diff --git a/ros/src/computing/perception/detection/packages/cv_tracker/launch/yolo2.launch b/ros/src/computing/perception/detection/packages/cv_tracker/launch/yolo2.launch index 40ae85e5471..1480bf5c15f 100644 --- a/ros/src/computing/perception/detection/packages/cv_tracker/launch/yolo2.launch +++ b/ros/src/computing/perception/detection/packages/cv_tracker/launch/yolo2.launch @@ -1,26 +1,21 @@ - - - - - - - - - - + + + + + + + + + + + + + + + + + + - - - - - - - - - - - - - diff --git a/ros/src/computing/perception/detection/packages/cv_tracker/nodes/yolo2/src/yolo2_node.cpp b/ros/src/computing/perception/detection/packages/cv_tracker/nodes/yolo2/src/yolo2_node.cpp index 11f919c34ce..c37edfe026f 100644 --- a/ros/src/computing/perception/detection/packages/cv_tracker/nodes/yolo2/src/yolo2_node.cpp +++ b/ros/src/computing/perception/detection/packages/cv_tracker/nodes/yolo2/src/yolo2_node.cpp @@ -191,7 +191,7 @@ class Yolo2DetectorNode void image_callback(const sensor_msgs::ImageConstPtr& in_image_message) { std::vector< RectClassScore > detections; - //darknet_image = yolo_detector_.convert_image(in_image_message); + //darknet_image_ = yolo_detector_.convert_image(in_image_message); darknet_image = convert_ipl_to_image(in_image_message); diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/CMakeLists.txt b/ros/src/computing/perception/detection/packages/yolo3_detector/CMakeLists.txt new file mode 100644 index 00000000000..58e8594b18a --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/CMakeLists.txt @@ -0,0 +1,154 @@ +cmake_minimum_required(VERSION 2.8.12) +project(yolo3_detector) + +find_package(catkin REQUIRED COMPONENTS + cv_bridge + image_transport + roscpp + sensor_msgs + std_msgs + autoware_msgs + ) + +find_package(CUDA) +find_package(OpenCV REQUIRED) +find_package(OpenMP) + +catkin_package(CATKIN_DEPENDS + cv_bridge + image_transport + roscpp + sensor_msgs + std_msgs + autoware_msgs + ) + +set(CMAKE_CXX_FLAGS "-std=c++11 -O3 -g -Wall ${CMAKE_CXX_FLAGS}") + +IF (CUDA_FOUND) + list(APPEND CUDA_NVCC_FLAGS "--std=c++11 -I$${PROJECT_SOURCE_DIR}/darknet/src -I${PROJECT_SOURCE_DIR}/src -DGPU") + SET(CUDA_PROPAGATE_HOST_FLAGS OFF) + + #darknet + cuda_add_library(yolo3_detector_lib SHARED + darknet/src/activation_kernels.cu + darknet/src/avgpool_layer_kernels.cu + darknet/src/convolutional_kernels.cu + darknet/src/crop_layer_kernels.cu + darknet/src/col2im_kernels.cu + darknet/src/blas_kernels.cu + darknet/src/deconvolutional_kernels.cu + darknet/src/dropout_layer_kernels.cu + darknet/src/im2col_kernels.cu + darknet/src/maxpool_layer_kernels.cu + + darknet/src/gemm.c + darknet/src/utils.c + darknet/src/cuda.c + darknet/src/deconvolutional_layer.c + darknet/src/convolutional_layer.c + darknet/src/list.c + darknet/src/image.c + darknet/src/activations.c + darknet/src/im2col.c + darknet/src/col2im.c + darknet/src/blas.c + darknet/src/crop_layer.c + darknet/src/dropout_layer.c + darknet/src/maxpool_layer.c + darknet/src/softmax_layer.c + darknet/src/data.c + darknet/src/matrix.c + darknet/src/network.c + darknet/src/connected_layer.c + darknet/src/cost_layer.c + darknet/src/parser.c + darknet/src/option_list.c + darknet/src/detection_layer.c + darknet/src/route_layer.c + darknet/src/upsample_layer.c + darknet/src/box.c + darknet/src/normalization_layer.c + darknet/src/avgpool_layer.c + darknet/src/layer.c + darknet/src/local_layer.c + darknet/src/shortcut_layer.c + darknet/src/logistic_layer.c + darknet/src/activation_layer.c + darknet/src/rnn_layer.c + darknet/src/gru_layer.c + darknet/src/crnn_layer.c + darknet/src/batchnorm_layer.c + darknet/src/region_layer.c + darknet/src/reorg_layer.c + darknet/src/tree.c + darknet/src/lstm_layer.c + darknet/src/l2norm_layer.c + darknet/src/yolo_layer.c + ) + + target_compile_definitions(yolo3_detector_lib PUBLIC -DGPU) + cuda_add_cublas_to_target(yolo3_detector_lib) + + if (OPENMP_FOUND) + set_target_properties(yolo3_detector_lib PROPERTIES + COMPILE_FLAGS ${OpenMP_CXX_FLAGS} + LINK_FLAGS ${OpenMP_CXX_FLAGS} + ) + endif () + + target_include_directories(yolo3_detector_lib PRIVATE + ${OpenCV_INCLUDE_DIR} + ${catkin_INCLUDE_DIRS} + ${Boost_INCLUDE_DIRS} + ${CUDA_INCLUDE_DIRS} + ${PROJECT_SOURCE_DIR}/darknet + ${PROJECT_SOURCE_DIR}/darknet/src + ${PROJECT_SOURCE_DIR}/src + ) + + target_link_libraries(yolo3_detector_lib + ${OpenCV_LIBRARIES} + ${catkin_LIBRARIES} + ${PCL_LIBRARIES} + ${Qt5Core_LIBRARIES} + ${CUDA_LIBRARIES} + ${CUDA_CUBLAS_LIBRARIES} + ${CUDA_curand_LIBRARY} + ) + + add_dependencies(yolo3_detector_lib + ${catkin_EXPORTED_TARGETS} + ) + + #ros node + cuda_add_executable(yolo3_detector + src/yolo3_node.cpp + src/yolo3_detector.cpp + src/yolo3_detector.h + ) + + target_compile_definitions(yolo3_detector PUBLIC -DGPU) + + target_include_directories(yolo3_detector PRIVATE + ${CUDA_INCLUDE_DIRS} + ${catkin_INCLUDE_DIRS} + ${PROJECT_SOURCE_DIR}/darknet + ${PROJECT_SOURCE_DIR}/darknet/src + ${PROJECT_SOURCE_DIR}/src + ) + + target_link_libraries(yolo3_detector + ${catkin_LIBRARIES} + ${OpenCV_LIBS} + ${CUDA_LIBRARIES} + ${CUDA_CUBLAS_LIBRARIES} + ${CUDA_curand_LIBRARY} + yolo3_detector_lib + ) + add_dependencies(yolo3_detector + ${catkin_EXPORTED_TARGETS} + ) +ELSE() + message("YOLO3 won't be built, CUDA was not found.") +ENDIF () \ No newline at end of file diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/README.md b/ros/src/computing/perception/detection/packages/yolo3_detector/README.md new file mode 100644 index 00000000000..8b1e794c1f3 --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/README.md @@ -0,0 +1,49 @@ +# Yolo3 Detector + +Autoware package based on Yolov3 image detector. + +### Requirements + +* NVIDIA GPU with CUDA installed +* Pretrained YOLOv3 model on COCO dataset. + +### How to launch + +* From a sourced terminal: + +`roslaunch yolo3_detector yolo3.launch` + +* From Runtime Manager: + +Computing Tab -> Detection/ cv_detector -> `yolo3_wa` + +### Parameters + +Launch file available parameters: + +|Parameter| Type| Description| +----------|-----|-------- +|`score_threshold`|*Double* |Detections with a confidence value larger than this value will be displayed. Default `0.5`.| +|`nms_threshold`|*Double*|Non-Maximum suppresion area threshold ratio to merge proposals. Default `0.45`.| +|`network_definition_file`|*String*|Network architecture definition configuration file. Default `yolov3.cfg`.| +|`pretrained_model_file`|*String*|Path to pretrained model. Default `yolov3.weights`.| +|`camera_id`|*String*|Camera workspace. Default `/`.| +|`image_src`|*String*|Image source topic. Default `/image_raw`.| + +### Subscribed topics + +|Topic|Type|Objective| +------|----|--------- +|`/image_raw`|`sensor_msgs/Image`|Source image stream to perform detection.| +|`/config/Yolo3`|`autoware_msgs/ConfigSsd`|Configuration adjustment for threshold.| + +### Published topics + +|Topic|Type|Objective| +------|----|--------- +|`/obj_car/image_obj`|`autoware_msgs/image_obj`|Contains the coordinates of the bounding box in image coordinates for objects detected as vehicles.| +|`/obj_person/image_obj`|`autoware_msgs/image_obj`|Contains the coordinates of the bounding box in image coordinates for objects detected as pedestrian.| + +### Video + +[![Yolo v3 Autoware](https://img.youtube.com/vi/pO4vM4ehI98/0.jpg)](https://www.youtube.com/watch?v=pO4vM4ehI98) diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/cfg/yolov3-voc.cfg b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/cfg/yolov3-voc.cfg new file mode 100644 index 00000000000..3f3e8dfb31b --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/cfg/yolov3-voc.cfg @@ -0,0 +1,785 @@ +[net] +# Testing + batch=1 + subdivisions=1 +# Training +# batch=64 +# subdivisions=16 +width=416 +height=416 +channels=3 +momentum=0.9 +decay=0.0005 +angle=0 +saturation = 1.5 +exposure = 1.5 +hue=.1 + +learning_rate=0.001 +burn_in=1000 +max_batches = 50200 +policy=steps +steps=40000,45000 +scales=.1,.1 + + + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +# Downsample + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +###################### + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=75 +activation=linear + +[yolo] +mask = 6,7,8 +anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 +classes=20 +num=9 +jitter=.3 +ignore_thresh = .5 +truth_thresh = 1 +random=1 + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[route] +layers = -1, 61 + + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=75 +activation=linear + +[yolo] +mask = 3,4,5 +anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 +classes=20 +num=9 +jitter=.3 +ignore_thresh = .5 +truth_thresh = 1 +random=1 + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[route] +layers = -1, 36 + + + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=75 +activation=linear + +[yolo] +mask = 0,1,2 +anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 +classes=20 +num=9 +jitter=.3 +ignore_thresh = .5 +truth_thresh = 1 +random=1 + diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/cfg/yolov3.cfg b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/cfg/yolov3.cfg new file mode 100644 index 00000000000..5f3ab621302 --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/cfg/yolov3.cfg @@ -0,0 +1,789 @@ +[net] +# Testing +batch=1 +subdivisions=1 +# Training +# batch=64 +# subdivisions=16 +width=416 +height=416 +channels=3 +momentum=0.9 +decay=0.0005 +angle=0 +saturation = 1.5 +exposure = 1.5 +hue=.1 + +learning_rate=0.001 +burn_in=1000 +max_batches = 500200 +policy=steps +steps=400000,450000 +scales=.1,.1 + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +# Downsample + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +###################### + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + + +[yolo] +mask = 6,7,8 +anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 +classes=80 +num=9 +jitter=.3 +ignore_thresh = .5 +truth_thresh = 1 +random=1 + + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[route] +layers = -1, 61 + + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + + +[yolo] +mask = 3,4,5 +anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 +classes=80 +num=9 +jitter=.3 +ignore_thresh = .5 +truth_thresh = 1 +random=1 + + + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[route] +layers = -1, 36 + + + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + + +[yolo] +mask = 0,1,2 +anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 +classes=80 +num=9 +jitter=.3 +ignore_thresh = .5 +truth_thresh = 1 +random=1 + diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/activation_kernels.cu b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/activation_kernels.cu new file mode 100644 index 00000000000..5852eb58e0e --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/activation_kernels.cu @@ -0,0 +1,200 @@ +#include "cuda_runtime.h" +#include "curand.h" +#include "cublas_v2.h" + +extern "C" { +#include "activations.h" +#include "cuda.h" +} + + +__device__ float lhtan_activate_kernel(float x) +{ + if(x < 0) return .001f*x; + if(x > 1) return .001f*(x-1.f) + 1.f; + return x; +} +__device__ float lhtan_gradient_kernel(float x) +{ + if(x > 0 && x < 1) return 1; + return .001; +} + +__device__ float hardtan_activate_kernel(float x) +{ + if (x < -1) return -1; + if (x > 1) return 1; + return x; +} +__device__ float linear_activate_kernel(float x){return x;} +__device__ float logistic_activate_kernel(float x){return 1.f/(1.f + expf(-x));} +__device__ float loggy_activate_kernel(float x){return 2.f/(1.f + expf(-x)) - 1;} +__device__ float relu_activate_kernel(float x){return x*(x>0);} +__device__ float elu_activate_kernel(float x){return (x >= 0)*x + (x < 0)*(expf(x)-1);} +__device__ float relie_activate_kernel(float x){return (x>0) ? x : .01f*x;} +__device__ float ramp_activate_kernel(float x){return x*(x>0)+.1f*x;} +__device__ float leaky_activate_kernel(float x){return (x>0) ? x : .1f*x;} +__device__ float tanh_activate_kernel(float x){return (2.f/(1 + expf(-2*x)) - 1);} +__device__ float plse_activate_kernel(float x) +{ + if(x < -4) return .01f * (x + 4); + if(x > 4) return .01f * (x - 4) + 1; + return .125f*x + .5f; +} +__device__ float stair_activate_kernel(float x) +{ + int n = floorf(x); + if (n%2 == 0) return floorf(x/2); + else return (x - n) + floorf(x/2); +} + + +__device__ float hardtan_gradient_kernel(float x) +{ + if (x > -1 && x < 1) return 1; + return 0; +} +__device__ float linear_gradient_kernel(float x){return 1;} +__device__ float logistic_gradient_kernel(float x){return (1-x)*x;} +__device__ float loggy_gradient_kernel(float x) +{ + float y = (x+1)/2; + return 2*(1-y)*y; +} +__device__ float relu_gradient_kernel(float x){return (x>0);} +__device__ float elu_gradient_kernel(float x){return (x >= 0) + (x < 0)*(x + 1);} +__device__ float relie_gradient_kernel(float x){return (x>0) ? 1 : .01f;} +__device__ float ramp_gradient_kernel(float x){return (x>0)+.1f;} +__device__ float leaky_gradient_kernel(float x){return (x>0) ? 1 : .1f;} +__device__ float tanh_gradient_kernel(float x){return 1-x*x;} +__device__ float plse_gradient_kernel(float x){return (x < 0 || x > 1) ? .01f : .125f;} +__device__ float stair_gradient_kernel(float x) +{ + if (floorf(x) == x) return 0; + return 1; +} + +__device__ float activate_kernel(float x, ACTIVATION a) +{ + switch(a){ + case LINEAR: + return linear_activate_kernel(x); + case LOGISTIC: + return logistic_activate_kernel(x); + case LOGGY: + return loggy_activate_kernel(x); + case RELU: + return relu_activate_kernel(x); + case ELU: + return elu_activate_kernel(x); + case RELIE: + return relie_activate_kernel(x); + case RAMP: + return ramp_activate_kernel(x); + case LEAKY: + return leaky_activate_kernel(x); + case TANH: + return tanh_activate_kernel(x); + case PLSE: + return plse_activate_kernel(x); + case STAIR: + return stair_activate_kernel(x); + case HARDTAN: + return hardtan_activate_kernel(x); + case LHTAN: + return lhtan_activate_kernel(x); + } + return 0; +} + +__device__ float gradient_kernel(float x, ACTIVATION a) +{ + switch(a){ + case LINEAR: + return linear_gradient_kernel(x); + case LOGISTIC: + return logistic_gradient_kernel(x); + case LOGGY: + return loggy_gradient_kernel(x); + case RELU: + return relu_gradient_kernel(x); + case ELU: + return elu_gradient_kernel(x); + case RELIE: + return relie_gradient_kernel(x); + case RAMP: + return ramp_gradient_kernel(x); + case LEAKY: + return leaky_gradient_kernel(x); + case TANH: + return tanh_gradient_kernel(x); + case PLSE: + return plse_gradient_kernel(x); + case STAIR: + return stair_gradient_kernel(x); + case HARDTAN: + return hardtan_gradient_kernel(x); + case LHTAN: + return lhtan_gradient_kernel(x); + } + return 0; +} + +__global__ void binary_gradient_array_kernel(float *x, float *dy, int n, int s, BINARY_ACTIVATION a, float *dx) +{ + int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + int i = id % s; + int b = id / s; + float x1 = x[b*s + i]; + float x2 = x[b*s + s/2 + i]; + if(id < n) { + float de = dy[id]; + dx[b*s + i] = x2*de; + dx[b*s + s/2 + i] = x1*de; + } +} + +extern "C" void binary_gradient_array_gpu(float *x, float *dx, int n, int size, BINARY_ACTIVATION a, float *y) +{ + binary_gradient_array_kernel<<>>(x, dx, n/2, size, a, y); + check_error(cudaPeekAtLastError()); +} +__global__ void binary_activate_array_kernel(float *x, int n, int s, BINARY_ACTIVATION a, float *y) +{ + int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + int i = id % s; + int b = id / s; + float x1 = x[b*s + i]; + float x2 = x[b*s + s/2 + i]; + if(id < n) y[id] = x1*x2; +} + +extern "C" void binary_activate_array_gpu(float *x, int n, int size, BINARY_ACTIVATION a, float *y) +{ + binary_activate_array_kernel<<>>(x, n/2, size, a, y); + check_error(cudaPeekAtLastError()); +} + +__global__ void activate_array_kernel(float *x, int n, ACTIVATION a) +{ + int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if(i < n) x[i] = activate_kernel(x[i], a); +} + +__global__ void gradient_array_kernel(float *x, int n, ACTIVATION a, float *delta) +{ + int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if(i < n) delta[i] *= gradient_kernel(x[i], a); +} + +extern "C" void activate_array_gpu(float *x, int n, ACTIVATION a) +{ + activate_array_kernel<<>>(x, n, a); + check_error(cudaPeekAtLastError()); +} + +extern "C" void gradient_array_gpu(float *x, int n, ACTIVATION a, float *delta) +{ + gradient_array_kernel<<>>(x, n, a, delta); + check_error(cudaPeekAtLastError()); +} diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/activation_layer.c b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/activation_layer.c new file mode 100644 index 00000000000..b4ba953967b --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/activation_layer.c @@ -0,0 +1,63 @@ +#include "activation_layer.h" +#include "utils.h" +#include "cuda.h" +#include "blas.h" +#include "gemm.h" + +#include +#include +#include +#include + +layer make_activation_layer(int batch, int inputs, ACTIVATION activation) +{ + layer l = {0}; + l.type = ACTIVE; + + l.inputs = inputs; + l.outputs = inputs; + l.batch=batch; + + l.output = calloc(batch*inputs, sizeof(float*)); + l.delta = calloc(batch*inputs, sizeof(float*)); + + l.forward = forward_activation_layer; + l.backward = backward_activation_layer; +#ifdef GPU + l.forward_gpu = forward_activation_layer_gpu; + l.backward_gpu = backward_activation_layer_gpu; + + l.output_gpu = cuda_make_array(l.output, inputs*batch); + l.delta_gpu = cuda_make_array(l.delta, inputs*batch); +#endif + l.activation = activation; + fprintf(stderr, "Activation Layer: %d inputs\n", inputs); + return l; +} + +void forward_activation_layer(layer l, network net) +{ + copy_cpu(l.outputs*l.batch, net.input, 1, l.output, 1); + activate_array(l.output, l.outputs*l.batch, l.activation); +} + +void backward_activation_layer(layer l, network net) +{ + gradient_array(l.output, l.outputs*l.batch, l.activation, l.delta); + copy_cpu(l.outputs*l.batch, l.delta, 1, net.delta, 1); +} + +#ifdef GPU + +void forward_activation_layer_gpu(layer l, network net) +{ + copy_gpu(l.outputs*l.batch, net.input_gpu, 1, l.output_gpu, 1); + activate_array_gpu(l.output_gpu, l.outputs*l.batch, l.activation); +} + +void backward_activation_layer_gpu(layer l, network net) +{ + gradient_array_gpu(l.output_gpu, l.outputs*l.batch, l.activation, l.delta_gpu); + copy_gpu(l.outputs*l.batch, l.delta_gpu, 1, net.delta_gpu, 1); +} +#endif diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/activation_layer.h b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/activation_layer.h new file mode 100644 index 00000000000..42118a84e83 --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/activation_layer.h @@ -0,0 +1,19 @@ +#ifndef ACTIVATION_LAYER_H +#define ACTIVATION_LAYER_H + +#include "activations.h" +#include "layer.h" +#include "network.h" + +layer make_activation_layer(int batch, int inputs, ACTIVATION activation); + +void forward_activation_layer(layer l, network net); +void backward_activation_layer(layer l, network net); + +#ifdef GPU +void forward_activation_layer_gpu(layer l, network net); +void backward_activation_layer_gpu(layer l, network net); +#endif + +#endif + diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/activations.c b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/activations.c new file mode 100644 index 00000000000..0cbb2f5546e --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/activations.c @@ -0,0 +1,143 @@ +#include "activations.h" + +#include +#include +#include +#include + +char *get_activation_string(ACTIVATION a) +{ + switch(a){ + case LOGISTIC: + return "logistic"; + case LOGGY: + return "loggy"; + case RELU: + return "relu"; + case ELU: + return "elu"; + case RELIE: + return "relie"; + case RAMP: + return "ramp"; + case LINEAR: + return "linear"; + case TANH: + return "tanh"; + case PLSE: + return "plse"; + case LEAKY: + return "leaky"; + case STAIR: + return "stair"; + case HARDTAN: + return "hardtan"; + case LHTAN: + return "lhtan"; + default: + break; + } + return "relu"; +} + +ACTIVATION get_activation(char *s) +{ + if (strcmp(s, "logistic")==0) return LOGISTIC; + if (strcmp(s, "loggy")==0) return LOGGY; + if (strcmp(s, "relu")==0) return RELU; + if (strcmp(s, "elu")==0) return ELU; + if (strcmp(s, "relie")==0) return RELIE; + if (strcmp(s, "plse")==0) return PLSE; + if (strcmp(s, "hardtan")==0) return HARDTAN; + if (strcmp(s, "lhtan")==0) return LHTAN; + if (strcmp(s, "linear")==0) return LINEAR; + if (strcmp(s, "ramp")==0) return RAMP; + if (strcmp(s, "leaky")==0) return LEAKY; + if (strcmp(s, "tanh")==0) return TANH; + if (strcmp(s, "stair")==0) return STAIR; + fprintf(stderr, "Couldn't find activation function %s, going with ReLU\n", s); + return RELU; +} + +float activate(float x, ACTIVATION a) +{ + switch(a){ + case LINEAR: + return linear_activate(x); + case LOGISTIC: + return logistic_activate(x); + case LOGGY: + return loggy_activate(x); + case RELU: + return relu_activate(x); + case ELU: + return elu_activate(x); + case RELIE: + return relie_activate(x); + case RAMP: + return ramp_activate(x); + case LEAKY: + return leaky_activate(x); + case TANH: + return tanh_activate(x); + case PLSE: + return plse_activate(x); + case STAIR: + return stair_activate(x); + case HARDTAN: + return hardtan_activate(x); + case LHTAN: + return lhtan_activate(x); + } + return 0; +} + +void activate_array(float *x, const int n, const ACTIVATION a) +{ + int i; + for(i = 0; i < n; ++i){ + x[i] = activate(x[i], a); + } +} + +float gradient(float x, ACTIVATION a) +{ + switch(a){ + case LINEAR: + return linear_gradient(x); + case LOGISTIC: + return logistic_gradient(x); + case LOGGY: + return loggy_gradient(x); + case RELU: + return relu_gradient(x); + case ELU: + return elu_gradient(x); + case RELIE: + return relie_gradient(x); + case RAMP: + return ramp_gradient(x); + case LEAKY: + return leaky_gradient(x); + case TANH: + return tanh_gradient(x); + case PLSE: + return plse_gradient(x); + case STAIR: + return stair_gradient(x); + case HARDTAN: + return hardtan_gradient(x); + case LHTAN: + return lhtan_gradient(x); + } + return 0; +} + +void gradient_array(const float *x, const int n, const ACTIVATION a, float *delta) +{ + int i; + for(i = 0; i < n; ++i){ + delta[i] *= gradient(x[i], a); + } +} + diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/activations.h b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/activations.h new file mode 100644 index 00000000000..d456dbe3c2b --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/activations.h @@ -0,0 +1,85 @@ +#ifndef ACTIVATIONS_H +#define ACTIVATIONS_H +#include "darknet.h" +#include "cuda.h" +#include "math.h" + +ACTIVATION get_activation(char *s); + +char *get_activation_string(ACTIVATION a); +float activate(float x, ACTIVATION a); +float gradient(float x, ACTIVATION a); +void gradient_array(const float *x, const int n, const ACTIVATION a, float *delta); +void activate_array(float *x, const int n, const ACTIVATION a); +#ifdef GPU +void activate_array_gpu(float *x, int n, ACTIVATION a); +void gradient_array_gpu(float *x, int n, ACTIVATION a, float *delta); +#endif + +static inline float stair_activate(float x) +{ + int n = floor(x); + if (n%2 == 0) return floor(x/2.); + else return (x - n) + floor(x/2.); +} +static inline float hardtan_activate(float x) +{ + if (x < -1) return -1; + if (x > 1) return 1; + return x; +} +static inline float linear_activate(float x){return x;} +static inline float logistic_activate(float x){return 1./(1. + exp(-x));} +static inline float loggy_activate(float x){return 2./(1. + exp(-x)) - 1;} +static inline float relu_activate(float x){return x*(x>0);} +static inline float elu_activate(float x){return (x >= 0)*x + (x < 0)*(exp(x)-1);} +static inline float relie_activate(float x){return (x>0) ? x : .01*x;} +static inline float ramp_activate(float x){return x*(x>0)+.1*x;} +static inline float leaky_activate(float x){return (x>0) ? x : .1*x;} +static inline float tanh_activate(float x){return (exp(2*x)-1)/(exp(2*x)+1);} +static inline float plse_activate(float x) +{ + if(x < -4) return .01 * (x + 4); + if(x > 4) return .01 * (x - 4) + 1; + return .125*x + .5; +} + +static inline float lhtan_activate(float x) +{ + if(x < 0) return .001*x; + if(x > 1) return .001*(x-1) + 1; + return x; +} +static inline float lhtan_gradient(float x) +{ + if(x > 0 && x < 1) return 1; + return .001; +} + +static inline float hardtan_gradient(float x) +{ + if (x > -1 && x < 1) return 1; + return 0; +} +static inline float linear_gradient(float x){return 1;} +static inline float logistic_gradient(float x){return (1-x)*x;} +static inline float loggy_gradient(float x) +{ + float y = (x+1.)/2.; + return 2*(1-y)*y; +} +static inline float stair_gradient(float x) +{ + if (floor(x) == x) return 0; + return 1; +} +static inline float relu_gradient(float x){return (x>0);} +static inline float elu_gradient(float x){return (x >= 0) + (x < 0)*(x + 1);} +static inline float relie_gradient(float x){return (x>0) ? 1 : .01;} +static inline float ramp_gradient(float x){return (x>0)+.1;} +static inline float leaky_gradient(float x){return (x>0) ? 1 : .1;} +static inline float tanh_gradient(float x){return 1-x*x;} +static inline float plse_gradient(float x){return (x < 0 || x > 1) ? .01 : .125;} + +#endif + diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/avgpool_layer.c b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/avgpool_layer.c new file mode 100644 index 00000000000..83034dbecf4 --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/avgpool_layer.c @@ -0,0 +1,71 @@ +#include "avgpool_layer.h" +#include "cuda.h" +#include + +avgpool_layer make_avgpool_layer(int batch, int w, int h, int c) +{ + fprintf(stderr, "avg %4d x%4d x%4d -> %4d\n", w, h, c, c); + avgpool_layer l = {0}; + l.type = AVGPOOL; + l.batch = batch; + l.h = h; + l.w = w; + l.c = c; + l.out_w = 1; + l.out_h = 1; + l.out_c = c; + l.outputs = l.out_c; + l.inputs = h*w*c; + int output_size = l.outputs * batch; + l.output = calloc(output_size, sizeof(float)); + l.delta = calloc(output_size, sizeof(float)); + l.forward = forward_avgpool_layer; + l.backward = backward_avgpool_layer; + #ifdef GPU + l.forward_gpu = forward_avgpool_layer_gpu; + l.backward_gpu = backward_avgpool_layer_gpu; + l.output_gpu = cuda_make_array(l.output, output_size); + l.delta_gpu = cuda_make_array(l.delta, output_size); + #endif + return l; +} + +void resize_avgpool_layer(avgpool_layer *l, int w, int h) +{ + l->w = w; + l->h = h; + l->inputs = h*w*l->c; +} + +void forward_avgpool_layer(const avgpool_layer l, network net) +{ + int b,i,k; + + for(b = 0; b < l.batch; ++b){ + for(k = 0; k < l.c; ++k){ + int out_index = k + b*l.c; + l.output[out_index] = 0; + for(i = 0; i < l.h*l.w; ++i){ + int in_index = i + l.h*l.w*(k + b*l.c); + l.output[out_index] += net.input[in_index]; + } + l.output[out_index] /= l.h*l.w; + } + } +} + +void backward_avgpool_layer(const avgpool_layer l, network net) +{ + int b,i,k; + + for(b = 0; b < l.batch; ++b){ + for(k = 0; k < l.c; ++k){ + int out_index = k + b*l.c; + for(i = 0; i < l.h*l.w; ++i){ + int in_index = i + l.h*l.w*(k + b*l.c); + net.delta[in_index] += l.delta[out_index] / (l.h*l.w); + } + } + } +} + diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/avgpool_layer.h b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/avgpool_layer.h new file mode 100644 index 00000000000..3bd356c4e39 --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/avgpool_layer.h @@ -0,0 +1,23 @@ +#ifndef AVGPOOL_LAYER_H +#define AVGPOOL_LAYER_H + +#include "image.h" +#include "cuda.h" +#include "layer.h" +#include "network.h" + +typedef layer avgpool_layer; + +image get_avgpool_image(avgpool_layer l); +avgpool_layer make_avgpool_layer(int batch, int w, int h, int c); +void resize_avgpool_layer(avgpool_layer *l, int w, int h); +void forward_avgpool_layer(const avgpool_layer l, network net); +void backward_avgpool_layer(const avgpool_layer l, network net); + +#ifdef GPU +void forward_avgpool_layer_gpu(avgpool_layer l, network net); +void backward_avgpool_layer_gpu(avgpool_layer l, network net); +#endif + +#endif + diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/avgpool_layer_kernels.cu b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/avgpool_layer_kernels.cu new file mode 100644 index 00000000000..a7eca3aeae9 --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/avgpool_layer_kernels.cu @@ -0,0 +1,61 @@ +#include "cuda_runtime.h" +#include "curand.h" +#include "cublas_v2.h" + +extern "C" { +#include "avgpool_layer.h" +#include "cuda.h" +} + +__global__ void forward_avgpool_layer_kernel(int n, int w, int h, int c, float *input, float *output) +{ + int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if(id >= n) return; + + int k = id % c; + id /= c; + int b = id; + + int i; + int out_index = (k + c*b); + output[out_index] = 0; + for(i = 0; i < w*h; ++i){ + int in_index = i + h*w*(k + b*c); + output[out_index] += input[in_index]; + } + output[out_index] /= w*h; +} + +__global__ void backward_avgpool_layer_kernel(int n, int w, int h, int c, float *in_delta, float *out_delta) +{ + int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if(id >= n) return; + + int k = id % c; + id /= c; + int b = id; + + int i; + int out_index = (k + c*b); + for(i = 0; i < w*h; ++i){ + int in_index = i + h*w*(k + b*c); + in_delta[in_index] += out_delta[out_index] / (w*h); + } +} + +extern "C" void forward_avgpool_layer_gpu(avgpool_layer layer, network net) +{ + size_t n = layer.c*layer.batch; + + forward_avgpool_layer_kernel<<>>(n, layer.w, layer.h, layer.c, net.input_gpu, layer.output_gpu); + check_error(cudaPeekAtLastError()); +} + +extern "C" void backward_avgpool_layer_gpu(avgpool_layer layer, network net) +{ + size_t n = layer.c*layer.batch; + + backward_avgpool_layer_kernel<<>>(n, layer.w, layer.h, layer.c, net.delta_gpu, layer.delta_gpu); + check_error(cudaPeekAtLastError()); +} + diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/batchnorm_layer.c b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/batchnorm_layer.c new file mode 100644 index 00000000000..ebff387cc4b --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/batchnorm_layer.c @@ -0,0 +1,279 @@ +#include "convolutional_layer.h" +#include "batchnorm_layer.h" +#include "blas.h" +#include + +layer make_batchnorm_layer(int batch, int w, int h, int c) +{ + fprintf(stderr, "Batch Normalization Layer: %d x %d x %d image\n", w,h,c); + layer l = {0}; + l.type = BATCHNORM; + l.batch = batch; + l.h = l.out_h = h; + l.w = l.out_w = w; + l.c = l.out_c = c; + l.output = calloc(h * w * c * batch, sizeof(float)); + l.delta = calloc(h * w * c * batch, sizeof(float)); + l.inputs = w*h*c; + l.outputs = l.inputs; + + l.scales = calloc(c, sizeof(float)); + l.scale_updates = calloc(c, sizeof(float)); + l.biases = calloc(c, sizeof(float)); + l.bias_updates = calloc(c, sizeof(float)); + int i; + for(i = 0; i < c; ++i){ + l.scales[i] = 1; + } + + l.mean = calloc(c, sizeof(float)); + l.variance = calloc(c, sizeof(float)); + + l.rolling_mean = calloc(c, sizeof(float)); + l.rolling_variance = calloc(c, sizeof(float)); + + l.forward = forward_batchnorm_layer; + l.backward = backward_batchnorm_layer; +#ifdef GPU + l.forward_gpu = forward_batchnorm_layer_gpu; + l.backward_gpu = backward_batchnorm_layer_gpu; + + l.output_gpu = cuda_make_array(l.output, h * w * c * batch); + l.delta_gpu = cuda_make_array(l.delta, h * w * c * batch); + + l.biases_gpu = cuda_make_array(l.biases, c); + l.bias_updates_gpu = cuda_make_array(l.bias_updates, c); + + l.scales_gpu = cuda_make_array(l.scales, c); + l.scale_updates_gpu = cuda_make_array(l.scale_updates, c); + + l.mean_gpu = cuda_make_array(l.mean, c); + l.variance_gpu = cuda_make_array(l.variance, c); + + l.rolling_mean_gpu = cuda_make_array(l.mean, c); + l.rolling_variance_gpu = cuda_make_array(l.variance, c); + + l.mean_delta_gpu = cuda_make_array(l.mean, c); + l.variance_delta_gpu = cuda_make_array(l.variance, c); + + l.x_gpu = cuda_make_array(l.output, l.batch*l.outputs); + l.x_norm_gpu = cuda_make_array(l.output, l.batch*l.outputs); + #ifdef CUDNN + cudnnCreateTensorDescriptor(&l.normTensorDesc); + cudnnCreateTensorDescriptor(&l.dstTensorDesc); + cudnnSetTensor4dDescriptor(l.dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l.batch, l.out_c, l.out_h, l.out_w); + cudnnSetTensor4dDescriptor(l.normTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, 1, l.out_c, 1, 1); + + #endif +#endif + return l; +} + +void backward_scale_cpu(float *x_norm, float *delta, int batch, int n, int size, float *scale_updates) +{ + int i,b,f; + for(f = 0; f < n; ++f){ + float sum = 0; + for(b = 0; b < batch; ++b){ + for(i = 0; i < size; ++i){ + int index = i + size*(f + n*b); + sum += delta[index] * x_norm[index]; + } + } + scale_updates[f] += sum; + } +} + +void mean_delta_cpu(float *delta, float *variance, int batch, int filters, int spatial, float *mean_delta) +{ + + int i,j,k; + for(i = 0; i < filters; ++i){ + mean_delta[i] = 0; + for (j = 0; j < batch; ++j) { + for (k = 0; k < spatial; ++k) { + int index = j*filters*spatial + i*spatial + k; + mean_delta[i] += delta[index]; + } + } + mean_delta[i] *= (-1./sqrt(variance[i] + .00001f)); + } +} +void variance_delta_cpu(float *x, float *delta, float *mean, float *variance, int batch, int filters, int spatial, float *variance_delta) +{ + + int i,j,k; + for(i = 0; i < filters; ++i){ + variance_delta[i] = 0; + for(j = 0; j < batch; ++j){ + for(k = 0; k < spatial; ++k){ + int index = j*filters*spatial + i*spatial + k; + variance_delta[i] += delta[index]*(x[index] - mean[i]); + } + } + variance_delta[i] *= -.5 * pow(variance[i] + .00001f, (float)(-3./2.)); + } +} +void normalize_delta_cpu(float *x, float *mean, float *variance, float *mean_delta, float *variance_delta, int batch, int filters, int spatial, float *delta) +{ + int f, j, k; + for(j = 0; j < batch; ++j){ + for(f = 0; f < filters; ++f){ + for(k = 0; k < spatial; ++k){ + int index = j*filters*spatial + f*spatial + k; + delta[index] = delta[index] * 1./(sqrt(variance[f] + .00001f)) + variance_delta[f] * 2. * (x[index] - mean[f]) / (spatial * batch) + mean_delta[f]/(spatial*batch); + } + } + } +} + +void resize_batchnorm_layer(layer *layer, int w, int h) +{ + fprintf(stderr, "Not implemented\n"); +} + +void forward_batchnorm_layer(layer l, network net) +{ + if(l.type == BATCHNORM) copy_cpu(l.outputs*l.batch, net.input, 1, l.output, 1); + copy_cpu(l.outputs*l.batch, l.output, 1, l.x, 1); + if(net.train){ + mean_cpu(l.output, l.batch, l.out_c, l.out_h*l.out_w, l.mean); + variance_cpu(l.output, l.mean, l.batch, l.out_c, l.out_h*l.out_w, l.variance); + + scal_cpu(l.out_c, .99, l.rolling_mean, 1); + axpy_cpu(l.out_c, .01, l.mean, 1, l.rolling_mean, 1); + scal_cpu(l.out_c, .99, l.rolling_variance, 1); + axpy_cpu(l.out_c, .01, l.variance, 1, l.rolling_variance, 1); + + normalize_cpu(l.output, l.mean, l.variance, l.batch, l.out_c, l.out_h*l.out_w); + copy_cpu(l.outputs*l.batch, l.output, 1, l.x_norm, 1); + } else { + normalize_cpu(l.output, l.rolling_mean, l.rolling_variance, l.batch, l.out_c, l.out_h*l.out_w); + } + scale_bias(l.output, l.scales, l.batch, l.out_c, l.out_h*l.out_w); + add_bias(l.output, l.biases, l.batch, l.out_c, l.out_h*l.out_w); +} + +void backward_batchnorm_layer(layer l, network net) +{ + if(!net.train){ + l.mean = l.rolling_mean; + l.variance = l.rolling_variance; + } + backward_bias(l.bias_updates, l.delta, l.batch, l.out_c, l.out_w*l.out_h); + backward_scale_cpu(l.x_norm, l.delta, l.batch, l.out_c, l.out_w*l.out_h, l.scale_updates); + + scale_bias(l.delta, l.scales, l.batch, l.out_c, l.out_h*l.out_w); + + mean_delta_cpu(l.delta, l.variance, l.batch, l.out_c, l.out_w*l.out_h, l.mean_delta); + variance_delta_cpu(l.x, l.delta, l.mean, l.variance, l.batch, l.out_c, l.out_w*l.out_h, l.variance_delta); + normalize_delta_cpu(l.x, l.mean, l.variance, l.mean_delta, l.variance_delta, l.batch, l.out_c, l.out_w*l.out_h, l.delta); + if(l.type == BATCHNORM) copy_cpu(l.outputs*l.batch, l.delta, 1, net.delta, 1); +} + +#ifdef GPU + +void pull_batchnorm_layer(layer l) +{ + cuda_pull_array(l.scales_gpu, l.scales, l.c); + cuda_pull_array(l.rolling_mean_gpu, l.rolling_mean, l.c); + cuda_pull_array(l.rolling_variance_gpu, l.rolling_variance, l.c); +} +void push_batchnorm_layer(layer l) +{ + cuda_push_array(l.scales_gpu, l.scales, l.c); + cuda_push_array(l.rolling_mean_gpu, l.rolling_mean, l.c); + cuda_push_array(l.rolling_variance_gpu, l.rolling_variance, l.c); +} + +void forward_batchnorm_layer_gpu(layer l, network net) +{ + if(l.type == BATCHNORM) copy_gpu(l.outputs*l.batch, net.input_gpu, 1, l.output_gpu, 1); + copy_gpu(l.outputs*l.batch, l.output_gpu, 1, l.x_gpu, 1); + if (net.train) { +#ifdef CUDNN + float one = 1; + float zero = 0; + cudnnBatchNormalizationForwardTraining(cudnn_handle(), + CUDNN_BATCHNORM_SPATIAL, + &one, + &zero, + l.dstTensorDesc, + l.x_gpu, + l.dstTensorDesc, + l.output_gpu, + l.normTensorDesc, + l.scales_gpu, + l.biases_gpu, + .01, + l.rolling_mean_gpu, + l.rolling_variance_gpu, + .00001, + l.mean_gpu, + l.variance_gpu); +#else + fast_mean_gpu(l.output_gpu, l.batch, l.out_c, l.out_h*l.out_w, l.mean_gpu); + fast_variance_gpu(l.output_gpu, l.mean_gpu, l.batch, l.out_c, l.out_h*l.out_w, l.variance_gpu); + + scal_gpu(l.out_c, .99, l.rolling_mean_gpu, 1); + axpy_gpu(l.out_c, .01, l.mean_gpu, 1, l.rolling_mean_gpu, 1); + scal_gpu(l.out_c, .99, l.rolling_variance_gpu, 1); + axpy_gpu(l.out_c, .01, l.variance_gpu, 1, l.rolling_variance_gpu, 1); + + copy_gpu(l.outputs*l.batch, l.output_gpu, 1, l.x_gpu, 1); + normalize_gpu(l.output_gpu, l.mean_gpu, l.variance_gpu, l.batch, l.out_c, l.out_h*l.out_w); + copy_gpu(l.outputs*l.batch, l.output_gpu, 1, l.x_norm_gpu, 1); + + scale_bias_gpu(l.output_gpu, l.scales_gpu, l.batch, l.out_c, l.out_h*l.out_w); + add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.out_c, l.out_w*l.out_h); +#endif + } else { + normalize_gpu(l.output_gpu, l.rolling_mean_gpu, l.rolling_variance_gpu, l.batch, l.out_c, l.out_h*l.out_w); + scale_bias_gpu(l.output_gpu, l.scales_gpu, l.batch, l.out_c, l.out_h*l.out_w); + add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.out_c, l.out_w*l.out_h); + } + +} + +void backward_batchnorm_layer_gpu(layer l, network net) +{ + if(!net.train){ + l.mean_gpu = l.rolling_mean_gpu; + l.variance_gpu = l.rolling_variance_gpu; + } +#ifdef CUDNN + float one = 1; + float zero = 0; + cudnnBatchNormalizationBackward(cudnn_handle(), + CUDNN_BATCHNORM_SPATIAL, + &one, + &zero, + &one, + &one, + l.dstTensorDesc, + l.x_gpu, + l.dstTensorDesc, + l.delta_gpu, + l.dstTensorDesc, + l.x_norm_gpu, + l.normTensorDesc, + l.scales_gpu, + l.scale_updates_gpu, + l.bias_updates_gpu, + .00001, + l.mean_gpu, + l.variance_gpu); + copy_gpu(l.outputs*l.batch, l.x_norm_gpu, 1, l.delta_gpu, 1); +#else + backward_bias_gpu(l.bias_updates_gpu, l.delta_gpu, l.batch, l.out_c, l.out_w*l.out_h); + backward_scale_gpu(l.x_norm_gpu, l.delta_gpu, l.batch, l.out_c, l.out_w*l.out_h, l.scale_updates_gpu); + + scale_bias_gpu(l.delta_gpu, l.scales_gpu, l.batch, l.out_c, l.out_h*l.out_w); + + fast_mean_delta_gpu(l.delta_gpu, l.variance_gpu, l.batch, l.out_c, l.out_w*l.out_h, l.mean_delta_gpu); + fast_variance_delta_gpu(l.x_gpu, l.delta_gpu, l.mean_gpu, l.variance_gpu, l.batch, l.out_c, l.out_w*l.out_h, l.variance_delta_gpu); + normalize_delta_gpu(l.x_gpu, l.mean_gpu, l.variance_gpu, l.mean_delta_gpu, l.variance_delta_gpu, l.batch, l.out_c, l.out_w*l.out_h, l.delta_gpu); +#endif + if(l.type == BATCHNORM) copy_gpu(l.outputs*l.batch, l.delta_gpu, 1, net.delta_gpu, 1); +} +#endif diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/batchnorm_layer.h b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/batchnorm_layer.h new file mode 100644 index 00000000000..25a18a3c8f2 --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/batchnorm_layer.h @@ -0,0 +1,19 @@ +#ifndef BATCHNORM_LAYER_H +#define BATCHNORM_LAYER_H + +#include "image.h" +#include "layer.h" +#include "network.h" + +layer make_batchnorm_layer(int batch, int w, int h, int c); +void forward_batchnorm_layer(layer l, network net); +void backward_batchnorm_layer(layer l, network net); + +#ifdef GPU +void forward_batchnorm_layer_gpu(layer l, network net); +void backward_batchnorm_layer_gpu(layer l, network net); +void pull_batchnorm_layer(layer l); +void push_batchnorm_layer(layer l); +#endif + +#endif diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/blas.c b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/blas.c new file mode 100644 index 00000000000..9e1604449ba --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/blas.c @@ -0,0 +1,351 @@ +#include "blas.h" + +#include +#include +#include +#include +#include +#include +void reorg_cpu(float *x, int w, int h, int c, int batch, int stride, int forward, float *out) +{ + int b,i,j,k; + int out_c = c/(stride*stride); + + for(b = 0; b < batch; ++b){ + for(k = 0; k < c; ++k){ + for(j = 0; j < h; ++j){ + for(i = 0; i < w; ++i){ + int in_index = i + w*(j + h*(k + c*b)); + int c2 = k % out_c; + int offset = k / out_c; + int w2 = i*stride + offset % stride; + int h2 = j*stride + offset / stride; + int out_index = w2 + w*stride*(h2 + h*stride*(c2 + out_c*b)); + if(forward) out[out_index] = x[in_index]; + else out[in_index] = x[out_index]; + } + } + } + } +} + +void flatten(float *x, int size, int layers, int batch, int forward) +{ + float *swap = calloc(size*layers*batch, sizeof(float)); + int i,c,b; + for(b = 0; b < batch; ++b){ + for(c = 0; c < layers; ++c){ + for(i = 0; i < size; ++i){ + int i1 = b*layers*size + c*size + i; + int i2 = b*layers*size + i*layers + c; + if (forward) swap[i2] = x[i1]; + else swap[i1] = x[i2]; + } + } + } + memcpy(x, swap, size*layers*batch*sizeof(float)); + free(swap); +} + +void weighted_sum_cpu(float *a, float *b, float *s, int n, float *c) +{ + int i; + for(i = 0; i < n; ++i){ + c[i] = s[i]*a[i] + (1-s[i])*(b ? b[i] : 0); + } +} + +void weighted_delta_cpu(float *a, float *b, float *s, float *da, float *db, float *ds, int n, float *dc) +{ + int i; + for(i = 0; i < n; ++i){ + if(da) da[i] += dc[i] * s[i]; + if(db) db[i] += dc[i] * (1-s[i]); + ds[i] += dc[i] * (a[i] - b[i]); + } +} + +void shortcut_cpu(int batch, int w1, int h1, int c1, float *add, int w2, int h2, int c2, float s1, float s2, float *out) +{ + int stride = w1/w2; + int sample = w2/w1; + assert(stride == h1/h2); + assert(sample == h2/h1); + if(stride < 1) stride = 1; + if(sample < 1) sample = 1; + int minw = (w1 < w2) ? w1 : w2; + int minh = (h1 < h2) ? h1 : h2; + int minc = (c1 < c2) ? c1 : c2; + + int i,j,k,b; + for(b = 0; b < batch; ++b){ + for(k = 0; k < minc; ++k){ + for(j = 0; j < minh; ++j){ + for(i = 0; i < minw; ++i){ + int out_index = i*sample + w2*(j*sample + h2*(k + c2*b)); + int add_index = i*stride + w1*(j*stride + h1*(k + c1*b)); + out[out_index] = s1*out[out_index] + s2*add[add_index]; + } + } + } + } +} + +void mean_cpu(float *x, int batch, int filters, int spatial, float *mean) +{ + float scale = 1./(batch * spatial); + int i,j,k; + for(i = 0; i < filters; ++i){ + mean[i] = 0; + for(j = 0; j < batch; ++j){ + for(k = 0; k < spatial; ++k){ + int index = j*filters*spatial + i*spatial + k; + mean[i] += x[index]; + } + } + mean[i] *= scale; + } +} + +void variance_cpu(float *x, float *mean, int batch, int filters, int spatial, float *variance) +{ + float scale = 1./(batch * spatial - 1); + int i,j,k; + for(i = 0; i < filters; ++i){ + variance[i] = 0; + for(j = 0; j < batch; ++j){ + for(k = 0; k < spatial; ++k){ + int index = j*filters*spatial + i*spatial + k; + variance[i] += pow((x[index] - mean[i]), 2); + } + } + variance[i] *= scale; + } +} + +void l2normalize_cpu(float *x, float *dx, int batch, int filters, int spatial) +{ + int b,f,i; + for(b = 0; b < batch; ++b){ + for(i = 0; i < spatial; ++i){ + float sum = 0; + for(f = 0; f < filters; ++f){ + int index = b*filters*spatial + f*spatial + i; + sum += powf(x[index], 2); + } + sum = sqrtf(sum); + for(f = 0; f < filters; ++f){ + int index = b*filters*spatial + f*spatial + i; + x[index] /= sum; + dx[index] = (1 - x[index]) / sum; + } + } + } +} + + +void normalize_cpu(float *x, float *mean, float *variance, int batch, int filters, int spatial) +{ + int b, f, i; + for(b = 0; b < batch; ++b){ + for(f = 0; f < filters; ++f){ + for(i = 0; i < spatial; ++i){ + int index = b*filters*spatial + f*spatial + i; + x[index] = (x[index] - mean[f])/(sqrt(variance[f]) + .000001f); + } + } + } +} + +void const_cpu(int N, float ALPHA, float *X, int INCX) +{ + int i; + for(i = 0; i < N; ++i) X[i*INCX] = ALPHA; +} + +void mul_cpu(int N, float *X, int INCX, float *Y, int INCY) +{ + int i; + for(i = 0; i < N; ++i) Y[i*INCY] *= X[i*INCX]; +} + +void pow_cpu(int N, float ALPHA, float *X, int INCX, float *Y, int INCY) +{ + int i; + for(i = 0; i < N; ++i) Y[i*INCY] = pow(X[i*INCX], ALPHA); +} + +void axpy_cpu(int N, float ALPHA, float *X, int INCX, float *Y, int INCY) +{ + int i; + for(i = 0; i < N; ++i) Y[i*INCY] += ALPHA*X[i*INCX]; +} + +void scal_cpu(int N, float ALPHA, float *X, int INCX) +{ + int i; + for(i = 0; i < N; ++i) X[i*INCX] *= ALPHA; +} + +void fill_cpu(int N, float ALPHA, float *X, int INCX) +{ + int i; + for(i = 0; i < N; ++i) X[i*INCX] = ALPHA; +} + +void deinter_cpu(int NX, float *X, int NY, float *Y, int B, float *OUT) +{ + int i, j; + int index = 0; + for(j = 0; j < B; ++j) { + for(i = 0; i < NX; ++i){ + if(X) X[j*NX + i] += OUT[index]; + ++index; + } + for(i = 0; i < NY; ++i){ + if(Y) Y[j*NY + i] += OUT[index]; + ++index; + } + } +} + +void inter_cpu(int NX, float *X, int NY, float *Y, int B, float *OUT) +{ + int i, j; + int index = 0; + for(j = 0; j < B; ++j) { + for(i = 0; i < NX; ++i){ + OUT[index++] = X[j*NX + i]; + } + for(i = 0; i < NY; ++i){ + OUT[index++] = Y[j*NY + i]; + } + } +} + +void copy_cpu(int N, float *X, int INCX, float *Y, int INCY) +{ + int i; + for(i = 0; i < N; ++i) Y[i*INCY] = X[i*INCX]; +} + +void mult_add_into_cpu(int N, float *X, float *Y, float *Z) +{ + int i; + for(i = 0; i < N; ++i) Z[i] += X[i]*Y[i]; +} + +void smooth_l1_cpu(int n, float *pred, float *truth, float *delta, float *error) +{ + int i; + for(i = 0; i < n; ++i){ + float diff = truth[i] - pred[i]; + float abs_val = fabs(diff); + if(abs_val < 1) { + error[i] = diff * diff; + delta[i] = diff; + } + else { + error[i] = 2*abs_val - 1; + delta[i] = (diff < 0) ? 1 : -1; + } + } +} + +void l1_cpu(int n, float *pred, float *truth, float *delta, float *error) +{ + int i; + for(i = 0; i < n; ++i){ + float diff = truth[i] - pred[i]; + error[i] = fabs(diff); + delta[i] = diff > 0 ? 1 : -1; + } +} + +void softmax_x_ent_cpu(int n, float *pred, float *truth, float *delta, float *error) +{ + int i; + for(i = 0; i < n; ++i){ + float t = truth[i]; + float p = pred[i]; + error[i] = (t) ? -log(p) : 0; + delta[i] = t-p; + } +} + +void logistic_x_ent_cpu(int n, float *pred, float *truth, float *delta, float *error) +{ + int i; + for(i = 0; i < n; ++i){ + float t = truth[i]; + float p = pred[i]; + error[i] = -t*log(p) - (1-t)*log(1-p); + delta[i] = t-p; + } +} + +void l2_cpu(int n, float *pred, float *truth, float *delta, float *error) +{ + int i; + for(i = 0; i < n; ++i){ + float diff = truth[i] - pred[i]; + error[i] = diff * diff; + delta[i] = diff; + } +} + +float dot_cpu(int N, float *X, int INCX, float *Y, int INCY) +{ + int i; + float dot = 0; + for(i = 0; i < N; ++i) dot += X[i*INCX] * Y[i*INCY]; + return dot; +} + +void softmax(float *input, int n, float temp, int stride, float *output) +{ + int i; + float sum = 0; + float largest = -FLT_MAX; + for(i = 0; i < n; ++i){ + if(input[i*stride] > largest) largest = input[i*stride]; + } + for(i = 0; i < n; ++i){ + float e = exp(input[i*stride]/temp - largest/temp); + sum += e; + output[i*stride] = e; + } + for(i = 0; i < n; ++i){ + output[i*stride] /= sum; + } +} + + +void softmax_cpu(float *input, int n, int batch, int batch_offset, int groups, int group_offset, int stride, float temp, float *output) +{ + int g, b; + for(b = 0; b < batch; ++b){ + for(g = 0; g < groups; ++g){ + softmax(input + b*batch_offset + g*group_offset, n, temp, stride, output + b*batch_offset + g*group_offset); + } + } +} + +void upsample_cpu(float *in, int w, int h, int c, int batch, int stride, int forward, float scale, float *out) +{ + int i, j, k, b; + for(b = 0; b < batch; ++b){ + for(k = 0; k < c; ++k){ + for(j = 0; j < h*stride; ++j){ + for(i = 0; i < w*stride; ++i){ + int in_index = b*w*h*c + k*w*h + (j/stride)*w + i/stride; + int out_index = b*w*h*c*stride*stride + k*w*h*stride*stride + j*w*stride + i; + if(forward) out[out_index] = scale*in[in_index]; + else in[in_index] += scale*out[out_index]; + } + } + } + } +} + + diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/blas.h b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/blas.h new file mode 100644 index 00000000000..707291dead0 --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/blas.h @@ -0,0 +1,105 @@ +#ifndef BLAS_H +#define BLAS_H +#include "darknet.h" + +void flatten(float *x, int size, int layers, int batch, int forward); +void pm(int M, int N, float *A); +float *random_matrix(int rows, int cols); +void time_random_matrix(int TA, int TB, int m, int k, int n); +void reorg_cpu(float *x, int w, int h, int c, int batch, int stride, int forward, float *out); + +void test_blas(); + +void inter_cpu(int NX, float *X, int NY, float *Y, int B, float *OUT); +void deinter_cpu(int NX, float *X, int NY, float *Y, int B, float *OUT); +void mult_add_into_cpu(int N, float *X, float *Y, float *Z); + +void const_cpu(int N, float ALPHA, float *X, int INCX); +void constrain_gpu(int N, float ALPHA, float * X, int INCX); +void pow_cpu(int N, float ALPHA, float *X, int INCX, float *Y, int INCY); +void mul_cpu(int N, float *X, int INCX, float *Y, int INCY); + +int test_gpu_blas(); +void shortcut_cpu(int batch, int w1, int h1, int c1, float *add, int w2, int h2, int c2, float s1, float s2, float *out); + +void mean_cpu(float *x, int batch, int filters, int spatial, float *mean); +void variance_cpu(float *x, float *mean, int batch, int filters, int spatial, float *variance); + +void scale_bias(float *output, float *scales, int batch, int n, int size); +void backward_scale_cpu(float *x_norm, float *delta, int batch, int n, int size, float *scale_updates); +void mean_delta_cpu(float *delta, float *variance, int batch, int filters, int spatial, float *mean_delta); +void variance_delta_cpu(float *x, float *delta, float *mean, float *variance, int batch, int filters, int spatial, float *variance_delta); +void normalize_delta_cpu(float *x, float *mean, float *variance, float *mean_delta, float *variance_delta, int batch, int filters, int spatial, float *delta); +void l2normalize_cpu(float *x, float *dx, int batch, int filters, int spatial); + +void smooth_l1_cpu(int n, float *pred, float *truth, float *delta, float *error); +void l2_cpu(int n, float *pred, float *truth, float *delta, float *error); +void l1_cpu(int n, float *pred, float *truth, float *delta, float *error); +void logistic_x_ent_cpu(int n, float *pred, float *truth, float *delta, float *error); +void softmax_x_ent_cpu(int n, float *pred, float *truth, float *delta, float *error); +void weighted_sum_cpu(float *a, float *b, float *s, int num, float *c); +void weighted_delta_cpu(float *a, float *b, float *s, float *da, float *db, float *ds, int n, float *dc); + +void softmax(float *input, int n, float temp, int stride, float *output); +void softmax_cpu(float *input, int n, int batch, int batch_offset, int groups, int group_offset, int stride, float temp, float *output); +void upsample_cpu(float *in, int w, int h, int c, int batch, int stride, int forward, float scale, float *out); + +#ifdef GPU +#include "cuda.h" +#include "tree.h" + +void axpy_gpu(int N, float ALPHA, float * X, int INCX, float * Y, int INCY); +void axpy_gpu_offset(int N, float ALPHA, float * X, int OFFX, int INCX, float * Y, int OFFY, int INCY); +void copy_gpu(int N, float * X, int INCX, float * Y, int INCY); +void copy_gpu_offset(int N, float * X, int OFFX, int INCX, float * Y, int OFFY, int INCY); +void add_gpu(int N, float ALPHA, float * X, int INCX); +void supp_gpu(int N, float ALPHA, float * X, int INCX); +void mask_gpu(int N, float * X, float mask_num, float * mask, float val); +void scale_mask_gpu(int N, float * X, float mask_num, float * mask, float scale); +void const_gpu(int N, float ALPHA, float *X, int INCX); +void pow_gpu(int N, float ALPHA, float *X, int INCX, float *Y, int INCY); +void mul_gpu(int N, float *X, int INCX, float *Y, int INCY); + +void mean_gpu(float *x, int batch, int filters, int spatial, float *mean); +void variance_gpu(float *x, float *mean, int batch, int filters, int spatial, float *variance); +void normalize_gpu(float *x, float *mean, float *variance, int batch, int filters, int spatial); +void l2normalize_gpu(float *x, float *dx, int batch, int filters, int spatial); + +void normalize_delta_gpu(float *x, float *mean, float *variance, float *mean_delta, float *variance_delta, int batch, int filters, int spatial, float *delta); + +void fast_mean_delta_gpu(float *delta, float *variance, int batch, int filters, int spatial, float *mean_delta); +void fast_variance_delta_gpu(float *x, float *delta, float *mean, float *variance, int batch, int filters, int spatial, float *variance_delta); + +void fast_variance_gpu(float *x, float *mean, int batch, int filters, int spatial, float *variance); +void fast_mean_gpu(float *x, int batch, int filters, int spatial, float *mean); +void shortcut_gpu(int batch, int w1, int h1, int c1, float *add, int w2, int h2, int c2, float s1, float s2, float *out); +void scale_bias_gpu(float *output, float *biases, int batch, int n, int size); +void backward_scale_gpu(float *x_norm, float *delta, int batch, int n, int size, float *scale_updates); +void scale_bias_gpu(float *output, float *biases, int batch, int n, int size); +void add_bias_gpu(float *output, float *biases, int batch, int n, int size); +void backward_bias_gpu(float *bias_updates, float *delta, int batch, int n, int size); + +void logistic_x_ent_gpu(int n, float *pred, float *truth, float *delta, float *error); +void softmax_x_ent_gpu(int n, float *pred, float *truth, float *delta, float *error); +void smooth_l1_gpu(int n, float *pred, float *truth, float *delta, float *error); +void l2_gpu(int n, float *pred, float *truth, float *delta, float *error); +void l1_gpu(int n, float *pred, float *truth, float *delta, float *error); +void wgan_gpu(int n, float *pred, float *truth, float *delta, float *error); +void weighted_delta_gpu(float *a, float *b, float *s, float *da, float *db, float *ds, int num, float *dc); +void weighted_sum_gpu(float *a, float *b, float *s, int num, float *c); +void mult_add_into_gpu(int num, float *a, float *b, float *c); +void inter_gpu(int NX, float *X, int NY, float *Y, int B, float *OUT); +void deinter_gpu(int NX, float *X, int NY, float *Y, int B, float *OUT); + +void reorg_gpu(float *x, int w, int h, int c, int batch, int stride, int forward, float *out); + +void softmax_gpu(float *input, int n, int batch, int batch_offset, int groups, int group_offset, int stride, float temp, float *output); +void adam_update_gpu(float *w, float *d, float *m, float *v, float B1, float B2, float eps, float decay, float rate, int n, int batch, int t); +void adam_gpu(int n, float *x, float *m, float *v, float B1, float B2, float rate, float eps, int t); + +void flatten_gpu(float *x, int spatial, int layers, int batch, int forward, float *out); +void softmax_tree(float *input, int spatial, int batch, int stride, float temp, float *output, tree hier); +void upsample_gpu(float *in, int w, int h, int c, int batch, int stride, int forward, float scale, float *out); + +#endif +#endif diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/blas_kernels.cu b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/blas_kernels.cu new file mode 100644 index 00000000000..47e82179170 --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/blas_kernels.cu @@ -0,0 +1,1035 @@ +#include "cuda_runtime.h" +#include "curand.h" +#include "cublas_v2.h" +#include + +extern "C" { +#include "blas.h" +#include "cuda.h" +#include "utils.h" +} + +__global__ void scale_bias_kernel(float *output, float *biases, int n, int size) +{ + int offset = blockIdx.x * blockDim.x + threadIdx.x; + int filter = blockIdx.y; + int batch = blockIdx.z; + + if(offset < size) output[(batch*n+filter)*size + offset] *= biases[filter]; +} + +void scale_bias_gpu(float *output, float *biases, int batch, int n, int size) +{ + dim3 dimGrid((size-1)/BLOCK + 1, n, batch); + dim3 dimBlock(BLOCK, 1, 1); + + scale_bias_kernel<<>>(output, biases, n, size); + check_error(cudaPeekAtLastError()); +} + +__global__ void backward_scale_kernel(float *x_norm, float *delta, int batch, int n, int size, float *scale_updates) +{ + __shared__ float part[BLOCK]; + int i,b; + int filter = blockIdx.x; + int p = threadIdx.x; + float sum = 0; + for(b = 0; b < batch; ++b){ + for(i = 0; i < size; i += BLOCK){ + int index = p + i + size*(filter + n*b); + sum += (p+i < size) ? delta[index]*x_norm[index] : 0; + } + } + part[p] = sum; + __syncthreads(); + if (p == 0) { + for(i = 0; i < BLOCK; ++i) scale_updates[filter] += part[i]; + } +} + +void backward_scale_gpu(float *x_norm, float *delta, int batch, int n, int size, float *scale_updates) +{ + backward_scale_kernel<<>>(x_norm, delta, batch, n, size, scale_updates); + check_error(cudaPeekAtLastError()); +} + +__global__ void add_bias_kernel(float *output, float *biases, int batch, int n, int size) +{ + int index = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if (index >= n*size*batch) return; + int i = index % size; + index /= size; + int j = index % n; + index /= n; + int k = index; + + output[(k*n+j)*size + i] += biases[j]; +} + +void add_bias_gpu(float *output, float *biases, int batch, int n, int size) +{ + int num = n*size*batch; + + add_bias_kernel<<>>(output, biases, batch, n, size); + check_error(cudaPeekAtLastError()); +} + +__global__ void backward_bias_conn_kernel(float *bias_updates, float *delta, int batch, int n) +{ + int index = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if (index >= n) return; + int b; + float sum = 0; + for(b = 0; b < batch; ++b){ + int i = b*n + index; + sum += delta[i]; + } + bias_updates[index] += sum; +} + +__global__ void backward_bias_kernel(float *bias_updates, float *delta, int batch, int n, int size) +{ + __shared__ float part[BLOCK]; + int i,b; + int filter = blockIdx.x; + int p = threadIdx.x; + float sum = 0; + for(b = 0; b < batch; ++b){ + for(i = 0; i < size; i += BLOCK){ + int index = p + i + size*(filter + n*b); + sum += (p+i < size) ? delta[index] : 0; + } + } + part[p] = sum; + __syncthreads(); + if (p == 0) { + for(i = 0; i < BLOCK; ++i) bias_updates[filter] += part[i]; + } +} + +void backward_bias_gpu(float *bias_updates, float *delta, int batch, int n, int size) +{ + if(size == 1){ + backward_bias_conn_kernel<<>>(bias_updates, delta, batch, n); + }else{ + backward_bias_kernel<<>>(bias_updates, delta, batch, n, size); + } + check_error(cudaPeekAtLastError()); +} + +/* +__global__ void dot_kernel(float *output, float scale, int batch, int n, int size, float *delta) +{ + int index = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + int f1 = index / n; + int f2 = index % n; + if (f2 <= f1) return; + + float sum = 0; + float norm1 = 0; + float norm2 = 0; + int b, i; + for(b = 0; b < batch; ++b){ + for(i = 0; i < size; ++i){ + int i1 = b * size * n + f1 * size + i; + int i2 = b * size * n + f2 * size + i; + sum += output[i1] * output[i2]; + norm1 += output[i1] * output[i1]; + norm2 += output[i2] * output[i2]; + } + } + norm1 = sqrt(norm1); + norm2 = sqrt(norm2); + float norm = norm1 * norm2; + sum = sum / norm; + for(b = 0; b < batch; ++b){ + for(i = 0; i < size; ++i){ + int i1 = b * size * n + f1 * size + i; + int i2 = b * size * n + f2 * size + i; + delta[i1] += - scale * sum * output[i2] / norm; + delta[i2] += - scale * sum * output[i1] / norm; + } + } +} + +void dot_error_gpu(layer l) +{ + dot_kernel<<>>(l.output_gpu, l.dot, l.batch, l.n, l.out_w * l.out_h, l.delta_gpu); + check_error(cudaPeekAtLastError()); +} +*/ + + +__global__ void adam_kernel(int N, float *x, float *m, float *v, float B1, float B2, float rate, float eps, int t) +{ + int index = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if (index >= N) return; + + float mhat = m[index] / (1.f - powf(B1, t)); + float vhat = v[index] / (1.f - powf(B2, t)); + + x[index] = x[index] + rate * mhat / (sqrtf(vhat) + eps); +} + +extern "C" void adam_gpu(int n, float *x, float *m, float *v, float B1, float B2, float rate, float eps, int t) +{ + adam_kernel<<>>(n, x, m, v, B1, B2, rate, eps, t); + check_error(cudaPeekAtLastError()); +} + +extern "C" void adam_update_gpu(float *w, float *d, float *m, float *v, float B1, float B2, float eps, float decay, float rate, int n, int batch, int t) +{ + scal_gpu(n, B1, m, 1); + scal_gpu(n, B2, v, 1); + axpy_gpu(n, -decay*batch, w, 1, d, 1); + + axpy_gpu(n, (1-B1), d, 1, m, 1); + mul_gpu(n, d, 1, d, 1); + axpy_gpu(n, (1-B2), d, 1, v, 1); + + adam_gpu(n, w, m, v, B1, B2, rate, eps, t); + fill_gpu(n, 0, d, 1); +} + +__global__ void normalize_kernel(int N, float *x, float *mean, float *variance, int batch, int filters, int spatial) +{ + int index = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if (index >= N) return; + int f = (index/spatial)%filters; + + x[index] = (x[index] - mean[f])/(sqrtf(variance[f] + .00001f)); +} + +__global__ void normalize_delta_kernel(int N, float *x, float *mean, float *variance, float *mean_delta, float *variance_delta, int batch, int filters, int spatial, float *delta) +{ + int index = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if (index >= N) return; + int f = (index/spatial)%filters; + + delta[index] = delta[index] * 1.f/(sqrtf(variance[f] + .00001f)) + variance_delta[f] * 2.f * (x[index] - mean[f]) / (spatial * batch) + mean_delta[f]/(spatial*batch); +} + +extern "C" void normalize_delta_gpu(float *x, float *mean, float *variance, float *mean_delta, float *variance_delta, int batch, int filters, int spatial, float *delta) +{ + size_t N = batch*filters*spatial; + normalize_delta_kernel<<>>(N, x, mean, variance, mean_delta, variance_delta, batch, filters, spatial, delta); + check_error(cudaPeekAtLastError()); +} + +__global__ void variance_delta_kernel(float *x, float *delta, float *mean, float *variance, int batch, int filters, int spatial, float *variance_delta) +{ + int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if (i >= filters) return; + int j,k; + variance_delta[i] = 0; + for(j = 0; j < batch; ++j){ + for(k = 0; k < spatial; ++k){ + int index = j*filters*spatial + i*spatial + k; + variance_delta[i] += delta[index]*(x[index] - mean[i]); + } + } + variance_delta[i] *= -.5f * powf(variance[i] + .00001f, (float)(-3.f/2.f)); +} + +__global__ void accumulate_kernel(float *x, int n, int groups, float *sum) +{ + int k; + int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if (i >= groups) return; + sum[i] = 0; + for(k = 0; k < n; ++k){ + sum[i] += x[k*groups + i]; + } +} + +__global__ void fast_mean_delta_kernel(float *delta, float *variance, int batch, int filters, int spatial, float *mean_delta) +{ + const int threads = BLOCK; + __shared__ float local[threads]; + + int id = threadIdx.x; + local[id] = 0; + + int filter = blockIdx.x; + + int i, j; + for(j = 0; j < batch; ++j){ + for(i = 0; i < spatial; i += threads){ + int index = j*spatial*filters + filter*spatial + i + id; + local[id] += (i+id < spatial) ? delta[index] : 0; + } + } + + __syncthreads(); + + if(id == 0){ + mean_delta[filter] = 0; + for(i = 0; i < threads; ++i){ + mean_delta[filter] += local[i]; + } + mean_delta[filter] *= (-1.f/sqrtf(variance[filter] + .00001f)); + } +} + +__global__ void fast_variance_delta_kernel(float *x, float *delta, float *mean, float *variance, int batch, int filters, int spatial, float *variance_delta) +{ + const int threads = BLOCK; + __shared__ float local[threads]; + + int id = threadIdx.x; + local[id] = 0; + + int filter = blockIdx.x; + + int i, j; + for(j = 0; j < batch; ++j){ + for(i = 0; i < spatial; i += threads){ + int index = j*spatial*filters + filter*spatial + i + id; + + local[id] += (i+id < spatial) ? delta[index]*(x[index] - mean[filter]) : 0; + } + } + + __syncthreads(); + + if(id == 0){ + variance_delta[filter] = 0; + for(i = 0; i < threads; ++i){ + variance_delta[filter] += local[i]; + } + variance_delta[filter] *= -.5f * powf(variance[filter] + .00001f, (float)(-3.f/2.f)); + } +} + + +__global__ void mean_delta_kernel(float *delta, float *variance, int batch, int filters, int spatial, float *mean_delta) +{ + int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if (i >= filters) return; + int j,k; + mean_delta[i] = 0; + for (j = 0; j < batch; ++j) { + for (k = 0; k < spatial; ++k) { + int index = j*filters*spatial + i*spatial + k; + mean_delta[i] += delta[index]; + } + } + mean_delta[i] *= (-1.f/sqrtf(variance[i] + .00001f)); +} + +extern "C" void mean_delta_gpu(float *delta, float *variance, int batch, int filters, int spatial, float *mean_delta) +{ + mean_delta_kernel<<>>(delta, variance, batch, filters, spatial, mean_delta); + check_error(cudaPeekAtLastError()); +} + +extern "C" void fast_mean_delta_gpu(float *delta, float *variance, int batch, int filters, int spatial, float *mean_delta) +{ + fast_mean_delta_kernel<<>>(delta, variance, batch, filters, spatial, mean_delta); + check_error(cudaPeekAtLastError()); +} + +extern "C" void fast_variance_delta_gpu(float *x, float *delta, float *mean, float *variance, int batch, int filters, int spatial, float *variance_delta) +{ + fast_variance_delta_kernel<<>>(x, delta, mean, variance, batch, filters, spatial, variance_delta); + check_error(cudaPeekAtLastError()); +} + +__global__ void mean_kernel(float *x, int batch, int filters, int spatial, float *mean) +{ + float scale = 1.f/(batch * spatial); + int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if (i >= filters) return; + int j,k; + mean[i] = 0; + for(j = 0; j < batch; ++j){ + for(k = 0; k < spatial; ++k){ + int index = j*filters*spatial + i*spatial + k; + mean[i] += x[index]; + } + } + mean[i] *= scale; +} + +__global__ void variance_kernel(float *x, float *mean, int batch, int filters, int spatial, float *variance) +{ + float scale = 1.f/(batch * spatial - 1); + int j,k; + int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if (i >= filters) return; + variance[i] = 0; + for(j = 0; j < batch; ++j){ + for(k = 0; k < spatial; ++k){ + int index = j*filters*spatial + i*spatial + k; + variance[i] += powf((x[index] - mean[i]), 2); + } + } + variance[i] *= scale; +} + +__global__ void reorg_kernel(int N, float *x, int w, int h, int c, int batch, int stride, int forward, float *out) +{ + int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if(i >= N) return; + int in_index = i; + int in_w = i%w; + i = i/w; + int in_h = i%h; + i = i/h; + int in_c = i%c; + i = i/c; + int b = i%batch; + + int out_c = c/(stride*stride); + + int c2 = in_c % out_c; + int offset = in_c / out_c; + int w2 = in_w*stride + offset % stride; + int h2 = in_h*stride + offset / stride; + //printf("%d\n", offset); + int out_index = w2 + w*stride*(h2 + h*stride*(c2 + out_c*b)); + + // printf("%d %d %d\n", w2, h2, c2); + //printf("%d %d\n", in_index, out_index); + //if(out_index >= N || out_index < 0) printf("bad bad bad \n"); + + if(forward) out[out_index] = x[in_index]; + else out[in_index] = x[out_index]; + //if(forward) out[1] = x[1]; + //else out[0] = x[0]; +} + +__global__ void axpy_kernel(int N, float ALPHA, float *X, int OFFX, int INCX, float *Y, int OFFY, int INCY) +{ + int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if(i < N) Y[OFFY+i*INCY] += ALPHA*X[OFFX+i*INCX]; +} + +__global__ void pow_kernel(int N, float ALPHA, float *X, int INCX, float *Y, int INCY) +{ + int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if(i < N) Y[i*INCY] = pow(X[i*INCX], ALPHA); +} + +__global__ void const_kernel(int N, float ALPHA, float *X, int INCX) +{ + int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if(i < N) X[i*INCX] = ALPHA; +} + +__global__ void constrain_kernel(int N, float ALPHA, float *X, int INCX) +{ + int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if(i < N) X[i*INCX] = fminf(ALPHA, fmaxf(-ALPHA, X[i*INCX])); +} + +__global__ void supp_kernel(int N, float ALPHA, float *X, int INCX) +{ + int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if(i < N) { + if((X[i*INCX] * X[i*INCX]) < (ALPHA * ALPHA)) X[i*INCX] = 0; + } +} + +__global__ void add_kernel(int N, float ALPHA, float *X, int INCX) +{ + int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if(i < N) X[i*INCX] += ALPHA; +} + +__global__ void scal_kernel(int N, float ALPHA, float *X, int INCX) +{ + int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if(i < N) X[i*INCX] *= ALPHA; +} + +__global__ void fill_kernel(int N, float ALPHA, float *X, int INCX) +{ + int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if(i < N) X[i*INCX] = ALPHA; +} + +__global__ void copy_kernel(int N, float *X, int OFFX, int INCX, float *Y, int OFFY, int INCY) +{ + int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if(i < N) Y[i*INCY + OFFY] = X[i*INCX + OFFX]; +} + +__global__ void mul_kernel(int N, float *X, int INCX, float *Y, int INCY) +{ + int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if(i < N) Y[i*INCY] *= X[i*INCX]; +} + + +extern "C" void normalize_gpu(float *x, float *mean, float *variance, int batch, int filters, int spatial) +{ + size_t N = batch*filters*spatial; + normalize_kernel<<>>(N, x, mean, variance, batch, filters, spatial); + check_error(cudaPeekAtLastError()); +} + +__global__ void l2norm_kernel(int N, float *x, float *dx, int batch, int filters, int spatial) +{ + int index = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if (index >= N) return; + int b = index / spatial; + int i = index % spatial; + int f; + float sum = 0; + for(f = 0; f < filters; ++f){ + int index = b*filters*spatial + f*spatial + i; + sum += powf(x[index], 2); + } + sum = sqrtf(sum); + if(sum == 0) sum = 1; + //printf("%f\n", sum); + for(f = 0; f < filters; ++f){ + int index = b*filters*spatial + f*spatial + i; + x[index] /= sum; + dx[index] = (1 - x[index]) / sum; + } +} + +extern "C" void l2normalize_gpu(float *x, float *dx, int batch, int filters, int spatial) +{ + size_t N = batch*spatial; + l2norm_kernel<<>>(N, x, dx, batch, filters, spatial); + check_error(cudaPeekAtLastError()); +} + +__global__ void fast_mean_kernel(float *x, int batch, int filters, int spatial, float *mean) +{ + const int threads = BLOCK; + __shared__ float local[threads]; + + int id = threadIdx.x; + local[id] = 0; + + int filter = blockIdx.x; + + int i, j; + for(j = 0; j < batch; ++j){ + for(i = 0; i < spatial; i += threads){ + int index = j*spatial*filters + filter*spatial + i + id; + local[id] += (i+id < spatial) ? x[index] : 0; + } + } + + __syncthreads(); + + if(id == 0){ + mean[filter] = 0; + for(i = 0; i < threads; ++i){ + mean[filter] += local[i]; + } + mean[filter] /= spatial * batch; + } +} + +__global__ void fast_variance_kernel(float *x, float *mean, int batch, int filters, int spatial, float *variance) +{ + const int threads = BLOCK; + __shared__ float local[threads]; + + int id = threadIdx.x; + local[id] = 0; + + int filter = blockIdx.x; + + int i, j; + for(j = 0; j < batch; ++j){ + for(i = 0; i < spatial; i += threads){ + int index = j*spatial*filters + filter*spatial + i + id; + + local[id] += (i+id < spatial) ? powf((x[index] - mean[filter]), 2) : 0; + } + } + + __syncthreads(); + + if(id == 0){ + variance[filter] = 0; + for(i = 0; i < threads; ++i){ + variance[filter] += local[i]; + } + variance[filter] /= (spatial * batch - 1); + } +} + +extern "C" void fast_mean_gpu(float *x, int batch, int filters, int spatial, float *mean) +{ + fast_mean_kernel<<>>(x, batch, filters, spatial, mean); + check_error(cudaPeekAtLastError()); +} + +extern "C" void fast_variance_gpu(float *x, float *mean, int batch, int filters, int spatial, float *variance) +{ + fast_variance_kernel<<>>(x, mean, batch, filters, spatial, variance); + check_error(cudaPeekAtLastError()); +} + + +extern "C" void mean_gpu(float *x, int batch, int filters, int spatial, float *mean) +{ + mean_kernel<<>>(x, batch, filters, spatial, mean); + check_error(cudaPeekAtLastError()); +} + +extern "C" void variance_gpu(float *x, float *mean, int batch, int filters, int spatial, float *variance) +{ + variance_kernel<<>>(x, mean, batch, filters, spatial, variance); + check_error(cudaPeekAtLastError()); +} + +extern "C" void axpy_gpu(int N, float ALPHA, float * X, int INCX, float * Y, int INCY) +{ + axpy_gpu_offset(N, ALPHA, X, 0, INCX, Y, 0, INCY); +} + +extern "C" void pow_gpu(int N, float ALPHA, float * X, int INCX, float * Y, int INCY) +{ + pow_kernel<<>>(N, ALPHA, X, INCX, Y, INCY); + check_error(cudaPeekAtLastError()); +} + +extern "C" void axpy_gpu_offset(int N, float ALPHA, float * X, int OFFX, int INCX, float * Y, int OFFY, int INCY) +{ + axpy_kernel<<>>(N, ALPHA, X, OFFX, INCX, Y, OFFY, INCY); + check_error(cudaPeekAtLastError()); +} + +extern "C" void copy_gpu(int N, float * X, int INCX, float * Y, int INCY) +{ + copy_gpu_offset(N, X, 0, INCX, Y, 0, INCY); +} + +extern "C" void mul_gpu(int N, float * X, int INCX, float * Y, int INCY) +{ + mul_kernel<<>>(N, X, INCX, Y, INCY); + check_error(cudaPeekAtLastError()); +} + +extern "C" void copy_gpu_offset(int N, float * X, int OFFX, int INCX, float * Y, int OFFY, int INCY) +{ + copy_kernel<<>>(N, X, OFFX, INCX, Y, OFFY, INCY); + check_error(cudaPeekAtLastError()); +} + +__global__ void flatten_kernel(int N, float *x, int spatial, int layers, int batch, int forward, float *out) +{ + int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if(i >= N) return; + int in_s = i%spatial; + i = i/spatial; + int in_c = i%layers; + i = i/layers; + int b = i; + + int i1 = b*layers*spatial + in_c*spatial + in_s; + int i2 = b*layers*spatial + in_s*layers + in_c; + + if (forward) out[i2] = x[i1]; + else out[i1] = x[i2]; +} + +extern "C" void flatten_gpu(float *x, int spatial, int layers, int batch, int forward, float *out) +{ + int size = spatial*batch*layers; + flatten_kernel<<>>(size, x, spatial, layers, batch, forward, out); + check_error(cudaPeekAtLastError()); +} + +extern "C" void reorg_gpu(float *x, int w, int h, int c, int batch, int stride, int forward, float *out) +{ + int size = w*h*c*batch; + reorg_kernel<<>>(size, x, w, h, c, batch, stride, forward, out); + check_error(cudaPeekAtLastError()); +} + +__global__ void mask_kernel(int n, float *x, float mask_num, float *mask, float val) +{ + int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if(i < n && mask[i] == mask_num) x[i] = val; +} + +extern "C" void mask_gpu(int N, float * X, float mask_num, float * mask, float val) +{ + mask_kernel<<>>(N, X, mask_num, mask, val); + check_error(cudaPeekAtLastError()); +} + +__global__ void scale_mask_kernel(int n, float *x, float mask_num, float *mask, float scale) +{ + int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if(i < n && mask[i] == mask_num) x[i] *= scale; +} + +extern "C" void scale_mask_gpu(int N, float * X, float mask_num, float * mask, float scale) +{ + scale_mask_kernel<<>>(N, X, mask_num, mask, scale); + check_error(cudaPeekAtLastError()); +} + +extern "C" void const_gpu(int N, float ALPHA, float * X, int INCX) +{ + const_kernel<<>>(N, ALPHA, X, INCX); + check_error(cudaPeekAtLastError()); +} + +extern "C" void constrain_gpu(int N, float ALPHA, float * X, int INCX) +{ + constrain_kernel<<>>(N, ALPHA, X, INCX); + check_error(cudaPeekAtLastError()); +} + + +extern "C" void add_gpu(int N, float ALPHA, float * X, int INCX) +{ + add_kernel<<>>(N, ALPHA, X, INCX); + check_error(cudaPeekAtLastError()); +} + +extern "C" void scal_gpu(int N, float ALPHA, float * X, int INCX) +{ + scal_kernel<<>>(N, ALPHA, X, INCX); + check_error(cudaPeekAtLastError()); +} + +extern "C" void supp_gpu(int N, float ALPHA, float * X, int INCX) +{ + supp_kernel<<>>(N, ALPHA, X, INCX); + check_error(cudaPeekAtLastError()); +} + +extern "C" void fill_gpu(int N, float ALPHA, float * X, int INCX) +{ + fill_kernel<<>>(N, ALPHA, X, INCX); + check_error(cudaPeekAtLastError()); +} + +__global__ void shortcut_kernel(int size, int minw, int minh, int minc, int stride, int sample, int batch, int w1, int h1, int c1, float *add, int w2, int h2, int c2, float s1, float s2, float *out) +{ + int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if (id >= size) return; + int i = id % minw; + id /= minw; + int j = id % minh; + id /= minh; + int k = id % minc; + id /= minc; + int b = id % batch; + + int out_index = i*sample + w2*(j*sample + h2*(k + c2*b)); + int add_index = i*stride + w1*(j*stride + h1*(k + c1*b)); + out[out_index] = s1*out[out_index] + s2*add[add_index]; + //out[out_index] += add[add_index]; +} + +extern "C" void shortcut_gpu(int batch, int w1, int h1, int c1, float *add, int w2, int h2, int c2, float s1, float s2, float *out) +{ + int minw = (w1 < w2) ? w1 : w2; + int minh = (h1 < h2) ? h1 : h2; + int minc = (c1 < c2) ? c1 : c2; + + int stride = w1/w2; + int sample = w2/w1; + assert(stride == h1/h2); + assert(sample == h2/h1); + if(stride < 1) stride = 1; + if(sample < 1) sample = 1; + + int size = batch * minw * minh * minc; + shortcut_kernel<<>>(size, minw, minh, minc, stride, sample, batch, w1, h1, c1, add, w2, h2, c2, s1, s2, out); + check_error(cudaPeekAtLastError()); +} + +__global__ void smooth_l1_kernel(int n, float *pred, float *truth, float *delta, float *error) +{ + int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if(i < n){ + float diff = truth[i] - pred[i]; + float abs_val = fabsf(diff); + if(abs_val < 1) { + error[i] = diff * diff; + delta[i] = diff; + } + else { + error[i] = 2*abs_val - 1; + delta[i] = (diff > 0) ? 1 : -1; + } + } +} + +extern "C" void smooth_l1_gpu(int n, float *pred, float *truth, float *delta, float *error) +{ + smooth_l1_kernel<<>>(n, pred, truth, delta, error); + check_error(cudaPeekAtLastError()); +} + +__global__ void softmax_x_ent_kernel(int n, float *pred, float *truth, float *delta, float *error) +{ + int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if(i < n){ + float t = truth[i]; + float p = pred[i]; + error[i] = (t) ? -log(p) : 0; + delta[i] = t-p; + } +} + +extern "C" void softmax_x_ent_gpu(int n, float *pred, float *truth, float *delta, float *error) +{ + softmax_x_ent_kernel<<>>(n, pred, truth, delta, error); + check_error(cudaPeekAtLastError()); +} + +__global__ void logistic_x_ent_kernel(int n, float *pred, float *truth, float *delta, float *error) +{ + int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if(i < n){ + float t = truth[i]; + float p = pred[i]; + error[i] = -t*log(p+.0000001) - (1-t)*log(1-p+.0000001); + delta[i] = t-p; + } +} + +extern "C" void logistic_x_ent_gpu(int n, float *pred, float *truth, float *delta, float *error) +{ + logistic_x_ent_kernel<<>>(n, pred, truth, delta, error); + check_error(cudaPeekAtLastError()); +} + +__global__ void l2_kernel(int n, float *pred, float *truth, float *delta, float *error) +{ + int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if(i < n){ + float diff = truth[i] - pred[i]; + error[i] = diff * diff; //I know this is technically wrong, deal with it. + delta[i] = diff; + } +} + +extern "C" void l2_gpu(int n, float *pred, float *truth, float *delta, float *error) +{ + l2_kernel<<>>(n, pred, truth, delta, error); + check_error(cudaPeekAtLastError()); +} + +__global__ void l1_kernel(int n, float *pred, float *truth, float *delta, float *error) +{ + int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if(i < n){ + float diff = truth[i] - pred[i]; + error[i] = abs(diff); + delta[i] = (diff > 0) ? 1 : -1; + } +} + +extern "C" void l1_gpu(int n, float *pred, float *truth, float *delta, float *error) +{ + l1_kernel<<>>(n, pred, truth, delta, error); + check_error(cudaPeekAtLastError()); +} + +__global__ void wgan_kernel(int n, float *pred, float *truth, float *delta, float *error) +{ + int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if(i < n){ + error[i] = truth[i] ? -pred[i] : pred[i]; + delta[i] = (truth[i] > 0) ? 1 : -1; + } +} + +extern "C" void wgan_gpu(int n, float *pred, float *truth, float *delta, float *error) +{ + wgan_kernel<<>>(n, pred, truth, delta, error); + check_error(cudaPeekAtLastError()); +} + + + + +__global__ void weighted_sum_kernel(int n, float *a, float *b, float *s, float *c) +{ + int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if(i < n){ + c[i] = s[i]*a[i] + (1-s[i])*(b ? b[i] : 0); + } +} + +__global__ void deinter_kernel(int NX, float *X, int NY, float *Y, int B, float *OUT) +{ + int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if(i < (NX+NY)*B){ + int b = i / (NX+NY); + int j = i % (NX+NY); + if (j < NX){ + if(X) X[b*NX + j] += OUT[i]; + } else { + if(Y) Y[b*NY + j - NX] += OUT[i]; + } + } +} + +extern "C" void deinter_gpu(int NX, float *X, int NY, float *Y, int B, float *OUT) +{ + deinter_kernel<<>>(NX, X, NY, Y, B, OUT); + check_error(cudaPeekAtLastError()); +} + +__global__ void inter_kernel(int NX, float *X, int NY, float *Y, int B, float *OUT) +{ + int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if(i < (NX+NY)*B){ + int b = i / (NX+NY); + int j = i % (NX+NY); + if (j < NX){ + OUT[i] = X[b*NX + j]; + } else { + OUT[i] = Y[b*NY + j - NX]; + } + } +} + +extern "C" void inter_gpu(int NX, float *X, int NY, float *Y, int B, float *OUT) +{ + inter_kernel<<>>(NX, X, NY, Y, B, OUT); + check_error(cudaPeekAtLastError()); +} + +extern "C" void weighted_sum_gpu(float *a, float *b, float *s, int num, float *c) +{ + weighted_sum_kernel<<>>(num, a, b, s, c); + check_error(cudaPeekAtLastError()); +} + +__global__ void weighted_delta_kernel(int n, float *a, float *b, float *s, float *da, float *db, float *ds, float *dc) +{ + int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if(i < n){ + if(da) da[i] += dc[i] * s[i]; + if(db) db[i] += dc[i] * (1-s[i]); + ds[i] += dc[i] * (a[i] - b[i]); + } +} + +extern "C" void weighted_delta_gpu(float *a, float *b, float *s, float *da, float *db, float *ds, int num, float *dc) +{ + weighted_delta_kernel<<>>(num, a, b, s, da, db, ds, dc); + check_error(cudaPeekAtLastError()); +} + +__global__ void mult_add_into_kernel(int n, float *a, float *b, float *c) +{ + int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if(i < n){ + c[i] += a[i]*b[i]; + } +} + +extern "C" void mult_add_into_gpu(int num, float *a, float *b, float *c) +{ + mult_add_into_kernel<<>>(num, a, b, c); + check_error(cudaPeekAtLastError()); +} + + +__device__ void softmax_device(float *input, int n, float temp, int stride, float *output) +{ + int i; + float sum = 0; + float largest = -INFINITY; + for(i = 0; i < n; ++i){ + int val = input[i*stride]; + largest = (val>largest) ? val : largest; + } + for(i = 0; i < n; ++i){ + float e = expf(input[i*stride]/temp - largest/temp); + sum += e; + output[i*stride] = e; + } + for(i = 0; i < n; ++i){ + output[i*stride] /= sum; + } +} + + +__global__ void softmax_tree_kernel(float *input, int spatial, int batch, int stride, float temp, float *output, int groups, int *group_size, int *group_offset) +{ + int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if (id >= spatial*batch*groups) return; + int s = id % spatial; + id = id / spatial; + int g = id % groups; + int b = id / groups; + int goff = group_offset[g]*spatial; + int boff = b*stride; + softmax_device(input + goff + boff + s, group_size[g], temp, spatial, output + goff + boff + s); +} + +extern "C" void softmax_tree(float *input, int spatial, int batch, int stride, float temp, float *output, tree hier) +{ + int *tree_groups_size = cuda_make_int_array(hier.group_size, hier.groups); + int *tree_groups_offset = cuda_make_int_array(hier.group_offset, hier.groups); + /* + static int *tree_groups_size = 0; + static int *tree_groups_offset = 0; + if(!tree_groups_size){ + tree_groups_size = cuda_make_int_array(hier.group_size, hier.groups); + tree_groups_offset = cuda_make_int_array(hier.group_offset, hier.groups); + } + */ + int num = spatial*batch*hier.groups; + softmax_tree_kernel<<>>(input, spatial, batch, stride, temp, output, hier.groups, tree_groups_size, tree_groups_offset); + check_error(cudaPeekAtLastError()); + cuda_free((float *)tree_groups_size); + cuda_free((float *)tree_groups_offset); +} + +__global__ void softmax_kernel(float *input, int n, int batch, int batch_offset, int groups, int group_offset, int stride, float temp, float *output) +{ + int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if (id >= batch*groups) return; + int b = id / groups; + int g = id % groups; + softmax_device(input + b*batch_offset + g*group_offset, n, temp, stride, output + b*batch_offset + g*group_offset); +} + +extern "C" void softmax_gpu(float *input, int n, int batch, int batch_offset, int groups, int group_offset, int stride, float temp, float *output) +{ + softmax_kernel<<>>(input, n, batch, batch_offset, groups, group_offset, stride, temp, output); + check_error(cudaPeekAtLastError()); +} + + +__global__ void upsample_kernel(size_t N, float *x, int w, int h, int c, int batch, int stride, int forward, float scale, float *out) +{ + size_t i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if(i >= N) return; + int out_index = i; + int out_w = i%(w*stride); + i = i/(w*stride); + int out_h = i%(h*stride); + i = i/(h*stride); + int out_c = i%c; + i = i/c; + int b = i%batch; + + int in_w = out_w / stride; + int in_h = out_h / stride; + int in_c = out_c; + + int in_index = b*w*h*c + in_c*w*h + in_h*w + in_w; + + + if(forward) out[out_index] += scale * x[in_index]; + else atomicAdd(x+in_index, scale * out[out_index]); +} +extern "C" void upsample_gpu(float *in, int w, int h, int c, int batch, int stride, int forward, float scale, float *out) +{ + size_t size = w*h*c*batch*stride*stride; + upsample_kernel<<>>(size, in, w, h, c, batch, stride, forward, scale, out); + check_error(cudaPeekAtLastError()); +} diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/box.c b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/box.c new file mode 100644 index 00000000000..8a1772c9ae0 --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/box.c @@ -0,0 +1,357 @@ +#include "box.h" +#include +#include +#include + +int nms_comparator(const void *pa, const void *pb) +{ + detection a = *(detection *)pa; + detection b = *(detection *)pb; + float diff = 0; + if(b.sort_class >= 0){ + diff = a.prob[b.sort_class] - b.prob[b.sort_class]; + } else { + diff = a.objectness - b.objectness; + } + if(diff < 0) return 1; + else if(diff > 0) return -1; + return 0; +} + +void do_nms_obj(detection *dets, int total, int classes, float thresh) +{ + int i, j, k; + k = total-1; + for(i = 0; i <= k; ++i){ + if(dets[i].objectness == 0){ + detection swap = dets[i]; + dets[i] = dets[k]; + dets[k] = swap; + --k; + --i; + } + } + total = k+1; + + for(i = 0; i < total; ++i){ + dets[i].sort_class = -1; + } + + qsort(dets, total, sizeof(detection), nms_comparator); + for(i = 0; i < total; ++i){ + if(dets[i].objectness == 0) continue; + box a = dets[i].bbox; + for(j = i+1; j < total; ++j){ + if(dets[j].objectness == 0) continue; + box b = dets[j].bbox; + if (box_iou(a, b) > thresh){ + dets[j].objectness = 0; + for(k = 0; k < classes; ++k){ + dets[j].prob[k] = 0; + } + } + } + } +} + + +void do_nms_sort(detection *dets, int total, int classes, float thresh) +{ + int i, j, k; + k = total-1; + for(i = 0; i <= k; ++i){ + if(dets[i].objectness == 0){ + detection swap = dets[i]; + dets[i] = dets[k]; + dets[k] = swap; + --k; + --i; + } + } + total = k+1; + + for(k = 0; k < classes; ++k){ + for(i = 0; i < total; ++i){ + dets[i].sort_class = k; + } + qsort(dets, total, sizeof(detection), nms_comparator); + for(i = 0; i < total; ++i){ + if(dets[i].prob[k] == 0) continue; + box a = dets[i].bbox; + for(j = i+1; j < total; ++j){ + box b = dets[j].bbox; + if (box_iou(a, b) > thresh){ + dets[j].prob[k] = 0; + } + } + } + } +} + +box float_to_box(float *f, int stride) +{ + box b = {0}; + b.x = f[0]; + b.y = f[1*stride]; + b.w = f[2*stride]; + b.h = f[3*stride]; + return b; +} + +dbox derivative(box a, box b) +{ + dbox d; + d.dx = 0; + d.dw = 0; + float l1 = a.x - a.w/2; + float l2 = b.x - b.w/2; + if (l1 > l2){ + d.dx -= 1; + d.dw += .5; + } + float r1 = a.x + a.w/2; + float r2 = b.x + b.w/2; + if(r1 < r2){ + d.dx += 1; + d.dw += .5; + } + if (l1 > r2) { + d.dx = -1; + d.dw = 0; + } + if (r1 < l2){ + d.dx = 1; + d.dw = 0; + } + + d.dy = 0; + d.dh = 0; + float t1 = a.y - a.h/2; + float t2 = b.y - b.h/2; + if (t1 > t2){ + d.dy -= 1; + d.dh += .5; + } + float b1 = a.y + a.h/2; + float b2 = b.y + b.h/2; + if(b1 < b2){ + d.dy += 1; + d.dh += .5; + } + if (t1 > b2) { + d.dy = -1; + d.dh = 0; + } + if (b1 < t2){ + d.dy = 1; + d.dh = 0; + } + return d; +} + +float overlap(float x1, float w1, float x2, float w2) +{ + float l1 = x1 - w1/2; + float l2 = x2 - w2/2; + float left = l1 > l2 ? l1 : l2; + float r1 = x1 + w1/2; + float r2 = x2 + w2/2; + float right = r1 < r2 ? r1 : r2; + return right - left; +} + +float box_intersection(box a, box b) +{ + float w = overlap(a.x, a.w, b.x, b.w); + float h = overlap(a.y, a.h, b.y, b.h); + if(w < 0 || h < 0) return 0; + float area = w*h; + return area; +} + +float box_union(box a, box b) +{ + float i = box_intersection(a, b); + float u = a.w*a.h + b.w*b.h - i; + return u; +} + +float box_iou(box a, box b) +{ + return box_intersection(a, b)/box_union(a, b); +} + +float box_rmse(box a, box b) +{ + return sqrt(pow(a.x-b.x, 2) + + pow(a.y-b.y, 2) + + pow(a.w-b.w, 2) + + pow(a.h-b.h, 2)); +} + +dbox dintersect(box a, box b) +{ + float w = overlap(a.x, a.w, b.x, b.w); + float h = overlap(a.y, a.h, b.y, b.h); + dbox dover = derivative(a, b); + dbox di; + + di.dw = dover.dw*h; + di.dx = dover.dx*h; + di.dh = dover.dh*w; + di.dy = dover.dy*w; + + return di; +} + +dbox dunion(box a, box b) +{ + dbox du; + + dbox di = dintersect(a, b); + du.dw = a.h - di.dw; + du.dh = a.w - di.dh; + du.dx = -di.dx; + du.dy = -di.dy; + + return du; +} + + +void test_dunion() +{ + box a = {0, 0, 1, 1}; + box dxa= {0+.0001, 0, 1, 1}; + box dya= {0, 0+.0001, 1, 1}; + box dwa= {0, 0, 1+.0001, 1}; + box dha= {0, 0, 1, 1+.0001}; + + box b = {.5, .5, .2, .2}; + dbox di = dunion(a,b); + printf("Union: %f %f %f %f\n", di.dx, di.dy, di.dw, di.dh); + float inter = box_union(a, b); + float xinter = box_union(dxa, b); + float yinter = box_union(dya, b); + float winter = box_union(dwa, b); + float hinter = box_union(dha, b); + xinter = (xinter - inter)/(.0001); + yinter = (yinter - inter)/(.0001); + winter = (winter - inter)/(.0001); + hinter = (hinter - inter)/(.0001); + printf("Union Manual %f %f %f %f\n", xinter, yinter, winter, hinter); +} +void test_dintersect() +{ + box a = {0, 0, 1, 1}; + box dxa= {0+.0001, 0, 1, 1}; + box dya= {0, 0+.0001, 1, 1}; + box dwa= {0, 0, 1+.0001, 1}; + box dha= {0, 0, 1, 1+.0001}; + + box b = {.5, .5, .2, .2}; + dbox di = dintersect(a,b); + printf("Inter: %f %f %f %f\n", di.dx, di.dy, di.dw, di.dh); + float inter = box_intersection(a, b); + float xinter = box_intersection(dxa, b); + float yinter = box_intersection(dya, b); + float winter = box_intersection(dwa, b); + float hinter = box_intersection(dha, b); + xinter = (xinter - inter)/(.0001); + yinter = (yinter - inter)/(.0001); + winter = (winter - inter)/(.0001); + hinter = (hinter - inter)/(.0001); + printf("Inter Manual %f %f %f %f\n", xinter, yinter, winter, hinter); +} + +void test_box() +{ + test_dintersect(); + test_dunion(); + box a = {0, 0, 1, 1}; + box dxa= {0+.00001, 0, 1, 1}; + box dya= {0, 0+.00001, 1, 1}; + box dwa= {0, 0, 1+.00001, 1}; + box dha= {0, 0, 1, 1+.00001}; + + box b = {.5, 0, .2, .2}; + + float iou = box_iou(a,b); + iou = (1-iou)*(1-iou); + printf("%f\n", iou); + dbox d = diou(a, b); + printf("%f %f %f %f\n", d.dx, d.dy, d.dw, d.dh); + + float xiou = box_iou(dxa, b); + float yiou = box_iou(dya, b); + float wiou = box_iou(dwa, b); + float hiou = box_iou(dha, b); + xiou = ((1-xiou)*(1-xiou) - iou)/(.00001); + yiou = ((1-yiou)*(1-yiou) - iou)/(.00001); + wiou = ((1-wiou)*(1-wiou) - iou)/(.00001); + hiou = ((1-hiou)*(1-hiou) - iou)/(.00001); + printf("manual %f %f %f %f\n", xiou, yiou, wiou, hiou); +} + +dbox diou(box a, box b) +{ + float u = box_union(a,b); + float i = box_intersection(a,b); + dbox di = dintersect(a,b); + dbox du = dunion(a,b); + dbox dd = {0,0,0,0}; + + if(i <= 0 || 1) { + dd.dx = b.x - a.x; + dd.dy = b.y - a.y; + dd.dw = b.w - a.w; + dd.dh = b.h - a.h; + return dd; + } + + dd.dx = 2*pow((1-(i/u)),1)*(di.dx*u - du.dx*i)/(u*u); + dd.dy = 2*pow((1-(i/u)),1)*(di.dy*u - du.dy*i)/(u*u); + dd.dw = 2*pow((1-(i/u)),1)*(di.dw*u - du.dw*i)/(u*u); + dd.dh = 2*pow((1-(i/u)),1)*(di.dh*u - du.dh*i)/(u*u); + return dd; +} + + +void do_nms(box *boxes, float **probs, int total, int classes, float thresh) +{ + int i, j, k; + for(i = 0; i < total; ++i){ + int any = 0; + for(k = 0; k < classes; ++k) any = any || (probs[i][k] > 0); + if(!any) { + continue; + } + for(j = i+1; j < total; ++j){ + if (box_iou(boxes[i], boxes[j]) > thresh){ + for(k = 0; k < classes; ++k){ + if (probs[i][k] < probs[j][k]) probs[i][k] = 0; + else probs[j][k] = 0; + } + } + } + } +} + +box encode_box(box b, box anchor) +{ + box encode; + encode.x = (b.x - anchor.x) / anchor.w; + encode.y = (b.y - anchor.y) / anchor.h; + encode.w = log2(b.w / anchor.w); + encode.h = log2(b.h / anchor.h); + return encode; +} + +box decode_box(box b, box anchor) +{ + box decode; + decode.x = b.x * anchor.w + anchor.x; + decode.y = b.y * anchor.h + anchor.y; + decode.w = pow(2., b.w) * anchor.w; + decode.h = pow(2., b.h) * anchor.h; + return decode; +} diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/box.h b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/box.h new file mode 100644 index 00000000000..dda3e59100c --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/box.h @@ -0,0 +1,14 @@ +#ifndef BOX_H +#define BOX_H +#include "darknet.h" + +typedef struct{ + float dx, dy, dw, dh; +} dbox; + +float box_rmse(box a, box b); +dbox diou(box a, box b); +box decode_box(box b, box anchor); +box encode_box(box b, box anchor); + +#endif diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/classifier.h b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/classifier.h new file mode 100644 index 00000000000..8b137891791 --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/classifier.h @@ -0,0 +1 @@ + diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/col2im.c b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/col2im.c new file mode 100644 index 00000000000..5c4605e1974 --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/col2im.c @@ -0,0 +1,39 @@ +#include +#include +void col2im_add_pixel(float *im, int height, int width, int channels, + int row, int col, int channel, int pad, float val) +{ + row -= pad; + col -= pad; + + if (row < 0 || col < 0 || + row >= height || col >= width) return; + im[col + width*(row + height*channel)] += val; +} +//This one might be too, can't remember. +void col2im_cpu(float* data_col, + int channels, int height, int width, + int ksize, int stride, int pad, float* data_im) +{ + int c,h,w; + int height_col = (height + 2*pad - ksize) / stride + 1; + int width_col = (width + 2*pad - ksize) / stride + 1; + + int channels_col = channels * ksize * ksize; + for (c = 0; c < channels_col; ++c) { + int w_offset = c % ksize; + int h_offset = (c / ksize) % ksize; + int c_im = c / ksize / ksize; + for (h = 0; h < height_col; ++h) { + for (w = 0; w < width_col; ++w) { + int im_row = h_offset + h * stride; + int im_col = w_offset + w * stride; + int col_index = (c * height_col + h) * width_col + w; + double val = data_col[col_index]; + col2im_add_pixel(data_im, height, width, channels, + im_row, im_col, c_im, pad, val); + } + } + } +} + diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/col2im.h b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/col2im.h new file mode 100644 index 00000000000..3fbe05307db --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/col2im.h @@ -0,0 +1,13 @@ +#ifndef COL2IM_H +#define COL2IM_H + +void col2im_cpu(float* data_col, + int channels, int height, int width, + int ksize, int stride, int pad, float* data_im); + +#ifdef GPU +void col2im_gpu(float *data_col, + int channels, int height, int width, + int ksize, int stride, int pad, float *data_im); +#endif +#endif diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/col2im_kernels.cu b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/col2im_kernels.cu new file mode 100644 index 00000000000..ba45e0fdec9 --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/col2im_kernels.cu @@ -0,0 +1,58 @@ +#include "cuda_runtime.h" +#include "curand.h" +#include "cublas_v2.h" + +extern "C" { +#include "col2im.h" +#include "cuda.h" +} + +// src: https://github.com/BVLC/caffe/blob/master/src/caffe/util/im2col.cu +// You may also want to read: https://github.com/BVLC/caffe/blob/master/LICENSE + +__global__ void col2im_gpu_kernel(const int n, const float* data_col, + const int height, const int width, const int ksize, + const int pad, + const int stride, + const int height_col, const int width_col, + float *data_im) { + int index = blockIdx.x*blockDim.x+threadIdx.x; + for(; index < n; index += blockDim.x*gridDim.x){ + float val = 0; + int w = index % width + pad; + int h = (index / width) % height + pad; + int c = index / (width * height); + // compute the start and end of the output + int w_col_start = (w < ksize) ? 0 : (w - ksize) / stride + 1; + int w_col_end = min(w / stride + 1, width_col); + int h_col_start = (h < ksize) ? 0 : (h - ksize) / stride + 1; + int h_col_end = min(h / stride + 1, height_col); + // equivalent implementation + int offset = + (c * ksize * ksize + h * ksize + w) * height_col * width_col; + int coeff_h_col = (1 - stride * ksize * height_col) * width_col; + int coeff_w_col = (1 - stride * height_col * width_col); + for (int h_col = h_col_start; h_col < h_col_end; ++h_col) { + for (int w_col = w_col_start; w_col < w_col_end; ++w_col) { + val += data_col[offset + h_col * coeff_h_col + w_col * coeff_w_col]; + } + } + data_im[index] += val; + } +} + +void col2im_gpu(float *data_col, + int channels, int height, int width, + int ksize, int stride, int pad, float *data_im){ + // We are going to launch channels * height_col * width_col kernels, each + // kernel responsible for copying a single-channel grid. + int height_col = (height + 2 * pad - ksize) / stride + 1; + int width_col = (width + 2 * pad - ksize) / stride + 1; + int num_kernels = channels * height * width; + col2im_gpu_kernel<<<(num_kernels+BLOCK-1)/BLOCK, + BLOCK>>>( + num_kernels, data_col, height, width, ksize, pad, + stride, height_col, + width_col, data_im); +} + diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/connected_layer.c b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/connected_layer.c new file mode 100644 index 00000000000..353f4e5677b --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/connected_layer.c @@ -0,0 +1,336 @@ +#include "connected_layer.h" +#include "convolutional_layer.h" +#include "batchnorm_layer.h" +#include "utils.h" +#include "cuda.h" +#include "blas.h" +#include "gemm.h" + +#include +#include +#include +#include + +layer make_connected_layer(int batch, int inputs, int outputs, ACTIVATION activation, int batch_normalize, int adam) +{ + int i; + layer l = {0}; + l.learning_rate_scale = 1; + l.type = CONNECTED; + + l.inputs = inputs; + l.outputs = outputs; + l.batch=batch; + l.batch_normalize = batch_normalize; + l.h = 1; + l.w = 1; + l.c = inputs; + l.out_h = 1; + l.out_w = 1; + l.out_c = outputs; + + l.output = calloc(batch*outputs, sizeof(float)); + l.delta = calloc(batch*outputs, sizeof(float)); + + l.weight_updates = calloc(inputs*outputs, sizeof(float)); + l.bias_updates = calloc(outputs, sizeof(float)); + + l.weights = calloc(outputs*inputs, sizeof(float)); + l.biases = calloc(outputs, sizeof(float)); + + l.forward = forward_connected_layer; + l.backward = backward_connected_layer; + l.update = update_connected_layer; + + //float scale = 1./sqrt(inputs); + float scale = sqrt(2./inputs); + for(i = 0; i < outputs*inputs; ++i){ + l.weights[i] = scale*rand_uniform(-1, 1); + } + + for(i = 0; i < outputs; ++i){ + l.biases[i] = 0; + } + + if(adam){ + l.m = calloc(l.inputs*l.outputs, sizeof(float)); + l.v = calloc(l.inputs*l.outputs, sizeof(float)); + l.bias_m = calloc(l.outputs, sizeof(float)); + l.scale_m = calloc(l.outputs, sizeof(float)); + l.bias_v = calloc(l.outputs, sizeof(float)); + l.scale_v = calloc(l.outputs, sizeof(float)); + } + if(batch_normalize){ + l.scales = calloc(outputs, sizeof(float)); + l.scale_updates = calloc(outputs, sizeof(float)); + for(i = 0; i < outputs; ++i){ + l.scales[i] = 1; + } + + l.mean = calloc(outputs, sizeof(float)); + l.mean_delta = calloc(outputs, sizeof(float)); + l.variance = calloc(outputs, sizeof(float)); + l.variance_delta = calloc(outputs, sizeof(float)); + + l.rolling_mean = calloc(outputs, sizeof(float)); + l.rolling_variance = calloc(outputs, sizeof(float)); + + l.x = calloc(batch*outputs, sizeof(float)); + l.x_norm = calloc(batch*outputs, sizeof(float)); + } + +#ifdef GPU + l.forward_gpu = forward_connected_layer_gpu; + l.backward_gpu = backward_connected_layer_gpu; + l.update_gpu = update_connected_layer_gpu; + + l.weights_gpu = cuda_make_array(l.weights, outputs*inputs); + l.biases_gpu = cuda_make_array(l.biases, outputs); + + l.weight_updates_gpu = cuda_make_array(l.weight_updates, outputs*inputs); + l.bias_updates_gpu = cuda_make_array(l.bias_updates, outputs); + + l.output_gpu = cuda_make_array(l.output, outputs*batch); + l.delta_gpu = cuda_make_array(l.delta, outputs*batch); + if (adam) { + l.m_gpu = cuda_make_array(0, inputs*outputs); + l.v_gpu = cuda_make_array(0, inputs*outputs); + l.bias_m_gpu = cuda_make_array(0, outputs); + l.bias_v_gpu = cuda_make_array(0, outputs); + l.scale_m_gpu = cuda_make_array(0, outputs); + l.scale_v_gpu = cuda_make_array(0, outputs); + } + + if(batch_normalize){ + l.mean_gpu = cuda_make_array(l.mean, outputs); + l.variance_gpu = cuda_make_array(l.variance, outputs); + + l.rolling_mean_gpu = cuda_make_array(l.mean, outputs); + l.rolling_variance_gpu = cuda_make_array(l.variance, outputs); + + l.mean_delta_gpu = cuda_make_array(l.mean, outputs); + l.variance_delta_gpu = cuda_make_array(l.variance, outputs); + + l.scales_gpu = cuda_make_array(l.scales, outputs); + l.scale_updates_gpu = cuda_make_array(l.scale_updates, outputs); + + l.x_gpu = cuda_make_array(l.output, l.batch*outputs); + l.x_norm_gpu = cuda_make_array(l.output, l.batch*outputs); +#ifdef CUDNN + cudnnCreateTensorDescriptor(&l.normTensorDesc); + cudnnCreateTensorDescriptor(&l.dstTensorDesc); + cudnnSetTensor4dDescriptor(l.dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l.batch, l.out_c, l.out_h, l.out_w); + cudnnSetTensor4dDescriptor(l.normTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, 1, l.out_c, 1, 1); +#endif + } +#endif + l.activation = activation; + fprintf(stderr, "connected %4d -> %4d\n", inputs, outputs); + return l; +} + +void update_connected_layer(layer l, update_args a) +{ + float learning_rate = a.learning_rate*l.learning_rate_scale; + float momentum = a.momentum; + float decay = a.decay; + int batch = a.batch; + axpy_cpu(l.outputs, learning_rate/batch, l.bias_updates, 1, l.biases, 1); + scal_cpu(l.outputs, momentum, l.bias_updates, 1); + + if(l.batch_normalize){ + axpy_cpu(l.outputs, learning_rate/batch, l.scale_updates, 1, l.scales, 1); + scal_cpu(l.outputs, momentum, l.scale_updates, 1); + } + + axpy_cpu(l.inputs*l.outputs, -decay*batch, l.weights, 1, l.weight_updates, 1); + axpy_cpu(l.inputs*l.outputs, learning_rate/batch, l.weight_updates, 1, l.weights, 1); + scal_cpu(l.inputs*l.outputs, momentum, l.weight_updates, 1); +} + +void forward_connected_layer(layer l, network net) +{ + fill_cpu(l.outputs*l.batch, 0, l.output, 1); + int m = l.batch; + int k = l.inputs; + int n = l.outputs; + float *a = net.input; + float *b = l.weights; + float *c = l.output; + gemm(0,1,m,n,k,1,a,k,b,k,1,c,n); + if(l.batch_normalize){ + forward_batchnorm_layer(l, net); + } else { + add_bias(l.output, l.biases, l.batch, l.outputs, 1); + } + activate_array(l.output, l.outputs*l.batch, l.activation); +} + +void backward_connected_layer(layer l, network net) +{ + gradient_array(l.output, l.outputs*l.batch, l.activation, l.delta); + + if(l.batch_normalize){ + backward_batchnorm_layer(l, net); + } else { + backward_bias(l.bias_updates, l.delta, l.batch, l.outputs, 1); + } + + int m = l.outputs; + int k = l.batch; + int n = l.inputs; + float *a = l.delta; + float *b = net.input; + float *c = l.weight_updates; + gemm(1,0,m,n,k,1,a,m,b,n,1,c,n); + + m = l.batch; + k = l.outputs; + n = l.inputs; + + a = l.delta; + b = l.weights; + c = net.delta; + + if(c) gemm(0,0,m,n,k,1,a,k,b,n,1,c,n); +} + + +void denormalize_connected_layer(layer l) +{ + int i, j; + for(i = 0; i < l.outputs; ++i){ + float scale = l.scales[i]/sqrt(l.rolling_variance[i] + .000001); + for(j = 0; j < l.inputs; ++j){ + l.weights[i*l.inputs + j] *= scale; + } + l.biases[i] -= l.rolling_mean[i] * scale; + l.scales[i] = 1; + l.rolling_mean[i] = 0; + l.rolling_variance[i] = 1; + } +} + + +void statistics_connected_layer(layer l) +{ + if(l.batch_normalize){ + printf("Scales "); + print_statistics(l.scales, l.outputs); + /* + printf("Rolling Mean "); + print_statistics(l.rolling_mean, l.outputs); + printf("Rolling Variance "); + print_statistics(l.rolling_variance, l.outputs); + */ + } + printf("Biases "); + print_statistics(l.biases, l.outputs); + printf("Weights "); + print_statistics(l.weights, l.outputs); +} + +#ifdef GPU + +void pull_connected_layer(layer l) +{ + cuda_pull_array(l.weights_gpu, l.weights, l.inputs*l.outputs); + cuda_pull_array(l.biases_gpu, l.biases, l.outputs); + cuda_pull_array(l.weight_updates_gpu, l.weight_updates, l.inputs*l.outputs); + cuda_pull_array(l.bias_updates_gpu, l.bias_updates, l.outputs); + if (l.batch_normalize){ + cuda_pull_array(l.scales_gpu, l.scales, l.outputs); + cuda_pull_array(l.rolling_mean_gpu, l.rolling_mean, l.outputs); + cuda_pull_array(l.rolling_variance_gpu, l.rolling_variance, l.outputs); + } +} + +void push_connected_layer(layer l) +{ + cuda_push_array(l.weights_gpu, l.weights, l.inputs*l.outputs); + cuda_push_array(l.biases_gpu, l.biases, l.outputs); + cuda_push_array(l.weight_updates_gpu, l.weight_updates, l.inputs*l.outputs); + cuda_push_array(l.bias_updates_gpu, l.bias_updates, l.outputs); + if (l.batch_normalize){ + cuda_push_array(l.scales_gpu, l.scales, l.outputs); + cuda_push_array(l.rolling_mean_gpu, l.rolling_mean, l.outputs); + cuda_push_array(l.rolling_variance_gpu, l.rolling_variance, l.outputs); + } +} + +void update_connected_layer_gpu(layer l, update_args a) +{ + float learning_rate = a.learning_rate*l.learning_rate_scale; + float momentum = a.momentum; + float decay = a.decay; + int batch = a.batch; + if(a.adam){ + adam_update_gpu(l.weights_gpu, l.weight_updates_gpu, l.m_gpu, l.v_gpu, a.B1, a.B2, a.eps, decay, learning_rate, l.inputs*l.outputs, batch, a.t); + adam_update_gpu(l.biases_gpu, l.bias_updates_gpu, l.bias_m_gpu, l.bias_v_gpu, a.B1, a.B2, a.eps, decay, learning_rate, l.outputs, batch, a.t); + if(l.scales_gpu){ + adam_update_gpu(l.scales_gpu, l.scale_updates_gpu, l.scale_m_gpu, l.scale_v_gpu, a.B1, a.B2, a.eps, decay, learning_rate, l.outputs, batch, a.t); + } + }else{ + axpy_gpu(l.outputs, learning_rate/batch, l.bias_updates_gpu, 1, l.biases_gpu, 1); + scal_gpu(l.outputs, momentum, l.bias_updates_gpu, 1); + + if(l.batch_normalize){ + axpy_gpu(l.outputs, learning_rate/batch, l.scale_updates_gpu, 1, l.scales_gpu, 1); + scal_gpu(l.outputs, momentum, l.scale_updates_gpu, 1); + } + + axpy_gpu(l.inputs*l.outputs, -decay*batch, l.weights_gpu, 1, l.weight_updates_gpu, 1); + axpy_gpu(l.inputs*l.outputs, learning_rate/batch, l.weight_updates_gpu, 1, l.weights_gpu, 1); + scal_gpu(l.inputs*l.outputs, momentum, l.weight_updates_gpu, 1); + } +} + +void forward_connected_layer_gpu(layer l, network net) +{ + fill_gpu(l.outputs*l.batch, 0, l.output_gpu, 1); + + int m = l.batch; + int k = l.inputs; + int n = l.outputs; + float * a = net.input_gpu; + float * b = l.weights_gpu; + float * c = l.output_gpu; + gemm_gpu(0,1,m,n,k,1,a,k,b,k,1,c,n); + + if (l.batch_normalize) { + forward_batchnorm_layer_gpu(l, net); + } else { + add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.outputs, 1); + } + activate_array_gpu(l.output_gpu, l.outputs*l.batch, l.activation); +} + +void backward_connected_layer_gpu(layer l, network net) +{ + constrain_gpu(l.outputs*l.batch, 1, l.delta_gpu, 1); + gradient_array_gpu(l.output_gpu, l.outputs*l.batch, l.activation, l.delta_gpu); + if(l.batch_normalize){ + backward_batchnorm_layer_gpu(l, net); + } else { + backward_bias_gpu(l.bias_updates_gpu, l.delta_gpu, l.batch, l.outputs, 1); + } + + int m = l.outputs; + int k = l.batch; + int n = l.inputs; + float * a = l.delta_gpu; + float * b = net.input_gpu; + float * c = l.weight_updates_gpu; + gemm_gpu(1,0,m,n,k,1,a,m,b,n,1,c,n); + + m = l.batch; + k = l.outputs; + n = l.inputs; + + a = l.delta_gpu; + b = l.weights_gpu; + c = net.delta_gpu; + + if(c) gemm_gpu(0,0,m,n,k,1,a,k,b,n,1,c,n); +} +#endif diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/connected_layer.h b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/connected_layer.h new file mode 100644 index 00000000000..6727a964eaa --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/connected_layer.h @@ -0,0 +1,23 @@ +#ifndef CONNECTED_LAYER_H +#define CONNECTED_LAYER_H + +#include "activations.h" +#include "layer.h" +#include "network.h" + +layer make_connected_layer(int batch, int inputs, int outputs, ACTIVATION activation, int batch_normalize, int adam); + +void forward_connected_layer(layer l, network net); +void backward_connected_layer(layer l, network net); +void update_connected_layer(layer l, update_args a); + +#ifdef GPU +void forward_connected_layer_gpu(layer l, network net); +void backward_connected_layer_gpu(layer l, network net); +void update_connected_layer_gpu(layer l, update_args a); +void push_connected_layer(layer l); +void pull_connected_layer(layer l); +#endif + +#endif + diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/convolutional_kernels.cu b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/convolutional_kernels.cu new file mode 100644 index 00000000000..8fa2ab2ef5d --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/convolutional_kernels.cu @@ -0,0 +1,322 @@ +#include "cuda_runtime.h" +#include "curand.h" +#include "cublas_v2.h" + +extern "C" { +#include "convolutional_layer.h" +#include "batchnorm_layer.h" +#include "gemm.h" +#include "blas.h" +#include "im2col.h" +#include "col2im.h" +#include "utils.h" +#include "cuda.h" +} + +__global__ void binarize_kernel(float *x, int n, float *binary) +{ + int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if (i >= n) return; + binary[i] = (x[i] >= 0) ? 1 : -1; +} + +void binarize_gpu(float *x, int n, float *binary) +{ + binarize_kernel<<>>(x, n, binary); + check_error(cudaPeekAtLastError()); +} + +__global__ void binarize_input_kernel(float *input, int n, int size, float *binary) +{ + int s = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if (s >= size) return; + int i = 0; + float mean = 0; + for(i = 0; i < n; ++i){ + mean += fabsf(input[i*size + s]); + } + mean = mean / n; + for(i = 0; i < n; ++i){ + binary[i*size + s] = (input[i*size + s] > 0) ? mean : -mean; + } +} + +void binarize_input_gpu(float *input, int n, int size, float *binary) +{ + binarize_input_kernel<<>>(input, n, size, binary); + check_error(cudaPeekAtLastError()); +} + + +__global__ void binarize_weights_kernel(float *weights, int n, int size, float *binary) +{ + int f = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if (f >= n) return; + int i = 0; + float mean = 0; + for(i = 0; i < size; ++i){ + mean += fabsf(weights[f*size + i]); + } + mean = mean / size; + for(i = 0; i < size; ++i){ + binary[f*size + i] = (weights[f*size + i] > 0) ? mean : -mean; + //binary[f*size + i] = weights[f*size + i]; + } +} + +void binarize_weights_gpu(float *weights, int n, int size, float *binary) +{ + binarize_weights_kernel<<>>(weights, n, size, binary); + check_error(cudaPeekAtLastError()); +} + +void forward_convolutional_layer_gpu(convolutional_layer l, network net) +{ + fill_gpu(l.outputs*l.batch, 0, l.output_gpu, 1); + if(l.binary){ + binarize_weights_gpu(l.weights_gpu, l.n, l.c/l.groups*l.size*l.size, l.binary_weights_gpu); + swap_binary(&l); + } + + if(l.xnor){ + binarize_weights_gpu(l.weights_gpu, l.n, l.c/l.groups*l.size*l.size, l.binary_weights_gpu); + swap_binary(&l); + binarize_gpu(net.input_gpu, l.c*l.h*l.w*l.batch, l.binary_input_gpu); + net.input_gpu = l.binary_input_gpu; + } + +#ifdef CUDNN + float one = 1; + cudnnConvolutionForward(cudnn_handle(), + &one, + l.srcTensorDesc, + net.input_gpu, + l.weightDesc, + l.weights_gpu, + l.convDesc, + l.fw_algo, + net.workspace, + l.workspace_size, + &one, + l.dstTensorDesc, + l.output_gpu); + +#else + int i, j; + int m = l.n/l.groups; + int k = l.size*l.size*l.c/l.groups; + int n = l.out_w*l.out_h; + for(i = 0; i < l.batch; ++i){ + for(j = 0; j < l.groups; ++j){ + float *a = l.weights_gpu + j*l.nweights/l.groups; + float *b = net.workspace; + float *c = l.output_gpu + (i*l.groups + j)*n*m; + + im2col_gpu(net.input_gpu + (i*l.groups + j)*l.c/l.groups*l.h*l.w, + l.c/l.groups, l.h, l.w, l.size, l.stride, l.pad, b); + gemm_gpu(0,0,m,n,k,1,a,k,b,n,1,c,n); + } + } +#endif + + if (l.batch_normalize) { + forward_batchnorm_layer_gpu(l, net); + } else { + add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.n, l.out_w*l.out_h); + } + + activate_array_gpu(l.output_gpu, l.outputs*l.batch, l.activation); + //if(l.dot > 0) dot_error_gpu(l); + if(l.binary || l.xnor) swap_binary(&l); +} + +__global__ void smooth_kernel(float *x, int n, int w, int h, int c, int size, float rate, float *delta) +{ + int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if(id >= n) return; + + int j = id % w; + id /= w; + int i = id % h; + id /= h; + int k = id % c; + id /= c; + int b = id; + + int w_offset = -(size/2.f); + int h_offset = -(size/2.f); + + int out_index = j + w*(i + h*(k + c*b)); + int l, m; + for(l = 0; l < size; ++l){ + for(m = 0; m < size; ++m){ + int cur_h = h_offset + i + l; + int cur_w = w_offset + j + m; + int index = cur_w + w*(cur_h + h*(k + b*c)); + int valid = (cur_h >= 0 && cur_h < h && + cur_w >= 0 && cur_w < w); + delta[out_index] += valid ? rate*(x[index] - x[out_index]) : 0; + } + } +} + +extern "C" void smooth_layer(layer l, int size, float rate) +{ + int h = l.out_h; + int w = l.out_w; + int c = l.out_c; + + size_t n = h*w*c*l.batch; + + smooth_kernel<<>>(l.output_gpu, n, l.w, l.h, l.c, size, rate, l.delta_gpu); + check_error(cudaPeekAtLastError()); +} + +void backward_convolutional_layer_gpu(convolutional_layer l, network net) +{ + if(l.smooth){ + smooth_layer(l, 5, l.smooth); + } + //constrain_gpu(l.outputs*l.batch, 1, l.delta_gpu, 1); + gradient_array_gpu(l.output_gpu, l.outputs*l.batch, l.activation, l.delta_gpu); + + + if(l.batch_normalize){ + backward_batchnorm_layer_gpu(l, net); + } else { + backward_bias_gpu(l.bias_updates_gpu, l.delta_gpu, l.batch, l.n, l.out_w*l.out_h); + } + float *original_input = net.input_gpu; + + if(l.xnor) net.input_gpu = l.binary_input_gpu; +#ifdef CUDNN + float one = 1; + cudnnConvolutionBackwardFilter(cudnn_handle(), + &one, + l.srcTensorDesc, + net.input_gpu, + l.ddstTensorDesc, + l.delta_gpu, + l.convDesc, + l.bf_algo, + net.workspace, + l.workspace_size, + &one, + l.dweightDesc, + l.weight_updates_gpu); + + if(net.delta_gpu){ + if(l.binary || l.xnor) swap_binary(&l); + cudnnConvolutionBackwardData(cudnn_handle(), + &one, + l.weightDesc, + l.weights_gpu, + l.ddstTensorDesc, + l.delta_gpu, + l.convDesc, + l.bd_algo, + net.workspace, + l.workspace_size, + &one, + l.dsrcTensorDesc, + net.delta_gpu); + if(l.binary || l.xnor) swap_binary(&l); + if(l.xnor) gradient_array_gpu(original_input, l.batch*l.c*l.h*l.w, HARDTAN, net.delta_gpu); + } + +#else + int m = l.n/l.groups; + int n = l.size*l.size*l.c/l.groups; + int k = l.out_w*l.out_h; + + int i, j; + for(i = 0; i < l.batch; ++i){ + for(j = 0; j < l.groups; ++j){ + float *a = l.delta_gpu + (i*l.groups + j)*m*k; + float *b = net.workspace; + float *c = l.weight_updates_gpu + j*l.nweights/l.groups; + + float *im = net.input_gpu+(i*l.groups + j)*l.c/l.groups*l.h*l.w; + + im2col_gpu(im, l.c/l.groups, l.h, l.w, + l.size, l.stride, l.pad, b); + gemm_gpu(0,1,m,n,k,1,a,k,b,k,1,c,n); + + if(net.delta_gpu){ + if(l.binary || l.xnor) swap_binary(&l); + a = l.weights_gpu + j*l.nweights/l.groups; + b = l.delta_gpu + (i*l.groups + j)*m*k; + c = net.workspace; + + gemm_gpu(1,0,n,k,m,1,a,n,b,k,0,c,k); + + col2im_gpu(net.workspace, l.c/l.groups, l.h, l.w, l.size, l.stride, + l.pad, net.delta_gpu + (i*l.groups + j)*l.c/l.groups*l.h*l.w); + if(l.binary || l.xnor) { + swap_binary(&l); + } + } + if(l.xnor) gradient_array_gpu(original_input + i*l.c*l.h*l.w, l.c*l.h*l.w, HARDTAN, net.delta_gpu + i*l.c*l.h*l.w); + } + } +#endif +} + +void pull_convolutional_layer(layer l) +{ + cuda_pull_array(l.weights_gpu, l.weights, l.nweights); + cuda_pull_array(l.biases_gpu, l.biases, l.n); + cuda_pull_array(l.weight_updates_gpu, l.weight_updates, l.nweights); + cuda_pull_array(l.bias_updates_gpu, l.bias_updates, l.n); + if (l.batch_normalize){ + cuda_pull_array(l.scales_gpu, l.scales, l.n); + cuda_pull_array(l.rolling_mean_gpu, l.rolling_mean, l.n); + cuda_pull_array(l.rolling_variance_gpu, l.rolling_variance, l.n); + } +} + +void push_convolutional_layer(layer l) +{ + cuda_push_array(l.weights_gpu, l.weights, l.nweights); + cuda_push_array(l.biases_gpu, l.biases, l.n); + cuda_push_array(l.weight_updates_gpu, l.weight_updates, l.nweights); + cuda_push_array(l.bias_updates_gpu, l.bias_updates, l.n); + if (l.batch_normalize){ + cuda_push_array(l.scales_gpu, l.scales, l.n); + cuda_push_array(l.rolling_mean_gpu, l.rolling_mean, l.n); + cuda_push_array(l.rolling_variance_gpu, l.rolling_variance, l.n); + } +} + +void update_convolutional_layer_gpu(layer l, update_args a) +{ + float learning_rate = a.learning_rate*l.learning_rate_scale; + float momentum = a.momentum; + float decay = a.decay; + int batch = a.batch; + + if(a.adam){ + adam_update_gpu(l.weights_gpu, l.weight_updates_gpu, l.m_gpu, l.v_gpu, a.B1, a.B2, a.eps, decay, learning_rate, l.nweights, batch, a.t); + adam_update_gpu(l.biases_gpu, l.bias_updates_gpu, l.bias_m_gpu, l.bias_v_gpu, a.B1, a.B2, a.eps, decay, learning_rate, l.n, batch, a.t); + if(l.scales_gpu){ + adam_update_gpu(l.scales_gpu, l.scale_updates_gpu, l.scale_m_gpu, l.scale_v_gpu, a.B1, a.B2, a.eps, decay, learning_rate, l.n, batch, a.t); + } + }else{ + axpy_gpu(l.nweights, -decay*batch, l.weights_gpu, 1, l.weight_updates_gpu, 1); + axpy_gpu(l.nweights, learning_rate/batch, l.weight_updates_gpu, 1, l.weights_gpu, 1); + scal_gpu(l.nweights, momentum, l.weight_updates_gpu, 1); + + axpy_gpu(l.n, learning_rate/batch, l.bias_updates_gpu, 1, l.biases_gpu, 1); + scal_gpu(l.n, momentum, l.bias_updates_gpu, 1); + + if(l.scales_gpu){ + axpy_gpu(l.n, learning_rate/batch, l.scale_updates_gpu, 1, l.scales_gpu, 1); + scal_gpu(l.n, momentum, l.scale_updates_gpu, 1); + } + } + if(l.clip){ + constrain_gpu(l.nweights, l.clip, l.weights_gpu, 1); + } +} + + diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/convolutional_layer.c b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/convolutional_layer.c new file mode 100644 index 00000000000..e4fb9bde047 --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/convolutional_layer.c @@ -0,0 +1,608 @@ +#include "convolutional_layer.h" +#include "utils.h" +#include "batchnorm_layer.h" +#include "im2col.h" +#include "col2im.h" +#include "blas.h" +#include "gemm.h" +#include +#include + +#ifdef AI2 +#include "xnor_layer.h" +#endif + +void swap_binary(convolutional_layer *l) +{ + float *swap = l->weights; + l->weights = l->binary_weights; + l->binary_weights = swap; + +#ifdef GPU + swap = l->weights_gpu; + l->weights_gpu = l->binary_weights_gpu; + l->binary_weights_gpu = swap; +#endif +} + +void binarize_weights(float *weights, int n, int size, float *binary) +{ + int i, f; + for(f = 0; f < n; ++f){ + float mean = 0; + for(i = 0; i < size; ++i){ + mean += fabs(weights[f*size + i]); + } + mean = mean / size; + for(i = 0; i < size; ++i){ + binary[f*size + i] = (weights[f*size + i] > 0) ? mean : -mean; + } + } +} + +void binarize_cpu(float *input, int n, float *binary) +{ + int i; + for(i = 0; i < n; ++i){ + binary[i] = (input[i] > 0) ? 1 : -1; + } +} + +void binarize_input(float *input, int n, int size, float *binary) +{ + int i, s; + for(s = 0; s < size; ++s){ + float mean = 0; + for(i = 0; i < n; ++i){ + mean += fabs(input[i*size + s]); + } + mean = mean / n; + for(i = 0; i < n; ++i){ + binary[i*size + s] = (input[i*size + s] > 0) ? mean : -mean; + } + } +} + +int convolutional_out_height(convolutional_layer l) +{ + return (l.h + 2*l.pad - l.size) / l.stride + 1; +} + +int convolutional_out_width(convolutional_layer l) +{ + return (l.w + 2*l.pad - l.size) / l.stride + 1; +} + +image get_convolutional_image(convolutional_layer l) +{ + return float_to_image(l.out_w,l.out_h,l.out_c,l.output); +} + +image get_convolutional_delta(convolutional_layer l) +{ + return float_to_image(l.out_w,l.out_h,l.out_c,l.delta); +} + +static size_t get_workspace_size(layer l){ +#ifdef CUDNN + if(gpu_index >= 0){ + size_t most = 0; + size_t s = 0; + cudnnGetConvolutionForwardWorkspaceSize(cudnn_handle(), + l.srcTensorDesc, + l.weightDesc, + l.convDesc, + l.dstTensorDesc, + l.fw_algo, + &s); + if (s > most) most = s; + cudnnGetConvolutionBackwardFilterWorkspaceSize(cudnn_handle(), + l.srcTensorDesc, + l.ddstTensorDesc, + l.convDesc, + l.dweightDesc, + l.bf_algo, + &s); + if (s > most) most = s; + cudnnGetConvolutionBackwardDataWorkspaceSize(cudnn_handle(), + l.weightDesc, + l.ddstTensorDesc, + l.convDesc, + l.dsrcTensorDesc, + l.bd_algo, + &s); + if (s > most) most = s; + return most; + } +#endif + return (size_t)l.out_h*l.out_w*l.size*l.size*l.c/l.groups*sizeof(float); +} + +#ifdef GPU +#ifdef CUDNN +void cudnn_convolutional_setup(layer *l) +{ + cudnnSetTensor4dDescriptor(l->dsrcTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l->batch, l->c, l->h, l->w); + cudnnSetTensor4dDescriptor(l->ddstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l->batch, l->out_c, l->out_h, l->out_w); + + cudnnSetTensor4dDescriptor(l->srcTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l->batch, l->c, l->h, l->w); + cudnnSetTensor4dDescriptor(l->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l->batch, l->out_c, l->out_h, l->out_w); + cudnnSetTensor4dDescriptor(l->normTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, 1, l->out_c, 1, 1); + + cudnnSetFilter4dDescriptor(l->dweightDesc, CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, l->n, l->c/l->groups, l->size, l->size); + cudnnSetFilter4dDescriptor(l->weightDesc, CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, l->n, l->c/l->groups, l->size, l->size); + #if CUDNN_MAJOR >= 6 + cudnnSetConvolution2dDescriptor(l->convDesc, l->pad, l->pad, l->stride, l->stride, 1, 1, CUDNN_CROSS_CORRELATION, CUDNN_DATA_FLOAT); + #else + cudnnSetConvolution2dDescriptor(l->convDesc, l->pad, l->pad, l->stride, l->stride, 1, 1, CUDNN_CROSS_CORRELATION); + #endif + + #if CUDNN_MAJOR >= 7 + cudnnSetConvolutionGroupCount(l->convDesc, l->groups); + #else + if(l->groups > 1){ + error("CUDNN < 7 doesn't support groups, please upgrade!"); + } + #endif + + cudnnGetConvolutionForwardAlgorithm(cudnn_handle(), + l->srcTensorDesc, + l->weightDesc, + l->convDesc, + l->dstTensorDesc, + CUDNN_CONVOLUTION_FWD_SPECIFY_WORKSPACE_LIMIT, + 4000000000, + &l->fw_algo); + cudnnGetConvolutionBackwardDataAlgorithm(cudnn_handle(), + l->weightDesc, + l->ddstTensorDesc, + l->convDesc, + l->dsrcTensorDesc, + CUDNN_CONVOLUTION_BWD_DATA_SPECIFY_WORKSPACE_LIMIT, + 4000000000, + &l->bd_algo); + cudnnGetConvolutionBackwardFilterAlgorithm(cudnn_handle(), + l->srcTensorDesc, + l->ddstTensorDesc, + l->convDesc, + l->dweightDesc, + CUDNN_CONVOLUTION_BWD_FILTER_SPECIFY_WORKSPACE_LIMIT, + 4000000000, + &l->bf_algo); +} +#endif +#endif + +convolutional_layer make_convolutional_layer(int batch, int h, int w, int c, int n, int groups, int size, int stride, int padding, ACTIVATION activation, int batch_normalize, int binary, int xnor, int adam) +{ + int i; + convolutional_layer l = {0}; + l.type = CONVOLUTIONAL; + + l.groups = groups; + l.h = h; + l.w = w; + l.c = c; + l.n = n; + l.binary = binary; + l.xnor = xnor; + l.batch = batch; + l.stride = stride; + l.size = size; + l.pad = padding; + l.batch_normalize = batch_normalize; + + l.weights = calloc(c/groups*n*size*size, sizeof(float)); + l.weight_updates = calloc(c/groups*n*size*size, sizeof(float)); + + l.biases = calloc(n, sizeof(float)); + l.bias_updates = calloc(n, sizeof(float)); + + l.nweights = c/groups*n*size*size; + l.nbiases = n; + + // float scale = 1./sqrt(size*size*c); + float scale = sqrt(2./(size*size*c/l.groups)); + //printf("convscale %f\n", scale); + //scale = .02; + //for(i = 0; i < c*n*size*size; ++i) l.weights[i] = scale*rand_uniform(-1, 1); + for(i = 0; i < l.nweights; ++i) l.weights[i] = scale*rand_normal(); + int out_w = convolutional_out_width(l); + int out_h = convolutional_out_height(l); + l.out_h = out_h; + l.out_w = out_w; + l.out_c = n; + l.outputs = l.out_h * l.out_w * l.out_c; + l.inputs = l.w * l.h * l.c; + + l.output = calloc(l.batch*l.outputs, sizeof(float)); + l.delta = calloc(l.batch*l.outputs, sizeof(float)); + + l.forward = forward_convolutional_layer; + l.backward = backward_convolutional_layer; + l.update = update_convolutional_layer; + if(binary){ + l.binary_weights = calloc(l.nweights, sizeof(float)); + l.cweights = calloc(l.nweights, sizeof(char)); + l.scales = calloc(n, sizeof(float)); + } + if(xnor){ + l.binary_weights = calloc(l.nweights, sizeof(float)); + l.binary_input = calloc(l.inputs*l.batch, sizeof(float)); + } + + if(batch_normalize){ + l.scales = calloc(n, sizeof(float)); + l.scale_updates = calloc(n, sizeof(float)); + for(i = 0; i < n; ++i){ + l.scales[i] = 1; + } + + l.mean = calloc(n, sizeof(float)); + l.variance = calloc(n, sizeof(float)); + + l.mean_delta = calloc(n, sizeof(float)); + l.variance_delta = calloc(n, sizeof(float)); + + l.rolling_mean = calloc(n, sizeof(float)); + l.rolling_variance = calloc(n, sizeof(float)); + l.x = calloc(l.batch*l.outputs, sizeof(float)); + l.x_norm = calloc(l.batch*l.outputs, sizeof(float)); + } + if(adam){ + l.m = calloc(l.nweights, sizeof(float)); + l.v = calloc(l.nweights, sizeof(float)); + l.bias_m = calloc(n, sizeof(float)); + l.scale_m = calloc(n, sizeof(float)); + l.bias_v = calloc(n, sizeof(float)); + l.scale_v = calloc(n, sizeof(float)); + } + +#ifdef GPU + l.forward_gpu = forward_convolutional_layer_gpu; + l.backward_gpu = backward_convolutional_layer_gpu; + l.update_gpu = update_convolutional_layer_gpu; + + if(gpu_index >= 0){ + if (adam) { + l.m_gpu = cuda_make_array(l.m, l.nweights); + l.v_gpu = cuda_make_array(l.v, l.nweights); + l.bias_m_gpu = cuda_make_array(l.bias_m, n); + l.bias_v_gpu = cuda_make_array(l.bias_v, n); + l.scale_m_gpu = cuda_make_array(l.scale_m, n); + l.scale_v_gpu = cuda_make_array(l.scale_v, n); + } + + l.weights_gpu = cuda_make_array(l.weights, l.nweights); + l.weight_updates_gpu = cuda_make_array(l.weight_updates, l.nweights); + + l.biases_gpu = cuda_make_array(l.biases, n); + l.bias_updates_gpu = cuda_make_array(l.bias_updates, n); + + l.delta_gpu = cuda_make_array(l.delta, l.batch*out_h*out_w*n); + l.output_gpu = cuda_make_array(l.output, l.batch*out_h*out_w*n); + + if(binary){ + l.binary_weights_gpu = cuda_make_array(l.weights, l.nweights); + } + if(xnor){ + l.binary_weights_gpu = cuda_make_array(l.weights, l.nweights); + l.binary_input_gpu = cuda_make_array(0, l.inputs*l.batch); + } + + if(batch_normalize){ + l.mean_gpu = cuda_make_array(l.mean, n); + l.variance_gpu = cuda_make_array(l.variance, n); + + l.rolling_mean_gpu = cuda_make_array(l.mean, n); + l.rolling_variance_gpu = cuda_make_array(l.variance, n); + + l.mean_delta_gpu = cuda_make_array(l.mean, n); + l.variance_delta_gpu = cuda_make_array(l.variance, n); + + l.scales_gpu = cuda_make_array(l.scales, n); + l.scale_updates_gpu = cuda_make_array(l.scale_updates, n); + + l.x_gpu = cuda_make_array(l.output, l.batch*out_h*out_w*n); + l.x_norm_gpu = cuda_make_array(l.output, l.batch*out_h*out_w*n); + } +#ifdef CUDNN + cudnnCreateTensorDescriptor(&l.normTensorDesc); + cudnnCreateTensorDescriptor(&l.srcTensorDesc); + cudnnCreateTensorDescriptor(&l.dstTensorDesc); + cudnnCreateFilterDescriptor(&l.weightDesc); + cudnnCreateTensorDescriptor(&l.dsrcTensorDesc); + cudnnCreateTensorDescriptor(&l.ddstTensorDesc); + cudnnCreateFilterDescriptor(&l.dweightDesc); + cudnnCreateConvolutionDescriptor(&l.convDesc); + cudnn_convolutional_setup(&l); +#endif + } +#endif + l.workspace_size = get_workspace_size(l); + l.activation = activation; + + fprintf(stderr, "conv %5d %2d x%2d /%2d %4d x%4d x%4d -> %4d x%4d x%4d %5.3f BFLOPs\n", n, size, size, stride, w, h, c, l.out_w, l.out_h, l.out_c, (2.0 * l.n * l.size*l.size*l.c/l.groups * l.out_h*l.out_w)/1000000000.); + + return l; +} + +void denormalize_convolutional_layer(convolutional_layer l) +{ + int i, j; + for(i = 0; i < l.n; ++i){ + float scale = l.scales[i]/sqrt(l.rolling_variance[i] + .00001); + for(j = 0; j < l.c/l.groups*l.size*l.size; ++j){ + l.weights[i*l.c/l.groups*l.size*l.size + j] *= scale; + } + l.biases[i] -= l.rolling_mean[i] * scale; + l.scales[i] = 1; + l.rolling_mean[i] = 0; + l.rolling_variance[i] = 1; + } +} + +/* +void test_convolutional_layer() +{ + convolutional_layer l = make_convolutional_layer(1, 5, 5, 3, 2, 5, 2, 1, LEAKY, 1, 0, 0, 0); + l.batch_normalize = 1; + float data[] = {1,1,1,1,1, + 1,1,1,1,1, + 1,1,1,1,1, + 1,1,1,1,1, + 1,1,1,1,1, + 2,2,2,2,2, + 2,2,2,2,2, + 2,2,2,2,2, + 2,2,2,2,2, + 2,2,2,2,2, + 3,3,3,3,3, + 3,3,3,3,3, + 3,3,3,3,3, + 3,3,3,3,3, + 3,3,3,3,3}; + //net.input = data; + //forward_convolutional_layer(l); +} +*/ + +void resize_convolutional_layer(convolutional_layer *l, int w, int h) +{ + l->w = w; + l->h = h; + int out_w = convolutional_out_width(*l); + int out_h = convolutional_out_height(*l); + + l->out_w = out_w; + l->out_h = out_h; + + l->outputs = l->out_h * l->out_w * l->out_c; + l->inputs = l->w * l->h * l->c; + + l->output = realloc(l->output, l->batch*l->outputs*sizeof(float)); + l->delta = realloc(l->delta, l->batch*l->outputs*sizeof(float)); + if(l->batch_normalize){ + l->x = realloc(l->x, l->batch*l->outputs*sizeof(float)); + l->x_norm = realloc(l->x_norm, l->batch*l->outputs*sizeof(float)); + } + +#ifdef GPU + cuda_free(l->delta_gpu); + cuda_free(l->output_gpu); + + l->delta_gpu = cuda_make_array(l->delta, l->batch*l->outputs); + l->output_gpu = cuda_make_array(l->output, l->batch*l->outputs); + + if(l->batch_normalize){ + cuda_free(l->x_gpu); + cuda_free(l->x_norm_gpu); + + l->x_gpu = cuda_make_array(l->output, l->batch*l->outputs); + l->x_norm_gpu = cuda_make_array(l->output, l->batch*l->outputs); + } +#ifdef CUDNN + cudnn_convolutional_setup(l); +#endif +#endif + l->workspace_size = get_workspace_size(*l); +} + +void add_bias(float *output, float *biases, int batch, int n, int size) +{ + int i,j,b; + for(b = 0; b < batch; ++b){ + for(i = 0; i < n; ++i){ + for(j = 0; j < size; ++j){ + output[(b*n + i)*size + j] += biases[i]; + } + } + } +} + +void scale_bias(float *output, float *scales, int batch, int n, int size) +{ + int i,j,b; + for(b = 0; b < batch; ++b){ + for(i = 0; i < n; ++i){ + for(j = 0; j < size; ++j){ + output[(b*n + i)*size + j] *= scales[i]; + } + } + } +} + +void backward_bias(float *bias_updates, float *delta, int batch, int n, int size) +{ + int i,b; + for(b = 0; b < batch; ++b){ + for(i = 0; i < n; ++i){ + bias_updates[i] += sum_array(delta+size*(i+b*n), size); + } + } +} + +void forward_convolutional_layer(convolutional_layer l, network net) +{ + int i, j; + + fill_cpu(l.outputs*l.batch, 0, l.output, 1); + + if(l.xnor){ + binarize_weights(l.weights, l.n, l.c/l.groups*l.size*l.size, l.binary_weights); + swap_binary(&l); + binarize_cpu(net.input, l.c*l.h*l.w*l.batch, l.binary_input); + net.input = l.binary_input; + } + + int m = l.n/l.groups; + int k = l.size*l.size*l.c/l.groups; + int n = l.out_w*l.out_h; + for(i = 0; i < l.batch; ++i){ + for(j = 0; j < l.groups; ++j){ + float *a = l.weights + j*l.nweights/l.groups; + float *b = net.workspace; + float *c = l.output + (i*l.groups + j)*n*m; + + im2col_cpu(net.input + (i*l.groups + j)*l.c/l.groups*l.h*l.w, + l.c/l.groups, l.h, l.w, l.size, l.stride, l.pad, b); + gemm(0,0,m,n,k,1,a,k,b,n,1,c,n); + } + } + + if(l.batch_normalize){ + forward_batchnorm_layer(l, net); + } else { + add_bias(l.output, l.biases, l.batch, l.n, l.out_h*l.out_w); + } + + activate_array(l.output, l.outputs*l.batch, l.activation); + if(l.binary || l.xnor) swap_binary(&l); +} + +void backward_convolutional_layer(convolutional_layer l, network net) +{ + int i, j; + int m = l.n/l.groups; + int n = l.size*l.size*l.c/l.groups; + int k = l.out_w*l.out_h; + + gradient_array(l.output, l.outputs*l.batch, l.activation, l.delta); + + if(l.batch_normalize){ + backward_batchnorm_layer(l, net); + } else { + backward_bias(l.bias_updates, l.delta, l.batch, l.n, k); + } + + for(i = 0; i < l.batch; ++i){ + for(j = 0; j < l.groups; ++j){ + float *a = l.delta + (i*l.groups + j)*m*k; + float *b = net.workspace; + float *c = l.weight_updates + j*l.nweights/l.groups; + + float *im = net.input+(i*l.groups + j)*l.c/l.groups*l.h*l.w; + + im2col_cpu(im, l.c/l.groups, l.h, l.w, + l.size, l.stride, l.pad, b); + gemm(0,1,m,n,k,1,a,k,b,k,1,c,n); + + if(net.delta){ + a = l.weights + j*l.nweights/l.groups; + b = l.delta + (i*l.groups + j)*m*k; + c = net.workspace; + + gemm(1,0,n,k,m,1,a,n,b,k,0,c,k); + + col2im_cpu(net.workspace, l.c/l.groups, l.h, l.w, l.size, l.stride, + l.pad, net.delta + (i*l.groups + j)*l.c/l.groups*l.h*l.w); + } + } + } +} + +void update_convolutional_layer(convolutional_layer l, update_args a) +{ + float learning_rate = a.learning_rate*l.learning_rate_scale; + float momentum = a.momentum; + float decay = a.decay; + int batch = a.batch; + + axpy_cpu(l.n, learning_rate/batch, l.bias_updates, 1, l.biases, 1); + scal_cpu(l.n, momentum, l.bias_updates, 1); + + if(l.scales){ + axpy_cpu(l.n, learning_rate/batch, l.scale_updates, 1, l.scales, 1); + scal_cpu(l.n, momentum, l.scale_updates, 1); + } + + axpy_cpu(l.nweights, -decay*batch, l.weights, 1, l.weight_updates, 1); + axpy_cpu(l.nweights, learning_rate/batch, l.weight_updates, 1, l.weights, 1); + scal_cpu(l.nweights, momentum, l.weight_updates, 1); +} + + +image get_convolutional_weight(convolutional_layer l, int i) +{ + int h = l.size; + int w = l.size; + int c = l.c/l.groups; + return float_to_image(w,h,c,l.weights+i*h*w*c); +} + +void rgbgr_weights(convolutional_layer l) +{ + int i; + for(i = 0; i < l.n; ++i){ + image im = get_convolutional_weight(l, i); + if (im.c == 3) { + rgbgr_image(im); + } + } +} + +void rescale_weights(convolutional_layer l, float scale, float trans) +{ + int i; + for(i = 0; i < l.n; ++i){ + image im = get_convolutional_weight(l, i); + if (im.c == 3) { + scale_image(im, scale); + float sum = sum_array(im.data, im.w*im.h*im.c); + l.biases[i] += sum*trans; + } + } +} + +image *get_weights(convolutional_layer l) +{ + image *weights = calloc(l.n, sizeof(image)); + int i; + for(i = 0; i < l.n; ++i){ + weights[i] = copy_image(get_convolutional_weight(l, i)); + normalize_image(weights[i]); + /* + char buff[256]; + sprintf(buff, "filter%d", i); + save_image(weights[i], buff); + */ + } + //error("hey"); + return weights; +} + +image *visualize_convolutional_layer(convolutional_layer l, char *window, image *prev_weights) +{ + image *single_weights = get_weights(l); + show_images(single_weights, l.n, window); + + image delta = get_convolutional_image(l); + image dc = collapse_image_layers(delta, 1); + char buff[256]; + sprintf(buff, "%s: Output", window); + //show_image(dc, buff); + //save_image(dc, buff); + free_image(dc); + return single_weights; +} + diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/convolutional_layer.h b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/convolutional_layer.h new file mode 100644 index 00000000000..6c261f5fc23 --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/convolutional_layer.h @@ -0,0 +1,50 @@ +#ifndef CONVOLUTIONAL_LAYER_H +#define CONVOLUTIONAL_LAYER_H + +#include "cuda.h" +#include "image.h" +#include "activations.h" +#include "layer.h" +#include "network.h" + +typedef layer convolutional_layer; + +#ifdef GPU +void forward_convolutional_layer_gpu(convolutional_layer layer, network net); +void backward_convolutional_layer_gpu(convolutional_layer layer, network net); +void update_convolutional_layer_gpu(convolutional_layer layer, update_args a); + +void push_convolutional_layer(convolutional_layer layer); +void pull_convolutional_layer(convolutional_layer layer); + +void add_bias_gpu(float *output, float *biases, int batch, int n, int size); +void backward_bias_gpu(float *bias_updates, float *delta, int batch, int n, int size); +void adam_update_gpu(float *w, float *d, float *m, float *v, float B1, float B2, float eps, float decay, float rate, int n, int batch, int t); +#ifdef CUDNN +void cudnn_convolutional_setup(layer *l); +#endif +#endif + +convolutional_layer make_convolutional_layer(int batch, int h, int w, int c, int n, int groups, int size, int stride, int padding, ACTIVATION activation, int batch_normalize, int binary, int xnor, int adam); +void resize_convolutional_layer(convolutional_layer *layer, int w, int h); +void forward_convolutional_layer(const convolutional_layer layer, network net); +void update_convolutional_layer(convolutional_layer layer, update_args a); +image *visualize_convolutional_layer(convolutional_layer layer, char *window, image *prev_weights); +void binarize_weights(float *weights, int n, int size, float *binary); +void swap_binary(convolutional_layer *l); +void binarize_weights2(float *weights, int n, int size, char *binary, float *scales); + +void backward_convolutional_layer(convolutional_layer layer, network net); + +void add_bias(float *output, float *biases, int batch, int n, int size); +void backward_bias(float *bias_updates, float *delta, int batch, int n, int size); + +image get_convolutional_image(convolutional_layer layer); +image get_convolutional_delta(convolutional_layer layer); +image get_convolutional_weight(convolutional_layer layer, int i); + +int convolutional_out_height(convolutional_layer layer); +int convolutional_out_width(convolutional_layer layer); + +#endif + diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/cost_layer.c b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/cost_layer.c new file mode 100644 index 00000000000..2138ff2617a --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/cost_layer.c @@ -0,0 +1,176 @@ +#include "cost_layer.h" +#include "utils.h" +#include "cuda.h" +#include "blas.h" +#include +#include +#include +#include + +COST_TYPE get_cost_type(char *s) +{ + if (strcmp(s, "seg")==0) return SEG; + if (strcmp(s, "sse")==0) return SSE; + if (strcmp(s, "masked")==0) return MASKED; + if (strcmp(s, "smooth")==0) return SMOOTH; + if (strcmp(s, "L1")==0) return L1; + if (strcmp(s, "wgan")==0) return WGAN; + fprintf(stderr, "Couldn't find cost type %s, going with SSE\n", s); + return SSE; +} + +char *get_cost_string(COST_TYPE a) +{ + switch(a){ + case SEG: + return "seg"; + case SSE: + return "sse"; + case MASKED: + return "masked"; + case SMOOTH: + return "smooth"; + case L1: + return "L1"; + case WGAN: + return "wgan"; + } + return "sse"; +} + +cost_layer make_cost_layer(int batch, int inputs, COST_TYPE cost_type, float scale) +{ + fprintf(stderr, "cost %4d\n", inputs); + cost_layer l = {0}; + l.type = COST; + + l.scale = scale; + l.batch = batch; + l.inputs = inputs; + l.outputs = inputs; + l.cost_type = cost_type; + l.delta = calloc(inputs*batch, sizeof(float)); + l.output = calloc(inputs*batch, sizeof(float)); + l.cost = calloc(1, sizeof(float)); + + l.forward = forward_cost_layer; + l.backward = backward_cost_layer; + #ifdef GPU + l.forward_gpu = forward_cost_layer_gpu; + l.backward_gpu = backward_cost_layer_gpu; + + l.delta_gpu = cuda_make_array(l.output, inputs*batch); + l.output_gpu = cuda_make_array(l.delta, inputs*batch); + #endif + return l; +} + +void resize_cost_layer(cost_layer *l, int inputs) +{ + l->inputs = inputs; + l->outputs = inputs; + l->delta = realloc(l->delta, inputs*l->batch*sizeof(float)); + l->output = realloc(l->output, inputs*l->batch*sizeof(float)); +#ifdef GPU + cuda_free(l->delta_gpu); + cuda_free(l->output_gpu); + l->delta_gpu = cuda_make_array(l->delta, inputs*l->batch); + l->output_gpu = cuda_make_array(l->output, inputs*l->batch); +#endif +} + +void forward_cost_layer(cost_layer l, network net) +{ + if (!net.truth) return; + if(l.cost_type == MASKED){ + int i; + for(i = 0; i < l.batch*l.inputs; ++i){ + if(net.truth[i] == SECRET_NUM) net.input[i] = SECRET_NUM; + } + } + if(l.cost_type == SMOOTH){ + smooth_l1_cpu(l.batch*l.inputs, net.input, net.truth, l.delta, l.output); + }else if(l.cost_type == L1){ + l1_cpu(l.batch*l.inputs, net.input, net.truth, l.delta, l.output); + } else { + l2_cpu(l.batch*l.inputs, net.input, net.truth, l.delta, l.output); + } + l.cost[0] = sum_array(l.output, l.batch*l.inputs); +} + +void backward_cost_layer(const cost_layer l, network net) +{ + axpy_cpu(l.batch*l.inputs, l.scale, l.delta, 1, net.delta, 1); +} + +#ifdef GPU + +void pull_cost_layer(cost_layer l) +{ + cuda_pull_array(l.delta_gpu, l.delta, l.batch*l.inputs); +} + +void push_cost_layer(cost_layer l) +{ + cuda_push_array(l.delta_gpu, l.delta, l.batch*l.inputs); +} + +int float_abs_compare (const void * a, const void * b) +{ + float fa = *(const float*) a; + if(fa < 0) fa = -fa; + float fb = *(const float*) b; + if(fb < 0) fb = -fb; + return (fa > fb) - (fa < fb); +} + +void forward_cost_layer_gpu(cost_layer l, network net) +{ + if (!net.truth) return; + if(l.smooth){ + scal_gpu(l.batch*l.inputs, (1-l.smooth), net.truth_gpu, 1); + add_gpu(l.batch*l.inputs, l.smooth * 1./l.inputs, net.truth_gpu, 1); + } + + if(l.cost_type == SMOOTH){ + smooth_l1_gpu(l.batch*l.inputs, net.input_gpu, net.truth_gpu, l.delta_gpu, l.output_gpu); + } else if (l.cost_type == L1){ + l1_gpu(l.batch*l.inputs, net.input_gpu, net.truth_gpu, l.delta_gpu, l.output_gpu); + } else if (l.cost_type == WGAN){ + wgan_gpu(l.batch*l.inputs, net.input_gpu, net.truth_gpu, l.delta_gpu, l.output_gpu); + } else { + l2_gpu(l.batch*l.inputs, net.input_gpu, net.truth_gpu, l.delta_gpu, l.output_gpu); + } + + if (l.cost_type == SEG && l.noobject_scale != 1) { + scale_mask_gpu(l.batch*l.inputs, l.delta_gpu, 0, net.truth_gpu, l.noobject_scale); + scale_mask_gpu(l.batch*l.inputs, l.output_gpu, 0, net.truth_gpu, l.noobject_scale); + } + if (l.cost_type == MASKED) { + mask_gpu(l.batch*l.inputs, net.delta_gpu, SECRET_NUM, net.truth_gpu, 0); + } + + if(l.ratio){ + cuda_pull_array(l.delta_gpu, l.delta, l.batch*l.inputs); + qsort(l.delta, l.batch*l.inputs, sizeof(float), float_abs_compare); + int n = (1-l.ratio) * l.batch*l.inputs; + float thresh = l.delta[n]; + thresh = 0; + printf("%f\n", thresh); + supp_gpu(l.batch*l.inputs, thresh, l.delta_gpu, 1); + } + + if(l.thresh){ + supp_gpu(l.batch*l.inputs, l.thresh*1./l.inputs, l.delta_gpu, 1); + } + + cuda_pull_array(l.output_gpu, l.output, l.batch*l.inputs); + l.cost[0] = sum_array(l.output, l.batch*l.inputs); +} + +void backward_cost_layer_gpu(const cost_layer l, network net) +{ + axpy_gpu(l.batch*l.inputs, l.scale, l.delta_gpu, 1, net.delta_gpu, 1); +} +#endif + diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/cost_layer.h b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/cost_layer.h new file mode 100644 index 00000000000..ceb64de00bf --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/cost_layer.h @@ -0,0 +1,20 @@ +#ifndef COST_LAYER_H +#define COST_LAYER_H +#include "layer.h" +#include "network.h" + +typedef layer cost_layer; + +COST_TYPE get_cost_type(char *s); +char *get_cost_string(COST_TYPE a); +cost_layer make_cost_layer(int batch, int inputs, COST_TYPE type, float scale); +void forward_cost_layer(const cost_layer l, network net); +void backward_cost_layer(const cost_layer l, network net); +void resize_cost_layer(cost_layer *l, int inputs); + +#ifdef GPU +void forward_cost_layer_gpu(cost_layer l, network net); +void backward_cost_layer_gpu(const cost_layer l, network net); +#endif + +#endif diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/crnn_layer.c b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/crnn_layer.c new file mode 100644 index 00000000000..7dd29f62b7a --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/crnn_layer.c @@ -0,0 +1,283 @@ +#include "crnn_layer.h" +#include "convolutional_layer.h" +#include "utils.h" +#include "cuda.h" +#include "blas.h" +#include "gemm.h" + +#include +#include +#include +#include + +static void increment_layer(layer *l, int steps) +{ + int num = l->outputs*l->batch*steps; + l->output += num; + l->delta += num; + l->x += num; + l->x_norm += num; + +#ifdef GPU + l->output_gpu += num; + l->delta_gpu += num; + l->x_gpu += num; + l->x_norm_gpu += num; +#endif +} + +layer make_crnn_layer(int batch, int h, int w, int c, int hidden_filters, int output_filters, int steps, ACTIVATION activation, int batch_normalize) +{ + fprintf(stderr, "CRNN Layer: %d x %d x %d image, %d filters\n", h,w,c,output_filters); + batch = batch / steps; + layer l = {0}; + l.batch = batch; + l.type = CRNN; + l.steps = steps; + l.h = h; + l.w = w; + l.c = c; + l.out_h = h; + l.out_w = w; + l.out_c = output_filters; + l.inputs = h*w*c; + l.hidden = h * w * hidden_filters; + l.outputs = l.out_h * l.out_w * l.out_c; + + l.state = calloc(l.hidden*batch*(steps+1), sizeof(float)); + + l.input_layer = malloc(sizeof(layer)); + fprintf(stderr, "\t\t"); + *(l.input_layer) = make_convolutional_layer(batch*steps, h, w, c, hidden_filters, 1, 3, 1, 1, activation, batch_normalize, 0, 0, 0); + l.input_layer->batch = batch; + + l.self_layer = malloc(sizeof(layer)); + fprintf(stderr, "\t\t"); + *(l.self_layer) = make_convolutional_layer(batch*steps, h, w, hidden_filters, hidden_filters, 1, 3, 1, 1, activation, batch_normalize, 0, 0, 0); + l.self_layer->batch = batch; + + l.output_layer = malloc(sizeof(layer)); + fprintf(stderr, "\t\t"); + *(l.output_layer) = make_convolutional_layer(batch*steps, h, w, hidden_filters, output_filters, 1, 3, 1, 1, activation, batch_normalize, 0, 0, 0); + l.output_layer->batch = batch; + + l.output = l.output_layer->output; + l.delta = l.output_layer->delta; + + l.forward = forward_crnn_layer; + l.backward = backward_crnn_layer; + l.update = update_crnn_layer; + +#ifdef GPU + l.forward_gpu = forward_crnn_layer_gpu; + l.backward_gpu = backward_crnn_layer_gpu; + l.update_gpu = update_crnn_layer_gpu; + + l.state_gpu = cuda_make_array(l.state, l.hidden*batch*(steps+1)); + l.output_gpu = l.output_layer->output_gpu; + l.delta_gpu = l.output_layer->delta_gpu; +#endif + + return l; +} + +void update_crnn_layer(layer l, update_args a) +{ + update_convolutional_layer(*(l.input_layer), a); + update_convolutional_layer(*(l.self_layer), a); + update_convolutional_layer(*(l.output_layer), a); +} + +void forward_crnn_layer(layer l, network net) +{ + network s = net; + s.train = net.train; + int i; + layer input_layer = *(l.input_layer); + layer self_layer = *(l.self_layer); + layer output_layer = *(l.output_layer); + + fill_cpu(l.outputs * l.batch * l.steps, 0, output_layer.delta, 1); + fill_cpu(l.hidden * l.batch * l.steps, 0, self_layer.delta, 1); + fill_cpu(l.hidden * l.batch * l.steps, 0, input_layer.delta, 1); + if(net.train) fill_cpu(l.hidden * l.batch, 0, l.state, 1); + + for (i = 0; i < l.steps; ++i) { + s.input = net.input; + forward_convolutional_layer(input_layer, s); + + s.input = l.state; + forward_convolutional_layer(self_layer, s); + + float *old_state = l.state; + if(net.train) l.state += l.hidden*l.batch; + if(l.shortcut){ + copy_cpu(l.hidden * l.batch, old_state, 1, l.state, 1); + }else{ + fill_cpu(l.hidden * l.batch, 0, l.state, 1); + } + axpy_cpu(l.hidden * l.batch, 1, input_layer.output, 1, l.state, 1); + axpy_cpu(l.hidden * l.batch, 1, self_layer.output, 1, l.state, 1); + + s.input = l.state; + forward_convolutional_layer(output_layer, s); + + net.input += l.inputs*l.batch; + increment_layer(&input_layer, 1); + increment_layer(&self_layer, 1); + increment_layer(&output_layer, 1); + } +} + +void backward_crnn_layer(layer l, network net) +{ + network s = net; + int i; + layer input_layer = *(l.input_layer); + layer self_layer = *(l.self_layer); + layer output_layer = *(l.output_layer); + + increment_layer(&input_layer, l.steps-1); + increment_layer(&self_layer, l.steps-1); + increment_layer(&output_layer, l.steps-1); + + l.state += l.hidden*l.batch*l.steps; + for (i = l.steps-1; i >= 0; --i) { + copy_cpu(l.hidden * l.batch, input_layer.output, 1, l.state, 1); + axpy_cpu(l.hidden * l.batch, 1, self_layer.output, 1, l.state, 1); + + s.input = l.state; + s.delta = self_layer.delta; + backward_convolutional_layer(output_layer, s); + + l.state -= l.hidden*l.batch; + /* + if(i > 0){ + copy_cpu(l.hidden * l.batch, input_layer.output - l.hidden*l.batch, 1, l.state, 1); + axpy_cpu(l.hidden * l.batch, 1, self_layer.output - l.hidden*l.batch, 1, l.state, 1); + }else{ + fill_cpu(l.hidden * l.batch, 0, l.state, 1); + } + */ + + s.input = l.state; + s.delta = self_layer.delta - l.hidden*l.batch; + if (i == 0) s.delta = 0; + backward_convolutional_layer(self_layer, s); + + copy_cpu(l.hidden*l.batch, self_layer.delta, 1, input_layer.delta, 1); + if (i > 0 && l.shortcut) axpy_cpu(l.hidden*l.batch, 1, self_layer.delta, 1, self_layer.delta - l.hidden*l.batch, 1); + s.input = net.input + i*l.inputs*l.batch; + if(net.delta) s.delta = net.delta + i*l.inputs*l.batch; + else s.delta = 0; + backward_convolutional_layer(input_layer, s); + + increment_layer(&input_layer, -1); + increment_layer(&self_layer, -1); + increment_layer(&output_layer, -1); + } +} + +#ifdef GPU + +void pull_crnn_layer(layer l) +{ + pull_convolutional_layer(*(l.input_layer)); + pull_convolutional_layer(*(l.self_layer)); + pull_convolutional_layer(*(l.output_layer)); +} + +void push_crnn_layer(layer l) +{ + push_convolutional_layer(*(l.input_layer)); + push_convolutional_layer(*(l.self_layer)); + push_convolutional_layer(*(l.output_layer)); +} + +void update_crnn_layer_gpu(layer l, update_args a) +{ + update_convolutional_layer_gpu(*(l.input_layer), a); + update_convolutional_layer_gpu(*(l.self_layer), a); + update_convolutional_layer_gpu(*(l.output_layer), a); +} + +void forward_crnn_layer_gpu(layer l, network net) +{ + network s = net; + int i; + layer input_layer = *(l.input_layer); + layer self_layer = *(l.self_layer); + layer output_layer = *(l.output_layer); + + fill_gpu(l.outputs * l.batch * l.steps, 0, output_layer.delta_gpu, 1); + fill_gpu(l.hidden * l.batch * l.steps, 0, self_layer.delta_gpu, 1); + fill_gpu(l.hidden * l.batch * l.steps, 0, input_layer.delta_gpu, 1); + if(net.train) fill_gpu(l.hidden * l.batch, 0, l.state_gpu, 1); + + for (i = 0; i < l.steps; ++i) { + s.input_gpu = net.input_gpu; + forward_convolutional_layer_gpu(input_layer, s); + + s.input_gpu = l.state_gpu; + forward_convolutional_layer_gpu(self_layer, s); + + float *old_state = l.state_gpu; + if(net.train) l.state_gpu += l.hidden*l.batch; + if(l.shortcut){ + copy_gpu(l.hidden * l.batch, old_state, 1, l.state_gpu, 1); + }else{ + fill_gpu(l.hidden * l.batch, 0, l.state_gpu, 1); + } + axpy_gpu(l.hidden * l.batch, 1, input_layer.output_gpu, 1, l.state_gpu, 1); + axpy_gpu(l.hidden * l.batch, 1, self_layer.output_gpu, 1, l.state_gpu, 1); + + s.input_gpu = l.state_gpu; + forward_convolutional_layer_gpu(output_layer, s); + + net.input_gpu += l.inputs*l.batch; + increment_layer(&input_layer, 1); + increment_layer(&self_layer, 1); + increment_layer(&output_layer, 1); + } +} + +void backward_crnn_layer_gpu(layer l, network net) +{ + network s = net; + s.train = net.train; + int i; + layer input_layer = *(l.input_layer); + layer self_layer = *(l.self_layer); + layer output_layer = *(l.output_layer); + increment_layer(&input_layer, l.steps - 1); + increment_layer(&self_layer, l.steps - 1); + increment_layer(&output_layer, l.steps - 1); + l.state_gpu += l.hidden*l.batch*l.steps; + for (i = l.steps-1; i >= 0; --i) { + copy_gpu(l.hidden * l.batch, input_layer.output_gpu, 1, l.state_gpu, 1); + axpy_gpu(l.hidden * l.batch, 1, self_layer.output_gpu, 1, l.state_gpu, 1); + + s.input_gpu = l.state_gpu; + s.delta_gpu = self_layer.delta_gpu; + backward_convolutional_layer_gpu(output_layer, s); + + l.state_gpu -= l.hidden*l.batch; + + s.input_gpu = l.state_gpu; + s.delta_gpu = self_layer.delta_gpu - l.hidden*l.batch; + if (i == 0) s.delta_gpu = 0; + backward_convolutional_layer_gpu(self_layer, s); + + copy_gpu(l.hidden*l.batch, self_layer.delta_gpu, 1, input_layer.delta_gpu, 1); + if (i > 0 && l.shortcut) axpy_gpu(l.hidden*l.batch, 1, self_layer.delta_gpu, 1, self_layer.delta_gpu - l.hidden*l.batch, 1); + s.input_gpu = net.input_gpu + i*l.inputs*l.batch; + if(net.delta_gpu) s.delta_gpu = net.delta_gpu + i*l.inputs*l.batch; + else s.delta_gpu = 0; + backward_convolutional_layer_gpu(input_layer, s); + + increment_layer(&input_layer, -1); + increment_layer(&self_layer, -1); + increment_layer(&output_layer, -1); + } +} +#endif diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/crnn_layer.h b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/crnn_layer.h new file mode 100644 index 00000000000..515f378354e --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/crnn_layer.h @@ -0,0 +1,24 @@ + +#ifndef CRNN_LAYER_H +#define CRNN_LAYER_H + +#include "activations.h" +#include "layer.h" +#include "network.h" + +layer make_crnn_layer(int batch, int h, int w, int c, int hidden_filters, int output_filters, int steps, ACTIVATION activation, int batch_normalize); + +void forward_crnn_layer(layer l, network net); +void backward_crnn_layer(layer l, network net); +void update_crnn_layer(layer l, update_args a); + +#ifdef GPU +void forward_crnn_layer_gpu(layer l, network net); +void backward_crnn_layer_gpu(layer l, network net); +void update_crnn_layer_gpu(layer l, update_args a); +void push_crnn_layer(layer l); +void pull_crnn_layer(layer l); +#endif + +#endif + diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/crop_layer.c b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/crop_layer.c new file mode 100644 index 00000000000..3b918529e64 --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/crop_layer.c @@ -0,0 +1,103 @@ +#include "crop_layer.h" +#include "cuda.h" +#include + +image get_crop_image(crop_layer l) +{ + int h = l.out_h; + int w = l.out_w; + int c = l.out_c; + return float_to_image(w,h,c,l.output); +} + +void backward_crop_layer(const crop_layer l, network net){} +void backward_crop_layer_gpu(const crop_layer l, network net){} + +crop_layer make_crop_layer(int batch, int h, int w, int c, int crop_height, int crop_width, int flip, float angle, float saturation, float exposure) +{ + fprintf(stderr, "Crop Layer: %d x %d -> %d x %d x %d image\n", h,w,crop_height,crop_width,c); + crop_layer l = {0}; + l.type = CROP; + l.batch = batch; + l.h = h; + l.w = w; + l.c = c; + l.scale = (float)crop_height / h; + l.flip = flip; + l.angle = angle; + l.saturation = saturation; + l.exposure = exposure; + l.out_w = crop_width; + l.out_h = crop_height; + l.out_c = c; + l.inputs = l.w * l.h * l.c; + l.outputs = l.out_w * l.out_h * l.out_c; + l.output = calloc(l.outputs*batch, sizeof(float)); + l.forward = forward_crop_layer; + l.backward = backward_crop_layer; + + #ifdef GPU + l.forward_gpu = forward_crop_layer_gpu; + l.backward_gpu = backward_crop_layer_gpu; + l.output_gpu = cuda_make_array(l.output, l.outputs*batch); + l.rand_gpu = cuda_make_array(0, l.batch*8); + #endif + return l; +} + +void resize_crop_layer(layer *l, int w, int h) +{ + l->w = w; + l->h = h; + + l->out_w = l->scale*w; + l->out_h = l->scale*h; + + l->inputs = l->w * l->h * l->c; + l->outputs = l->out_h * l->out_w * l->out_c; + + l->output = realloc(l->output, l->batch*l->outputs*sizeof(float)); + #ifdef GPU + cuda_free(l->output_gpu); + l->output_gpu = cuda_make_array(l->output, l->outputs*l->batch); + #endif +} + + +void forward_crop_layer(const crop_layer l, network net) +{ + int i,j,c,b,row,col; + int index; + int count = 0; + int flip = (l.flip && rand()%2); + int dh = rand()%(l.h - l.out_h + 1); + int dw = rand()%(l.w - l.out_w + 1); + float scale = 2; + float trans = -1; + if(l.noadjust){ + scale = 1; + trans = 0; + } + if(!net.train){ + flip = 0; + dh = (l.h - l.out_h)/2; + dw = (l.w - l.out_w)/2; + } + for(b = 0; b < l.batch; ++b){ + for(c = 0; c < l.c; ++c){ + for(i = 0; i < l.out_h; ++i){ + for(j = 0; j < l.out_w; ++j){ + if(flip){ + col = l.w - dw - j - 1; + }else{ + col = j + dw; + } + row = i + dh; + index = col+l.w*(row+l.h*(c + l.c*b)); + l.output[count++] = net.input[index]*scale + trans; + } + } + } + } +} + diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/crop_layer.h b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/crop_layer.h new file mode 100644 index 00000000000..3b5883c47d6 --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/crop_layer.h @@ -0,0 +1,20 @@ +#ifndef CROP_LAYER_H +#define CROP_LAYER_H + +#include "image.h" +#include "layer.h" +#include "network.h" + +typedef layer crop_layer; + +image get_crop_image(crop_layer l); +crop_layer make_crop_layer(int batch, int h, int w, int c, int crop_height, int crop_width, int flip, float angle, float saturation, float exposure); +void forward_crop_layer(const crop_layer l, network net); +void resize_crop_layer(layer *l, int w, int h); + +#ifdef GPU +void forward_crop_layer_gpu(crop_layer l, network net); +#endif + +#endif + diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/crop_layer_kernels.cu b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/crop_layer_kernels.cu new file mode 100644 index 00000000000..b5b9f554627 --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/crop_layer_kernels.cu @@ -0,0 +1,225 @@ +#include "cuda_runtime.h" +#include "curand.h" +#include "cublas_v2.h" + +extern "C" { +#include "crop_layer.h" +#include "utils.h" +#include "cuda.h" +#include "image.h" +} + +__device__ float get_pixel_kernel(float *image, int w, int h, int x, int y, int c) +{ + if(x < 0 || x >= w || y < 0 || y >= h) return 0; + return image[x + w*(y + c*h)]; +} + +__device__ float3 rgb_to_hsv_kernel(float3 rgb) +{ + float r = rgb.x; + float g = rgb.y; + float b = rgb.z; + + float h, s, v; + float max = (r > g) ? ( (r > b) ? r : b) : ( (g > b) ? g : b); + float min = (r < g) ? ( (r < b) ? r : b) : ( (g < b) ? g : b); + float delta = max - min; + v = max; + if(max == 0){ + s = 0; + h = -1; + }else{ + s = delta/max; + if(r == max){ + h = (g - b) / delta; + } else if (g == max) { + h = 2 + (b - r) / delta; + } else { + h = 4 + (r - g) / delta; + } + if (h < 0) h += 6; + } + return make_float3(h, s, v); +} + +__device__ float3 hsv_to_rgb_kernel(float3 hsv) +{ + float h = hsv.x; + float s = hsv.y; + float v = hsv.z; + + float r, g, b; + float f, p, q, t; + + if (s == 0) { + r = g = b = v; + } else { + int index = (int) floorf(h); + f = h - index; + p = v*(1-s); + q = v*(1-s*f); + t = v*(1-s*(1-f)); + if(index == 0){ + r = v; g = t; b = p; + } else if(index == 1){ + r = q; g = v; b = p; + } else if(index == 2){ + r = p; g = v; b = t; + } else if(index == 3){ + r = p; g = q; b = v; + } else if(index == 4){ + r = t; g = p; b = v; + } else { + r = v; g = p; b = q; + } + } + r = (r < 0) ? 0 : ((r > 1) ? 1 : r); + g = (g < 0) ? 0 : ((g > 1) ? 1 : g); + b = (b < 0) ? 0 : ((b > 1) ? 1 : b); + return make_float3(r, g, b); +} + +__device__ float bilinear_interpolate_kernel(float *image, int w, int h, float x, float y, int c) +{ + int ix = (int) floorf(x); + int iy = (int) floorf(y); + + float dx = x - ix; + float dy = y - iy; + + float val = (1-dy) * (1-dx) * get_pixel_kernel(image, w, h, ix, iy, c) + + dy * (1-dx) * get_pixel_kernel(image, w, h, ix, iy+1, c) + + (1-dy) * dx * get_pixel_kernel(image, w, h, ix+1, iy, c) + + dy * dx * get_pixel_kernel(image, w, h, ix+1, iy+1, c); + return val; +} + +__global__ void levels_image_kernel(float *image, float *rand, int batch, int w, int h, int train, float saturation, float exposure, float translate, float scale, float shift) +{ + int size = batch * w * h; + int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if(id >= size) return; + int x = id % w; + id /= w; + int y = id % h; + id /= h; + float rshift = rand[0]; + float gshift = rand[1]; + float bshift = rand[2]; + float r0 = rand[8*id + 0]; + float r1 = rand[8*id + 1]; + float r2 = rand[8*id + 2]; + float r3 = rand[8*id + 3]; + + saturation = r0*(saturation - 1) + 1; + saturation = (r1 > .5f) ? 1.f/saturation : saturation; + exposure = r2*(exposure - 1) + 1; + exposure = (r3 > .5f) ? 1.f/exposure : exposure; + + size_t offset = id * h * w * 3; + image += offset; + float r = image[x + w*(y + h*0)]; + float g = image[x + w*(y + h*1)]; + float b = image[x + w*(y + h*2)]; + float3 rgb = make_float3(r,g,b); + if(train){ + float3 hsv = rgb_to_hsv_kernel(rgb); + hsv.y *= saturation; + hsv.z *= exposure; + rgb = hsv_to_rgb_kernel(hsv); + } else { + shift = 0; + } + image[x + w*(y + h*0)] = rgb.x*scale + translate + (rshift - .5f)*shift; + image[x + w*(y + h*1)] = rgb.y*scale + translate + (gshift - .5f)*shift; + image[x + w*(y + h*2)] = rgb.z*scale + translate + (bshift - .5f)*shift; +} + +__global__ void forward_crop_layer_kernel(float *input, float *rand, int size, int c, int h, int w, int crop_height, int crop_width, int train, int flip, float angle, float *output) +{ + int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if(id >= size) return; + + float cx = w/2.f; + float cy = h/2.f; + + int count = id; + int j = id % crop_width; + id /= crop_width; + int i = id % crop_height; + id /= crop_height; + int k = id % c; + id /= c; + int b = id; + + float r4 = rand[8*b + 4]; + float r5 = rand[8*b + 5]; + float r6 = rand[8*b + 6]; + float r7 = rand[8*b + 7]; + + float dw = (w - crop_width)*r4; + float dh = (h - crop_height)*r5; + flip = (flip && (r6 > .5f)); + angle = 2*angle*r7 - angle; + if(!train){ + dw = (w - crop_width)/2.f; + dh = (h - crop_height)/2.f; + flip = 0; + angle = 0; + } + + input += w*h*c*b; + + float x = (flip) ? w - dw - j - 1 : j + dw; + float y = i + dh; + + float rx = cosf(angle)*(x-cx) - sinf(angle)*(y-cy) + cx; + float ry = sinf(angle)*(x-cx) + cosf(angle)*(y-cy) + cy; + + output[count] = bilinear_interpolate_kernel(input, w, h, rx, ry, k); +} + +extern "C" void forward_crop_layer_gpu(crop_layer layer, network net) +{ + cuda_random(layer.rand_gpu, layer.batch*8); + + float radians = layer.angle*3.14159265f/180.f; + + float scale = 2; + float translate = -1; + if(layer.noadjust){ + scale = 1; + translate = 0; + } + + int size = layer.batch * layer.w * layer.h; + + levels_image_kernel<<>>(net.input_gpu, layer.rand_gpu, layer.batch, layer.w, layer.h, net.train, layer.saturation, layer.exposure, translate, scale, layer.shift); + check_error(cudaPeekAtLastError()); + + size = layer.batch*layer.c*layer.out_w*layer.out_h; + + forward_crop_layer_kernel<<>>(net.input_gpu, layer.rand_gpu, size, layer.c, layer.h, layer.w, layer.out_h, layer.out_w, net.train, layer.flip, radians, layer.output_gpu); + check_error(cudaPeekAtLastError()); + +/* + cuda_pull_array(layer.output_gpu, layer.output, size); + image im = float_to_image(layer.crop_width, layer.crop_height, layer.c, layer.output + 0*(size/layer.batch)); + image im2 = float_to_image(layer.crop_width, layer.crop_height, layer.c, layer.output + 1*(size/layer.batch)); + image im3 = float_to_image(layer.crop_width, layer.crop_height, layer.c, layer.output + 2*(size/layer.batch)); + + translate_image(im, -translate); + scale_image(im, 1/scale); + translate_image(im2, -translate); + scale_image(im2, 1/scale); + translate_image(im3, -translate); + scale_image(im3, 1/scale); + + show_image(im, "cropped"); + show_image(im2, "cropped2"); + show_image(im3, "cropped3"); + cvWaitKey(0); + */ +} + diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/cuda.c b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/cuda.c new file mode 100644 index 00000000000..48aba6e4012 --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/cuda.c @@ -0,0 +1,178 @@ +int gpu_index = 0; + +#ifdef GPU + +#include "cuda.h" +#include "utils.h" +#include "blas.h" +#include +#include +#include + +void cuda_set_device(int n) +{ + gpu_index = n; + cudaError_t status = cudaSetDevice(n); + check_error(status); +} + +int cuda_get_device() +{ + int n = 0; + cudaError_t status = cudaGetDevice(&n); + check_error(status); + return n; +} + +void check_error(cudaError_t status) +{ + //cudaDeviceSynchronize(); + cudaError_t status2 = cudaGetLastError(); + if (status != cudaSuccess) + { + const char *s = cudaGetErrorString(status); + char buffer[256]; + printf("CUDA Error: %s\n", s); + assert(0); + snprintf(buffer, 256, "CUDA Error: %s", s); + error(buffer); + } + if (status2 != cudaSuccess) + { + const char *s = cudaGetErrorString(status); + char buffer[256]; + printf("CUDA Error Prev: %s\n", s); + assert(0); + snprintf(buffer, 256, "CUDA Error Prev: %s", s); + error(buffer); + } +} + +dim3 cuda_gridsize(size_t n){ + size_t k = (n-1) / BLOCK + 1; + size_t x = k; + size_t y = 1; + if(x > 65535){ + x = ceil(sqrt(k)); + y = (n-1)/(x*BLOCK) + 1; + } + dim3 d = {x, y, 1}; + //printf("%ld %ld %ld %ld\n", n, x, y, x*y*BLOCK); + return d; +} + +#ifdef CUDNN +cudnnHandle_t cudnn_handle() +{ + static int init[16] = {0}; + static cudnnHandle_t handle[16]; + int i = cuda_get_device(); + if(!init[i]) { + cudnnCreate(&handle[i]); + init[i] = 1; + } + return handle[i]; +} +#endif + +cublasHandle_t blas_handle() +{ + static int init[16] = {0}; + static cublasHandle_t handle[16]; + int i = cuda_get_device(); + if(!init[i]) { + cublasCreate(&handle[i]); + init[i] = 1; + } + return handle[i]; +} + +float *cuda_make_array(float *x, size_t n) +{ + float *x_gpu; + size_t size = sizeof(float)*n; + cudaError_t status = cudaMalloc((void **)&x_gpu, size); + check_error(status); + if(x){ + status = cudaMemcpy(x_gpu, x, size, cudaMemcpyHostToDevice); + check_error(status); + } else { + fill_gpu(n, 0, x_gpu, 1); + } + if(!x_gpu) error("Cuda malloc failed\n"); + return x_gpu; +} + +void cuda_random(float *x_gpu, size_t n) +{ + static curandGenerator_t gen[16]; + static int init[16] = {0}; + int i = cuda_get_device(); + if(!init[i]){ + curandCreateGenerator(&gen[i], CURAND_RNG_PSEUDO_DEFAULT); + curandSetPseudoRandomGeneratorSeed(gen[i], time(0)); + init[i] = 1; + } + curandGenerateUniform(gen[i], x_gpu, n); + check_error(cudaPeekAtLastError()); +} + +float cuda_compare(float *x_gpu, float *x, size_t n, char *s) +{ + float *tmp = calloc(n, sizeof(float)); + cuda_pull_array(x_gpu, tmp, n); + //int i; + //for(i = 0; i < n; ++i) printf("%f %f\n", tmp[i], x[i]); + axpy_cpu(n, -1, x, 1, tmp, 1); + float err = dot_cpu(n, tmp, 1, tmp, 1); + printf("Error %s: %f\n", s, sqrt(err/n)); + free(tmp); + return err; +} + +int *cuda_make_int_array(int *x, size_t n) +{ + int *x_gpu; + size_t size = sizeof(int)*n; + cudaError_t status = cudaMalloc((void **)&x_gpu, size); + check_error(status); + if(x){ + status = cudaMemcpy(x_gpu, x, size, cudaMemcpyHostToDevice); + check_error(status); + } + if(!x_gpu) error("Cuda malloc failed\n"); + return x_gpu; +} + +void cuda_free(float *x_gpu) +{ + cudaError_t status = cudaFree(x_gpu); + check_error(status); +} + +void cuda_push_array(float *x_gpu, float *x, size_t n) +{ + size_t size = sizeof(float)*n; + cudaError_t status = cudaMemcpy(x_gpu, x, size, cudaMemcpyHostToDevice); + check_error(status); +} + +void cuda_pull_array(float *x_gpu, float *x, size_t n) +{ + size_t size = sizeof(float)*n; + cudaError_t status = cudaMemcpy(x, x_gpu, size, cudaMemcpyDeviceToHost); + check_error(status); +} + +float cuda_mag_array(float *x_gpu, size_t n) +{ + float *temp = calloc(n, sizeof(float)); + cuda_pull_array(x_gpu, temp, n); + float m = mag_array(temp, n); + free(temp); + return m; +} +#else +void cuda_set_device(int n){} + +#endif diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/cuda.h b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/cuda.h new file mode 100644 index 00000000000..a1bc2160ded --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/cuda.h @@ -0,0 +1,20 @@ +#ifndef CUDA_H +#define CUDA_H + +#include "darknet.h" + +#ifdef GPU + +void check_error(cudaError_t status); +cublasHandle_t blas_handle(); +int *cuda_make_int_array(int *x, size_t n); +void cuda_random(float *x_gpu, size_t n); +float cuda_compare(float *x_gpu, float *x, size_t n, char *s); +dim3 cuda_gridsize(size_t n); + +#ifdef CUDNN +cudnnHandle_t cudnn_handle(); +#endif + +#endif +#endif diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/darknet.h b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/darknet.h new file mode 100644 index 00000000000..9327ea8ca06 --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/darknet.h @@ -0,0 +1,800 @@ +#ifndef DARKNET_API +#define DARKNET_API +#include +#include +#include +#include + +#define SECRET_NUM -1234 +extern int gpu_index; + +#ifdef GPU + #define BLOCK 512 + + #include "cuda_runtime.h" + #include "curand.h" + #include "cublas_v2.h" + + #ifdef CUDNN + #include "cudnn.h" + #endif +#endif + +#ifndef __cplusplus + #ifdef OPENCV + #include "opencv2/highgui/highgui_c.h" + #include "opencv2/imgproc/imgproc_c.h" + #include "opencv2/core/version.hpp" + #if CV_MAJOR_VERSION == 3 + #include "opencv2/videoio/videoio_c.h" + #include "opencv2/imgcodecs/imgcodecs_c.h" + #endif + #endif +#endif + +typedef struct{ + int classes; + char **names; +} metadata; + +metadata get_metadata(char *file); + +typedef struct{ + int *leaf; + int n; + int *parent; + int *child; + int *group; + char **name; + + int groups; + int *group_size; + int *group_offset; +} tree; +tree *read_tree(char *filename); + +typedef enum{ + LOGISTIC, RELU, RELIE, LINEAR, RAMP, TANH, PLSE, LEAKY, ELU, LOGGY, STAIR, HARDTAN, LHTAN +} ACTIVATION; + +typedef enum{ + MULT, ADD, SUB, DIV +} BINARY_ACTIVATION; + +typedef enum { + CONVOLUTIONAL, + DECONVOLUTIONAL, + CONNECTED, + MAXPOOL, + SOFTMAX, + DETECTION, + DROPOUT, + CROP, + ROUTE, + COST, + NORMALIZATION, + AVGPOOL, + LOCAL, + SHORTCUT, + ACTIVE, + RNN, + GRU, + LSTM, + CRNN, + BATCHNORM, + NETWORK, + XNOR, + REGION, + YOLO, + REORG, + UPSAMPLE, + LOGXENT, + L2NORM, + BLANK +} LAYER_TYPE; + +typedef enum{ + SSE, MASKED, L1, SEG, SMOOTH,WGAN +} COST_TYPE; + +typedef struct{ + int batch; + float learning_rate; + float momentum; + float decay; + int adam; + float B1; + float B2; + float eps; + int t; +} update_args; + +struct network; +typedef struct network network; + +struct layer; +typedef struct layer layer; + +struct layer{ + LAYER_TYPE type; + ACTIVATION activation; + COST_TYPE cost_type; + void (*forward) (struct layer, struct network); + void (*backward) (struct layer, struct network); + void (*update) (struct layer, update_args); + void (*forward_gpu) (struct layer, struct network); + void (*backward_gpu) (struct layer, struct network); + void (*update_gpu) (struct layer, update_args); + int batch_normalize; + int shortcut; + int batch; + int forced; + int flipped; + int inputs; + int outputs; + int nweights; + int nbiases; + int extra; + int truths; + int h,w,c; + int out_h, out_w, out_c; + int n; + int max_boxes; + int groups; + int size; + int side; + int stride; + int reverse; + int flatten; + int spatial; + int pad; + int sqrt; + int flip; + int index; + int binary; + int xnor; + int steps; + int hidden; + int truth; + float smooth; + float dot; + float angle; + float jitter; + float saturation; + float exposure; + float shift; + float ratio; + float learning_rate_scale; + float clip; + int softmax; + int classes; + int coords; + int background; + int rescore; + int objectness; + int joint; + int noadjust; + int reorg; + int log; + int tanh; + int *mask; + int total; + + float alpha; + float beta; + float kappa; + + float coord_scale; + float object_scale; + float noobject_scale; + float mask_scale; + float class_scale; + int bias_match; + int random; + float ignore_thresh; + float truth_thresh; + float thresh; + float focus; + int classfix; + int absolute; + + int onlyforward; + int stopbackward; + int dontload; + int dontsave; + int dontloadscales; + + float temperature; + float probability; + float scale; + + char * cweights; + int * indexes; + int * input_layers; + int * input_sizes; + int * map; + float * rand; + float * cost; + float * state; + float * prev_state; + float * forgot_state; + float * forgot_delta; + float * state_delta; + float * combine_cpu; + float * combine_delta_cpu; + + float * concat; + float * concat_delta; + + float * binary_weights; + + float * biases; + float * bias_updates; + + float * scales; + float * scale_updates; + + float * weights; + float * weight_updates; + + float * delta; + float * output; + float * loss; + float * squared; + float * norms; + + float * spatial_mean; + float * mean; + float * variance; + + float * mean_delta; + float * variance_delta; + + float * rolling_mean; + float * rolling_variance; + + float * x; + float * x_norm; + + float * m; + float * v; + + float * bias_m; + float * bias_v; + float * scale_m; + float * scale_v; + + + float *z_cpu; + float *r_cpu; + float *h_cpu; + float * prev_state_cpu; + + float *temp_cpu; + float *temp2_cpu; + float *temp3_cpu; + + float *dh_cpu; + float *hh_cpu; + float *prev_cell_cpu; + float *cell_cpu; + float *f_cpu; + float *i_cpu; + float *g_cpu; + float *o_cpu; + float *c_cpu; + float *dc_cpu; + + float * binary_input; + + struct layer *input_layer; + struct layer *self_layer; + struct layer *output_layer; + + struct layer *reset_layer; + struct layer *update_layer; + struct layer *state_layer; + + struct layer *input_gate_layer; + struct layer *state_gate_layer; + struct layer *input_save_layer; + struct layer *state_save_layer; + struct layer *input_state_layer; + struct layer *state_state_layer; + + struct layer *input_z_layer; + struct layer *state_z_layer; + + struct layer *input_r_layer; + struct layer *state_r_layer; + + struct layer *input_h_layer; + struct layer *state_h_layer; + + struct layer *wz; + struct layer *uz; + struct layer *wr; + struct layer *ur; + struct layer *wh; + struct layer *uh; + struct layer *uo; + struct layer *wo; + struct layer *uf; + struct layer *wf; + struct layer *ui; + struct layer *wi; + struct layer *ug; + struct layer *wg; + + tree *softmax_tree; + + size_t workspace_size; + +#ifdef GPU + int *indexes_gpu; + + float *z_gpu; + float *r_gpu; + float *h_gpu; + + float *temp_gpu; + float *temp2_gpu; + float *temp3_gpu; + + float *dh_gpu; + float *hh_gpu; + float *prev_cell_gpu; + float *cell_gpu; + float *f_gpu; + float *i_gpu; + float *g_gpu; + float *o_gpu; + float *c_gpu; + float *dc_gpu; + + float *m_gpu; + float *v_gpu; + float *bias_m_gpu; + float *scale_m_gpu; + float *bias_v_gpu; + float *scale_v_gpu; + + float * combine_gpu; + float * combine_delta_gpu; + + float * prev_state_gpu; + float * forgot_state_gpu; + float * forgot_delta_gpu; + float * state_gpu; + float * state_delta_gpu; + float * gate_gpu; + float * gate_delta_gpu; + float * save_gpu; + float * save_delta_gpu; + float * concat_gpu; + float * concat_delta_gpu; + + float * binary_input_gpu; + float * binary_weights_gpu; + + float * mean_gpu; + float * variance_gpu; + + float * rolling_mean_gpu; + float * rolling_variance_gpu; + + float * variance_delta_gpu; + float * mean_delta_gpu; + + float * x_gpu; + float * x_norm_gpu; + float * weights_gpu; + float * weight_updates_gpu; + float * weight_change_gpu; + + float * biases_gpu; + float * bias_updates_gpu; + float * bias_change_gpu; + + float * scales_gpu; + float * scale_updates_gpu; + float * scale_change_gpu; + + float * output_gpu; + float * loss_gpu; + float * delta_gpu; + float * rand_gpu; + float * squared_gpu; + float * norms_gpu; +#ifdef CUDNN + cudnnTensorDescriptor_t srcTensorDesc, dstTensorDesc; + cudnnTensorDescriptor_t dsrcTensorDesc, ddstTensorDesc; + cudnnTensorDescriptor_t normTensorDesc; + cudnnFilterDescriptor_t weightDesc; + cudnnFilterDescriptor_t dweightDesc; + cudnnConvolutionDescriptor_t convDesc; + cudnnConvolutionFwdAlgo_t fw_algo; + cudnnConvolutionBwdDataAlgo_t bd_algo; + cudnnConvolutionBwdFilterAlgo_t bf_algo; +#endif +#endif +}; + +void free_layer(layer); + +typedef enum { + CONSTANT, STEP, EXP, POLY, STEPS, SIG, RANDOM +} learning_rate_policy; + +typedef struct network{ + int n; + int batch; + size_t *seen; + int *t; + float epoch; + int subdivisions; + layer *layers; + float *output; + learning_rate_policy policy; + + float learning_rate; + float momentum; + float decay; + float gamma; + float scale; + float power; + int time_steps; + int step; + int max_batches; + float *scales; + int *steps; + int num_steps; + int burn_in; + + int adam; + float B1; + float B2; + float eps; + + int inputs; + int outputs; + int truths; + int notruth; + int h, w, c; + int max_crop; + int min_crop; + float max_ratio; + float min_ratio; + int center; + float angle; + float aspect; + float exposure; + float saturation; + float hue; + int random; + + int gpu_index; + tree *hierarchy; + + float *input; + float *truth; + float *delta; + float *workspace; + int train; + int index; + float *cost; + float clip; + +#ifdef GPU + float *input_gpu; + float *truth_gpu; + float *delta_gpu; + float *output_gpu; +#endif + +} network; + +typedef struct { + int w; + int h; + float scale; + float rad; + float dx; + float dy; + float aspect; +} augment_args; + +typedef struct { + int w; + int h; + int c; + float *data; +} image; + +typedef struct{ + float x, y, w, h; +} box; + +typedef struct detection{ + box bbox; + int classes; + float *prob; + float *mask; + float objectness; + int sort_class; +} detection; + +typedef struct matrix{ + int rows, cols; + float **vals; +} matrix; + + +typedef struct{ + int w, h; + matrix X; + matrix y; + int shallow; + int *num_boxes; + box **boxes; +} data; + +typedef enum { + CLASSIFICATION_DATA, DETECTION_DATA, CAPTCHA_DATA, REGION_DATA, IMAGE_DATA, COMPARE_DATA, WRITING_DATA, SWAG_DATA, TAG_DATA, OLD_CLASSIFICATION_DATA, STUDY_DATA, DET_DATA, SUPER_DATA, LETTERBOX_DATA, REGRESSION_DATA, SEGMENTATION_DATA, INSTANCE_DATA +} data_type; + +typedef struct load_args{ + int threads; + char **paths; + char *path; + int n; + int m; + char **labels; + int h; + int w; + int out_w; + int out_h; + int nh; + int nw; + int num_boxes; + int min, max, size; + int classes; + int background; + int scale; + int center; + int coords; + float jitter; + float angle; + float aspect; + float saturation; + float exposure; + float hue; + data *d; + image *im; + image *resized; + data_type type; + tree *hierarchy; +} load_args; + +typedef struct{ + int id; + float x,y,w,h; + float left, right, top, bottom; +} box_label; + + +network *load_network(char *cfg, char *weights, int clear); +load_args get_base_args(network *net); + +void free_data(data d); + +typedef struct node{ + void *val; + struct node *next; + struct node *prev; +} node; + +typedef struct list{ + int size; + node *front; + node *back; +} list; + +pthread_t load_data(load_args args); +list *read_data_cfg(char *filename); +list *read_cfg(char *filename); +unsigned char *read_file(char *filename); +data resize_data(data orig, int w, int h); +data *tile_data(data orig, int divs, int size); +data select_data(data *orig, int *inds); + +void forward_network(network *net); +void backward_network(network *net); +void update_network(network *net); + + +float dot_cpu(int N, float *X, int INCX, float *Y, int INCY); +void axpy_cpu(int N, float ALPHA, float *X, int INCX, float *Y, int INCY); +void copy_cpu(int N, float *X, int INCX, float *Y, int INCY); +void scal_cpu(int N, float ALPHA, float *X, int INCX); +void fill_cpu(int N, float ALPHA, float * X, int INCX); +void normalize_cpu(float *x, float *mean, float *variance, int batch, int filters, int spatial); +void softmax(float *input, int n, float temp, int stride, float *output); + +int best_3d_shift_r(image a, image b, int min, int max); +#ifdef GPU +void axpy_gpu(int N, float ALPHA, float * X, int INCX, float * Y, int INCY); +void fill_gpu(int N, float ALPHA, float * X, int INCX); +void scal_gpu(int N, float ALPHA, float * X, int INCX); +void copy_gpu(int N, float * X, int INCX, float * Y, int INCY); + +void cuda_set_device(int n); +void cuda_free(float *x_gpu); +float *cuda_make_array(float *x, size_t n); +void cuda_pull_array(float *x_gpu, float *x, size_t n); +float cuda_mag_array(float *x_gpu, size_t n); +void cuda_push_array(float *x_gpu, float *x, size_t n); + +void forward_network_gpu(network *net); +void backward_network_gpu(network *net); +void update_network_gpu(network *net); + +float train_networks(network **nets, int n, data d, int interval); +void sync_nets(network **nets, int n, int interval); +void harmless_update_network_gpu(network *net); +#endif +image get_label(image **characters, char *string, int size); +void draw_label(image a, int r, int c, image label, const float *rgb); +void save_image_png(image im, const char *name); +void get_next_batch(data d, int n, int offset, float *X, float *y); +void grayscale_image_3c(image im); +void normalize_image(image p); +void matrix_to_csv(matrix m); +float train_network_sgd(network *net, data d, int n); +void rgbgr_image(image im); +data copy_data(data d); +data concat_data(data d1, data d2); +data load_cifar10_data(char *filename); +float matrix_topk_accuracy(matrix truth, matrix guess, int k); +void matrix_add_matrix(matrix from, matrix to); +void scale_matrix(matrix m, float scale); +matrix csv_to_matrix(char *filename); +float *network_accuracies(network *net, data d, int n); +float train_network_datum(network *net); +image make_random_image(int w, int h, int c); + +void denormalize_connected_layer(layer l); +void denormalize_convolutional_layer(layer l); +void statistics_connected_layer(layer l); +void rescale_weights(layer l, float scale, float trans); +void rgbgr_weights(layer l); +image *get_weights(layer l); + +void demo(char *cfgfile, char *weightfile, float thresh, int cam_index, const char *filename, char **names, int classes, int frame_skip, char *prefix, int avg, float hier_thresh, int w, int h, int fps, int fullscreen); +void get_detection_detections(layer l, int w, int h, float thresh, detection *dets); + +char *option_find_str(list *l, char *key, char *def); +int option_find_int(list *l, char *key, int def); +int option_find_int_quiet(list *l, char *key, int def); + +network *parse_network_cfg(char *filename); +void save_weights(network *net, char *filename); +void load_weights(network *net, char *filename); +void save_weights_upto(network *net, char *filename, int cutoff); +void load_weights_upto(network *net, char *filename, int start, int cutoff); + +void zero_objectness(layer l); +void get_region_detections(layer l, int w, int h, int netw, int neth, float thresh, int *map, float tree_thresh, int relative, detection *dets); +int get_yolo_detections(layer l, int w, int h, int netw, int neth, float thresh, int *map, int relative, detection *dets); +void free_network(network *net); +void set_batch_network(network *net, int b); +void set_temp_network(network *net, float t); +image load_image(char *filename, int w, int h, int c); +image load_image_color(char *filename, int w, int h); +image make_image(int w, int h, int c); +image resize_image(image im, int w, int h); +void censor_image(image im, int dx, int dy, int w, int h); +image letterbox_image(image im, int w, int h); +image crop_image(image im, int dx, int dy, int w, int h); +image center_crop_image(image im, int w, int h); +image resize_min(image im, int min); +image resize_max(image im, int max); +image threshold_image(image im, float thresh); +image mask_to_rgb(image mask); +int resize_network(network *net, int w, int h); +void free_matrix(matrix m); +void test_resize(char *filename); +void save_image(image p, const char *name); +void show_image(image p, const char *name); +image copy_image(image p); +void draw_box_width(image a, int x1, int y1, int x2, int y2, int w, float r, float g, float b); +float get_current_rate(network *net); +void composite_3d(char *f1, char *f2, char *out, int delta); +data load_data_old(char **paths, int n, int m, char **labels, int k, int w, int h); +size_t get_current_batch(network *net); +void constrain_image(image im); +image get_network_image_layer(network *net, int i); +layer get_network_output_layer(network *net); +void top_predictions(network *net, int n, int *index); +void flip_image(image a); +image float_to_image(int w, int h, int c, float *data); +void ghost_image(image source, image dest, int dx, int dy); +float network_accuracy(network *net, data d); +void random_distort_image(image im, float hue, float saturation, float exposure); +void fill_image(image m, float s); +image grayscale_image(image im); +void rotate_image_cw(image im, int times); +double what_time_is_it_now(); +image rotate_image(image m, float rad); +void visualize_network(network *net); +float box_iou(box a, box b); +data load_all_cifar10(); +box_label *read_boxes(char *filename, int *n); +box float_to_box(float *f, int stride); +void draw_detections(image im, detection *dets, int num, float thresh, char **names, image **alphabet, int classes); + +matrix network_predict_data(network *net, data test); +image **load_alphabet(); +image get_network_image(network *net); +float *network_predict(network *net, float *input); + +int network_width(network *net); +int network_height(network *net); +float *network_predict_image(network *net, image im); +void network_detect(network *net, image im, float thresh, float hier_thresh, float nms, detection *dets); +detection *get_network_boxes(network *net, int w, int h, float thresh, float hier, int *map, int relative, int *num); +void free_detections(detection *dets, int n); + +void reset_network_state(network *net, int b); + +char **get_labels(char *filename); +void do_nms_obj(detection *dets, int total, int classes, float thresh); +void do_nms_sort(detection *dets, int total, int classes, float thresh); + +matrix make_matrix(int rows, int cols); + +#ifndef __cplusplus +#ifdef OPENCV +image get_image_from_stream(CvCapture *cap); +#endif +#endif +void free_image(image m); +float train_network(network *net, data d); +pthread_t load_data_in_thread(load_args args); +void load_data_blocking(load_args args); +list *get_paths(char *filename); +void hierarchy_predictions(float *predictions, int n, tree *hier, int only_leaves, int stride); +void change_leaves(tree *t, char *leaf_list); + +int find_int_arg(int argc, char **argv, char *arg, int def); +float find_float_arg(int argc, char **argv, char *arg, float def); +int find_arg(int argc, char* argv[], char *arg); +char *find_char_arg(int argc, char **argv, char *arg, char *def); +char *basecfg(char *cfgfile); +void find_replace(char *str, char *orig, char *rep, char *output); +void free_ptrs(void **ptrs, int n); +char *fgetl(FILE *fp); +void strip(char *s); +float sec(clock_t clocks); +void **list_to_array(list *l); +void top_k(float *a, int n, int k, int *index); +int *read_map(char *filename); +void error(const char *s); +int max_index(float *a, int n); +int max_int_index(int *a, int n); +int sample_array(float *a, int n); +int *random_index_order(int min, int max); +void free_list(list *l); +float mse_array(float *a, int n); +float variance_array(float *a, int n); +float mag_array(float *a, int n); +void scale_array(float *a, int n, float s); +float mean_array(float *a, int n); +float sum_array(float *a, int n); +void normalize_array(float *a, int n); +int *read_intlist(char *s, int *n, int d); +size_t rand_size_t(); +float rand_normal(); +float rand_uniform(float min, float max); + +#endif diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/data.c b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/data.c new file mode 100644 index 00000000000..51900f2600f --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/data.c @@ -0,0 +1,1603 @@ +#include "data.h" +#include "utils.h" +#include "image.h" +#include "cuda.h" + +#include +#include +#include + +pthread_mutex_t mutex = PTHREAD_MUTEX_INITIALIZER; + +list *get_paths(char *filename) +{ + char *path; + FILE *file = fopen(filename, "r"); + if(!file) file_error(filename); + list *lines = make_list(); + while((path=fgetl(file))){ + list_insert(lines, path); + } + fclose(file); + return lines; +} + +/* +char **get_random_paths_indexes(char **paths, int n, int m, int *indexes) +{ + char **random_paths = calloc(n, sizeof(char*)); + int i; + pthread_mutex_lock(&mutex); + for(i = 0; i < n; ++i){ + int index = rand()%m; + indexes[i] = index; + random_paths[i] = paths[index]; + if(i == 0) printf("%s\n", paths[index]); + } + pthread_mutex_unlock(&mutex); + return random_paths; +} +*/ + +char **get_random_paths(char **paths, int n, int m) +{ + char **random_paths = calloc(n, sizeof(char*)); + int i; + pthread_mutex_lock(&mutex); + for(i = 0; i < n; ++i){ + int index = rand()%m; + random_paths[i] = paths[index]; + //if(i == 0) printf("%s\n", paths[index]); + } + pthread_mutex_unlock(&mutex); + return random_paths; +} + +char **find_replace_paths(char **paths, int n, char *find, char *replace) +{ + char **replace_paths = calloc(n, sizeof(char*)); + int i; + for(i = 0; i < n; ++i){ + char replaced[4096]; + find_replace(paths[i], find, replace, replaced); + replace_paths[i] = copy_string(replaced); + } + return replace_paths; +} + +matrix load_image_paths_gray(char **paths, int n, int w, int h) +{ + int i; + matrix X; + X.rows = n; + X.vals = calloc(X.rows, sizeof(float*)); + X.cols = 0; + + for(i = 0; i < n; ++i){ + image im = load_image(paths[i], w, h, 3); + + image gray = grayscale_image(im); + free_image(im); + im = gray; + + X.vals[i] = im.data; + X.cols = im.h*im.w*im.c; + } + return X; +} + +matrix load_image_paths(char **paths, int n, int w, int h) +{ + int i; + matrix X; + X.rows = n; + X.vals = calloc(X.rows, sizeof(float*)); + X.cols = 0; + + for(i = 0; i < n; ++i){ + image im = load_image_color(paths[i], w, h); + X.vals[i] = im.data; + X.cols = im.h*im.w*im.c; + } + return X; +} + +matrix load_image_augment_paths(char **paths, int n, int min, int max, int size, float angle, float aspect, float hue, float saturation, float exposure, int center) +{ + int i; + matrix X; + X.rows = n; + X.vals = calloc(X.rows, sizeof(float*)); + X.cols = 0; + + for(i = 0; i < n; ++i){ + image im = load_image_color(paths[i], 0, 0); + image crop; + if(center){ + crop = center_crop_image(im, size, size); + } else { + crop = random_augment_image(im, angle, aspect, min, max, size, size); + } + int flip = rand()%2; + if (flip) flip_image(crop); + random_distort_image(crop, hue, saturation, exposure); + + /* + show_image(im, "orig"); + show_image(crop, "crop"); + cvWaitKey(0); + */ + free_image(im); + X.vals[i] = crop.data; + X.cols = crop.h*crop.w*crop.c; + } + return X; +} + + +box_label *read_boxes(char *filename, int *n) +{ + FILE *file = fopen(filename, "r"); + if(!file) file_error(filename); + float x, y, h, w; + int id; + int count = 0; + int size = 64; + box_label *boxes = calloc(size, sizeof(box_label)); + while(fscanf(file, "%d %f %f %f %f", &id, &x, &y, &w, &h) == 5){ + if(count == size) { + size = size * 2; + boxes = realloc(boxes, size*sizeof(box_label)); + } + boxes[count].id = id; + boxes[count].x = x; + boxes[count].y = y; + boxes[count].h = h; + boxes[count].w = w; + boxes[count].left = x - w/2; + boxes[count].right = x + w/2; + boxes[count].top = y - h/2; + boxes[count].bottom = y + h/2; + ++count; + } + fclose(file); + *n = count; + return boxes; +} + +void randomize_boxes(box_label *b, int n) +{ + int i; + for(i = 0; i < n; ++i){ + box_label swap = b[i]; + int index = rand()%n; + b[i] = b[index]; + b[index] = swap; + } +} + +void correct_boxes(box_label *boxes, int n, float dx, float dy, float sx, float sy, int flip) +{ + int i; + for(i = 0; i < n; ++i){ + if(boxes[i].x == 0 && boxes[i].y == 0) { + boxes[i].x = 999999; + boxes[i].y = 999999; + boxes[i].w = 999999; + boxes[i].h = 999999; + continue; + } + boxes[i].left = boxes[i].left * sx - dx; + boxes[i].right = boxes[i].right * sx - dx; + boxes[i].top = boxes[i].top * sy - dy; + boxes[i].bottom = boxes[i].bottom* sy - dy; + + if(flip){ + float swap = boxes[i].left; + boxes[i].left = 1. - boxes[i].right; + boxes[i].right = 1. - swap; + } + + boxes[i].left = constrain(0, 1, boxes[i].left); + boxes[i].right = constrain(0, 1, boxes[i].right); + boxes[i].top = constrain(0, 1, boxes[i].top); + boxes[i].bottom = constrain(0, 1, boxes[i].bottom); + + boxes[i].x = (boxes[i].left+boxes[i].right)/2; + boxes[i].y = (boxes[i].top+boxes[i].bottom)/2; + boxes[i].w = (boxes[i].right - boxes[i].left); + boxes[i].h = (boxes[i].bottom - boxes[i].top); + + boxes[i].w = constrain(0, 1, boxes[i].w); + boxes[i].h = constrain(0, 1, boxes[i].h); + } +} + +void fill_truth_swag(char *path, float *truth, int classes, int flip, float dx, float dy, float sx, float sy) +{ + char labelpath[4096]; + find_replace(path, "images", "labels", labelpath); + find_replace(labelpath, "JPEGImages", "labels", labelpath); + find_replace(labelpath, ".jpg", ".txt", labelpath); + find_replace(labelpath, ".JPG", ".txt", labelpath); + find_replace(labelpath, ".JPEG", ".txt", labelpath); + + int count = 0; + box_label *boxes = read_boxes(labelpath, &count); + randomize_boxes(boxes, count); + correct_boxes(boxes, count, dx, dy, sx, sy, flip); + float x,y,w,h; + int id; + int i; + + for (i = 0; i < count && i < 90; ++i) { + x = boxes[i].x; + y = boxes[i].y; + w = boxes[i].w; + h = boxes[i].h; + id = boxes[i].id; + + if (w < .0 || h < .0) continue; + + int index = (4+classes) * i; + + truth[index++] = x; + truth[index++] = y; + truth[index++] = w; + truth[index++] = h; + + if (id < classes) truth[index+id] = 1; + } + free(boxes); +} + +void fill_truth_region(char *path, float *truth, int classes, int num_boxes, int flip, float dx, float dy, float sx, float sy) +{ + char labelpath[4096]; + find_replace(path, "images", "labels", labelpath); + find_replace(labelpath, "JPEGImages", "labels", labelpath); + + find_replace(labelpath, ".jpg", ".txt", labelpath); + find_replace(labelpath, ".png", ".txt", labelpath); + find_replace(labelpath, ".JPG", ".txt", labelpath); + find_replace(labelpath, ".JPEG", ".txt", labelpath); + int count = 0; + box_label *boxes = read_boxes(labelpath, &count); + randomize_boxes(boxes, count); + correct_boxes(boxes, count, dx, dy, sx, sy, flip); + float x,y,w,h; + int id; + int i; + + for (i = 0; i < count; ++i) { + x = boxes[i].x; + y = boxes[i].y; + w = boxes[i].w; + h = boxes[i].h; + id = boxes[i].id; + + if (w < .005 || h < .005) continue; + + int col = (int)(x*num_boxes); + int row = (int)(y*num_boxes); + + x = x*num_boxes - col; + y = y*num_boxes - row; + + int index = (col+row*num_boxes)*(5+classes); + if (truth[index]) continue; + truth[index++] = 1; + + if (id < classes) truth[index+id] = 1; + index += classes; + + truth[index++] = x; + truth[index++] = y; + truth[index++] = w; + truth[index++] = h; + } + free(boxes); +} + +void load_rle(image im, int *rle, int n) +{ + int count = 0; + int curr = 0; + int i,j; + for(i = 0; i < n; ++i){ + for(j = 0; j < rle[i]; ++j){ + im.data[count++] = curr; + } + curr = 1 - curr; + } + for(; count < im.h*im.w*im.c; ++count){ + im.data[count] = curr; + } +} + +void or_image(image src, image dest, int c) +{ + int i; + for(i = 0; i < src.w*src.h; ++i){ + if(src.data[i]) dest.data[dest.w*dest.h*c + i] = 1; + } +} + +void exclusive_image(image src) +{ + int k, j, i; + int s = src.w*src.h; + for(k = 0; k < src.c-1; ++k){ + for(i = 0; i < s; ++i){ + if (src.data[k*s + i]){ + for(j = k+1; j < src.c; ++j){ + src.data[j*s + i] = 0; + } + } + } + } +} + +box bound_image(image im) +{ + int x,y; + int minx = im.w; + int miny = im.h; + int maxx = 0; + int maxy = 0; + for(y = 0; y < im.h; ++y){ + for(x = 0; x < im.w; ++x){ + if(im.data[y*im.w + x]){ + minx = (x < minx) ? x : minx; + miny = (y < miny) ? y : miny; + maxx = (x > maxx) ? x : maxx; + maxy = (y > maxy) ? y : maxy; + } + } + } + box b = {minx, miny, maxx-minx + 1, maxy-miny + 1}; + //printf("%f %f %f %f\n", b.x, b.y, b.w, b.h); + return b; +} + +void fill_truth_iseg(char *path, int num_boxes, float *truth, int classes, int w, int h, augment_args aug, int flip, int mw, int mh) +{ + char labelpath[4096]; + find_replace(path, "images", "mask", labelpath); + find_replace(labelpath, "JPEGImages", "mask", labelpath); + find_replace(labelpath, ".jpg", ".txt", labelpath); + find_replace(labelpath, ".JPG", ".txt", labelpath); + find_replace(labelpath, ".JPEG", ".txt", labelpath); + FILE *file = fopen(labelpath, "r"); + if(!file) file_error(labelpath); + char buff[32788]; + int id; + int i = 0; + image part = make_image(w, h, 1); + while((fscanf(file, "%d %s", &id, buff) == 2) && i < num_boxes){ + int n = 0; + int *rle = read_intlist(buff, &n, 0); + load_rle(part, rle, n); + image sized = rotate_crop_image(part, aug.rad, aug.scale, aug.w, aug.h, aug.dx, aug.dy, aug.aspect); + if(flip) flip_image(sized); + box b = bound_image(sized); + if(b.w > 0){ + image crop = crop_image(sized, b.x, b.y, b.w, b.h); + image mask = resize_image(crop, mw, mh); + truth[i*(4 + mw*mh + 1) + 0] = (b.x + b.w/2.)/sized.w; + truth[i*(4 + mw*mh + 1) + 1] = (b.y + b.h/2.)/sized.h; + truth[i*(4 + mw*mh + 1) + 2] = b.w/sized.w; + truth[i*(4 + mw*mh + 1) + 3] = b.h/sized.h; + int j; + for(j = 0; j < mw*mh; ++j){ + truth[i*(4 + mw*mh + 1) + 4 + j] = mask.data[j]; + } + truth[i*(4 + mw*mh + 1) + 4 + mw*mh] = id; + free_image(crop); + free_image(mask); + ++i; + } + free_image(sized); + free(rle); + } + fclose(file); + free_image(part); +} + + +void fill_truth_detection(char *path, int num_boxes, float *truth, int classes, int flip, float dx, float dy, float sx, float sy) +{ + char labelpath[4096]; + find_replace(path, "images", "labels", labelpath); + find_replace(labelpath, "JPEGImages", "labels", labelpath); + + find_replace(labelpath, "raw", "labels", labelpath); + find_replace(labelpath, ".jpg", ".txt", labelpath); + find_replace(labelpath, ".png", ".txt", labelpath); + find_replace(labelpath, ".JPG", ".txt", labelpath); + find_replace(labelpath, ".JPEG", ".txt", labelpath); + int count = 0; + box_label *boxes = read_boxes(labelpath, &count); + randomize_boxes(boxes, count); + correct_boxes(boxes, count, dx, dy, sx, sy, flip); + if(count > num_boxes) count = num_boxes; + float x,y,w,h; + int id; + int i; + int sub = 0; + + for (i = 0; i < count; ++i) { + x = boxes[i].x; + y = boxes[i].y; + w = boxes[i].w; + h = boxes[i].h; + id = boxes[i].id; + + if ((w < .001 || h < .001)) { + ++sub; + continue; + } + + truth[(i-sub)*5+0] = x; + truth[(i-sub)*5+1] = y; + truth[(i-sub)*5+2] = w; + truth[(i-sub)*5+3] = h; + truth[(i-sub)*5+4] = id; + } + free(boxes); +} + +#define NUMCHARS 37 + +void print_letters(float *pred, int n) +{ + int i; + for(i = 0; i < n; ++i){ + int index = max_index(pred+i*NUMCHARS, NUMCHARS); + printf("%c", int_to_alphanum(index)); + } + printf("\n"); +} + +void fill_truth_captcha(char *path, int n, float *truth) +{ + char *begin = strrchr(path, '/'); + ++begin; + int i; + for(i = 0; i < strlen(begin) && i < n && begin[i] != '.'; ++i){ + int index = alphanum_to_int(begin[i]); + if(index > 35) printf("Bad %c\n", begin[i]); + truth[i*NUMCHARS+index] = 1; + } + for(;i < n; ++i){ + truth[i*NUMCHARS + NUMCHARS-1] = 1; + } +} + +data load_data_captcha(char **paths, int n, int m, int k, int w, int h) +{ + if(m) paths = get_random_paths(paths, n, m); + data d = {0}; + d.shallow = 0; + d.X = load_image_paths(paths, n, w, h); + d.y = make_matrix(n, k*NUMCHARS); + int i; + for(i = 0; i < n; ++i){ + fill_truth_captcha(paths[i], k, d.y.vals[i]); + } + if(m) free(paths); + return d; +} + +data load_data_captcha_encode(char **paths, int n, int m, int w, int h) +{ + if(m) paths = get_random_paths(paths, n, m); + data d = {0}; + d.shallow = 0; + d.X = load_image_paths(paths, n, w, h); + d.X.cols = 17100; + d.y = d.X; + if(m) free(paths); + return d; +} + +void fill_truth(char *path, char **labels, int k, float *truth) +{ + int i; + memset(truth, 0, k*sizeof(float)); + int count = 0; + for(i = 0; i < k; ++i){ + if(strstr(path, labels[i])){ + truth[i] = 1; + ++count; + //printf("%s %s %d\n", path, labels[i], i); + } + } + if(count != 1 && (k != 1 || count != 0)) printf("Too many or too few labels: %d, %s\n", count, path); +} + +void fill_hierarchy(float *truth, int k, tree *hierarchy) +{ + int j; + for(j = 0; j < k; ++j){ + if(truth[j]){ + int parent = hierarchy->parent[j]; + while(parent >= 0){ + truth[parent] = 1; + parent = hierarchy->parent[parent]; + } + } + } + int i; + int count = 0; + for(j = 0; j < hierarchy->groups; ++j){ + //printf("%d\n", count); + int mask = 1; + for(i = 0; i < hierarchy->group_size[j]; ++i){ + if(truth[count + i]){ + mask = 0; + break; + } + } + if (mask) { + for(i = 0; i < hierarchy->group_size[j]; ++i){ + truth[count + i] = SECRET_NUM; + } + } + count += hierarchy->group_size[j]; + } +} + +matrix load_regression_labels_paths(char **paths, int n, int k) +{ + matrix y = make_matrix(n, k); + int i,j; + for(i = 0; i < n; ++i){ + char labelpath[4096]; + find_replace(paths[i], "images", "labels", labelpath); + find_replace(labelpath, "JPEGImages", "labels", labelpath); + find_replace(labelpath, ".BMP", ".txt", labelpath); + find_replace(labelpath, ".JPEG", ".txt", labelpath); + find_replace(labelpath, ".JPG", ".txt", labelpath); + find_replace(labelpath, ".JPeG", ".txt", labelpath); + find_replace(labelpath, ".Jpeg", ".txt", labelpath); + find_replace(labelpath, ".PNG", ".txt", labelpath); + find_replace(labelpath, ".TIF", ".txt", labelpath); + find_replace(labelpath, ".bmp", ".txt", labelpath); + find_replace(labelpath, ".jpeg", ".txt", labelpath); + find_replace(labelpath, ".jpg", ".txt", labelpath); + find_replace(labelpath, ".png", ".txt", labelpath); + find_replace(labelpath, ".tif", ".txt", labelpath); + + FILE *file = fopen(labelpath, "r"); + for(j = 0; j < k; ++j){ + fscanf(file, "%f", &(y.vals[i][j])); + } + fclose(file); + } + return y; +} + +matrix load_labels_paths(char **paths, int n, char **labels, int k, tree *hierarchy) +{ + matrix y = make_matrix(n, k); + int i; + for(i = 0; i < n && labels; ++i){ + fill_truth(paths[i], labels, k, y.vals[i]); + if(hierarchy){ + fill_hierarchy(y.vals[i], k, hierarchy); + } + } + return y; +} + +matrix load_tags_paths(char **paths, int n, int k) +{ + matrix y = make_matrix(n, k); + int i; + //int count = 0; + for(i = 0; i < n; ++i){ + char label[4096]; + find_replace(paths[i], "images", "labels", label); + find_replace(label, ".jpg", ".txt", label); + FILE *file = fopen(label, "r"); + if (!file) continue; + //++count; + int tag; + while(fscanf(file, "%d", &tag) == 1){ + if(tag < k){ + y.vals[i][tag] = 1; + } + } + fclose(file); + } + //printf("%d/%d\n", count, n); + return y; +} + +char **get_labels(char *filename) +{ + list *plist = get_paths(filename); + char **labels = (char **)list_to_array(plist); + free_list(plist); + return labels; +} + +void free_data(data d) +{ + if(!d.shallow){ + free_matrix(d.X); + free_matrix(d.y); + }else{ + free(d.X.vals); + free(d.y.vals); + } +} + +image get_segmentation_image(char *path, int w, int h, int classes) +{ + char labelpath[4096]; + find_replace(path, "images", "mask", labelpath); + find_replace(labelpath, "JPEGImages", "mask", labelpath); + find_replace(labelpath, ".jpg", ".txt", labelpath); + find_replace(labelpath, ".JPG", ".txt", labelpath); + find_replace(labelpath, ".JPEG", ".txt", labelpath); + image mask = make_image(w, h, classes); + FILE *file = fopen(labelpath, "r"); + if(!file) file_error(labelpath); + char buff[32788]; + int id; + image part = make_image(w, h, 1); + while(fscanf(file, "%d %s", &id, buff) == 2){ + int n = 0; + int *rle = read_intlist(buff, &n, 0); + load_rle(part, rle, n); + or_image(part, mask, id); + free(rle); + } + //exclusive_image(mask); + fclose(file); + free_image(part); + return mask; +} + +image get_segmentation_image2(char *path, int w, int h, int classes) +{ + char labelpath[4096]; + find_replace(path, "images", "mask", labelpath); + find_replace(labelpath, "JPEGImages", "mask", labelpath); + find_replace(labelpath, ".jpg", ".txt", labelpath); + find_replace(labelpath, ".JPG", ".txt", labelpath); + find_replace(labelpath, ".JPEG", ".txt", labelpath); + image mask = make_image(w, h, classes+1); + int i; + for(i = 0; i < w*h; ++i){ + mask.data[w*h*classes + i] = 1; + } + FILE *file = fopen(labelpath, "r"); + if(!file) file_error(labelpath); + char buff[32788]; + int id; + image part = make_image(w, h, 1); + while(fscanf(file, "%d %s", &id, buff) == 2){ + int n = 0; + int *rle = read_intlist(buff, &n, 0); + load_rle(part, rle, n); + or_image(part, mask, id); + for(i = 0; i < w*h; ++i){ + if(part.data[i]) mask.data[w*h*classes + i] = 0; + } + free(rle); + } + //exclusive_image(mask); + fclose(file); + free_image(part); + return mask; +} + +data load_data_seg(int n, char **paths, int m, int w, int h, int classes, int min, int max, float angle, float aspect, float hue, float saturation, float exposure, int div) +{ + char **random_paths = get_random_paths(paths, n, m); + int i; + data d = {0}; + d.shallow = 0; + + d.X.rows = n; + d.X.vals = calloc(d.X.rows, sizeof(float*)); + d.X.cols = h*w*3; + + + d.y.rows = n; + d.y.cols = h*w*classes/div/div; + d.y.vals = calloc(d.X.rows, sizeof(float*)); + + for(i = 0; i < n; ++i){ + image orig = load_image_color(random_paths[i], 0, 0); + augment_args a = random_augment_args(orig, angle, aspect, min, max, w, h); + image sized = rotate_crop_image(orig, a.rad, a.scale, a.w, a.h, a.dx, a.dy, a.aspect); + + int flip = rand()%2; + if(flip) flip_image(sized); + random_distort_image(sized, hue, saturation, exposure); + d.X.vals[i] = sized.data; + + image mask = get_segmentation_image(random_paths[i], orig.w, orig.h, classes); + //image mask = make_image(orig.w, orig.h, classes+1); + image sized_m = rotate_crop_image(mask, a.rad, a.scale/div, a.w/div, a.h/div, a.dx/div, a.dy/div, a.aspect); + + if(flip) flip_image(sized_m); + d.y.vals[i] = sized_m.data; + + free_image(orig); + free_image(mask); + + /* + image rgb = mask_to_rgb(sized_m, classes); + show_image(rgb, "part"); + show_image(sized, "orig"); + cvWaitKey(0); + free_image(rgb); + */ + } + free(random_paths); + return d; +} + +data load_data_iseg(int n, char **paths, int m, int w, int h, int classes, int boxes, int coords, int min, int max, float angle, float aspect, float hue, float saturation, float exposure) +{ + char **random_paths = get_random_paths(paths, n, m); + int i; + data d = {0}; + d.shallow = 0; + + d.X.rows = n; + d.X.vals = calloc(d.X.rows, sizeof(float*)); + d.X.cols = h*w*3; + + d.y = make_matrix(n, (coords+1)*boxes); + + for(i = 0; i < n; ++i){ + image orig = load_image_color(random_paths[i], 0, 0); + augment_args a = random_augment_args(orig, angle, aspect, min, max, w, h); + image sized = rotate_crop_image(orig, a.rad, a.scale, a.w, a.h, a.dx, a.dy, a.aspect); + + int flip = rand()%2; + if(flip) flip_image(sized); + random_distort_image(sized, hue, saturation, exposure); + d.X.vals[i] = sized.data; + //show_image(sized, "image"); + + fill_truth_iseg(random_paths[i], boxes, d.y.vals[i], classes, orig.w, orig.h, a, flip, 14, 14); + + free_image(orig); + + /* + image rgb = mask_to_rgb(sized_m, classes); + show_image(rgb, "part"); + show_image(sized, "orig"); + cvWaitKey(0); + free_image(rgb); + */ + } + free(random_paths); + return d; +} + +data load_data_region(int n, char **paths, int m, int w, int h, int size, int classes, float jitter, float hue, float saturation, float exposure) +{ + char **random_paths = get_random_paths(paths, n, m); + int i; + data d = {0}; + d.shallow = 0; + + d.X.rows = n; + d.X.vals = calloc(d.X.rows, sizeof(float*)); + d.X.cols = h*w*3; + + + int k = size*size*(5+classes); + d.y = make_matrix(n, k); + for(i = 0; i < n; ++i){ + image orig = load_image_color(random_paths[i], 0, 0); + + int oh = orig.h; + int ow = orig.w; + + int dw = (ow*jitter); + int dh = (oh*jitter); + + int pleft = rand_uniform(-dw, dw); + int pright = rand_uniform(-dw, dw); + int ptop = rand_uniform(-dh, dh); + int pbot = rand_uniform(-dh, dh); + + int swidth = ow - pleft - pright; + int sheight = oh - ptop - pbot; + + float sx = (float)swidth / ow; + float sy = (float)sheight / oh; + + int flip = rand()%2; + image cropped = crop_image(orig, pleft, ptop, swidth, sheight); + + float dx = ((float)pleft/ow)/sx; + float dy = ((float)ptop /oh)/sy; + + image sized = resize_image(cropped, w, h); + if(flip) flip_image(sized); + random_distort_image(sized, hue, saturation, exposure); + d.X.vals[i] = sized.data; + + fill_truth_region(random_paths[i], d.y.vals[i], classes, size, flip, dx, dy, 1./sx, 1./sy); + + free_image(orig); + free_image(cropped); + } + free(random_paths); + return d; +} + +data load_data_compare(int n, char **paths, int m, int classes, int w, int h) +{ + if(m) paths = get_random_paths(paths, 2*n, m); + int i,j; + data d = {0}; + d.shallow = 0; + + d.X.rows = n; + d.X.vals = calloc(d.X.rows, sizeof(float*)); + d.X.cols = h*w*6; + + int k = 2*(classes); + d.y = make_matrix(n, k); + for(i = 0; i < n; ++i){ + image im1 = load_image_color(paths[i*2], w, h); + image im2 = load_image_color(paths[i*2+1], w, h); + + d.X.vals[i] = calloc(d.X.cols, sizeof(float)); + memcpy(d.X.vals[i], im1.data, h*w*3*sizeof(float)); + memcpy(d.X.vals[i] + h*w*3, im2.data, h*w*3*sizeof(float)); + + int id; + float iou; + + char imlabel1[4096]; + char imlabel2[4096]; + find_replace(paths[i*2], "imgs", "labels", imlabel1); + find_replace(imlabel1, "jpg", "txt", imlabel1); + FILE *fp1 = fopen(imlabel1, "r"); + + while(fscanf(fp1, "%d %f", &id, &iou) == 2){ + if (d.y.vals[i][2*id] < iou) d.y.vals[i][2*id] = iou; + } + + find_replace(paths[i*2+1], "imgs", "labels", imlabel2); + find_replace(imlabel2, "jpg", "txt", imlabel2); + FILE *fp2 = fopen(imlabel2, "r"); + + while(fscanf(fp2, "%d %f", &id, &iou) == 2){ + if (d.y.vals[i][2*id + 1] < iou) d.y.vals[i][2*id + 1] = iou; + } + + for (j = 0; j < classes; ++j){ + if (d.y.vals[i][2*j] > .5 && d.y.vals[i][2*j+1] < .5){ + d.y.vals[i][2*j] = 1; + d.y.vals[i][2*j+1] = 0; + } else if (d.y.vals[i][2*j] < .5 && d.y.vals[i][2*j+1] > .5){ + d.y.vals[i][2*j] = 0; + d.y.vals[i][2*j+1] = 1; + } else { + d.y.vals[i][2*j] = SECRET_NUM; + d.y.vals[i][2*j+1] = SECRET_NUM; + } + } + fclose(fp1); + fclose(fp2); + + free_image(im1); + free_image(im2); + } + if(m) free(paths); + return d; +} + +data load_data_swag(char **paths, int n, int classes, float jitter) +{ + int index = rand()%n; + char *random_path = paths[index]; + + image orig = load_image_color(random_path, 0, 0); + int h = orig.h; + int w = orig.w; + + data d = {0}; + d.shallow = 0; + d.w = w; + d.h = h; + + d.X.rows = 1; + d.X.vals = calloc(d.X.rows, sizeof(float*)); + d.X.cols = h*w*3; + + int k = (4+classes)*90; + d.y = make_matrix(1, k); + + int dw = w*jitter; + int dh = h*jitter; + + int pleft = rand_uniform(-dw, dw); + int pright = rand_uniform(-dw, dw); + int ptop = rand_uniform(-dh, dh); + int pbot = rand_uniform(-dh, dh); + + int swidth = w - pleft - pright; + int sheight = h - ptop - pbot; + + float sx = (float)swidth / w; + float sy = (float)sheight / h; + + int flip = rand()%2; + image cropped = crop_image(orig, pleft, ptop, swidth, sheight); + + float dx = ((float)pleft/w)/sx; + float dy = ((float)ptop /h)/sy; + + image sized = resize_image(cropped, w, h); + if(flip) flip_image(sized); + d.X.vals[0] = sized.data; + + fill_truth_swag(random_path, d.y.vals[0], classes, flip, dx, dy, 1./sx, 1./sy); + + free_image(orig); + free_image(cropped); + + return d; +} + +data load_data_detection(int n, char **paths, int m, int w, int h, int boxes, int classes, float jitter, float hue, float saturation, float exposure) +{ + char **random_paths = get_random_paths(paths, n, m); + int i; + data d = {0}; + d.shallow = 0; + + d.X.rows = n; + d.X.vals = calloc(d.X.rows, sizeof(float*)); + d.X.cols = h*w*3; + + d.y = make_matrix(n, 5*boxes); + for(i = 0; i < n; ++i){ + image orig = load_image_color(random_paths[i], 0, 0); + image sized = make_image(w, h, orig.c); + fill_image(sized, .5); + + float dw = jitter * orig.w; + float dh = jitter * orig.h; + + float new_ar = (orig.w + rand_uniform(-dw, dw)) / (orig.h + rand_uniform(-dh, dh)); + float scale = rand_uniform(.25, 2); + + float nw, nh; + + if(new_ar < 1){ + nh = scale * h; + nw = nh * new_ar; + } else { + nw = scale * w; + nh = nw / new_ar; + } + + float dx = rand_uniform(0, w - nw); + float dy = rand_uniform(0, h - nh); + + place_image(orig, nw, nh, dx, dy, sized); + + random_distort_image(sized, hue, saturation, exposure); + + int flip = rand()%2; + if(flip) flip_image(sized); + d.X.vals[i] = sized.data; + + + fill_truth_detection(random_paths[i], boxes, d.y.vals[i], classes, flip, -dx/w, -dy/h, nw/w, nh/h); + + free_image(orig); + } + free(random_paths); + return d; +} + +void *load_thread(void *ptr) +{ + //printf("Loading data: %d\n", rand()); + load_args a = *(struct load_args*)ptr; + if(a.exposure == 0) a.exposure = 1; + if(a.saturation == 0) a.saturation = 1; + if(a.aspect == 0) a.aspect = 1; + + if (a.type == OLD_CLASSIFICATION_DATA){ + *a.d = load_data_old(a.paths, a.n, a.m, a.labels, a.classes, a.w, a.h); + } else if (a.type == REGRESSION_DATA){ + *a.d = load_data_regression(a.paths, a.n, a.m, a.classes, a.min, a.max, a.size, a.angle, a.aspect, a.hue, a.saturation, a.exposure); + } else if (a.type == CLASSIFICATION_DATA){ + *a.d = load_data_augment(a.paths, a.n, a.m, a.labels, a.classes, a.hierarchy, a.min, a.max, a.size, a.angle, a.aspect, a.hue, a.saturation, a.exposure, a.center); + } else if (a.type == SUPER_DATA){ + *a.d = load_data_super(a.paths, a.n, a.m, a.w, a.h, a.scale); + } else if (a.type == WRITING_DATA){ + *a.d = load_data_writing(a.paths, a.n, a.m, a.w, a.h, a.out_w, a.out_h); + } else if (a.type == INSTANCE_DATA){ + *a.d = load_data_iseg(a.n, a.paths, a.m, a.w, a.h, a.classes, a.num_boxes, a.coords, a.min, a.max, a.angle, a.aspect, a.hue, a.saturation, a.exposure); + } else if (a.type == SEGMENTATION_DATA){ + *a.d = load_data_seg(a.n, a.paths, a.m, a.w, a.h, a.classes, a.min, a.max, a.angle, a.aspect, a.hue, a.saturation, a.exposure, a.scale); + } else if (a.type == REGION_DATA){ + *a.d = load_data_region(a.n, a.paths, a.m, a.w, a.h, a.num_boxes, a.classes, a.jitter, a.hue, a.saturation, a.exposure); + } else if (a.type == DETECTION_DATA){ + *a.d = load_data_detection(a.n, a.paths, a.m, a.w, a.h, a.num_boxes, a.classes, a.jitter, a.hue, a.saturation, a.exposure); + } else if (a.type == SWAG_DATA){ + *a.d = load_data_swag(a.paths, a.n, a.classes, a.jitter); + } else if (a.type == COMPARE_DATA){ + *a.d = load_data_compare(a.n, a.paths, a.m, a.classes, a.w, a.h); + } else if (a.type == IMAGE_DATA){ + *(a.im) = load_image_color(a.path, 0, 0); + *(a.resized) = resize_image(*(a.im), a.w, a.h); + } else if (a.type == LETTERBOX_DATA){ + *(a.im) = load_image_color(a.path, 0, 0); + *(a.resized) = letterbox_image(*(a.im), a.w, a.h); + } else if (a.type == TAG_DATA){ + *a.d = load_data_tag(a.paths, a.n, a.m, a.classes, a.min, a.max, a.size, a.angle, a.aspect, a.hue, a.saturation, a.exposure); + } + free(ptr); + return 0; +} + +pthread_t load_data_in_thread(load_args args) +{ + pthread_t thread; + struct load_args *ptr = calloc(1, sizeof(struct load_args)); + *ptr = args; + if(pthread_create(&thread, 0, load_thread, ptr)) error("Thread creation failed"); + return thread; +} + +void *load_threads(void *ptr) +{ + int i; + load_args args = *(load_args *)ptr; + if (args.threads == 0) args.threads = 1; + data *out = args.d; + int total = args.n; + free(ptr); + data *buffers = calloc(args.threads, sizeof(data)); + pthread_t *threads = calloc(args.threads, sizeof(pthread_t)); + for(i = 0; i < args.threads; ++i){ + args.d = buffers + i; + args.n = (i+1) * total/args.threads - i * total/args.threads; + threads[i] = load_data_in_thread(args); + } + for(i = 0; i < args.threads; ++i){ + pthread_join(threads[i], 0); + } + *out = concat_datas(buffers, args.threads); + out->shallow = 0; + for(i = 0; i < args.threads; ++i){ + buffers[i].shallow = 1; + free_data(buffers[i]); + } + free(buffers); + free(threads); + return 0; +} + +void load_data_blocking(load_args args) +{ + struct load_args *ptr = calloc(1, sizeof(struct load_args)); + *ptr = args; + load_thread(ptr); +} + +pthread_t load_data(load_args args) +{ + pthread_t thread; + struct load_args *ptr = calloc(1, sizeof(struct load_args)); + *ptr = args; + if(pthread_create(&thread, 0, load_threads, ptr)) error("Thread creation failed"); + return thread; +} + +data load_data_writing(char **paths, int n, int m, int w, int h, int out_w, int out_h) +{ + if(m) paths = get_random_paths(paths, n, m); + char **replace_paths = find_replace_paths(paths, n, ".png", "-label.png"); + data d = {0}; + d.shallow = 0; + d.X = load_image_paths(paths, n, w, h); + d.y = load_image_paths_gray(replace_paths, n, out_w, out_h); + if(m) free(paths); + int i; + for(i = 0; i < n; ++i) free(replace_paths[i]); + free(replace_paths); + return d; +} + +data load_data_old(char **paths, int n, int m, char **labels, int k, int w, int h) +{ + if(m) paths = get_random_paths(paths, n, m); + data d = {0}; + d.shallow = 0; + d.X = load_image_paths(paths, n, w, h); + d.y = load_labels_paths(paths, n, labels, k, 0); + if(m) free(paths); + return d; +} + +/* + data load_data_study(char **paths, int n, int m, char **labels, int k, int min, int max, int size, float angle, float aspect, float hue, float saturation, float exposure) + { + data d = {0}; + d.indexes = calloc(n, sizeof(int)); + if(m) paths = get_random_paths_indexes(paths, n, m, d.indexes); + d.shallow = 0; + d.X = load_image_augment_paths(paths, n, min, max, size, angle, aspect, hue, saturation, exposure); + d.y = load_labels_paths(paths, n, labels, k); + if(m) free(paths); + return d; + } + */ + +data load_data_super(char **paths, int n, int m, int w, int h, int scale) +{ + if(m) paths = get_random_paths(paths, n, m); + data d = {0}; + d.shallow = 0; + + int i; + d.X.rows = n; + d.X.vals = calloc(n, sizeof(float*)); + d.X.cols = w*h*3; + + d.y.rows = n; + d.y.vals = calloc(n, sizeof(float*)); + d.y.cols = w*scale * h*scale * 3; + + for(i = 0; i < n; ++i){ + image im = load_image_color(paths[i], 0, 0); + image crop = random_crop_image(im, w*scale, h*scale); + int flip = rand()%2; + if (flip) flip_image(crop); + image resize = resize_image(crop, w, h); + d.X.vals[i] = resize.data; + d.y.vals[i] = crop.data; + free_image(im); + } + + if(m) free(paths); + return d; +} + +data load_data_regression(char **paths, int n, int m, int k, int min, int max, int size, float angle, float aspect, float hue, float saturation, float exposure) +{ + if(m) paths = get_random_paths(paths, n, m); + data d = {0}; + d.shallow = 0; + d.X = load_image_augment_paths(paths, n, min, max, size, angle, aspect, hue, saturation, exposure, 0); + d.y = load_regression_labels_paths(paths, n, k); + if(m) free(paths); + return d; +} + +data select_data(data *orig, int *inds) +{ + data d = {0}; + d.shallow = 1; + d.w = orig[0].w; + d.h = orig[0].h; + + d.X.rows = orig[0].X.rows; + d.y.rows = orig[0].X.rows; + + d.X.cols = orig[0].X.cols; + d.y.cols = orig[0].y.cols; + + d.X.vals = calloc(orig[0].X.rows, sizeof(float *)); + d.y.vals = calloc(orig[0].y.rows, sizeof(float *)); + int i; + for(i = 0; i < d.X.rows; ++i){ + d.X.vals[i] = orig[inds[i]].X.vals[i]; + d.y.vals[i] = orig[inds[i]].y.vals[i]; + } + return d; +} + +data *tile_data(data orig, int divs, int size) +{ + data *ds = calloc(divs*divs, sizeof(data)); + int i, j; + #pragma omp parallel for + for(i = 0; i < divs*divs; ++i){ + data d; + d.shallow = 0; + d.w = orig.w/divs * size; + d.h = orig.h/divs * size; + d.X.rows = orig.X.rows; + d.X.cols = d.w*d.h*3; + d.X.vals = calloc(d.X.rows, sizeof(float*)); + + d.y = copy_matrix(orig.y); + #pragma omp parallel for + for(j = 0; j < orig.X.rows; ++j){ + int x = (i%divs) * orig.w / divs - (d.w - orig.w/divs)/2; + int y = (i/divs) * orig.h / divs - (d.h - orig.h/divs)/2; + image im = float_to_image(orig.w, orig.h, 3, orig.X.vals[j]); + d.X.vals[j] = crop_image(im, x, y, d.w, d.h).data; + } + ds[i] = d; + } + return ds; +} + +data resize_data(data orig, int w, int h) +{ + data d = {0}; + d.shallow = 0; + d.w = w; + d.h = h; + int i; + d.X.rows = orig.X.rows; + d.X.cols = w*h*3; + d.X.vals = calloc(d.X.rows, sizeof(float*)); + + d.y = copy_matrix(orig.y); + #pragma omp parallel for + for(i = 0; i < orig.X.rows; ++i){ + image im = float_to_image(orig.w, orig.h, 3, orig.X.vals[i]); + d.X.vals[i] = resize_image(im, w, h).data; + } + return d; +} + +data load_data_augment(char **paths, int n, int m, char **labels, int k, tree *hierarchy, int min, int max, int size, float angle, float aspect, float hue, float saturation, float exposure, int center) +{ + if(m) paths = get_random_paths(paths, n, m); + data d = {0}; + d.shallow = 0; + d.w=size; + d.h=size; + d.X = load_image_augment_paths(paths, n, min, max, size, angle, aspect, hue, saturation, exposure, center); + d.y = load_labels_paths(paths, n, labels, k, hierarchy); + if(m) free(paths); + return d; +} + +data load_data_tag(char **paths, int n, int m, int k, int min, int max, int size, float angle, float aspect, float hue, float saturation, float exposure) +{ + if(m) paths = get_random_paths(paths, n, m); + data d = {0}; + d.w = size; + d.h = size; + d.shallow = 0; + d.X = load_image_augment_paths(paths, n, min, max, size, angle, aspect, hue, saturation, exposure, 0); + d.y = load_tags_paths(paths, n, k); + if(m) free(paths); + return d; +} + +matrix concat_matrix(matrix m1, matrix m2) +{ + int i, count = 0; + matrix m; + m.cols = m1.cols; + m.rows = m1.rows+m2.rows; + m.vals = calloc(m1.rows + m2.rows, sizeof(float*)); + for(i = 0; i < m1.rows; ++i){ + m.vals[count++] = m1.vals[i]; + } + for(i = 0; i < m2.rows; ++i){ + m.vals[count++] = m2.vals[i]; + } + return m; +} + +data concat_data(data d1, data d2) +{ + data d = {0}; + d.shallow = 1; + d.X = concat_matrix(d1.X, d2.X); + d.y = concat_matrix(d1.y, d2.y); + d.w = d1.w; + d.h = d1.h; + return d; +} + +data concat_datas(data *d, int n) +{ + int i; + data out = {0}; + for(i = 0; i < n; ++i){ + data new = concat_data(d[i], out); + free_data(out); + out = new; + } + return out; +} + +data load_categorical_data_csv(char *filename, int target, int k) +{ + data d = {0}; + d.shallow = 0; + matrix X = csv_to_matrix(filename); + float *truth_1d = pop_column(&X, target); + float **truth = one_hot_encode(truth_1d, X.rows, k); + matrix y; + y.rows = X.rows; + y.cols = k; + y.vals = truth; + d.X = X; + d.y = y; + free(truth_1d); + return d; +} + +data load_cifar10_data(char *filename) +{ + data d = {0}; + d.shallow = 0; + long i,j; + matrix X = make_matrix(10000, 3072); + matrix y = make_matrix(10000, 10); + d.X = X; + d.y = y; + + FILE *fp = fopen(filename, "rb"); + if(!fp) file_error(filename); + for(i = 0; i < 10000; ++i){ + unsigned char bytes[3073]; + fread(bytes, 1, 3073, fp); + int class = bytes[0]; + y.vals[i][class] = 1; + for(j = 0; j < X.cols; ++j){ + X.vals[i][j] = (double)bytes[j+1]; + } + } + scale_data_rows(d, 1./255); + //normalize_data_rows(d); + fclose(fp); + return d; +} + +void get_random_batch(data d, int n, float *X, float *y) +{ + int j; + for(j = 0; j < n; ++j){ + int index = rand()%d.X.rows; + memcpy(X+j*d.X.cols, d.X.vals[index], d.X.cols*sizeof(float)); + memcpy(y+j*d.y.cols, d.y.vals[index], d.y.cols*sizeof(float)); + } +} + +void get_next_batch(data d, int n, int offset, float *X, float *y) +{ + int j; + for(j = 0; j < n; ++j){ + int index = offset + j; + memcpy(X+j*d.X.cols, d.X.vals[index], d.X.cols*sizeof(float)); + if(y) memcpy(y+j*d.y.cols, d.y.vals[index], d.y.cols*sizeof(float)); + } +} + +void smooth_data(data d) +{ + int i, j; + float scale = 1. / d.y.cols; + float eps = .1; + for(i = 0; i < d.y.rows; ++i){ + for(j = 0; j < d.y.cols; ++j){ + d.y.vals[i][j] = eps * scale + (1-eps) * d.y.vals[i][j]; + } + } +} + +data load_all_cifar10() +{ + data d = {0}; + d.shallow = 0; + int i,j,b; + matrix X = make_matrix(50000, 3072); + matrix y = make_matrix(50000, 10); + d.X = X; + d.y = y; + + + for(b = 0; b < 5; ++b){ + char buff[256]; + sprintf(buff, "data/cifar/cifar-10-batches-bin/data_batch_%d.bin", b+1); + FILE *fp = fopen(buff, "rb"); + if(!fp) file_error(buff); + for(i = 0; i < 10000; ++i){ + unsigned char bytes[3073]; + fread(bytes, 1, 3073, fp); + int class = bytes[0]; + y.vals[i+b*10000][class] = 1; + for(j = 0; j < X.cols; ++j){ + X.vals[i+b*10000][j] = (double)bytes[j+1]; + } + } + fclose(fp); + } + //normalize_data_rows(d); + scale_data_rows(d, 1./255); + smooth_data(d); + return d; +} + +data load_go(char *filename) +{ + FILE *fp = fopen(filename, "rb"); + matrix X = make_matrix(3363059, 361); + matrix y = make_matrix(3363059, 361); + int row, col; + + if(!fp) file_error(filename); + char *label; + int count = 0; + while((label = fgetl(fp))){ + int i; + if(count == X.rows){ + X = resize_matrix(X, count*2); + y = resize_matrix(y, count*2); + } + sscanf(label, "%d %d", &row, &col); + char *board = fgetl(fp); + + int index = row*19 + col; + y.vals[count][index] = 1; + + for(i = 0; i < 19*19; ++i){ + float val = 0; + if(board[i] == '1') val = 1; + else if(board[i] == '2') val = -1; + X.vals[count][i] = val; + } + ++count; + free(label); + free(board); + } + X = resize_matrix(X, count); + y = resize_matrix(y, count); + + data d = {0}; + d.shallow = 0; + d.X = X; + d.y = y; + + + fclose(fp); + + return d; +} + + +void randomize_data(data d) +{ + int i; + for(i = d.X.rows-1; i > 0; --i){ + int index = rand()%i; + float *swap = d.X.vals[index]; + d.X.vals[index] = d.X.vals[i]; + d.X.vals[i] = swap; + + swap = d.y.vals[index]; + d.y.vals[index] = d.y.vals[i]; + d.y.vals[i] = swap; + } +} + +void scale_data_rows(data d, float s) +{ + int i; + for(i = 0; i < d.X.rows; ++i){ + scale_array(d.X.vals[i], d.X.cols, s); + } +} + +void translate_data_rows(data d, float s) +{ + int i; + for(i = 0; i < d.X.rows; ++i){ + translate_array(d.X.vals[i], d.X.cols, s); + } +} + +data copy_data(data d) +{ + data c = {0}; + c.w = d.w; + c.h = d.h; + c.shallow = 0; + c.num_boxes = d.num_boxes; + c.boxes = d.boxes; + c.X = copy_matrix(d.X); + c.y = copy_matrix(d.y); + return c; +} + +void normalize_data_rows(data d) +{ + int i; + for(i = 0; i < d.X.rows; ++i){ + normalize_array(d.X.vals[i], d.X.cols); + } +} + +data get_data_part(data d, int part, int total) +{ + data p = {0}; + p.shallow = 1; + p.X.rows = d.X.rows * (part + 1) / total - d.X.rows * part / total; + p.y.rows = d.y.rows * (part + 1) / total - d.y.rows * part / total; + p.X.cols = d.X.cols; + p.y.cols = d.y.cols; + p.X.vals = d.X.vals + d.X.rows * part / total; + p.y.vals = d.y.vals + d.y.rows * part / total; + return p; +} + +data get_random_data(data d, int num) +{ + data r = {0}; + r.shallow = 1; + + r.X.rows = num; + r.y.rows = num; + + r.X.cols = d.X.cols; + r.y.cols = d.y.cols; + + r.X.vals = calloc(num, sizeof(float *)); + r.y.vals = calloc(num, sizeof(float *)); + + int i; + for(i = 0; i < num; ++i){ + int index = rand()%d.X.rows; + r.X.vals[i] = d.X.vals[index]; + r.y.vals[i] = d.y.vals[index]; + } + return r; +} + +data *split_data(data d, int part, int total) +{ + data *split = calloc(2, sizeof(data)); + int i; + int start = part*d.X.rows/total; + int end = (part+1)*d.X.rows/total; + data train; + data test; + train.shallow = test.shallow = 1; + + test.X.rows = test.y.rows = end-start; + train.X.rows = train.y.rows = d.X.rows - (end-start); + train.X.cols = test.X.cols = d.X.cols; + train.y.cols = test.y.cols = d.y.cols; + + train.X.vals = calloc(train.X.rows, sizeof(float*)); + test.X.vals = calloc(test.X.rows, sizeof(float*)); + train.y.vals = calloc(train.y.rows, sizeof(float*)); + test.y.vals = calloc(test.y.rows, sizeof(float*)); + + for(i = 0; i < start; ++i){ + train.X.vals[i] = d.X.vals[i]; + train.y.vals[i] = d.y.vals[i]; + } + for(i = start; i < end; ++i){ + test.X.vals[i-start] = d.X.vals[i]; + test.y.vals[i-start] = d.y.vals[i]; + } + for(i = end; i < d.X.rows; ++i){ + train.X.vals[i-(end-start)] = d.X.vals[i]; + train.y.vals[i-(end-start)] = d.y.vals[i]; + } + split[0] = train; + split[1] = test; + return split; +} + diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/data.h b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/data.h new file mode 100644 index 00000000000..781906f8743 --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/data.h @@ -0,0 +1,50 @@ +#ifndef DATA_H +#define DATA_H +#include + +#include "darknet.h" +#include "matrix.h" +#include "list.h" +#include "image.h" +#include "tree.h" + +static inline float distance_from_edge(int x, int max) +{ + int dx = (max/2) - x; + if (dx < 0) dx = -dx; + dx = (max/2) + 1 - dx; + dx *= 2; + float dist = (float)dx/max; + if (dist > 1) dist = 1; + return dist; +} +void load_data_blocking(load_args args); + + +void print_letters(float *pred, int n); +data load_data_captcha(char **paths, int n, int m, int k, int w, int h); +data load_data_captcha_encode(char **paths, int n, int m, int w, int h); +data load_data_detection(int n, char **paths, int m, int w, int h, int boxes, int classes, float jitter, float hue, float saturation, float exposure); +data load_data_tag(char **paths, int n, int m, int k, int min, int max, int size, float angle, float aspect, float hue, float saturation, float exposure); +matrix load_image_augment_paths(char **paths, int n, int min, int max, int size, float angle, float aspect, float hue, float saturation, float exposure, int center); +data load_data_super(char **paths, int n, int m, int w, int h, int scale); +data load_data_augment(char **paths, int n, int m, char **labels, int k, tree *hierarchy, int min, int max, int size, float angle, float aspect, float hue, float saturation, float exposure, int center); +data load_data_regression(char **paths, int n, int m, int classes, int min, int max, int size, float angle, float aspect, float hue, float saturation, float exposure); +data load_go(char *filename); + + +data load_data_writing(char **paths, int n, int m, int w, int h, int out_w, int out_h); + +void get_random_batch(data d, int n, float *X, float *y); +data get_data_part(data d, int part, int total); +data get_random_data(data d, int num); +data load_categorical_data_csv(char *filename, int target, int k); +void normalize_data_rows(data d); +void scale_data_rows(data d, float s); +void translate_data_rows(data d, float s); +void randomize_data(data d); +data *split_data(data d, int part, int total); +data concat_datas(data *d, int n); +void fill_truth(char *path, char **labels, int k, float *truth); + +#endif diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/deconvolutional_kernels.cu b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/deconvolutional_kernels.cu new file mode 100644 index 00000000000..8267dcfa25b --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/deconvolutional_kernels.cu @@ -0,0 +1,139 @@ +#include "cuda_runtime.h" +#include "curand.h" +#include "cublas_v2.h" + +extern "C" { +#include "convolutional_layer.h" +#include "deconvolutional_layer.h" +#include "batchnorm_layer.h" +#include "gemm.h" +#include "blas.h" +#include "im2col.h" +#include "col2im.h" +#include "utils.h" +#include "cuda.h" +} + +extern "C" void forward_deconvolutional_layer_gpu(layer l, network net) +{ + int i; + + int m = l.size*l.size*l.n; + int n = l.h*l.w; + int k = l.c; + + fill_gpu(l.outputs*l.batch, 0, l.output_gpu, 1); + + for(i = 0; i < l.batch; ++i){ + float *a = l.weights_gpu; + float *b = net.input_gpu + i*l.c*l.h*l.w; + float *c = net.workspace; + + gemm_gpu(1,0,m,n,k,1,a,m,b,n,0,c,n); + + col2im_gpu(net.workspace, l.out_c, l.out_h, l.out_w, l.size, l.stride, l.pad, l.output_gpu+i*l.outputs); + } + if (l.batch_normalize) { + forward_batchnorm_layer_gpu(l, net); + } else { + add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.n, l.out_w*l.out_h); + } + activate_array_gpu(l.output_gpu, l.batch*l.n*l.out_w*l.out_h, l.activation); +} + +extern "C" void backward_deconvolutional_layer_gpu(layer l, network net) +{ + int i; + + //constrain_gpu(l.outputs*l.batch, 1, l.delta_gpu, 1); + gradient_array_gpu(l.output_gpu, l.outputs*l.batch, l.activation, l.delta_gpu); + + if(l.batch_normalize){ + backward_batchnorm_layer_gpu(l, net); + } else { + backward_bias_gpu(l.bias_updates_gpu, l.delta_gpu, l.batch, l.n, l.out_w*l.out_h); + } + + //if(net.delta_gpu) memset(net.delta_gpu, 0, l.batch*l.h*l.w*l.c*sizeof(float)); + + for(i = 0; i < l.batch; ++i){ + int m = l.c; + int n = l.size*l.size*l.n; + int k = l.h*l.w; + + float *a = net.input_gpu + i*m*k; + float *b = net.workspace; + float *c = l.weight_updates_gpu; + + im2col_gpu(l.delta_gpu + i*l.outputs, l.out_c, l.out_h, l.out_w, + l.size, l.stride, l.pad, b); + gemm_gpu(0,1,m,n,k,1,a,k,b,k,1,c,n); + + if(net.delta_gpu){ + int m = l.c; + int n = l.h*l.w; + int k = l.size*l.size*l.n; + + float *a = l.weights_gpu; + float *b = net.workspace; + float *c = net.delta_gpu + i*n*m; + + gemm_gpu(0,0,m,n,k,1,a,k,b,n,1,c,n); + } + } +} + +extern "C" void pull_deconvolutional_layer(layer l) +{ + cuda_pull_array(l.weights_gpu, l.weights, l.c*l.n*l.size*l.size); + cuda_pull_array(l.biases_gpu, l.biases, l.n); + cuda_pull_array(l.weight_updates_gpu, l.weight_updates, l.c*l.n*l.size*l.size); + cuda_pull_array(l.bias_updates_gpu, l.bias_updates, l.n); + if (l.batch_normalize){ + cuda_pull_array(l.scales_gpu, l.scales, l.n); + cuda_pull_array(l.rolling_mean_gpu, l.rolling_mean, l.n); + cuda_pull_array(l.rolling_variance_gpu, l.rolling_variance, l.n); + } +} + +extern "C" void push_deconvolutional_layer(layer l) +{ + cuda_push_array(l.weights_gpu, l.weights, l.c*l.n*l.size*l.size); + cuda_push_array(l.biases_gpu, l.biases, l.n); + cuda_push_array(l.weight_updates_gpu, l.weight_updates, l.c*l.n*l.size*l.size); + cuda_push_array(l.bias_updates_gpu, l.bias_updates, l.n); + if (l.batch_normalize){ + cuda_push_array(l.scales_gpu, l.scales, l.n); + cuda_push_array(l.rolling_mean_gpu, l.rolling_mean, l.n); + cuda_push_array(l.rolling_variance_gpu, l.rolling_variance, l.n); + } +} + +void update_deconvolutional_layer_gpu(layer l, update_args a) +{ + float learning_rate = a.learning_rate*l.learning_rate_scale; + float momentum = a.momentum; + float decay = a.decay; + int batch = a.batch; + + if(a.adam){ + adam_update_gpu(l.weights_gpu, l.weight_updates_gpu, l.m_gpu, l.v_gpu, a.B1, a.B2, a.eps, decay, learning_rate, l.nweights, batch, a.t); + adam_update_gpu(l.biases_gpu, l.bias_updates_gpu, l.bias_m_gpu, l.bias_v_gpu, a.B1, a.B2, a.eps, decay, learning_rate, l.n, batch, a.t); + if(l.scales_gpu){ + adam_update_gpu(l.scales_gpu, l.scale_updates_gpu, l.scale_m_gpu, l.scale_v_gpu, a.B1, a.B2, a.eps, decay, learning_rate, l.n, batch, a.t); + } + }else{ + axpy_gpu(l.nweights, -decay*batch, l.weights_gpu, 1, l.weight_updates_gpu, 1); + axpy_gpu(l.nweights, learning_rate/batch, l.weight_updates_gpu, 1, l.weights_gpu, 1); + scal_gpu(l.nweights, momentum, l.weight_updates_gpu, 1); + + axpy_gpu(l.n, learning_rate/batch, l.bias_updates_gpu, 1, l.biases_gpu, 1); + scal_gpu(l.n, momentum, l.bias_updates_gpu, 1); + + if(l.scales_gpu){ + axpy_gpu(l.n, learning_rate/batch, l.scale_updates_gpu, 1, l.scales_gpu, 1); + scal_gpu(l.n, momentum, l.scale_updates_gpu, 1); + } + } +} + diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/deconvolutional_layer.c b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/deconvolutional_layer.c new file mode 100644 index 00000000000..00c0e85771d --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/deconvolutional_layer.c @@ -0,0 +1,312 @@ +#include "deconvolutional_layer.h" +#include "convolutional_layer.h" +#include "batchnorm_layer.h" +#include "utils.h" +#include "im2col.h" +#include "col2im.h" +#include "blas.h" +#include "gemm.h" + +#include +#include + + +static size_t get_workspace_size(layer l){ + return (size_t)l.h*l.w*l.size*l.size*l.n*sizeof(float); +} + +void bilinear_init(layer l) +{ + int i,j,f; + float center = (l.size-1) / 2.; + for(f = 0; f < l.n; ++f){ + for(j = 0; j < l.size; ++j){ + for(i = 0; i < l.size; ++i){ + float val = (1 - fabs(i - center)) * (1 - fabs(j - center)); + int c = f%l.c; + int ind = f*l.size*l.size*l.c + c*l.size*l.size + j*l.size + i; + l.weights[ind] = val; + } + } + } +} + + +layer make_deconvolutional_layer(int batch, int h, int w, int c, int n, int size, int stride, int padding, ACTIVATION activation, int batch_normalize, int adam) +{ + int i; + layer l = {0}; + l.type = DECONVOLUTIONAL; + + l.h = h; + l.w = w; + l.c = c; + l.n = n; + l.batch = batch; + l.stride = stride; + l.size = size; + + l.nweights = c*n*size*size; + l.nbiases = n; + + l.weights = calloc(c*n*size*size, sizeof(float)); + l.weight_updates = calloc(c*n*size*size, sizeof(float)); + + l.biases = calloc(n, sizeof(float)); + l.bias_updates = calloc(n, sizeof(float)); + //float scale = n/(size*size*c); + //printf("scale: %f\n", scale); + float scale = .02; + for(i = 0; i < c*n*size*size; ++i) l.weights[i] = scale*rand_normal(); + //bilinear_init(l); + for(i = 0; i < n; ++i){ + l.biases[i] = 0; + } + l.pad = padding; + + l.out_h = (l.h - 1) * l.stride + l.size - 2*l.pad; + l.out_w = (l.w - 1) * l.stride + l.size - 2*l.pad; + l.out_c = n; + l.outputs = l.out_w * l.out_h * l.out_c; + l.inputs = l.w * l.h * l.c; + + scal_cpu(l.nweights, (float)l.out_w*l.out_h/(l.w*l.h), l.weights, 1); + + l.output = calloc(l.batch*l.outputs, sizeof(float)); + l.delta = calloc(l.batch*l.outputs, sizeof(float)); + + l.forward = forward_deconvolutional_layer; + l.backward = backward_deconvolutional_layer; + l.update = update_deconvolutional_layer; + + l.batch_normalize = batch_normalize; + + if(batch_normalize){ + l.scales = calloc(n, sizeof(float)); + l.scale_updates = calloc(n, sizeof(float)); + for(i = 0; i < n; ++i){ + l.scales[i] = 1; + } + + l.mean = calloc(n, sizeof(float)); + l.variance = calloc(n, sizeof(float)); + + l.mean_delta = calloc(n, sizeof(float)); + l.variance_delta = calloc(n, sizeof(float)); + + l.rolling_mean = calloc(n, sizeof(float)); + l.rolling_variance = calloc(n, sizeof(float)); + l.x = calloc(l.batch*l.outputs, sizeof(float)); + l.x_norm = calloc(l.batch*l.outputs, sizeof(float)); + } + if(adam){ + l.m = calloc(c*n*size*size, sizeof(float)); + l.v = calloc(c*n*size*size, sizeof(float)); + l.bias_m = calloc(n, sizeof(float)); + l.scale_m = calloc(n, sizeof(float)); + l.bias_v = calloc(n, sizeof(float)); + l.scale_v = calloc(n, sizeof(float)); + } + +#ifdef GPU + l.forward_gpu = forward_deconvolutional_layer_gpu; + l.backward_gpu = backward_deconvolutional_layer_gpu; + l.update_gpu = update_deconvolutional_layer_gpu; + + if(gpu_index >= 0){ + + if (adam) { + l.m_gpu = cuda_make_array(l.m, c*n*size*size); + l.v_gpu = cuda_make_array(l.v, c*n*size*size); + l.bias_m_gpu = cuda_make_array(l.bias_m, n); + l.bias_v_gpu = cuda_make_array(l.bias_v, n); + l.scale_m_gpu = cuda_make_array(l.scale_m, n); + l.scale_v_gpu = cuda_make_array(l.scale_v, n); + } + l.weights_gpu = cuda_make_array(l.weights, c*n*size*size); + l.weight_updates_gpu = cuda_make_array(l.weight_updates, c*n*size*size); + + l.biases_gpu = cuda_make_array(l.biases, n); + l.bias_updates_gpu = cuda_make_array(l.bias_updates, n); + + l.delta_gpu = cuda_make_array(l.delta, l.batch*l.out_h*l.out_w*n); + l.output_gpu = cuda_make_array(l.output, l.batch*l.out_h*l.out_w*n); + + if(batch_normalize){ + l.mean_gpu = cuda_make_array(0, n); + l.variance_gpu = cuda_make_array(0, n); + + l.rolling_mean_gpu = cuda_make_array(0, n); + l.rolling_variance_gpu = cuda_make_array(0, n); + + l.mean_delta_gpu = cuda_make_array(0, n); + l.variance_delta_gpu = cuda_make_array(0, n); + + l.scales_gpu = cuda_make_array(l.scales, n); + l.scale_updates_gpu = cuda_make_array(0, n); + + l.x_gpu = cuda_make_array(0, l.batch*l.out_h*l.out_w*n); + l.x_norm_gpu = cuda_make_array(0, l.batch*l.out_h*l.out_w*n); + } + } + #ifdef CUDNN + cudnnCreateTensorDescriptor(&l.dstTensorDesc); + cudnnCreateTensorDescriptor(&l.normTensorDesc); + cudnnSetTensor4dDescriptor(l.dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l.batch, l.out_c, l.out_h, l.out_w); + cudnnSetTensor4dDescriptor(l.normTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, 1, l.out_c, 1, 1); + #endif +#endif + + l.activation = activation; + l.workspace_size = get_workspace_size(l); + + fprintf(stderr, "deconv%5d %2d x%2d /%2d %4d x%4d x%4d -> %4d x%4d x%4d\n", n, size, size, stride, w, h, c, l.out_w, l.out_h, l.out_c); + + return l; +} + +void denormalize_deconvolutional_layer(layer l) +{ + int i, j; + for(i = 0; i < l.n; ++i){ + float scale = l.scales[i]/sqrt(l.rolling_variance[i] + .00001); + for(j = 0; j < l.c*l.size*l.size; ++j){ + l.weights[i*l.c*l.size*l.size + j] *= scale; + } + l.biases[i] -= l.rolling_mean[i] * scale; + l.scales[i] = 1; + l.rolling_mean[i] = 0; + l.rolling_variance[i] = 1; + } +} + +void resize_deconvolutional_layer(layer *l, int h, int w) +{ + l->h = h; + l->w = w; + l->out_h = (l->h - 1) * l->stride + l->size - 2*l->pad; + l->out_w = (l->w - 1) * l->stride + l->size - 2*l->pad; + + l->outputs = l->out_h * l->out_w * l->out_c; + l->inputs = l->w * l->h * l->c; + + l->output = realloc(l->output, l->batch*l->outputs*sizeof(float)); + l->delta = realloc(l->delta, l->batch*l->outputs*sizeof(float)); + if(l->batch_normalize){ + l->x = realloc(l->x, l->batch*l->outputs*sizeof(float)); + l->x_norm = realloc(l->x_norm, l->batch*l->outputs*sizeof(float)); + } + +#ifdef GPU + cuda_free(l->delta_gpu); + cuda_free(l->output_gpu); + + l->delta_gpu = cuda_make_array(l->delta, l->batch*l->outputs); + l->output_gpu = cuda_make_array(l->output, l->batch*l->outputs); + + if(l->batch_normalize){ + cuda_free(l->x_gpu); + cuda_free(l->x_norm_gpu); + + l->x_gpu = cuda_make_array(l->output, l->batch*l->outputs); + l->x_norm_gpu = cuda_make_array(l->output, l->batch*l->outputs); + } + #ifdef CUDNN + cudnnSetTensor4dDescriptor(l->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l->batch, l->out_c, l->out_h, l->out_w); + cudnnSetTensor4dDescriptor(l->normTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, 1, l->out_c, 1, 1); + #endif +#endif + l->workspace_size = get_workspace_size(*l); +} + +void forward_deconvolutional_layer(const layer l, network net) +{ + int i; + + int m = l.size*l.size*l.n; + int n = l.h*l.w; + int k = l.c; + + fill_cpu(l.outputs*l.batch, 0, l.output, 1); + + for(i = 0; i < l.batch; ++i){ + float *a = l.weights; + float *b = net.input + i*l.c*l.h*l.w; + float *c = net.workspace; + + gemm_cpu(1,0,m,n,k,1,a,m,b,n,0,c,n); + + col2im_cpu(net.workspace, l.out_c, l.out_h, l.out_w, l.size, l.stride, l.pad, l.output+i*l.outputs); + } + if (l.batch_normalize) { + forward_batchnorm_layer(l, net); + } else { + add_bias(l.output, l.biases, l.batch, l.n, l.out_w*l.out_h); + } + activate_array(l.output, l.batch*l.n*l.out_w*l.out_h, l.activation); +} + +void backward_deconvolutional_layer(layer l, network net) +{ + int i; + + gradient_array(l.output, l.outputs*l.batch, l.activation, l.delta); + + if(l.batch_normalize){ + backward_batchnorm_layer(l, net); + } else { + backward_bias(l.bias_updates, l.delta, l.batch, l.n, l.out_w*l.out_h); + } + + //if(net.delta) memset(net.delta, 0, l.batch*l.h*l.w*l.c*sizeof(float)); + + for(i = 0; i < l.batch; ++i){ + int m = l.c; + int n = l.size*l.size*l.n; + int k = l.h*l.w; + + float *a = net.input + i*m*k; + float *b = net.workspace; + float *c = l.weight_updates; + + im2col_cpu(l.delta + i*l.outputs, l.out_c, l.out_h, l.out_w, + l.size, l.stride, l.pad, b); + gemm_cpu(0,1,m,n,k,1,a,k,b,k,1,c,n); + + if(net.delta){ + int m = l.c; + int n = l.h*l.w; + int k = l.size*l.size*l.n; + + float *a = l.weights; + float *b = net.workspace; + float *c = net.delta + i*n*m; + + gemm_cpu(0,0,m,n,k,1,a,k,b,n,1,c,n); + } + } +} + +void update_deconvolutional_layer(layer l, update_args a) +{ + float learning_rate = a.learning_rate*l.learning_rate_scale; + float momentum = a.momentum; + float decay = a.decay; + int batch = a.batch; + + int size = l.size*l.size*l.c*l.n; + axpy_cpu(l.n, learning_rate/batch, l.bias_updates, 1, l.biases, 1); + scal_cpu(l.n, momentum, l.bias_updates, 1); + + if(l.scales){ + axpy_cpu(l.n, learning_rate/batch, l.scale_updates, 1, l.scales, 1); + scal_cpu(l.n, momentum, l.scale_updates, 1); + } + + axpy_cpu(size, -decay*batch, l.weights, 1, l.weight_updates, 1); + axpy_cpu(size, learning_rate/batch, l.weight_updates, 1, l.weights, 1); + scal_cpu(size, momentum, l.weight_updates, 1); +} + + + diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/deconvolutional_layer.h b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/deconvolutional_layer.h new file mode 100644 index 00000000000..b254fb91e69 --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/deconvolutional_layer.h @@ -0,0 +1,25 @@ +#ifndef DECONVOLUTIONAL_LAYER_H +#define DECONVOLUTIONAL_LAYER_H + +#include "cuda.h" +#include "image.h" +#include "activations.h" +#include "layer.h" +#include "network.h" + +#ifdef GPU +void forward_deconvolutional_layer_gpu(layer l, network net); +void backward_deconvolutional_layer_gpu(layer l, network net); +void update_deconvolutional_layer_gpu(layer l, update_args a); +void push_deconvolutional_layer(layer l); +void pull_deconvolutional_layer(layer l); +#endif + +layer make_deconvolutional_layer(int batch, int h, int w, int c, int n, int size, int stride, int padding, ACTIVATION activation, int batch_normalize, int adam); +void resize_deconvolutional_layer(layer *l, int h, int w); +void forward_deconvolutional_layer(const layer l, network net); +void update_deconvolutional_layer(layer l, update_args a); +void backward_deconvolutional_layer(layer l, network net); + +#endif + diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/demo.c b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/demo.c new file mode 100644 index 00000000000..492c360d4ba --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/demo.c @@ -0,0 +1,364 @@ +#include "network.h" +#include "detection_layer.h" +#include "region_layer.h" +#include "cost_layer.h" +#include "utils.h" +#include "parser.h" +#include "box.h" +#include "image.h" +#include "demo.h" +#include + +#define DEMO 1 + +#ifdef OPENCV + +static char **demo_names; +static image **demo_alphabet; +static int demo_classes; + +static network *net; +static image buff [3]; +static image buff_letter[3]; +static int buff_index = 0; +static CvCapture * cap; +static IplImage * ipl; +static float fps = 0; +static float demo_thresh = 0; +static float demo_hier = .5; +static int running = 0; + +static int demo_frame = 3; +static int demo_index = 0; +static float **predictions; +static float *avg; +static int demo_done = 0; +static int demo_total = 0; +double demo_time; + +detection *get_network_boxes(network *net, int w, int h, float thresh, float hier, int *map, int relative, int *num); + +int size_network(network *net) +{ + int i; + int count = 0; + for(i = 0; i < net->n; ++i){ + layer l = net->layers[i]; + if(l.type == YOLO || l.type == REGION || l.type == DETECTION){ + count += l.outputs; + } + } + return count; +} + +void remember_network(network *net) +{ + int i; + int count = 0; + for(i = 0; i < net->n; ++i){ + layer l = net->layers[i]; + if(l.type == YOLO || l.type == REGION || l.type == DETECTION){ + memcpy(predictions[demo_index] + count, net->layers[i].output, sizeof(float) * l.outputs); + count += l.outputs; + } + } +} + +detection *avg_predictions(network *net, int *nboxes) +{ + int i, j; + int count = 0; + fill_cpu(demo_total, 0, avg, 1); + for(j = 0; j < demo_frame; ++j){ + axpy_cpu(demo_total, 1./demo_frame, predictions[j], 1, avg, 1); + } + for(i = 0; i < net->n; ++i){ + layer l = net->layers[i]; + if(l.type == YOLO || l.type == REGION || l.type == DETECTION){ + memcpy(l.output, avg + count, sizeof(float) * l.outputs); + count += l.outputs; + } + } + detection *dets = get_network_boxes(net, buff[0].w, buff[0].h, demo_thresh, demo_hier, 0, 1, nboxes); + return dets; +} + +void *detect_in_thread(void *ptr) +{ + running = 1; + float nms = .4; + + layer l = net->layers[net->n-1]; + float *X = buff_letter[(buff_index+2)%3].data; + network_predict(net, X); + + /* + if(l.type == DETECTION){ + get_detection_boxes(l, 1, 1, demo_thresh, probs, boxes, 0); + } else */ + remember_network(net); + detection *dets = 0; + int nboxes = 0; + dets = avg_predictions(net, &nboxes); + + + /* + int i,j; + box zero = {0}; + int classes = l.classes; + for(i = 0; i < demo_detections; ++i){ + avg[i].objectness = 0; + avg[i].bbox = zero; + memset(avg[i].prob, 0, classes*sizeof(float)); + for(j = 0; j < demo_frame; ++j){ + axpy_cpu(classes, 1./demo_frame, dets[j][i].prob, 1, avg[i].prob, 1); + avg[i].objectness += dets[j][i].objectness * 1./demo_frame; + avg[i].bbox.x += dets[j][i].bbox.x * 1./demo_frame; + avg[i].bbox.y += dets[j][i].bbox.y * 1./demo_frame; + avg[i].bbox.w += dets[j][i].bbox.w * 1./demo_frame; + avg[i].bbox.h += dets[j][i].bbox.h * 1./demo_frame; + } + //copy_cpu(classes, dets[0][i].prob, 1, avg[i].prob, 1); + //avg[i].objectness = dets[0][i].objectness; + } + */ + + if (nms > 0) do_nms_obj(dets, nboxes, l.classes, nms); + + printf("\033[2J"); + printf("\033[1;1H"); + printf("\nFPS:%.1f\n",fps); + printf("Objects:\n\n"); + image display = buff[(buff_index+2) % 3]; + draw_detections(display, dets, nboxes, demo_thresh, demo_names, demo_alphabet, demo_classes); + free_detections(dets, nboxes); + + demo_index = (demo_index + 1)%demo_frame; + running = 0; + return 0; +} + +void *fetch_in_thread(void *ptr) +{ + int status = fill_image_from_stream(cap, buff[buff_index]); + letterbox_image_into(buff[buff_index], net->w, net->h, buff_letter[buff_index]); + if(status == 0) demo_done = 1; + return 0; +} + +void *display_in_thread(void *ptr) +{ + show_image_cv(buff[(buff_index + 1)%3], "Demo", ipl); + int c = cvWaitKey(1); + if (c != -1) c = c%256; + if (c == 27) { + demo_done = 1; + return 0; + } else if (c == 82) { + demo_thresh += .02; + } else if (c == 84) { + demo_thresh -= .02; + if(demo_thresh <= .02) demo_thresh = .02; + } else if (c == 83) { + demo_hier += .02; + } else if (c == 81) { + demo_hier -= .02; + if(demo_hier <= .0) demo_hier = .0; + } + return 0; +} + +void *display_loop(void *ptr) +{ + while(1){ + display_in_thread(0); + } +} + +void *detect_loop(void *ptr) +{ + while(1){ + detect_in_thread(0); + } +} + +void demo(char *cfgfile, char *weightfile, float thresh, int cam_index, const char *filename, char **names, int classes, int delay, char *prefix, int avg_frames, float hier, int w, int h, int frames, int fullscreen) +{ + //demo_frame = avg_frames; + image **alphabet = load_alphabet(); + demo_names = names; + demo_alphabet = alphabet; + demo_classes = classes; + demo_thresh = thresh; + demo_hier = hier; + printf("Demo\n"); + net = load_network(cfgfile, weightfile, 0); + set_batch_network(net, 1); + pthread_t detect_thread; + pthread_t fetch_thread; + + srand(2222222); + + int i; + demo_total = size_network(net); + predictions = calloc(demo_frame, sizeof(float*)); + for (i = 0; i < demo_frame; ++i){ + predictions[i] = calloc(demo_total, sizeof(float)); + } + avg = calloc(demo_total, sizeof(float)); + + if(filename){ + printf("video file: %s\n", filename); + cap = cvCaptureFromFile(filename); + }else{ + cap = cvCaptureFromCAM(cam_index); + + if(w){ + cvSetCaptureProperty(cap, CV_CAP_PROP_FRAME_WIDTH, w); + } + if(h){ + cvSetCaptureProperty(cap, CV_CAP_PROP_FRAME_HEIGHT, h); + } + if(frames){ + cvSetCaptureProperty(cap, CV_CAP_PROP_FPS, frames); + } + } + + if(!cap) error("Couldn't connect to webcam.\n"); + + buff[0] = get_image_from_stream(cap); + buff[1] = copy_image(buff[0]); + buff[2] = copy_image(buff[0]); + buff_letter[0] = letterbox_image(buff[0], net->w, net->h); + buff_letter[1] = letterbox_image(buff[0], net->w, net->h); + buff_letter[2] = letterbox_image(buff[0], net->w, net->h); + ipl = cvCreateImage(cvSize(buff[0].w,buff[0].h), IPL_DEPTH_8U, buff[0].c); + + int count = 0; + if(!prefix){ + cvNamedWindow("Demo", CV_WINDOW_NORMAL); + if(fullscreen){ + cvSetWindowProperty("Demo", CV_WND_PROP_FULLSCREEN, CV_WINDOW_FULLSCREEN); + } else { + cvMoveWindow("Demo", 0, 0); + cvResizeWindow("Demo", 1352, 1013); + } + } + + demo_time = what_time_is_it_now(); + + while(!demo_done){ + buff_index = (buff_index + 1) %3; + if(pthread_create(&fetch_thread, 0, fetch_in_thread, 0)) error("Thread creation failed"); + if(pthread_create(&detect_thread, 0, detect_in_thread, 0)) error("Thread creation failed"); + if(!prefix){ + fps = 1./(what_time_is_it_now() - demo_time); + demo_time = what_time_is_it_now(); + display_in_thread(0); + }else{ + char name[256]; + sprintf(name, "%s_%08d", prefix, count); + save_image(buff[(buff_index + 1)%3], name); + } + pthread_join(fetch_thread, 0); + pthread_join(detect_thread, 0); + ++count; + } +} + +/* + void demo_compare(char *cfg1, char *weight1, char *cfg2, char *weight2, float thresh, int cam_index, const char *filename, char **names, int classes, int delay, char *prefix, int avg_frames, float hier, int w, int h, int frames, int fullscreen) + { + demo_frame = avg_frames; + predictions = calloc(demo_frame, sizeof(float*)); + image **alphabet = load_alphabet(); + demo_names = names; + demo_alphabet = alphabet; + demo_classes = classes; + demo_thresh = thresh; + demo_hier = hier; + printf("Demo\n"); + net = load_network(cfg1, weight1, 0); + set_batch_network(net, 1); + pthread_t detect_thread; + pthread_t fetch_thread; + + srand(2222222); + + if(filename){ + printf("video file: %s\n", filename); + cap = cvCaptureFromFile(filename); + }else{ + cap = cvCaptureFromCAM(cam_index); + + if(w){ + cvSetCaptureProperty(cap, CV_CAP_PROP_FRAME_WIDTH, w); + } + if(h){ + cvSetCaptureProperty(cap, CV_CAP_PROP_FRAME_HEIGHT, h); + } + if(frames){ + cvSetCaptureProperty(cap, CV_CAP_PROP_FPS, frames); + } + } + + if(!cap) error("Couldn't connect to webcam.\n"); + + layer l = net->layers[net->n-1]; + demo_detections = l.n*l.w*l.h; + int j; + + avg = (float *) calloc(l.outputs, sizeof(float)); + for(j = 0; j < demo_frame; ++j) predictions[j] = (float *) calloc(l.outputs, sizeof(float)); + + boxes = (box *)calloc(l.w*l.h*l.n, sizeof(box)); + probs = (float **)calloc(l.w*l.h*l.n, sizeof(float *)); + for(j = 0; j < l.w*l.h*l.n; ++j) probs[j] = (float *)calloc(l.classes+1, sizeof(float)); + + buff[0] = get_image_from_stream(cap); + buff[1] = copy_image(buff[0]); + buff[2] = copy_image(buff[0]); + buff_letter[0] = letterbox_image(buff[0], net->w, net->h); + buff_letter[1] = letterbox_image(buff[0], net->w, net->h); + buff_letter[2] = letterbox_image(buff[0], net->w, net->h); + ipl = cvCreateImage(cvSize(buff[0].w,buff[0].h), IPL_DEPTH_8U, buff[0].c); + + int count = 0; + if(!prefix){ + cvNamedWindow("Demo", CV_WINDOW_NORMAL); + if(fullscreen){ + cvSetWindowProperty("Demo", CV_WND_PROP_FULLSCREEN, CV_WINDOW_FULLSCREEN); + } else { + cvMoveWindow("Demo", 0, 0); + cvResizeWindow("Demo", 1352, 1013); + } + } + + demo_time = what_time_is_it_now(); + + while(!demo_done){ +buff_index = (buff_index + 1) %3; +if(pthread_create(&fetch_thread, 0, fetch_in_thread, 0)) error("Thread creation failed"); +if(pthread_create(&detect_thread, 0, detect_in_thread, 0)) error("Thread creation failed"); +if(!prefix){ + fps = 1./(what_time_is_it_now() - demo_time); + demo_time = what_time_is_it_now(); + display_in_thread(0); +}else{ + char name[256]; + sprintf(name, "%s_%08d", prefix, count); + save_image(buff[(buff_index + 1)%3], name); +} +pthread_join(fetch_thread, 0); +pthread_join(detect_thread, 0); +++count; +} +} +*/ +#else +void demo(char *cfgfile, char *weightfile, float thresh, int cam_index, const char *filename, char **names, int classes, int delay, char *prefix, int avg, float hier, int w, int h, int frames, int fullscreen) +{ + fprintf(stderr, "Demo needs OpenCV for webcam images.\n"); +} +#endif + diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/demo.h b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/demo.h new file mode 100644 index 00000000000..86e46541d1a --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/demo.h @@ -0,0 +1,6 @@ +#ifndef DEMO_H +#define DEMO_H + +#include "image.h" + +#endif diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/detection_layer.c b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/detection_layer.c new file mode 100644 index 00000000000..d0e0194b1db --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/detection_layer.c @@ -0,0 +1,275 @@ +#include "detection_layer.h" +#include "activations.h" +#include "softmax_layer.h" +#include "blas.h" +#include "box.h" +#include "cuda.h" +#include "utils.h" + +#include +#include +#include +#include + +detection_layer make_detection_layer(int batch, int inputs, int n, int side, int classes, int coords, int rescore) +{ + detection_layer l = {0}; + l.type = DETECTION; + + l.n = n; + l.batch = batch; + l.inputs = inputs; + l.classes = classes; + l.coords = coords; + l.rescore = rescore; + l.side = side; + l.w = side; + l.h = side; + assert(side*side*((1 + l.coords)*l.n + l.classes) == inputs); + l.cost = calloc(1, sizeof(float)); + l.outputs = l.inputs; + l.truths = l.side*l.side*(1+l.coords+l.classes); + l.output = calloc(batch*l.outputs, sizeof(float)); + l.delta = calloc(batch*l.outputs, sizeof(float)); + + l.forward = forward_detection_layer; + l.backward = backward_detection_layer; +#ifdef GPU + l.forward_gpu = forward_detection_layer_gpu; + l.backward_gpu = backward_detection_layer_gpu; + l.output_gpu = cuda_make_array(l.output, batch*l.outputs); + l.delta_gpu = cuda_make_array(l.delta, batch*l.outputs); +#endif + + fprintf(stderr, "Detection Layer\n"); + srand(0); + + return l; +} + +void forward_detection_layer(const detection_layer l, network net) +{ + int locations = l.side*l.side; + int i,j; + memcpy(l.output, net.input, l.outputs*l.batch*sizeof(float)); + //if(l.reorg) reorg(l.output, l.w*l.h, size*l.n, l.batch, 1); + int b; + if (l.softmax){ + for(b = 0; b < l.batch; ++b){ + int index = b*l.inputs; + for (i = 0; i < locations; ++i) { + int offset = i*l.classes; + softmax(l.output + index + offset, l.classes, 1, 1, + l.output + index + offset); + } + } + } + if(net.train){ + float avg_iou = 0; + float avg_cat = 0; + float avg_allcat = 0; + float avg_obj = 0; + float avg_anyobj = 0; + int count = 0; + *(l.cost) = 0; + int size = l.inputs * l.batch; + memset(l.delta, 0, size * sizeof(float)); + for (b = 0; b < l.batch; ++b){ + int index = b*l.inputs; + for (i = 0; i < locations; ++i) { + int truth_index = (b*locations + i)*(1+l.coords+l.classes); + int is_obj = net.truth[truth_index]; + for (j = 0; j < l.n; ++j) { + int p_index = index + locations*l.classes + i*l.n + j; + l.delta[p_index] = l.noobject_scale*(0 - l.output[p_index]); + *(l.cost) += l.noobject_scale*pow(l.output[p_index], 2); + avg_anyobj += l.output[p_index]; + } + + int best_index = -1; + float best_iou = 0; + float best_rmse = 20; + + if (!is_obj){ + continue; + } + + int class_index = index + i*l.classes; + for(j = 0; j < l.classes; ++j) { + l.delta[class_index+j] = l.class_scale * (net.truth[truth_index+1+j] - l.output[class_index+j]); + *(l.cost) += l.class_scale * pow(net.truth[truth_index+1+j] - l.output[class_index+j], 2); + if(net.truth[truth_index + 1 + j]) avg_cat += l.output[class_index+j]; + avg_allcat += l.output[class_index+j]; + } + + box truth = float_to_box(net.truth + truth_index + 1 + l.classes, 1); + truth.x /= l.side; + truth.y /= l.side; + + for(j = 0; j < l.n; ++j){ + int box_index = index + locations*(l.classes + l.n) + (i*l.n + j) * l.coords; + box out = float_to_box(l.output + box_index, 1); + out.x /= l.side; + out.y /= l.side; + + if (l.sqrt){ + out.w = out.w*out.w; + out.h = out.h*out.h; + } + + float iou = box_iou(out, truth); + //iou = 0; + float rmse = box_rmse(out, truth); + if(best_iou > 0 || iou > 0){ + if(iou > best_iou){ + best_iou = iou; + best_index = j; + } + }else{ + if(rmse < best_rmse){ + best_rmse = rmse; + best_index = j; + } + } + } + + if(l.forced){ + if(truth.w*truth.h < .1){ + best_index = 1; + }else{ + best_index = 0; + } + } + if(l.random && *(net.seen) < 64000){ + best_index = rand()%l.n; + } + + int box_index = index + locations*(l.classes + l.n) + (i*l.n + best_index) * l.coords; + int tbox_index = truth_index + 1 + l.classes; + + box out = float_to_box(l.output + box_index, 1); + out.x /= l.side; + out.y /= l.side; + if (l.sqrt) { + out.w = out.w*out.w; + out.h = out.h*out.h; + } + float iou = box_iou(out, truth); + + //printf("%d,", best_index); + int p_index = index + locations*l.classes + i*l.n + best_index; + *(l.cost) -= l.noobject_scale * pow(l.output[p_index], 2); + *(l.cost) += l.object_scale * pow(1-l.output[p_index], 2); + avg_obj += l.output[p_index]; + l.delta[p_index] = l.object_scale * (1.-l.output[p_index]); + + if(l.rescore){ + l.delta[p_index] = l.object_scale * (iou - l.output[p_index]); + } + + l.delta[box_index+0] = l.coord_scale*(net.truth[tbox_index + 0] - l.output[box_index + 0]); + l.delta[box_index+1] = l.coord_scale*(net.truth[tbox_index + 1] - l.output[box_index + 1]); + l.delta[box_index+2] = l.coord_scale*(net.truth[tbox_index + 2] - l.output[box_index + 2]); + l.delta[box_index+3] = l.coord_scale*(net.truth[tbox_index + 3] - l.output[box_index + 3]); + if(l.sqrt){ + l.delta[box_index+2] = l.coord_scale*(sqrt(net.truth[tbox_index + 2]) - l.output[box_index + 2]); + l.delta[box_index+3] = l.coord_scale*(sqrt(net.truth[tbox_index + 3]) - l.output[box_index + 3]); + } + + *(l.cost) += pow(1-iou, 2); + avg_iou += iou; + ++count; + } + } + + if(0){ + float *costs = calloc(l.batch*locations*l.n, sizeof(float)); + for (b = 0; b < l.batch; ++b) { + int index = b*l.inputs; + for (i = 0; i < locations; ++i) { + for (j = 0; j < l.n; ++j) { + int p_index = index + locations*l.classes + i*l.n + j; + costs[b*locations*l.n + i*l.n + j] = l.delta[p_index]*l.delta[p_index]; + } + } + } + int indexes[100]; + top_k(costs, l.batch*locations*l.n, 100, indexes); + float cutoff = costs[indexes[99]]; + for (b = 0; b < l.batch; ++b) { + int index = b*l.inputs; + for (i = 0; i < locations; ++i) { + for (j = 0; j < l.n; ++j) { + int p_index = index + locations*l.classes + i*l.n + j; + if (l.delta[p_index]*l.delta[p_index] < cutoff) l.delta[p_index] = 0; + } + } + } + free(costs); + } + + + *(l.cost) = pow(mag_array(l.delta, l.outputs * l.batch), 2); + + + printf("Detection Avg IOU: %f, Pos Cat: %f, All Cat: %f, Pos Obj: %f, Any Obj: %f, count: %d\n", avg_iou/count, avg_cat/count, avg_allcat/(count*l.classes), avg_obj/count, avg_anyobj/(l.batch*locations*l.n), count); + //if(l.reorg) reorg(l.delta, l.w*l.h, size*l.n, l.batch, 0); + } +} + +void backward_detection_layer(const detection_layer l, network net) +{ + axpy_cpu(l.batch*l.inputs, 1, l.delta, 1, net.delta, 1); +} + +void get_detection_detections(layer l, int w, int h, float thresh, detection *dets) +{ + int i,j,n; + float *predictions = l.output; + //int per_cell = 5*num+classes; + for (i = 0; i < l.side*l.side; ++i){ + int row = i / l.side; + int col = i % l.side; + for(n = 0; n < l.n; ++n){ + int index = i*l.n + n; + int p_index = l.side*l.side*l.classes + i*l.n + n; + float scale = predictions[p_index]; + int box_index = l.side*l.side*(l.classes + l.n) + (i*l.n + n)*4; + box b; + b.x = (predictions[box_index + 0] + col) / l.side * w; + b.y = (predictions[box_index + 1] + row) / l.side * h; + b.w = pow(predictions[box_index + 2], (l.sqrt?2:1)) * w; + b.h = pow(predictions[box_index + 3], (l.sqrt?2:1)) * h; + dets[index].bbox = b; + dets[index].objectness = scale; + for(j = 0; j < l.classes; ++j){ + int class_index = i*l.classes; + float prob = scale*predictions[class_index+j]; + dets[index].prob[j] = (prob > thresh) ? prob : 0; + } + } + } +} + +#ifdef GPU + +void forward_detection_layer_gpu(const detection_layer l, network net) +{ + if(!net.train){ + copy_gpu(l.batch*l.inputs, net.input_gpu, 1, l.output_gpu, 1); + return; + } + + cuda_pull_array(net.input_gpu, net.input, l.batch*l.inputs); + forward_detection_layer(l, net); + cuda_push_array(l.output_gpu, l.output, l.batch*l.outputs); + cuda_push_array(l.delta_gpu, l.delta, l.batch*l.inputs); +} + +void backward_detection_layer_gpu(detection_layer l, network net) +{ + axpy_gpu(l.batch*l.inputs, 1, l.delta_gpu, 1, net.delta_gpu, 1); + //copy_gpu(l.batch*l.inputs, l.delta_gpu, 1, net.delta_gpu, 1); +} +#endif + diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/detection_layer.h b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/detection_layer.h new file mode 100644 index 00000000000..1c818535700 --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/detection_layer.h @@ -0,0 +1,18 @@ +#ifndef DETECTION_LAYER_H +#define DETECTION_LAYER_H + +#include "layer.h" +#include "network.h" + +typedef layer detection_layer; + +detection_layer make_detection_layer(int batch, int inputs, int n, int size, int classes, int coords, int rescore); +void forward_detection_layer(const detection_layer l, network net); +void backward_detection_layer(const detection_layer l, network net); + +#ifdef GPU +void forward_detection_layer_gpu(const detection_layer l, network net); +void backward_detection_layer_gpu(detection_layer l, network net); +#endif + +#endif diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/dropout_layer.c b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/dropout_layer.c new file mode 100644 index 00000000000..780554fb371 --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/dropout_layer.c @@ -0,0 +1,60 @@ +#include "dropout_layer.h" +#include "utils.h" +#include "cuda.h" +#include +#include + +dropout_layer make_dropout_layer(int batch, int inputs, float probability) +{ + dropout_layer l = {0}; + l.type = DROPOUT; + l.probability = probability; + l.inputs = inputs; + l.outputs = inputs; + l.batch = batch; + l.rand = calloc(inputs*batch, sizeof(float)); + l.scale = 1./(1.-probability); + l.forward = forward_dropout_layer; + l.backward = backward_dropout_layer; + #ifdef GPU + l.forward_gpu = forward_dropout_layer_gpu; + l.backward_gpu = backward_dropout_layer_gpu; + l.rand_gpu = cuda_make_array(l.rand, inputs*batch); + #endif + fprintf(stderr, "dropout p = %.2f %4d -> %4d\n", probability, inputs, inputs); + return l; +} + +void resize_dropout_layer(dropout_layer *l, int inputs) +{ + l->rand = realloc(l->rand, l->inputs*l->batch*sizeof(float)); + #ifdef GPU + cuda_free(l->rand_gpu); + + l->rand_gpu = cuda_make_array(l->rand, inputs*l->batch); + #endif +} + +void forward_dropout_layer(dropout_layer l, network net) +{ + int i; + if (!net.train) return; + for(i = 0; i < l.batch * l.inputs; ++i){ + float r = rand_uniform(0, 1); + l.rand[i] = r; + if(r < l.probability) net.input[i] = 0; + else net.input[i] *= l.scale; + } +} + +void backward_dropout_layer(dropout_layer l, network net) +{ + int i; + if(!net.delta) return; + for(i = 0; i < l.batch * l.inputs; ++i){ + float r = l.rand[i]; + if(r < l.probability) net.delta[i] = 0; + else net.delta[i] *= l.scale; + } +} + diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/dropout_layer.h b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/dropout_layer.h new file mode 100644 index 00000000000..01f94d4d7d1 --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/dropout_layer.h @@ -0,0 +1,20 @@ +#ifndef DROPOUT_LAYER_H +#define DROPOUT_LAYER_H + +#include "layer.h" +#include "network.h" + +typedef layer dropout_layer; + +dropout_layer make_dropout_layer(int batch, int inputs, float probability); + +void forward_dropout_layer(dropout_layer l, network net); +void backward_dropout_layer(dropout_layer l, network net); +void resize_dropout_layer(dropout_layer *l, int inputs); + +#ifdef GPU +void forward_dropout_layer_gpu(dropout_layer l, network net); +void backward_dropout_layer_gpu(dropout_layer l, network net); + +#endif +#endif diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/dropout_layer_kernels.cu b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/dropout_layer_kernels.cu new file mode 100644 index 00000000000..bd12b678758 --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/dropout_layer_kernels.cu @@ -0,0 +1,41 @@ +#include "cuda_runtime.h" +#include "curand.h" +#include "cublas_v2.h" + +extern "C" { +#include "dropout_layer.h" +#include "cuda.h" +#include "utils.h" +} + +__global__ void yoloswag420blazeit360noscope(float *input, int size, float *rand, float prob, float scale) +{ + int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if(id < size) input[id] = (rand[id] < prob) ? 0 : input[id]*scale; +} + +void forward_dropout_layer_gpu(dropout_layer layer, network net) +{ + if (!net.train) return; + int size = layer.inputs*layer.batch; + cuda_random(layer.rand_gpu, size); + /* + int i; + for(i = 0; i < size; ++i){ + layer.rand[i] = rand_uniform(); + } + cuda_push_array(layer.rand_gpu, layer.rand, size); + */ + + yoloswag420blazeit360noscope<<>>(net.input_gpu, size, layer.rand_gpu, layer.probability, layer.scale); + check_error(cudaPeekAtLastError()); +} + +void backward_dropout_layer_gpu(dropout_layer layer, network net) +{ + if(!net.delta_gpu) return; + int size = layer.inputs*layer.batch; + + yoloswag420blazeit360noscope<<>>(net.delta_gpu, size, layer.rand_gpu, layer.probability, layer.scale); + check_error(cudaPeekAtLastError()); +} diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/gemm.c b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/gemm.c new file mode 100644 index 00000000000..648027f2cdf --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/gemm.c @@ -0,0 +1,324 @@ +#include "gemm.h" +#include "utils.h" +#include "cuda.h" +#include +#include +#include + +void gemm_bin(int M, int N, int K, float ALPHA, + char *A, int lda, + float *B, int ldb, + float *C, int ldc) +{ + int i,j,k; + for(i = 0; i < M; ++i){ + for(k = 0; k < K; ++k){ + char A_PART = A[i*lda+k]; + if(A_PART){ + for(j = 0; j < N; ++j){ + C[i*ldc+j] += B[k*ldb+j]; + } + } else { + for(j = 0; j < N; ++j){ + C[i*ldc+j] -= B[k*ldb+j]; + } + } + } + } +} + +float *random_matrix(int rows, int cols) +{ + int i; + float *m = calloc(rows*cols, sizeof(float)); + for(i = 0; i < rows*cols; ++i){ + m[i] = (float)rand()/RAND_MAX; + } + return m; +} + +void time_random_matrix(int TA, int TB, int m, int k, int n) +{ + float *a; + if(!TA) a = random_matrix(m,k); + else a = random_matrix(k,m); + int lda = (!TA)?k:m; + float *b; + if(!TB) b = random_matrix(k,n); + else b = random_matrix(n,k); + int ldb = (!TB)?n:k; + + float *c = random_matrix(m,n); + int i; + clock_t start = clock(), end; + for(i = 0; i<10; ++i){ + gemm_cpu(TA,TB,m,n,k,1,a,lda,b,ldb,1,c,n); + } + end = clock(); + printf("Matrix Multiplication %dx%d * %dx%d, TA=%d, TB=%d: %lf ms\n",m,k,k,n, TA, TB, (float)(end-start)/CLOCKS_PER_SEC); + free(a); + free(b); + free(c); +} + + +void gemm(int TA, int TB, int M, int N, int K, float ALPHA, + float *A, int lda, + float *B, int ldb, + float BETA, + float *C, int ldc) +{ + gemm_cpu( TA, TB, M, N, K, ALPHA,A,lda, B, ldb,BETA,C,ldc); +} + +void gemm_nn(int M, int N, int K, float ALPHA, + float *A, int lda, + float *B, int ldb, + float *C, int ldc) +{ + int i,j,k; + #pragma omp parallel for + for(i = 0; i < M; ++i){ + for(k = 0; k < K; ++k){ + register float A_PART = ALPHA*A[i*lda+k]; + for(j = 0; j < N; ++j){ + C[i*ldc+j] += A_PART*B[k*ldb+j]; + } + } + } +} + +void gemm_nt(int M, int N, int K, float ALPHA, + float *A, int lda, + float *B, int ldb, + float *C, int ldc) +{ + int i,j,k; + #pragma omp parallel for + for(i = 0; i < M; ++i){ + for(j = 0; j < N; ++j){ + register float sum = 0; + for(k = 0; k < K; ++k){ + sum += ALPHA*A[i*lda+k]*B[j*ldb + k]; + } + C[i*ldc+j] += sum; + } + } +} + +void gemm_tn(int M, int N, int K, float ALPHA, + float *A, int lda, + float *B, int ldb, + float *C, int ldc) +{ + int i,j,k; + #pragma omp parallel for + for(i = 0; i < M; ++i){ + for(k = 0; k < K; ++k){ + register float A_PART = ALPHA*A[k*lda+i]; + for(j = 0; j < N; ++j){ + C[i*ldc+j] += A_PART*B[k*ldb+j]; + } + } + } +} + +void gemm_tt(int M, int N, int K, float ALPHA, + float *A, int lda, + float *B, int ldb, + float *C, int ldc) +{ + int i,j,k; + #pragma omp parallel for + for(i = 0; i < M; ++i){ + for(j = 0; j < N; ++j){ + register float sum = 0; + for(k = 0; k < K; ++k){ + sum += ALPHA*A[i+k*lda]*B[k+j*ldb]; + } + C[i*ldc+j] += sum; + } + } +} + + +void gemm_cpu(int TA, int TB, int M, int N, int K, float ALPHA, + float *A, int lda, + float *B, int ldb, + float BETA, + float *C, int ldc) +{ + //printf("cpu: %d %d %d %d %d %f %d %d %f %d\n",TA, TB, M, N, K, ALPHA, lda, ldb, BETA, ldc); + int i, j; + for(i = 0; i < M; ++i){ + for(j = 0; j < N; ++j){ + C[i*ldc + j] *= BETA; + } + } + if(!TA && !TB) + gemm_nn(M, N, K, ALPHA,A,lda, B, ldb,C,ldc); + else if(TA && !TB) + gemm_tn(M, N, K, ALPHA,A,lda, B, ldb,C,ldc); + else if(!TA && TB) + gemm_nt(M, N, K, ALPHA,A,lda, B, ldb,C,ldc); + else + gemm_tt(M, N, K, ALPHA,A,lda, B, ldb,C,ldc); +} + +#ifdef GPU + +#include + +void gemm_gpu(int TA, int TB, int M, int N, int K, float ALPHA, + float *A_gpu, int lda, + float *B_gpu, int ldb, + float BETA, + float *C_gpu, int ldc) +{ + cublasHandle_t handle = blas_handle(); + cudaError_t status = cublasSgemm(handle, (TB ? CUBLAS_OP_T : CUBLAS_OP_N), + (TA ? CUBLAS_OP_T : CUBLAS_OP_N), N, M, K, &ALPHA, B_gpu, ldb, A_gpu, lda, &BETA, C_gpu, ldc); + check_error(status); +} + +#include +#include +#include +#include + +void time_gpu_random_matrix(int TA, int TB, int m, int k, int n) +{ + float *a; + if(!TA) a = random_matrix(m,k); + else a = random_matrix(k,m); + int lda = (!TA)?k:m; + float *b; + if(!TB) b = random_matrix(k,n); + else b = random_matrix(n,k); + int ldb = (!TB)?n:k; + + float *c = random_matrix(m,n); + int i; + clock_t start = clock(), end; + for(i = 0; i<32; ++i){ + gemm_gpu(TA,TB,m,n,k,1,a,lda,b,ldb,1,c,n); + } + end = clock(); + printf("Matrix Multiplication %dx%d * %dx%d, TA=%d, TB=%d: %lf s\n",m,k,k,n, TA, TB, (float)(end-start)/CLOCKS_PER_SEC); + free(a); + free(b); + free(c); +} + +void time_gpu(int TA, int TB, int m, int k, int n) +{ + int iter = 10; + float *a = random_matrix(m,k); + float *b = random_matrix(k,n); + + int lda = (!TA)?k:m; + int ldb = (!TB)?n:k; + + float *c = random_matrix(m,n); + + float *a_cl = cuda_make_array(a, m*k); + float *b_cl = cuda_make_array(b, k*n); + float *c_cl = cuda_make_array(c, m*n); + + int i; + clock_t start = clock(), end; + for(i = 0; i +#include +#include +#include + +static void increment_layer(layer *l, int steps) +{ + int num = l->outputs*l->batch*steps; + l->output += num; + l->delta += num; + l->x += num; + l->x_norm += num; + +#ifdef GPU + l->output_gpu += num; + l->delta_gpu += num; + l->x_gpu += num; + l->x_norm_gpu += num; +#endif +} + +layer make_gru_layer(int batch, int inputs, int outputs, int steps, int batch_normalize, int adam) +{ + fprintf(stderr, "GRU Layer: %d inputs, %d outputs\n", inputs, outputs); + batch = batch / steps; + layer l = {0}; + l.batch = batch; + l.type = GRU; + l.steps = steps; + l.inputs = inputs; + + l.uz = malloc(sizeof(layer)); + fprintf(stderr, "\t\t"); + *(l.uz) = make_connected_layer(batch*steps, inputs, outputs, LINEAR, batch_normalize, adam); + l.uz->batch = batch; + + l.wz = malloc(sizeof(layer)); + fprintf(stderr, "\t\t"); + *(l.wz) = make_connected_layer(batch*steps, outputs, outputs, LINEAR, batch_normalize, adam); + l.wz->batch = batch; + + l.ur = malloc(sizeof(layer)); + fprintf(stderr, "\t\t"); + *(l.ur) = make_connected_layer(batch*steps, inputs, outputs, LINEAR, batch_normalize, adam); + l.ur->batch = batch; + + l.wr = malloc(sizeof(layer)); + fprintf(stderr, "\t\t"); + *(l.wr) = make_connected_layer(batch*steps, outputs, outputs, LINEAR, batch_normalize, adam); + l.wr->batch = batch; + + + + l.uh = malloc(sizeof(layer)); + fprintf(stderr, "\t\t"); + *(l.uh) = make_connected_layer(batch*steps, inputs, outputs, LINEAR, batch_normalize, adam); + l.uh->batch = batch; + + l.wh = malloc(sizeof(layer)); + fprintf(stderr, "\t\t"); + *(l.wh) = make_connected_layer(batch*steps, outputs, outputs, LINEAR, batch_normalize, adam); + l.wh->batch = batch; + + l.batch_normalize = batch_normalize; + + + l.outputs = outputs; + l.output = calloc(outputs*batch*steps, sizeof(float)); + l.delta = calloc(outputs*batch*steps, sizeof(float)); + l.state = calloc(outputs*batch, sizeof(float)); + l.prev_state = calloc(outputs*batch, sizeof(float)); + l.forgot_state = calloc(outputs*batch, sizeof(float)); + l.forgot_delta = calloc(outputs*batch, sizeof(float)); + + l.r_cpu = calloc(outputs*batch, sizeof(float)); + l.z_cpu = calloc(outputs*batch, sizeof(float)); + l.h_cpu = calloc(outputs*batch, sizeof(float)); + + l.forward = forward_gru_layer; + l.backward = backward_gru_layer; + l.update = update_gru_layer; + +#ifdef GPU + l.forward_gpu = forward_gru_layer_gpu; + l.backward_gpu = backward_gru_layer_gpu; + l.update_gpu = update_gru_layer_gpu; + + l.forgot_state_gpu = cuda_make_array(0, batch*outputs); + l.forgot_delta_gpu = cuda_make_array(0, batch*outputs); + l.prev_state_gpu = cuda_make_array(0, batch*outputs); + l.state_gpu = cuda_make_array(0, batch*outputs); + l.output_gpu = cuda_make_array(0, batch*outputs*steps); + l.delta_gpu = cuda_make_array(0, batch*outputs*steps); + l.r_gpu = cuda_make_array(0, batch*outputs); + l.z_gpu = cuda_make_array(0, batch*outputs); + l.h_gpu = cuda_make_array(0, batch*outputs); + +#ifdef CUDNN + cudnnSetTensor4dDescriptor(l.uz->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.uz->out_c, l.uz->out_h, l.uz->out_w); + cudnnSetTensor4dDescriptor(l.uh->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.uh->out_c, l.uh->out_h, l.uh->out_w); + cudnnSetTensor4dDescriptor(l.ur->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.ur->out_c, l.ur->out_h, l.ur->out_w); + cudnnSetTensor4dDescriptor(l.wz->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.wz->out_c, l.wz->out_h, l.wz->out_w); + cudnnSetTensor4dDescriptor(l.wh->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.wh->out_c, l.wh->out_h, l.wh->out_w); + cudnnSetTensor4dDescriptor(l.wr->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.wr->out_c, l.wr->out_h, l.wr->out_w); +#endif +#endif + + return l; +} + +void update_gru_layer(layer l, update_args a) +{ + update_connected_layer(*(l.ur), a); + update_connected_layer(*(l.uz), a); + update_connected_layer(*(l.uh), a); + update_connected_layer(*(l.wr), a); + update_connected_layer(*(l.wz), a); + update_connected_layer(*(l.wh), a); +} + +void forward_gru_layer(layer l, network net) +{ + network s = net; + s.train = net.train; + int i; + layer uz = *(l.uz); + layer ur = *(l.ur); + layer uh = *(l.uh); + + layer wz = *(l.wz); + layer wr = *(l.wr); + layer wh = *(l.wh); + + fill_cpu(l.outputs * l.batch * l.steps, 0, uz.delta, 1); + fill_cpu(l.outputs * l.batch * l.steps, 0, ur.delta, 1); + fill_cpu(l.outputs * l.batch * l.steps, 0, uh.delta, 1); + + fill_cpu(l.outputs * l.batch * l.steps, 0, wz.delta, 1); + fill_cpu(l.outputs * l.batch * l.steps, 0, wr.delta, 1); + fill_cpu(l.outputs * l.batch * l.steps, 0, wh.delta, 1); + if(net.train) { + fill_cpu(l.outputs * l.batch * l.steps, 0, l.delta, 1); + copy_cpu(l.outputs*l.batch, l.state, 1, l.prev_state, 1); + } + + for (i = 0; i < l.steps; ++i) { + s.input = l.state; + forward_connected_layer(wz, s); + forward_connected_layer(wr, s); + + s.input = net.input; + forward_connected_layer(uz, s); + forward_connected_layer(ur, s); + forward_connected_layer(uh, s); + + + copy_cpu(l.outputs*l.batch, uz.output, 1, l.z_cpu, 1); + axpy_cpu(l.outputs*l.batch, 1, wz.output, 1, l.z_cpu, 1); + + copy_cpu(l.outputs*l.batch, ur.output, 1, l.r_cpu, 1); + axpy_cpu(l.outputs*l.batch, 1, wr.output, 1, l.r_cpu, 1); + + activate_array(l.z_cpu, l.outputs*l.batch, LOGISTIC); + activate_array(l.r_cpu, l.outputs*l.batch, LOGISTIC); + + copy_cpu(l.outputs*l.batch, l.state, 1, l.forgot_state, 1); + mul_cpu(l.outputs*l.batch, l.r_cpu, 1, l.forgot_state, 1); + + s.input = l.forgot_state; + forward_connected_layer(wh, s); + + copy_cpu(l.outputs*l.batch, uh.output, 1, l.h_cpu, 1); + axpy_cpu(l.outputs*l.batch, 1, wh.output, 1, l.h_cpu, 1); + + if(l.tanh){ + activate_array(l.h_cpu, l.outputs*l.batch, TANH); + } else { + activate_array(l.h_cpu, l.outputs*l.batch, LOGISTIC); + } + + weighted_sum_cpu(l.state, l.h_cpu, l.z_cpu, l.outputs*l.batch, l.output); + + copy_cpu(l.outputs*l.batch, l.output, 1, l.state, 1); + + net.input += l.inputs*l.batch; + l.output += l.outputs*l.batch; + increment_layer(&uz, 1); + increment_layer(&ur, 1); + increment_layer(&uh, 1); + + increment_layer(&wz, 1); + increment_layer(&wr, 1); + increment_layer(&wh, 1); + } +} + +void backward_gru_layer(layer l, network net) +{ +} + +#ifdef GPU + +void pull_gru_layer(layer l) +{ +} + +void push_gru_layer(layer l) +{ +} + +void update_gru_layer_gpu(layer l, update_args a) +{ + update_connected_layer_gpu(*(l.ur), a); + update_connected_layer_gpu(*(l.uz), a); + update_connected_layer_gpu(*(l.uh), a); + update_connected_layer_gpu(*(l.wr), a); + update_connected_layer_gpu(*(l.wz), a); + update_connected_layer_gpu(*(l.wh), a); +} + +void forward_gru_layer_gpu(layer l, network net) +{ + network s = {0}; + s.train = net.train; + int i; + layer uz = *(l.uz); + layer ur = *(l.ur); + layer uh = *(l.uh); + + layer wz = *(l.wz); + layer wr = *(l.wr); + layer wh = *(l.wh); + + fill_gpu(l.outputs * l.batch * l.steps, 0, uz.delta_gpu, 1); + fill_gpu(l.outputs * l.batch * l.steps, 0, ur.delta_gpu, 1); + fill_gpu(l.outputs * l.batch * l.steps, 0, uh.delta_gpu, 1); + + fill_gpu(l.outputs * l.batch * l.steps, 0, wz.delta_gpu, 1); + fill_gpu(l.outputs * l.batch * l.steps, 0, wr.delta_gpu, 1); + fill_gpu(l.outputs * l.batch * l.steps, 0, wh.delta_gpu, 1); + if(net.train) { + fill_gpu(l.outputs * l.batch * l.steps, 0, l.delta_gpu, 1); + copy_gpu(l.outputs*l.batch, l.state_gpu, 1, l.prev_state_gpu, 1); + } + + for (i = 0; i < l.steps; ++i) { + s.input_gpu = l.state_gpu; + forward_connected_layer_gpu(wz, s); + forward_connected_layer_gpu(wr, s); + + s.input_gpu = net.input_gpu; + forward_connected_layer_gpu(uz, s); + forward_connected_layer_gpu(ur, s); + forward_connected_layer_gpu(uh, s); + + copy_gpu(l.outputs*l.batch, uz.output_gpu, 1, l.z_gpu, 1); + axpy_gpu(l.outputs*l.batch, 1, wz.output_gpu, 1, l.z_gpu, 1); + + copy_gpu(l.outputs*l.batch, ur.output_gpu, 1, l.r_gpu, 1); + axpy_gpu(l.outputs*l.batch, 1, wr.output_gpu, 1, l.r_gpu, 1); + + activate_array_gpu(l.z_gpu, l.outputs*l.batch, LOGISTIC); + activate_array_gpu(l.r_gpu, l.outputs*l.batch, LOGISTIC); + + copy_gpu(l.outputs*l.batch, l.state_gpu, 1, l.forgot_state_gpu, 1); + mul_gpu(l.outputs*l.batch, l.r_gpu, 1, l.forgot_state_gpu, 1); + + s.input_gpu = l.forgot_state_gpu; + forward_connected_layer_gpu(wh, s); + + copy_gpu(l.outputs*l.batch, uh.output_gpu, 1, l.h_gpu, 1); + axpy_gpu(l.outputs*l.batch, 1, wh.output_gpu, 1, l.h_gpu, 1); + + if(l.tanh){ + activate_array_gpu(l.h_gpu, l.outputs*l.batch, TANH); + } else { + activate_array_gpu(l.h_gpu, l.outputs*l.batch, LOGISTIC); + } + + weighted_sum_gpu(l.state_gpu, l.h_gpu, l.z_gpu, l.outputs*l.batch, l.output_gpu); + copy_gpu(l.outputs*l.batch, l.output_gpu, 1, l.state_gpu, 1); + + net.input_gpu += l.inputs*l.batch; + l.output_gpu += l.outputs*l.batch; + increment_layer(&uz, 1); + increment_layer(&ur, 1); + increment_layer(&uh, 1); + + increment_layer(&wz, 1); + increment_layer(&wr, 1); + increment_layer(&wh, 1); + } +} + +void backward_gru_layer_gpu(layer l, network net) +{ + network s = {0}; + s.train = net.train; + int i; + layer uz = *(l.uz); + layer ur = *(l.ur); + layer uh = *(l.uh); + + layer wz = *(l.wz); + layer wr = *(l.wr); + layer wh = *(l.wh); + + increment_layer(&uz, l.steps - 1); + increment_layer(&ur, l.steps - 1); + increment_layer(&uh, l.steps - 1); + + increment_layer(&wz, l.steps - 1); + increment_layer(&wr, l.steps - 1); + increment_layer(&wh, l.steps - 1); + + net.input_gpu += l.inputs*l.batch*(l.steps-1); + if(net.delta_gpu) net.delta_gpu += l.inputs*l.batch*(l.steps-1); + l.output_gpu += l.outputs*l.batch*(l.steps-1); + l.delta_gpu += l.outputs*l.batch*(l.steps-1); + float *end_state = l.output_gpu; + for (i = l.steps-1; i >= 0; --i) { + if(i != 0) copy_gpu(l.outputs*l.batch, l.output_gpu - l.outputs*l.batch, 1, l.state_gpu, 1); + else copy_gpu(l.outputs*l.batch, l.prev_state_gpu, 1, l.state_gpu, 1); + float *prev_delta_gpu = (i == 0) ? 0 : l.delta_gpu - l.outputs*l.batch; + + copy_gpu(l.outputs*l.batch, uz.output_gpu, 1, l.z_gpu, 1); + axpy_gpu(l.outputs*l.batch, 1, wz.output_gpu, 1, l.z_gpu, 1); + + copy_gpu(l.outputs*l.batch, ur.output_gpu, 1, l.r_gpu, 1); + axpy_gpu(l.outputs*l.batch, 1, wr.output_gpu, 1, l.r_gpu, 1); + + activate_array_gpu(l.z_gpu, l.outputs*l.batch, LOGISTIC); + activate_array_gpu(l.r_gpu, l.outputs*l.batch, LOGISTIC); + + copy_gpu(l.outputs*l.batch, uh.output_gpu, 1, l.h_gpu, 1); + axpy_gpu(l.outputs*l.batch, 1, wh.output_gpu, 1, l.h_gpu, 1); + + if(l.tanh){ + activate_array_gpu(l.h_gpu, l.outputs*l.batch, TANH); + } else { + activate_array_gpu(l.h_gpu, l.outputs*l.batch, LOGISTIC); + } + + weighted_delta_gpu(l.state_gpu, l.h_gpu, l.z_gpu, prev_delta_gpu, uh.delta_gpu, uz.delta_gpu, l.outputs*l.batch, l.delta_gpu); + + if(l.tanh){ + gradient_array_gpu(l.h_gpu, l.outputs*l.batch, TANH, uh.delta_gpu); + } else { + gradient_array_gpu(l.h_gpu, l.outputs*l.batch, LOGISTIC, uh.delta_gpu); + } + + copy_gpu(l.outputs*l.batch, uh.delta_gpu, 1, wh.delta_gpu, 1); + + copy_gpu(l.outputs*l.batch, l.state_gpu, 1, l.forgot_state_gpu, 1); + mul_gpu(l.outputs*l.batch, l.r_gpu, 1, l.forgot_state_gpu, 1); + fill_gpu(l.outputs*l.batch, 0, l.forgot_delta_gpu, 1); + + s.input_gpu = l.forgot_state_gpu; + s.delta_gpu = l.forgot_delta_gpu; + + backward_connected_layer_gpu(wh, s); + if(prev_delta_gpu) mult_add_into_gpu(l.outputs*l.batch, l.forgot_delta_gpu, l.r_gpu, prev_delta_gpu); + mult_add_into_gpu(l.outputs*l.batch, l.forgot_delta_gpu, l.state_gpu, ur.delta_gpu); + + gradient_array_gpu(l.r_gpu, l.outputs*l.batch, LOGISTIC, ur.delta_gpu); + copy_gpu(l.outputs*l.batch, ur.delta_gpu, 1, wr.delta_gpu, 1); + + gradient_array_gpu(l.z_gpu, l.outputs*l.batch, LOGISTIC, uz.delta_gpu); + copy_gpu(l.outputs*l.batch, uz.delta_gpu, 1, wz.delta_gpu, 1); + + s.input_gpu = l.state_gpu; + s.delta_gpu = prev_delta_gpu; + + backward_connected_layer_gpu(wr, s); + backward_connected_layer_gpu(wz, s); + + s.input_gpu = net.input_gpu; + s.delta_gpu = net.delta_gpu; + + backward_connected_layer_gpu(uh, s); + backward_connected_layer_gpu(ur, s); + backward_connected_layer_gpu(uz, s); + + + net.input_gpu -= l.inputs*l.batch; + if(net.delta_gpu) net.delta_gpu -= l.inputs*l.batch; + l.output_gpu -= l.outputs*l.batch; + l.delta_gpu -= l.outputs*l.batch; + increment_layer(&uz, -1); + increment_layer(&ur, -1); + increment_layer(&uh, -1); + + increment_layer(&wz, -1); + increment_layer(&wr, -1); + increment_layer(&wh, -1); + } + copy_gpu(l.outputs*l.batch, end_state, 1, l.state_gpu, 1); +} +#endif diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/gru_layer.h b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/gru_layer.h new file mode 100644 index 00000000000..9067942e949 --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/gru_layer.h @@ -0,0 +1,24 @@ + +#ifndef GRU_LAYER_H +#define GRU_LAYER_H + +#include "activations.h" +#include "layer.h" +#include "network.h" + +layer make_gru_layer(int batch, int inputs, int outputs, int steps, int batch_normalize, int adam); + +void forward_gru_layer(layer l, network state); +void backward_gru_layer(layer l, network state); +void update_gru_layer(layer l, update_args a); + +#ifdef GPU +void forward_gru_layer_gpu(layer l, network state); +void backward_gru_layer_gpu(layer l, network state); +void update_gru_layer_gpu(layer l, update_args a); +void push_gru_layer(layer l); +void pull_gru_layer(layer l); +#endif + +#endif + diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/im2col.c b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/im2col.c new file mode 100644 index 00000000000..69ec98a9d12 --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/im2col.c @@ -0,0 +1,40 @@ +#include "im2col.h" +#include +float im2col_get_pixel(float *im, int height, int width, int channels, + int row, int col, int channel, int pad) +{ + row -= pad; + col -= pad; + + if (row < 0 || col < 0 || + row >= height || col >= width) return 0; + return im[col + width*(row + height*channel)]; +} + +//From Berkeley Vision's Caffe! +//https://github.com/BVLC/caffe/blob/master/LICENSE +void im2col_cpu(float* data_im, + int channels, int height, int width, + int ksize, int stride, int pad, float* data_col) +{ + int c,h,w; + int height_col = (height + 2*pad - ksize) / stride + 1; + int width_col = (width + 2*pad - ksize) / stride + 1; + + int channels_col = channels * ksize * ksize; + for (c = 0; c < channels_col; ++c) { + int w_offset = c % ksize; + int h_offset = (c / ksize) % ksize; + int c_im = c / ksize / ksize; + for (h = 0; h < height_col; ++h) { + for (w = 0; w < width_col; ++w) { + int im_row = h_offset + h * stride; + int im_col = w_offset + w * stride; + int col_index = (c * height_col + h) * width_col + w; + data_col[col_index] = im2col_get_pixel(data_im, height, width, channels, + im_row, im_col, c_im, pad); + } + } + } +} + diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/im2col.h b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/im2col.h new file mode 100644 index 00000000000..02c4247fad9 --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/im2col.h @@ -0,0 +1,15 @@ +#ifndef IM2COL_H +#define IM2COL_H + +void im2col_cpu(float* data_im, + int channels, int height, int width, + int ksize, int stride, int pad, float* data_col); + +#ifdef GPU + +void im2col_gpu(float *im, + int channels, int height, int width, + int ksize, int stride, int pad,float *data_col); + +#endif +#endif diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/im2col_kernels.cu b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/im2col_kernels.cu new file mode 100644 index 00000000000..07b5e67989a --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/im2col_kernels.cu @@ -0,0 +1,61 @@ +#include "cuda_runtime.h" +#include "curand.h" +#include "cublas_v2.h" + +extern "C" { +#include "im2col.h" +#include "cuda.h" +} + +// src: https://github.com/BVLC/caffe/blob/master/src/caffe/util/im2col.cu +// You may also want to read: https://github.com/BVLC/caffe/blob/master/LICENSE + +__global__ void im2col_gpu_kernel(const int n, const float* data_im, + const int height, const int width, const int ksize, + const int pad, + const int stride, + const int height_col, const int width_col, + float *data_col) { + int index = blockIdx.x*blockDim.x+threadIdx.x; + for(; index < n; index += blockDim.x*gridDim.x){ + int w_out = index % width_col; + int h_index = index / width_col; + int h_out = h_index % height_col; + int channel_in = h_index / height_col; + int channel_out = channel_in * ksize * ksize; + int h_in = h_out * stride - pad; + int w_in = w_out * stride - pad; + float* data_col_ptr = data_col; + data_col_ptr += (channel_out * height_col + h_out) * width_col + w_out; + const float* data_im_ptr = data_im; + data_im_ptr += (channel_in * height + h_in) * width + w_in; + for (int i = 0; i < ksize; ++i) { + for (int j = 0; j < ksize; ++j) { + int h = h_in + i; + int w = w_in + j; + + *data_col_ptr = (h >= 0 && w >= 0 && h < height && w < width) ? + data_im_ptr[i * width + j] : 0; + + //*data_col_ptr = data_im_ptr[ii * width + jj]; + + data_col_ptr += height_col * width_col; + } + } + } +} + +void im2col_gpu(float *im, + int channels, int height, int width, + int ksize, int stride, int pad, float *data_col){ + // We are going to launch channels * height_col * width_col kernels, each + // kernel responsible for copying a single-channel grid. + int height_col = (height + 2 * pad - ksize) / stride + 1; + int width_col = (width + 2 * pad - ksize) / stride + 1; + int num_kernels = channels * height_col * width_col; + im2col_gpu_kernel<<<(num_kernels+BLOCK-1)/BLOCK, + BLOCK>>>( + num_kernels, im, height, width, ksize, pad, + stride, height_col, + width_col, data_col); +} diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/image.c b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/image.c new file mode 100644 index 00000000000..786acb089e3 --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/image.c @@ -0,0 +1,1615 @@ +#include "image.h" +#include "utils.h" +#include "blas.h" +#include "cuda.h" +#include +#include + +#define STB_IMAGE_IMPLEMENTATION +#include "stb_image.h" +#define STB_IMAGE_WRITE_IMPLEMENTATION +#include "stb_image_write.h" + +int windows = 0; + +float colors[6][3] = { {1,0,1}, {0,0,1},{0,1,1},{0,1,0},{1,1,0},{1,0,0} }; + +float get_color(int c, int x, int max) +{ + float ratio = ((float)x/max)*5; + int i = floor(ratio); + int j = ceil(ratio); + ratio -= i; + float r = (1-ratio) * colors[i][c] + ratio*colors[j][c]; + //printf("%f\n", r); + return r; +} + +image mask_to_rgb(image mask) +{ + int n = mask.c; + image im = make_image(mask.w, mask.h, 3); + int i, j; + for(j = 0; j < n; ++j){ + int offset = j*123457 % n; + float red = get_color(2,offset,n); + float green = get_color(1,offset,n); + float blue = get_color(0,offset,n); + for(i = 0; i < im.w*im.h; ++i){ + im.data[i + 0*im.w*im.h] += mask.data[j*im.h*im.w + i]*red; + im.data[i + 1*im.w*im.h] += mask.data[j*im.h*im.w + i]*green; + im.data[i + 2*im.w*im.h] += mask.data[j*im.h*im.w + i]*blue; + } + } + return im; +} + +static float get_pixel(image m, int x, int y, int c) +{ + assert(x < m.w && y < m.h && c < m.c); + return m.data[c*m.h*m.w + y*m.w + x]; +} +static float get_pixel_extend(image m, int x, int y, int c) +{ + if(x < 0 || x >= m.w || y < 0 || y >= m.h) return 0; + /* + if(x < 0) x = 0; + if(x >= m.w) x = m.w-1; + if(y < 0) y = 0; + if(y >= m.h) y = m.h-1; + */ + if(c < 0 || c >= m.c) return 0; + return get_pixel(m, x, y, c); +} +static void set_pixel(image m, int x, int y, int c, float val) +{ + if (x < 0 || y < 0 || c < 0 || x >= m.w || y >= m.h || c >= m.c) return; + assert(x < m.w && y < m.h && c < m.c); + m.data[c*m.h*m.w + y*m.w + x] = val; +} +static void add_pixel(image m, int x, int y, int c, float val) +{ + assert(x < m.w && y < m.h && c < m.c); + m.data[c*m.h*m.w + y*m.w + x] += val; +} + +static float bilinear_interpolate(image im, float x, float y, int c) +{ + int ix = (int) floorf(x); + int iy = (int) floorf(y); + + float dx = x - ix; + float dy = y - iy; + + float val = (1-dy) * (1-dx) * get_pixel_extend(im, ix, iy, c) + + dy * (1-dx) * get_pixel_extend(im, ix, iy+1, c) + + (1-dy) * dx * get_pixel_extend(im, ix+1, iy, c) + + dy * dx * get_pixel_extend(im, ix+1, iy+1, c); + return val; +} + + +void composite_image(image source, image dest, int dx, int dy) +{ + int x,y,k; + for(k = 0; k < source.c; ++k){ + for(y = 0; y < source.h; ++y){ + for(x = 0; x < source.w; ++x){ + float val = get_pixel(source, x, y, k); + float val2 = get_pixel_extend(dest, dx+x, dy+y, k); + set_pixel(dest, dx+x, dy+y, k, val * val2); + } + } + } +} + +image border_image(image a, int border) +{ + image b = make_image(a.w + 2*border, a.h + 2*border, a.c); + int x,y,k; + for(k = 0; k < b.c; ++k){ + for(y = 0; y < b.h; ++y){ + for(x = 0; x < b.w; ++x){ + float val = get_pixel_extend(a, x - border, y - border, k); + if(x - border < 0 || x - border >= a.w || y - border < 0 || y - border >= a.h) val = 1; + set_pixel(b, x, y, k, val); + } + } + } + return b; +} + +image tile_images(image a, image b, int dx) +{ + if(a.w == 0) return copy_image(b); + image c = make_image(a.w + b.w + dx, (a.h > b.h) ? a.h : b.h, (a.c > b.c) ? a.c : b.c); + fill_cpu(c.w*c.h*c.c, 1, c.data, 1); + embed_image(a, c, 0, 0); + composite_image(b, c, a.w + dx, 0); + return c; +} + +image get_label(image **characters, char *string, int size) +{ + size = size/10; + if(size > 7) size = 7; + image label = make_empty_image(0,0,0); + while(*string){ + image l = characters[size][(int)*string]; + image n = tile_images(label, l, -size - 1 + (size+1)/2); + free_image(label); + label = n; + ++string; + } + image b = border_image(label, label.h*.25); + free_image(label); + return b; +} + +void draw_label(image a, int r, int c, image label, const float *rgb) +{ + int w = label.w; + int h = label.h; + if (r - h >= 0) r = r - h; + + int i, j, k; + for(j = 0; j < h && j + r < a.h; ++j){ + for(i = 0; i < w && i + c < a.w; ++i){ + for(k = 0; k < label.c; ++k){ + float val = get_pixel(label, i, j, k); + set_pixel(a, i+c, j+r, k, rgb[k] * val); + } + } + } +} + +void draw_box(image a, int x1, int y1, int x2, int y2, float r, float g, float b) +{ + //normalize_image(a); + int i; + if(x1 < 0) x1 = 0; + if(x1 >= a.w) x1 = a.w-1; + if(x2 < 0) x2 = 0; + if(x2 >= a.w) x2 = a.w-1; + + if(y1 < 0) y1 = 0; + if(y1 >= a.h) y1 = a.h-1; + if(y2 < 0) y2 = 0; + if(y2 >= a.h) y2 = a.h-1; + + for(i = x1; i <= x2; ++i){ + a.data[i + y1*a.w + 0*a.w*a.h] = r; + a.data[i + y2*a.w + 0*a.w*a.h] = r; + + a.data[i + y1*a.w + 1*a.w*a.h] = g; + a.data[i + y2*a.w + 1*a.w*a.h] = g; + + a.data[i + y1*a.w + 2*a.w*a.h] = b; + a.data[i + y2*a.w + 2*a.w*a.h] = b; + } + for(i = y1; i <= y2; ++i){ + a.data[x1 + i*a.w + 0*a.w*a.h] = r; + a.data[x2 + i*a.w + 0*a.w*a.h] = r; + + a.data[x1 + i*a.w + 1*a.w*a.h] = g; + a.data[x2 + i*a.w + 1*a.w*a.h] = g; + + a.data[x1 + i*a.w + 2*a.w*a.h] = b; + a.data[x2 + i*a.w + 2*a.w*a.h] = b; + } +} + +void draw_box_width(image a, int x1, int y1, int x2, int y2, int w, float r, float g, float b) +{ + int i; + for(i = 0; i < w; ++i){ + draw_box(a, x1+i, y1+i, x2-i, y2-i, r, g, b); + } +} + +void draw_bbox(image a, box bbox, int w, float r, float g, float b) +{ + int left = (bbox.x-bbox.w/2)*a.w; + int right = (bbox.x+bbox.w/2)*a.w; + int top = (bbox.y-bbox.h/2)*a.h; + int bot = (bbox.y+bbox.h/2)*a.h; + + int i; + for(i = 0; i < w; ++i){ + draw_box(a, left+i, top+i, right-i, bot-i, r, g, b); + } +} + +image **load_alphabet() +{ + int i, j; + const int nsize = 8; + image **alphabets = calloc(nsize, sizeof(image)); + for(j = 0; j < nsize; ++j){ + alphabets[j] = calloc(128, sizeof(image)); + for(i = 32; i < 127; ++i){ + char buff[256]; + sprintf(buff, "data/labels/%d_%d.png", i, j); + alphabets[j][i] = load_image_color(buff, 0, 0); + } + } + return alphabets; +} + +void draw_detections(image im, detection *dets, int num, float thresh, char **names, image **alphabet, int classes) +{ + int i,j; + + for(i = 0; i < num; ++i){ + char labelstr[4096] = {0}; + int class = -1; + for(j = 0; j < classes; ++j){ + if (dets[i].prob[j] > thresh){ + if (class < 0) { + strcat(labelstr, names[j]); + class = j; + } else { + strcat(labelstr, ", "); + strcat(labelstr, names[j]); + } + printf("%s: %.0f%%\n", names[j], dets[i].prob[j]*100); + } + } + if(class >= 0){ + int width = im.h * .006; + + /* + if(0){ + width = pow(prob, 1./2.)*10+1; + alphabet = 0; + } + */ + + //printf("%d %s: %.0f%%\n", i, names[class], prob*100); + int offset = class*123457 % classes; + float red = get_color(2,offset,classes); + float green = get_color(1,offset,classes); + float blue = get_color(0,offset,classes); + float rgb[3]; + + //width = prob*20+2; + + rgb[0] = red; + rgb[1] = green; + rgb[2] = blue; + box b = dets[i].bbox; + //printf("%f %f %f %f\n", b.x, b.y, b.w, b.h); + + int left = (b.x-b.w/2.)*im.w; + int right = (b.x+b.w/2.)*im.w; + int top = (b.y-b.h/2.)*im.h; + int bot = (b.y+b.h/2.)*im.h; + + if(left < 0) left = 0; + if(right > im.w-1) right = im.w-1; + if(top < 0) top = 0; + if(bot > im.h-1) bot = im.h-1; + + draw_box_width(im, left, top, right, bot, width, red, green, blue); + if (alphabet) { + image label = get_label(alphabet, labelstr, (im.h*.03)); + draw_label(im, top + width, left, label, rgb); + free_image(label); + } + if (dets[i].mask){ + image mask = float_to_image(14, 14, 1, dets[i].mask); + image resized_mask = resize_image(mask, b.w*im.w, b.h*im.h); + image tmask = threshold_image(resized_mask, .5); + embed_image(tmask, im, left, top); + free_image(mask); + free_image(resized_mask); + free_image(tmask); + } + } + } +} + +void transpose_image(image im) +{ + assert(im.w == im.h); + int n, m; + int c; + for(c = 0; c < im.c; ++c){ + for(n = 0; n < im.w-1; ++n){ + for(m = n + 1; m < im.w; ++m){ + float swap = im.data[m + im.w*(n + im.h*c)]; + im.data[m + im.w*(n + im.h*c)] = im.data[n + im.w*(m + im.h*c)]; + im.data[n + im.w*(m + im.h*c)] = swap; + } + } + } +} + +void rotate_image_cw(image im, int times) +{ + assert(im.w == im.h); + times = (times + 400) % 4; + int i, x, y, c; + int n = im.w; + for(i = 0; i < times; ++i){ + for(c = 0; c < im.c; ++c){ + for(x = 0; x < n/2; ++x){ + for(y = 0; y < (n-1)/2 + 1; ++y){ + float temp = im.data[y + im.w*(x + im.h*c)]; + im.data[y + im.w*(x + im.h*c)] = im.data[n-1-x + im.w*(y + im.h*c)]; + im.data[n-1-x + im.w*(y + im.h*c)] = im.data[n-1-y + im.w*(n-1-x + im.h*c)]; + im.data[n-1-y + im.w*(n-1-x + im.h*c)] = im.data[x + im.w*(n-1-y + im.h*c)]; + im.data[x + im.w*(n-1-y + im.h*c)] = temp; + } + } + } + } +} + +void flip_image(image a) +{ + int i,j,k; + for(k = 0; k < a.c; ++k){ + for(i = 0; i < a.h; ++i){ + for(j = 0; j < a.w/2; ++j){ + int index = j + a.w*(i + a.h*(k)); + int flip = (a.w - j - 1) + a.w*(i + a.h*(k)); + float swap = a.data[flip]; + a.data[flip] = a.data[index]; + a.data[index] = swap; + } + } + } +} + +image image_distance(image a, image b) +{ + int i,j; + image dist = make_image(a.w, a.h, 1); + for(i = 0; i < a.c; ++i){ + for(j = 0; j < a.h*a.w; ++j){ + dist.data[j] += pow(a.data[i*a.h*a.w+j]-b.data[i*a.h*a.w+j],2); + } + } + for(j = 0; j < a.h*a.w; ++j){ + dist.data[j] = sqrt(dist.data[j]); + } + return dist; +} + +void ghost_image(image source, image dest, int dx, int dy) +{ + int x,y,k; + float max_dist = sqrt((-source.w/2. + .5)*(-source.w/2. + .5)); + for(k = 0; k < source.c; ++k){ + for(y = 0; y < source.h; ++y){ + for(x = 0; x < source.w; ++x){ + float dist = sqrt((x - source.w/2. + .5)*(x - source.w/2. + .5) + (y - source.h/2. + .5)*(y - source.h/2. + .5)); + float alpha = (1 - dist/max_dist); + if(alpha < 0) alpha = 0; + float v1 = get_pixel(source, x,y,k); + float v2 = get_pixel(dest, dx+x,dy+y,k); + float val = alpha*v1 + (1-alpha)*v2; + set_pixel(dest, dx+x, dy+y, k, val); + } + } + } +} + +void blocky_image(image im, int s) +{ + int i,j,k; + for(k = 0; k < im.c; ++k){ + for(j = 0; j < im.h; ++j){ + for(i = 0; i < im.w; ++i){ + im.data[i + im.w*(j + im.h*k)] = im.data[i/s*s + im.w*(j/s*s + im.h*k)]; + } + } + } +} + +void censor_image(image im, int dx, int dy, int w, int h) +{ + int i,j,k; + int s = 32; + if(dx < 0) dx = 0; + if(dy < 0) dy = 0; + + for(k = 0; k < im.c; ++k){ + for(j = dy; j < dy + h && j < im.h; ++j){ + for(i = dx; i < dx + w && i < im.w; ++i){ + im.data[i + im.w*(j + im.h*k)] = im.data[i/s*s + im.w*(j/s*s + im.h*k)]; + //im.data[i + j*im.w + k*im.w*im.h] = 0; + } + } + } +} + +void embed_image(image source, image dest, int dx, int dy) +{ + int x,y,k; + for(k = 0; k < source.c; ++k){ + for(y = 0; y < source.h; ++y){ + for(x = 0; x < source.w; ++x){ + float val = get_pixel(source, x,y,k); + set_pixel(dest, dx+x, dy+y, k, val); + } + } + } +} + +image collapse_image_layers(image source, int border) +{ + int h = source.h; + h = (h+border)*source.c - border; + image dest = make_image(source.w, h, 1); + int i; + for(i = 0; i < source.c; ++i){ + image layer = get_image_layer(source, i); + int h_offset = i*(source.h+border); + embed_image(layer, dest, 0, h_offset); + free_image(layer); + } + return dest; +} + +void constrain_image(image im) +{ + int i; + for(i = 0; i < im.w*im.h*im.c; ++i){ + if(im.data[i] < 0) im.data[i] = 0; + if(im.data[i] > 1) im.data[i] = 1; + } +} + +void normalize_image(image p) +{ + int i; + float min = 9999999; + float max = -999999; + + for(i = 0; i < p.h*p.w*p.c; ++i){ + float v = p.data[i]; + if(v < min) min = v; + if(v > max) max = v; + } + if(max - min < .000000001){ + min = 0; + max = 1; + } + for(i = 0; i < p.c*p.w*p.h; ++i){ + p.data[i] = (p.data[i] - min)/(max-min); + } +} + +void normalize_image2(image p) +{ + float *min = calloc(p.c, sizeof(float)); + float *max = calloc(p.c, sizeof(float)); + int i,j; + for(i = 0; i < p.c; ++i) min[i] = max[i] = p.data[i*p.h*p.w]; + + for(j = 0; j < p.c; ++j){ + for(i = 0; i < p.h*p.w; ++i){ + float v = p.data[i+j*p.h*p.w]; + if(v < min[j]) min[j] = v; + if(v > max[j]) max[j] = v; + } + } + for(i = 0; i < p.c; ++i){ + if(max[i] - min[i] < .000000001){ + min[i] = 0; + max[i] = 1; + } + } + for(j = 0; j < p.c; ++j){ + for(i = 0; i < p.w*p.h; ++i){ + p.data[i+j*p.h*p.w] = (p.data[i+j*p.h*p.w] - min[j])/(max[j]-min[j]); + } + } + free(min); + free(max); +} + +void copy_image_into(image src, image dest) +{ + memcpy(dest.data, src.data, src.h*src.w*src.c*sizeof(float)); +} + +image copy_image(image p) +{ + image copy = p; + copy.data = calloc(p.h*p.w*p.c, sizeof(float)); + memcpy(copy.data, p.data, p.h*p.w*p.c*sizeof(float)); + return copy; +} + +void rgbgr_image(image im) +{ + int i; + for(i = 0; i < im.w*im.h; ++i){ + float swap = im.data[i]; + im.data[i] = im.data[i+im.w*im.h*2]; + im.data[i+im.w*im.h*2] = swap; + } +} + +#ifdef OPENCV +void show_image_cv(image p, const char *name, IplImage *disp) +{ + int x,y,k; + if(p.c == 3) rgbgr_image(p); + //normalize_image(copy); + + char buff[256]; + //sprintf(buff, "%s (%d)", name, windows); + sprintf(buff, "%s", name); + + int step = disp->widthStep; + cvNamedWindow(buff, CV_WINDOW_NORMAL); + //cvMoveWindow(buff, 100*(windows%10) + 200*(windows/10), 100*(windows%10)); + ++windows; + for(y = 0; y < p.h; ++y){ + for(x = 0; x < p.w; ++x){ + for(k= 0; k < p.c; ++k){ + disp->imageData[y*step + x*p.c + k] = (unsigned char)(get_pixel(p,x,y,k)*255); + } + } + } + if(0){ + int w = 448; + int h = w*p.h/p.w; + if(h > 1000){ + h = 1000; + w = h*p.w/p.h; + } + IplImage *buffer = disp; + disp = cvCreateImage(cvSize(w, h), buffer->depth, buffer->nChannels); + cvResize(buffer, disp, CV_INTER_LINEAR); + cvReleaseImage(&buffer); + } + cvShowImage(buff, disp); +} +#endif + +void show_image(image p, const char *name) +{ +#ifdef OPENCV + IplImage *disp = cvCreateImage(cvSize(p.w,p.h), IPL_DEPTH_8U, p.c); + image copy = copy_image(p); + constrain_image(copy); + show_image_cv(copy, name, disp); + free_image(copy); + cvReleaseImage(&disp); +#else + fprintf(stderr, "Not compiled with OpenCV, saving to %s.png instead\n", name); + save_image(p, name); +#endif +} + +#ifdef OPENCV + +void ipl_into_image(IplImage* src, image im) +{ + unsigned char *data = (unsigned char *)src->imageData; + int h = src->height; + int w = src->width; + int c = src->nChannels; + int step = src->widthStep; + int i, j, k; + + for(i = 0; i < h; ++i){ + for(k= 0; k < c; ++k){ + for(j = 0; j < w; ++j){ + im.data[k*w*h + i*w + j] = data[i*step + j*c + k]/255.; + } + } + } +} + +image ipl_to_image(IplImage* src) +{ + int h = src->height; + int w = src->width; + int c = src->nChannels; + image out = make_image(w, h, c); + ipl_into_image(src, out); + return out; +} + +image load_image_cv(char *filename, int channels) +{ + IplImage* src = 0; + int flag = -1; + if (channels == 0) flag = -1; + else if (channels == 1) flag = 0; + else if (channels == 3) flag = 1; + else { + fprintf(stderr, "OpenCV can't force load with %d channels\n", channels); + } + + if( (src = cvLoadImage(filename, flag)) == 0 ) + { + fprintf(stderr, "Cannot load image \"%s\"\n", filename); + char buff[256]; + sprintf(buff, "echo %s >> bad.list", filename); + system(buff); + return make_image(10,10,3); + //exit(0); + } + image out = ipl_to_image(src); + cvReleaseImage(&src); + rgbgr_image(out); + return out; +} + +void flush_stream_buffer(CvCapture *cap, int n) +{ + int i; + for(i = 0; i < n; ++i) { + cvQueryFrame(cap); + } +} + +image get_image_from_stream(CvCapture *cap) +{ + IplImage* src = cvQueryFrame(cap); + if (!src) return make_empty_image(0,0,0); + image im = ipl_to_image(src); + rgbgr_image(im); + return im; +} + +int fill_image_from_stream(CvCapture *cap, image im) +{ + IplImage* src = cvQueryFrame(cap); + if (!src) return 0; + ipl_into_image(src, im); + rgbgr_image(im); + return 1; +} + +void save_image_jpg(image p, const char *name) +{ + image copy = copy_image(p); + if(p.c == 3) rgbgr_image(copy); + int x,y,k; + + char buff[256]; + sprintf(buff, "%s.jpg", name); + + IplImage *disp = cvCreateImage(cvSize(p.w,p.h), IPL_DEPTH_8U, p.c); + int step = disp->widthStep; + for(y = 0; y < p.h; ++y){ + for(x = 0; x < p.w; ++x){ + for(k= 0; k < p.c; ++k){ + disp->imageData[y*step + x*p.c + k] = (unsigned char)(get_pixel(copy,x,y,k)*255); + } + } + } + cvSaveImage(buff, disp,0); + cvReleaseImage(&disp); + free_image(copy); +} +#endif + +void save_image_png(image im, const char *name) +{ + char buff[256]; + //sprintf(buff, "%s (%d)", name, windows); + sprintf(buff, "%s.png", name); + unsigned char *data = calloc(im.w*im.h*im.c, sizeof(char)); + int i,k; + for(k = 0; k < im.c; ++k){ + for(i = 0; i < im.w*im.h; ++i){ + data[i*im.c+k] = (unsigned char) (255*im.data[i + k*im.w*im.h]); + } + } + int success = stbi_write_png(buff, im.w, im.h, im.c, data, im.w*im.c); + free(data); + if(!success) fprintf(stderr, "Failed to write image %s\n", buff); +} + +void save_image(image im, const char *name) +{ +#ifdef OPENCV + save_image_jpg(im, name); +#else + save_image_png(im, name); +#endif +} + + +void show_image_layers(image p, char *name) +{ + int i; + char buff[256]; + for(i = 0; i < p.c; ++i){ + sprintf(buff, "%s - Layer %d", name, i); + image layer = get_image_layer(p, i); + show_image(layer, buff); + free_image(layer); + } +} + +void show_image_collapsed(image p, char *name) +{ + image c = collapse_image_layers(p, 1); + show_image(c, name); + free_image(c); +} + +image make_empty_image(int w, int h, int c) +{ + image out; + out.data = 0; + out.h = h; + out.w = w; + out.c = c; + return out; +} + +image make_image(int w, int h, int c) +{ + image out = make_empty_image(w,h,c); + out.data = calloc(h*w*c, sizeof(float)); + return out; +} + +image make_random_image(int w, int h, int c) +{ + image out = make_empty_image(w,h,c); + out.data = calloc(h*w*c, sizeof(float)); + int i; + for(i = 0; i < w*h*c; ++i){ + out.data[i] = (rand_normal() * .25) + .5; + } + return out; +} + +image float_to_image(int w, int h, int c, float *data) +{ + image out = make_empty_image(w,h,c); + out.data = data; + return out; +} + +void place_image(image im, int w, int h, int dx, int dy, image canvas) +{ + int x, y, c; + for(c = 0; c < im.c; ++c){ + for(y = 0; y < h; ++y){ + for(x = 0; x < w; ++x){ + float rx = ((float)x / w) * im.w; + float ry = ((float)y / h) * im.h; + float val = bilinear_interpolate(im, rx, ry, c); + set_pixel(canvas, x + dx, y + dy, c, val); + } + } + } +} + +image center_crop_image(image im, int w, int h) +{ + int m = (im.w < im.h) ? im.w : im.h; + image c = crop_image(im, (im.w - m) / 2, (im.h - m)/2, m, m); + image r = resize_image(c, w, h); + free_image(c); + return r; +} + +image rotate_crop_image(image im, float rad, float s, int w, int h, float dx, float dy, float aspect) +{ + int x, y, c; + float cx = im.w/2.; + float cy = im.h/2.; + image rot = make_image(w, h, im.c); + for(c = 0; c < im.c; ++c){ + for(y = 0; y < h; ++y){ + for(x = 0; x < w; ++x){ + float rx = cos(rad)*((x - w/2.)/s*aspect + dx/s*aspect) - sin(rad)*((y - h/2.)/s + dy/s) + cx; + float ry = sin(rad)*((x - w/2.)/s*aspect + dx/s*aspect) + cos(rad)*((y - h/2.)/s + dy/s) + cy; + float val = bilinear_interpolate(im, rx, ry, c); + set_pixel(rot, x, y, c, val); + } + } + } + return rot; +} + +image rotate_image(image im, float rad) +{ + int x, y, c; + float cx = im.w/2.; + float cy = im.h/2.; + image rot = make_image(im.w, im.h, im.c); + for(c = 0; c < im.c; ++c){ + for(y = 0; y < im.h; ++y){ + for(x = 0; x < im.w; ++x){ + float rx = cos(rad)*(x-cx) - sin(rad)*(y-cy) + cx; + float ry = sin(rad)*(x-cx) + cos(rad)*(y-cy) + cy; + float val = bilinear_interpolate(im, rx, ry, c); + set_pixel(rot, x, y, c, val); + } + } + } + return rot; +} + +void fill_image(image m, float s) +{ + int i; + for(i = 0; i < m.h*m.w*m.c; ++i) m.data[i] = s; +} + +void translate_image(image m, float s) +{ + int i; + for(i = 0; i < m.h*m.w*m.c; ++i) m.data[i] += s; +} + +void scale_image(image m, float s) +{ + int i; + for(i = 0; i < m.h*m.w*m.c; ++i) m.data[i] *= s; +} + +image crop_image(image im, int dx, int dy, int w, int h) +{ + image cropped = make_image(w, h, im.c); + int i, j, k; + for(k = 0; k < im.c; ++k){ + for(j = 0; j < h; ++j){ + for(i = 0; i < w; ++i){ + int r = j + dy; + int c = i + dx; + float val = 0; + r = constrain_int(r, 0, im.h-1); + c = constrain_int(c, 0, im.w-1); + val = get_pixel(im, c, r, k); + set_pixel(cropped, i, j, k, val); + } + } + } + return cropped; +} + +int best_3d_shift_r(image a, image b, int min, int max) +{ + if(min == max) return min; + int mid = floor((min + max) / 2.); + image c1 = crop_image(b, 0, mid, b.w, b.h); + image c2 = crop_image(b, 0, mid+1, b.w, b.h); + float d1 = dist_array(c1.data, a.data, a.w*a.h*a.c, 10); + float d2 = dist_array(c2.data, a.data, a.w*a.h*a.c, 10); + free_image(c1); + free_image(c2); + if(d1 < d2) return best_3d_shift_r(a, b, min, mid); + else return best_3d_shift_r(a, b, mid+1, max); +} + +int best_3d_shift(image a, image b, int min, int max) +{ + int i; + int best = 0; + float best_distance = FLT_MAX; + for(i = min; i <= max; i += 2){ + image c = crop_image(b, 0, i, b.w, b.h); + float d = dist_array(c.data, a.data, a.w*a.h*a.c, 100); + if(d < best_distance){ + best_distance = d; + best = i; + } + printf("%d %f\n", i, d); + free_image(c); + } + return best; +} + +void composite_3d(char *f1, char *f2, char *out, int delta) +{ + if(!out) out = "out"; + image a = load_image(f1, 0,0,0); + image b = load_image(f2, 0,0,0); + int shift = best_3d_shift_r(a, b, -a.h/100, a.h/100); + + image c1 = crop_image(b, 10, shift, b.w, b.h); + float d1 = dist_array(c1.data, a.data, a.w*a.h*a.c, 100); + image c2 = crop_image(b, -10, shift, b.w, b.h); + float d2 = dist_array(c2.data, a.data, a.w*a.h*a.c, 100); + + if(d2 < d1 && 0){ + image swap = a; + a = b; + b = swap; + shift = -shift; + printf("swapped, %d\n", shift); + } + else{ + printf("%d\n", shift); + } + + image c = crop_image(b, delta, shift, a.w, a.h); + int i; + for(i = 0; i < c.w*c.h; ++i){ + c.data[i] = a.data[i]; + } +#ifdef OPENCV + save_image_jpg(c, out); +#else + save_image(c, out); +#endif +} + +void letterbox_image_into(image im, int w, int h, image boxed) +{ + int new_w = im.w; + int new_h = im.h; + if (((float)w/im.w) < ((float)h/im.h)) { + new_w = w; + new_h = (im.h * w)/im.w; + } else { + new_h = h; + new_w = (im.w * h)/im.h; + } + image resized = resize_image(im, new_w, new_h); + embed_image(resized, boxed, (w-new_w)/2, (h-new_h)/2); + free_image(resized); +} + +image letterbox_image(image im, int w, int h) +{ + int new_w = im.w; + int new_h = im.h; + if (((float)w/im.w) < ((float)h/im.h)) { + new_w = w; + new_h = (im.h * w)/im.w; + } else { + new_h = h; + new_w = (im.w * h)/im.h; + } + image resized = resize_image(im, new_w, new_h); + image boxed = make_image(w, h, im.c); + fill_image(boxed, .5); + //int i; + //for(i = 0; i < boxed.w*boxed.h*boxed.c; ++i) boxed.data[i] = 0; + embed_image(resized, boxed, (w-new_w)/2, (h-new_h)/2); + free_image(resized); + return boxed; +} + +image resize_max(image im, int max) +{ + int w = im.w; + int h = im.h; + if(w > h){ + h = (h * max) / w; + w = max; + } else { + w = (w * max) / h; + h = max; + } + if(w == im.w && h == im.h) return im; + image resized = resize_image(im, w, h); + return resized; +} + +image resize_min(image im, int min) +{ + int w = im.w; + int h = im.h; + if(w < h){ + h = (h * min) / w; + w = min; + } else { + w = (w * min) / h; + h = min; + } + if(w == im.w && h == im.h) return im; + image resized = resize_image(im, w, h); + return resized; +} + +image random_crop_image(image im, int w, int h) +{ + int dx = rand_int(0, im.w - w); + int dy = rand_int(0, im.h - h); + image crop = crop_image(im, dx, dy, w, h); + return crop; +} + +augment_args random_augment_args(image im, float angle, float aspect, int low, int high, int w, int h) +{ + augment_args a = {0}; + aspect = rand_scale(aspect); + int r = rand_int(low, high); + int min = (im.h < im.w*aspect) ? im.h : im.w*aspect; + float scale = (float)r / min; + + float rad = rand_uniform(-angle, angle) * TWO_PI / 360.; + + float dx = (im.w*scale/aspect - w) / 2.; + float dy = (im.h*scale - w) / 2.; + //if(dx < 0) dx = 0; + //if(dy < 0) dy = 0; + dx = rand_uniform(-dx, dx); + dy = rand_uniform(-dy, dy); + + a.rad = rad; + a.scale = scale; + a.w = w; + a.h = h; + a.dx = dx; + a.dy = dy; + a.aspect = aspect; + return a; +} + +image random_augment_image(image im, float angle, float aspect, int low, int high, int w, int h) +{ + augment_args a = random_augment_args(im, angle, aspect, low, high, w, h); + image crop = rotate_crop_image(im, a.rad, a.scale, a.w, a.h, a.dx, a.dy, a.aspect); + return crop; +} + +float three_way_max(float a, float b, float c) +{ + return (a > b) ? ( (a > c) ? a : c) : ( (b > c) ? b : c) ; +} + +float three_way_min(float a, float b, float c) +{ + return (a < b) ? ( (a < c) ? a : c) : ( (b < c) ? b : c) ; +} + +void yuv_to_rgb(image im) +{ + assert(im.c == 3); + int i, j; + float r, g, b; + float y, u, v; + for(j = 0; j < im.h; ++j){ + for(i = 0; i < im.w; ++i){ + y = get_pixel(im, i , j, 0); + u = get_pixel(im, i , j, 1); + v = get_pixel(im, i , j, 2); + + r = y + 1.13983*v; + g = y + -.39465*u + -.58060*v; + b = y + 2.03211*u; + + set_pixel(im, i, j, 0, r); + set_pixel(im, i, j, 1, g); + set_pixel(im, i, j, 2, b); + } + } +} + +void rgb_to_yuv(image im) +{ + assert(im.c == 3); + int i, j; + float r, g, b; + float y, u, v; + for(j = 0; j < im.h; ++j){ + for(i = 0; i < im.w; ++i){ + r = get_pixel(im, i , j, 0); + g = get_pixel(im, i , j, 1); + b = get_pixel(im, i , j, 2); + + y = .299*r + .587*g + .114*b; + u = -.14713*r + -.28886*g + .436*b; + v = .615*r + -.51499*g + -.10001*b; + + set_pixel(im, i, j, 0, y); + set_pixel(im, i, j, 1, u); + set_pixel(im, i, j, 2, v); + } + } +} + +// http://www.cs.rit.edu/~ncs/color/t_convert.html +void rgb_to_hsv(image im) +{ + assert(im.c == 3); + int i, j; + float r, g, b; + float h, s, v; + for(j = 0; j < im.h; ++j){ + for(i = 0; i < im.w; ++i){ + r = get_pixel(im, i , j, 0); + g = get_pixel(im, i , j, 1); + b = get_pixel(im, i , j, 2); + float max = three_way_max(r,g,b); + float min = three_way_min(r,g,b); + float delta = max - min; + v = max; + if(max == 0){ + s = 0; + h = 0; + }else{ + s = delta/max; + if(r == max){ + h = (g - b) / delta; + } else if (g == max) { + h = 2 + (b - r) / delta; + } else { + h = 4 + (r - g) / delta; + } + if (h < 0) h += 6; + h = h/6.; + } + set_pixel(im, i, j, 0, h); + set_pixel(im, i, j, 1, s); + set_pixel(im, i, j, 2, v); + } + } +} + +void hsv_to_rgb(image im) +{ + assert(im.c == 3); + int i, j; + float r, g, b; + float h, s, v; + float f, p, q, t; + for(j = 0; j < im.h; ++j){ + for(i = 0; i < im.w; ++i){ + h = 6 * get_pixel(im, i , j, 0); + s = get_pixel(im, i , j, 1); + v = get_pixel(im, i , j, 2); + if (s == 0) { + r = g = b = v; + } else { + int index = floor(h); + f = h - index; + p = v*(1-s); + q = v*(1-s*f); + t = v*(1-s*(1-f)); + if(index == 0){ + r = v; g = t; b = p; + } else if(index == 1){ + r = q; g = v; b = p; + } else if(index == 2){ + r = p; g = v; b = t; + } else if(index == 3){ + r = p; g = q; b = v; + } else if(index == 4){ + r = t; g = p; b = v; + } else { + r = v; g = p; b = q; + } + } + set_pixel(im, i, j, 0, r); + set_pixel(im, i, j, 1, g); + set_pixel(im, i, j, 2, b); + } + } +} + +void grayscale_image_3c(image im) +{ + assert(im.c == 3); + int i, j, k; + float scale[] = {0.299, 0.587, 0.114}; + for(j = 0; j < im.h; ++j){ + for(i = 0; i < im.w; ++i){ + float val = 0; + for(k = 0; k < 3; ++k){ + val += scale[k]*get_pixel(im, i, j, k); + } + im.data[0*im.h*im.w + im.w*j + i] = val; + im.data[1*im.h*im.w + im.w*j + i] = val; + im.data[2*im.h*im.w + im.w*j + i] = val; + } + } +} + +image grayscale_image(image im) +{ + assert(im.c == 3); + int i, j, k; + image gray = make_image(im.w, im.h, 1); + float scale[] = {0.299, 0.587, 0.114}; + for(k = 0; k < im.c; ++k){ + for(j = 0; j < im.h; ++j){ + for(i = 0; i < im.w; ++i){ + gray.data[i+im.w*j] += scale[k]*get_pixel(im, i, j, k); + } + } + } + return gray; +} + +image threshold_image(image im, float thresh) +{ + int i; + image t = make_image(im.w, im.h, im.c); + for(i = 0; i < im.w*im.h*im.c; ++i){ + t.data[i] = im.data[i]>thresh ? 1 : 0; + } + return t; +} + +image blend_image(image fore, image back, float alpha) +{ + assert(fore.w == back.w && fore.h == back.h && fore.c == back.c); + image blend = make_image(fore.w, fore.h, fore.c); + int i, j, k; + for(k = 0; k < fore.c; ++k){ + for(j = 0; j < fore.h; ++j){ + for(i = 0; i < fore.w; ++i){ + float val = alpha * get_pixel(fore, i, j, k) + + (1 - alpha)* get_pixel(back, i, j, k); + set_pixel(blend, i, j, k, val); + } + } + } + return blend; +} + +void scale_image_channel(image im, int c, float v) +{ + int i, j; + for(j = 0; j < im.h; ++j){ + for(i = 0; i < im.w; ++i){ + float pix = get_pixel(im, i, j, c); + pix = pix*v; + set_pixel(im, i, j, c, pix); + } + } +} + +void translate_image_channel(image im, int c, float v) +{ + int i, j; + for(j = 0; j < im.h; ++j){ + for(i = 0; i < im.w; ++i){ + float pix = get_pixel(im, i, j, c); + pix = pix+v; + set_pixel(im, i, j, c, pix); + } + } +} + +image binarize_image(image im) +{ + image c = copy_image(im); + int i; + for(i = 0; i < im.w * im.h * im.c; ++i){ + if(c.data[i] > .5) c.data[i] = 1; + else c.data[i] = 0; + } + return c; +} + +void saturate_image(image im, float sat) +{ + rgb_to_hsv(im); + scale_image_channel(im, 1, sat); + hsv_to_rgb(im); + constrain_image(im); +} + +void hue_image(image im, float hue) +{ + rgb_to_hsv(im); + int i; + for(i = 0; i < im.w*im.h; ++i){ + im.data[i] = im.data[i] + hue; + if (im.data[i] > 1) im.data[i] -= 1; + if (im.data[i] < 0) im.data[i] += 1; + } + hsv_to_rgb(im); + constrain_image(im); +} + +void exposure_image(image im, float sat) +{ + rgb_to_hsv(im); + scale_image_channel(im, 2, sat); + hsv_to_rgb(im); + constrain_image(im); +} + +void distort_image(image im, float hue, float sat, float val) +{ + rgb_to_hsv(im); + scale_image_channel(im, 1, sat); + scale_image_channel(im, 2, val); + int i; + for(i = 0; i < im.w*im.h; ++i){ + im.data[i] = im.data[i] + hue; + if (im.data[i] > 1) im.data[i] -= 1; + if (im.data[i] < 0) im.data[i] += 1; + } + hsv_to_rgb(im); + constrain_image(im); +} + +void random_distort_image(image im, float hue, float saturation, float exposure) +{ + float dhue = rand_uniform(-hue, hue); + float dsat = rand_scale(saturation); + float dexp = rand_scale(exposure); + distort_image(im, dhue, dsat, dexp); +} + +void saturate_exposure_image(image im, float sat, float exposure) +{ + rgb_to_hsv(im); + scale_image_channel(im, 1, sat); + scale_image_channel(im, 2, exposure); + hsv_to_rgb(im); + constrain_image(im); +} + +image resize_image(image im, int w, int h) +{ + image resized = make_image(w, h, im.c); + image part = make_image(w, im.h, im.c); + int r, c, k; + float w_scale = (float)(im.w - 1) / (w - 1); + float h_scale = (float)(im.h - 1) / (h - 1); + for(k = 0; k < im.c; ++k){ + for(r = 0; r < im.h; ++r){ + for(c = 0; c < w; ++c){ + float val = 0; + if(c == w-1 || im.w == 1){ + val = get_pixel(im, im.w-1, r, k); + } else { + float sx = c*w_scale; + int ix = (int) sx; + float dx = sx - ix; + val = (1 - dx) * get_pixel(im, ix, r, k) + dx * get_pixel(im, ix+1, r, k); + } + set_pixel(part, c, r, k, val); + } + } + } + for(k = 0; k < im.c; ++k){ + for(r = 0; r < h; ++r){ + float sy = r*h_scale; + int iy = (int) sy; + float dy = sy - iy; + for(c = 0; c < w; ++c){ + float val = (1-dy) * get_pixel(part, c, iy, k); + set_pixel(resized, c, r, k, val); + } + if(r == h-1 || im.h == 1) continue; + for(c = 0; c < w; ++c){ + float val = dy * get_pixel(part, c, iy+1, k); + add_pixel(resized, c, r, k, val); + } + } + } + + free_image(part); + return resized; +} + + +void test_resize(char *filename) +{ + image im = load_image(filename, 0,0, 3); + float mag = mag_array(im.data, im.w*im.h*im.c); + printf("L2 Norm: %f\n", mag); + image gray = grayscale_image(im); + + image c1 = copy_image(im); + image c2 = copy_image(im); + image c3 = copy_image(im); + image c4 = copy_image(im); + distort_image(c1, .1, 1.5, 1.5); + distort_image(c2, -.1, .66666, .66666); + distort_image(c3, .1, 1.5, .66666); + distort_image(c4, .1, .66666, 1.5); + + + show_image(im, "Original"); + show_image(gray, "Gray"); + show_image(c1, "C1"); + show_image(c2, "C2"); + show_image(c3, "C3"); + show_image(c4, "C4"); +#ifdef OPENCV + while(1){ + image aug = random_augment_image(im, 0, .75, 320, 448, 320, 320); + show_image(aug, "aug"); + free_image(aug); + + + float exposure = 1.15; + float saturation = 1.15; + float hue = .05; + + image c = copy_image(im); + + float dexp = rand_scale(exposure); + float dsat = rand_scale(saturation); + float dhue = rand_uniform(-hue, hue); + + distort_image(c, dhue, dsat, dexp); + show_image(c, "rand"); + printf("%f %f %f\n", dhue, dsat, dexp); + free_image(c); + cvWaitKey(0); + } +#endif +} + + +image load_image_stb(char *filename, int channels) +{ + int w, h, c; + unsigned char *data = stbi_load(filename, &w, &h, &c, channels); + if (!data) { + fprintf(stderr, "Cannot load image \"%s\"\nSTB Reason: %s\n", filename, stbi_failure_reason()); + exit(0); + } + if(channels) c = channels; + int i,j,k; + image im = make_image(w, h, c); + for(k = 0; k < c; ++k){ + for(j = 0; j < h; ++j){ + for(i = 0; i < w; ++i){ + int dst_index = i + w*j + w*h*k; + int src_index = k + c*i + c*w*j; + im.data[dst_index] = (float)data[src_index]/255.; + } + } + } + free(data); + return im; +} + +image load_image(char *filename, int w, int h, int c) +{ +#ifdef OPENCV + image out = load_image_cv(filename, c); +#else + image out = load_image_stb(filename, c); +#endif + + if((h && w) && (h != out.h || w != out.w)){ + image resized = resize_image(out, w, h); + free_image(out); + out = resized; + } + return out; +} + +image load_image_color(char *filename, int w, int h) +{ + return load_image(filename, w, h, 3); +} + +image get_image_layer(image m, int l) +{ + image out = make_image(m.w, m.h, 1); + int i; + for(i = 0; i < m.h*m.w; ++i){ + out.data[i] = m.data[i+l*m.h*m.w]; + } + return out; +} +void print_image(image m) +{ + int i, j, k; + for(i =0 ; i < m.c; ++i){ + for(j =0 ; j < m.h; ++j){ + for(k = 0; k < m.w; ++k){ + printf("%.2lf, ", m.data[i*m.h*m.w + j*m.w + k]); + if(k > 30) break; + } + printf("\n"); + if(j > 30) break; + } + printf("\n"); + } + printf("\n"); +} + +image collapse_images_vert(image *ims, int n) +{ + int color = 1; + int border = 1; + int h,w,c; + w = ims[0].w; + h = (ims[0].h + border) * n - border; + c = ims[0].c; + if(c != 3 || !color){ + w = (w+border)*c - border; + c = 1; + } + + image filters = make_image(w, h, c); + int i,j; + for(i = 0; i < n; ++i){ + int h_offset = i*(ims[0].h+border); + image copy = copy_image(ims[i]); + //normalize_image(copy); + if(c == 3 && color){ + embed_image(copy, filters, 0, h_offset); + } + else{ + for(j = 0; j < copy.c; ++j){ + int w_offset = j*(ims[0].w+border); + image layer = get_image_layer(copy, j); + embed_image(layer, filters, w_offset, h_offset); + free_image(layer); + } + } + free_image(copy); + } + return filters; +} + +image collapse_images_horz(image *ims, int n) +{ + int color = 1; + int border = 1; + int h,w,c; + int size = ims[0].h; + h = size; + w = (ims[0].w + border) * n - border; + c = ims[0].c; + if(c != 3 || !color){ + h = (h+border)*c - border; + c = 1; + } + + image filters = make_image(w, h, c); + int i,j; + for(i = 0; i < n; ++i){ + int w_offset = i*(size+border); + image copy = copy_image(ims[i]); + //normalize_image(copy); + if(c == 3 && color){ + embed_image(copy, filters, w_offset, 0); + } + else{ + for(j = 0; j < copy.c; ++j){ + int h_offset = j*(size+border); + image layer = get_image_layer(copy, j); + embed_image(layer, filters, w_offset, h_offset); + free_image(layer); + } + } + free_image(copy); + } + return filters; +} + +void show_image_normalized(image im, const char *name) +{ + image c = copy_image(im); + normalize_image(c); + show_image(c, name); + free_image(c); +} + +void show_images(image *ims, int n, char *window) +{ + image m = collapse_images_vert(ims, n); + /* + int w = 448; + int h = ((float)m.h/m.w) * 448; + if(h > 896){ + h = 896; + w = ((float)m.w/m.h) * 896; + } + image sized = resize_image(m, w, h); + */ + normalize_image(m); + save_image(m, window); + show_image(m, window); + free_image(m); +} + +void free_image(image m) +{ + if(m.data){ + free(m.data); + } +} diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/image.h b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/image.h new file mode 100644 index 00000000000..789cf18da01 --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/image.h @@ -0,0 +1,64 @@ +#ifndef IMAGE_H +#define IMAGE_H + +#include +#include +#include +#include +#include +#include "box.h" +#include "darknet.h" + +#ifndef __cplusplus +#ifdef OPENCV +int fill_image_from_stream(CvCapture *cap, image im); +image ipl_to_image(IplImage* src); +void ipl_into_image(IplImage* src, image im); +void flush_stream_buffer(CvCapture *cap, int n); +void show_image_cv(image p, const char *name, IplImage *disp); +#endif +#endif + +float get_color(int c, int x, int max); +void draw_box(image a, int x1, int y1, int x2, int y2, float r, float g, float b); +void draw_bbox(image a, box bbox, int w, float r, float g, float b); +void write_label(image a, int r, int c, image *characters, char *string, float *rgb); +image image_distance(image a, image b); +void scale_image(image m, float s); +image rotate_crop_image(image im, float rad, float s, int w, int h, float dx, float dy, float aspect); +image random_crop_image(image im, int w, int h); +image random_augment_image(image im, float angle, float aspect, int low, int high, int w, int h); +augment_args random_augment_args(image im, float angle, float aspect, int low, int high, int w, int h); +void letterbox_image_into(image im, int w, int h, image boxed); +image resize_max(image im, int max); +void translate_image(image m, float s); +void embed_image(image source, image dest, int dx, int dy); +void place_image(image im, int w, int h, int dx, int dy, image canvas); +void saturate_image(image im, float sat); +void exposure_image(image im, float sat); +void distort_image(image im, float hue, float sat, float val); +void saturate_exposure_image(image im, float sat, float exposure); +void rgb_to_hsv(image im); +void hsv_to_rgb(image im); +void yuv_to_rgb(image im); +void rgb_to_yuv(image im); + + +image collapse_image_layers(image source, int border); +image collapse_images_horz(image *ims, int n); +image collapse_images_vert(image *ims, int n); + +void show_image_normalized(image im, const char *name); +void show_images(image *ims, int n, char *window); +void show_image_layers(image p, char *name); +void show_image_collapsed(image p, char *name); + +void print_image(image m); + +image make_empty_image(int w, int h, int c); +void copy_image_into(image src, image dest); + +image get_image_layer(image m, int l); + +#endif + diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/l2norm_layer.c b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/l2norm_layer.c new file mode 100644 index 00000000000..d099479b4c0 --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/l2norm_layer.c @@ -0,0 +1,63 @@ +#include "l2norm_layer.h" +#include "activations.h" +#include "blas.h" +#include "cuda.h" + +#include +#include +#include +#include +#include + +layer make_l2norm_layer(int batch, int inputs) +{ + fprintf(stderr, "l2norm %4d\n", inputs); + layer l = {0}; + l.type = L2NORM; + l.batch = batch; + l.inputs = inputs; + l.outputs = inputs; + l.output = calloc(inputs*batch, sizeof(float)); + l.scales = calloc(inputs*batch, sizeof(float)); + l.delta = calloc(inputs*batch, sizeof(float)); + + l.forward = forward_l2norm_layer; + l.backward = backward_l2norm_layer; + #ifdef GPU + l.forward_gpu = forward_l2norm_layer_gpu; + l.backward_gpu = backward_l2norm_layer_gpu; + + l.output_gpu = cuda_make_array(l.output, inputs*batch); + l.scales_gpu = cuda_make_array(l.output, inputs*batch); + l.delta_gpu = cuda_make_array(l.delta, inputs*batch); + #endif + return l; +} + +void forward_l2norm_layer(const layer l, network net) +{ + copy_cpu(l.outputs*l.batch, net.input, 1, l.output, 1); + l2normalize_cpu(l.output, l.scales, l.batch, l.out_c, l.out_w*l.out_h); +} + +void backward_l2norm_layer(const layer l, network net) +{ + //axpy_cpu(l.inputs*l.batch, 1, l.scales, 1, l.delta, 1); + axpy_cpu(l.inputs*l.batch, 1, l.delta, 1, net.delta, 1); +} + +#ifdef GPU + +void forward_l2norm_layer_gpu(const layer l, network net) +{ + copy_gpu(l.outputs*l.batch, net.input_gpu, 1, l.output_gpu, 1); + l2normalize_gpu(l.output_gpu, l.scales_gpu, l.batch, l.out_c, l.out_w*l.out_h); +} + +void backward_l2norm_layer_gpu(const layer l, network net) +{ + axpy_gpu(l.batch*l.inputs, 1, l.scales_gpu, 1, l.delta_gpu, 1); + axpy_gpu(l.batch*l.inputs, 1, l.delta_gpu, 1, net.delta_gpu, 1); +} + +#endif diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/l2norm_layer.h b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/l2norm_layer.h new file mode 100644 index 00000000000..1ca6f710f01 --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/l2norm_layer.h @@ -0,0 +1,15 @@ +#ifndef L2NORM_LAYER_H +#define L2NORM_LAYER_H +#include "layer.h" +#include "network.h" + +layer make_l2norm_layer(int batch, int inputs); +void forward_l2norm_layer(const layer l, network net); +void backward_l2norm_layer(const layer l, network net); + +#ifdef GPU +void forward_l2norm_layer_gpu(const layer l, network net); +void backward_l2norm_layer_gpu(const layer l, network net); +#endif + +#endif diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/layer.c b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/layer.c new file mode 100644 index 00000000000..c27b4776421 --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/layer.c @@ -0,0 +1,97 @@ +#include "layer.h" +#include "cuda.h" + +#include + +void free_layer(layer l) +{ + if(l.type == DROPOUT){ + if(l.rand) free(l.rand); +#ifdef GPU + if(l.rand_gpu) cuda_free(l.rand_gpu); +#endif + return; + } + if(l.cweights) free(l.cweights); + if(l.indexes) free(l.indexes); + if(l.input_layers) free(l.input_layers); + if(l.input_sizes) free(l.input_sizes); + if(l.map) free(l.map); + if(l.rand) free(l.rand); + if(l.cost) free(l.cost); + if(l.state) free(l.state); + if(l.prev_state) free(l.prev_state); + if(l.forgot_state) free(l.forgot_state); + if(l.forgot_delta) free(l.forgot_delta); + if(l.state_delta) free(l.state_delta); + if(l.concat) free(l.concat); + if(l.concat_delta) free(l.concat_delta); + if(l.binary_weights) free(l.binary_weights); + if(l.biases) free(l.biases); + if(l.bias_updates) free(l.bias_updates); + if(l.scales) free(l.scales); + if(l.scale_updates) free(l.scale_updates); + if(l.weights) free(l.weights); + if(l.weight_updates) free(l.weight_updates); + if(l.delta) free(l.delta); + if(l.output) free(l.output); + if(l.squared) free(l.squared); + if(l.norms) free(l.norms); + if(l.spatial_mean) free(l.spatial_mean); + if(l.mean) free(l.mean); + if(l.variance) free(l.variance); + if(l.mean_delta) free(l.mean_delta); + if(l.variance_delta) free(l.variance_delta); + if(l.rolling_mean) free(l.rolling_mean); + if(l.rolling_variance) free(l.rolling_variance); + if(l.x) free(l.x); + if(l.x_norm) free(l.x_norm); + if(l.m) free(l.m); + if(l.v) free(l.v); + if(l.z_cpu) free(l.z_cpu); + if(l.r_cpu) free(l.r_cpu); + if(l.h_cpu) free(l.h_cpu); + if(l.binary_input) free(l.binary_input); + +#ifdef GPU + if(l.indexes_gpu) cuda_free((float *)l.indexes_gpu); + + if(l.z_gpu) cuda_free(l.z_gpu); + if(l.r_gpu) cuda_free(l.r_gpu); + if(l.h_gpu) cuda_free(l.h_gpu); + if(l.m_gpu) cuda_free(l.m_gpu); + if(l.v_gpu) cuda_free(l.v_gpu); + if(l.prev_state_gpu) cuda_free(l.prev_state_gpu); + if(l.forgot_state_gpu) cuda_free(l.forgot_state_gpu); + if(l.forgot_delta_gpu) cuda_free(l.forgot_delta_gpu); + if(l.state_gpu) cuda_free(l.state_gpu); + if(l.state_delta_gpu) cuda_free(l.state_delta_gpu); + if(l.gate_gpu) cuda_free(l.gate_gpu); + if(l.gate_delta_gpu) cuda_free(l.gate_delta_gpu); + if(l.save_gpu) cuda_free(l.save_gpu); + if(l.save_delta_gpu) cuda_free(l.save_delta_gpu); + if(l.concat_gpu) cuda_free(l.concat_gpu); + if(l.concat_delta_gpu) cuda_free(l.concat_delta_gpu); + if(l.binary_input_gpu) cuda_free(l.binary_input_gpu); + if(l.binary_weights_gpu) cuda_free(l.binary_weights_gpu); + if(l.mean_gpu) cuda_free(l.mean_gpu); + if(l.variance_gpu) cuda_free(l.variance_gpu); + if(l.rolling_mean_gpu) cuda_free(l.rolling_mean_gpu); + if(l.rolling_variance_gpu) cuda_free(l.rolling_variance_gpu); + if(l.variance_delta_gpu) cuda_free(l.variance_delta_gpu); + if(l.mean_delta_gpu) cuda_free(l.mean_delta_gpu); + if(l.x_gpu) cuda_free(l.x_gpu); + if(l.x_norm_gpu) cuda_free(l.x_norm_gpu); + if(l.weights_gpu) cuda_free(l.weights_gpu); + if(l.weight_updates_gpu) cuda_free(l.weight_updates_gpu); + if(l.biases_gpu) cuda_free(l.biases_gpu); + if(l.bias_updates_gpu) cuda_free(l.bias_updates_gpu); + if(l.scales_gpu) cuda_free(l.scales_gpu); + if(l.scale_updates_gpu) cuda_free(l.scale_updates_gpu); + if(l.output_gpu) cuda_free(l.output_gpu); + if(l.delta_gpu) cuda_free(l.delta_gpu); + if(l.rand_gpu) cuda_free(l.rand_gpu); + if(l.squared_gpu) cuda_free(l.squared_gpu); + if(l.norms_gpu) cuda_free(l.norms_gpu); +#endif +} diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/layer.h b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/layer.h new file mode 100644 index 00000000000..af6cd2ab505 --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/layer.h @@ -0,0 +1 @@ +#include "darknet.h" diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/list.c b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/list.c new file mode 100644 index 00000000000..0e4165d3780 --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/list.c @@ -0,0 +1,92 @@ +#include +#include +#include "list.h" + +list *make_list() +{ + list *l = malloc(sizeof(list)); + l->size = 0; + l->front = 0; + l->back = 0; + return l; +} + +/* +void transfer_node(list *s, list *d, node *n) +{ + node *prev, *next; + prev = n->prev; + next = n->next; + if(prev) prev->next = next; + if(next) next->prev = prev; + --s->size; + if(s->front == n) s->front = next; + if(s->back == n) s->back = prev; +} +*/ + +void *list_pop(list *l){ + if(!l->back) return 0; + node *b = l->back; + void *val = b->val; + l->back = b->prev; + if(l->back) l->back->next = 0; + free(b); + --l->size; + + return val; +} + +void list_insert(list *l, void *val) +{ + node *new = malloc(sizeof(node)); + new->val = val; + new->next = 0; + + if(!l->back){ + l->front = new; + new->prev = 0; + }else{ + l->back->next = new; + new->prev = l->back; + } + l->back = new; + ++l->size; +} + +void free_node(node *n) +{ + node *next; + while(n) { + next = n->next; + free(n); + n = next; + } +} + +void free_list(list *l) +{ + free_node(l->front); + free(l); +} + +void free_list_contents(list *l) +{ + node *n = l->front; + while(n){ + free(n->val); + n = n->next; + } +} + +void **list_to_array(list *l) +{ + void **a = calloc(l->size, sizeof(void*)); + int count = 0; + node *n = l->front; + while(n){ + a[count++] = n->val; + n = n->next; + } + return a; +} diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/list.h b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/list.h new file mode 100644 index 00000000000..6b445c717c2 --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/list.h @@ -0,0 +1,13 @@ +#ifndef LIST_H +#define LIST_H +#include "darknet.h" + +list *make_list(); +int list_find(list *l, void *val); + +void list_insert(list *, void *); + + +void free_list_contents(list *l); + +#endif diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/local_layer.c b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/local_layer.c new file mode 100644 index 00000000000..74f6910a8fd --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/local_layer.c @@ -0,0 +1,293 @@ +#include "local_layer.h" +#include "utils.h" +#include "im2col.h" +#include "col2im.h" +#include "blas.h" +#include "gemm.h" +#include +#include + +int local_out_height(local_layer l) +{ + int h = l.h; + if (!l.pad) h -= l.size; + else h -= 1; + return h/l.stride + 1; +} + +int local_out_width(local_layer l) +{ + int w = l.w; + if (!l.pad) w -= l.size; + else w -= 1; + return w/l.stride + 1; +} + +local_layer make_local_layer(int batch, int h, int w, int c, int n, int size, int stride, int pad, ACTIVATION activation) +{ + int i; + local_layer l = {0}; + l.type = LOCAL; + + l.h = h; + l.w = w; + l.c = c; + l.n = n; + l.batch = batch; + l.stride = stride; + l.size = size; + l.pad = pad; + + int out_h = local_out_height(l); + int out_w = local_out_width(l); + int locations = out_h*out_w; + l.out_h = out_h; + l.out_w = out_w; + l.out_c = n; + l.outputs = l.out_h * l.out_w * l.out_c; + l.inputs = l.w * l.h * l.c; + + l.weights = calloc(c*n*size*size*locations, sizeof(float)); + l.weight_updates = calloc(c*n*size*size*locations, sizeof(float)); + + l.biases = calloc(l.outputs, sizeof(float)); + l.bias_updates = calloc(l.outputs, sizeof(float)); + + // float scale = 1./sqrt(size*size*c); + float scale = sqrt(2./(size*size*c)); + for(i = 0; i < c*n*size*size; ++i) l.weights[i] = scale*rand_uniform(-1,1); + + l.output = calloc(l.batch*out_h * out_w * n, sizeof(float)); + l.delta = calloc(l.batch*out_h * out_w * n, sizeof(float)); + + l.workspace_size = out_h*out_w*size*size*c; + + l.forward = forward_local_layer; + l.backward = backward_local_layer; + l.update = update_local_layer; + +#ifdef GPU + l.forward_gpu = forward_local_layer_gpu; + l.backward_gpu = backward_local_layer_gpu; + l.update_gpu = update_local_layer_gpu; + + l.weights_gpu = cuda_make_array(l.weights, c*n*size*size*locations); + l.weight_updates_gpu = cuda_make_array(l.weight_updates, c*n*size*size*locations); + + l.biases_gpu = cuda_make_array(l.biases, l.outputs); + l.bias_updates_gpu = cuda_make_array(l.bias_updates, l.outputs); + + l.delta_gpu = cuda_make_array(l.delta, l.batch*out_h*out_w*n); + l.output_gpu = cuda_make_array(l.output, l.batch*out_h*out_w*n); + +#endif + l.activation = activation; + + fprintf(stderr, "Local Layer: %d x %d x %d image, %d filters -> %d x %d x %d image\n", h,w,c,n, out_h, out_w, n); + + return l; +} + +void forward_local_layer(const local_layer l, network net) +{ + int out_h = local_out_height(l); + int out_w = local_out_width(l); + int i, j; + int locations = out_h * out_w; + + for(i = 0; i < l.batch; ++i){ + copy_cpu(l.outputs, l.biases, 1, l.output + i*l.outputs, 1); + } + + for(i = 0; i < l.batch; ++i){ + float *input = net.input + i*l.w*l.h*l.c; + im2col_cpu(input, l.c, l.h, l.w, + l.size, l.stride, l.pad, net.workspace); + float *output = l.output + i*l.outputs; + for(j = 0; j < locations; ++j){ + float *a = l.weights + j*l.size*l.size*l.c*l.n; + float *b = net.workspace + j; + float *c = output + j; + + int m = l.n; + int n = 1; + int k = l.size*l.size*l.c; + + gemm(0,0,m,n,k,1,a,k,b,locations,1,c,locations); + } + } + activate_array(l.output, l.outputs*l.batch, l.activation); +} + +void backward_local_layer(local_layer l, network net) +{ + int i, j; + int locations = l.out_w*l.out_h; + + gradient_array(l.output, l.outputs*l.batch, l.activation, l.delta); + + for(i = 0; i < l.batch; ++i){ + axpy_cpu(l.outputs, 1, l.delta + i*l.outputs, 1, l.bias_updates, 1); + } + + for(i = 0; i < l.batch; ++i){ + float *input = net.input + i*l.w*l.h*l.c; + im2col_cpu(input, l.c, l.h, l.w, + l.size, l.stride, l.pad, net.workspace); + + for(j = 0; j < locations; ++j){ + float *a = l.delta + i*l.outputs + j; + float *b = net.workspace + j; + float *c = l.weight_updates + j*l.size*l.size*l.c*l.n; + int m = l.n; + int n = l.size*l.size*l.c; + int k = 1; + + gemm(0,1,m,n,k,1,a,locations,b,locations,1,c,n); + } + + if(net.delta){ + for(j = 0; j < locations; ++j){ + float *a = l.weights + j*l.size*l.size*l.c*l.n; + float *b = l.delta + i*l.outputs + j; + float *c = net.workspace + j; + + int m = l.size*l.size*l.c; + int n = 1; + int k = l.n; + + gemm(1,0,m,n,k,1,a,m,b,locations,0,c,locations); + } + + col2im_cpu(net.workspace, l.c, l.h, l.w, l.size, l.stride, l.pad, net.delta+i*l.c*l.h*l.w); + } + } +} + +void update_local_layer(local_layer l, update_args a) +{ + float learning_rate = a.learning_rate*l.learning_rate_scale; + float momentum = a.momentum; + float decay = a.decay; + int batch = a.batch; + + int locations = l.out_w*l.out_h; + int size = l.size*l.size*l.c*l.n*locations; + axpy_cpu(l.outputs, learning_rate/batch, l.bias_updates, 1, l.biases, 1); + scal_cpu(l.outputs, momentum, l.bias_updates, 1); + + axpy_cpu(size, -decay*batch, l.weights, 1, l.weight_updates, 1); + axpy_cpu(size, learning_rate/batch, l.weight_updates, 1, l.weights, 1); + scal_cpu(size, momentum, l.weight_updates, 1); +} + +#ifdef GPU + +void forward_local_layer_gpu(const local_layer l, network net) +{ + int out_h = local_out_height(l); + int out_w = local_out_width(l); + int i, j; + int locations = out_h * out_w; + + for(i = 0; i < l.batch; ++i){ + copy_gpu(l.outputs, l.biases_gpu, 1, l.output_gpu + i*l.outputs, 1); + } + + for(i = 0; i < l.batch; ++i){ + float *input = net.input_gpu + i*l.w*l.h*l.c; + im2col_gpu(input, l.c, l.h, l.w, + l.size, l.stride, l.pad, net.workspace); + float *output = l.output_gpu + i*l.outputs; + for(j = 0; j < locations; ++j){ + float *a = l.weights_gpu + j*l.size*l.size*l.c*l.n; + float *b = net.workspace + j; + float *c = output + j; + + int m = l.n; + int n = 1; + int k = l.size*l.size*l.c; + + gemm_gpu(0,0,m,n,k,1,a,k,b,locations,1,c,locations); + } + } + activate_array_gpu(l.output_gpu, l.outputs*l.batch, l.activation); +} + +void backward_local_layer_gpu(local_layer l, network net) +{ + int i, j; + int locations = l.out_w*l.out_h; + + gradient_array_gpu(l.output_gpu, l.outputs*l.batch, l.activation, l.delta_gpu); + for(i = 0; i < l.batch; ++i){ + axpy_gpu(l.outputs, 1, l.delta_gpu + i*l.outputs, 1, l.bias_updates_gpu, 1); + } + + for(i = 0; i < l.batch; ++i){ + float *input = net.input_gpu + i*l.w*l.h*l.c; + im2col_gpu(input, l.c, l.h, l.w, + l.size, l.stride, l.pad, net.workspace); + + for(j = 0; j < locations; ++j){ + float *a = l.delta_gpu + i*l.outputs + j; + float *b = net.workspace + j; + float *c = l.weight_updates_gpu + j*l.size*l.size*l.c*l.n; + int m = l.n; + int n = l.size*l.size*l.c; + int k = 1; + + gemm_gpu(0,1,m,n,k,1,a,locations,b,locations,1,c,n); + } + + if(net.delta_gpu){ + for(j = 0; j < locations; ++j){ + float *a = l.weights_gpu + j*l.size*l.size*l.c*l.n; + float *b = l.delta_gpu + i*l.outputs + j; + float *c = net.workspace + j; + + int m = l.size*l.size*l.c; + int n = 1; + int k = l.n; + + gemm_gpu(1,0,m,n,k,1,a,m,b,locations,0,c,locations); + } + + col2im_gpu(net.workspace, l.c, l.h, l.w, l.size, l.stride, l.pad, net.delta_gpu+i*l.c*l.h*l.w); + } + } +} + +void update_local_layer_gpu(local_layer l, update_args a) +{ + float learning_rate = a.learning_rate*l.learning_rate_scale; + float momentum = a.momentum; + float decay = a.decay; + int batch = a.batch; + + int locations = l.out_w*l.out_h; + int size = l.size*l.size*l.c*l.n*locations; + axpy_gpu(l.outputs, learning_rate/batch, l.bias_updates_gpu, 1, l.biases_gpu, 1); + scal_gpu(l.outputs, momentum, l.bias_updates_gpu, 1); + + axpy_gpu(size, -decay*batch, l.weights_gpu, 1, l.weight_updates_gpu, 1); + axpy_gpu(size, learning_rate/batch, l.weight_updates_gpu, 1, l.weights_gpu, 1); + scal_gpu(size, momentum, l.weight_updates_gpu, 1); +} + +void pull_local_layer(local_layer l) +{ + int locations = l.out_w*l.out_h; + int size = l.size*l.size*l.c*l.n*locations; + cuda_pull_array(l.weights_gpu, l.weights, size); + cuda_pull_array(l.biases_gpu, l.biases, l.outputs); +} + +void push_local_layer(local_layer l) +{ + int locations = l.out_w*l.out_h; + int size = l.size*l.size*l.c*l.n*locations; + cuda_push_array(l.weights_gpu, l.weights, size); + cuda_push_array(l.biases_gpu, l.biases, l.outputs); +} +#endif diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/local_layer.h b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/local_layer.h new file mode 100644 index 00000000000..776e572f420 --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/local_layer.h @@ -0,0 +1,31 @@ +#ifndef LOCAL_LAYER_H +#define LOCAL_LAYER_H + +#include "cuda.h" +#include "image.h" +#include "activations.h" +#include "layer.h" +#include "network.h" + +typedef layer local_layer; + +#ifdef GPU +void forward_local_layer_gpu(local_layer layer, network net); +void backward_local_layer_gpu(local_layer layer, network net); +void update_local_layer_gpu(local_layer layer, update_args a); + +void push_local_layer(local_layer layer); +void pull_local_layer(local_layer layer); +#endif + +local_layer make_local_layer(int batch, int h, int w, int c, int n, int size, int stride, int pad, ACTIVATION activation); + +void forward_local_layer(const local_layer layer, network net); +void backward_local_layer(local_layer layer, network net); +void update_local_layer(local_layer layer, update_args a); + +void bias_output(float *output, float *biases, int batch, int n, int size); +void backward_bias(float *bias_updates, float *delta, int batch, int n, int size); + +#endif + diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/logistic_layer.c b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/logistic_layer.c new file mode 100644 index 00000000000..b2b3d6b1ccf --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/logistic_layer.c @@ -0,0 +1,71 @@ +#include "logistic_layer.h" +#include "activations.h" +#include "blas.h" +#include "cuda.h" + +#include +#include +#include +#include +#include + +layer make_logistic_layer(int batch, int inputs) +{ + fprintf(stderr, "logistic x entropy %4d\n", inputs); + layer l = {0}; + l.type = LOGXENT; + l.batch = batch; + l.inputs = inputs; + l.outputs = inputs; + l.loss = calloc(inputs*batch, sizeof(float)); + l.output = calloc(inputs*batch, sizeof(float)); + l.delta = calloc(inputs*batch, sizeof(float)); + l.cost = calloc(1, sizeof(float)); + + l.forward = forward_logistic_layer; + l.backward = backward_logistic_layer; + #ifdef GPU + l.forward_gpu = forward_logistic_layer_gpu; + l.backward_gpu = backward_logistic_layer_gpu; + + l.output_gpu = cuda_make_array(l.output, inputs*batch); + l.loss_gpu = cuda_make_array(l.loss, inputs*batch); + l.delta_gpu = cuda_make_array(l.delta, inputs*batch); + #endif + return l; +} + +void forward_logistic_layer(const layer l, network net) +{ + copy_cpu(l.outputs*l.batch, net.input, 1, l.output, 1); + activate_array(l.output, l.outputs*l.batch, LOGISTIC); + if(net.truth){ + logistic_x_ent_cpu(l.batch*l.inputs, l.output, net.truth, l.delta, l.loss); + l.cost[0] = sum_array(l.loss, l.batch*l.inputs); + } +} + +void backward_logistic_layer(const layer l, network net) +{ + axpy_cpu(l.inputs*l.batch, 1, l.delta, 1, net.delta, 1); +} + +#ifdef GPU + +void forward_logistic_layer_gpu(const layer l, network net) +{ + copy_gpu(l.outputs*l.batch, net.input_gpu, 1, l.output_gpu, 1); + activate_array_gpu(l.output_gpu, l.outputs*l.batch, LOGISTIC); + if(net.truth){ + logistic_x_ent_gpu(l.batch*l.inputs, l.output_gpu, net.truth_gpu, l.delta_gpu, l.loss_gpu); + cuda_pull_array(l.loss_gpu, l.loss, l.batch*l.inputs); + l.cost[0] = sum_array(l.loss, l.batch*l.inputs); + } +} + +void backward_logistic_layer_gpu(const layer l, network net) +{ + axpy_gpu(l.batch*l.inputs, 1, l.delta_gpu, 1, net.delta_gpu, 1); +} + +#endif diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/logistic_layer.h b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/logistic_layer.h new file mode 100644 index 00000000000..9c25bee3c2a --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/logistic_layer.h @@ -0,0 +1,15 @@ +#ifndef LOGISTIC_LAYER_H +#define LOGISTIC_LAYER_H +#include "layer.h" +#include "network.h" + +layer make_logistic_layer(int batch, int inputs); +void forward_logistic_layer(const layer l, network net); +void backward_logistic_layer(const layer l, network net); + +#ifdef GPU +void forward_logistic_layer_gpu(const layer l, network net); +void backward_logistic_layer_gpu(const layer l, network net); +#endif + +#endif diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/lstm_layer.c b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/lstm_layer.c new file mode 100644 index 00000000000..fb07de20228 --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/lstm_layer.c @@ -0,0 +1,626 @@ +#include "lstm_layer.h" +#include "connected_layer.h" +#include "utils.h" +#include "cuda.h" +#include "blas.h" +#include "gemm.h" + +#include +#include +#include +#include + +static void increment_layer(layer *l, int steps) +{ + int num = l->outputs*l->batch*steps; + l->output += num; + l->delta += num; + l->x += num; + l->x_norm += num; + +#ifdef GPU + l->output_gpu += num; + l->delta_gpu += num; + l->x_gpu += num; + l->x_norm_gpu += num; +#endif +} + +layer make_lstm_layer(int batch, int inputs, int outputs, int steps, int batch_normalize, int adam) +{ + fprintf(stderr, "LSTM Layer: %d inputs, %d outputs\n", inputs, outputs); + batch = batch / steps; + layer l = { 0 }; + l.batch = batch; + l.type = LSTM; + l.steps = steps; + l.inputs = inputs; + + l.uf = malloc(sizeof(layer)); + fprintf(stderr, "\t\t"); + *(l.uf) = make_connected_layer(batch*steps, inputs, outputs, LINEAR, batch_normalize, adam); + l.uf->batch = batch; + + l.ui = malloc(sizeof(layer)); + fprintf(stderr, "\t\t"); + *(l.ui) = make_connected_layer(batch*steps, inputs, outputs, LINEAR, batch_normalize, adam); + l.ui->batch = batch; + + l.ug = malloc(sizeof(layer)); + fprintf(stderr, "\t\t"); + *(l.ug) = make_connected_layer(batch*steps, inputs, outputs, LINEAR, batch_normalize, adam); + l.ug->batch = batch; + + l.uo = malloc(sizeof(layer)); + fprintf(stderr, "\t\t"); + *(l.uo) = make_connected_layer(batch*steps, inputs, outputs, LINEAR, batch_normalize, adam); + l.uo->batch = batch; + + l.wf = malloc(sizeof(layer)); + fprintf(stderr, "\t\t"); + *(l.wf) = make_connected_layer(batch*steps, outputs, outputs, LINEAR, batch_normalize, adam); + l.wf->batch = batch; + + l.wi = malloc(sizeof(layer)); + fprintf(stderr, "\t\t"); + *(l.wi) = make_connected_layer(batch*steps, outputs, outputs, LINEAR, batch_normalize, adam); + l.wi->batch = batch; + + l.wg = malloc(sizeof(layer)); + fprintf(stderr, "\t\t"); + *(l.wg) = make_connected_layer(batch*steps, outputs, outputs, LINEAR, batch_normalize, adam); + l.wg->batch = batch; + + l.wo = malloc(sizeof(layer)); + fprintf(stderr, "\t\t"); + *(l.wo) = make_connected_layer(batch*steps, outputs, outputs, LINEAR, batch_normalize, adam); + l.wo->batch = batch; + + l.batch_normalize = batch_normalize; + l.outputs = outputs; + + l.output = calloc(outputs*batch*steps, sizeof(float)); + l.state = calloc(outputs*batch, sizeof(float)); + + l.forward = forward_lstm_layer; + l.update = update_lstm_layer; + + l.prev_state_cpu = calloc(batch*outputs, sizeof(float)); + l.prev_cell_cpu = calloc(batch*outputs, sizeof(float)); + l.cell_cpu = calloc(batch*outputs*steps, sizeof(float)); + + l.f_cpu = calloc(batch*outputs, sizeof(float)); + l.i_cpu = calloc(batch*outputs, sizeof(float)); + l.g_cpu = calloc(batch*outputs, sizeof(float)); + l.o_cpu = calloc(batch*outputs, sizeof(float)); + l.c_cpu = calloc(batch*outputs, sizeof(float)); + l.h_cpu = calloc(batch*outputs, sizeof(float)); + l.temp_cpu = calloc(batch*outputs, sizeof(float)); + l.temp2_cpu = calloc(batch*outputs, sizeof(float)); + l.temp3_cpu = calloc(batch*outputs, sizeof(float)); + l.dc_cpu = calloc(batch*outputs, sizeof(float)); + l.dh_cpu = calloc(batch*outputs, sizeof(float)); + +#ifdef GPU + l.forward_gpu = forward_lstm_layer_gpu; + l.backward_gpu = backward_lstm_layer_gpu; + l.update_gpu = update_lstm_layer_gpu; + + l.output_gpu = cuda_make_array(0, batch*outputs*steps); + l.delta_gpu = cuda_make_array(0, batch*l.outputs*steps); + + l.prev_state_gpu = cuda_make_array(0, batch*outputs); + l.prev_cell_gpu = cuda_make_array(0, batch*outputs); + l.cell_gpu = cuda_make_array(0, batch*outputs*steps); + + l.f_gpu = cuda_make_array(0, batch*outputs); + l.i_gpu = cuda_make_array(0, batch*outputs); + l.g_gpu = cuda_make_array(0, batch*outputs); + l.o_gpu = cuda_make_array(0, batch*outputs); + l.c_gpu = cuda_make_array(0, batch*outputs); + l.h_gpu = cuda_make_array(0, batch*outputs); + l.temp_gpu = cuda_make_array(0, batch*outputs); + l.temp2_gpu = cuda_make_array(0, batch*outputs); + l.temp3_gpu = cuda_make_array(0, batch*outputs); + l.dc_gpu = cuda_make_array(0, batch*outputs); + l.dh_gpu = cuda_make_array(0, batch*outputs); +#ifdef CUDNN + cudnnSetTensor4dDescriptor(l.wf->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.wf->out_c, l.wf->out_h, l.wf->out_w); + cudnnSetTensor4dDescriptor(l.wi->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.wi->out_c, l.wi->out_h, l.wi->out_w); + cudnnSetTensor4dDescriptor(l.wg->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.wg->out_c, l.wg->out_h, l.wg->out_w); + cudnnSetTensor4dDescriptor(l.wo->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.wo->out_c, l.wo->out_h, l.wo->out_w); + + cudnnSetTensor4dDescriptor(l.uf->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.uf->out_c, l.uf->out_h, l.uf->out_w); + cudnnSetTensor4dDescriptor(l.ui->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.ui->out_c, l.ui->out_h, l.ui->out_w); + cudnnSetTensor4dDescriptor(l.ug->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.ug->out_c, l.ug->out_h, l.ug->out_w); + cudnnSetTensor4dDescriptor(l.uo->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.uo->out_c, l.uo->out_h, l.uo->out_w); +#endif + +#endif + + return l; +} + +void update_lstm_layer(layer l, update_args a) +{ + update_connected_layer(*(l.wf), a); + update_connected_layer(*(l.wi), a); + update_connected_layer(*(l.wg), a); + update_connected_layer(*(l.wo), a); + update_connected_layer(*(l.uf), a); + update_connected_layer(*(l.ui), a); + update_connected_layer(*(l.ug), a); + update_connected_layer(*(l.uo), a); +} + +void forward_lstm_layer(layer l, network state) +{ + network s = { 0 }; + s.train = state.train; + int i; + layer wf = *(l.wf); + layer wi = *(l.wi); + layer wg = *(l.wg); + layer wo = *(l.wo); + + layer uf = *(l.uf); + layer ui = *(l.ui); + layer ug = *(l.ug); + layer uo = *(l.uo); + + fill_cpu(l.outputs * l.batch * l.steps, 0, wf.delta, 1); + fill_cpu(l.outputs * l.batch * l.steps, 0, wi.delta, 1); + fill_cpu(l.outputs * l.batch * l.steps, 0, wg.delta, 1); + fill_cpu(l.outputs * l.batch * l.steps, 0, wo.delta, 1); + + fill_cpu(l.outputs * l.batch * l.steps, 0, uf.delta, 1); + fill_cpu(l.outputs * l.batch * l.steps, 0, ui.delta, 1); + fill_cpu(l.outputs * l.batch * l.steps, 0, ug.delta, 1); + fill_cpu(l.outputs * l.batch * l.steps, 0, uo.delta, 1); + if (state.train) { + fill_cpu(l.outputs * l.batch * l.steps, 0, l.delta, 1); + } + + for (i = 0; i < l.steps; ++i) { + s.input = l.h_cpu; + forward_connected_layer(wf, s); + forward_connected_layer(wi, s); + forward_connected_layer(wg, s); + forward_connected_layer(wo, s); + + s.input = state.input; + forward_connected_layer(uf, s); + forward_connected_layer(ui, s); + forward_connected_layer(ug, s); + forward_connected_layer(uo, s); + + copy_cpu(l.outputs*l.batch, wf.output, 1, l.f_cpu, 1); + axpy_cpu(l.outputs*l.batch, 1, uf.output, 1, l.f_cpu, 1); + + copy_cpu(l.outputs*l.batch, wi.output, 1, l.i_cpu, 1); + axpy_cpu(l.outputs*l.batch, 1, ui.output, 1, l.i_cpu, 1); + + copy_cpu(l.outputs*l.batch, wg.output, 1, l.g_cpu, 1); + axpy_cpu(l.outputs*l.batch, 1, ug.output, 1, l.g_cpu, 1); + + copy_cpu(l.outputs*l.batch, wo.output, 1, l.o_cpu, 1); + axpy_cpu(l.outputs*l.batch, 1, uo.output, 1, l.o_cpu, 1); + + activate_array(l.f_cpu, l.outputs*l.batch, LOGISTIC); + activate_array(l.i_cpu, l.outputs*l.batch, LOGISTIC); + activate_array(l.g_cpu, l.outputs*l.batch, TANH); + activate_array(l.o_cpu, l.outputs*l.batch, LOGISTIC); + + copy_cpu(l.outputs*l.batch, l.i_cpu, 1, l.temp_cpu, 1); + mul_cpu(l.outputs*l.batch, l.g_cpu, 1, l.temp_cpu, 1); + mul_cpu(l.outputs*l.batch, l.f_cpu, 1, l.c_cpu, 1); + axpy_cpu(l.outputs*l.batch, 1, l.temp_cpu, 1, l.c_cpu, 1); + + copy_cpu(l.outputs*l.batch, l.c_cpu, 1, l.h_cpu, 1); + activate_array(l.h_cpu, l.outputs*l.batch, TANH); + mul_cpu(l.outputs*l.batch, l.o_cpu, 1, l.h_cpu, 1); + + copy_cpu(l.outputs*l.batch, l.c_cpu, 1, l.cell_cpu, 1); + copy_cpu(l.outputs*l.batch, l.h_cpu, 1, l.output, 1); + + state.input += l.inputs*l.batch; + l.output += l.outputs*l.batch; + l.cell_cpu += l.outputs*l.batch; + + increment_layer(&wf, 1); + increment_layer(&wi, 1); + increment_layer(&wg, 1); + increment_layer(&wo, 1); + + increment_layer(&uf, 1); + increment_layer(&ui, 1); + increment_layer(&ug, 1); + increment_layer(&uo, 1); + } +} + +void backward_lstm_layer(layer l, network state) +{ + network s = { 0 }; + s.train = state.train; + int i; + layer wf = *(l.wf); + layer wi = *(l.wi); + layer wg = *(l.wg); + layer wo = *(l.wo); + + layer uf = *(l.uf); + layer ui = *(l.ui); + layer ug = *(l.ug); + layer uo = *(l.uo); + + increment_layer(&wf, l.steps - 1); + increment_layer(&wi, l.steps - 1); + increment_layer(&wg, l.steps - 1); + increment_layer(&wo, l.steps - 1); + + increment_layer(&uf, l.steps - 1); + increment_layer(&ui, l.steps - 1); + increment_layer(&ug, l.steps - 1); + increment_layer(&uo, l.steps - 1); + + state.input += l.inputs*l.batch*(l.steps - 1); + if (state.delta) state.delta += l.inputs*l.batch*(l.steps - 1); + + l.output += l.outputs*l.batch*(l.steps - 1); + l.cell_cpu += l.outputs*l.batch*(l.steps - 1); + l.delta += l.outputs*l.batch*(l.steps - 1); + + for (i = l.steps - 1; i >= 0; --i) { + if (i != 0) copy_cpu(l.outputs*l.batch, l.cell_cpu - l.outputs*l.batch, 1, l.prev_cell_cpu, 1); + copy_cpu(l.outputs*l.batch, l.cell_cpu, 1, l.c_cpu, 1); + if (i != 0) copy_cpu(l.outputs*l.batch, l.output - l.outputs*l.batch, 1, l.prev_state_cpu, 1); + copy_cpu(l.outputs*l.batch, l.output, 1, l.h_cpu, 1); + + l.dh_cpu = (i == 0) ? 0 : l.delta - l.outputs*l.batch; + + copy_cpu(l.outputs*l.batch, wf.output, 1, l.f_cpu, 1); + axpy_cpu(l.outputs*l.batch, 1, uf.output, 1, l.f_cpu, 1); + + copy_cpu(l.outputs*l.batch, wi.output, 1, l.i_cpu, 1); + axpy_cpu(l.outputs*l.batch, 1, ui.output, 1, l.i_cpu, 1); + + copy_cpu(l.outputs*l.batch, wg.output, 1, l.g_cpu, 1); + axpy_cpu(l.outputs*l.batch, 1, ug.output, 1, l.g_cpu, 1); + + copy_cpu(l.outputs*l.batch, wo.output, 1, l.o_cpu, 1); + axpy_cpu(l.outputs*l.batch, 1, uo.output, 1, l.o_cpu, 1); + + activate_array(l.f_cpu, l.outputs*l.batch, LOGISTIC); + activate_array(l.i_cpu, l.outputs*l.batch, LOGISTIC); + activate_array(l.g_cpu, l.outputs*l.batch, TANH); + activate_array(l.o_cpu, l.outputs*l.batch, LOGISTIC); + + copy_cpu(l.outputs*l.batch, l.delta, 1, l.temp3_cpu, 1); + + copy_cpu(l.outputs*l.batch, l.c_cpu, 1, l.temp_cpu, 1); + activate_array(l.temp_cpu, l.outputs*l.batch, TANH); + + copy_cpu(l.outputs*l.batch, l.temp3_cpu, 1, l.temp2_cpu, 1); + mul_cpu(l.outputs*l.batch, l.o_cpu, 1, l.temp2_cpu, 1); + + gradient_array(l.temp_cpu, l.outputs*l.batch, TANH, l.temp2_cpu); + axpy_cpu(l.outputs*l.batch, 1, l.dc_cpu, 1, l.temp2_cpu, 1); + + copy_cpu(l.outputs*l.batch, l.c_cpu, 1, l.temp_cpu, 1); + activate_array(l.temp_cpu, l.outputs*l.batch, TANH); + mul_cpu(l.outputs*l.batch, l.temp3_cpu, 1, l.temp_cpu, 1); + gradient_array(l.o_cpu, l.outputs*l.batch, LOGISTIC, l.temp_cpu); + copy_cpu(l.outputs*l.batch, l.temp_cpu, 1, wo.delta, 1); + s.input = l.prev_state_cpu; + s.delta = l.dh_cpu; + backward_connected_layer(wo, s); + + copy_cpu(l.outputs*l.batch, l.temp_cpu, 1, uo.delta, 1); + s.input = state.input; + s.delta = state.delta; + backward_connected_layer(uo, s); + + copy_cpu(l.outputs*l.batch, l.temp2_cpu, 1, l.temp_cpu, 1); + mul_cpu(l.outputs*l.batch, l.i_cpu, 1, l.temp_cpu, 1); + gradient_array(l.g_cpu, l.outputs*l.batch, TANH, l.temp_cpu); + copy_cpu(l.outputs*l.batch, l.temp_cpu, 1, wg.delta, 1); + s.input = l.prev_state_cpu; + s.delta = l.dh_cpu; + backward_connected_layer(wg, s); + + copy_cpu(l.outputs*l.batch, l.temp_cpu, 1, ug.delta, 1); + s.input = state.input; + s.delta = state.delta; + backward_connected_layer(ug, s); + + copy_cpu(l.outputs*l.batch, l.temp2_cpu, 1, l.temp_cpu, 1); + mul_cpu(l.outputs*l.batch, l.g_cpu, 1, l.temp_cpu, 1); + gradient_array(l.i_cpu, l.outputs*l.batch, LOGISTIC, l.temp_cpu); + copy_cpu(l.outputs*l.batch, l.temp_cpu, 1, wi.delta, 1); + s.input = l.prev_state_cpu; + s.delta = l.dh_cpu; + backward_connected_layer(wi, s); + + copy_cpu(l.outputs*l.batch, l.temp_cpu, 1, ui.delta, 1); + s.input = state.input; + s.delta = state.delta; + backward_connected_layer(ui, s); + + copy_cpu(l.outputs*l.batch, l.temp2_cpu, 1, l.temp_cpu, 1); + mul_cpu(l.outputs*l.batch, l.prev_cell_cpu, 1, l.temp_cpu, 1); + gradient_array(l.f_cpu, l.outputs*l.batch, LOGISTIC, l.temp_cpu); + copy_cpu(l.outputs*l.batch, l.temp_cpu, 1, wf.delta, 1); + s.input = l.prev_state_cpu; + s.delta = l.dh_cpu; + backward_connected_layer(wf, s); + + copy_cpu(l.outputs*l.batch, l.temp_cpu, 1, uf.delta, 1); + s.input = state.input; + s.delta = state.delta; + backward_connected_layer(uf, s); + + copy_cpu(l.outputs*l.batch, l.temp2_cpu, 1, l.temp_cpu, 1); + mul_cpu(l.outputs*l.batch, l.f_cpu, 1, l.temp_cpu, 1); + copy_cpu(l.outputs*l.batch, l.temp_cpu, 1, l.dc_cpu, 1); + + state.input -= l.inputs*l.batch; + if (state.delta) state.delta -= l.inputs*l.batch; + l.output -= l.outputs*l.batch; + l.cell_cpu -= l.outputs*l.batch; + l.delta -= l.outputs*l.batch; + + increment_layer(&wf, -1); + increment_layer(&wi, -1); + increment_layer(&wg, -1); + increment_layer(&wo, -1); + + increment_layer(&uf, -1); + increment_layer(&ui, -1); + increment_layer(&ug, -1); + increment_layer(&uo, -1); + } +} + +#ifdef GPU +void update_lstm_layer_gpu(layer l, update_args a) +{ + update_connected_layer_gpu(*(l.wf), a); + update_connected_layer_gpu(*(l.wi), a); + update_connected_layer_gpu(*(l.wg), a); + update_connected_layer_gpu(*(l.wo), a); + update_connected_layer_gpu(*(l.uf), a); + update_connected_layer_gpu(*(l.ui), a); + update_connected_layer_gpu(*(l.ug), a); + update_connected_layer_gpu(*(l.uo), a); +} + +void forward_lstm_layer_gpu(layer l, network state) +{ + network s = { 0 }; + s.train = state.train; + int i; + layer wf = *(l.wf); + layer wi = *(l.wi); + layer wg = *(l.wg); + layer wo = *(l.wo); + + layer uf = *(l.uf); + layer ui = *(l.ui); + layer ug = *(l.ug); + layer uo = *(l.uo); + + fill_gpu(l.outputs * l.batch * l.steps, 0, wf.delta_gpu, 1); + fill_gpu(l.outputs * l.batch * l.steps, 0, wi.delta_gpu, 1); + fill_gpu(l.outputs * l.batch * l.steps, 0, wg.delta_gpu, 1); + fill_gpu(l.outputs * l.batch * l.steps, 0, wo.delta_gpu, 1); + + fill_gpu(l.outputs * l.batch * l.steps, 0, uf.delta_gpu, 1); + fill_gpu(l.outputs * l.batch * l.steps, 0, ui.delta_gpu, 1); + fill_gpu(l.outputs * l.batch * l.steps, 0, ug.delta_gpu, 1); + fill_gpu(l.outputs * l.batch * l.steps, 0, uo.delta_gpu, 1); + if (state.train) { + fill_gpu(l.outputs * l.batch * l.steps, 0, l.delta_gpu, 1); + } + + for (i = 0; i < l.steps; ++i) { + s.input_gpu = l.h_gpu; + forward_connected_layer_gpu(wf, s); + forward_connected_layer_gpu(wi, s); + forward_connected_layer_gpu(wg, s); + forward_connected_layer_gpu(wo, s); + + s.input_gpu = state.input_gpu; + forward_connected_layer_gpu(uf, s); + forward_connected_layer_gpu(ui, s); + forward_connected_layer_gpu(ug, s); + forward_connected_layer_gpu(uo, s); + + copy_gpu(l.outputs*l.batch, wf.output_gpu, 1, l.f_gpu, 1); + axpy_gpu(l.outputs*l.batch, 1, uf.output_gpu, 1, l.f_gpu, 1); + + copy_gpu(l.outputs*l.batch, wi.output_gpu, 1, l.i_gpu, 1); + axpy_gpu(l.outputs*l.batch, 1, ui.output_gpu, 1, l.i_gpu, 1); + + copy_gpu(l.outputs*l.batch, wg.output_gpu, 1, l.g_gpu, 1); + axpy_gpu(l.outputs*l.batch, 1, ug.output_gpu, 1, l.g_gpu, 1); + + copy_gpu(l.outputs*l.batch, wo.output_gpu, 1, l.o_gpu, 1); + axpy_gpu(l.outputs*l.batch, 1, uo.output_gpu, 1, l.o_gpu, 1); + + activate_array_gpu(l.f_gpu, l.outputs*l.batch, LOGISTIC); + activate_array_gpu(l.i_gpu, l.outputs*l.batch, LOGISTIC); + activate_array_gpu(l.g_gpu, l.outputs*l.batch, TANH); + activate_array_gpu(l.o_gpu, l.outputs*l.batch, LOGISTIC); + + copy_gpu(l.outputs*l.batch, l.i_gpu, 1, l.temp_gpu, 1); + mul_gpu(l.outputs*l.batch, l.g_gpu, 1, l.temp_gpu, 1); + mul_gpu(l.outputs*l.batch, l.f_gpu, 1, l.c_gpu, 1); + axpy_gpu(l.outputs*l.batch, 1, l.temp_gpu, 1, l.c_gpu, 1); + + copy_gpu(l.outputs*l.batch, l.c_gpu, 1, l.h_gpu, 1); + activate_array_gpu(l.h_gpu, l.outputs*l.batch, TANH); + mul_gpu(l.outputs*l.batch, l.o_gpu, 1, l.h_gpu, 1); + + copy_gpu(l.outputs*l.batch, l.c_gpu, 1, l.cell_gpu, 1); + copy_gpu(l.outputs*l.batch, l.h_gpu, 1, l.output_gpu, 1); + + state.input_gpu += l.inputs*l.batch; + l.output_gpu += l.outputs*l.batch; + l.cell_gpu += l.outputs*l.batch; + + increment_layer(&wf, 1); + increment_layer(&wi, 1); + increment_layer(&wg, 1); + increment_layer(&wo, 1); + + increment_layer(&uf, 1); + increment_layer(&ui, 1); + increment_layer(&ug, 1); + increment_layer(&uo, 1); + } +} + +void backward_lstm_layer_gpu(layer l, network state) +{ + network s = { 0 }; + s.train = state.train; + int i; + layer wf = *(l.wf); + layer wi = *(l.wi); + layer wg = *(l.wg); + layer wo = *(l.wo); + + layer uf = *(l.uf); + layer ui = *(l.ui); + layer ug = *(l.ug); + layer uo = *(l.uo); + + increment_layer(&wf, l.steps - 1); + increment_layer(&wi, l.steps - 1); + increment_layer(&wg, l.steps - 1); + increment_layer(&wo, l.steps - 1); + + increment_layer(&uf, l.steps - 1); + increment_layer(&ui, l.steps - 1); + increment_layer(&ug, l.steps - 1); + increment_layer(&uo, l.steps - 1); + + state.input_gpu += l.inputs*l.batch*(l.steps - 1); + if (state.delta_gpu) state.delta_gpu += l.inputs*l.batch*(l.steps - 1); + + l.output_gpu += l.outputs*l.batch*(l.steps - 1); + l.cell_gpu += l.outputs*l.batch*(l.steps - 1); + l.delta_gpu += l.outputs*l.batch*(l.steps - 1); + + for (i = l.steps - 1; i >= 0; --i) { + if (i != 0) copy_gpu(l.outputs*l.batch, l.cell_gpu - l.outputs*l.batch, 1, l.prev_cell_gpu, 1); + copy_gpu(l.outputs*l.batch, l.cell_gpu, 1, l.c_gpu, 1); + if (i != 0) copy_gpu(l.outputs*l.batch, l.output_gpu - l.outputs*l.batch, 1, l.prev_state_gpu, 1); + copy_gpu(l.outputs*l.batch, l.output_gpu, 1, l.h_gpu, 1); + + l.dh_gpu = (i == 0) ? 0 : l.delta_gpu - l.outputs*l.batch; + + copy_gpu(l.outputs*l.batch, wf.output_gpu, 1, l.f_gpu, 1); + axpy_gpu(l.outputs*l.batch, 1, uf.output_gpu, 1, l.f_gpu, 1); + + copy_gpu(l.outputs*l.batch, wi.output_gpu, 1, l.i_gpu, 1); + axpy_gpu(l.outputs*l.batch, 1, ui.output_gpu, 1, l.i_gpu, 1); + + copy_gpu(l.outputs*l.batch, wg.output_gpu, 1, l.g_gpu, 1); + axpy_gpu(l.outputs*l.batch, 1, ug.output_gpu, 1, l.g_gpu, 1); + + copy_gpu(l.outputs*l.batch, wo.output_gpu, 1, l.o_gpu, 1); + axpy_gpu(l.outputs*l.batch, 1, uo.output_gpu, 1, l.o_gpu, 1); + + activate_array_gpu(l.f_gpu, l.outputs*l.batch, LOGISTIC); + activate_array_gpu(l.i_gpu, l.outputs*l.batch, LOGISTIC); + activate_array_gpu(l.g_gpu, l.outputs*l.batch, TANH); + activate_array_gpu(l.o_gpu, l.outputs*l.batch, LOGISTIC); + + copy_gpu(l.outputs*l.batch, l.delta_gpu, 1, l.temp3_gpu, 1); + + copy_gpu(l.outputs*l.batch, l.c_gpu, 1, l.temp_gpu, 1); + activate_array_gpu(l.temp_gpu, l.outputs*l.batch, TANH); + + copy_gpu(l.outputs*l.batch, l.temp3_gpu, 1, l.temp2_gpu, 1); + mul_gpu(l.outputs*l.batch, l.o_gpu, 1, l.temp2_gpu, 1); + + gradient_array_gpu(l.temp_gpu, l.outputs*l.batch, TANH, l.temp2_gpu); + axpy_gpu(l.outputs*l.batch, 1, l.dc_gpu, 1, l.temp2_gpu, 1); + + copy_gpu(l.outputs*l.batch, l.c_gpu, 1, l.temp_gpu, 1); + activate_array_gpu(l.temp_gpu, l.outputs*l.batch, TANH); + mul_gpu(l.outputs*l.batch, l.temp3_gpu, 1, l.temp_gpu, 1); + gradient_array_gpu(l.o_gpu, l.outputs*l.batch, LOGISTIC, l.temp_gpu); + copy_gpu(l.outputs*l.batch, l.temp_gpu, 1, wo.delta_gpu, 1); + s.input_gpu = l.prev_state_gpu; + s.delta_gpu = l.dh_gpu; + backward_connected_layer_gpu(wo, s); + + copy_gpu(l.outputs*l.batch, l.temp_gpu, 1, uo.delta_gpu, 1); + s.input_gpu = state.input_gpu; + s.delta_gpu = state.delta_gpu; + backward_connected_layer_gpu(uo, s); + + copy_gpu(l.outputs*l.batch, l.temp2_gpu, 1, l.temp_gpu, 1); + mul_gpu(l.outputs*l.batch, l.i_gpu, 1, l.temp_gpu, 1); + gradient_array_gpu(l.g_gpu, l.outputs*l.batch, TANH, l.temp_gpu); + copy_gpu(l.outputs*l.batch, l.temp_gpu, 1, wg.delta_gpu, 1); + s.input_gpu = l.prev_state_gpu; + s.delta_gpu = l.dh_gpu; + backward_connected_layer_gpu(wg, s); + + copy_gpu(l.outputs*l.batch, l.temp_gpu, 1, ug.delta_gpu, 1); + s.input_gpu = state.input_gpu; + s.delta_gpu = state.delta_gpu; + backward_connected_layer_gpu(ug, s); + + copy_gpu(l.outputs*l.batch, l.temp2_gpu, 1, l.temp_gpu, 1); + mul_gpu(l.outputs*l.batch, l.g_gpu, 1, l.temp_gpu, 1); + gradient_array_gpu(l.i_gpu, l.outputs*l.batch, LOGISTIC, l.temp_gpu); + copy_gpu(l.outputs*l.batch, l.temp_gpu, 1, wi.delta_gpu, 1); + s.input_gpu = l.prev_state_gpu; + s.delta_gpu = l.dh_gpu; + backward_connected_layer_gpu(wi, s); + + copy_gpu(l.outputs*l.batch, l.temp_gpu, 1, ui.delta_gpu, 1); + s.input_gpu = state.input_gpu; + s.delta_gpu = state.delta_gpu; + backward_connected_layer_gpu(ui, s); + + copy_gpu(l.outputs*l.batch, l.temp2_gpu, 1, l.temp_gpu, 1); + mul_gpu(l.outputs*l.batch, l.prev_cell_gpu, 1, l.temp_gpu, 1); + gradient_array_gpu(l.f_gpu, l.outputs*l.batch, LOGISTIC, l.temp_gpu); + copy_gpu(l.outputs*l.batch, l.temp_gpu, 1, wf.delta_gpu, 1); + s.input_gpu = l.prev_state_gpu; + s.delta_gpu = l.dh_gpu; + backward_connected_layer_gpu(wf, s); + + copy_gpu(l.outputs*l.batch, l.temp_gpu, 1, uf.delta_gpu, 1); + s.input_gpu = state.input_gpu; + s.delta_gpu = state.delta_gpu; + backward_connected_layer_gpu(uf, s); + + copy_gpu(l.outputs*l.batch, l.temp2_gpu, 1, l.temp_gpu, 1); + mul_gpu(l.outputs*l.batch, l.f_gpu, 1, l.temp_gpu, 1); + copy_gpu(l.outputs*l.batch, l.temp_gpu, 1, l.dc_gpu, 1); + + state.input_gpu -= l.inputs*l.batch; + if (state.delta_gpu) state.delta_gpu -= l.inputs*l.batch; + l.output_gpu -= l.outputs*l.batch; + l.cell_gpu -= l.outputs*l.batch; + l.delta_gpu -= l.outputs*l.batch; + + increment_layer(&wf, -1); + increment_layer(&wi, -1); + increment_layer(&wg, -1); + increment_layer(&wo, -1); + + increment_layer(&uf, -1); + increment_layer(&ui, -1); + increment_layer(&ug, -1); + increment_layer(&uo, -1); + } +} +#endif diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/lstm_layer.h b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/lstm_layer.h new file mode 100644 index 00000000000..b9f07e6424b --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/lstm_layer.h @@ -0,0 +1,20 @@ +#ifndef LSTM_LAYER_H +#define LSTM_LAYER_H + +#include "activations.h" +#include "layer.h" +#include "network.h" +#define USET + +layer make_lstm_layer(int batch, int inputs, int outputs, int steps, int batch_normalize, int adam); + +void forward_lstm_layer(layer l, network net); +void update_lstm_layer(layer l, update_args a); + +#ifdef GPU +void forward_lstm_layer_gpu(layer l, network net); +void backward_lstm_layer_gpu(layer l, network net); +void update_lstm_layer_gpu(layer l, update_args a); + +#endif +#endif diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/matrix.c b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/matrix.c new file mode 100644 index 00000000000..799916bff01 --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/matrix.c @@ -0,0 +1,196 @@ +#include "matrix.h" +#include "utils.h" +#include "blas.h" +#include +#include +#include +#include +#include + +void free_matrix(matrix m) +{ + int i; + for(i = 0; i < m.rows; ++i) free(m.vals[i]); + free(m.vals); +} + +float matrix_topk_accuracy(matrix truth, matrix guess, int k) +{ + int *indexes = calloc(k, sizeof(int)); + int n = truth.cols; + int i,j; + int correct = 0; + for(i = 0; i < truth.rows; ++i){ + top_k(guess.vals[i], n, k, indexes); + for(j = 0; j < k; ++j){ + int class = indexes[j]; + if(truth.vals[i][class]){ + ++correct; + break; + } + } + } + free(indexes); + return (float)correct/truth.rows; +} + +void scale_matrix(matrix m, float scale) +{ + int i,j; + for(i = 0; i < m.rows; ++i){ + for(j = 0; j < m.cols; ++j){ + m.vals[i][j] *= scale; + } + } +} + +matrix resize_matrix(matrix m, int size) +{ + int i; + if (m.rows == size) return m; + if (m.rows < size) { + m.vals = realloc(m.vals, size*sizeof(float*)); + for (i = m.rows; i < size; ++i) { + m.vals[i] = calloc(m.cols, sizeof(float)); + } + } else if (m.rows > size) { + for (i = size; i < m.rows; ++i) { + free(m.vals[i]); + } + m.vals = realloc(m.vals, size*sizeof(float*)); + } + m.rows = size; + return m; +} + +void matrix_add_matrix(matrix from, matrix to) +{ + assert(from.rows == to.rows && from.cols == to.cols); + int i,j; + for(i = 0; i < from.rows; ++i){ + for(j = 0; j < from.cols; ++j){ + to.vals[i][j] += from.vals[i][j]; + } + } +} + +matrix copy_matrix(matrix m) +{ + matrix c = {0}; + c.rows = m.rows; + c.cols = m.cols; + c.vals = calloc(c.rows, sizeof(float *)); + int i; + for(i = 0; i < c.rows; ++i){ + c.vals[i] = calloc(c.cols, sizeof(float)); + copy_cpu(c.cols, m.vals[i], 1, c.vals[i], 1); + } + return c; +} + +matrix make_matrix(int rows, int cols) +{ + int i; + matrix m; + m.rows = rows; + m.cols = cols; + m.vals = calloc(m.rows, sizeof(float *)); + for(i = 0; i < m.rows; ++i){ + m.vals[i] = calloc(m.cols, sizeof(float)); + } + return m; +} + +matrix hold_out_matrix(matrix *m, int n) +{ + int i; + matrix h; + h.rows = n; + h.cols = m->cols; + h.vals = calloc(h.rows, sizeof(float *)); + for(i = 0; i < n; ++i){ + int index = rand()%m->rows; + h.vals[i] = m->vals[index]; + m->vals[index] = m->vals[--(m->rows)]; + } + return h; +} + +float *pop_column(matrix *m, int c) +{ + float *col = calloc(m->rows, sizeof(float)); + int i, j; + for(i = 0; i < m->rows; ++i){ + col[i] = m->vals[i][c]; + for(j = c; j < m->cols-1; ++j){ + m->vals[i][j] = m->vals[i][j+1]; + } + } + --m->cols; + return col; +} + +matrix csv_to_matrix(char *filename) +{ + FILE *fp = fopen(filename, "r"); + if(!fp) file_error(filename); + + matrix m; + m.cols = -1; + + char *line; + + int n = 0; + int size = 1024; + m.vals = calloc(size, sizeof(float*)); + while((line = fgetl(fp))){ + if(m.cols == -1) m.cols = count_fields(line); + if(n == size){ + size *= 2; + m.vals = realloc(m.vals, size*sizeof(float*)); + } + m.vals[n] = parse_fields(line, m.cols); + free(line); + ++n; + } + m.vals = realloc(m.vals, n*sizeof(float*)); + m.rows = n; + return m; +} + +void matrix_to_csv(matrix m) +{ + int i, j; + + for(i = 0; i < m.rows; ++i){ + for(j = 0; j < m.cols; ++j){ + if(j > 0) printf(","); + printf("%.17g", m.vals[i][j]); + } + printf("\n"); + } +} + +void print_matrix(matrix m) +{ + int i, j; + printf("%d X %d Matrix:\n",m.rows, m.cols); + printf(" __"); + for(j = 0; j < 16*m.cols-1; ++j) printf(" "); + printf("__ \n"); + + printf("| "); + for(j = 0; j < 16*m.cols-1; ++j) printf(" "); + printf(" |\n"); + + for(i = 0; i < m.rows; ++i){ + printf("| "); + for(j = 0; j < m.cols; ++j){ + printf("%15.7f ", m.vals[i][j]); + } + printf(" |\n"); + } + printf("|__"); + for(j = 0; j < 16*m.cols-1; ++j) printf(" "); + printf("__|\n"); +} diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/matrix.h b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/matrix.h new file mode 100644 index 00000000000..879acd70d26 --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/matrix.h @@ -0,0 +1,13 @@ +#ifndef MATRIX_H +#define MATRIX_H +#include "darknet.h" + +matrix copy_matrix(matrix m); +void print_matrix(matrix m); + +matrix hold_out_matrix(matrix *m, int n); +matrix resize_matrix(matrix m, int size); + +float *pop_column(matrix *m, int c); + +#endif diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/maxpool_layer.c b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/maxpool_layer.c new file mode 100644 index 00000000000..17dedf7a14c --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/maxpool_layer.c @@ -0,0 +1,127 @@ +#include "maxpool_layer.h" +#include "cuda.h" +#include + +image get_maxpool_image(maxpool_layer l) +{ + int h = l.out_h; + int w = l.out_w; + int c = l.c; + return float_to_image(w,h,c,l.output); +} + +image get_maxpool_delta(maxpool_layer l) +{ + int h = l.out_h; + int w = l.out_w; + int c = l.c; + return float_to_image(w,h,c,l.delta); +} + +maxpool_layer make_maxpool_layer(int batch, int h, int w, int c, int size, int stride, int padding) +{ + maxpool_layer l = {0}; + l.type = MAXPOOL; + l.batch = batch; + l.h = h; + l.w = w; + l.c = c; + l.pad = padding; + l.out_w = (w + 2*padding)/stride; + l.out_h = (h + 2*padding)/stride; + l.out_c = c; + l.outputs = l.out_h * l.out_w * l.out_c; + l.inputs = h*w*c; + l.size = size; + l.stride = stride; + int output_size = l.out_h * l.out_w * l.out_c * batch; + l.indexes = calloc(output_size, sizeof(int)); + l.output = calloc(output_size, sizeof(float)); + l.delta = calloc(output_size, sizeof(float)); + l.forward = forward_maxpool_layer; + l.backward = backward_maxpool_layer; + #ifdef GPU + l.forward_gpu = forward_maxpool_layer_gpu; + l.backward_gpu = backward_maxpool_layer_gpu; + l.indexes_gpu = cuda_make_int_array(0, output_size); + l.output_gpu = cuda_make_array(l.output, output_size); + l.delta_gpu = cuda_make_array(l.delta, output_size); + #endif + fprintf(stderr, "max %d x %d / %d %4d x%4d x%4d -> %4d x%4d x%4d\n", size, size, stride, w, h, c, l.out_w, l.out_h, l.out_c); + return l; +} + +void resize_maxpool_layer(maxpool_layer *l, int w, int h) +{ + l->h = h; + l->w = w; + l->inputs = h*w*l->c; + + l->out_w = (w + 2*l->pad)/l->stride; + l->out_h = (h + 2*l->pad)/l->stride; + l->outputs = l->out_w * l->out_h * l->c; + int output_size = l->outputs * l->batch; + + l->indexes = realloc(l->indexes, output_size * sizeof(int)); + l->output = realloc(l->output, output_size * sizeof(float)); + l->delta = realloc(l->delta, output_size * sizeof(float)); + + #ifdef GPU + cuda_free((float *)l->indexes_gpu); + cuda_free(l->output_gpu); + cuda_free(l->delta_gpu); + l->indexes_gpu = cuda_make_int_array(0, output_size); + l->output_gpu = cuda_make_array(l->output, output_size); + l->delta_gpu = cuda_make_array(l->delta, output_size); + #endif +} + +void forward_maxpool_layer(const maxpool_layer l, network net) +{ + int b,i,j,k,m,n; + int w_offset = -l.pad; + int h_offset = -l.pad; + + int h = l.out_h; + int w = l.out_w; + int c = l.c; + + for(b = 0; b < l.batch; ++b){ + for(k = 0; k < c; ++k){ + for(i = 0; i < h; ++i){ + for(j = 0; j < w; ++j){ + int out_index = j + w*(i + h*(k + c*b)); + float max = -FLT_MAX; + int max_i = -1; + for(n = 0; n < l.size; ++n){ + for(m = 0; m < l.size; ++m){ + int cur_h = h_offset + i*l.stride + n; + int cur_w = w_offset + j*l.stride + m; + int index = cur_w + l.w*(cur_h + l.h*(k + b*l.c)); + int valid = (cur_h >= 0 && cur_h < l.h && + cur_w >= 0 && cur_w < l.w); + float val = (valid != 0) ? net.input[index] : -FLT_MAX; + max_i = (val > max) ? index : max_i; + max = (val > max) ? val : max; + } + } + l.output[out_index] = max; + l.indexes[out_index] = max_i; + } + } + } + } +} + +void backward_maxpool_layer(const maxpool_layer l, network net) +{ + int i; + int h = l.out_h; + int w = l.out_w; + int c = l.c; + for(i = 0; i < h*w*c*l.batch; ++i){ + int index = l.indexes[i]; + net.delta[index] += l.delta[i]; + } +} + diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/maxpool_layer.h b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/maxpool_layer.h new file mode 100644 index 00000000000..ceb5190716c --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/maxpool_layer.h @@ -0,0 +1,23 @@ +#ifndef MAXPOOL_LAYER_H +#define MAXPOOL_LAYER_H + +#include "image.h" +#include "cuda.h" +#include "layer.h" +#include "network.h" + +typedef layer maxpool_layer; + +image get_maxpool_image(maxpool_layer l); +maxpool_layer make_maxpool_layer(int batch, int h, int w, int c, int size, int stride, int padding); +void resize_maxpool_layer(maxpool_layer *l, int w, int h); +void forward_maxpool_layer(const maxpool_layer l, network net); +void backward_maxpool_layer(const maxpool_layer l, network net); + +#ifdef GPU +void forward_maxpool_layer_gpu(maxpool_layer l, network net); +void backward_maxpool_layer_gpu(maxpool_layer l, network net); +#endif + +#endif + diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/maxpool_layer_kernels.cu b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/maxpool_layer_kernels.cu new file mode 100644 index 00000000000..3202e84b695 --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/maxpool_layer_kernels.cu @@ -0,0 +1,106 @@ +#include "cuda_runtime.h" +#include "curand.h" +#include "cublas_v2.h" + +extern "C" { +#include "maxpool_layer.h" +#include "cuda.h" +} + +__global__ void forward_maxpool_layer_kernel(int n, int in_h, int in_w, int in_c, int stride, int size, int pad, float *input, float *output, int *indexes) +{ + int h = (in_h + 2*pad)/stride; + int w = (in_w + 2*pad)/stride; + int c = in_c; + + int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if(id >= n) return; + + int j = id % w; + id /= w; + int i = id % h; + id /= h; + int k = id % c; + id /= c; + int b = id; + + int w_offset = -pad; + int h_offset = -pad; + + int out_index = j + w*(i + h*(k + c*b)); + float max = -INFINITY; + int max_i = -1; + int l, m; + for(l = 0; l < size; ++l){ + for(m = 0; m < size; ++m){ + int cur_h = h_offset + i*stride + l; + int cur_w = w_offset + j*stride + m; + int index = cur_w + in_w*(cur_h + in_h*(k + b*in_c)); + int valid = (cur_h >= 0 && cur_h < in_h && + cur_w >= 0 && cur_w < in_w); + float val = (valid != 0) ? input[index] : -INFINITY; + max_i = (val > max) ? index : max_i; + max = (val > max) ? val : max; + } + } + output[out_index] = max; + indexes[out_index] = max_i; +} + +__global__ void backward_maxpool_layer_kernel(int n, int in_h, int in_w, int in_c, int stride, int size, int pad, float *delta, float *prev_delta, int *indexes) +{ + int h = (in_h + 2*pad)/stride; + int w = (in_w + 2*pad)/stride; + int c = in_c; + int area = (size-1)/stride; + + int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x; + if(id >= n) return; + + int index = id; + int j = id % in_w; + id /= in_w; + int i = id % in_h; + id /= in_h; + int k = id % in_c; + id /= in_c; + int b = id; + + int w_offset = -pad; + int h_offset = -pad; + + float d = 0; + int l, m; + for(l = -area; l < area+1; ++l){ + for(m = -area; m < area+1; ++m){ + int out_w = (j-w_offset)/stride + m; + int out_h = (i-h_offset)/stride + l; + int out_index = out_w + w*(out_h + h*(k + c*b)); + int valid = (out_w >= 0 && out_w < w && + out_h >= 0 && out_h < h); + d += (valid && indexes[out_index] == index) ? delta[out_index] : 0; + } + } + prev_delta[index] += d; +} + +extern "C" void forward_maxpool_layer_gpu(maxpool_layer layer, network net) +{ + int h = layer.out_h; + int w = layer.out_w; + int c = layer.c; + + size_t n = h*w*c*layer.batch; + + forward_maxpool_layer_kernel<<>>(n, layer.h, layer.w, layer.c, layer.stride, layer.size, layer.pad, net.input_gpu, layer.output_gpu, layer.indexes_gpu); + check_error(cudaPeekAtLastError()); +} + +extern "C" void backward_maxpool_layer_gpu(maxpool_layer layer, network net) +{ + size_t n = layer.h*layer.w*layer.c*layer.batch; + + backward_maxpool_layer_kernel<<>>(n, layer.h, layer.w, layer.c, layer.stride, layer.size, layer.pad, layer.delta_gpu, net.delta_gpu, layer.indexes_gpu); + check_error(cudaPeekAtLastError()); +} + diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/network.c b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/network.c new file mode 100644 index 00000000000..aaab7997b5e --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/network.c @@ -0,0 +1,1129 @@ +#include +#include +#include +#include "network.h" +#include "image.h" +#include "data.h" +#include "utils.h" +#include "blas.h" + +#include "crop_layer.h" +#include "connected_layer.h" +#include "gru_layer.h" +#include "rnn_layer.h" +#include "crnn_layer.h" +#include "local_layer.h" +#include "convolutional_layer.h" +#include "activation_layer.h" +#include "detection_layer.h" +#include "region_layer.h" +#include "yolo_layer.h" +#include "normalization_layer.h" +#include "batchnorm_layer.h" +#include "maxpool_layer.h" +#include "reorg_layer.h" +#include "avgpool_layer.h" +#include "cost_layer.h" +#include "softmax_layer.h" +#include "dropout_layer.h" +#include "route_layer.h" +#include "upsample_layer.h" +#include "shortcut_layer.h" +#include "parser.h" +#include "data.h" + +load_args get_base_args(network *net) +{ + load_args args = {0}; + args.w = net->w; + args.h = net->h; + args.size = net->w; + + args.min = net->min_crop; + args.max = net->max_crop; + args.angle = net->angle; + args.aspect = net->aspect; + args.exposure = net->exposure; + args.center = net->center; + args.saturation = net->saturation; + args.hue = net->hue; + return args; +} + +network *load_network(char *cfg, char *weights, int clear) +{ + network *net = parse_network_cfg(cfg); + if(weights && weights[0] != 0){ + load_weights(net, weights); + } + if(clear) (*net->seen) = 0; + return net; +} + +size_t get_current_batch(network *net) +{ + size_t batch_num = (*net->seen)/(net->batch*net->subdivisions); + return batch_num; +} + +void reset_network_state(network *net, int b) +{ + int i; + for (i = 0; i < net->n; ++i) { + #ifdef GPU + layer l = net->layers[i]; + if(l.state_gpu){ + fill_gpu(l.outputs, 0, l.state_gpu + l.outputs*b, 1); + } + if(l.h_gpu){ + fill_gpu(l.outputs, 0, l.h_gpu + l.outputs*b, 1); + } + #endif + } +} + +void reset_rnn(network *net) +{ + reset_network_state(net, 0); +} + +float get_current_rate(network *net) +{ + size_t batch_num = get_current_batch(net); + int i; + float rate; + if (batch_num < net->burn_in) return net->learning_rate * pow((float)batch_num / net->burn_in, net->power); + switch (net->policy) { + case CONSTANT: + return net->learning_rate; + case STEP: + return net->learning_rate * pow(net->scale, batch_num/net->step); + case STEPS: + rate = net->learning_rate; + for(i = 0; i < net->num_steps; ++i){ + if(net->steps[i] > batch_num) return rate; + rate *= net->scales[i]; + } + return rate; + case EXP: + return net->learning_rate * pow(net->gamma, batch_num); + case POLY: + return net->learning_rate * pow(1 - (float)batch_num / net->max_batches, net->power); + case RANDOM: + return net->learning_rate * pow(rand_uniform(0,1), net->power); + case SIG: + return net->learning_rate * (1./(1.+exp(net->gamma*(batch_num - net->step)))); + default: + fprintf(stderr, "Policy is weird!\n"); + return net->learning_rate; + } +} + +char *get_layer_string(LAYER_TYPE a) +{ + switch(a){ + case CONVOLUTIONAL: + return "convolutional"; + case ACTIVE: + return "activation"; + case LOCAL: + return "local"; + case DECONVOLUTIONAL: + return "deconvolutional"; + case CONNECTED: + return "connected"; + case RNN: + return "rnn"; + case GRU: + return "gru"; + case LSTM: + return "lstm"; + case CRNN: + return "crnn"; + case MAXPOOL: + return "maxpool"; + case REORG: + return "reorg"; + case AVGPOOL: + return "avgpool"; + case SOFTMAX: + return "softmax"; + case DETECTION: + return "detection"; + case REGION: + return "region"; + case YOLO: + return "yolo"; + case DROPOUT: + return "dropout"; + case CROP: + return "crop"; + case COST: + return "cost"; + case ROUTE: + return "route"; + case SHORTCUT: + return "shortcut"; + case NORMALIZATION: + return "normalization"; + case BATCHNORM: + return "batchnorm"; + default: + break; + } + return "none"; +} + +network *make_network(int n) +{ + network *net = calloc(1, sizeof(network)); + net->n = n; + net->layers = calloc(net->n, sizeof(layer)); + net->seen = calloc(1, sizeof(size_t)); + net->t = calloc(1, sizeof(int)); + net->cost = calloc(1, sizeof(float)); + return net; +} + +void forward_network(network *netp) +{ +#ifdef GPU + if(netp->gpu_index >= 0){ + forward_network_gpu(netp); + return; + } +#endif + network net = *netp; + int i; + for(i = 0; i < net.n; ++i){ + net.index = i; + layer l = net.layers[i]; + if(l.delta){ + fill_cpu(l.outputs * l.batch, 0, l.delta, 1); + } + l.forward(l, net); + net.input = l.output; + if(l.truth) { + net.truth = l.output; + } + } + calc_network_cost(netp); +} + +void update_network(network *netp) +{ +#ifdef GPU + if(netp->gpu_index >= 0){ + update_network_gpu(netp); + return; + } +#endif + network net = *netp; + int i; + update_args a = {0}; + a.batch = net.batch*net.subdivisions; + a.learning_rate = get_current_rate(netp); + a.momentum = net.momentum; + a.decay = net.decay; + a.adam = net.adam; + a.B1 = net.B1; + a.B2 = net.B2; + a.eps = net.eps; + ++*net.t; + a.t = *net.t; + + for(i = 0; i < net.n; ++i){ + layer l = net.layers[i]; + if(l.update){ + l.update(l, a); + } + } +} + +void calc_network_cost(network *netp) +{ + network net = *netp; + int i; + float sum = 0; + int count = 0; + for(i = 0; i < net.n; ++i){ + if(net.layers[i].cost){ + sum += net.layers[i].cost[0]; + ++count; + } + } + *net.cost = sum/count; +} + +int get_predicted_class_network(network *net) +{ + return max_index(net->output, net->outputs); +} + +void backward_network(network *netp) +{ +#ifdef GPU + if(netp->gpu_index >= 0){ + backward_network_gpu(netp); + return; + } +#endif + network net = *netp; + int i; + network orig = net; + for(i = net.n-1; i >= 0; --i){ + layer l = net.layers[i]; + if(l.stopbackward) break; + if(i == 0){ + net = orig; + }else{ + layer prev = net.layers[i-1]; + net.input = prev.output; + net.delta = prev.delta; + } + net.index = i; + l.backward(l, net); + } +} + +float train_network_datum(network *net) +{ + *net->seen += net->batch; + net->train = 1; + forward_network(net); + backward_network(net); + float error = *net->cost; + if(((*net->seen)/net->batch)%net->subdivisions == 0) update_network(net); + return error; +} + +float train_network_sgd(network *net, data d, int n) +{ + int batch = net->batch; + + int i; + float sum = 0; + for(i = 0; i < n; ++i){ + get_random_batch(d, batch, net->input, net->truth); + float err = train_network_datum(net); + sum += err; + } + return (float)sum/(n*batch); +} + +float train_network(network *net, data d) +{ + assert(d.X.rows % net->batch == 0); + int batch = net->batch; + int n = d.X.rows / batch; + + int i; + float sum = 0; + for(i = 0; i < n; ++i){ + get_next_batch(d, batch, i*batch, net->input, net->truth); + float err = train_network_datum(net); + sum += err; + } + return (float)sum/(n*batch); +} + +void set_temp_network(network *net, float t) +{ + int i; + for(i = 0; i < net->n; ++i){ + net->layers[i].temperature = t; + } +} + + +void set_batch_network(network *net, int b) +{ + net->batch = b; + int i; + for(i = 0; i < net->n; ++i){ + net->layers[i].batch = b; +#ifdef CUDNN + if(net->layers[i].type == CONVOLUTIONAL){ + cudnn_convolutional_setup(net->layers + i); + } + if(net->layers[i].type == DECONVOLUTIONAL){ + layer *l = net->layers + i; + cudnnSetTensor4dDescriptor(l->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, 1, l->out_c, l->out_h, l->out_w); + cudnnSetTensor4dDescriptor(l->normTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, 1, l->out_c, 1, 1); + } +#endif + } +} + +int resize_network(network *net, int w, int h) +{ +#ifdef GPU + cuda_set_device(net->gpu_index); + cuda_free(net->workspace); +#endif + int i; + //if(w == net->w && h == net->h) return 0; + net->w = w; + net->h = h; + int inputs = 0; + size_t workspace_size = 0; + //fprintf(stderr, "Resizing to %d x %d...\n", w, h); + //fflush(stderr); + for (i = 0; i < net->n; ++i){ + layer l = net->layers[i]; + if(l.type == CONVOLUTIONAL){ + resize_convolutional_layer(&l, w, h); + }else if(l.type == CROP){ + resize_crop_layer(&l, w, h); + }else if(l.type == MAXPOOL){ + resize_maxpool_layer(&l, w, h); + }else if(l.type == REGION){ + resize_region_layer(&l, w, h); + }else if(l.type == YOLO){ + resize_yolo_layer(&l, w, h); + }else if(l.type == ROUTE){ + resize_route_layer(&l, net); + }else if(l.type == SHORTCUT){ + resize_shortcut_layer(&l, w, h); + }else if(l.type == UPSAMPLE){ + resize_upsample_layer(&l, w, h); + }else if(l.type == REORG){ + resize_reorg_layer(&l, w, h); + }else if(l.type == AVGPOOL){ + resize_avgpool_layer(&l, w, h); + }else if(l.type == NORMALIZATION){ + resize_normalization_layer(&l, w, h); + }else if(l.type == COST){ + resize_cost_layer(&l, inputs); + }else{ + error("Cannot resize this type of layer"); + } + if(l.workspace_size > workspace_size) workspace_size = l.workspace_size; + if(l.workspace_size > 2000000000) assert(0); + inputs = l.outputs; + net->layers[i] = l; + w = l.out_w; + h = l.out_h; + if(l.type == AVGPOOL) break; + } + layer out = get_network_output_layer(net); + net->inputs = net->layers[0].inputs; + net->outputs = out.outputs; + net->truths = out.outputs; + if(net->layers[net->n-1].truths) net->truths = net->layers[net->n-1].truths; + net->output = out.output; + free(net->input); + free(net->truth); + net->input = calloc(net->inputs*net->batch, sizeof(float)); + net->truth = calloc(net->truths*net->batch, sizeof(float)); +#ifdef GPU + if(gpu_index >= 0){ + cuda_free(net->input_gpu); + cuda_free(net->truth_gpu); + net->input_gpu = cuda_make_array(net->input, net->inputs*net->batch); + net->truth_gpu = cuda_make_array(net->truth, net->truths*net->batch); + if(workspace_size){ + net->workspace = cuda_make_array(0, (workspace_size-1)/sizeof(float)+1); + } + }else { + free(net->workspace); + net->workspace = calloc(1, workspace_size); + } +#else + free(net->workspace); + net->workspace = calloc(1, workspace_size); +#endif + //fprintf(stderr, " Done!\n"); + return 0; +} + +layer get_network_detection_layer(network *net) +{ + int i; + for(i = 0; i < net->n; ++i){ + if(net->layers[i].type == DETECTION){ + return net->layers[i]; + } + } + fprintf(stderr, "Detection layer not found!!\n"); + layer l = {0}; + return l; +} + +image get_network_image_layer(network *net, int i) +{ + layer l = net->layers[i]; +#ifdef GPU + //cuda_pull_array(l.output_gpu, l.output, l.outputs); +#endif + if (l.out_w && l.out_h && l.out_c){ + return float_to_image(l.out_w, l.out_h, l.out_c, l.output); + } + image def = {0}; + return def; +} + +image get_network_image(network *net) +{ + int i; + for(i = net->n-1; i >= 0; --i){ + image m = get_network_image_layer(net, i); + if(m.h != 0) return m; + } + image def = {0}; + return def; +} + +void visualize_network(network *net) +{ + image *prev = 0; + int i; + char buff[256]; + for(i = 0; i < net->n; ++i){ + sprintf(buff, "Layer %d", i); + layer l = net->layers[i]; + if(l.type == CONVOLUTIONAL){ + prev = visualize_convolutional_layer(l, buff, prev); + } + } +} + +void top_predictions(network *net, int k, int *index) +{ + top_k(net->output, net->outputs, k, index); +} + + +float *network_predict(network *net, float *input) +{ + network orig = *net; + net->input = input; + net->truth = 0; + net->train = 0; + net->delta = 0; + forward_network(net); + float *out = net->output; + *net = orig; + return out; +} + +int num_detections(network *net, float thresh) +{ + int i; + int s = 0; + for(i = 0; i < net->n; ++i){ + layer l = net->layers[i]; + if(l.type == YOLO){ + s += yolo_num_detections(l, thresh); + } + if(l.type == DETECTION || l.type == REGION){ + s += l.w*l.h*l.n; + } + } + return s; +} + +detection *make_network_boxes(network *net, float thresh, int *num) +{ + layer l = net->layers[net->n - 1]; + int i; + int nboxes = num_detections(net, thresh); + if(num) *num = nboxes; + detection *dets = calloc(nboxes, sizeof(detection)); + for(i = 0; i < nboxes; ++i){ + dets[i].prob = calloc(l.classes, sizeof(float)); + if(l.coords > 4){ + dets[i].mask = calloc(l.coords-4, sizeof(float)); + } + } + return dets; +} + +void fill_network_boxes(network *net, int w, int h, float thresh, float hier, int *map, int relative, detection *dets) +{ + int j; + for(j = 0; j < net->n; ++j){ + layer l = net->layers[j]; + if(l.type == YOLO){ + int count = get_yolo_detections(l, w, h, net->w, net->h, thresh, map, relative, dets); + dets += count; + } + if(l.type == REGION){ + get_region_detections(l, w, h, net->w, net->h, thresh, map, hier, relative, dets); + dets += l.w*l.h*l.n; + } + if(l.type == DETECTION){ + get_detection_detections(l, w, h, thresh, dets); + dets += l.w*l.h*l.n; + } + } +} + +detection *get_network_boxes(network *net, int w, int h, float thresh, float hier, int *map, int relative, int *num) +{ + detection *dets = make_network_boxes(net, thresh, num); + fill_network_boxes(net, w, h, thresh, hier, map, relative, dets); + return dets; +} + +void free_detections(detection *dets, int n) +{ + int i; + for(i = 0; i < n; ++i){ + free(dets[i].prob); + if(dets[i].mask) free(dets[i].mask); + } + free(dets); +} + +float *network_predict_image(network *net, image im) +{ + image imr = letterbox_image(im, net->w, net->h); + set_batch_network(net, 1); + float *p = network_predict(net, imr.data); + free_image(imr); + return p; +} + +int network_width(network *net){return net->w;} +int network_height(network *net){return net->h;} + +matrix network_predict_data_multi(network *net, data test, int n) +{ + int i,j,b,m; + int k = net->outputs; + matrix pred = make_matrix(test.X.rows, k); + float *X = calloc(net->batch*test.X.rows, sizeof(float)); + for(i = 0; i < test.X.rows; i += net->batch){ + for(b = 0; b < net->batch; ++b){ + if(i+b == test.X.rows) break; + memcpy(X+b*test.X.cols, test.X.vals[i+b], test.X.cols*sizeof(float)); + } + for(m = 0; m < n; ++m){ + float *out = network_predict(net, X); + for(b = 0; b < net->batch; ++b){ + if(i+b == test.X.rows) break; + for(j = 0; j < k; ++j){ + pred.vals[i+b][j] += out[j+b*k]/n; + } + } + } + } + free(X); + return pred; +} + +matrix network_predict_data(network *net, data test) +{ + int i,j,b; + int k = net->outputs; + matrix pred = make_matrix(test.X.rows, k); + float *X = calloc(net->batch*test.X.cols, sizeof(float)); + for(i = 0; i < test.X.rows; i += net->batch){ + for(b = 0; b < net->batch; ++b){ + if(i+b == test.X.rows) break; + memcpy(X+b*test.X.cols, test.X.vals[i+b], test.X.cols*sizeof(float)); + } + float *out = network_predict(net, X); + for(b = 0; b < net->batch; ++b){ + if(i+b == test.X.rows) break; + for(j = 0; j < k; ++j){ + pred.vals[i+b][j] = out[j+b*k]; + } + } + } + free(X); + return pred; +} + +void print_network(network *net) +{ + int i,j; + for(i = 0; i < net->n; ++i){ + layer l = net->layers[i]; + float *output = l.output; + int n = l.outputs; + float mean = mean_array(output, n); + float vari = variance_array(output, n); + fprintf(stderr, "Layer %d - Mean: %f, Variance: %f\n",i,mean, vari); + if(n > 100) n = 100; + for(j = 0; j < n; ++j) fprintf(stderr, "%f, ", output[j]); + if(n == 100)fprintf(stderr,".....\n"); + fprintf(stderr, "\n"); + } +} + +void compare_networks(network *n1, network *n2, data test) +{ + matrix g1 = network_predict_data(n1, test); + matrix g2 = network_predict_data(n2, test); + int i; + int a,b,c,d; + a = b = c = d = 0; + for(i = 0; i < g1.rows; ++i){ + int truth = max_index(test.y.vals[i], test.y.cols); + int p1 = max_index(g1.vals[i], g1.cols); + int p2 = max_index(g2.vals[i], g2.cols); + if(p1 == truth){ + if(p2 == truth) ++d; + else ++c; + }else{ + if(p2 == truth) ++b; + else ++a; + } + } + printf("%5d %5d\n%5d %5d\n", a, b, c, d); + float num = pow((abs(b - c) - 1.), 2.); + float den = b + c; + printf("%f\n", num/den); +} + +float network_accuracy(network *net, data d) +{ + matrix guess = network_predict_data(net, d); + float acc = matrix_topk_accuracy(d.y, guess,1); + free_matrix(guess); + return acc; +} + +float *network_accuracies(network *net, data d, int n) +{ + static float acc[2]; + matrix guess = network_predict_data(net, d); + acc[0] = matrix_topk_accuracy(d.y, guess, 1); + acc[1] = matrix_topk_accuracy(d.y, guess, n); + free_matrix(guess); + return acc; +} + +layer get_network_output_layer(network *net) +{ + int i; + for(i = net->n - 1; i >= 0; --i){ + if(net->layers[i].type != COST) break; + } + return net->layers[i]; +} + +float network_accuracy_multi(network *net, data d, int n) +{ + matrix guess = network_predict_data_multi(net, d, n); + float acc = matrix_topk_accuracy(d.y, guess,1); + free_matrix(guess); + return acc; +} + +void free_network(network *net) +{ + int i; + for(i = 0; i < net->n; ++i){ + free_layer(net->layers[i]); + } + free(net->layers); + if(net->input) free(net->input); + if(net->truth) free(net->truth); +#ifdef GPU + if(net->input_gpu) cuda_free(net->input_gpu); + if(net->truth_gpu) cuda_free(net->truth_gpu); +#endif + free(net); +} + +// Some day... +// ^ What the hell is this comment for? + + +layer network_output_layer(network *net) +{ + int i; + for(i = net->n - 1; i >= 0; --i){ + if(net->layers[i].type != COST) break; + } + return net->layers[i]; +} + +int network_inputs(network *net) +{ + return net->layers[0].inputs; +} + +int network_outputs(network *net) +{ + return network_output_layer(net).outputs; +} + +float *network_output(network *net) +{ + return network_output_layer(net).output; +} + +#ifdef GPU + +void forward_network_gpu(network *netp) +{ + network net = *netp; + cuda_set_device(net.gpu_index); + cuda_push_array(net.input_gpu, net.input, net.inputs*net.batch); + if(net.truth){ + cuda_push_array(net.truth_gpu, net.truth, net.truths*net.batch); + } + + int i; + for(i = 0; i < net.n; ++i){ + net.index = i; + layer l = net.layers[i]; + if(l.delta_gpu){ + fill_gpu(l.outputs * l.batch, 0, l.delta_gpu, 1); + } + l.forward_gpu(l, net); + net.input_gpu = l.output_gpu; + net.input = l.output; + if(l.truth) { + net.truth_gpu = l.output_gpu; + net.truth = l.output; + } + } + pull_network_output(netp); + calc_network_cost(netp); +} + +void backward_network_gpu(network *netp) +{ + int i; + network net = *netp; + network orig = net; + cuda_set_device(net.gpu_index); + for(i = net.n-1; i >= 0; --i){ + layer l = net.layers[i]; + if(l.stopbackward) break; + if(i == 0){ + net = orig; + }else{ + layer prev = net.layers[i-1]; + net.input = prev.output; + net.delta = prev.delta; + net.input_gpu = prev.output_gpu; + net.delta_gpu = prev.delta_gpu; + } + net.index = i; + l.backward_gpu(l, net); + } +} + +void update_network_gpu(network *netp) +{ + network net = *netp; + cuda_set_device(net.gpu_index); + int i; + update_args a = {0}; + a.batch = net.batch*net.subdivisions; + a.learning_rate = get_current_rate(netp); + a.momentum = net.momentum; + a.decay = net.decay; + a.adam = net.adam; + a.B1 = net.B1; + a.B2 = net.B2; + a.eps = net.eps; + ++*net.t; + a.t = (*net.t); + + for(i = 0; i < net.n; ++i){ + layer l = net.layers[i]; + if(l.update_gpu){ + l.update_gpu(l, a); + } + } +} + +void harmless_update_network_gpu(network *netp) +{ + network net = *netp; + cuda_set_device(net.gpu_index); + int i; + for(i = 0; i < net.n; ++i){ + layer l = net.layers[i]; + if(l.weight_updates_gpu) fill_gpu(l.nweights, 0, l.weight_updates_gpu, 1); + if(l.bias_updates_gpu) fill_gpu(l.nbiases, 0, l.bias_updates_gpu, 1); + if(l.scale_updates_gpu) fill_gpu(l.nbiases, 0, l.scale_updates_gpu, 1); + } +} + +typedef struct { + network *net; + data d; + float *err; +} train_args; + +void *train_thread(void *ptr) +{ + train_args args = *(train_args*)ptr; + free(ptr); + cuda_set_device(args.net->gpu_index); + *args.err = train_network(args.net, args.d); + return 0; +} + +pthread_t train_network_in_thread(network *net, data d, float *err) +{ + pthread_t thread; + train_args *ptr = (train_args *)calloc(1, sizeof(train_args)); + ptr->net = net; + ptr->d = d; + ptr->err = err; + if(pthread_create(&thread, 0, train_thread, ptr)) error("Thread creation failed"); + return thread; +} + +void merge_weights(layer l, layer base) +{ + if (l.type == CONVOLUTIONAL) { + axpy_cpu(l.n, 1, l.bias_updates, 1, base.biases, 1); + axpy_cpu(l.nweights, 1, l.weight_updates, 1, base.weights, 1); + if (l.scales) { + axpy_cpu(l.n, 1, l.scale_updates, 1, base.scales, 1); + } + } else if(l.type == CONNECTED) { + axpy_cpu(l.outputs, 1, l.bias_updates, 1, base.biases, 1); + axpy_cpu(l.outputs*l.inputs, 1, l.weight_updates, 1, base.weights, 1); + } +} + +void scale_weights(layer l, float s) +{ + if (l.type == CONVOLUTIONAL) { + scal_cpu(l.n, s, l.biases, 1); + scal_cpu(l.nweights, s, l.weights, 1); + if (l.scales) { + scal_cpu(l.n, s, l.scales, 1); + } + } else if(l.type == CONNECTED) { + scal_cpu(l.outputs, s, l.biases, 1); + scal_cpu(l.outputs*l.inputs, s, l.weights, 1); + } +} + + +void pull_weights(layer l) +{ + if(l.type == CONVOLUTIONAL || l.type == DECONVOLUTIONAL){ + cuda_pull_array(l.biases_gpu, l.bias_updates, l.n); + cuda_pull_array(l.weights_gpu, l.weight_updates, l.nweights); + if(l.scales) cuda_pull_array(l.scales_gpu, l.scale_updates, l.n); + } else if(l.type == CONNECTED){ + cuda_pull_array(l.biases_gpu, l.bias_updates, l.outputs); + cuda_pull_array(l.weights_gpu, l.weight_updates, l.outputs*l.inputs); + } +} + +void push_weights(layer l) +{ + if(l.type == CONVOLUTIONAL || l.type == DECONVOLUTIONAL){ + cuda_push_array(l.biases_gpu, l.biases, l.n); + cuda_push_array(l.weights_gpu, l.weights, l.nweights); + if(l.scales) cuda_push_array(l.scales_gpu, l.scales, l.n); + } else if(l.type == CONNECTED){ + cuda_push_array(l.biases_gpu, l.biases, l.outputs); + cuda_push_array(l.weights_gpu, l.weights, l.outputs*l.inputs); + } +} + +void distribute_weights(layer l, layer base) +{ + if (l.type == CONVOLUTIONAL || l.type == DECONVOLUTIONAL) { + cuda_push_array(l.biases_gpu, base.biases, l.n); + cuda_push_array(l.weights_gpu, base.weights, l.nweights); + if (base.scales) cuda_push_array(l.scales_gpu, base.scales, l.n); + } else if (l.type == CONNECTED) { + cuda_push_array(l.biases_gpu, base.biases, l.outputs); + cuda_push_array(l.weights_gpu, base.weights, l.outputs*l.inputs); + } +} + + +/* + + void pull_updates(layer l) + { + if(l.type == CONVOLUTIONAL){ + cuda_pull_array(l.bias_updates_gpu, l.bias_updates, l.n); + cuda_pull_array(l.weight_updates_gpu, l.weight_updates, l.nweights); + if(l.scale_updates) cuda_pull_array(l.scale_updates_gpu, l.scale_updates, l.n); + } else if(l.type == CONNECTED){ + cuda_pull_array(l.bias_updates_gpu, l.bias_updates, l.outputs); + cuda_pull_array(l.weight_updates_gpu, l.weight_updates, l.outputs*l.inputs); + } + } + + void push_updates(layer l) + { + if(l.type == CONVOLUTIONAL){ + cuda_push_array(l.bias_updates_gpu, l.bias_updates, l.n); + cuda_push_array(l.weight_updates_gpu, l.weight_updates, l.nweights); + if(l.scale_updates) cuda_push_array(l.scale_updates_gpu, l.scale_updates, l.n); + } else if(l.type == CONNECTED){ + cuda_push_array(l.bias_updates_gpu, l.bias_updates, l.outputs); + cuda_push_array(l.weight_updates_gpu, l.weight_updates, l.outputs*l.inputs); + } + } + + void update_layer(layer l, network net) + { + int update_batch = net.batch*net.subdivisions; + float rate = get_current_rate(net); + l.t = get_current_batch(net); + if(l.update_gpu){ + l.update_gpu(l, update_batch, rate*l.learning_rate_scale, net.momentum, net.decay); + } + } + void merge_updates(layer l, layer base) + { + if (l.type == CONVOLUTIONAL) { + axpy_cpu(l.n, 1, l.bias_updates, 1, base.bias_updates, 1); + axpy_cpu(l.nweights, 1, l.weight_updates, 1, base.weight_updates, 1); + if (l.scale_updates) { + axpy_cpu(l.n, 1, l.scale_updates, 1, base.scale_updates, 1); + } + } else if(l.type == CONNECTED) { + axpy_cpu(l.outputs, 1, l.bias_updates, 1, base.bias_updates, 1); + axpy_cpu(l.outputs*l.inputs, 1, l.weight_updates, 1, base.weight_updates, 1); + } + } + + void distribute_updates(layer l, layer base) + { + if(l.type == CONVOLUTIONAL || l.type == DECONVOLUTIONAL){ + cuda_push_array(l.bias_updates_gpu, base.bias_updates, l.n); + cuda_push_array(l.weight_updates_gpu, base.weight_updates, l.nweights); + if(base.scale_updates) cuda_push_array(l.scale_updates_gpu, base.scale_updates, l.n); + } else if(l.type == CONNECTED){ + cuda_push_array(l.bias_updates_gpu, base.bias_updates, l.outputs); + cuda_push_array(l.weight_updates_gpu, base.weight_updates, l.outputs*l.inputs); + } + } + */ + +/* + void sync_layer(network *nets, int n, int j) + { + int i; + network net = nets[0]; + layer base = net.layers[j]; + scale_weights(base, 0); + for (i = 0; i < n; ++i) { + cuda_set_device(nets[i].gpu_index); + layer l = nets[i].layers[j]; + pull_weights(l); + merge_weights(l, base); + } + scale_weights(base, 1./n); + for (i = 0; i < n; ++i) { + cuda_set_device(nets[i].gpu_index); + layer l = nets[i].layers[j]; + distribute_weights(l, base); + } + } + */ + +void sync_layer(network **nets, int n, int j) +{ + int i; + network *net = nets[0]; + layer base = net->layers[j]; + scale_weights(base, 0); + for (i = 0; i < n; ++i) { + cuda_set_device(nets[i]->gpu_index); + layer l = nets[i]->layers[j]; + pull_weights(l); + merge_weights(l, base); + } + scale_weights(base, 1./n); + for (i = 0; i < n; ++i) { + cuda_set_device(nets[i]->gpu_index); + layer l = nets[i]->layers[j]; + distribute_weights(l, base); + } +} + +typedef struct{ + network **nets; + int n; + int j; +} sync_args; + +void *sync_layer_thread(void *ptr) +{ + sync_args args = *(sync_args*)ptr; + sync_layer(args.nets, args.n, args.j); + free(ptr); + return 0; +} + +pthread_t sync_layer_in_thread(network **nets, int n, int j) +{ + pthread_t thread; + sync_args *ptr = (sync_args *)calloc(1, sizeof(sync_args)); + ptr->nets = nets; + ptr->n = n; + ptr->j = j; + if(pthread_create(&thread, 0, sync_layer_thread, ptr)) error("Thread creation failed"); + return thread; +} + +void sync_nets(network **nets, int n, int interval) +{ + int j; + int layers = nets[0]->n; + pthread_t *threads = (pthread_t *) calloc(layers, sizeof(pthread_t)); + + *(nets[0]->seen) += interval * (n-1) * nets[0]->batch * nets[0]->subdivisions; + for (j = 0; j < n; ++j){ + *(nets[j]->seen) = *(nets[0]->seen); + } + for (j = 0; j < layers; ++j) { + threads[j] = sync_layer_in_thread(nets, n, j); + } + for (j = 0; j < layers; ++j) { + pthread_join(threads[j], 0); + } + free(threads); +} + +float train_networks(network **nets, int n, data d, int interval) +{ + int i; + int batch = nets[0]->batch; + int subdivisions = nets[0]->subdivisions; + assert(batch * subdivisions * n == d.X.rows); + pthread_t *threads = (pthread_t *) calloc(n, sizeof(pthread_t)); + float *errors = (float *) calloc(n, sizeof(float)); + + float sum = 0; + for(i = 0; i < n; ++i){ + data p = get_data_part(d, i, n); + threads[i] = train_network_in_thread(nets[i], p, errors + i); + } + for(i = 0; i < n; ++i){ + pthread_join(threads[i], 0); + //printf("%f\n", errors[i]); + sum += errors[i]; + } + //cudaDeviceSynchronize(); + if (get_current_batch(nets[0]) % interval == 0) { + printf("Syncing... "); + fflush(stdout); + sync_nets(nets, n, interval); + printf("Done!\n"); + } + //cudaDeviceSynchronize(); + free(threads); + free(errors); + return (float)sum/(n); +} + +void pull_network_output(network *net) +{ + layer l = get_network_output_layer(net); + cuda_pull_array(l.output_gpu, l.output, l.outputs*l.batch); +} + +#endif diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/network.h b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/network.h new file mode 100644 index 00000000000..1b0dfd1aaa3 --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/network.h @@ -0,0 +1,29 @@ +// Oh boy, why am I about to do this.... +#ifndef NETWORK_H +#define NETWORK_H +#include "darknet.h" + +#include "image.h" +#include "layer.h" +#include "data.h" +#include "tree.h" + + +#ifdef GPU +void pull_network_output(network *net); +#endif + +void compare_networks(network *n1, network *n2, data d); +char *get_layer_string(LAYER_TYPE a); + +network *make_network(int n); + + +float network_accuracy_multi(network *net, data d, int n); +int get_predicted_class_network(network *net); +void print_network(network *net); +int resize_network(network *net, int w, int h); +void calc_network_cost(network *net); + +#endif + diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/normalization_layer.c b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/normalization_layer.c new file mode 100644 index 00000000000..424714fe865 --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/normalization_layer.c @@ -0,0 +1,151 @@ +#include "normalization_layer.h" +#include "blas.h" + +#include + +layer make_normalization_layer(int batch, int w, int h, int c, int size, float alpha, float beta, float kappa) +{ + fprintf(stderr, "Local Response Normalization Layer: %d x %d x %d image, %d size\n", w,h,c,size); + layer layer = {0}; + layer.type = NORMALIZATION; + layer.batch = batch; + layer.h = layer.out_h = h; + layer.w = layer.out_w = w; + layer.c = layer.out_c = c; + layer.kappa = kappa; + layer.size = size; + layer.alpha = alpha; + layer.beta = beta; + layer.output = calloc(h * w * c * batch, sizeof(float)); + layer.delta = calloc(h * w * c * batch, sizeof(float)); + layer.squared = calloc(h * w * c * batch, sizeof(float)); + layer.norms = calloc(h * w * c * batch, sizeof(float)); + layer.inputs = w*h*c; + layer.outputs = layer.inputs; + + layer.forward = forward_normalization_layer; + layer.backward = backward_normalization_layer; + #ifdef GPU + layer.forward_gpu = forward_normalization_layer_gpu; + layer.backward_gpu = backward_normalization_layer_gpu; + + layer.output_gpu = cuda_make_array(layer.output, h * w * c * batch); + layer.delta_gpu = cuda_make_array(layer.delta, h * w * c * batch); + layer.squared_gpu = cuda_make_array(layer.squared, h * w * c * batch); + layer.norms_gpu = cuda_make_array(layer.norms, h * w * c * batch); + #endif + return layer; +} + +void resize_normalization_layer(layer *layer, int w, int h) +{ + int c = layer->c; + int batch = layer->batch; + layer->h = h; + layer->w = w; + layer->out_h = h; + layer->out_w = w; + layer->inputs = w*h*c; + layer->outputs = layer->inputs; + layer->output = realloc(layer->output, h * w * c * batch * sizeof(float)); + layer->delta = realloc(layer->delta, h * w * c * batch * sizeof(float)); + layer->squared = realloc(layer->squared, h * w * c * batch * sizeof(float)); + layer->norms = realloc(layer->norms, h * w * c * batch * sizeof(float)); +#ifdef GPU + cuda_free(layer->output_gpu); + cuda_free(layer->delta_gpu); + cuda_free(layer->squared_gpu); + cuda_free(layer->norms_gpu); + layer->output_gpu = cuda_make_array(layer->output, h * w * c * batch); + layer->delta_gpu = cuda_make_array(layer->delta, h * w * c * batch); + layer->squared_gpu = cuda_make_array(layer->squared, h * w * c * batch); + layer->norms_gpu = cuda_make_array(layer->norms, h * w * c * batch); +#endif +} + +void forward_normalization_layer(const layer layer, network net) +{ + int k,b; + int w = layer.w; + int h = layer.h; + int c = layer.c; + scal_cpu(w*h*c*layer.batch, 0, layer.squared, 1); + + for(b = 0; b < layer.batch; ++b){ + float *squared = layer.squared + w*h*c*b; + float *norms = layer.norms + w*h*c*b; + float *input = net.input + w*h*c*b; + pow_cpu(w*h*c, 2, input, 1, squared, 1); + + const_cpu(w*h, layer.kappa, norms, 1); + for(k = 0; k < layer.size/2; ++k){ + axpy_cpu(w*h, layer.alpha, squared + w*h*k, 1, norms, 1); + } + + for(k = 1; k < layer.c; ++k){ + copy_cpu(w*h, norms + w*h*(k-1), 1, norms + w*h*k, 1); + int prev = k - ((layer.size-1)/2) - 1; + int next = k + (layer.size/2); + if(prev >= 0) axpy_cpu(w*h, -layer.alpha, squared + w*h*prev, 1, norms + w*h*k, 1); + if(next < layer.c) axpy_cpu(w*h, layer.alpha, squared + w*h*next, 1, norms + w*h*k, 1); + } + } + pow_cpu(w*h*c*layer.batch, -layer.beta, layer.norms, 1, layer.output, 1); + mul_cpu(w*h*c*layer.batch, net.input, 1, layer.output, 1); +} + +void backward_normalization_layer(const layer layer, network net) +{ + // TODO This is approximate ;-) + // Also this should add in to delta instead of overwritting. + + int w = layer.w; + int h = layer.h; + int c = layer.c; + pow_cpu(w*h*c*layer.batch, -layer.beta, layer.norms, 1, net.delta, 1); + mul_cpu(w*h*c*layer.batch, layer.delta, 1, net.delta, 1); +} + +#ifdef GPU +void forward_normalization_layer_gpu(const layer layer, network net) +{ + int k,b; + int w = layer.w; + int h = layer.h; + int c = layer.c; + scal_gpu(w*h*c*layer.batch, 0, layer.squared_gpu, 1); + + for(b = 0; b < layer.batch; ++b){ + float *squared = layer.squared_gpu + w*h*c*b; + float *norms = layer.norms_gpu + w*h*c*b; + float *input = net.input_gpu + w*h*c*b; + pow_gpu(w*h*c, 2, input, 1, squared, 1); + + const_gpu(w*h, layer.kappa, norms, 1); + for(k = 0; k < layer.size/2; ++k){ + axpy_gpu(w*h, layer.alpha, squared + w*h*k, 1, norms, 1); + } + + for(k = 1; k < layer.c; ++k){ + copy_gpu(w*h, norms + w*h*(k-1), 1, norms + w*h*k, 1); + int prev = k - ((layer.size-1)/2) - 1; + int next = k + (layer.size/2); + if(prev >= 0) axpy_gpu(w*h, -layer.alpha, squared + w*h*prev, 1, norms + w*h*k, 1); + if(next < layer.c) axpy_gpu(w*h, layer.alpha, squared + w*h*next, 1, norms + w*h*k, 1); + } + } + pow_gpu(w*h*c*layer.batch, -layer.beta, layer.norms_gpu, 1, layer.output_gpu, 1); + mul_gpu(w*h*c*layer.batch, net.input_gpu, 1, layer.output_gpu, 1); +} + +void backward_normalization_layer_gpu(const layer layer, network net) +{ + // TODO This is approximate ;-) + + int w = layer.w; + int h = layer.h; + int c = layer.c; + pow_gpu(w*h*c*layer.batch, -layer.beta, layer.norms_gpu, 1, net.delta_gpu, 1); + mul_gpu(w*h*c*layer.batch, layer.delta_gpu, 1, net.delta_gpu, 1); +} +#endif diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/normalization_layer.h b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/normalization_layer.h new file mode 100644 index 00000000000..665baa50662 --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/normalization_layer.h @@ -0,0 +1,19 @@ +#ifndef NORMALIZATION_LAYER_H +#define NORMALIZATION_LAYER_H + +#include "image.h" +#include "layer.h" +#include "network.h" + +layer make_normalization_layer(int batch, int w, int h, int c, int size, float alpha, float beta, float kappa); +void resize_normalization_layer(layer *layer, int h, int w); +void forward_normalization_layer(const layer layer, network net); +void backward_normalization_layer(const layer layer, network net); +void visualize_normalization_layer(layer layer, char *window); + +#ifdef GPU +void forward_normalization_layer_gpu(const layer layer, network net); +void backward_normalization_layer_gpu(const layer layer, network net); +#endif + +#endif diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/option_list.c b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/option_list.c new file mode 100644 index 00000000000..2f52781f809 --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/option_list.c @@ -0,0 +1,140 @@ +#include +#include +#include +#include "option_list.h" +#include "utils.h" + +list *read_data_cfg(char *filename) +{ + FILE *file = fopen(filename, "r"); + if(file == 0) file_error(filename); + char *line; + int nu = 0; + list *options = make_list(); + while((line=fgetl(file)) != 0){ + ++ nu; + strip(line); + switch(line[0]){ + case '\0': + case '#': + case ';': + free(line); + break; + default: + if(!read_option(line, options)){ + fprintf(stderr, "Config file error line %d, could parse: %s\n", nu, line); + free(line); + } + break; + } + } + fclose(file); + return options; +} + +metadata get_metadata(char *file) +{ + metadata m = {0}; + list *options = read_data_cfg(file); + + char *name_list = option_find_str(options, "names", 0); + if(!name_list) name_list = option_find_str(options, "labels", 0); + if(!name_list) { + fprintf(stderr, "No names or labels found\n"); + } else { + m.names = get_labels(name_list); + } + m.classes = option_find_int(options, "classes", 2); + free_list(options); + return m; +} + +int read_option(char *s, list *options) +{ + size_t i; + size_t len = strlen(s); + char *val = 0; + for(i = 0; i < len; ++i){ + if(s[i] == '='){ + s[i] = '\0'; + val = s+i+1; + break; + } + } + if(i == len-1) return 0; + char *key = s; + option_insert(options, key, val); + return 1; +} + +void option_insert(list *l, char *key, char *val) +{ + kvp *p = malloc(sizeof(kvp)); + p->key = key; + p->val = val; + p->used = 0; + list_insert(l, p); +} + +void option_unused(list *l) +{ + node *n = l->front; + while(n){ + kvp *p = (kvp *)n->val; + if(!p->used){ + fprintf(stderr, "Unused field: '%s = %s'\n", p->key, p->val); + } + n = n->next; + } +} + +char *option_find(list *l, char *key) +{ + node *n = l->front; + while(n){ + kvp *p = (kvp *)n->val; + if(strcmp(p->key, key) == 0){ + p->used = 1; + return p->val; + } + n = n->next; + } + return 0; +} +char *option_find_str(list *l, char *key, char *def) +{ + char *v = option_find(l, key); + if(v) return v; + if(def) fprintf(stderr, "%s: Using default '%s'\n", key, def); + return def; +} + +int option_find_int(list *l, char *key, int def) +{ + char *v = option_find(l, key); + if(v) return atoi(v); + fprintf(stderr, "%s: Using default '%d'\n", key, def); + return def; +} + +int option_find_int_quiet(list *l, char *key, int def) +{ + char *v = option_find(l, key); + if(v) return atoi(v); + return def; +} + +float option_find_float_quiet(list *l, char *key, float def) +{ + char *v = option_find(l, key); + if(v) return atof(v); + return def; +} + +float option_find_float(list *l, char *key, float def) +{ + char *v = option_find(l, key); + if(v) return atof(v); + fprintf(stderr, "%s: Using default '%lf'\n", key, def); + return def; +} diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/option_list.h b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/option_list.h new file mode 100644 index 00000000000..844bd8724b7 --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/option_list.h @@ -0,0 +1,19 @@ +#ifndef OPTION_LIST_H +#define OPTION_LIST_H +#include "list.h" + +typedef struct{ + char *key; + char *val; + int used; +} kvp; + + +int read_option(char *s, list *options); +void option_insert(list *l, char *key, char *val); +char *option_find(list *l, char *key); +float option_find_float(list *l, char *key, float def); +float option_find_float_quiet(list *l, char *key, float def); +void option_unused(list *l); + +#endif diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/parser.c b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/parser.c new file mode 100644 index 00000000000..8b43f3c540d --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/parser.c @@ -0,0 +1,1296 @@ +#include +#include +#include +#include + +#include "activation_layer.h" +#include "logistic_layer.h" +#include "l2norm_layer.h" +#include "activations.h" +#include "avgpool_layer.h" +#include "batchnorm_layer.h" +#include "blas.h" +#include "connected_layer.h" +#include "deconvolutional_layer.h" +#include "convolutional_layer.h" +#include "cost_layer.h" +#include "crnn_layer.h" +#include "crop_layer.h" +#include "detection_layer.h" +#include "dropout_layer.h" +#include "gru_layer.h" +#include "list.h" +#include "local_layer.h" +#include "maxpool_layer.h" +#include "normalization_layer.h" +#include "option_list.h" +#include "parser.h" +#include "region_layer.h" +#include "yolo_layer.h" +#include "reorg_layer.h" +#include "rnn_layer.h" +#include "route_layer.h" +#include "upsample_layer.h" +#include "shortcut_layer.h" +#include "softmax_layer.h" +#include "lstm_layer.h" +#include "utils.h" + +typedef struct{ + char *type; + list *options; +}section; + +list *read_cfg(char *filename); + +LAYER_TYPE string_to_layer_type(char * type) +{ + + if (strcmp(type, "[shortcut]")==0) return SHORTCUT; + if (strcmp(type, "[crop]")==0) return CROP; + if (strcmp(type, "[cost]")==0) return COST; + if (strcmp(type, "[detection]")==0) return DETECTION; + if (strcmp(type, "[region]")==0) return REGION; + if (strcmp(type, "[yolo]")==0) return YOLO; + if (strcmp(type, "[local]")==0) return LOCAL; + if (strcmp(type, "[conv]")==0 + || strcmp(type, "[convolutional]")==0) return CONVOLUTIONAL; + if (strcmp(type, "[deconv]")==0 + || strcmp(type, "[deconvolutional]")==0) return DECONVOLUTIONAL; + if (strcmp(type, "[activation]")==0) return ACTIVE; + if (strcmp(type, "[logistic]")==0) return LOGXENT; + if (strcmp(type, "[l2norm]")==0) return L2NORM; + if (strcmp(type, "[net]")==0 + || strcmp(type, "[network]")==0) return NETWORK; + if (strcmp(type, "[crnn]")==0) return CRNN; + if (strcmp(type, "[gru]")==0) return GRU; + if (strcmp(type, "[lstm]") == 0) return LSTM; + if (strcmp(type, "[rnn]")==0) return RNN; + if (strcmp(type, "[conn]")==0 + || strcmp(type, "[connected]")==0) return CONNECTED; + if (strcmp(type, "[max]")==0 + || strcmp(type, "[maxpool]")==0) return MAXPOOL; + if (strcmp(type, "[reorg]")==0) return REORG; + if (strcmp(type, "[avg]")==0 + || strcmp(type, "[avgpool]")==0) return AVGPOOL; + if (strcmp(type, "[dropout]")==0) return DROPOUT; + if (strcmp(type, "[lrn]")==0 + || strcmp(type, "[normalization]")==0) return NORMALIZATION; + if (strcmp(type, "[batchnorm]")==0) return BATCHNORM; + if (strcmp(type, "[soft]")==0 + || strcmp(type, "[softmax]")==0) return SOFTMAX; + if (strcmp(type, "[route]")==0) return ROUTE; + if (strcmp(type, "[upsample]")==0) return UPSAMPLE; + return BLANK; +} + +void free_section(section *s) +{ + free(s->type); + node *n = s->options->front; + while(n){ + kvp *pair = (kvp *)n->val; + free(pair->key); + free(pair); + node *next = n->next; + free(n); + n = next; + } + free(s->options); + free(s); +} + +void parse_data(char *data, float *a, int n) +{ + int i; + if(!data) return; + char *curr = data; + char *next = data; + int done = 0; + for(i = 0; i < n && !done; ++i){ + while(*++next !='\0' && *next != ','); + if(*next == '\0') done = 1; + *next = '\0'; + sscanf(curr, "%g", &a[i]); + curr = next+1; + } +} + +typedef struct size_params{ + int batch; + int inputs; + int h; + int w; + int c; + int index; + int time_steps; + network *net; +} size_params; + +local_layer parse_local(list *options, size_params params) +{ + int n = option_find_int(options, "filters",1); + int size = option_find_int(options, "size",1); + int stride = option_find_int(options, "stride",1); + int pad = option_find_int(options, "pad",0); + char *activation_s = option_find_str(options, "activation", "logistic"); + ACTIVATION activation = get_activation(activation_s); + + int batch,h,w,c; + h = params.h; + w = params.w; + c = params.c; + batch=params.batch; + if(!(h && w && c)) error("Layer before local layer must output image."); + + local_layer layer = make_local_layer(batch,h,w,c,n,size,stride,pad,activation); + + return layer; +} + +layer parse_deconvolutional(list *options, size_params params) +{ + int n = option_find_int(options, "filters",1); + int size = option_find_int(options, "size",1); + int stride = option_find_int(options, "stride",1); + + char *activation_s = option_find_str(options, "activation", "logistic"); + ACTIVATION activation = get_activation(activation_s); + + int batch,h,w,c; + h = params.h; + w = params.w; + c = params.c; + batch=params.batch; + if(!(h && w && c)) error("Layer before deconvolutional layer must output image."); + int batch_normalize = option_find_int_quiet(options, "batch_normalize", 0); + int pad = option_find_int_quiet(options, "pad",0); + int padding = option_find_int_quiet(options, "padding",0); + if(pad) padding = size/2; + + layer l = make_deconvolutional_layer(batch,h,w,c,n,size,stride,padding, activation, batch_normalize, params.net->adam); + + return l; +} + + +convolutional_layer parse_convolutional(list *options, size_params params) +{ + int n = option_find_int(options, "filters",1); + int size = option_find_int(options, "size",1); + int stride = option_find_int(options, "stride",1); + int pad = option_find_int_quiet(options, "pad",0); + int padding = option_find_int_quiet(options, "padding",0); + int groups = option_find_int_quiet(options, "groups", 1); + if(pad) padding = size/2; + + char *activation_s = option_find_str(options, "activation", "logistic"); + ACTIVATION activation = get_activation(activation_s); + + int batch,h,w,c; + h = params.h; + w = params.w; + c = params.c; + batch=params.batch; + if(!(h && w && c)) error("Layer before convolutional layer must output image."); + int batch_normalize = option_find_int_quiet(options, "batch_normalize", 0); + int binary = option_find_int_quiet(options, "binary", 0); + int xnor = option_find_int_quiet(options, "xnor", 0); + + convolutional_layer layer = make_convolutional_layer(batch,h,w,c,n,groups,size,stride,padding,activation, batch_normalize, binary, xnor, params.net->adam); + layer.flipped = option_find_int_quiet(options, "flipped", 0); + layer.dot = option_find_float_quiet(options, "dot", 0); + + return layer; +} + +layer parse_crnn(list *options, size_params params) +{ + int output_filters = option_find_int(options, "output_filters",1); + int hidden_filters = option_find_int(options, "hidden_filters",1); + char *activation_s = option_find_str(options, "activation", "logistic"); + ACTIVATION activation = get_activation(activation_s); + int batch_normalize = option_find_int_quiet(options, "batch_normalize", 0); + + layer l = make_crnn_layer(params.batch, params.w, params.h, params.c, hidden_filters, output_filters, params.time_steps, activation, batch_normalize); + + l.shortcut = option_find_int_quiet(options, "shortcut", 0); + + return l; +} + +layer parse_rnn(list *options, size_params params) +{ + int output = option_find_int(options, "output",1); + char *activation_s = option_find_str(options, "activation", "logistic"); + ACTIVATION activation = get_activation(activation_s); + int batch_normalize = option_find_int_quiet(options, "batch_normalize", 0); + + layer l = make_rnn_layer(params.batch, params.inputs, output, params.time_steps, activation, batch_normalize, params.net->adam); + + l.shortcut = option_find_int_quiet(options, "shortcut", 0); + + return l; +} + +layer parse_gru(list *options, size_params params) +{ + int output = option_find_int(options, "output",1); + int batch_normalize = option_find_int_quiet(options, "batch_normalize", 0); + + layer l = make_gru_layer(params.batch, params.inputs, output, params.time_steps, batch_normalize, params.net->adam); + l.tanh = option_find_int_quiet(options, "tanh", 0); + + return l; +} + +layer parse_lstm(list *options, size_params params) +{ + int output = option_find_int(options, "output", 1); + int batch_normalize = option_find_int_quiet(options, "batch_normalize", 0); + + layer l = make_lstm_layer(params.batch, params.inputs, output, params.time_steps, batch_normalize, params.net->adam); + + return l; +} + +layer parse_connected(list *options, size_params params) +{ + int output = option_find_int(options, "output",1); + char *activation_s = option_find_str(options, "activation", "logistic"); + ACTIVATION activation = get_activation(activation_s); + int batch_normalize = option_find_int_quiet(options, "batch_normalize", 0); + + layer l = make_connected_layer(params.batch, params.inputs, output, activation, batch_normalize, params.net->adam); + return l; +} + +softmax_layer parse_softmax(list *options, size_params params) +{ + int groups = option_find_int_quiet(options, "groups",1); + softmax_layer layer = make_softmax_layer(params.batch, params.inputs, groups); + layer.temperature = option_find_float_quiet(options, "temperature", 1); + char *tree_file = option_find_str(options, "tree", 0); + if (tree_file) layer.softmax_tree = read_tree(tree_file); + layer.w = params.w; + layer.h = params.h; + layer.c = params.c; + layer.spatial = option_find_float_quiet(options, "spatial", 0); + return layer; +} + +int *parse_yolo_mask(char *a, int *num) +{ + int *mask = 0; + if(a){ + int len = strlen(a); + int n = 1; + int i; + for(i = 0; i < len; ++i){ + if (a[i] == ',') ++n; + } + mask = calloc(n, sizeof(int)); + for(i = 0; i < n; ++i){ + int val = atoi(a); + mask[i] = val; + a = strchr(a, ',')+1; + } + *num = n; + } + return mask; +} + +layer parse_yolo(list *options, size_params params) +{ + int classes = option_find_int(options, "classes", 20); + int total = option_find_int(options, "num", 1); + int num = total; + + char *a = option_find_str(options, "mask", 0); + int *mask = parse_yolo_mask(a, &num); + layer l = make_yolo_layer(params.batch, params.w, params.h, num, total, mask, classes); + assert(l.outputs == params.inputs); + + l.max_boxes = option_find_int_quiet(options, "max",90); + l.jitter = option_find_float(options, "jitter", .2); + + l.ignore_thresh = option_find_float(options, "ignore_thresh", .5); + l.truth_thresh = option_find_float(options, "truth_thresh", 1); + l.random = option_find_int_quiet(options, "random", 0); + + char *map_file = option_find_str(options, "map", 0); + if (map_file) l.map = read_map(map_file); + + a = option_find_str(options, "anchors", 0); + if(a){ + int len = strlen(a); + int n = 1; + int i; + for(i = 0; i < len; ++i){ + if (a[i] == ',') ++n; + } + for(i = 0; i < n; ++i){ + float bias = atof(a); + l.biases[i] = bias; + a = strchr(a, ',')+1; + } + } + return l; +} + +layer parse_region(list *options, size_params params) +{ + int coords = option_find_int(options, "coords", 4); + int classes = option_find_int(options, "classes", 20); + int num = option_find_int(options, "num", 1); + + layer l = make_region_layer(params.batch, params.w, params.h, num, classes, coords); + assert(l.outputs == params.inputs); + + l.log = option_find_int_quiet(options, "log", 0); + l.sqrt = option_find_int_quiet(options, "sqrt", 0); + + l.softmax = option_find_int(options, "softmax", 0); + l.background = option_find_int_quiet(options, "background", 0); + l.max_boxes = option_find_int_quiet(options, "max",30); + l.jitter = option_find_float(options, "jitter", .2); + l.rescore = option_find_int_quiet(options, "rescore",0); + + l.thresh = option_find_float(options, "thresh", .5); + l.classfix = option_find_int_quiet(options, "classfix", 0); + l.absolute = option_find_int_quiet(options, "absolute", 0); + l.random = option_find_int_quiet(options, "random", 0); + + l.coord_scale = option_find_float(options, "coord_scale", 1); + l.object_scale = option_find_float(options, "object_scale", 1); + l.noobject_scale = option_find_float(options, "noobject_scale", 1); + l.mask_scale = option_find_float(options, "mask_scale", 1); + l.class_scale = option_find_float(options, "class_scale", 1); + l.bias_match = option_find_int_quiet(options, "bias_match",0); + + char *tree_file = option_find_str(options, "tree", 0); + if (tree_file) l.softmax_tree = read_tree(tree_file); + char *map_file = option_find_str(options, "map", 0); + if (map_file) l.map = read_map(map_file); + + char *a = option_find_str(options, "anchors", 0); + if(a){ + int len = strlen(a); + int n = 1; + int i; + for(i = 0; i < len; ++i){ + if (a[i] == ',') ++n; + } + for(i = 0; i < n; ++i){ + float bias = atof(a); + l.biases[i] = bias; + a = strchr(a, ',')+1; + } + } + return l; +} + +detection_layer parse_detection(list *options, size_params params) +{ + int coords = option_find_int(options, "coords", 1); + int classes = option_find_int(options, "classes", 1); + int rescore = option_find_int(options, "rescore", 0); + int num = option_find_int(options, "num", 1); + int side = option_find_int(options, "side", 7); + detection_layer layer = make_detection_layer(params.batch, params.inputs, num, side, classes, coords, rescore); + + layer.softmax = option_find_int(options, "softmax", 0); + layer.sqrt = option_find_int(options, "sqrt", 0); + + layer.max_boxes = option_find_int_quiet(options, "max",90); + layer.coord_scale = option_find_float(options, "coord_scale", 1); + layer.forced = option_find_int(options, "forced", 0); + layer.object_scale = option_find_float(options, "object_scale", 1); + layer.noobject_scale = option_find_float(options, "noobject_scale", 1); + layer.class_scale = option_find_float(options, "class_scale", 1); + layer.jitter = option_find_float(options, "jitter", .2); + layer.random = option_find_int_quiet(options, "random", 0); + layer.reorg = option_find_int_quiet(options, "reorg", 0); + return layer; +} + +cost_layer parse_cost(list *options, size_params params) +{ + char *type_s = option_find_str(options, "type", "sse"); + COST_TYPE type = get_cost_type(type_s); + float scale = option_find_float_quiet(options, "scale",1); + cost_layer layer = make_cost_layer(params.batch, params.inputs, type, scale); + layer.ratio = option_find_float_quiet(options, "ratio",0); + layer.noobject_scale = option_find_float_quiet(options, "noobj", 1); + layer.thresh = option_find_float_quiet(options, "thresh",0); + return layer; +} + +crop_layer parse_crop(list *options, size_params params) +{ + int crop_height = option_find_int(options, "crop_height",1); + int crop_width = option_find_int(options, "crop_width",1); + int flip = option_find_int(options, "flip",0); + float angle = option_find_float(options, "angle",0); + float saturation = option_find_float(options, "saturation",1); + float exposure = option_find_float(options, "exposure",1); + + int batch,h,w,c; + h = params.h; + w = params.w; + c = params.c; + batch=params.batch; + if(!(h && w && c)) error("Layer before crop layer must output image."); + + int noadjust = option_find_int_quiet(options, "noadjust",0); + + crop_layer l = make_crop_layer(batch,h,w,c,crop_height,crop_width,flip, angle, saturation, exposure); + l.shift = option_find_float(options, "shift", 0); + l.noadjust = noadjust; + return l; +} + +layer parse_reorg(list *options, size_params params) +{ + int stride = option_find_int(options, "stride",1); + int reverse = option_find_int_quiet(options, "reverse",0); + int flatten = option_find_int_quiet(options, "flatten",0); + int extra = option_find_int_quiet(options, "extra",0); + + int batch,h,w,c; + h = params.h; + w = params.w; + c = params.c; + batch=params.batch; + if(!(h && w && c)) error("Layer before reorg layer must output image."); + + layer layer = make_reorg_layer(batch,w,h,c,stride,reverse, flatten, extra); + return layer; +} + +maxpool_layer parse_maxpool(list *options, size_params params) +{ + int stride = option_find_int(options, "stride",1); + int size = option_find_int(options, "size",stride); + int padding = option_find_int_quiet(options, "padding", (size-1)/2); + + int batch,h,w,c; + h = params.h; + w = params.w; + c = params.c; + batch=params.batch; + if(!(h && w && c)) error("Layer before maxpool layer must output image."); + + maxpool_layer layer = make_maxpool_layer(batch,h,w,c,size,stride,padding); + return layer; +} + +avgpool_layer parse_avgpool(list *options, size_params params) +{ + int batch,w,h,c; + w = params.w; + h = params.h; + c = params.c; + batch=params.batch; + if(!(h && w && c)) error("Layer before avgpool layer must output image."); + + avgpool_layer layer = make_avgpool_layer(batch,w,h,c); + return layer; +} + +dropout_layer parse_dropout(list *options, size_params params) +{ + float probability = option_find_float(options, "probability", .5); + dropout_layer layer = make_dropout_layer(params.batch, params.inputs, probability); + layer.out_w = params.w; + layer.out_h = params.h; + layer.out_c = params.c; + return layer; +} + +layer parse_normalization(list *options, size_params params) +{ + float alpha = option_find_float(options, "alpha", .0001); + float beta = option_find_float(options, "beta" , .75); + float kappa = option_find_float(options, "kappa", 1); + int size = option_find_int(options, "size", 5); + layer l = make_normalization_layer(params.batch, params.w, params.h, params.c, size, alpha, beta, kappa); + return l; +} + +layer parse_batchnorm(list *options, size_params params) +{ + layer l = make_batchnorm_layer(params.batch, params.w, params.h, params.c); + return l; +} + +layer parse_shortcut(list *options, size_params params, network *net) +{ + char *l = option_find(options, "from"); + int index = atoi(l); + if(index < 0) index = params.index + index; + + int batch = params.batch; + layer from = net->layers[index]; + + layer s = make_shortcut_layer(batch, index, params.w, params.h, params.c, from.out_w, from.out_h, from.out_c); + + char *activation_s = option_find_str(options, "activation", "linear"); + ACTIVATION activation = get_activation(activation_s); + s.activation = activation; + s.alpha = option_find_float_quiet(options, "alpha", 1); + s.beta = option_find_float_quiet(options, "beta", 1); + return s; +} + + +layer parse_l2norm(list *options, size_params params) +{ + layer l = make_l2norm_layer(params.batch, params.inputs); + l.h = l.out_h = params.h; + l.w = l.out_w = params.w; + l.c = l.out_c = params.c; + return l; +} + + +layer parse_logistic(list *options, size_params params) +{ + layer l = make_logistic_layer(params.batch, params.inputs); + l.h = l.out_h = params.h; + l.w = l.out_w = params.w; + l.c = l.out_c = params.c; + return l; +} + +layer parse_activation(list *options, size_params params) +{ + char *activation_s = option_find_str(options, "activation", "linear"); + ACTIVATION activation = get_activation(activation_s); + + layer l = make_activation_layer(params.batch, params.inputs, activation); + + l.h = l.out_h = params.h; + l.w = l.out_w = params.w; + l.c = l.out_c = params.c; + + return l; +} + +layer parse_upsample(list *options, size_params params, network *net) +{ + + int stride = option_find_int(options, "stride",2); + layer l = make_upsample_layer(params.batch, params.w, params.h, params.c, stride); + l.scale = option_find_float_quiet(options, "scale", 1); + return l; +} + +route_layer parse_route(list *options, size_params params, network *net) +{ + char *l = option_find(options, "layers"); + int len = strlen(l); + if(!l) error("Route Layer must specify input layers"); + int n = 1; + int i; + for(i = 0; i < len; ++i){ + if (l[i] == ',') ++n; + } + + int *layers = calloc(n, sizeof(int)); + int *sizes = calloc(n, sizeof(int)); + for(i = 0; i < n; ++i){ + int index = atoi(l); + l = strchr(l, ',')+1; + if(index < 0) index = params.index + index; + layers[i] = index; + sizes[i] = net->layers[index].outputs; + } + int batch = params.batch; + + route_layer layer = make_route_layer(batch, n, layers, sizes); + + convolutional_layer first = net->layers[layers[0]]; + layer.out_w = first.out_w; + layer.out_h = first.out_h; + layer.out_c = first.out_c; + for(i = 1; i < n; ++i){ + int index = layers[i]; + convolutional_layer next = net->layers[index]; + if(next.out_w == first.out_w && next.out_h == first.out_h){ + layer.out_c += next.out_c; + }else{ + layer.out_h = layer.out_w = layer.out_c = 0; + } + } + + return layer; +} + +learning_rate_policy get_policy(char *s) +{ + if (strcmp(s, "random")==0) return RANDOM; + if (strcmp(s, "poly")==0) return POLY; + if (strcmp(s, "constant")==0) return CONSTANT; + if (strcmp(s, "step")==0) return STEP; + if (strcmp(s, "exp")==0) return EXP; + if (strcmp(s, "sigmoid")==0) return SIG; + if (strcmp(s, "steps")==0) return STEPS; + fprintf(stderr, "Couldn't find policy %s, going with constant\n", s); + return CONSTANT; +} + +void parse_net_options(list *options, network *net) +{ + net->batch = option_find_int(options, "batch",1); + net->learning_rate = option_find_float(options, "learning_rate", .001); + net->momentum = option_find_float(options, "momentum", .9); + net->decay = option_find_float(options, "decay", .0001); + int subdivs = option_find_int(options, "subdivisions",1); + net->time_steps = option_find_int_quiet(options, "time_steps",1); + net->notruth = option_find_int_quiet(options, "notruth",0); + net->batch /= subdivs; + net->batch *= net->time_steps; + net->subdivisions = subdivs; + net->random = option_find_int_quiet(options, "random", 0); + + net->adam = option_find_int_quiet(options, "adam", 0); + if(net->adam){ + net->B1 = option_find_float(options, "B1", .9); + net->B2 = option_find_float(options, "B2", .999); + net->eps = option_find_float(options, "eps", .0000001); + } + + net->h = option_find_int_quiet(options, "height",0); + net->w = option_find_int_quiet(options, "width",0); + net->c = option_find_int_quiet(options, "channels",0); + net->inputs = option_find_int_quiet(options, "inputs", net->h * net->w * net->c); + net->max_crop = option_find_int_quiet(options, "max_crop",net->w*2); + net->min_crop = option_find_int_quiet(options, "min_crop",net->w); + net->max_ratio = option_find_float_quiet(options, "max_ratio", (float) net->max_crop / net->w); + net->min_ratio = option_find_float_quiet(options, "min_ratio", (float) net->min_crop / net->w); + net->center = option_find_int_quiet(options, "center",0); + net->clip = option_find_float_quiet(options, "clip", 0); + + net->angle = option_find_float_quiet(options, "angle", 0); + net->aspect = option_find_float_quiet(options, "aspect", 1); + net->saturation = option_find_float_quiet(options, "saturation", 1); + net->exposure = option_find_float_quiet(options, "exposure", 1); + net->hue = option_find_float_quiet(options, "hue", 0); + + if(!net->inputs && !(net->h && net->w && net->c)) error("No input parameters supplied"); + + char *policy_s = option_find_str(options, "policy", "constant"); + net->policy = get_policy(policy_s); + net->burn_in = option_find_int_quiet(options, "burn_in", 0); + net->power = option_find_float_quiet(options, "power", 4); + if(net->policy == STEP){ + net->step = option_find_int(options, "step", 1); + net->scale = option_find_float(options, "scale", 1); + } else if (net->policy == STEPS){ + char *l = option_find(options, "steps"); + char *p = option_find(options, "scales"); + if(!l || !p) error("STEPS policy must have steps and scales in cfg file"); + + int len = strlen(l); + int n = 1; + int i; + for(i = 0; i < len; ++i){ + if (l[i] == ',') ++n; + } + int *steps = calloc(n, sizeof(int)); + float *scales = calloc(n, sizeof(float)); + for(i = 0; i < n; ++i){ + int step = atoi(l); + float scale = atof(p); + l = strchr(l, ',')+1; + p = strchr(p, ',')+1; + steps[i] = step; + scales[i] = scale; + } + net->scales = scales; + net->steps = steps; + net->num_steps = n; + } else if (net->policy == EXP){ + net->gamma = option_find_float(options, "gamma", 1); + } else if (net->policy == SIG){ + net->gamma = option_find_float(options, "gamma", 1); + net->step = option_find_int(options, "step", 1); + } else if (net->policy == POLY || net->policy == RANDOM){ + } + net->max_batches = option_find_int(options, "max_batches", 0); +} + +int is_network(section *s) +{ + return (strcmp(s->type, "[net]")==0 + || strcmp(s->type, "[network]")==0); +} + +network *parse_network_cfg(char *filename) +{ + list *sections = read_cfg(filename); + node *n = sections->front; + if(!n) error("Config file has no sections"); + network *net = make_network(sections->size - 1); + net->gpu_index = gpu_index; + size_params params; + + section *s = (section *)n->val; + list *options = s->options; + if(!is_network(s)) error("First section must be [net] or [network]"); + parse_net_options(options, net); + + params.h = net->h; + params.w = net->w; + params.c = net->c; + params.inputs = net->inputs; + params.batch = net->batch; + params.time_steps = net->time_steps; + params.net = net; + + size_t workspace_size = 0; + n = n->next; + int count = 0; + free_section(s); + fprintf(stderr, "layer filters size input output\n"); + while(n){ + params.index = count; + fprintf(stderr, "%5d ", count); + s = (section *)n->val; + options = s->options; + layer l = {0}; + LAYER_TYPE lt = string_to_layer_type(s->type); + if(lt == CONVOLUTIONAL){ + l = parse_convolutional(options, params); + }else if(lt == DECONVOLUTIONAL){ + l = parse_deconvolutional(options, params); + }else if(lt == LOCAL){ + l = parse_local(options, params); + }else if(lt == ACTIVE){ + l = parse_activation(options, params); + }else if(lt == LOGXENT){ + l = parse_logistic(options, params); + }else if(lt == L2NORM){ + l = parse_l2norm(options, params); + }else if(lt == RNN){ + l = parse_rnn(options, params); + }else if(lt == GRU){ + l = parse_gru(options, params); + }else if (lt == LSTM) { + l = parse_lstm(options, params); + }else if(lt == CRNN){ + l = parse_crnn(options, params); + }else if(lt == CONNECTED){ + l = parse_connected(options, params); + }else if(lt == CROP){ + l = parse_crop(options, params); + }else if(lt == COST){ + l = parse_cost(options, params); + }else if(lt == REGION){ + l = parse_region(options, params); + }else if(lt == YOLO){ + l = parse_yolo(options, params); + }else if(lt == DETECTION){ + l = parse_detection(options, params); + }else if(lt == SOFTMAX){ + l = parse_softmax(options, params); + net->hierarchy = l.softmax_tree; + }else if(lt == NORMALIZATION){ + l = parse_normalization(options, params); + }else if(lt == BATCHNORM){ + l = parse_batchnorm(options, params); + }else if(lt == MAXPOOL){ + l = parse_maxpool(options, params); + }else if(lt == REORG){ + l = parse_reorg(options, params); + }else if(lt == AVGPOOL){ + l = parse_avgpool(options, params); + }else if(lt == ROUTE){ + l = parse_route(options, params, net); + }else if(lt == UPSAMPLE){ + l = parse_upsample(options, params, net); + }else if(lt == SHORTCUT){ + l = parse_shortcut(options, params, net); + }else if(lt == DROPOUT){ + l = parse_dropout(options, params); + l.output = net->layers[count-1].output; + l.delta = net->layers[count-1].delta; +#ifdef GPU + l.output_gpu = net->layers[count-1].output_gpu; + l.delta_gpu = net->layers[count-1].delta_gpu; +#endif + }else{ + fprintf(stderr, "Type not recognized: %s\n", s->type); + } + l.clip = net->clip; + l.truth = option_find_int_quiet(options, "truth", 0); + l.onlyforward = option_find_int_quiet(options, "onlyforward", 0); + l.stopbackward = option_find_int_quiet(options, "stopbackward", 0); + l.dontsave = option_find_int_quiet(options, "dontsave", 0); + l.dontload = option_find_int_quiet(options, "dontload", 0); + l.dontloadscales = option_find_int_quiet(options, "dontloadscales", 0); + l.learning_rate_scale = option_find_float_quiet(options, "learning_rate", 1); + l.smooth = option_find_float_quiet(options, "smooth", 0); + option_unused(options); + net->layers[count] = l; + if (l.workspace_size > workspace_size) workspace_size = l.workspace_size; + free_section(s); + n = n->next; + ++count; + if(n){ + params.h = l.out_h; + params.w = l.out_w; + params.c = l.out_c; + params.inputs = l.outputs; + } + } + free_list(sections); + layer out = get_network_output_layer(net); + net->outputs = out.outputs; + net->truths = out.outputs; + if(net->layers[net->n-1].truths) net->truths = net->layers[net->n-1].truths; + net->output = out.output; + net->input = calloc(net->inputs*net->batch, sizeof(float)); + net->truth = calloc(net->truths*net->batch, sizeof(float)); +#ifdef GPU + net->output_gpu = out.output_gpu; + net->input_gpu = cuda_make_array(net->input, net->inputs*net->batch); + net->truth_gpu = cuda_make_array(net->truth, net->truths*net->batch); +#endif + if(workspace_size){ + //printf("%ld\n", workspace_size); +#ifdef GPU + if(gpu_index >= 0){ + net->workspace = cuda_make_array(0, (workspace_size-1)/sizeof(float)+1); + }else { + net->workspace = calloc(1, workspace_size); + } +#else + net->workspace = calloc(1, workspace_size); +#endif + } + return net; +} + +list *read_cfg(char *filename) +{ + FILE *file = fopen(filename, "r"); + if(file == 0) file_error(filename); + char *line; + int nu = 0; + list *options = make_list(); + section *current = 0; + while((line=fgetl(file)) != 0){ + ++ nu; + strip(line); + switch(line[0]){ + case '[': + current = malloc(sizeof(section)); + list_insert(options, current); + current->options = make_list(); + current->type = line; + break; + case '\0': + case '#': + case ';': + free(line); + break; + default: + if(!read_option(line, current->options)){ + fprintf(stderr, "Config file error line %d, could parse: %s\n", nu, line); + free(line); + } + break; + } + } + fclose(file); + return options; +} + +void save_convolutional_weights_binary(layer l, FILE *fp) +{ +#ifdef GPU + if(gpu_index >= 0){ + pull_convolutional_layer(l); + } +#endif + binarize_weights(l.weights, l.n, l.c*l.size*l.size, l.binary_weights); + int size = l.c*l.size*l.size; + int i, j, k; + fwrite(l.biases, sizeof(float), l.n, fp); + if (l.batch_normalize){ + fwrite(l.scales, sizeof(float), l.n, fp); + fwrite(l.rolling_mean, sizeof(float), l.n, fp); + fwrite(l.rolling_variance, sizeof(float), l.n, fp); + } + for(i = 0; i < l.n; ++i){ + float mean = l.binary_weights[i*size]; + if(mean < 0) mean = -mean; + fwrite(&mean, sizeof(float), 1, fp); + for(j = 0; j < size/8; ++j){ + int index = i*size + j*8; + unsigned char c = 0; + for(k = 0; k < 8; ++k){ + if (j*8 + k >= size) break; + if (l.binary_weights[index + k] > 0) c = (c | 1<= 0){ + pull_convolutional_layer(l); + } +#endif + int num = l.nweights; + fwrite(l.biases, sizeof(float), l.n, fp); + if (l.batch_normalize){ + fwrite(l.scales, sizeof(float), l.n, fp); + fwrite(l.rolling_mean, sizeof(float), l.n, fp); + fwrite(l.rolling_variance, sizeof(float), l.n, fp); + } + fwrite(l.weights, sizeof(float), num, fp); +} + +void save_batchnorm_weights(layer l, FILE *fp) +{ +#ifdef GPU + if(gpu_index >= 0){ + pull_batchnorm_layer(l); + } +#endif + fwrite(l.scales, sizeof(float), l.c, fp); + fwrite(l.rolling_mean, sizeof(float), l.c, fp); + fwrite(l.rolling_variance, sizeof(float), l.c, fp); +} + +void save_connected_weights(layer l, FILE *fp) +{ +#ifdef GPU + if(gpu_index >= 0){ + pull_connected_layer(l); + } +#endif + fwrite(l.biases, sizeof(float), l.outputs, fp); + fwrite(l.weights, sizeof(float), l.outputs*l.inputs, fp); + if (l.batch_normalize){ + fwrite(l.scales, sizeof(float), l.outputs, fp); + fwrite(l.rolling_mean, sizeof(float), l.outputs, fp); + fwrite(l.rolling_variance, sizeof(float), l.outputs, fp); + } +} + +void save_weights_upto(network *net, char *filename, int cutoff) +{ +#ifdef GPU + if(net->gpu_index >= 0){ + cuda_set_device(net->gpu_index); + } +#endif + fprintf(stderr, "Saving weights to %s\n", filename); + FILE *fp = fopen(filename, "wb"); + if(!fp) file_error(filename); + + int major = 0; + int minor = 2; + int revision = 0; + fwrite(&major, sizeof(int), 1, fp); + fwrite(&minor, sizeof(int), 1, fp); + fwrite(&revision, sizeof(int), 1, fp); + fwrite(net->seen, sizeof(size_t), 1, fp); + + int i; + for(i = 0; i < net->n && i < cutoff; ++i){ + layer l = net->layers[i]; + if (l.dontsave) continue; + if(l.type == CONVOLUTIONAL || l.type == DECONVOLUTIONAL){ + save_convolutional_weights(l, fp); + } if(l.type == CONNECTED){ + save_connected_weights(l, fp); + } if(l.type == BATCHNORM){ + save_batchnorm_weights(l, fp); + } if(l.type == RNN){ + save_connected_weights(*(l.input_layer), fp); + save_connected_weights(*(l.self_layer), fp); + save_connected_weights(*(l.output_layer), fp); + } if (l.type == LSTM) { + save_connected_weights(*(l.wi), fp); + save_connected_weights(*(l.wf), fp); + save_connected_weights(*(l.wo), fp); + save_connected_weights(*(l.wg), fp); + save_connected_weights(*(l.ui), fp); + save_connected_weights(*(l.uf), fp); + save_connected_weights(*(l.uo), fp); + save_connected_weights(*(l.ug), fp); + } if (l.type == GRU) { + if(1){ + save_connected_weights(*(l.wz), fp); + save_connected_weights(*(l.wr), fp); + save_connected_weights(*(l.wh), fp); + save_connected_weights(*(l.uz), fp); + save_connected_weights(*(l.ur), fp); + save_connected_weights(*(l.uh), fp); + }else{ + save_connected_weights(*(l.reset_layer), fp); + save_connected_weights(*(l.update_layer), fp); + save_connected_weights(*(l.state_layer), fp); + } + } if(l.type == CRNN){ + save_convolutional_weights(*(l.input_layer), fp); + save_convolutional_weights(*(l.self_layer), fp); + save_convolutional_weights(*(l.output_layer), fp); + } if(l.type == LOCAL){ +#ifdef GPU + if(gpu_index >= 0){ + pull_local_layer(l); + } +#endif + int locations = l.out_w*l.out_h; + int size = l.size*l.size*l.c*l.n*locations; + fwrite(l.biases, sizeof(float), l.outputs, fp); + fwrite(l.weights, sizeof(float), size, fp); + } + } + fclose(fp); +} +void save_weights(network *net, char *filename) +{ + save_weights_upto(net, filename, net->n); +} + +void transpose_matrix(float *a, int rows, int cols) +{ + float *transpose = calloc(rows*cols, sizeof(float)); + int x, y; + for(x = 0; x < rows; ++x){ + for(y = 0; y < cols; ++y){ + transpose[y*rows + x] = a[x*cols + y]; + } + } + memcpy(a, transpose, rows*cols*sizeof(float)); + free(transpose); +} + +void load_connected_weights(layer l, FILE *fp, int transpose) +{ + fread(l.biases, sizeof(float), l.outputs, fp); + fread(l.weights, sizeof(float), l.outputs*l.inputs, fp); + if(transpose){ + transpose_matrix(l.weights, l.inputs, l.outputs); + } + //printf("Biases: %f mean %f variance\n", mean_array(l.biases, l.outputs), variance_array(l.biases, l.outputs)); + //printf("Weights: %f mean %f variance\n", mean_array(l.weights, l.outputs*l.inputs), variance_array(l.weights, l.outputs*l.inputs)); + if (l.batch_normalize && (!l.dontloadscales)){ + fread(l.scales, sizeof(float), l.outputs, fp); + fread(l.rolling_mean, sizeof(float), l.outputs, fp); + fread(l.rolling_variance, sizeof(float), l.outputs, fp); + //printf("Scales: %f mean %f variance\n", mean_array(l.scales, l.outputs), variance_array(l.scales, l.outputs)); + //printf("rolling_mean: %f mean %f variance\n", mean_array(l.rolling_mean, l.outputs), variance_array(l.rolling_mean, l.outputs)); + //printf("rolling_variance: %f mean %f variance\n", mean_array(l.rolling_variance, l.outputs), variance_array(l.rolling_variance, l.outputs)); + } +#ifdef GPU + if(gpu_index >= 0){ + push_connected_layer(l); + } +#endif +} + +void load_batchnorm_weights(layer l, FILE *fp) +{ + fread(l.scales, sizeof(float), l.c, fp); + fread(l.rolling_mean, sizeof(float), l.c, fp); + fread(l.rolling_variance, sizeof(float), l.c, fp); +#ifdef GPU + if(gpu_index >= 0){ + push_batchnorm_layer(l); + } +#endif +} + +void load_convolutional_weights_binary(layer l, FILE *fp) +{ + fread(l.biases, sizeof(float), l.n, fp); + if (l.batch_normalize && (!l.dontloadscales)){ + fread(l.scales, sizeof(float), l.n, fp); + fread(l.rolling_mean, sizeof(float), l.n, fp); + fread(l.rolling_variance, sizeof(float), l.n, fp); + } + int size = l.c*l.size*l.size; + int i, j, k; + for(i = 0; i < l.n; ++i){ + float mean = 0; + fread(&mean, sizeof(float), 1, fp); + for(j = 0; j < size/8; ++j){ + int index = i*size + j*8; + unsigned char c = 0; + fread(&c, sizeof(char), 1, fp); + for(k = 0; k < 8; ++k){ + if (j*8 + k >= size) break; + l.weights[index + k] = (c & 1<= 0){ + push_convolutional_layer(l); + } +#endif +} + +void load_convolutional_weights(layer l, FILE *fp) +{ + if(l.binary){ + //load_convolutional_weights_binary(l, fp); + //return; + } + int num = l.nweights; + fread(l.biases, sizeof(float), l.n, fp); + if (l.batch_normalize && (!l.dontloadscales)){ + fread(l.scales, sizeof(float), l.n, fp); + fread(l.rolling_mean, sizeof(float), l.n, fp); + fread(l.rolling_variance, sizeof(float), l.n, fp); + if(0){ + int i; + for(i = 0; i < l.n; ++i){ + printf("%g, ", l.rolling_mean[i]); + } + printf("\n"); + for(i = 0; i < l.n; ++i){ + printf("%g, ", l.rolling_variance[i]); + } + printf("\n"); + } + if(0){ + fill_cpu(l.n, 0, l.rolling_mean, 1); + fill_cpu(l.n, 0, l.rolling_variance, 1); + } + if(0){ + int i; + for(i = 0; i < l.n; ++i){ + printf("%g, ", l.rolling_mean[i]); + } + printf("\n"); + for(i = 0; i < l.n; ++i){ + printf("%g, ", l.rolling_variance[i]); + } + printf("\n"); + } + } + fread(l.weights, sizeof(float), num, fp); + //if(l.c == 3) scal_cpu(num, 1./256, l.weights, 1); + if (l.flipped) { + transpose_matrix(l.weights, l.c*l.size*l.size, l.n); + } + //if (l.binary) binarize_weights(l.weights, l.n, l.c*l.size*l.size, l.weights); +#ifdef GPU + if(gpu_index >= 0){ + push_convolutional_layer(l); + } +#endif +} + + +void load_weights_upto(network *net, char *filename, int start, int cutoff) +{ +#ifdef GPU + if(net->gpu_index >= 0){ + cuda_set_device(net->gpu_index); + } +#endif + fprintf(stderr, "Loading weights from %s...", filename); + fflush(stdout); + FILE *fp = fopen(filename, "rb"); + if(!fp) file_error(filename); + + int major; + int minor; + int revision; + fread(&major, sizeof(int), 1, fp); + fread(&minor, sizeof(int), 1, fp); + fread(&revision, sizeof(int), 1, fp); + if ((major*10 + minor) >= 2 && major < 1000 && minor < 1000){ + fread(net->seen, sizeof(size_t), 1, fp); + } else { + int iseen = 0; + fread(&iseen, sizeof(int), 1, fp); + *net->seen = iseen; + } + int transpose = (major > 1000) || (minor > 1000); + + int i; + for(i = start; i < net->n && i < cutoff; ++i){ + layer l = net->layers[i]; + if (l.dontload) continue; + if(l.type == CONVOLUTIONAL || l.type == DECONVOLUTIONAL){ + load_convolutional_weights(l, fp); + } + if(l.type == CONNECTED){ + load_connected_weights(l, fp, transpose); + } + if(l.type == BATCHNORM){ + load_batchnorm_weights(l, fp); + } + if(l.type == CRNN){ + load_convolutional_weights(*(l.input_layer), fp); + load_convolutional_weights(*(l.self_layer), fp); + load_convolutional_weights(*(l.output_layer), fp); + } + if(l.type == RNN){ + load_connected_weights(*(l.input_layer), fp, transpose); + load_connected_weights(*(l.self_layer), fp, transpose); + load_connected_weights(*(l.output_layer), fp, transpose); + } + if (l.type == LSTM) { + load_connected_weights(*(l.wi), fp, transpose); + load_connected_weights(*(l.wf), fp, transpose); + load_connected_weights(*(l.wo), fp, transpose); + load_connected_weights(*(l.wg), fp, transpose); + load_connected_weights(*(l.ui), fp, transpose); + load_connected_weights(*(l.uf), fp, transpose); + load_connected_weights(*(l.uo), fp, transpose); + load_connected_weights(*(l.ug), fp, transpose); + } + if (l.type == GRU) { + if(1){ + load_connected_weights(*(l.wz), fp, transpose); + load_connected_weights(*(l.wr), fp, transpose); + load_connected_weights(*(l.wh), fp, transpose); + load_connected_weights(*(l.uz), fp, transpose); + load_connected_weights(*(l.ur), fp, transpose); + load_connected_weights(*(l.uh), fp, transpose); + }else{ + load_connected_weights(*(l.reset_layer), fp, transpose); + load_connected_weights(*(l.update_layer), fp, transpose); + load_connected_weights(*(l.state_layer), fp, transpose); + } + } + if(l.type == LOCAL){ + int locations = l.out_w*l.out_h; + int size = l.size*l.size*l.c*l.n*locations; + fread(l.biases, sizeof(float), l.outputs, fp); + fread(l.weights, sizeof(float), size, fp); +#ifdef GPU + if(gpu_index >= 0){ + push_local_layer(l); + } +#endif + } + } + fprintf(stderr, "Done!\n"); + fclose(fp); +} + +void load_weights(network *net, char *filename) +{ + load_weights_upto(net, filename, 0, net->n); +} + diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/parser.h b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/parser.h new file mode 100644 index 00000000000..81aef2c86f3 --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/parser.h @@ -0,0 +1,9 @@ +#ifndef PARSER_H +#define PARSER_H +#include "darknet.h" +#include "network.h" + +void save_network(network net, char *filename); +void save_weights_double(network net, char *filename); + +#endif diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/region_layer.c b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/region_layer.c new file mode 100644 index 00000000000..179f5e32a60 --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/region_layer.c @@ -0,0 +1,507 @@ +#include "region_layer.h" +#include "activations.h" +#include "blas.h" +#include "box.h" +#include "cuda.h" +#include "utils.h" + +#include +#include +#include +#include + +layer make_region_layer(int batch, int w, int h, int n, int classes, int coords) +{ + layer l = {0}; + l.type = REGION; + + l.n = n; + l.batch = batch; + l.h = h; + l.w = w; + l.c = n*(classes + coords + 1); + l.out_w = l.w; + l.out_h = l.h; + l.out_c = l.c; + l.classes = classes; + l.coords = coords; + l.cost = calloc(1, sizeof(float)); + l.biases = calloc(n*2, sizeof(float)); + l.bias_updates = calloc(n*2, sizeof(float)); + l.outputs = h*w*n*(classes + coords + 1); + l.inputs = l.outputs; + l.truths = 30*(l.coords + 1); + l.delta = calloc(batch*l.outputs, sizeof(float)); + l.output = calloc(batch*l.outputs, sizeof(float)); + int i; + for(i = 0; i < n*2; ++i){ + l.biases[i] = .5; + } + + l.forward = forward_region_layer; + l.backward = backward_region_layer; +#ifdef GPU + l.forward_gpu = forward_region_layer_gpu; + l.backward_gpu = backward_region_layer_gpu; + l.output_gpu = cuda_make_array(l.output, batch*l.outputs); + l.delta_gpu = cuda_make_array(l.delta, batch*l.outputs); +#endif + + fprintf(stderr, "detection\n"); + srand(0); + + return l; +} + +void resize_region_layer(layer *l, int w, int h) +{ + l->w = w; + l->h = h; + + l->outputs = h*w*l->n*(l->classes + l->coords + 1); + l->inputs = l->outputs; + + l->output = realloc(l->output, l->batch*l->outputs*sizeof(float)); + l->delta = realloc(l->delta, l->batch*l->outputs*sizeof(float)); + +#ifdef GPU + cuda_free(l->delta_gpu); + cuda_free(l->output_gpu); + + l->delta_gpu = cuda_make_array(l->delta, l->batch*l->outputs); + l->output_gpu = cuda_make_array(l->output, l->batch*l->outputs); +#endif +} + +box get_region_box(float *x, float *biases, int n, int index, int i, int j, int w, int h, int stride) +{ + box b; + b.x = (i + x[index + 0*stride]) / w; + b.y = (j + x[index + 1*stride]) / h; + b.w = exp(x[index + 2*stride]) * biases[2*n] / w; + b.h = exp(x[index + 3*stride]) * biases[2*n+1] / h; + return b; +} + +float delta_region_box(box truth, float *x, float *biases, int n, int index, int i, int j, int w, int h, float *delta, float scale, int stride) +{ + box pred = get_region_box(x, biases, n, index, i, j, w, h, stride); + float iou = box_iou(pred, truth); + + float tx = (truth.x*w - i); + float ty = (truth.y*h - j); + float tw = log(truth.w*w / biases[2*n]); + float th = log(truth.h*h / biases[2*n + 1]); + + delta[index + 0*stride] = scale * (tx - x[index + 0*stride]); + delta[index + 1*stride] = scale * (ty - x[index + 1*stride]); + delta[index + 2*stride] = scale * (tw - x[index + 2*stride]); + delta[index + 3*stride] = scale * (th - x[index + 3*stride]); + return iou; +} + +void delta_region_mask(float *truth, float *x, int n, int index, float *delta, int stride, int scale) +{ + int i; + for(i = 0; i < n; ++i){ + delta[index + i*stride] = scale*(truth[i] - x[index + i*stride]); + } +} + + +void delta_region_class(float *output, float *delta, int index, int class, int classes, tree *hier, float scale, int stride, float *avg_cat, int tag) +{ + int i, n; + if(hier){ + float pred = 1; + while(class >= 0){ + pred *= output[index + stride*class]; + int g = hier->group[class]; + int offset = hier->group_offset[g]; + for(i = 0; i < hier->group_size[g]; ++i){ + delta[index + stride*(offset + i)] = scale * (0 - output[index + stride*(offset + i)]); + } + delta[index + stride*class] = scale * (1 - output[index + stride*class]); + + class = hier->parent[class]; + } + *avg_cat += pred; + } else { + if (delta[index] && tag){ + delta[index + stride*class] = scale * (1 - output[index + stride*class]); + return; + } + for(n = 0; n < classes; ++n){ + delta[index + stride*n] = scale * (((n == class)?1 : 0) - output[index + stride*n]); + if(n == class) *avg_cat += output[index + stride*n]; + } + } +} + +float logit(float x) +{ + return log(x/(1.-x)); +} + +float tisnan(float x) +{ + return (x != x); +} + +int entry_index(layer l, int batch, int location, int entry) +{ + int n = location / (l.w*l.h); + int loc = location % (l.w*l.h); + return batch*l.outputs + n*l.w*l.h*(l.coords+l.classes+1) + entry*l.w*l.h + loc; +} + +void forward_region_layer(const layer l, network net) +{ + int i,j,b,t,n; + memcpy(l.output, net.input, l.outputs*l.batch*sizeof(float)); + +#ifndef GPU + for (b = 0; b < l.batch; ++b){ + for(n = 0; n < l.n; ++n){ + int index = entry_index(l, b, n*l.w*l.h, 0); + activate_array(l.output + index, 2*l.w*l.h, LOGISTIC); + index = entry_index(l, b, n*l.w*l.h, l.coords); + if(!l.background) activate_array(l.output + index, l.w*l.h, LOGISTIC); + index = entry_index(l, b, n*l.w*l.h, l.coords + 1); + if(!l.softmax && !l.softmax_tree) activate_array(l.output + index, l.classes*l.w*l.h, LOGISTIC); + } + } + if (l.softmax_tree){ + int i; + int count = l.coords + 1; + for (i = 0; i < l.softmax_tree->groups; ++i) { + int group_size = l.softmax_tree->group_size[i]; + softmax_cpu(net.input + count, group_size, l.batch, l.inputs, l.n*l.w*l.h, 1, l.n*l.w*l.h, l.temperature, l.output + count); + count += group_size; + } + } else if (l.softmax){ + int index = entry_index(l, 0, 0, l.coords + !l.background); + softmax_cpu(net.input + index, l.classes + l.background, l.batch*l.n, l.inputs/l.n, l.w*l.h, 1, l.w*l.h, 1, l.output + index); + } +#endif + + memset(l.delta, 0, l.outputs * l.batch * sizeof(float)); + if(!net.train) return; + float avg_iou = 0; + float recall = 0; + float avg_cat = 0; + float avg_obj = 0; + float avg_anyobj = 0; + int count = 0; + int class_count = 0; + *(l.cost) = 0; + for (b = 0; b < l.batch; ++b) { + if(l.softmax_tree){ + int onlyclass = 0; + for(t = 0; t < 30; ++t){ + box truth = float_to_box(net.truth + t*(l.coords + 1) + b*l.truths, 1); + if(!truth.x) break; + int class = net.truth[t*(l.coords + 1) + b*l.truths + l.coords]; + float maxp = 0; + int maxi = 0; + if(truth.x > 100000 && truth.y > 100000){ + for(n = 0; n < l.n*l.w*l.h; ++n){ + int class_index = entry_index(l, b, n, l.coords + 1); + int obj_index = entry_index(l, b, n, l.coords); + float scale = l.output[obj_index]; + l.delta[obj_index] = l.noobject_scale * (0 - l.output[obj_index]); + float p = scale*get_hierarchy_probability(l.output + class_index, l.softmax_tree, class, l.w*l.h); + if(p > maxp){ + maxp = p; + maxi = n; + } + } + int class_index = entry_index(l, b, maxi, l.coords + 1); + int obj_index = entry_index(l, b, maxi, l.coords); + delta_region_class(l.output, l.delta, class_index, class, l.classes, l.softmax_tree, l.class_scale, l.w*l.h, &avg_cat, !l.softmax); + if(l.output[obj_index] < .3) l.delta[obj_index] = l.object_scale * (.3 - l.output[obj_index]); + else l.delta[obj_index] = 0; + l.delta[obj_index] = 0; + ++class_count; + onlyclass = 1; + break; + } + } + if(onlyclass) continue; + } + for (j = 0; j < l.h; ++j) { + for (i = 0; i < l.w; ++i) { + for (n = 0; n < l.n; ++n) { + int box_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 0); + box pred = get_region_box(l.output, l.biases, n, box_index, i, j, l.w, l.h, l.w*l.h); + float best_iou = 0; + for(t = 0; t < 30; ++t){ + box truth = float_to_box(net.truth + t*(l.coords + 1) + b*l.truths, 1); + if(!truth.x) break; + float iou = box_iou(pred, truth); + if (iou > best_iou) { + best_iou = iou; + } + } + int obj_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, l.coords); + avg_anyobj += l.output[obj_index]; + l.delta[obj_index] = l.noobject_scale * (0 - l.output[obj_index]); + if(l.background) l.delta[obj_index] = l.noobject_scale * (1 - l.output[obj_index]); + if (best_iou > l.thresh) { + l.delta[obj_index] = 0; + } + + if(*(net.seen) < 12800){ + box truth = {0}; + truth.x = (i + .5)/l.w; + truth.y = (j + .5)/l.h; + truth.w = l.biases[2*n]/l.w; + truth.h = l.biases[2*n+1]/l.h; + delta_region_box(truth, l.output, l.biases, n, box_index, i, j, l.w, l.h, l.delta, .01, l.w*l.h); + } + } + } + } + for(t = 0; t < 30; ++t){ + box truth = float_to_box(net.truth + t*(l.coords + 1) + b*l.truths, 1); + + if(!truth.x) break; + float best_iou = 0; + int best_n = 0; + i = (truth.x * l.w); + j = (truth.y * l.h); + box truth_shift = truth; + truth_shift.x = 0; + truth_shift.y = 0; + for(n = 0; n < l.n; ++n){ + int box_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 0); + box pred = get_region_box(l.output, l.biases, n, box_index, i, j, l.w, l.h, l.w*l.h); + if(l.bias_match){ + pred.w = l.biases[2*n]/l.w; + pred.h = l.biases[2*n+1]/l.h; + } + pred.x = 0; + pred.y = 0; + float iou = box_iou(pred, truth_shift); + if (iou > best_iou){ + best_iou = iou; + best_n = n; + } + } + + int box_index = entry_index(l, b, best_n*l.w*l.h + j*l.w + i, 0); + float iou = delta_region_box(truth, l.output, l.biases, best_n, box_index, i, j, l.w, l.h, l.delta, l.coord_scale * (2 - truth.w*truth.h), l.w*l.h); + if(l.coords > 4){ + int mask_index = entry_index(l, b, best_n*l.w*l.h + j*l.w + i, 4); + delta_region_mask(net.truth + t*(l.coords + 1) + b*l.truths + 5, l.output, l.coords - 4, mask_index, l.delta, l.w*l.h, l.mask_scale); + } + if(iou > .5) recall += 1; + avg_iou += iou; + + int obj_index = entry_index(l, b, best_n*l.w*l.h + j*l.w + i, l.coords); + avg_obj += l.output[obj_index]; + l.delta[obj_index] = l.object_scale * (1 - l.output[obj_index]); + if (l.rescore) { + l.delta[obj_index] = l.object_scale * (iou - l.output[obj_index]); + } + if(l.background){ + l.delta[obj_index] = l.object_scale * (0 - l.output[obj_index]); + } + + int class = net.truth[t*(l.coords + 1) + b*l.truths + l.coords]; + if (l.map) class = l.map[class]; + int class_index = entry_index(l, b, best_n*l.w*l.h + j*l.w + i, l.coords + 1); + delta_region_class(l.output, l.delta, class_index, class, l.classes, l.softmax_tree, l.class_scale, l.w*l.h, &avg_cat, !l.softmax); + ++count; + ++class_count; + } + } + *(l.cost) = pow(mag_array(l.delta, l.outputs * l.batch), 2); + printf("Region Avg IOU: %f, Class: %f, Obj: %f, No Obj: %f, Avg Recall: %f, count: %d\n", avg_iou/count, avg_cat/class_count, avg_obj/count, avg_anyobj/(l.w*l.h*l.n*l.batch), recall/count, count); +} + +void backward_region_layer(const layer l, network net) +{ + /* + int b; + int size = l.coords + l.classes + 1; + for (b = 0; b < l.batch*l.n; ++b){ + int index = (b*size + 4)*l.w*l.h; + gradient_array(l.output + index, l.w*l.h, LOGISTIC, l.delta + index); + } + axpy_cpu(l.batch*l.inputs, 1, l.delta, 1, net.delta, 1); + */ +} + +void correct_region_boxes(detection *dets, int n, int w, int h, int netw, int neth, int relative) +{ + int i; + int new_w=0; + int new_h=0; + if (((float)netw/w) < ((float)neth/h)) { + new_w = netw; + new_h = (h * netw)/w; + } else { + new_h = neth; + new_w = (w * neth)/h; + } + for (i = 0; i < n; ++i){ + box b = dets[i].bbox; + b.x = (b.x - (netw - new_w)/2./netw) / ((float)new_w/netw); + b.y = (b.y - (neth - new_h)/2./neth) / ((float)new_h/neth); + b.w *= (float)netw/new_w; + b.h *= (float)neth/new_h; + if(!relative){ + b.x *= w; + b.w *= w; + b.y *= h; + b.h *= h; + } + dets[i].bbox = b; + } +} + +void get_region_detections(layer l, int w, int h, int netw, int neth, float thresh, int *map, float tree_thresh, int relative, detection *dets) +{ + int i,j,n,z; + float *predictions = l.output; + if (l.batch == 2) { + float *flip = l.output + l.outputs; + for (j = 0; j < l.h; ++j) { + for (i = 0; i < l.w/2; ++i) { + for (n = 0; n < l.n; ++n) { + for(z = 0; z < l.classes + l.coords + 1; ++z){ + int i1 = z*l.w*l.h*l.n + n*l.w*l.h + j*l.w + i; + int i2 = z*l.w*l.h*l.n + n*l.w*l.h + j*l.w + (l.w - i - 1); + float swap = flip[i1]; + flip[i1] = flip[i2]; + flip[i2] = swap; + if(z == 0){ + flip[i1] = -flip[i1]; + flip[i2] = -flip[i2]; + } + } + } + } + } + for(i = 0; i < l.outputs; ++i){ + l.output[i] = (l.output[i] + flip[i])/2.; + } + } + for (i = 0; i < l.w*l.h; ++i){ + int row = i / l.w; + int col = i % l.w; + for(n = 0; n < l.n; ++n){ + int index = n*l.w*l.h + i; + for(j = 0; j < l.classes; ++j){ + dets[index].prob[j] = 0; + } + int obj_index = entry_index(l, 0, n*l.w*l.h + i, l.coords); + int box_index = entry_index(l, 0, n*l.w*l.h + i, 0); + int mask_index = entry_index(l, 0, n*l.w*l.h + i, 4); + float scale = l.background ? 1 : predictions[obj_index]; + dets[index].bbox = get_region_box(predictions, l.biases, n, box_index, col, row, l.w, l.h, l.w*l.h); + dets[index].objectness = scale > thresh ? scale : 0; + if(dets[index].mask){ + for(j = 0; j < l.coords - 4; ++j){ + dets[index].mask[j] = l.output[mask_index + j*l.w*l.h]; + } + } + + int class_index = entry_index(l, 0, n*l.w*l.h + i, l.coords + !l.background); + if(l.softmax_tree){ + + hierarchy_predictions(predictions + class_index, l.classes, l.softmax_tree, 0, l.w*l.h); + if(map){ + for(j = 0; j < 200; ++j){ + int class_index = entry_index(l, 0, n*l.w*l.h + i, l.coords + 1 + map[j]); + float prob = scale*predictions[class_index]; + dets[index].prob[j] = (prob > thresh) ? prob : 0; + } + } else { + int j = hierarchy_top_prediction(predictions + class_index, l.softmax_tree, tree_thresh, l.w*l.h); + dets[index].prob[j] = (scale > thresh) ? scale : 0; + } + } else { + if(dets[index].objectness){ + for(j = 0; j < l.classes; ++j){ + int class_index = entry_index(l, 0, n*l.w*l.h + i, l.coords + 1 + j); + float prob = scale*predictions[class_index]; + dets[index].prob[j] = (prob > thresh) ? prob : 0; + } + } + } + } + } + correct_region_boxes(dets, l.w*l.h*l.n, w, h, netw, neth, relative); +} + +#ifdef GPU + +void forward_region_layer_gpu(const layer l, network net) +{ + copy_gpu(l.batch*l.inputs, net.input_gpu, 1, l.output_gpu, 1); + int b, n; + for (b = 0; b < l.batch; ++b){ + for(n = 0; n < l.n; ++n){ + int index = entry_index(l, b, n*l.w*l.h, 0); + activate_array_gpu(l.output_gpu + index, 2*l.w*l.h, LOGISTIC); + if(l.coords > 4){ + index = entry_index(l, b, n*l.w*l.h, 4); + activate_array_gpu(l.output_gpu + index, (l.coords - 4)*l.w*l.h, LOGISTIC); + } + index = entry_index(l, b, n*l.w*l.h, l.coords); + if(!l.background) activate_array_gpu(l.output_gpu + index, l.w*l.h, LOGISTIC); + index = entry_index(l, b, n*l.w*l.h, l.coords + 1); + if(!l.softmax && !l.softmax_tree) activate_array_gpu(l.output_gpu + index, l.classes*l.w*l.h, LOGISTIC); + } + } + if (l.softmax_tree){ + int index = entry_index(l, 0, 0, l.coords + 1); + softmax_tree(net.input_gpu + index, l.w*l.h, l.batch*l.n, l.inputs/l.n, 1, l.output_gpu + index, *l.softmax_tree); + } else if (l.softmax) { + int index = entry_index(l, 0, 0, l.coords + !l.background); + softmax_gpu(net.input_gpu + index, l.classes + l.background, l.batch*l.n, l.inputs/l.n, l.w*l.h, 1, l.w*l.h, 1, l.output_gpu + index); + } + if(!net.train || l.onlyforward){ + cuda_pull_array(l.output_gpu, l.output, l.batch*l.outputs); + return; + } + + cuda_pull_array(l.output_gpu, net.input, l.batch*l.inputs); + forward_region_layer(l, net); + //cuda_push_array(l.output_gpu, l.output, l.batch*l.outputs); + if(!net.train) return; + cuda_push_array(l.delta_gpu, l.delta, l.batch*l.outputs); +} + +void backward_region_layer_gpu(const layer l, network net) +{ + int b, n; + for (b = 0; b < l.batch; ++b){ + for(n = 0; n < l.n; ++n){ + int index = entry_index(l, b, n*l.w*l.h, 0); + gradient_array_gpu(l.output_gpu + index, 2*l.w*l.h, LOGISTIC, l.delta_gpu + index); + if(l.coords > 4){ + index = entry_index(l, b, n*l.w*l.h, 4); + gradient_array_gpu(l.output_gpu + index, (l.coords - 4)*l.w*l.h, LOGISTIC, l.delta_gpu + index); + } + index = entry_index(l, b, n*l.w*l.h, l.coords); + if(!l.background) gradient_array_gpu(l.output_gpu + index, l.w*l.h, LOGISTIC, l.delta_gpu + index); + } + } + axpy_gpu(l.batch*l.inputs, 1, l.delta_gpu, 1, net.delta_gpu, 1); +} +#endif + +void zero_objectness(layer l) +{ + int i, n; + for (i = 0; i < l.w*l.h; ++i){ + for(n = 0; n < l.n; ++n){ + int obj_index = entry_index(l, 0, n*l.w*l.h + i, l.coords); + l.output[obj_index] = 0; + } + } +} + diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/region_layer.h b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/region_layer.h new file mode 100644 index 00000000000..9f12fd187fd --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/region_layer.h @@ -0,0 +1,18 @@ +#ifndef REGION_LAYER_H +#define REGION_LAYER_H + +#include "darknet.h" +#include "layer.h" +#include "network.h" + +layer make_region_layer(int batch, int w, int h, int n, int classes, int coords); +void forward_region_layer(const layer l, network net); +void backward_region_layer(const layer l, network net); +void resize_region_layer(layer *l, int w, int h); + +#ifdef GPU +void forward_region_layer_gpu(const layer l, network net); +void backward_region_layer_gpu(layer l, network net); +#endif + +#endif diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/reorg_layer.c b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/reorg_layer.c new file mode 100644 index 00000000000..31d6b843676 --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/reorg_layer.c @@ -0,0 +1,173 @@ +#include "reorg_layer.h" +#include "cuda.h" +#include "blas.h" + +#include + + +layer make_reorg_layer(int batch, int w, int h, int c, int stride, int reverse, int flatten, int extra) +{ + layer l = {0}; + l.type = REORG; + l.batch = batch; + l.stride = stride; + l.extra = extra; + l.h = h; + l.w = w; + l.c = c; + l.flatten = flatten; + if(reverse){ + l.out_w = w*stride; + l.out_h = h*stride; + l.out_c = c/(stride*stride); + }else{ + l.out_w = w/stride; + l.out_h = h/stride; + l.out_c = c*(stride*stride); + } + l.reverse = reverse; + + l.outputs = l.out_h * l.out_w * l.out_c; + l.inputs = h*w*c; + if(l.extra){ + l.out_w = l.out_h = l.out_c = 0; + l.outputs = l.inputs + l.extra; + } + + if(extra){ + fprintf(stderr, "reorg %4d -> %4d\n", l.inputs, l.outputs); + } else { + fprintf(stderr, "reorg /%2d %4d x%4d x%4d -> %4d x%4d x%4d\n", stride, w, h, c, l.out_w, l.out_h, l.out_c); + } + int output_size = l.outputs * batch; + l.output = calloc(output_size, sizeof(float)); + l.delta = calloc(output_size, sizeof(float)); + + l.forward = forward_reorg_layer; + l.backward = backward_reorg_layer; +#ifdef GPU + l.forward_gpu = forward_reorg_layer_gpu; + l.backward_gpu = backward_reorg_layer_gpu; + + l.output_gpu = cuda_make_array(l.output, output_size); + l.delta_gpu = cuda_make_array(l.delta, output_size); +#endif + return l; +} + +void resize_reorg_layer(layer *l, int w, int h) +{ + int stride = l->stride; + int c = l->c; + + l->h = h; + l->w = w; + + if(l->reverse){ + l->out_w = w*stride; + l->out_h = h*stride; + l->out_c = c/(stride*stride); + }else{ + l->out_w = w/stride; + l->out_h = h/stride; + l->out_c = c*(stride*stride); + } + + l->outputs = l->out_h * l->out_w * l->out_c; + l->inputs = l->outputs; + int output_size = l->outputs * l->batch; + + l->output = realloc(l->output, output_size * sizeof(float)); + l->delta = realloc(l->delta, output_size * sizeof(float)); + +#ifdef GPU + cuda_free(l->output_gpu); + cuda_free(l->delta_gpu); + l->output_gpu = cuda_make_array(l->output, output_size); + l->delta_gpu = cuda_make_array(l->delta, output_size); +#endif +} + +void forward_reorg_layer(const layer l, network net) +{ + int i; + if(l.flatten){ + memcpy(l.output, net.input, l.outputs*l.batch*sizeof(float)); + if(l.reverse){ + flatten(l.output, l.w*l.h, l.c, l.batch, 0); + }else{ + flatten(l.output, l.w*l.h, l.c, l.batch, 1); + } + } else if (l.extra) { + for(i = 0; i < l.batch; ++i){ + copy_cpu(l.inputs, net.input + i*l.inputs, 1, l.output + i*l.outputs, 1); + } + } else if (l.reverse){ + reorg_cpu(net.input, l.w, l.h, l.c, l.batch, l.stride, 1, l.output); + } else { + reorg_cpu(net.input, l.w, l.h, l.c, l.batch, l.stride, 0, l.output); + } +} + +void backward_reorg_layer(const layer l, network net) +{ + int i; + if(l.flatten){ + memcpy(net.delta, l.delta, l.outputs*l.batch*sizeof(float)); + if(l.reverse){ + flatten(net.delta, l.w*l.h, l.c, l.batch, 1); + }else{ + flatten(net.delta, l.w*l.h, l.c, l.batch, 0); + } + } else if(l.reverse){ + reorg_cpu(l.delta, l.w, l.h, l.c, l.batch, l.stride, 0, net.delta); + } else if (l.extra) { + for(i = 0; i < l.batch; ++i){ + copy_cpu(l.inputs, l.delta + i*l.outputs, 1, net.delta + i*l.inputs, 1); + } + }else{ + reorg_cpu(l.delta, l.w, l.h, l.c, l.batch, l.stride, 1, net.delta); + } +} + +#ifdef GPU +void forward_reorg_layer_gpu(layer l, network net) +{ + int i; + if(l.flatten){ + if(l.reverse){ + flatten_gpu(net.input_gpu, l.w*l.h, l.c, l.batch, 0, l.output_gpu); + }else{ + flatten_gpu(net.input_gpu, l.w*l.h, l.c, l.batch, 1, l.output_gpu); + } + } else if (l.extra) { + for(i = 0; i < l.batch; ++i){ + copy_gpu(l.inputs, net.input_gpu + i*l.inputs, 1, l.output_gpu + i*l.outputs, 1); + } + } else if (l.reverse) { + reorg_gpu(net.input_gpu, l.w, l.h, l.c, l.batch, l.stride, 1, l.output_gpu); + }else { + reorg_gpu(net.input_gpu, l.w, l.h, l.c, l.batch, l.stride, 0, l.output_gpu); + } +} + +void backward_reorg_layer_gpu(layer l, network net) +{ + if(l.flatten){ + if(l.reverse){ + flatten_gpu(l.delta_gpu, l.w*l.h, l.c, l.batch, 1, net.delta_gpu); + }else{ + flatten_gpu(l.delta_gpu, l.w*l.h, l.c, l.batch, 0, net.delta_gpu); + } + } else if (l.extra) { + int i; + for(i = 0; i < l.batch; ++i){ + copy_gpu(l.inputs, l.delta_gpu + i*l.outputs, 1, net.delta_gpu + i*l.inputs, 1); + } + } else if(l.reverse){ + reorg_gpu(l.delta_gpu, l.w, l.h, l.c, l.batch, l.stride, 0, net.delta_gpu); + } else { + reorg_gpu(l.delta_gpu, l.w, l.h, l.c, l.batch, l.stride, 1, net.delta_gpu); + } +} +#endif diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/reorg_layer.h b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/reorg_layer.h new file mode 100644 index 00000000000..e6513a5f441 --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/reorg_layer.h @@ -0,0 +1,20 @@ +#ifndef REORG_LAYER_H +#define REORG_LAYER_H + +#include "image.h" +#include "cuda.h" +#include "layer.h" +#include "network.h" + +layer make_reorg_layer(int batch, int w, int h, int c, int stride, int reverse, int flatten, int extra); +void resize_reorg_layer(layer *l, int w, int h); +void forward_reorg_layer(const layer l, network net); +void backward_reorg_layer(const layer l, network net); + +#ifdef GPU +void forward_reorg_layer_gpu(layer l, network net); +void backward_reorg_layer_gpu(layer l, network net); +#endif + +#endif + diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/rnn_layer.c b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/rnn_layer.c new file mode 100644 index 00000000000..8c9b457e26e --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/rnn_layer.c @@ -0,0 +1,292 @@ +#include "rnn_layer.h" +#include "connected_layer.h" +#include "utils.h" +#include "cuda.h" +#include "blas.h" +#include "gemm.h" + +#include +#include +#include +#include + +static void increment_layer(layer *l, int steps) +{ + int num = l->outputs*l->batch*steps; + l->output += num; + l->delta += num; + l->x += num; + l->x_norm += num; + +#ifdef GPU + l->output_gpu += num; + l->delta_gpu += num; + l->x_gpu += num; + l->x_norm_gpu += num; +#endif +} + +layer make_rnn_layer(int batch, int inputs, int outputs, int steps, ACTIVATION activation, int batch_normalize, int adam) +{ + fprintf(stderr, "RNN Layer: %d inputs, %d outputs\n", inputs, outputs); + batch = batch / steps; + layer l = {0}; + l.batch = batch; + l.type = RNN; + l.steps = steps; + l.inputs = inputs; + + l.state = calloc(batch*outputs, sizeof(float)); + l.prev_state = calloc(batch*outputs, sizeof(float)); + + l.input_layer = malloc(sizeof(layer)); + fprintf(stderr, "\t\t"); + *(l.input_layer) = make_connected_layer(batch*steps, inputs, outputs, activation, batch_normalize, adam); + l.input_layer->batch = batch; + + l.self_layer = malloc(sizeof(layer)); + fprintf(stderr, "\t\t"); + *(l.self_layer) = make_connected_layer(batch*steps, outputs, outputs, activation, batch_normalize, adam); + l.self_layer->batch = batch; + + l.output_layer = malloc(sizeof(layer)); + fprintf(stderr, "\t\t"); + *(l.output_layer) = make_connected_layer(batch*steps, outputs, outputs, activation, batch_normalize, adam); + l.output_layer->batch = batch; + + l.outputs = outputs; + l.output = l.output_layer->output; + l.delta = l.output_layer->delta; + + l.forward = forward_rnn_layer; + l.backward = backward_rnn_layer; + l.update = update_rnn_layer; +#ifdef GPU + l.forward_gpu = forward_rnn_layer_gpu; + l.backward_gpu = backward_rnn_layer_gpu; + l.update_gpu = update_rnn_layer_gpu; + l.state_gpu = cuda_make_array(0, batch*outputs); + l.prev_state_gpu = cuda_make_array(0, batch*outputs); + l.output_gpu = l.output_layer->output_gpu; + l.delta_gpu = l.output_layer->delta_gpu; +#ifdef CUDNN + cudnnSetTensor4dDescriptor(l.input_layer->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.input_layer->out_c, l.input_layer->out_h, l.input_layer->out_w); + cudnnSetTensor4dDescriptor(l.self_layer->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.self_layer->out_c, l.self_layer->out_h, l.self_layer->out_w); + cudnnSetTensor4dDescriptor(l.output_layer->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, batch, l.output_layer->out_c, l.output_layer->out_h, l.output_layer->out_w); +#endif +#endif + + return l; +} + +void update_rnn_layer(layer l, update_args a) +{ + update_connected_layer(*(l.input_layer), a); + update_connected_layer(*(l.self_layer), a); + update_connected_layer(*(l.output_layer), a); +} + +void forward_rnn_layer(layer l, network net) +{ + network s = net; + s.train = net.train; + int i; + layer input_layer = *(l.input_layer); + layer self_layer = *(l.self_layer); + layer output_layer = *(l.output_layer); + + fill_cpu(l.outputs * l.batch * l.steps, 0, output_layer.delta, 1); + fill_cpu(l.outputs * l.batch * l.steps, 0, self_layer.delta, 1); + fill_cpu(l.outputs * l.batch * l.steps, 0, input_layer.delta, 1); + if(net.train) fill_cpu(l.outputs * l.batch, 0, l.state, 1); + + for (i = 0; i < l.steps; ++i) { + s.input = net.input; + forward_connected_layer(input_layer, s); + + s.input = l.state; + forward_connected_layer(self_layer, s); + + float *old_state = l.state; + if(net.train) l.state += l.outputs*l.batch; + if(l.shortcut){ + copy_cpu(l.outputs * l.batch, old_state, 1, l.state, 1); + }else{ + fill_cpu(l.outputs * l.batch, 0, l.state, 1); + } + axpy_cpu(l.outputs * l.batch, 1, input_layer.output, 1, l.state, 1); + axpy_cpu(l.outputs * l.batch, 1, self_layer.output, 1, l.state, 1); + + s.input = l.state; + forward_connected_layer(output_layer, s); + + net.input += l.inputs*l.batch; + increment_layer(&input_layer, 1); + increment_layer(&self_layer, 1); + increment_layer(&output_layer, 1); + } +} + +void backward_rnn_layer(layer l, network net) +{ + network s = net; + s.train = net.train; + int i; + layer input_layer = *(l.input_layer); + layer self_layer = *(l.self_layer); + layer output_layer = *(l.output_layer); + + increment_layer(&input_layer, l.steps-1); + increment_layer(&self_layer, l.steps-1); + increment_layer(&output_layer, l.steps-1); + + l.state += l.outputs*l.batch*l.steps; + for (i = l.steps-1; i >= 0; --i) { + copy_cpu(l.outputs * l.batch, input_layer.output, 1, l.state, 1); + axpy_cpu(l.outputs * l.batch, 1, self_layer.output, 1, l.state, 1); + + s.input = l.state; + s.delta = self_layer.delta; + backward_connected_layer(output_layer, s); + + l.state -= l.outputs*l.batch; + /* + if(i > 0){ + copy_cpu(l.outputs * l.batch, input_layer.output - l.outputs*l.batch, 1, l.state, 1); + axpy_cpu(l.outputs * l.batch, 1, self_layer.output - l.outputs*l.batch, 1, l.state, 1); + }else{ + fill_cpu(l.outputs * l.batch, 0, l.state, 1); + } + */ + + s.input = l.state; + s.delta = self_layer.delta - l.outputs*l.batch; + if (i == 0) s.delta = 0; + backward_connected_layer(self_layer, s); + + copy_cpu(l.outputs*l.batch, self_layer.delta, 1, input_layer.delta, 1); + if (i > 0 && l.shortcut) axpy_cpu(l.outputs*l.batch, 1, self_layer.delta, 1, self_layer.delta - l.outputs*l.batch, 1); + s.input = net.input + i*l.inputs*l.batch; + if(net.delta) s.delta = net.delta + i*l.inputs*l.batch; + else s.delta = 0; + backward_connected_layer(input_layer, s); + + increment_layer(&input_layer, -1); + increment_layer(&self_layer, -1); + increment_layer(&output_layer, -1); + } +} + +#ifdef GPU + +void pull_rnn_layer(layer l) +{ + pull_connected_layer(*(l.input_layer)); + pull_connected_layer(*(l.self_layer)); + pull_connected_layer(*(l.output_layer)); +} + +void push_rnn_layer(layer l) +{ + push_connected_layer(*(l.input_layer)); + push_connected_layer(*(l.self_layer)); + push_connected_layer(*(l.output_layer)); +} + +void update_rnn_layer_gpu(layer l, update_args a) +{ + update_connected_layer_gpu(*(l.input_layer), a); + update_connected_layer_gpu(*(l.self_layer), a); + update_connected_layer_gpu(*(l.output_layer), a); +} + +void forward_rnn_layer_gpu(layer l, network net) +{ + network s = {0}; + s.train = net.train; + int i; + layer input_layer = *(l.input_layer); + layer self_layer = *(l.self_layer); + layer output_layer = *(l.output_layer); + + fill_gpu(l.outputs * l.batch * l.steps, 0, output_layer.delta_gpu, 1); + fill_gpu(l.outputs * l.batch * l.steps, 0, self_layer.delta_gpu, 1); + fill_gpu(l.outputs * l.batch * l.steps, 0, input_layer.delta_gpu, 1); + + if(net.train) { + fill_gpu(l.outputs * l.batch * l.steps, 0, l.delta_gpu, 1); + copy_gpu(l.outputs*l.batch, l.state_gpu, 1, l.prev_state_gpu, 1); + } + + for (i = 0; i < l.steps; ++i) { + s.input_gpu = net.input_gpu; + forward_connected_layer_gpu(input_layer, s); + + s.input_gpu = l.state_gpu; + forward_connected_layer_gpu(self_layer, s); + + fill_gpu(l.outputs * l.batch, 0, l.state_gpu, 1); + axpy_gpu(l.outputs * l.batch, 1, input_layer.output_gpu, 1, l.state_gpu, 1); + axpy_gpu(l.outputs * l.batch, 1, self_layer.output_gpu, 1, l.state_gpu, 1); + + s.input_gpu = l.state_gpu; + forward_connected_layer_gpu(output_layer, s); + + net.input_gpu += l.inputs*l.batch; + increment_layer(&input_layer, 1); + increment_layer(&self_layer, 1); + increment_layer(&output_layer, 1); + } +} + +void backward_rnn_layer_gpu(layer l, network net) +{ + network s = {0}; + s.train = net.train; + int i; + layer input_layer = *(l.input_layer); + layer self_layer = *(l.self_layer); + layer output_layer = *(l.output_layer); + increment_layer(&input_layer, l.steps - 1); + increment_layer(&self_layer, l.steps - 1); + increment_layer(&output_layer, l.steps - 1); + float *last_input = input_layer.output_gpu; + float *last_self = self_layer.output_gpu; + for (i = l.steps-1; i >= 0; --i) { + fill_gpu(l.outputs * l.batch, 0, l.state_gpu, 1); + axpy_gpu(l.outputs * l.batch, 1, input_layer.output_gpu, 1, l.state_gpu, 1); + axpy_gpu(l.outputs * l.batch, 1, self_layer.output_gpu, 1, l.state_gpu, 1); + + s.input_gpu = l.state_gpu; + s.delta_gpu = self_layer.delta_gpu; + backward_connected_layer_gpu(output_layer, s); + + if(i != 0) { + fill_gpu(l.outputs * l.batch, 0, l.state_gpu, 1); + axpy_gpu(l.outputs * l.batch, 1, input_layer.output_gpu - l.outputs*l.batch, 1, l.state_gpu, 1); + axpy_gpu(l.outputs * l.batch, 1, self_layer.output_gpu - l.outputs*l.batch, 1, l.state_gpu, 1); + }else { + copy_gpu(l.outputs*l.batch, l.prev_state_gpu, 1, l.state_gpu, 1); + } + + copy_gpu(l.outputs*l.batch, self_layer.delta_gpu, 1, input_layer.delta_gpu, 1); + + s.input_gpu = l.state_gpu; + s.delta_gpu = (i > 0) ? self_layer.delta_gpu - l.outputs*l.batch : 0; + if (i == 0) s.delta_gpu = 0; + backward_connected_layer_gpu(self_layer, s); + + s.input_gpu = net.input_gpu + i*l.inputs*l.batch; + if(net.delta_gpu) s.delta_gpu = net.delta_gpu + i*l.inputs*l.batch; + else s.delta_gpu = 0; + backward_connected_layer_gpu(input_layer, s); + + increment_layer(&input_layer, -1); + increment_layer(&self_layer, -1); + increment_layer(&output_layer, -1); + } + fill_gpu(l.outputs * l.batch, 0, l.state_gpu, 1); + axpy_gpu(l.outputs * l.batch, 1, last_input, 1, l.state_gpu, 1); + axpy_gpu(l.outputs * l.batch, 1, last_self, 1, l.state_gpu, 1); +} +#endif diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/rnn_layer.h b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/rnn_layer.h new file mode 100644 index 00000000000..270a63ffafc --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/rnn_layer.h @@ -0,0 +1,25 @@ + +#ifndef RNN_LAYER_H +#define RNN_LAYER_H + +#include "activations.h" +#include "layer.h" +#include "network.h" +#define USET + +layer make_rnn_layer(int batch, int inputs, int outputs, int steps, ACTIVATION activation, int batch_normalize, int adam); + +void forward_rnn_layer(layer l, network net); +void backward_rnn_layer(layer l, network net); +void update_rnn_layer(layer l, update_args a); + +#ifdef GPU +void forward_rnn_layer_gpu(layer l, network net); +void backward_rnn_layer_gpu(layer l, network net); +void update_rnn_layer_gpu(layer l, update_args a); +void push_rnn_layer(layer l); +void pull_rnn_layer(layer l); +#endif + +#endif + diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/route_layer.c b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/route_layer.c new file mode 100644 index 00000000000..a8970a46001 --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/route_layer.c @@ -0,0 +1,134 @@ +#include "route_layer.h" +#include "cuda.h" +#include "blas.h" + +#include + +route_layer make_route_layer(int batch, int n, int *input_layers, int *input_sizes) +{ + fprintf(stderr,"route "); + route_layer l = {0}; + l.type = ROUTE; + l.batch = batch; + l.n = n; + l.input_layers = input_layers; + l.input_sizes = input_sizes; + int i; + int outputs = 0; + for(i = 0; i < n; ++i){ + fprintf(stderr," %d", input_layers[i]); + outputs += input_sizes[i]; + } + fprintf(stderr, "\n"); + l.outputs = outputs; + l.inputs = outputs; + l.delta = calloc(outputs*batch, sizeof(float)); + l.output = calloc(outputs*batch, sizeof(float));; + + l.forward = forward_route_layer; + l.backward = backward_route_layer; + #ifdef GPU + l.forward_gpu = forward_route_layer_gpu; + l.backward_gpu = backward_route_layer_gpu; + + l.delta_gpu = cuda_make_array(l.delta, outputs*batch); + l.output_gpu = cuda_make_array(l.output, outputs*batch); + #endif + return l; +} + +void resize_route_layer(route_layer *l, network *net) +{ + int i; + layer first = net->layers[l->input_layers[0]]; + l->out_w = first.out_w; + l->out_h = first.out_h; + l->out_c = first.out_c; + l->outputs = first.outputs; + l->input_sizes[0] = first.outputs; + for(i = 1; i < l->n; ++i){ + int index = l->input_layers[i]; + layer next = net->layers[index]; + l->outputs += next.outputs; + l->input_sizes[i] = next.outputs; + if(next.out_w == first.out_w && next.out_h == first.out_h){ + l->out_c += next.out_c; + }else{ + printf("%d %d, %d %d\n", next.out_w, next.out_h, first.out_w, first.out_h); + l->out_h = l->out_w = l->out_c = 0; + } + } + l->inputs = l->outputs; + l->delta = realloc(l->delta, l->outputs*l->batch*sizeof(float)); + l->output = realloc(l->output, l->outputs*l->batch*sizeof(float)); + +#ifdef GPU + cuda_free(l->output_gpu); + cuda_free(l->delta_gpu); + l->output_gpu = cuda_make_array(l->output, l->outputs*l->batch); + l->delta_gpu = cuda_make_array(l->delta, l->outputs*l->batch); +#endif + +} + +void forward_route_layer(const route_layer l, network net) +{ + int i, j; + int offset = 0; + for(i = 0; i < l.n; ++i){ + int index = l.input_layers[i]; + float *input = net.layers[index].output; + int input_size = l.input_sizes[i]; + for(j = 0; j < l.batch; ++j){ + copy_cpu(input_size, input + j*input_size, 1, l.output + offset + j*l.outputs, 1); + } + offset += input_size; + } +} + +void backward_route_layer(const route_layer l, network net) +{ + int i, j; + int offset = 0; + for(i = 0; i < l.n; ++i){ + int index = l.input_layers[i]; + float *delta = net.layers[index].delta; + int input_size = l.input_sizes[i]; + for(j = 0; j < l.batch; ++j){ + axpy_cpu(input_size, 1, l.delta + offset + j*l.outputs, 1, delta + j*input_size, 1); + } + offset += input_size; + } +} + +#ifdef GPU +void forward_route_layer_gpu(const route_layer l, network net) +{ + int i, j; + int offset = 0; + for(i = 0; i < l.n; ++i){ + int index = l.input_layers[i]; + float *input = net.layers[index].output_gpu; + int input_size = l.input_sizes[i]; + for(j = 0; j < l.batch; ++j){ + copy_gpu(input_size, input + j*input_size, 1, l.output_gpu + offset + j*l.outputs, 1); + } + offset += input_size; + } +} + +void backward_route_layer_gpu(const route_layer l, network net) +{ + int i, j; + int offset = 0; + for(i = 0; i < l.n; ++i){ + int index = l.input_layers[i]; + float *delta = net.layers[index].delta_gpu; + int input_size = l.input_sizes[i]; + for(j = 0; j < l.batch; ++j){ + axpy_gpu(input_size, 1, l.delta_gpu + offset + j*l.outputs, 1, delta + j*input_size, 1); + } + offset += input_size; + } +} +#endif diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/route_layer.h b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/route_layer.h new file mode 100644 index 00000000000..1d40330ff30 --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/route_layer.h @@ -0,0 +1,18 @@ +#ifndef ROUTE_LAYER_H +#define ROUTE_LAYER_H +#include "network.h" +#include "layer.h" + +typedef layer route_layer; + +route_layer make_route_layer(int batch, int n, int *input_layers, int *input_size); +void forward_route_layer(const route_layer l, network net); +void backward_route_layer(const route_layer l, network net); +void resize_route_layer(route_layer *l, network *net); + +#ifdef GPU +void forward_route_layer_gpu(const route_layer l, network net); +void backward_route_layer_gpu(const route_layer l, network net); +#endif + +#endif diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/shortcut_layer.c b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/shortcut_layer.c new file mode 100644 index 00000000000..49d17f56f66 --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/shortcut_layer.c @@ -0,0 +1,90 @@ +#include "shortcut_layer.h" +#include "cuda.h" +#include "blas.h" +#include "activations.h" + +#include +#include + +layer make_shortcut_layer(int batch, int index, int w, int h, int c, int w2, int h2, int c2) +{ + fprintf(stderr, "res %3d %4d x%4d x%4d -> %4d x%4d x%4d\n",index, w2,h2,c2, w,h,c); + layer l = {0}; + l.type = SHORTCUT; + l.batch = batch; + l.w = w2; + l.h = h2; + l.c = c2; + l.out_w = w; + l.out_h = h; + l.out_c = c; + l.outputs = w*h*c; + l.inputs = l.outputs; + + l.index = index; + + l.delta = calloc(l.outputs*batch, sizeof(float)); + l.output = calloc(l.outputs*batch, sizeof(float));; + + l.forward = forward_shortcut_layer; + l.backward = backward_shortcut_layer; + #ifdef GPU + l.forward_gpu = forward_shortcut_layer_gpu; + l.backward_gpu = backward_shortcut_layer_gpu; + + l.delta_gpu = cuda_make_array(l.delta, l.outputs*batch); + l.output_gpu = cuda_make_array(l.output, l.outputs*batch); + #endif + return l; +} + +void resize_shortcut_layer(layer *l, int w, int h) +{ + assert(l->w == l->out_w); + assert(l->h == l->out_h); + l->w = l->out_w = w; + l->h = l->out_h = h; + l->outputs = w*h*l->out_c; + l->inputs = l->outputs; + l->delta = realloc(l->delta, l->outputs*l->batch*sizeof(float)); + l->output = realloc(l->output, l->outputs*l->batch*sizeof(float)); + +#ifdef GPU + cuda_free(l->output_gpu); + cuda_free(l->delta_gpu); + l->output_gpu = cuda_make_array(l->output, l->outputs*l->batch); + l->delta_gpu = cuda_make_array(l->delta, l->outputs*l->batch); +#endif + +} + + +void forward_shortcut_layer(const layer l, network net) +{ + copy_cpu(l.outputs*l.batch, net.input, 1, l.output, 1); + shortcut_cpu(l.batch, l.w, l.h, l.c, net.layers[l.index].output, l.out_w, l.out_h, l.out_c, l.alpha, l.beta, l.output); + activate_array(l.output, l.outputs*l.batch, l.activation); +} + +void backward_shortcut_layer(const layer l, network net) +{ + gradient_array(l.output, l.outputs*l.batch, l.activation, l.delta); + axpy_cpu(l.outputs*l.batch, l.alpha, l.delta, 1, net.delta, 1); + shortcut_cpu(l.batch, l.out_w, l.out_h, l.out_c, l.delta, l.w, l.h, l.c, 1, l.beta, net.layers[l.index].delta); +} + +#ifdef GPU +void forward_shortcut_layer_gpu(const layer l, network net) +{ + copy_gpu(l.outputs*l.batch, net.input_gpu, 1, l.output_gpu, 1); + shortcut_gpu(l.batch, l.w, l.h, l.c, net.layers[l.index].output_gpu, l.out_w, l.out_h, l.out_c, l.alpha, l.beta, l.output_gpu); + activate_array_gpu(l.output_gpu, l.outputs*l.batch, l.activation); +} + +void backward_shortcut_layer_gpu(const layer l, network net) +{ + gradient_array_gpu(l.output_gpu, l.outputs*l.batch, l.activation, l.delta_gpu); + axpy_gpu(l.outputs*l.batch, l.alpha, l.delta_gpu, 1, net.delta_gpu, 1); + shortcut_gpu(l.batch, l.out_w, l.out_h, l.out_c, l.delta_gpu, l.w, l.h, l.c, 1, l.beta, net.layers[l.index].delta_gpu); +} +#endif diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/shortcut_layer.h b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/shortcut_layer.h new file mode 100644 index 00000000000..5f684fc1ead --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/shortcut_layer.h @@ -0,0 +1,17 @@ +#ifndef SHORTCUT_LAYER_H +#define SHORTCUT_LAYER_H + +#include "layer.h" +#include "network.h" + +layer make_shortcut_layer(int batch, int index, int w, int h, int c, int w2, int h2, int c2); +void forward_shortcut_layer(const layer l, network net); +void backward_shortcut_layer(const layer l, network net); +void resize_shortcut_layer(layer *l, int w, int h); + +#ifdef GPU +void forward_shortcut_layer_gpu(const layer l, network net); +void backward_shortcut_layer_gpu(const layer l, network net); +#endif + +#endif diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/softmax_layer.c b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/softmax_layer.c new file mode 100644 index 00000000000..afcc6342483 --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/softmax_layer.c @@ -0,0 +1,107 @@ +#include "softmax_layer.h" +#include "blas.h" +#include "cuda.h" + +#include +#include +#include +#include +#include + +softmax_layer make_softmax_layer(int batch, int inputs, int groups) +{ + assert(inputs%groups == 0); + fprintf(stderr, "softmax %4d\n", inputs); + softmax_layer l = {0}; + l.type = SOFTMAX; + l.batch = batch; + l.groups = groups; + l.inputs = inputs; + l.outputs = inputs; + l.loss = calloc(inputs*batch, sizeof(float)); + l.output = calloc(inputs*batch, sizeof(float)); + l.delta = calloc(inputs*batch, sizeof(float)); + l.cost = calloc(1, sizeof(float)); + + l.forward = forward_softmax_layer; + l.backward = backward_softmax_layer; + #ifdef GPU + l.forward_gpu = forward_softmax_layer_gpu; + l.backward_gpu = backward_softmax_layer_gpu; + + l.output_gpu = cuda_make_array(l.output, inputs*batch); + l.loss_gpu = cuda_make_array(l.loss, inputs*batch); + l.delta_gpu = cuda_make_array(l.delta, inputs*batch); + #endif + return l; +} + +void forward_softmax_layer(const softmax_layer l, network net) +{ + if(l.softmax_tree){ + int i; + int count = 0; + for (i = 0; i < l.softmax_tree->groups; ++i) { + int group_size = l.softmax_tree->group_size[i]; + softmax_cpu(net.input + count, group_size, l.batch, l.inputs, 1, 0, 1, l.temperature, l.output + count); + count += group_size; + } + } else { + softmax_cpu(net.input, l.inputs/l.groups, l.batch, l.inputs, l.groups, l.inputs/l.groups, 1, l.temperature, l.output); + } + + if(net.truth){ + softmax_x_ent_cpu(l.batch*l.inputs, l.output, net.truth, l.delta, l.loss); + l.cost[0] = sum_array(l.loss, l.batch*l.inputs); + } +} + +void backward_softmax_layer(const softmax_layer l, network net) +{ + axpy_cpu(l.inputs*l.batch, 1, l.delta, 1, net.delta, 1); +} + +#ifdef GPU + +void pull_softmax_layer_output(const softmax_layer layer) +{ + cuda_pull_array(layer.output_gpu, layer.output, layer.inputs*layer.batch); +} + +void forward_softmax_layer_gpu(const softmax_layer l, network net) +{ + if(l.softmax_tree){ + softmax_tree(net.input_gpu, 1, l.batch, l.inputs, l.temperature, l.output_gpu, *l.softmax_tree); + /* + int i; + int count = 0; + for (i = 0; i < l.softmax_tree->groups; ++i) { + int group_size = l.softmax_tree->group_size[i]; + softmax_gpu(net.input_gpu + count, group_size, l.batch, l.inputs, 1, 0, 1, l.temperature, l.output_gpu + count); + count += group_size; + } + */ + } else { + if(l.spatial){ + softmax_gpu(net.input_gpu, l.c, l.batch*l.c, l.inputs/l.c, l.w*l.h, 1, l.w*l.h, 1, l.output_gpu); + }else{ + softmax_gpu(net.input_gpu, l.inputs/l.groups, l.batch, l.inputs, l.groups, l.inputs/l.groups, 1, l.temperature, l.output_gpu); + } + } + if(net.truth){ + softmax_x_ent_gpu(l.batch*l.inputs, l.output_gpu, net.truth_gpu, l.delta_gpu, l.loss_gpu); + if(l.softmax_tree){ + mask_gpu(l.batch*l.inputs, l.delta_gpu, SECRET_NUM, net.truth_gpu, 0); + mask_gpu(l.batch*l.inputs, l.loss_gpu, SECRET_NUM, net.truth_gpu, 0); + } + cuda_pull_array(l.loss_gpu, l.loss, l.batch*l.inputs); + l.cost[0] = sum_array(l.loss, l.batch*l.inputs); + } +} + +void backward_softmax_layer_gpu(const softmax_layer layer, network net) +{ + axpy_gpu(layer.batch*layer.inputs, 1, layer.delta_gpu, 1, net.delta_gpu, 1); +} + +#endif diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/softmax_layer.h b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/softmax_layer.h new file mode 100644 index 00000000000..2e3ffe01a6c --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/softmax_layer.h @@ -0,0 +1,19 @@ +#ifndef SOFTMAX_LAYER_H +#define SOFTMAX_LAYER_H +#include "layer.h" +#include "network.h" + +typedef layer softmax_layer; + +void softmax_array(float *input, int n, float temp, float *output); +softmax_layer make_softmax_layer(int batch, int inputs, int groups); +void forward_softmax_layer(const softmax_layer l, network net); +void backward_softmax_layer(const softmax_layer l, network net); + +#ifdef GPU +void pull_softmax_layer_output(const softmax_layer l); +void forward_softmax_layer_gpu(const softmax_layer l, network net); +void backward_softmax_layer_gpu(const softmax_layer l, network net); +#endif + +#endif diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/stb_image.h b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/stb_image.h new file mode 100644 index 00000000000..d9c21bc813f --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/stb_image.h @@ -0,0 +1,7462 @@ +/* stb_image - v2.19 - public domain image loader - http://nothings.org/stb + no warranty implied; use at your own risk + + Do this: + #define STB_IMAGE_IMPLEMENTATION + before you include this file in *one* C or C++ file to create the implementation. + + // i.e. it should look like this: + #include ... + #include ... + #include ... + #define STB_IMAGE_IMPLEMENTATION + #include "stb_image.h" + + You can #define STBI_ASSERT(x) before the #include to avoid using assert.h. + And #define STBI_MALLOC, STBI_REALLOC, and STBI_FREE to avoid using malloc,realloc,free + + + QUICK NOTES: + Primarily of interest to game developers and other people who can + avoid problematic images and only need the trivial interface + + JPEG baseline & progressive (12 bpc/arithmetic not supported, same as stock IJG lib) + PNG 1/2/4/8/16-bit-per-channel + + TGA (not sure what subset, if a subset) + BMP non-1bpp, non-RLE + PSD (composited view only, no extra channels, 8/16 bit-per-channel) + + GIF (*comp always reports as 4-channel) + HDR (radiance rgbE format) + PIC (Softimage PIC) + PNM (PPM and PGM binary only) + + Animated GIF still needs a proper API, but here's one way to do it: + http://gist.github.com/urraka/685d9a6340b26b830d49 + + - decode from memory or through FILE (define STBI_NO_STDIO to remove code) + - decode from arbitrary I/O callbacks + - SIMD acceleration on x86/x64 (SSE2) and ARM (NEON) + + Full documentation under "DOCUMENTATION" below. + + +LICENSE + + See end of file for license information. + +RECENT REVISION HISTORY: + + 2.19 (2018-02-11) fix warning + 2.18 (2018-01-30) fix warnings + 2.17 (2018-01-29) bugfix, 1-bit BMP, 16-bitness query, fix warnings + 2.16 (2017-07-23) all functions have 16-bit variants; optimizations; bugfixes + 2.15 (2017-03-18) fix png-1,2,4; all Imagenet JPGs; no runtime SSE detection on GCC + 2.14 (2017-03-03) remove deprecated STBI_JPEG_OLD; fixes for Imagenet JPGs + 2.13 (2016-12-04) experimental 16-bit API, only for PNG so far; fixes + 2.12 (2016-04-02) fix typo in 2.11 PSD fix that caused crashes + 2.11 (2016-04-02) 16-bit PNGS; enable SSE2 in non-gcc x64 + RGB-format JPEG; remove white matting in PSD; + allocate large structures on the stack; + correct channel count for PNG & BMP + 2.10 (2016-01-22) avoid warning introduced in 2.09 + 2.09 (2016-01-16) 16-bit TGA; comments in PNM files; STBI_REALLOC_SIZED + + See end of file for full revision history. + + + ============================ Contributors ========================= + + Image formats Extensions, features + Sean Barrett (jpeg, png, bmp) Jetro Lauha (stbi_info) + Nicolas Schulz (hdr, psd) Martin "SpartanJ" Golini (stbi_info) + Jonathan Dummer (tga) James "moose2000" Brown (iPhone PNG) + Jean-Marc Lienher (gif) Ben "Disch" Wenger (io callbacks) + Tom Seddon (pic) Omar Cornut (1/2/4-bit PNG) + Thatcher Ulrich (psd) Nicolas Guillemot (vertical flip) + Ken Miller (pgm, ppm) Richard Mitton (16-bit PSD) + github:urraka (animated gif) Junggon Kim (PNM comments) + Christopher Forseth (animated gif) Daniel Gibson (16-bit TGA) + socks-the-fox (16-bit PNG) + Jeremy Sawicki (handle all ImageNet JPGs) + Optimizations & bugfixes Mikhail Morozov (1-bit BMP) + Fabian "ryg" Giesen Anael Seghezzi (is-16-bit query) + Arseny Kapoulkine + John-Mark Allen + + Bug & warning fixes + Marc LeBlanc David Woo Guillaume George Martins Mozeiko + Christpher Lloyd Jerry Jansson Joseph Thomson Phil Jordan + Dave Moore Roy Eltham Hayaki Saito Nathan Reed + Won Chun Luke Graham Johan Duparc Nick Verigakis + the Horde3D community Thomas Ruf Ronny Chevalier github:rlyeh + Janez Zemva John Bartholomew Michal Cichon github:romigrou + Jonathan Blow Ken Hamada Tero Hanninen github:svdijk + Laurent Gomila Cort Stratton Sergio Gonzalez github:snagar + Aruelien Pocheville Thibault Reuille Cass Everitt github:Zelex + Ryamond Barbiero Paul Du Bois Engin Manap github:grim210 + Aldo Culquicondor Philipp Wiesemann Dale Weiler github:sammyhw + Oriol Ferrer Mesia Josh Tobin Matthew Gregan github:phprus + Julian Raschke Gregory Mullen Baldur Karlsson github:poppolopoppo + Christian Floisand Kevin Schmidt github:darealshinji + Blazej Dariusz Roszkowski github:Michaelangel007 +*/ + +#ifndef STBI_INCLUDE_STB_IMAGE_H +#define STBI_INCLUDE_STB_IMAGE_H + +// DOCUMENTATION +// +// Limitations: +// - no 12-bit-per-channel JPEG +// - no JPEGs with arithmetic coding +// - GIF always returns *comp=4 +// +// Basic usage (see HDR discussion below for HDR usage): +// int x,y,n; +// unsigned char *data = stbi_load(filename, &x, &y, &n, 0); +// // ... process data if not NULL ... +// // ... x = width, y = height, n = # 8-bit components per pixel ... +// // ... replace '0' with '1'..'4' to force that many components per pixel +// // ... but 'n' will always be the number that it would have been if you said 0 +// stbi_image_free(data) +// +// Standard parameters: +// int *x -- outputs image width in pixels +// int *y -- outputs image height in pixels +// int *channels_in_file -- outputs # of image components in image file +// int desired_channels -- if non-zero, # of image components requested in result +// +// The return value from an image loader is an 'unsigned char *' which points +// to the pixel data, or NULL on an allocation failure or if the image is +// corrupt or invalid. The pixel data consists of *y scanlines of *x pixels, +// with each pixel consisting of N interleaved 8-bit components; the first +// pixel pointed to is top-left-most in the image. There is no padding between +// image scanlines or between pixels, regardless of format. The number of +// components N is 'desired_channels' if desired_channels is non-zero, or +// *channels_in_file otherwise. If desired_channels is non-zero, +// *channels_in_file has the number of components that _would_ have been +// output otherwise. E.g. if you set desired_channels to 4, you will always +// get RGBA output, but you can check *channels_in_file to see if it's trivially +// opaque because e.g. there were only 3 channels in the source image. +// +// An output image with N components has the following components interleaved +// in this order in each pixel: +// +// N=#comp components +// 1 grey +// 2 grey, alpha +// 3 red, green, blue +// 4 red, green, blue, alpha +// +// If image loading fails for any reason, the return value will be NULL, +// and *x, *y, *channels_in_file will be unchanged. The function +// stbi_failure_reason() can be queried for an extremely brief, end-user +// unfriendly explanation of why the load failed. Define STBI_NO_FAILURE_STRINGS +// to avoid compiling these strings at all, and STBI_FAILURE_USERMSG to get slightly +// more user-friendly ones. +// +// Paletted PNG, BMP, GIF, and PIC images are automatically depalettized. +// +// =========================================================================== +// +// Philosophy +// +// stb libraries are designed with the following priorities: +// +// 1. easy to use +// 2. easy to maintain +// 3. good performance +// +// Sometimes I let "good performance" creep up in priority over "easy to maintain", +// and for best performance I may provide less-easy-to-use APIs that give higher +// performance, in addition to the easy to use ones. Nevertheless, it's important +// to keep in mind that from the standpoint of you, a client of this library, +// all you care about is #1 and #3, and stb libraries DO NOT emphasize #3 above all. +// +// Some secondary priorities arise directly from the first two, some of which +// make more explicit reasons why performance can't be emphasized. +// +// - Portable ("ease of use") +// - Small source code footprint ("easy to maintain") +// - No dependencies ("ease of use") +// +// =========================================================================== +// +// I/O callbacks +// +// I/O callbacks allow you to read from arbitrary sources, like packaged +// files or some other source. Data read from callbacks are processed +// through a small internal buffer (currently 128 bytes) to try to reduce +// overhead. +// +// The three functions you must define are "read" (reads some bytes of data), +// "skip" (skips some bytes of data), "eof" (reports if the stream is at the end). +// +// =========================================================================== +// +// SIMD support +// +// The JPEG decoder will try to automatically use SIMD kernels on x86 when +// supported by the compiler. For ARM Neon support, you must explicitly +// request it. +// +// (The old do-it-yourself SIMD API is no longer supported in the current +// code.) +// +// On x86, SSE2 will automatically be used when available based on a run-time +// test; if not, the generic C versions are used as a fall-back. On ARM targets, +// the typical path is to have separate builds for NEON and non-NEON devices +// (at least this is true for iOS and Android). Therefore, the NEON support is +// toggled by a build flag: define STBI_NEON to get NEON loops. +// +// If for some reason you do not want to use any of SIMD code, or if +// you have issues compiling it, you can disable it entirely by +// defining STBI_NO_SIMD. +// +// =========================================================================== +// +// HDR image support (disable by defining STBI_NO_HDR) +// +// stb_image now supports loading HDR images in general, and currently +// the Radiance .HDR file format, although the support is provided +// generically. You can still load any file through the existing interface; +// if you attempt to load an HDR file, it will be automatically remapped to +// LDR, assuming gamma 2.2 and an arbitrary scale factor defaulting to 1; +// both of these constants can be reconfigured through this interface: +// +// stbi_hdr_to_ldr_gamma(2.2f); +// stbi_hdr_to_ldr_scale(1.0f); +// +// (note, do not use _inverse_ constants; stbi_image will invert them +// appropriately). +// +// Additionally, there is a new, parallel interface for loading files as +// (linear) floats to preserve the full dynamic range: +// +// float *data = stbi_loadf(filename, &x, &y, &n, 0); +// +// If you load LDR images through this interface, those images will +// be promoted to floating point values, run through the inverse of +// constants corresponding to the above: +// +// stbi_ldr_to_hdr_scale(1.0f); +// stbi_ldr_to_hdr_gamma(2.2f); +// +// Finally, given a filename (or an open file or memory block--see header +// file for details) containing image data, you can query for the "most +// appropriate" interface to use (that is, whether the image is HDR or +// not), using: +// +// stbi_is_hdr(char *filename); +// +// =========================================================================== +// +// iPhone PNG support: +// +// By default we convert iphone-formatted PNGs back to RGB, even though +// they are internally encoded differently. You can disable this conversion +// by by calling stbi_convert_iphone_png_to_rgb(0), in which case +// you will always just get the native iphone "format" through (which +// is BGR stored in RGB). +// +// Call stbi_set_unpremultiply_on_load(1) as well to force a divide per +// pixel to remove any premultiplied alpha *only* if the image file explicitly +// says there's premultiplied data (currently only happens in iPhone images, +// and only if iPhone convert-to-rgb processing is on). +// +// =========================================================================== +// +// ADDITIONAL CONFIGURATION +// +// - You can suppress implementation of any of the decoders to reduce +// your code footprint by #defining one or more of the following +// symbols before creating the implementation. +// +// STBI_NO_JPEG +// STBI_NO_PNG +// STBI_NO_BMP +// STBI_NO_PSD +// STBI_NO_TGA +// STBI_NO_GIF +// STBI_NO_HDR +// STBI_NO_PIC +// STBI_NO_PNM (.ppm and .pgm) +// +// - You can request *only* certain decoders and suppress all other ones +// (this will be more forward-compatible, as addition of new decoders +// doesn't require you to disable them explicitly): +// +// STBI_ONLY_JPEG +// STBI_ONLY_PNG +// STBI_ONLY_BMP +// STBI_ONLY_PSD +// STBI_ONLY_TGA +// STBI_ONLY_GIF +// STBI_ONLY_HDR +// STBI_ONLY_PIC +// STBI_ONLY_PNM (.ppm and .pgm) +// +// - If you use STBI_NO_PNG (or _ONLY_ without PNG), and you still +// want the zlib decoder to be available, #define STBI_SUPPORT_ZLIB +// + + +#ifndef STBI_NO_STDIO +#include +#endif // STBI_NO_STDIO + +#define STBI_VERSION 1 + +enum +{ + STBI_default = 0, // only used for desired_channels + + STBI_grey = 1, + STBI_grey_alpha = 2, + STBI_rgb = 3, + STBI_rgb_alpha = 4 +}; + +typedef unsigned char stbi_uc; +typedef unsigned short stbi_us; + +#ifdef __cplusplus +extern "C" { +#endif + +#ifdef STB_IMAGE_STATIC +#define STBIDEF static +#else +#define STBIDEF extern +#endif + +////////////////////////////////////////////////////////////////////////////// +// +// PRIMARY API - works on images of any type +// + +// +// load image by filename, open file, or memory buffer +// + +typedef struct +{ + int (*read) (void *user,char *data,int size); // fill 'data' with 'size' bytes. return number of bytes actually read + void (*skip) (void *user,int n); // skip the next 'n' bytes, or 'unget' the last -n bytes if negative + int (*eof) (void *user); // returns nonzero if we are at end of file/data +} stbi_io_callbacks; + +//////////////////////////////////// +// +// 8-bits-per-channel interface +// + +STBIDEF stbi_uc *stbi_load_from_memory (stbi_uc const *buffer, int len , int *x, int *y, int *channels_in_file, int desired_channels); +STBIDEF stbi_uc *stbi_load_from_callbacks(stbi_io_callbacks const *clbk , void *user, int *x, int *y, int *channels_in_file, int desired_channels); +#ifndef STBI_NO_GIF +STBIDEF stbi_uc *stbi_load_gif_from_memory(stbi_uc const *buffer, int len, int **delays, int *x, int *y, int *z, int *comp, int req_comp); +#endif + + +#ifndef STBI_NO_STDIO +STBIDEF stbi_uc *stbi_load (char const *filename, int *x, int *y, int *channels_in_file, int desired_channels); +STBIDEF stbi_uc *stbi_load_from_file (FILE *f, int *x, int *y, int *channels_in_file, int desired_channels); +// for stbi_load_from_file, file pointer is left pointing immediately after image +#endif + +//////////////////////////////////// +// +// 16-bits-per-channel interface +// + +STBIDEF stbi_us *stbi_load_16_from_memory (stbi_uc const *buffer, int len, int *x, int *y, int *channels_in_file, int desired_channels); +STBIDEF stbi_us *stbi_load_16_from_callbacks(stbi_io_callbacks const *clbk, void *user, int *x, int *y, int *channels_in_file, int desired_channels); + +#ifndef STBI_NO_STDIO +STBIDEF stbi_us *stbi_load_16 (char const *filename, int *x, int *y, int *channels_in_file, int desired_channels); +STBIDEF stbi_us *stbi_load_from_file_16(FILE *f, int *x, int *y, int *channels_in_file, int desired_channels); +#endif + +//////////////////////////////////// +// +// float-per-channel interface +// +#ifndef STBI_NO_LINEAR + STBIDEF float *stbi_loadf_from_memory (stbi_uc const *buffer, int len, int *x, int *y, int *channels_in_file, int desired_channels); + STBIDEF float *stbi_loadf_from_callbacks (stbi_io_callbacks const *clbk, void *user, int *x, int *y, int *channels_in_file, int desired_channels); + + #ifndef STBI_NO_STDIO + STBIDEF float *stbi_loadf (char const *filename, int *x, int *y, int *channels_in_file, int desired_channels); + STBIDEF float *stbi_loadf_from_file (FILE *f, int *x, int *y, int *channels_in_file, int desired_channels); + #endif +#endif + +#ifndef STBI_NO_HDR + STBIDEF void stbi_hdr_to_ldr_gamma(float gamma); + STBIDEF void stbi_hdr_to_ldr_scale(float scale); +#endif // STBI_NO_HDR + +#ifndef STBI_NO_LINEAR + STBIDEF void stbi_ldr_to_hdr_gamma(float gamma); + STBIDEF void stbi_ldr_to_hdr_scale(float scale); +#endif // STBI_NO_LINEAR + +// stbi_is_hdr is always defined, but always returns false if STBI_NO_HDR +STBIDEF int stbi_is_hdr_from_callbacks(stbi_io_callbacks const *clbk, void *user); +STBIDEF int stbi_is_hdr_from_memory(stbi_uc const *buffer, int len); +#ifndef STBI_NO_STDIO +STBIDEF int stbi_is_hdr (char const *filename); +STBIDEF int stbi_is_hdr_from_file(FILE *f); +#endif // STBI_NO_STDIO + + +// get a VERY brief reason for failure +// NOT THREADSAFE +STBIDEF const char *stbi_failure_reason (void); + +// free the loaded image -- this is just free() +STBIDEF void stbi_image_free (void *retval_from_stbi_load); + +// get image dimensions & components without fully decoding +STBIDEF int stbi_info_from_memory(stbi_uc const *buffer, int len, int *x, int *y, int *comp); +STBIDEF int stbi_info_from_callbacks(stbi_io_callbacks const *clbk, void *user, int *x, int *y, int *comp); +STBIDEF int stbi_is_16_bit_from_memory(stbi_uc const *buffer, int len); +STBIDEF int stbi_is_16_bit_from_callbacks(stbi_io_callbacks const *clbk, void *user); + +#ifndef STBI_NO_STDIO +STBIDEF int stbi_info (char const *filename, int *x, int *y, int *comp); +STBIDEF int stbi_info_from_file (FILE *f, int *x, int *y, int *comp); +STBIDEF int stbi_is_16_bit (char const *filename); +STBIDEF int stbi_is_16_bit_from_file(FILE *f); +#endif + + + +// for image formats that explicitly notate that they have premultiplied alpha, +// we just return the colors as stored in the file. set this flag to force +// unpremultiplication. results are undefined if the unpremultiply overflow. +STBIDEF void stbi_set_unpremultiply_on_load(int flag_true_if_should_unpremultiply); + +// indicate whether we should process iphone images back to canonical format, +// or just pass them through "as-is" +STBIDEF void stbi_convert_iphone_png_to_rgb(int flag_true_if_should_convert); + +// flip the image vertically, so the first pixel in the output array is the bottom left +STBIDEF void stbi_set_flip_vertically_on_load(int flag_true_if_should_flip); + +// ZLIB client - used by PNG, available for other purposes + +STBIDEF char *stbi_zlib_decode_malloc_guesssize(const char *buffer, int len, int initial_size, int *outlen); +STBIDEF char *stbi_zlib_decode_malloc_guesssize_headerflag(const char *buffer, int len, int initial_size, int *outlen, int parse_header); +STBIDEF char *stbi_zlib_decode_malloc(const char *buffer, int len, int *outlen); +STBIDEF int stbi_zlib_decode_buffer(char *obuffer, int olen, const char *ibuffer, int ilen); + +STBIDEF char *stbi_zlib_decode_noheader_malloc(const char *buffer, int len, int *outlen); +STBIDEF int stbi_zlib_decode_noheader_buffer(char *obuffer, int olen, const char *ibuffer, int ilen); + + +#ifdef __cplusplus +} +#endif + +// +// +//// end header file ///////////////////////////////////////////////////// +#endif // STBI_INCLUDE_STB_IMAGE_H + +#ifdef STB_IMAGE_IMPLEMENTATION + +#if defined(STBI_ONLY_JPEG) || defined(STBI_ONLY_PNG) || defined(STBI_ONLY_BMP) \ + || defined(STBI_ONLY_TGA) || defined(STBI_ONLY_GIF) || defined(STBI_ONLY_PSD) \ + || defined(STBI_ONLY_HDR) || defined(STBI_ONLY_PIC) || defined(STBI_ONLY_PNM) \ + || defined(STBI_ONLY_ZLIB) + #ifndef STBI_ONLY_JPEG + #define STBI_NO_JPEG + #endif + #ifndef STBI_ONLY_PNG + #define STBI_NO_PNG + #endif + #ifndef STBI_ONLY_BMP + #define STBI_NO_BMP + #endif + #ifndef STBI_ONLY_PSD + #define STBI_NO_PSD + #endif + #ifndef STBI_ONLY_TGA + #define STBI_NO_TGA + #endif + #ifndef STBI_ONLY_GIF + #define STBI_NO_GIF + #endif + #ifndef STBI_ONLY_HDR + #define STBI_NO_HDR + #endif + #ifndef STBI_ONLY_PIC + #define STBI_NO_PIC + #endif + #ifndef STBI_ONLY_PNM + #define STBI_NO_PNM + #endif +#endif + +#if defined(STBI_NO_PNG) && !defined(STBI_SUPPORT_ZLIB) && !defined(STBI_NO_ZLIB) +#define STBI_NO_ZLIB +#endif + + +#include +#include // ptrdiff_t on osx +#include +#include +#include + +#if !defined(STBI_NO_LINEAR) || !defined(STBI_NO_HDR) +#include // ldexp, pow +#endif + +#ifndef STBI_NO_STDIO +#include +#endif + +#ifndef STBI_ASSERT +#include +#define STBI_ASSERT(x) assert(x) +#endif + + +#ifndef _MSC_VER + #ifdef __cplusplus + #define stbi_inline inline + #else + #define stbi_inline + #endif +#else + #define stbi_inline __forceinline +#endif + + +#ifdef _MSC_VER +typedef unsigned short stbi__uint16; +typedef signed short stbi__int16; +typedef unsigned int stbi__uint32; +typedef signed int stbi__int32; +#else +#include +typedef uint16_t stbi__uint16; +typedef int16_t stbi__int16; +typedef uint32_t stbi__uint32; +typedef int32_t stbi__int32; +#endif + +// should produce compiler error if size is wrong +typedef unsigned char validate_uint32[sizeof(stbi__uint32)==4 ? 1 : -1]; + +#ifdef _MSC_VER +#define STBI_NOTUSED(v) (void)(v) +#else +#define STBI_NOTUSED(v) (void)sizeof(v) +#endif + +#ifdef _MSC_VER +#define STBI_HAS_LROTL +#endif + +#ifdef STBI_HAS_LROTL + #define stbi_lrot(x,y) _lrotl(x,y) +#else + #define stbi_lrot(x,y) (((x) << (y)) | ((x) >> (32 - (y)))) +#endif + +#if defined(STBI_MALLOC) && defined(STBI_FREE) && (defined(STBI_REALLOC) || defined(STBI_REALLOC_SIZED)) +// ok +#elif !defined(STBI_MALLOC) && !defined(STBI_FREE) && !defined(STBI_REALLOC) && !defined(STBI_REALLOC_SIZED) +// ok +#else +#error "Must define all or none of STBI_MALLOC, STBI_FREE, and STBI_REALLOC (or STBI_REALLOC_SIZED)." +#endif + +#ifndef STBI_MALLOC +#define STBI_MALLOC(sz) malloc(sz) +#define STBI_REALLOC(p,newsz) realloc(p,newsz) +#define STBI_FREE(p) free(p) +#endif + +#ifndef STBI_REALLOC_SIZED +#define STBI_REALLOC_SIZED(p,oldsz,newsz) STBI_REALLOC(p,newsz) +#endif + +// x86/x64 detection +#if defined(__x86_64__) || defined(_M_X64) +#define STBI__X64_TARGET +#elif defined(__i386) || defined(_M_IX86) +#define STBI__X86_TARGET +#endif + +#if defined(__GNUC__) && defined(STBI__X86_TARGET) && !defined(__SSE2__) && !defined(STBI_NO_SIMD) +// gcc doesn't support sse2 intrinsics unless you compile with -msse2, +// which in turn means it gets to use SSE2 everywhere. This is unfortunate, +// but previous attempts to provide the SSE2 functions with runtime +// detection caused numerous issues. The way architecture extensions are +// exposed in GCC/Clang is, sadly, not really suited for one-file libs. +// New behavior: if compiled with -msse2, we use SSE2 without any +// detection; if not, we don't use it at all. +#define STBI_NO_SIMD +#endif + +#if defined(__MINGW32__) && defined(STBI__X86_TARGET) && !defined(STBI_MINGW_ENABLE_SSE2) && !defined(STBI_NO_SIMD) +// Note that __MINGW32__ doesn't actually mean 32-bit, so we have to avoid STBI__X64_TARGET +// +// 32-bit MinGW wants ESP to be 16-byte aligned, but this is not in the +// Windows ABI and VC++ as well as Windows DLLs don't maintain that invariant. +// As a result, enabling SSE2 on 32-bit MinGW is dangerous when not +// simultaneously enabling "-mstackrealign". +// +// See https://github.com/nothings/stb/issues/81 for more information. +// +// So default to no SSE2 on 32-bit MinGW. If you've read this far and added +// -mstackrealign to your build settings, feel free to #define STBI_MINGW_ENABLE_SSE2. +#define STBI_NO_SIMD +#endif + +#if !defined(STBI_NO_SIMD) && (defined(STBI__X86_TARGET) || defined(STBI__X64_TARGET)) +#define STBI_SSE2 +#include + +#ifdef _MSC_VER + +#if _MSC_VER >= 1400 // not VC6 +#include // __cpuid +static int stbi__cpuid3(void) +{ + int info[4]; + __cpuid(info,1); + return info[3]; +} +#else +static int stbi__cpuid3(void) +{ + int res; + __asm { + mov eax,1 + cpuid + mov res,edx + } + return res; +} +#endif + +#define STBI_SIMD_ALIGN(type, name) __declspec(align(16)) type name + +static int stbi__sse2_available(void) +{ + int info3 = stbi__cpuid3(); + return ((info3 >> 26) & 1) != 0; +} +#else // assume GCC-style if not VC++ +#define STBI_SIMD_ALIGN(type, name) type name __attribute__((aligned(16))) + +static int stbi__sse2_available(void) +{ + // If we're even attempting to compile this on GCC/Clang, that means + // -msse2 is on, which means the compiler is allowed to use SSE2 + // instructions at will, and so are we. + return 1; +} +#endif +#endif + +// ARM NEON +#if defined(STBI_NO_SIMD) && defined(STBI_NEON) +#undef STBI_NEON +#endif + +#ifdef STBI_NEON +#include +// assume GCC or Clang on ARM targets +#define STBI_SIMD_ALIGN(type, name) type name __attribute__((aligned(16))) +#endif + +#ifndef STBI_SIMD_ALIGN +#define STBI_SIMD_ALIGN(type, name) type name +#endif + +/////////////////////////////////////////////// +// +// stbi__context struct and start_xxx functions + +// stbi__context structure is our basic context used by all images, so it +// contains all the IO context, plus some basic image information +typedef struct +{ + stbi__uint32 img_x, img_y; + int img_n, img_out_n; + + stbi_io_callbacks io; + void *io_user_data; + + int read_from_callbacks; + int buflen; + stbi_uc buffer_start[128]; + + stbi_uc *img_buffer, *img_buffer_end; + stbi_uc *img_buffer_original, *img_buffer_original_end; +} stbi__context; + + +static void stbi__refill_buffer(stbi__context *s); + +// initialize a memory-decode context +static void stbi__start_mem(stbi__context *s, stbi_uc const *buffer, int len) +{ + s->io.read = NULL; + s->read_from_callbacks = 0; + s->img_buffer = s->img_buffer_original = (stbi_uc *) buffer; + s->img_buffer_end = s->img_buffer_original_end = (stbi_uc *) buffer+len; +} + +// initialize a callback-based context +static void stbi__start_callbacks(stbi__context *s, stbi_io_callbacks *c, void *user) +{ + s->io = *c; + s->io_user_data = user; + s->buflen = sizeof(s->buffer_start); + s->read_from_callbacks = 1; + s->img_buffer_original = s->buffer_start; + stbi__refill_buffer(s); + s->img_buffer_original_end = s->img_buffer_end; +} + +#ifndef STBI_NO_STDIO + +static int stbi__stdio_read(void *user, char *data, int size) +{ + return (int) fread(data,1,size,(FILE*) user); +} + +static void stbi__stdio_skip(void *user, int n) +{ + fseek((FILE*) user, n, SEEK_CUR); +} + +static int stbi__stdio_eof(void *user) +{ + return feof((FILE*) user); +} + +static stbi_io_callbacks stbi__stdio_callbacks = +{ + stbi__stdio_read, + stbi__stdio_skip, + stbi__stdio_eof, +}; + +static void stbi__start_file(stbi__context *s, FILE *f) +{ + stbi__start_callbacks(s, &stbi__stdio_callbacks, (void *) f); +} + +//static void stop_file(stbi__context *s) { } + +#endif // !STBI_NO_STDIO + +static void stbi__rewind(stbi__context *s) +{ + // conceptually rewind SHOULD rewind to the beginning of the stream, + // but we just rewind to the beginning of the initial buffer, because + // we only use it after doing 'test', which only ever looks at at most 92 bytes + s->img_buffer = s->img_buffer_original; + s->img_buffer_end = s->img_buffer_original_end; +} + +enum +{ + STBI_ORDER_RGB, + STBI_ORDER_BGR +}; + +typedef struct +{ + int bits_per_channel; + int num_channels; + int channel_order; +} stbi__result_info; + +#ifndef STBI_NO_JPEG +static int stbi__jpeg_test(stbi__context *s); +static void *stbi__jpeg_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri); +static int stbi__jpeg_info(stbi__context *s, int *x, int *y, int *comp); +#endif + +#ifndef STBI_NO_PNG +static int stbi__png_test(stbi__context *s); +static void *stbi__png_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri); +static int stbi__png_info(stbi__context *s, int *x, int *y, int *comp); +static int stbi__png_is16(stbi__context *s); +#endif + +#ifndef STBI_NO_BMP +static int stbi__bmp_test(stbi__context *s); +static void *stbi__bmp_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri); +static int stbi__bmp_info(stbi__context *s, int *x, int *y, int *comp); +#endif + +#ifndef STBI_NO_TGA +static int stbi__tga_test(stbi__context *s); +static void *stbi__tga_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri); +static int stbi__tga_info(stbi__context *s, int *x, int *y, int *comp); +#endif + +#ifndef STBI_NO_PSD +static int stbi__psd_test(stbi__context *s); +static void *stbi__psd_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri, int bpc); +static int stbi__psd_info(stbi__context *s, int *x, int *y, int *comp); +static int stbi__psd_is16(stbi__context *s); +#endif + +#ifndef STBI_NO_HDR +static int stbi__hdr_test(stbi__context *s); +static float *stbi__hdr_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri); +static int stbi__hdr_info(stbi__context *s, int *x, int *y, int *comp); +#endif + +#ifndef STBI_NO_PIC +static int stbi__pic_test(stbi__context *s); +static void *stbi__pic_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri); +static int stbi__pic_info(stbi__context *s, int *x, int *y, int *comp); +#endif + +#ifndef STBI_NO_GIF +static int stbi__gif_test(stbi__context *s); +static void *stbi__gif_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri); +static void *stbi__load_gif_main(stbi__context *s, int **delays, int *x, int *y, int *z, int *comp, int req_comp); +static int stbi__gif_info(stbi__context *s, int *x, int *y, int *comp); +#endif + +#ifndef STBI_NO_PNM +static int stbi__pnm_test(stbi__context *s); +static void *stbi__pnm_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri); +static int stbi__pnm_info(stbi__context *s, int *x, int *y, int *comp); +#endif + +// this is not threadsafe +static const char *stbi__g_failure_reason; + +STBIDEF const char *stbi_failure_reason(void) +{ + return stbi__g_failure_reason; +} + +static int stbi__err(const char *str) +{ + stbi__g_failure_reason = str; + return 0; +} + +static void *stbi__malloc(size_t size) +{ + return STBI_MALLOC(size); +} + +// stb_image uses ints pervasively, including for offset calculations. +// therefore the largest decoded image size we can support with the +// current code, even on 64-bit targets, is INT_MAX. this is not a +// significant limitation for the intended use case. +// +// we do, however, need to make sure our size calculations don't +// overflow. hence a few helper functions for size calculations that +// multiply integers together, making sure that they're non-negative +// and no overflow occurs. + +// return 1 if the sum is valid, 0 on overflow. +// negative terms are considered invalid. +static int stbi__addsizes_valid(int a, int b) +{ + if (b < 0) return 0; + // now 0 <= b <= INT_MAX, hence also + // 0 <= INT_MAX - b <= INTMAX. + // And "a + b <= INT_MAX" (which might overflow) is the + // same as a <= INT_MAX - b (no overflow) + return a <= INT_MAX - b; +} + +// returns 1 if the product is valid, 0 on overflow. +// negative factors are considered invalid. +static int stbi__mul2sizes_valid(int a, int b) +{ + if (a < 0 || b < 0) return 0; + if (b == 0) return 1; // mul-by-0 is always safe + // portable way to check for no overflows in a*b + return a <= INT_MAX/b; +} + +// returns 1 if "a*b + add" has no negative terms/factors and doesn't overflow +static int stbi__mad2sizes_valid(int a, int b, int add) +{ + return stbi__mul2sizes_valid(a, b) && stbi__addsizes_valid(a*b, add); +} + +// returns 1 if "a*b*c + add" has no negative terms/factors and doesn't overflow +static int stbi__mad3sizes_valid(int a, int b, int c, int add) +{ + return stbi__mul2sizes_valid(a, b) && stbi__mul2sizes_valid(a*b, c) && + stbi__addsizes_valid(a*b*c, add); +} + +// returns 1 if "a*b*c*d + add" has no negative terms/factors and doesn't overflow +#if !defined(STBI_NO_LINEAR) || !defined(STBI_NO_HDR) +static int stbi__mad4sizes_valid(int a, int b, int c, int d, int add) +{ + return stbi__mul2sizes_valid(a, b) && stbi__mul2sizes_valid(a*b, c) && + stbi__mul2sizes_valid(a*b*c, d) && stbi__addsizes_valid(a*b*c*d, add); +} +#endif + +// mallocs with size overflow checking +static void *stbi__malloc_mad2(int a, int b, int add) +{ + if (!stbi__mad2sizes_valid(a, b, add)) return NULL; + return stbi__malloc(a*b + add); +} + +static void *stbi__malloc_mad3(int a, int b, int c, int add) +{ + if (!stbi__mad3sizes_valid(a, b, c, add)) return NULL; + return stbi__malloc(a*b*c + add); +} + +#if !defined(STBI_NO_LINEAR) || !defined(STBI_NO_HDR) +static void *stbi__malloc_mad4(int a, int b, int c, int d, int add) +{ + if (!stbi__mad4sizes_valid(a, b, c, d, add)) return NULL; + return stbi__malloc(a*b*c*d + add); +} +#endif + +// stbi__err - error +// stbi__errpf - error returning pointer to float +// stbi__errpuc - error returning pointer to unsigned char + +#ifdef STBI_NO_FAILURE_STRINGS + #define stbi__err(x,y) 0 +#elif defined(STBI_FAILURE_USERMSG) + #define stbi__err(x,y) stbi__err(y) +#else + #define stbi__err(x,y) stbi__err(x) +#endif + +#define stbi__errpf(x,y) ((float *)(size_t) (stbi__err(x,y)?NULL:NULL)) +#define stbi__errpuc(x,y) ((unsigned char *)(size_t) (stbi__err(x,y)?NULL:NULL)) + +STBIDEF void stbi_image_free(void *retval_from_stbi_load) +{ + STBI_FREE(retval_from_stbi_load); +} + +#ifndef STBI_NO_LINEAR +static float *stbi__ldr_to_hdr(stbi_uc *data, int x, int y, int comp); +#endif + +#ifndef STBI_NO_HDR +static stbi_uc *stbi__hdr_to_ldr(float *data, int x, int y, int comp); +#endif + +static int stbi__vertically_flip_on_load = 0; + +STBIDEF void stbi_set_flip_vertically_on_load(int flag_true_if_should_flip) +{ + stbi__vertically_flip_on_load = flag_true_if_should_flip; +} + +static void *stbi__load_main(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri, int bpc) +{ + memset(ri, 0, sizeof(*ri)); // make sure it's initialized if we add new fields + ri->bits_per_channel = 8; // default is 8 so most paths don't have to be changed + ri->channel_order = STBI_ORDER_RGB; // all current input & output are this, but this is here so we can add BGR order + ri->num_channels = 0; + + #ifndef STBI_NO_JPEG + if (stbi__jpeg_test(s)) return stbi__jpeg_load(s,x,y,comp,req_comp, ri); + #endif + #ifndef STBI_NO_PNG + if (stbi__png_test(s)) return stbi__png_load(s,x,y,comp,req_comp, ri); + #endif + #ifndef STBI_NO_BMP + if (stbi__bmp_test(s)) return stbi__bmp_load(s,x,y,comp,req_comp, ri); + #endif + #ifndef STBI_NO_GIF + if (stbi__gif_test(s)) return stbi__gif_load(s,x,y,comp,req_comp, ri); + #endif + #ifndef STBI_NO_PSD + if (stbi__psd_test(s)) return stbi__psd_load(s,x,y,comp,req_comp, ri, bpc); + #endif + #ifndef STBI_NO_PIC + if (stbi__pic_test(s)) return stbi__pic_load(s,x,y,comp,req_comp, ri); + #endif + #ifndef STBI_NO_PNM + if (stbi__pnm_test(s)) return stbi__pnm_load(s,x,y,comp,req_comp, ri); + #endif + + #ifndef STBI_NO_HDR + if (stbi__hdr_test(s)) { + float *hdr = stbi__hdr_load(s, x,y,comp,req_comp, ri); + return stbi__hdr_to_ldr(hdr, *x, *y, req_comp ? req_comp : *comp); + } + #endif + + #ifndef STBI_NO_TGA + // test tga last because it's a crappy test! + if (stbi__tga_test(s)) + return stbi__tga_load(s,x,y,comp,req_comp, ri); + #endif + + return stbi__errpuc("unknown image type", "Image not of any known type, or corrupt"); +} + +static stbi_uc *stbi__convert_16_to_8(stbi__uint16 *orig, int w, int h, int channels) +{ + int i; + int img_len = w * h * channels; + stbi_uc *reduced; + + reduced = (stbi_uc *) stbi__malloc(img_len); + if (reduced == NULL) return stbi__errpuc("outofmem", "Out of memory"); + + for (i = 0; i < img_len; ++i) + reduced[i] = (stbi_uc)((orig[i] >> 8) & 0xFF); // top half of each byte is sufficient approx of 16->8 bit scaling + + STBI_FREE(orig); + return reduced; +} + +static stbi__uint16 *stbi__convert_8_to_16(stbi_uc *orig, int w, int h, int channels) +{ + int i; + int img_len = w * h * channels; + stbi__uint16 *enlarged; + + enlarged = (stbi__uint16 *) stbi__malloc(img_len*2); + if (enlarged == NULL) return (stbi__uint16 *) stbi__errpuc("outofmem", "Out of memory"); + + for (i = 0; i < img_len; ++i) + enlarged[i] = (stbi__uint16)((orig[i] << 8) + orig[i]); // replicate to high and low byte, maps 0->0, 255->0xffff + + STBI_FREE(orig); + return enlarged; +} + +static void stbi__vertical_flip(void *image, int w, int h, int bytes_per_pixel) +{ + int row; + size_t bytes_per_row = (size_t)w * bytes_per_pixel; + stbi_uc temp[2048]; + stbi_uc *bytes = (stbi_uc *)image; + + for (row = 0; row < (h>>1); row++) { + stbi_uc *row0 = bytes + row*bytes_per_row; + stbi_uc *row1 = bytes + (h - row - 1)*bytes_per_row; + // swap row0 with row1 + size_t bytes_left = bytes_per_row; + while (bytes_left) { + size_t bytes_copy = (bytes_left < sizeof(temp)) ? bytes_left : sizeof(temp); + memcpy(temp, row0, bytes_copy); + memcpy(row0, row1, bytes_copy); + memcpy(row1, temp, bytes_copy); + row0 += bytes_copy; + row1 += bytes_copy; + bytes_left -= bytes_copy; + } + } +} + +static void stbi__vertical_flip_slices(void *image, int w, int h, int z, int bytes_per_pixel) +{ + int slice; + int slice_size = w * h * bytes_per_pixel; + + stbi_uc *bytes = (stbi_uc *)image; + for (slice = 0; slice < z; ++slice) { + stbi__vertical_flip(bytes, w, h, bytes_per_pixel); + bytes += slice_size; + } +} + +static unsigned char *stbi__load_and_postprocess_8bit(stbi__context *s, int *x, int *y, int *comp, int req_comp) +{ + stbi__result_info ri; + void *result = stbi__load_main(s, x, y, comp, req_comp, &ri, 8); + + if (result == NULL) + return NULL; + + if (ri.bits_per_channel != 8) { + STBI_ASSERT(ri.bits_per_channel == 16); + result = stbi__convert_16_to_8((stbi__uint16 *) result, *x, *y, req_comp == 0 ? *comp : req_comp); + ri.bits_per_channel = 8; + } + + // @TODO: move stbi__convert_format to here + + if (stbi__vertically_flip_on_load) { + int channels = req_comp ? req_comp : *comp; + stbi__vertical_flip(result, *x, *y, channels * sizeof(stbi_uc)); + } + + return (unsigned char *) result; +} + +static stbi__uint16 *stbi__load_and_postprocess_16bit(stbi__context *s, int *x, int *y, int *comp, int req_comp) +{ + stbi__result_info ri; + void *result = stbi__load_main(s, x, y, comp, req_comp, &ri, 16); + + if (result == NULL) + return NULL; + + if (ri.bits_per_channel != 16) { + STBI_ASSERT(ri.bits_per_channel == 8); + result = stbi__convert_8_to_16((stbi_uc *) result, *x, *y, req_comp == 0 ? *comp : req_comp); + ri.bits_per_channel = 16; + } + + // @TODO: move stbi__convert_format16 to here + // @TODO: special case RGB-to-Y (and RGBA-to-YA) for 8-bit-to-16-bit case to keep more precision + + if (stbi__vertically_flip_on_load) { + int channels = req_comp ? req_comp : *comp; + stbi__vertical_flip(result, *x, *y, channels * sizeof(stbi__uint16)); + } + + return (stbi__uint16 *) result; +} + +#if !defined(STBI_NO_HDR) || !defined(STBI_NO_LINEAR) +static void stbi__float_postprocess(float *result, int *x, int *y, int *comp, int req_comp) +{ + if (stbi__vertically_flip_on_load && result != NULL) { + int channels = req_comp ? req_comp : *comp; + stbi__vertical_flip(result, *x, *y, channels * sizeof(float)); + } +} +#endif + +#ifndef STBI_NO_STDIO + +static FILE *stbi__fopen(char const *filename, char const *mode) +{ + FILE *f; +#if defined(_MSC_VER) && _MSC_VER >= 1400 + if (0 != fopen_s(&f, filename, mode)) + f=0; +#else + f = fopen(filename, mode); +#endif + return f; +} + + +STBIDEF stbi_uc *stbi_load(char const *filename, int *x, int *y, int *comp, int req_comp) +{ + FILE *f = stbi__fopen(filename, "rb"); + unsigned char *result; + if (!f) return stbi__errpuc("can't fopen", "Unable to open file"); + result = stbi_load_from_file(f,x,y,comp,req_comp); + fclose(f); + return result; +} + +STBIDEF stbi_uc *stbi_load_from_file(FILE *f, int *x, int *y, int *comp, int req_comp) +{ + unsigned char *result; + stbi__context s; + stbi__start_file(&s,f); + result = stbi__load_and_postprocess_8bit(&s,x,y,comp,req_comp); + if (result) { + // need to 'unget' all the characters in the IO buffer + fseek(f, - (int) (s.img_buffer_end - s.img_buffer), SEEK_CUR); + } + return result; +} + +STBIDEF stbi__uint16 *stbi_load_from_file_16(FILE *f, int *x, int *y, int *comp, int req_comp) +{ + stbi__uint16 *result; + stbi__context s; + stbi__start_file(&s,f); + result = stbi__load_and_postprocess_16bit(&s,x,y,comp,req_comp); + if (result) { + // need to 'unget' all the characters in the IO buffer + fseek(f, - (int) (s.img_buffer_end - s.img_buffer), SEEK_CUR); + } + return result; +} + +STBIDEF stbi_us *stbi_load_16(char const *filename, int *x, int *y, int *comp, int req_comp) +{ + FILE *f = stbi__fopen(filename, "rb"); + stbi__uint16 *result; + if (!f) return (stbi_us *) stbi__errpuc("can't fopen", "Unable to open file"); + result = stbi_load_from_file_16(f,x,y,comp,req_comp); + fclose(f); + return result; +} + + +#endif //!STBI_NO_STDIO + +STBIDEF stbi_us *stbi_load_16_from_memory(stbi_uc const *buffer, int len, int *x, int *y, int *channels_in_file, int desired_channels) +{ + stbi__context s; + stbi__start_mem(&s,buffer,len); + return stbi__load_and_postprocess_16bit(&s,x,y,channels_in_file,desired_channels); +} + +STBIDEF stbi_us *stbi_load_16_from_callbacks(stbi_io_callbacks const *clbk, void *user, int *x, int *y, int *channels_in_file, int desired_channels) +{ + stbi__context s; + stbi__start_callbacks(&s, (stbi_io_callbacks *)clbk, user); + return stbi__load_and_postprocess_16bit(&s,x,y,channels_in_file,desired_channels); +} + +STBIDEF stbi_uc *stbi_load_from_memory(stbi_uc const *buffer, int len, int *x, int *y, int *comp, int req_comp) +{ + stbi__context s; + stbi__start_mem(&s,buffer,len); + return stbi__load_and_postprocess_8bit(&s,x,y,comp,req_comp); +} + +STBIDEF stbi_uc *stbi_load_from_callbacks(stbi_io_callbacks const *clbk, void *user, int *x, int *y, int *comp, int req_comp) +{ + stbi__context s; + stbi__start_callbacks(&s, (stbi_io_callbacks *) clbk, user); + return stbi__load_and_postprocess_8bit(&s,x,y,comp,req_comp); +} + +#ifndef STBI_NO_GIF +STBIDEF stbi_uc *stbi_load_gif_from_memory(stbi_uc const *buffer, int len, int **delays, int *x, int *y, int *z, int *comp, int req_comp) +{ + unsigned char *result; + stbi__context s; + stbi__start_mem(&s,buffer,len); + + result = (unsigned char*) stbi__load_gif_main(&s, delays, x, y, z, comp, req_comp); + if (stbi__vertically_flip_on_load) { + stbi__vertical_flip_slices( result, *x, *y, *z, *comp ); + } + + return result; +} +#endif + +#ifndef STBI_NO_LINEAR +static float *stbi__loadf_main(stbi__context *s, int *x, int *y, int *comp, int req_comp) +{ + unsigned char *data; + #ifndef STBI_NO_HDR + if (stbi__hdr_test(s)) { + stbi__result_info ri; + float *hdr_data = stbi__hdr_load(s,x,y,comp,req_comp, &ri); + if (hdr_data) + stbi__float_postprocess(hdr_data,x,y,comp,req_comp); + return hdr_data; + } + #endif + data = stbi__load_and_postprocess_8bit(s, x, y, comp, req_comp); + if (data) + return stbi__ldr_to_hdr(data, *x, *y, req_comp ? req_comp : *comp); + return stbi__errpf("unknown image type", "Image not of any known type, or corrupt"); +} + +STBIDEF float *stbi_loadf_from_memory(stbi_uc const *buffer, int len, int *x, int *y, int *comp, int req_comp) +{ + stbi__context s; + stbi__start_mem(&s,buffer,len); + return stbi__loadf_main(&s,x,y,comp,req_comp); +} + +STBIDEF float *stbi_loadf_from_callbacks(stbi_io_callbacks const *clbk, void *user, int *x, int *y, int *comp, int req_comp) +{ + stbi__context s; + stbi__start_callbacks(&s, (stbi_io_callbacks *) clbk, user); + return stbi__loadf_main(&s,x,y,comp,req_comp); +} + +#ifndef STBI_NO_STDIO +STBIDEF float *stbi_loadf(char const *filename, int *x, int *y, int *comp, int req_comp) +{ + float *result; + FILE *f = stbi__fopen(filename, "rb"); + if (!f) return stbi__errpf("can't fopen", "Unable to open file"); + result = stbi_loadf_from_file(f,x,y,comp,req_comp); + fclose(f); + return result; +} + +STBIDEF float *stbi_loadf_from_file(FILE *f, int *x, int *y, int *comp, int req_comp) +{ + stbi__context s; + stbi__start_file(&s,f); + return stbi__loadf_main(&s,x,y,comp,req_comp); +} +#endif // !STBI_NO_STDIO + +#endif // !STBI_NO_LINEAR + +// these is-hdr-or-not is defined independent of whether STBI_NO_LINEAR is +// defined, for API simplicity; if STBI_NO_LINEAR is defined, it always +// reports false! + +STBIDEF int stbi_is_hdr_from_memory(stbi_uc const *buffer, int len) +{ + #ifndef STBI_NO_HDR + stbi__context s; + stbi__start_mem(&s,buffer,len); + return stbi__hdr_test(&s); + #else + STBI_NOTUSED(buffer); + STBI_NOTUSED(len); + return 0; + #endif +} + +#ifndef STBI_NO_STDIO +STBIDEF int stbi_is_hdr (char const *filename) +{ + FILE *f = stbi__fopen(filename, "rb"); + int result=0; + if (f) { + result = stbi_is_hdr_from_file(f); + fclose(f); + } + return result; +} + +STBIDEF int stbi_is_hdr_from_file(FILE *f) +{ + #ifndef STBI_NO_HDR + long pos = ftell(f); + int res; + stbi__context s; + stbi__start_file(&s,f); + res = stbi__hdr_test(&s); + fseek(f, pos, SEEK_SET); + return res; + #else + STBI_NOTUSED(f); + return 0; + #endif +} +#endif // !STBI_NO_STDIO + +STBIDEF int stbi_is_hdr_from_callbacks(stbi_io_callbacks const *clbk, void *user) +{ + #ifndef STBI_NO_HDR + stbi__context s; + stbi__start_callbacks(&s, (stbi_io_callbacks *) clbk, user); + return stbi__hdr_test(&s); + #else + STBI_NOTUSED(clbk); + STBI_NOTUSED(user); + return 0; + #endif +} + +#ifndef STBI_NO_LINEAR +static float stbi__l2h_gamma=2.2f, stbi__l2h_scale=1.0f; + +STBIDEF void stbi_ldr_to_hdr_gamma(float gamma) { stbi__l2h_gamma = gamma; } +STBIDEF void stbi_ldr_to_hdr_scale(float scale) { stbi__l2h_scale = scale; } +#endif + +static float stbi__h2l_gamma_i=1.0f/2.2f, stbi__h2l_scale_i=1.0f; + +STBIDEF void stbi_hdr_to_ldr_gamma(float gamma) { stbi__h2l_gamma_i = 1/gamma; } +STBIDEF void stbi_hdr_to_ldr_scale(float scale) { stbi__h2l_scale_i = 1/scale; } + + +////////////////////////////////////////////////////////////////////////////// +// +// Common code used by all image loaders +// + +enum +{ + STBI__SCAN_load=0, + STBI__SCAN_type, + STBI__SCAN_header +}; + +static void stbi__refill_buffer(stbi__context *s) +{ + int n = (s->io.read)(s->io_user_data,(char*)s->buffer_start,s->buflen); + if (n == 0) { + // at end of file, treat same as if from memory, but need to handle case + // where s->img_buffer isn't pointing to safe memory, e.g. 0-byte file + s->read_from_callbacks = 0; + s->img_buffer = s->buffer_start; + s->img_buffer_end = s->buffer_start+1; + *s->img_buffer = 0; + } else { + s->img_buffer = s->buffer_start; + s->img_buffer_end = s->buffer_start + n; + } +} + +stbi_inline static stbi_uc stbi__get8(stbi__context *s) +{ + if (s->img_buffer < s->img_buffer_end) + return *s->img_buffer++; + if (s->read_from_callbacks) { + stbi__refill_buffer(s); + return *s->img_buffer++; + } + return 0; +} + +stbi_inline static int stbi__at_eof(stbi__context *s) +{ + if (s->io.read) { + if (!(s->io.eof)(s->io_user_data)) return 0; + // if feof() is true, check if buffer = end + // special case: we've only got the special 0 character at the end + if (s->read_from_callbacks == 0) return 1; + } + + return s->img_buffer >= s->img_buffer_end; +} + +static void stbi__skip(stbi__context *s, int n) +{ + if (n < 0) { + s->img_buffer = s->img_buffer_end; + return; + } + if (s->io.read) { + int blen = (int) (s->img_buffer_end - s->img_buffer); + if (blen < n) { + s->img_buffer = s->img_buffer_end; + (s->io.skip)(s->io_user_data, n - blen); + return; + } + } + s->img_buffer += n; +} + +static int stbi__getn(stbi__context *s, stbi_uc *buffer, int n) +{ + if (s->io.read) { + int blen = (int) (s->img_buffer_end - s->img_buffer); + if (blen < n) { + int res, count; + + memcpy(buffer, s->img_buffer, blen); + + count = (s->io.read)(s->io_user_data, (char*) buffer + blen, n - blen); + res = (count == (n-blen)); + s->img_buffer = s->img_buffer_end; + return res; + } + } + + if (s->img_buffer+n <= s->img_buffer_end) { + memcpy(buffer, s->img_buffer, n); + s->img_buffer += n; + return 1; + } else + return 0; +} + +static int stbi__get16be(stbi__context *s) +{ + int z = stbi__get8(s); + return (z << 8) + stbi__get8(s); +} + +static stbi__uint32 stbi__get32be(stbi__context *s) +{ + stbi__uint32 z = stbi__get16be(s); + return (z << 16) + stbi__get16be(s); +} + +#if defined(STBI_NO_BMP) && defined(STBI_NO_TGA) && defined(STBI_NO_GIF) +// nothing +#else +static int stbi__get16le(stbi__context *s) +{ + int z = stbi__get8(s); + return z + (stbi__get8(s) << 8); +} +#endif + +#ifndef STBI_NO_BMP +static stbi__uint32 stbi__get32le(stbi__context *s) +{ + stbi__uint32 z = stbi__get16le(s); + return z + (stbi__get16le(s) << 16); +} +#endif + +#define STBI__BYTECAST(x) ((stbi_uc) ((x) & 255)) // truncate int to byte without warnings + + +////////////////////////////////////////////////////////////////////////////// +// +// generic converter from built-in img_n to req_comp +// individual types do this automatically as much as possible (e.g. jpeg +// does all cases internally since it needs to colorspace convert anyway, +// and it never has alpha, so very few cases ). png can automatically +// interleave an alpha=255 channel, but falls back to this for other cases +// +// assume data buffer is malloced, so malloc a new one and free that one +// only failure mode is malloc failing + +static stbi_uc stbi__compute_y(int r, int g, int b) +{ + return (stbi_uc) (((r*77) + (g*150) + (29*b)) >> 8); +} + +static unsigned char *stbi__convert_format(unsigned char *data, int img_n, int req_comp, unsigned int x, unsigned int y) +{ + int i,j; + unsigned char *good; + + if (req_comp == img_n) return data; + STBI_ASSERT(req_comp >= 1 && req_comp <= 4); + + good = (unsigned char *) stbi__malloc_mad3(req_comp, x, y, 0); + if (good == NULL) { + STBI_FREE(data); + return stbi__errpuc("outofmem", "Out of memory"); + } + + for (j=0; j < (int) y; ++j) { + unsigned char *src = data + j * x * img_n ; + unsigned char *dest = good + j * x * req_comp; + + #define STBI__COMBO(a,b) ((a)*8+(b)) + #define STBI__CASE(a,b) case STBI__COMBO(a,b): for(i=x-1; i >= 0; --i, src += a, dest += b) + // convert source image with img_n components to one with req_comp components; + // avoid switch per pixel, so use switch per scanline and massive macros + switch (STBI__COMBO(img_n, req_comp)) { + STBI__CASE(1,2) { dest[0]=src[0], dest[1]=255; } break; + STBI__CASE(1,3) { dest[0]=dest[1]=dest[2]=src[0]; } break; + STBI__CASE(1,4) { dest[0]=dest[1]=dest[2]=src[0], dest[3]=255; } break; + STBI__CASE(2,1) { dest[0]=src[0]; } break; + STBI__CASE(2,3) { dest[0]=dest[1]=dest[2]=src[0]; } break; + STBI__CASE(2,4) { dest[0]=dest[1]=dest[2]=src[0], dest[3]=src[1]; } break; + STBI__CASE(3,4) { dest[0]=src[0],dest[1]=src[1],dest[2]=src[2],dest[3]=255; } break; + STBI__CASE(3,1) { dest[0]=stbi__compute_y(src[0],src[1],src[2]); } break; + STBI__CASE(3,2) { dest[0]=stbi__compute_y(src[0],src[1],src[2]), dest[1] = 255; } break; + STBI__CASE(4,1) { dest[0]=stbi__compute_y(src[0],src[1],src[2]); } break; + STBI__CASE(4,2) { dest[0]=stbi__compute_y(src[0],src[1],src[2]), dest[1] = src[3]; } break; + STBI__CASE(4,3) { dest[0]=src[0],dest[1]=src[1],dest[2]=src[2]; } break; + default: STBI_ASSERT(0); + } + #undef STBI__CASE + } + + STBI_FREE(data); + return good; +} + +static stbi__uint16 stbi__compute_y_16(int r, int g, int b) +{ + return (stbi__uint16) (((r*77) + (g*150) + (29*b)) >> 8); +} + +static stbi__uint16 *stbi__convert_format16(stbi__uint16 *data, int img_n, int req_comp, unsigned int x, unsigned int y) +{ + int i,j; + stbi__uint16 *good; + + if (req_comp == img_n) return data; + STBI_ASSERT(req_comp >= 1 && req_comp <= 4); + + good = (stbi__uint16 *) stbi__malloc(req_comp * x * y * 2); + if (good == NULL) { + STBI_FREE(data); + return (stbi__uint16 *) stbi__errpuc("outofmem", "Out of memory"); + } + + for (j=0; j < (int) y; ++j) { + stbi__uint16 *src = data + j * x * img_n ; + stbi__uint16 *dest = good + j * x * req_comp; + + #define STBI__COMBO(a,b) ((a)*8+(b)) + #define STBI__CASE(a,b) case STBI__COMBO(a,b): for(i=x-1; i >= 0; --i, src += a, dest += b) + // convert source image with img_n components to one with req_comp components; + // avoid switch per pixel, so use switch per scanline and massive macros + switch (STBI__COMBO(img_n, req_comp)) { + STBI__CASE(1,2) { dest[0]=src[0], dest[1]=0xffff; } break; + STBI__CASE(1,3) { dest[0]=dest[1]=dest[2]=src[0]; } break; + STBI__CASE(1,4) { dest[0]=dest[1]=dest[2]=src[0], dest[3]=0xffff; } break; + STBI__CASE(2,1) { dest[0]=src[0]; } break; + STBI__CASE(2,3) { dest[0]=dest[1]=dest[2]=src[0]; } break; + STBI__CASE(2,4) { dest[0]=dest[1]=dest[2]=src[0], dest[3]=src[1]; } break; + STBI__CASE(3,4) { dest[0]=src[0],dest[1]=src[1],dest[2]=src[2],dest[3]=0xffff; } break; + STBI__CASE(3,1) { dest[0]=stbi__compute_y_16(src[0],src[1],src[2]); } break; + STBI__CASE(3,2) { dest[0]=stbi__compute_y_16(src[0],src[1],src[2]), dest[1] = 0xffff; } break; + STBI__CASE(4,1) { dest[0]=stbi__compute_y_16(src[0],src[1],src[2]); } break; + STBI__CASE(4,2) { dest[0]=stbi__compute_y_16(src[0],src[1],src[2]), dest[1] = src[3]; } break; + STBI__CASE(4,3) { dest[0]=src[0],dest[1]=src[1],dest[2]=src[2]; } break; + default: STBI_ASSERT(0); + } + #undef STBI__CASE + } + + STBI_FREE(data); + return good; +} + +#ifndef STBI_NO_LINEAR +static float *stbi__ldr_to_hdr(stbi_uc *data, int x, int y, int comp) +{ + int i,k,n; + float *output; + if (!data) return NULL; + output = (float *) stbi__malloc_mad4(x, y, comp, sizeof(float), 0); + if (output == NULL) { STBI_FREE(data); return stbi__errpf("outofmem", "Out of memory"); } + // compute number of non-alpha components + if (comp & 1) n = comp; else n = comp-1; + for (i=0; i < x*y; ++i) { + for (k=0; k < n; ++k) { + output[i*comp + k] = (float) (pow(data[i*comp+k]/255.0f, stbi__l2h_gamma) * stbi__l2h_scale); + } + if (k < comp) output[i*comp + k] = data[i*comp+k]/255.0f; + } + STBI_FREE(data); + return output; +} +#endif + +#ifndef STBI_NO_HDR +#define stbi__float2int(x) ((int) (x)) +static stbi_uc *stbi__hdr_to_ldr(float *data, int x, int y, int comp) +{ + int i,k,n; + stbi_uc *output; + if (!data) return NULL; + output = (stbi_uc *) stbi__malloc_mad3(x, y, comp, 0); + if (output == NULL) { STBI_FREE(data); return stbi__errpuc("outofmem", "Out of memory"); } + // compute number of non-alpha components + if (comp & 1) n = comp; else n = comp-1; + for (i=0; i < x*y; ++i) { + for (k=0; k < n; ++k) { + float z = (float) pow(data[i*comp+k]*stbi__h2l_scale_i, stbi__h2l_gamma_i) * 255 + 0.5f; + if (z < 0) z = 0; + if (z > 255) z = 255; + output[i*comp + k] = (stbi_uc) stbi__float2int(z); + } + if (k < comp) { + float z = data[i*comp+k] * 255 + 0.5f; + if (z < 0) z = 0; + if (z > 255) z = 255; + output[i*comp + k] = (stbi_uc) stbi__float2int(z); + } + } + STBI_FREE(data); + return output; +} +#endif + +////////////////////////////////////////////////////////////////////////////// +// +// "baseline" JPEG/JFIF decoder +// +// simple implementation +// - doesn't support delayed output of y-dimension +// - simple interface (only one output format: 8-bit interleaved RGB) +// - doesn't try to recover corrupt jpegs +// - doesn't allow partial loading, loading multiple at once +// - still fast on x86 (copying globals into locals doesn't help x86) +// - allocates lots of intermediate memory (full size of all components) +// - non-interleaved case requires this anyway +// - allows good upsampling (see next) +// high-quality +// - upsampled channels are bilinearly interpolated, even across blocks +// - quality integer IDCT derived from IJG's 'slow' +// performance +// - fast huffman; reasonable integer IDCT +// - some SIMD kernels for common paths on targets with SSE2/NEON +// - uses a lot of intermediate memory, could cache poorly + +#ifndef STBI_NO_JPEG + +// huffman decoding acceleration +#define FAST_BITS 9 // larger handles more cases; smaller stomps less cache + +typedef struct +{ + stbi_uc fast[1 << FAST_BITS]; + // weirdly, repacking this into AoS is a 10% speed loss, instead of a win + stbi__uint16 code[256]; + stbi_uc values[256]; + stbi_uc size[257]; + unsigned int maxcode[18]; + int delta[17]; // old 'firstsymbol' - old 'firstcode' +} stbi__huffman; + +typedef struct +{ + stbi__context *s; + stbi__huffman huff_dc[4]; + stbi__huffman huff_ac[4]; + stbi__uint16 dequant[4][64]; + stbi__int16 fast_ac[4][1 << FAST_BITS]; + +// sizes for components, interleaved MCUs + int img_h_max, img_v_max; + int img_mcu_x, img_mcu_y; + int img_mcu_w, img_mcu_h; + +// definition of jpeg image component + struct + { + int id; + int h,v; + int tq; + int hd,ha; + int dc_pred; + + int x,y,w2,h2; + stbi_uc *data; + void *raw_data, *raw_coeff; + stbi_uc *linebuf; + short *coeff; // progressive only + int coeff_w, coeff_h; // number of 8x8 coefficient blocks + } img_comp[4]; + + stbi__uint32 code_buffer; // jpeg entropy-coded buffer + int code_bits; // number of valid bits + unsigned char marker; // marker seen while filling entropy buffer + int nomore; // flag if we saw a marker so must stop + + int progressive; + int spec_start; + int spec_end; + int succ_high; + int succ_low; + int eob_run; + int jfif; + int app14_color_transform; // Adobe APP14 tag + int rgb; + + int scan_n, order[4]; + int restart_interval, todo; + +// kernels + void (*idct_block_kernel)(stbi_uc *out, int out_stride, short data[64]); + void (*YCbCr_to_RGB_kernel)(stbi_uc *out, const stbi_uc *y, const stbi_uc *pcb, const stbi_uc *pcr, int count, int step); + stbi_uc *(*resample_row_hv_2_kernel)(stbi_uc *out, stbi_uc *in_near, stbi_uc *in_far, int w, int hs); +} stbi__jpeg; + +static int stbi__build_huffman(stbi__huffman *h, int *count) +{ + int i,j,k=0; + unsigned int code; + // build size list for each symbol (from JPEG spec) + for (i=0; i < 16; ++i) + for (j=0; j < count[i]; ++j) + h->size[k++] = (stbi_uc) (i+1); + h->size[k] = 0; + + // compute actual symbols (from jpeg spec) + code = 0; + k = 0; + for(j=1; j <= 16; ++j) { + // compute delta to add to code to compute symbol id + h->delta[j] = k - code; + if (h->size[k] == j) { + while (h->size[k] == j) + h->code[k++] = (stbi__uint16) (code++); + if (code-1 >= (1u << j)) return stbi__err("bad code lengths","Corrupt JPEG"); + } + // compute largest code + 1 for this size, preshifted as needed later + h->maxcode[j] = code << (16-j); + code <<= 1; + } + h->maxcode[j] = 0xffffffff; + + // build non-spec acceleration table; 255 is flag for not-accelerated + memset(h->fast, 255, 1 << FAST_BITS); + for (i=0; i < k; ++i) { + int s = h->size[i]; + if (s <= FAST_BITS) { + int c = h->code[i] << (FAST_BITS-s); + int m = 1 << (FAST_BITS-s); + for (j=0; j < m; ++j) { + h->fast[c+j] = (stbi_uc) i; + } + } + } + return 1; +} + +// build a table that decodes both magnitude and value of small ACs in +// one go. +static void stbi__build_fast_ac(stbi__int16 *fast_ac, stbi__huffman *h) +{ + int i; + for (i=0; i < (1 << FAST_BITS); ++i) { + stbi_uc fast = h->fast[i]; + fast_ac[i] = 0; + if (fast < 255) { + int rs = h->values[fast]; + int run = (rs >> 4) & 15; + int magbits = rs & 15; + int len = h->size[fast]; + + if (magbits && len + magbits <= FAST_BITS) { + // magnitude code followed by receive_extend code + int k = ((i << len) & ((1 << FAST_BITS) - 1)) >> (FAST_BITS - magbits); + int m = 1 << (magbits - 1); + if (k < m) k += (~0U << magbits) + 1; + // if the result is small enough, we can fit it in fast_ac table + if (k >= -128 && k <= 127) + fast_ac[i] = (stbi__int16) ((k * 256) + (run * 16) + (len + magbits)); + } + } + } +} + +static void stbi__grow_buffer_unsafe(stbi__jpeg *j) +{ + do { + unsigned int b = j->nomore ? 0 : stbi__get8(j->s); + if (b == 0xff) { + int c = stbi__get8(j->s); + while (c == 0xff) c = stbi__get8(j->s); // consume fill bytes + if (c != 0) { + j->marker = (unsigned char) c; + j->nomore = 1; + return; + } + } + j->code_buffer |= b << (24 - j->code_bits); + j->code_bits += 8; + } while (j->code_bits <= 24); +} + +// (1 << n) - 1 +static const stbi__uint32 stbi__bmask[17]={0,1,3,7,15,31,63,127,255,511,1023,2047,4095,8191,16383,32767,65535}; + +// decode a jpeg huffman value from the bitstream +stbi_inline static int stbi__jpeg_huff_decode(stbi__jpeg *j, stbi__huffman *h) +{ + unsigned int temp; + int c,k; + + if (j->code_bits < 16) stbi__grow_buffer_unsafe(j); + + // look at the top FAST_BITS and determine what symbol ID it is, + // if the code is <= FAST_BITS + c = (j->code_buffer >> (32 - FAST_BITS)) & ((1 << FAST_BITS)-1); + k = h->fast[c]; + if (k < 255) { + int s = h->size[k]; + if (s > j->code_bits) + return -1; + j->code_buffer <<= s; + j->code_bits -= s; + return h->values[k]; + } + + // naive test is to shift the code_buffer down so k bits are + // valid, then test against maxcode. To speed this up, we've + // preshifted maxcode left so that it has (16-k) 0s at the + // end; in other words, regardless of the number of bits, it + // wants to be compared against something shifted to have 16; + // that way we don't need to shift inside the loop. + temp = j->code_buffer >> 16; + for (k=FAST_BITS+1 ; ; ++k) + if (temp < h->maxcode[k]) + break; + if (k == 17) { + // error! code not found + j->code_bits -= 16; + return -1; + } + + if (k > j->code_bits) + return -1; + + // convert the huffman code to the symbol id + c = ((j->code_buffer >> (32 - k)) & stbi__bmask[k]) + h->delta[k]; + STBI_ASSERT((((j->code_buffer) >> (32 - h->size[c])) & stbi__bmask[h->size[c]]) == h->code[c]); + + // convert the id to a symbol + j->code_bits -= k; + j->code_buffer <<= k; + return h->values[c]; +} + +// bias[n] = (-1<code_bits < n) stbi__grow_buffer_unsafe(j); + + sgn = (stbi__int32)j->code_buffer >> 31; // sign bit is always in MSB + k = stbi_lrot(j->code_buffer, n); + STBI_ASSERT(n >= 0 && n < (int) (sizeof(stbi__bmask)/sizeof(*stbi__bmask))); + j->code_buffer = k & ~stbi__bmask[n]; + k &= stbi__bmask[n]; + j->code_bits -= n; + return k + (stbi__jbias[n] & ~sgn); +} + +// get some unsigned bits +stbi_inline static int stbi__jpeg_get_bits(stbi__jpeg *j, int n) +{ + unsigned int k; + if (j->code_bits < n) stbi__grow_buffer_unsafe(j); + k = stbi_lrot(j->code_buffer, n); + j->code_buffer = k & ~stbi__bmask[n]; + k &= stbi__bmask[n]; + j->code_bits -= n; + return k; +} + +stbi_inline static int stbi__jpeg_get_bit(stbi__jpeg *j) +{ + unsigned int k; + if (j->code_bits < 1) stbi__grow_buffer_unsafe(j); + k = j->code_buffer; + j->code_buffer <<= 1; + --j->code_bits; + return k & 0x80000000; +} + +// given a value that's at position X in the zigzag stream, +// where does it appear in the 8x8 matrix coded as row-major? +static const stbi_uc stbi__jpeg_dezigzag[64+15] = +{ + 0, 1, 8, 16, 9, 2, 3, 10, + 17, 24, 32, 25, 18, 11, 4, 5, + 12, 19, 26, 33, 40, 48, 41, 34, + 27, 20, 13, 6, 7, 14, 21, 28, + 35, 42, 49, 56, 57, 50, 43, 36, + 29, 22, 15, 23, 30, 37, 44, 51, + 58, 59, 52, 45, 38, 31, 39, 46, + 53, 60, 61, 54, 47, 55, 62, 63, + // let corrupt input sample past end + 63, 63, 63, 63, 63, 63, 63, 63, + 63, 63, 63, 63, 63, 63, 63 +}; + +// decode one 64-entry block-- +static int stbi__jpeg_decode_block(stbi__jpeg *j, short data[64], stbi__huffman *hdc, stbi__huffman *hac, stbi__int16 *fac, int b, stbi__uint16 *dequant) +{ + int diff,dc,k; + int t; + + if (j->code_bits < 16) stbi__grow_buffer_unsafe(j); + t = stbi__jpeg_huff_decode(j, hdc); + if (t < 0) return stbi__err("bad huffman code","Corrupt JPEG"); + + // 0 all the ac values now so we can do it 32-bits at a time + memset(data,0,64*sizeof(data[0])); + + diff = t ? stbi__extend_receive(j, t) : 0; + dc = j->img_comp[b].dc_pred + diff; + j->img_comp[b].dc_pred = dc; + data[0] = (short) (dc * dequant[0]); + + // decode AC components, see JPEG spec + k = 1; + do { + unsigned int zig; + int c,r,s; + if (j->code_bits < 16) stbi__grow_buffer_unsafe(j); + c = (j->code_buffer >> (32 - FAST_BITS)) & ((1 << FAST_BITS)-1); + r = fac[c]; + if (r) { // fast-AC path + k += (r >> 4) & 15; // run + s = r & 15; // combined length + j->code_buffer <<= s; + j->code_bits -= s; + // decode into unzigzag'd location + zig = stbi__jpeg_dezigzag[k++]; + data[zig] = (short) ((r >> 8) * dequant[zig]); + } else { + int rs = stbi__jpeg_huff_decode(j, hac); + if (rs < 0) return stbi__err("bad huffman code","Corrupt JPEG"); + s = rs & 15; + r = rs >> 4; + if (s == 0) { + if (rs != 0xf0) break; // end block + k += 16; + } else { + k += r; + // decode into unzigzag'd location + zig = stbi__jpeg_dezigzag[k++]; + data[zig] = (short) (stbi__extend_receive(j,s) * dequant[zig]); + } + } + } while (k < 64); + return 1; +} + +static int stbi__jpeg_decode_block_prog_dc(stbi__jpeg *j, short data[64], stbi__huffman *hdc, int b) +{ + int diff,dc; + int t; + if (j->spec_end != 0) return stbi__err("can't merge dc and ac", "Corrupt JPEG"); + + if (j->code_bits < 16) stbi__grow_buffer_unsafe(j); + + if (j->succ_high == 0) { + // first scan for DC coefficient, must be first + memset(data,0,64*sizeof(data[0])); // 0 all the ac values now + t = stbi__jpeg_huff_decode(j, hdc); + diff = t ? stbi__extend_receive(j, t) : 0; + + dc = j->img_comp[b].dc_pred + diff; + j->img_comp[b].dc_pred = dc; + data[0] = (short) (dc << j->succ_low); + } else { + // refinement scan for DC coefficient + if (stbi__jpeg_get_bit(j)) + data[0] += (short) (1 << j->succ_low); + } + return 1; +} + +// @OPTIMIZE: store non-zigzagged during the decode passes, +// and only de-zigzag when dequantizing +static int stbi__jpeg_decode_block_prog_ac(stbi__jpeg *j, short data[64], stbi__huffman *hac, stbi__int16 *fac) +{ + int k; + if (j->spec_start == 0) return stbi__err("can't merge dc and ac", "Corrupt JPEG"); + + if (j->succ_high == 0) { + int shift = j->succ_low; + + if (j->eob_run) { + --j->eob_run; + return 1; + } + + k = j->spec_start; + do { + unsigned int zig; + int c,r,s; + if (j->code_bits < 16) stbi__grow_buffer_unsafe(j); + c = (j->code_buffer >> (32 - FAST_BITS)) & ((1 << FAST_BITS)-1); + r = fac[c]; + if (r) { // fast-AC path + k += (r >> 4) & 15; // run + s = r & 15; // combined length + j->code_buffer <<= s; + j->code_bits -= s; + zig = stbi__jpeg_dezigzag[k++]; + data[zig] = (short) ((r >> 8) << shift); + } else { + int rs = stbi__jpeg_huff_decode(j, hac); + if (rs < 0) return stbi__err("bad huffman code","Corrupt JPEG"); + s = rs & 15; + r = rs >> 4; + if (s == 0) { + if (r < 15) { + j->eob_run = (1 << r); + if (r) + j->eob_run += stbi__jpeg_get_bits(j, r); + --j->eob_run; + break; + } + k += 16; + } else { + k += r; + zig = stbi__jpeg_dezigzag[k++]; + data[zig] = (short) (stbi__extend_receive(j,s) << shift); + } + } + } while (k <= j->spec_end); + } else { + // refinement scan for these AC coefficients + + short bit = (short) (1 << j->succ_low); + + if (j->eob_run) { + --j->eob_run; + for (k = j->spec_start; k <= j->spec_end; ++k) { + short *p = &data[stbi__jpeg_dezigzag[k]]; + if (*p != 0) + if (stbi__jpeg_get_bit(j)) + if ((*p & bit)==0) { + if (*p > 0) + *p += bit; + else + *p -= bit; + } + } + } else { + k = j->spec_start; + do { + int r,s; + int rs = stbi__jpeg_huff_decode(j, hac); // @OPTIMIZE see if we can use the fast path here, advance-by-r is so slow, eh + if (rs < 0) return stbi__err("bad huffman code","Corrupt JPEG"); + s = rs & 15; + r = rs >> 4; + if (s == 0) { + if (r < 15) { + j->eob_run = (1 << r) - 1; + if (r) + j->eob_run += stbi__jpeg_get_bits(j, r); + r = 64; // force end of block + } else { + // r=15 s=0 should write 16 0s, so we just do + // a run of 15 0s and then write s (which is 0), + // so we don't have to do anything special here + } + } else { + if (s != 1) return stbi__err("bad huffman code", "Corrupt JPEG"); + // sign bit + if (stbi__jpeg_get_bit(j)) + s = bit; + else + s = -bit; + } + + // advance by r + while (k <= j->spec_end) { + short *p = &data[stbi__jpeg_dezigzag[k++]]; + if (*p != 0) { + if (stbi__jpeg_get_bit(j)) + if ((*p & bit)==0) { + if (*p > 0) + *p += bit; + else + *p -= bit; + } + } else { + if (r == 0) { + *p = (short) s; + break; + } + --r; + } + } + } while (k <= j->spec_end); + } + } + return 1; +} + +// take a -128..127 value and stbi__clamp it and convert to 0..255 +stbi_inline static stbi_uc stbi__clamp(int x) +{ + // trick to use a single test to catch both cases + if ((unsigned int) x > 255) { + if (x < 0) return 0; + if (x > 255) return 255; + } + return (stbi_uc) x; +} + +#define stbi__f2f(x) ((int) (((x) * 4096 + 0.5))) +#define stbi__fsh(x) ((x) * 4096) + +// derived from jidctint -- DCT_ISLOW +#define STBI__IDCT_1D(s0,s1,s2,s3,s4,s5,s6,s7) \ + int t0,t1,t2,t3,p1,p2,p3,p4,p5,x0,x1,x2,x3; \ + p2 = s2; \ + p3 = s6; \ + p1 = (p2+p3) * stbi__f2f(0.5411961f); \ + t2 = p1 + p3*stbi__f2f(-1.847759065f); \ + t3 = p1 + p2*stbi__f2f( 0.765366865f); \ + p2 = s0; \ + p3 = s4; \ + t0 = stbi__fsh(p2+p3); \ + t1 = stbi__fsh(p2-p3); \ + x0 = t0+t3; \ + x3 = t0-t3; \ + x1 = t1+t2; \ + x2 = t1-t2; \ + t0 = s7; \ + t1 = s5; \ + t2 = s3; \ + t3 = s1; \ + p3 = t0+t2; \ + p4 = t1+t3; \ + p1 = t0+t3; \ + p2 = t1+t2; \ + p5 = (p3+p4)*stbi__f2f( 1.175875602f); \ + t0 = t0*stbi__f2f( 0.298631336f); \ + t1 = t1*stbi__f2f( 2.053119869f); \ + t2 = t2*stbi__f2f( 3.072711026f); \ + t3 = t3*stbi__f2f( 1.501321110f); \ + p1 = p5 + p1*stbi__f2f(-0.899976223f); \ + p2 = p5 + p2*stbi__f2f(-2.562915447f); \ + p3 = p3*stbi__f2f(-1.961570560f); \ + p4 = p4*stbi__f2f(-0.390180644f); \ + t3 += p1+p4; \ + t2 += p2+p3; \ + t1 += p2+p4; \ + t0 += p1+p3; + +static void stbi__idct_block(stbi_uc *out, int out_stride, short data[64]) +{ + int i,val[64],*v=val; + stbi_uc *o; + short *d = data; + + // columns + for (i=0; i < 8; ++i,++d, ++v) { + // if all zeroes, shortcut -- this avoids dequantizing 0s and IDCTing + if (d[ 8]==0 && d[16]==0 && d[24]==0 && d[32]==0 + && d[40]==0 && d[48]==0 && d[56]==0) { + // no shortcut 0 seconds + // (1|2|3|4|5|6|7)==0 0 seconds + // all separate -0.047 seconds + // 1 && 2|3 && 4|5 && 6|7: -0.047 seconds + int dcterm = d[0]*4; + v[0] = v[8] = v[16] = v[24] = v[32] = v[40] = v[48] = v[56] = dcterm; + } else { + STBI__IDCT_1D(d[ 0],d[ 8],d[16],d[24],d[32],d[40],d[48],d[56]) + // constants scaled things up by 1<<12; let's bring them back + // down, but keep 2 extra bits of precision + x0 += 512; x1 += 512; x2 += 512; x3 += 512; + v[ 0] = (x0+t3) >> 10; + v[56] = (x0-t3) >> 10; + v[ 8] = (x1+t2) >> 10; + v[48] = (x1-t2) >> 10; + v[16] = (x2+t1) >> 10; + v[40] = (x2-t1) >> 10; + v[24] = (x3+t0) >> 10; + v[32] = (x3-t0) >> 10; + } + } + + for (i=0, v=val, o=out; i < 8; ++i,v+=8,o+=out_stride) { + // no fast case since the first 1D IDCT spread components out + STBI__IDCT_1D(v[0],v[1],v[2],v[3],v[4],v[5],v[6],v[7]) + // constants scaled things up by 1<<12, plus we had 1<<2 from first + // loop, plus horizontal and vertical each scale by sqrt(8) so together + // we've got an extra 1<<3, so 1<<17 total we need to remove. + // so we want to round that, which means adding 0.5 * 1<<17, + // aka 65536. Also, we'll end up with -128 to 127 that we want + // to encode as 0..255 by adding 128, so we'll add that before the shift + x0 += 65536 + (128<<17); + x1 += 65536 + (128<<17); + x2 += 65536 + (128<<17); + x3 += 65536 + (128<<17); + // tried computing the shifts into temps, or'ing the temps to see + // if any were out of range, but that was slower + o[0] = stbi__clamp((x0+t3) >> 17); + o[7] = stbi__clamp((x0-t3) >> 17); + o[1] = stbi__clamp((x1+t2) >> 17); + o[6] = stbi__clamp((x1-t2) >> 17); + o[2] = stbi__clamp((x2+t1) >> 17); + o[5] = stbi__clamp((x2-t1) >> 17); + o[3] = stbi__clamp((x3+t0) >> 17); + o[4] = stbi__clamp((x3-t0) >> 17); + } +} + +#ifdef STBI_SSE2 +// sse2 integer IDCT. not the fastest possible implementation but it +// produces bit-identical results to the generic C version so it's +// fully "transparent". +static void stbi__idct_simd(stbi_uc *out, int out_stride, short data[64]) +{ + // This is constructed to match our regular (generic) integer IDCT exactly. + __m128i row0, row1, row2, row3, row4, row5, row6, row7; + __m128i tmp; + + // dot product constant: even elems=x, odd elems=y + #define dct_const(x,y) _mm_setr_epi16((x),(y),(x),(y),(x),(y),(x),(y)) + + // out(0) = c0[even]*x + c0[odd]*y (c0, x, y 16-bit, out 32-bit) + // out(1) = c1[even]*x + c1[odd]*y + #define dct_rot(out0,out1, x,y,c0,c1) \ + __m128i c0##lo = _mm_unpacklo_epi16((x),(y)); \ + __m128i c0##hi = _mm_unpackhi_epi16((x),(y)); \ + __m128i out0##_l = _mm_madd_epi16(c0##lo, c0); \ + __m128i out0##_h = _mm_madd_epi16(c0##hi, c0); \ + __m128i out1##_l = _mm_madd_epi16(c0##lo, c1); \ + __m128i out1##_h = _mm_madd_epi16(c0##hi, c1) + + // out = in << 12 (in 16-bit, out 32-bit) + #define dct_widen(out, in) \ + __m128i out##_l = _mm_srai_epi32(_mm_unpacklo_epi16(_mm_setzero_si128(), (in)), 4); \ + __m128i out##_h = _mm_srai_epi32(_mm_unpackhi_epi16(_mm_setzero_si128(), (in)), 4) + + // wide add + #define dct_wadd(out, a, b) \ + __m128i out##_l = _mm_add_epi32(a##_l, b##_l); \ + __m128i out##_h = _mm_add_epi32(a##_h, b##_h) + + // wide sub + #define dct_wsub(out, a, b) \ + __m128i out##_l = _mm_sub_epi32(a##_l, b##_l); \ + __m128i out##_h = _mm_sub_epi32(a##_h, b##_h) + + // butterfly a/b, add bias, then shift by "s" and pack + #define dct_bfly32o(out0, out1, a,b,bias,s) \ + { \ + __m128i abiased_l = _mm_add_epi32(a##_l, bias); \ + __m128i abiased_h = _mm_add_epi32(a##_h, bias); \ + dct_wadd(sum, abiased, b); \ + dct_wsub(dif, abiased, b); \ + out0 = _mm_packs_epi32(_mm_srai_epi32(sum_l, s), _mm_srai_epi32(sum_h, s)); \ + out1 = _mm_packs_epi32(_mm_srai_epi32(dif_l, s), _mm_srai_epi32(dif_h, s)); \ + } + + // 8-bit interleave step (for transposes) + #define dct_interleave8(a, b) \ + tmp = a; \ + a = _mm_unpacklo_epi8(a, b); \ + b = _mm_unpackhi_epi8(tmp, b) + + // 16-bit interleave step (for transposes) + #define dct_interleave16(a, b) \ + tmp = a; \ + a = _mm_unpacklo_epi16(a, b); \ + b = _mm_unpackhi_epi16(tmp, b) + + #define dct_pass(bias,shift) \ + { \ + /* even part */ \ + dct_rot(t2e,t3e, row2,row6, rot0_0,rot0_1); \ + __m128i sum04 = _mm_add_epi16(row0, row4); \ + __m128i dif04 = _mm_sub_epi16(row0, row4); \ + dct_widen(t0e, sum04); \ + dct_widen(t1e, dif04); \ + dct_wadd(x0, t0e, t3e); \ + dct_wsub(x3, t0e, t3e); \ + dct_wadd(x1, t1e, t2e); \ + dct_wsub(x2, t1e, t2e); \ + /* odd part */ \ + dct_rot(y0o,y2o, row7,row3, rot2_0,rot2_1); \ + dct_rot(y1o,y3o, row5,row1, rot3_0,rot3_1); \ + __m128i sum17 = _mm_add_epi16(row1, row7); \ + __m128i sum35 = _mm_add_epi16(row3, row5); \ + dct_rot(y4o,y5o, sum17,sum35, rot1_0,rot1_1); \ + dct_wadd(x4, y0o, y4o); \ + dct_wadd(x5, y1o, y5o); \ + dct_wadd(x6, y2o, y5o); \ + dct_wadd(x7, y3o, y4o); \ + dct_bfly32o(row0,row7, x0,x7,bias,shift); \ + dct_bfly32o(row1,row6, x1,x6,bias,shift); \ + dct_bfly32o(row2,row5, x2,x5,bias,shift); \ + dct_bfly32o(row3,row4, x3,x4,bias,shift); \ + } + + __m128i rot0_0 = dct_const(stbi__f2f(0.5411961f), stbi__f2f(0.5411961f) + stbi__f2f(-1.847759065f)); + __m128i rot0_1 = dct_const(stbi__f2f(0.5411961f) + stbi__f2f( 0.765366865f), stbi__f2f(0.5411961f)); + __m128i rot1_0 = dct_const(stbi__f2f(1.175875602f) + stbi__f2f(-0.899976223f), stbi__f2f(1.175875602f)); + __m128i rot1_1 = dct_const(stbi__f2f(1.175875602f), stbi__f2f(1.175875602f) + stbi__f2f(-2.562915447f)); + __m128i rot2_0 = dct_const(stbi__f2f(-1.961570560f) + stbi__f2f( 0.298631336f), stbi__f2f(-1.961570560f)); + __m128i rot2_1 = dct_const(stbi__f2f(-1.961570560f), stbi__f2f(-1.961570560f) + stbi__f2f( 3.072711026f)); + __m128i rot3_0 = dct_const(stbi__f2f(-0.390180644f) + stbi__f2f( 2.053119869f), stbi__f2f(-0.390180644f)); + __m128i rot3_1 = dct_const(stbi__f2f(-0.390180644f), stbi__f2f(-0.390180644f) + stbi__f2f( 1.501321110f)); + + // rounding biases in column/row passes, see stbi__idct_block for explanation. + __m128i bias_0 = _mm_set1_epi32(512); + __m128i bias_1 = _mm_set1_epi32(65536 + (128<<17)); + + // load + row0 = _mm_load_si128((const __m128i *) (data + 0*8)); + row1 = _mm_load_si128((const __m128i *) (data + 1*8)); + row2 = _mm_load_si128((const __m128i *) (data + 2*8)); + row3 = _mm_load_si128((const __m128i *) (data + 3*8)); + row4 = _mm_load_si128((const __m128i *) (data + 4*8)); + row5 = _mm_load_si128((const __m128i *) (data + 5*8)); + row6 = _mm_load_si128((const __m128i *) (data + 6*8)); + row7 = _mm_load_si128((const __m128i *) (data + 7*8)); + + // column pass + dct_pass(bias_0, 10); + + { + // 16bit 8x8 transpose pass 1 + dct_interleave16(row0, row4); + dct_interleave16(row1, row5); + dct_interleave16(row2, row6); + dct_interleave16(row3, row7); + + // transpose pass 2 + dct_interleave16(row0, row2); + dct_interleave16(row1, row3); + dct_interleave16(row4, row6); + dct_interleave16(row5, row7); + + // transpose pass 3 + dct_interleave16(row0, row1); + dct_interleave16(row2, row3); + dct_interleave16(row4, row5); + dct_interleave16(row6, row7); + } + + // row pass + dct_pass(bias_1, 17); + + { + // pack + __m128i p0 = _mm_packus_epi16(row0, row1); // a0a1a2a3...a7b0b1b2b3...b7 + __m128i p1 = _mm_packus_epi16(row2, row3); + __m128i p2 = _mm_packus_epi16(row4, row5); + __m128i p3 = _mm_packus_epi16(row6, row7); + + // 8bit 8x8 transpose pass 1 + dct_interleave8(p0, p2); // a0e0a1e1... + dct_interleave8(p1, p3); // c0g0c1g1... + + // transpose pass 2 + dct_interleave8(p0, p1); // a0c0e0g0... + dct_interleave8(p2, p3); // b0d0f0h0... + + // transpose pass 3 + dct_interleave8(p0, p2); // a0b0c0d0... + dct_interleave8(p1, p3); // a4b4c4d4... + + // store + _mm_storel_epi64((__m128i *) out, p0); out += out_stride; + _mm_storel_epi64((__m128i *) out, _mm_shuffle_epi32(p0, 0x4e)); out += out_stride; + _mm_storel_epi64((__m128i *) out, p2); out += out_stride; + _mm_storel_epi64((__m128i *) out, _mm_shuffle_epi32(p2, 0x4e)); out += out_stride; + _mm_storel_epi64((__m128i *) out, p1); out += out_stride; + _mm_storel_epi64((__m128i *) out, _mm_shuffle_epi32(p1, 0x4e)); out += out_stride; + _mm_storel_epi64((__m128i *) out, p3); out += out_stride; + _mm_storel_epi64((__m128i *) out, _mm_shuffle_epi32(p3, 0x4e)); + } + +#undef dct_const +#undef dct_rot +#undef dct_widen +#undef dct_wadd +#undef dct_wsub +#undef dct_bfly32o +#undef dct_interleave8 +#undef dct_interleave16 +#undef dct_pass +} + +#endif // STBI_SSE2 + +#ifdef STBI_NEON + +// NEON integer IDCT. should produce bit-identical +// results to the generic C version. +static void stbi__idct_simd(stbi_uc *out, int out_stride, short data[64]) +{ + int16x8_t row0, row1, row2, row3, row4, row5, row6, row7; + + int16x4_t rot0_0 = vdup_n_s16(stbi__f2f(0.5411961f)); + int16x4_t rot0_1 = vdup_n_s16(stbi__f2f(-1.847759065f)); + int16x4_t rot0_2 = vdup_n_s16(stbi__f2f( 0.765366865f)); + int16x4_t rot1_0 = vdup_n_s16(stbi__f2f( 1.175875602f)); + int16x4_t rot1_1 = vdup_n_s16(stbi__f2f(-0.899976223f)); + int16x4_t rot1_2 = vdup_n_s16(stbi__f2f(-2.562915447f)); + int16x4_t rot2_0 = vdup_n_s16(stbi__f2f(-1.961570560f)); + int16x4_t rot2_1 = vdup_n_s16(stbi__f2f(-0.390180644f)); + int16x4_t rot3_0 = vdup_n_s16(stbi__f2f( 0.298631336f)); + int16x4_t rot3_1 = vdup_n_s16(stbi__f2f( 2.053119869f)); + int16x4_t rot3_2 = vdup_n_s16(stbi__f2f( 3.072711026f)); + int16x4_t rot3_3 = vdup_n_s16(stbi__f2f( 1.501321110f)); + +#define dct_long_mul(out, inq, coeff) \ + int32x4_t out##_l = vmull_s16(vget_low_s16(inq), coeff); \ + int32x4_t out##_h = vmull_s16(vget_high_s16(inq), coeff) + +#define dct_long_mac(out, acc, inq, coeff) \ + int32x4_t out##_l = vmlal_s16(acc##_l, vget_low_s16(inq), coeff); \ + int32x4_t out##_h = vmlal_s16(acc##_h, vget_high_s16(inq), coeff) + +#define dct_widen(out, inq) \ + int32x4_t out##_l = vshll_n_s16(vget_low_s16(inq), 12); \ + int32x4_t out##_h = vshll_n_s16(vget_high_s16(inq), 12) + +// wide add +#define dct_wadd(out, a, b) \ + int32x4_t out##_l = vaddq_s32(a##_l, b##_l); \ + int32x4_t out##_h = vaddq_s32(a##_h, b##_h) + +// wide sub +#define dct_wsub(out, a, b) \ + int32x4_t out##_l = vsubq_s32(a##_l, b##_l); \ + int32x4_t out##_h = vsubq_s32(a##_h, b##_h) + +// butterfly a/b, then shift using "shiftop" by "s" and pack +#define dct_bfly32o(out0,out1, a,b,shiftop,s) \ + { \ + dct_wadd(sum, a, b); \ + dct_wsub(dif, a, b); \ + out0 = vcombine_s16(shiftop(sum_l, s), shiftop(sum_h, s)); \ + out1 = vcombine_s16(shiftop(dif_l, s), shiftop(dif_h, s)); \ + } + +#define dct_pass(shiftop, shift) \ + { \ + /* even part */ \ + int16x8_t sum26 = vaddq_s16(row2, row6); \ + dct_long_mul(p1e, sum26, rot0_0); \ + dct_long_mac(t2e, p1e, row6, rot0_1); \ + dct_long_mac(t3e, p1e, row2, rot0_2); \ + int16x8_t sum04 = vaddq_s16(row0, row4); \ + int16x8_t dif04 = vsubq_s16(row0, row4); \ + dct_widen(t0e, sum04); \ + dct_widen(t1e, dif04); \ + dct_wadd(x0, t0e, t3e); \ + dct_wsub(x3, t0e, t3e); \ + dct_wadd(x1, t1e, t2e); \ + dct_wsub(x2, t1e, t2e); \ + /* odd part */ \ + int16x8_t sum15 = vaddq_s16(row1, row5); \ + int16x8_t sum17 = vaddq_s16(row1, row7); \ + int16x8_t sum35 = vaddq_s16(row3, row5); \ + int16x8_t sum37 = vaddq_s16(row3, row7); \ + int16x8_t sumodd = vaddq_s16(sum17, sum35); \ + dct_long_mul(p5o, sumodd, rot1_0); \ + dct_long_mac(p1o, p5o, sum17, rot1_1); \ + dct_long_mac(p2o, p5o, sum35, rot1_2); \ + dct_long_mul(p3o, sum37, rot2_0); \ + dct_long_mul(p4o, sum15, rot2_1); \ + dct_wadd(sump13o, p1o, p3o); \ + dct_wadd(sump24o, p2o, p4o); \ + dct_wadd(sump23o, p2o, p3o); \ + dct_wadd(sump14o, p1o, p4o); \ + dct_long_mac(x4, sump13o, row7, rot3_0); \ + dct_long_mac(x5, sump24o, row5, rot3_1); \ + dct_long_mac(x6, sump23o, row3, rot3_2); \ + dct_long_mac(x7, sump14o, row1, rot3_3); \ + dct_bfly32o(row0,row7, x0,x7,shiftop,shift); \ + dct_bfly32o(row1,row6, x1,x6,shiftop,shift); \ + dct_bfly32o(row2,row5, x2,x5,shiftop,shift); \ + dct_bfly32o(row3,row4, x3,x4,shiftop,shift); \ + } + + // load + row0 = vld1q_s16(data + 0*8); + row1 = vld1q_s16(data + 1*8); + row2 = vld1q_s16(data + 2*8); + row3 = vld1q_s16(data + 3*8); + row4 = vld1q_s16(data + 4*8); + row5 = vld1q_s16(data + 5*8); + row6 = vld1q_s16(data + 6*8); + row7 = vld1q_s16(data + 7*8); + + // add DC bias + row0 = vaddq_s16(row0, vsetq_lane_s16(1024, vdupq_n_s16(0), 0)); + + // column pass + dct_pass(vrshrn_n_s32, 10); + + // 16bit 8x8 transpose + { +// these three map to a single VTRN.16, VTRN.32, and VSWP, respectively. +// whether compilers actually get this is another story, sadly. +#define dct_trn16(x, y) { int16x8x2_t t = vtrnq_s16(x, y); x = t.val[0]; y = t.val[1]; } +#define dct_trn32(x, y) { int32x4x2_t t = vtrnq_s32(vreinterpretq_s32_s16(x), vreinterpretq_s32_s16(y)); x = vreinterpretq_s16_s32(t.val[0]); y = vreinterpretq_s16_s32(t.val[1]); } +#define dct_trn64(x, y) { int16x8_t x0 = x; int16x8_t y0 = y; x = vcombine_s16(vget_low_s16(x0), vget_low_s16(y0)); y = vcombine_s16(vget_high_s16(x0), vget_high_s16(y0)); } + + // pass 1 + dct_trn16(row0, row1); // a0b0a2b2a4b4a6b6 + dct_trn16(row2, row3); + dct_trn16(row4, row5); + dct_trn16(row6, row7); + + // pass 2 + dct_trn32(row0, row2); // a0b0c0d0a4b4c4d4 + dct_trn32(row1, row3); + dct_trn32(row4, row6); + dct_trn32(row5, row7); + + // pass 3 + dct_trn64(row0, row4); // a0b0c0d0e0f0g0h0 + dct_trn64(row1, row5); + dct_trn64(row2, row6); + dct_trn64(row3, row7); + +#undef dct_trn16 +#undef dct_trn32 +#undef dct_trn64 + } + + // row pass + // vrshrn_n_s32 only supports shifts up to 16, we need + // 17. so do a non-rounding shift of 16 first then follow + // up with a rounding shift by 1. + dct_pass(vshrn_n_s32, 16); + + { + // pack and round + uint8x8_t p0 = vqrshrun_n_s16(row0, 1); + uint8x8_t p1 = vqrshrun_n_s16(row1, 1); + uint8x8_t p2 = vqrshrun_n_s16(row2, 1); + uint8x8_t p3 = vqrshrun_n_s16(row3, 1); + uint8x8_t p4 = vqrshrun_n_s16(row4, 1); + uint8x8_t p5 = vqrshrun_n_s16(row5, 1); + uint8x8_t p6 = vqrshrun_n_s16(row6, 1); + uint8x8_t p7 = vqrshrun_n_s16(row7, 1); + + // again, these can translate into one instruction, but often don't. +#define dct_trn8_8(x, y) { uint8x8x2_t t = vtrn_u8(x, y); x = t.val[0]; y = t.val[1]; } +#define dct_trn8_16(x, y) { uint16x4x2_t t = vtrn_u16(vreinterpret_u16_u8(x), vreinterpret_u16_u8(y)); x = vreinterpret_u8_u16(t.val[0]); y = vreinterpret_u8_u16(t.val[1]); } +#define dct_trn8_32(x, y) { uint32x2x2_t t = vtrn_u32(vreinterpret_u32_u8(x), vreinterpret_u32_u8(y)); x = vreinterpret_u8_u32(t.val[0]); y = vreinterpret_u8_u32(t.val[1]); } + + // sadly can't use interleaved stores here since we only write + // 8 bytes to each scan line! + + // 8x8 8-bit transpose pass 1 + dct_trn8_8(p0, p1); + dct_trn8_8(p2, p3); + dct_trn8_8(p4, p5); + dct_trn8_8(p6, p7); + + // pass 2 + dct_trn8_16(p0, p2); + dct_trn8_16(p1, p3); + dct_trn8_16(p4, p6); + dct_trn8_16(p5, p7); + + // pass 3 + dct_trn8_32(p0, p4); + dct_trn8_32(p1, p5); + dct_trn8_32(p2, p6); + dct_trn8_32(p3, p7); + + // store + vst1_u8(out, p0); out += out_stride; + vst1_u8(out, p1); out += out_stride; + vst1_u8(out, p2); out += out_stride; + vst1_u8(out, p3); out += out_stride; + vst1_u8(out, p4); out += out_stride; + vst1_u8(out, p5); out += out_stride; + vst1_u8(out, p6); out += out_stride; + vst1_u8(out, p7); + +#undef dct_trn8_8 +#undef dct_trn8_16 +#undef dct_trn8_32 + } + +#undef dct_long_mul +#undef dct_long_mac +#undef dct_widen +#undef dct_wadd +#undef dct_wsub +#undef dct_bfly32o +#undef dct_pass +} + +#endif // STBI_NEON + +#define STBI__MARKER_none 0xff +// if there's a pending marker from the entropy stream, return that +// otherwise, fetch from the stream and get a marker. if there's no +// marker, return 0xff, which is never a valid marker value +static stbi_uc stbi__get_marker(stbi__jpeg *j) +{ + stbi_uc x; + if (j->marker != STBI__MARKER_none) { x = j->marker; j->marker = STBI__MARKER_none; return x; } + x = stbi__get8(j->s); + if (x != 0xff) return STBI__MARKER_none; + while (x == 0xff) + x = stbi__get8(j->s); // consume repeated 0xff fill bytes + return x; +} + +// in each scan, we'll have scan_n components, and the order +// of the components is specified by order[] +#define STBI__RESTART(x) ((x) >= 0xd0 && (x) <= 0xd7) + +// after a restart interval, stbi__jpeg_reset the entropy decoder and +// the dc prediction +static void stbi__jpeg_reset(stbi__jpeg *j) +{ + j->code_bits = 0; + j->code_buffer = 0; + j->nomore = 0; + j->img_comp[0].dc_pred = j->img_comp[1].dc_pred = j->img_comp[2].dc_pred = j->img_comp[3].dc_pred = 0; + j->marker = STBI__MARKER_none; + j->todo = j->restart_interval ? j->restart_interval : 0x7fffffff; + j->eob_run = 0; + // no more than 1<<31 MCUs if no restart_interal? that's plenty safe, + // since we don't even allow 1<<30 pixels +} + +static int stbi__parse_entropy_coded_data(stbi__jpeg *z) +{ + stbi__jpeg_reset(z); + if (!z->progressive) { + if (z->scan_n == 1) { + int i,j; + STBI_SIMD_ALIGN(short, data[64]); + int n = z->order[0]; + // non-interleaved data, we just need to process one block at a time, + // in trivial scanline order + // number of blocks to do just depends on how many actual "pixels" this + // component has, independent of interleaved MCU blocking and such + int w = (z->img_comp[n].x+7) >> 3; + int h = (z->img_comp[n].y+7) >> 3; + for (j=0; j < h; ++j) { + for (i=0; i < w; ++i) { + int ha = z->img_comp[n].ha; + if (!stbi__jpeg_decode_block(z, data, z->huff_dc+z->img_comp[n].hd, z->huff_ac+ha, z->fast_ac[ha], n, z->dequant[z->img_comp[n].tq])) return 0; + z->idct_block_kernel(z->img_comp[n].data+z->img_comp[n].w2*j*8+i*8, z->img_comp[n].w2, data); + // every data block is an MCU, so countdown the restart interval + if (--z->todo <= 0) { + if (z->code_bits < 24) stbi__grow_buffer_unsafe(z); + // if it's NOT a restart, then just bail, so we get corrupt data + // rather than no data + if (!STBI__RESTART(z->marker)) return 1; + stbi__jpeg_reset(z); + } + } + } + return 1; + } else { // interleaved + int i,j,k,x,y; + STBI_SIMD_ALIGN(short, data[64]); + for (j=0; j < z->img_mcu_y; ++j) { + for (i=0; i < z->img_mcu_x; ++i) { + // scan an interleaved mcu... process scan_n components in order + for (k=0; k < z->scan_n; ++k) { + int n = z->order[k]; + // scan out an mcu's worth of this component; that's just determined + // by the basic H and V specified for the component + for (y=0; y < z->img_comp[n].v; ++y) { + for (x=0; x < z->img_comp[n].h; ++x) { + int x2 = (i*z->img_comp[n].h + x)*8; + int y2 = (j*z->img_comp[n].v + y)*8; + int ha = z->img_comp[n].ha; + if (!stbi__jpeg_decode_block(z, data, z->huff_dc+z->img_comp[n].hd, z->huff_ac+ha, z->fast_ac[ha], n, z->dequant[z->img_comp[n].tq])) return 0; + z->idct_block_kernel(z->img_comp[n].data+z->img_comp[n].w2*y2+x2, z->img_comp[n].w2, data); + } + } + } + // after all interleaved components, that's an interleaved MCU, + // so now count down the restart interval + if (--z->todo <= 0) { + if (z->code_bits < 24) stbi__grow_buffer_unsafe(z); + if (!STBI__RESTART(z->marker)) return 1; + stbi__jpeg_reset(z); + } + } + } + return 1; + } + } else { + if (z->scan_n == 1) { + int i,j; + int n = z->order[0]; + // non-interleaved data, we just need to process one block at a time, + // in trivial scanline order + // number of blocks to do just depends on how many actual "pixels" this + // component has, independent of interleaved MCU blocking and such + int w = (z->img_comp[n].x+7) >> 3; + int h = (z->img_comp[n].y+7) >> 3; + for (j=0; j < h; ++j) { + for (i=0; i < w; ++i) { + short *data = z->img_comp[n].coeff + 64 * (i + j * z->img_comp[n].coeff_w); + if (z->spec_start == 0) { + if (!stbi__jpeg_decode_block_prog_dc(z, data, &z->huff_dc[z->img_comp[n].hd], n)) + return 0; + } else { + int ha = z->img_comp[n].ha; + if (!stbi__jpeg_decode_block_prog_ac(z, data, &z->huff_ac[ha], z->fast_ac[ha])) + return 0; + } + // every data block is an MCU, so countdown the restart interval + if (--z->todo <= 0) { + if (z->code_bits < 24) stbi__grow_buffer_unsafe(z); + if (!STBI__RESTART(z->marker)) return 1; + stbi__jpeg_reset(z); + } + } + } + return 1; + } else { // interleaved + int i,j,k,x,y; + for (j=0; j < z->img_mcu_y; ++j) { + for (i=0; i < z->img_mcu_x; ++i) { + // scan an interleaved mcu... process scan_n components in order + for (k=0; k < z->scan_n; ++k) { + int n = z->order[k]; + // scan out an mcu's worth of this component; that's just determined + // by the basic H and V specified for the component + for (y=0; y < z->img_comp[n].v; ++y) { + for (x=0; x < z->img_comp[n].h; ++x) { + int x2 = (i*z->img_comp[n].h + x); + int y2 = (j*z->img_comp[n].v + y); + short *data = z->img_comp[n].coeff + 64 * (x2 + y2 * z->img_comp[n].coeff_w); + if (!stbi__jpeg_decode_block_prog_dc(z, data, &z->huff_dc[z->img_comp[n].hd], n)) + return 0; + } + } + } + // after all interleaved components, that's an interleaved MCU, + // so now count down the restart interval + if (--z->todo <= 0) { + if (z->code_bits < 24) stbi__grow_buffer_unsafe(z); + if (!STBI__RESTART(z->marker)) return 1; + stbi__jpeg_reset(z); + } + } + } + return 1; + } + } +} + +static void stbi__jpeg_dequantize(short *data, stbi__uint16 *dequant) +{ + int i; + for (i=0; i < 64; ++i) + data[i] *= dequant[i]; +} + +static void stbi__jpeg_finish(stbi__jpeg *z) +{ + if (z->progressive) { + // dequantize and idct the data + int i,j,n; + for (n=0; n < z->s->img_n; ++n) { + int w = (z->img_comp[n].x+7) >> 3; + int h = (z->img_comp[n].y+7) >> 3; + for (j=0; j < h; ++j) { + for (i=0; i < w; ++i) { + short *data = z->img_comp[n].coeff + 64 * (i + j * z->img_comp[n].coeff_w); + stbi__jpeg_dequantize(data, z->dequant[z->img_comp[n].tq]); + z->idct_block_kernel(z->img_comp[n].data+z->img_comp[n].w2*j*8+i*8, z->img_comp[n].w2, data); + } + } + } + } +} + +static int stbi__process_marker(stbi__jpeg *z, int m) +{ + int L; + switch (m) { + case STBI__MARKER_none: // no marker found + return stbi__err("expected marker","Corrupt JPEG"); + + case 0xDD: // DRI - specify restart interval + if (stbi__get16be(z->s) != 4) return stbi__err("bad DRI len","Corrupt JPEG"); + z->restart_interval = stbi__get16be(z->s); + return 1; + + case 0xDB: // DQT - define quantization table + L = stbi__get16be(z->s)-2; + while (L > 0) { + int q = stbi__get8(z->s); + int p = q >> 4, sixteen = (p != 0); + int t = q & 15,i; + if (p != 0 && p != 1) return stbi__err("bad DQT type","Corrupt JPEG"); + if (t > 3) return stbi__err("bad DQT table","Corrupt JPEG"); + + for (i=0; i < 64; ++i) + z->dequant[t][stbi__jpeg_dezigzag[i]] = (stbi__uint16)(sixteen ? stbi__get16be(z->s) : stbi__get8(z->s)); + L -= (sixteen ? 129 : 65); + } + return L==0; + + case 0xC4: // DHT - define huffman table + L = stbi__get16be(z->s)-2; + while (L > 0) { + stbi_uc *v; + int sizes[16],i,n=0; + int q = stbi__get8(z->s); + int tc = q >> 4; + int th = q & 15; + if (tc > 1 || th > 3) return stbi__err("bad DHT header","Corrupt JPEG"); + for (i=0; i < 16; ++i) { + sizes[i] = stbi__get8(z->s); + n += sizes[i]; + } + L -= 17; + if (tc == 0) { + if (!stbi__build_huffman(z->huff_dc+th, sizes)) return 0; + v = z->huff_dc[th].values; + } else { + if (!stbi__build_huffman(z->huff_ac+th, sizes)) return 0; + v = z->huff_ac[th].values; + } + for (i=0; i < n; ++i) + v[i] = stbi__get8(z->s); + if (tc != 0) + stbi__build_fast_ac(z->fast_ac[th], z->huff_ac + th); + L -= n; + } + return L==0; + } + + // check for comment block or APP blocks + if ((m >= 0xE0 && m <= 0xEF) || m == 0xFE) { + L = stbi__get16be(z->s); + if (L < 2) { + if (m == 0xFE) + return stbi__err("bad COM len","Corrupt JPEG"); + else + return stbi__err("bad APP len","Corrupt JPEG"); + } + L -= 2; + + if (m == 0xE0 && L >= 5) { // JFIF APP0 segment + static const unsigned char tag[5] = {'J','F','I','F','\0'}; + int ok = 1; + int i; + for (i=0; i < 5; ++i) + if (stbi__get8(z->s) != tag[i]) + ok = 0; + L -= 5; + if (ok) + z->jfif = 1; + } else if (m == 0xEE && L >= 12) { // Adobe APP14 segment + static const unsigned char tag[6] = {'A','d','o','b','e','\0'}; + int ok = 1; + int i; + for (i=0; i < 6; ++i) + if (stbi__get8(z->s) != tag[i]) + ok = 0; + L -= 6; + if (ok) { + stbi__get8(z->s); // version + stbi__get16be(z->s); // flags0 + stbi__get16be(z->s); // flags1 + z->app14_color_transform = stbi__get8(z->s); // color transform + L -= 6; + } + } + + stbi__skip(z->s, L); + return 1; + } + + return stbi__err("unknown marker","Corrupt JPEG"); +} + +// after we see SOS +static int stbi__process_scan_header(stbi__jpeg *z) +{ + int i; + int Ls = stbi__get16be(z->s); + z->scan_n = stbi__get8(z->s); + if (z->scan_n < 1 || z->scan_n > 4 || z->scan_n > (int) z->s->img_n) return stbi__err("bad SOS component count","Corrupt JPEG"); + if (Ls != 6+2*z->scan_n) return stbi__err("bad SOS len","Corrupt JPEG"); + for (i=0; i < z->scan_n; ++i) { + int id = stbi__get8(z->s), which; + int q = stbi__get8(z->s); + for (which = 0; which < z->s->img_n; ++which) + if (z->img_comp[which].id == id) + break; + if (which == z->s->img_n) return 0; // no match + z->img_comp[which].hd = q >> 4; if (z->img_comp[which].hd > 3) return stbi__err("bad DC huff","Corrupt JPEG"); + z->img_comp[which].ha = q & 15; if (z->img_comp[which].ha > 3) return stbi__err("bad AC huff","Corrupt JPEG"); + z->order[i] = which; + } + + { + int aa; + z->spec_start = stbi__get8(z->s); + z->spec_end = stbi__get8(z->s); // should be 63, but might be 0 + aa = stbi__get8(z->s); + z->succ_high = (aa >> 4); + z->succ_low = (aa & 15); + if (z->progressive) { + if (z->spec_start > 63 || z->spec_end > 63 || z->spec_start > z->spec_end || z->succ_high > 13 || z->succ_low > 13) + return stbi__err("bad SOS", "Corrupt JPEG"); + } else { + if (z->spec_start != 0) return stbi__err("bad SOS","Corrupt JPEG"); + if (z->succ_high != 0 || z->succ_low != 0) return stbi__err("bad SOS","Corrupt JPEG"); + z->spec_end = 63; + } + } + + return 1; +} + +static int stbi__free_jpeg_components(stbi__jpeg *z, int ncomp, int why) +{ + int i; + for (i=0; i < ncomp; ++i) { + if (z->img_comp[i].raw_data) { + STBI_FREE(z->img_comp[i].raw_data); + z->img_comp[i].raw_data = NULL; + z->img_comp[i].data = NULL; + } + if (z->img_comp[i].raw_coeff) { + STBI_FREE(z->img_comp[i].raw_coeff); + z->img_comp[i].raw_coeff = 0; + z->img_comp[i].coeff = 0; + } + if (z->img_comp[i].linebuf) { + STBI_FREE(z->img_comp[i].linebuf); + z->img_comp[i].linebuf = NULL; + } + } + return why; +} + +static int stbi__process_frame_header(stbi__jpeg *z, int scan) +{ + stbi__context *s = z->s; + int Lf,p,i,q, h_max=1,v_max=1,c; + Lf = stbi__get16be(s); if (Lf < 11) return stbi__err("bad SOF len","Corrupt JPEG"); // JPEG + p = stbi__get8(s); if (p != 8) return stbi__err("only 8-bit","JPEG format not supported: 8-bit only"); // JPEG baseline + s->img_y = stbi__get16be(s); if (s->img_y == 0) return stbi__err("no header height", "JPEG format not supported: delayed height"); // Legal, but we don't handle it--but neither does IJG + s->img_x = stbi__get16be(s); if (s->img_x == 0) return stbi__err("0 width","Corrupt JPEG"); // JPEG requires + c = stbi__get8(s); + if (c != 3 && c != 1 && c != 4) return stbi__err("bad component count","Corrupt JPEG"); + s->img_n = c; + for (i=0; i < c; ++i) { + z->img_comp[i].data = NULL; + z->img_comp[i].linebuf = NULL; + } + + if (Lf != 8+3*s->img_n) return stbi__err("bad SOF len","Corrupt JPEG"); + + z->rgb = 0; + for (i=0; i < s->img_n; ++i) { + static const unsigned char rgb[3] = { 'R', 'G', 'B' }; + z->img_comp[i].id = stbi__get8(s); + if (s->img_n == 3 && z->img_comp[i].id == rgb[i]) + ++z->rgb; + q = stbi__get8(s); + z->img_comp[i].h = (q >> 4); if (!z->img_comp[i].h || z->img_comp[i].h > 4) return stbi__err("bad H","Corrupt JPEG"); + z->img_comp[i].v = q & 15; if (!z->img_comp[i].v || z->img_comp[i].v > 4) return stbi__err("bad V","Corrupt JPEG"); + z->img_comp[i].tq = stbi__get8(s); if (z->img_comp[i].tq > 3) return stbi__err("bad TQ","Corrupt JPEG"); + } + + if (scan != STBI__SCAN_load) return 1; + + if (!stbi__mad3sizes_valid(s->img_x, s->img_y, s->img_n, 0)) return stbi__err("too large", "Image too large to decode"); + + for (i=0; i < s->img_n; ++i) { + if (z->img_comp[i].h > h_max) h_max = z->img_comp[i].h; + if (z->img_comp[i].v > v_max) v_max = z->img_comp[i].v; + } + + // compute interleaved mcu info + z->img_h_max = h_max; + z->img_v_max = v_max; + z->img_mcu_w = h_max * 8; + z->img_mcu_h = v_max * 8; + // these sizes can't be more than 17 bits + z->img_mcu_x = (s->img_x + z->img_mcu_w-1) / z->img_mcu_w; + z->img_mcu_y = (s->img_y + z->img_mcu_h-1) / z->img_mcu_h; + + for (i=0; i < s->img_n; ++i) { + // number of effective pixels (e.g. for non-interleaved MCU) + z->img_comp[i].x = (s->img_x * z->img_comp[i].h + h_max-1) / h_max; + z->img_comp[i].y = (s->img_y * z->img_comp[i].v + v_max-1) / v_max; + // to simplify generation, we'll allocate enough memory to decode + // the bogus oversized data from using interleaved MCUs and their + // big blocks (e.g. a 16x16 iMCU on an image of width 33); we won't + // discard the extra data until colorspace conversion + // + // img_mcu_x, img_mcu_y: <=17 bits; comp[i].h and .v are <=4 (checked earlier) + // so these muls can't overflow with 32-bit ints (which we require) + z->img_comp[i].w2 = z->img_mcu_x * z->img_comp[i].h * 8; + z->img_comp[i].h2 = z->img_mcu_y * z->img_comp[i].v * 8; + z->img_comp[i].coeff = 0; + z->img_comp[i].raw_coeff = 0; + z->img_comp[i].linebuf = NULL; + z->img_comp[i].raw_data = stbi__malloc_mad2(z->img_comp[i].w2, z->img_comp[i].h2, 15); + if (z->img_comp[i].raw_data == NULL) + return stbi__free_jpeg_components(z, i+1, stbi__err("outofmem", "Out of memory")); + // align blocks for idct using mmx/sse + z->img_comp[i].data = (stbi_uc*) (((size_t) z->img_comp[i].raw_data + 15) & ~15); + if (z->progressive) { + // w2, h2 are multiples of 8 (see above) + z->img_comp[i].coeff_w = z->img_comp[i].w2 / 8; + z->img_comp[i].coeff_h = z->img_comp[i].h2 / 8; + z->img_comp[i].raw_coeff = stbi__malloc_mad3(z->img_comp[i].w2, z->img_comp[i].h2, sizeof(short), 15); + if (z->img_comp[i].raw_coeff == NULL) + return stbi__free_jpeg_components(z, i+1, stbi__err("outofmem", "Out of memory")); + z->img_comp[i].coeff = (short*) (((size_t) z->img_comp[i].raw_coeff + 15) & ~15); + } + } + + return 1; +} + +// use comparisons since in some cases we handle more than one case (e.g. SOF) +#define stbi__DNL(x) ((x) == 0xdc) +#define stbi__SOI(x) ((x) == 0xd8) +#define stbi__EOI(x) ((x) == 0xd9) +#define stbi__SOF(x) ((x) == 0xc0 || (x) == 0xc1 || (x) == 0xc2) +#define stbi__SOS(x) ((x) == 0xda) + +#define stbi__SOF_progressive(x) ((x) == 0xc2) + +static int stbi__decode_jpeg_header(stbi__jpeg *z, int scan) +{ + int m; + z->jfif = 0; + z->app14_color_transform = -1; // valid values are 0,1,2 + z->marker = STBI__MARKER_none; // initialize cached marker to empty + m = stbi__get_marker(z); + if (!stbi__SOI(m)) return stbi__err("no SOI","Corrupt JPEG"); + if (scan == STBI__SCAN_type) return 1; + m = stbi__get_marker(z); + while (!stbi__SOF(m)) { + if (!stbi__process_marker(z,m)) return 0; + m = stbi__get_marker(z); + while (m == STBI__MARKER_none) { + // some files have extra padding after their blocks, so ok, we'll scan + if (stbi__at_eof(z->s)) return stbi__err("no SOF", "Corrupt JPEG"); + m = stbi__get_marker(z); + } + } + z->progressive = stbi__SOF_progressive(m); + if (!stbi__process_frame_header(z, scan)) return 0; + return 1; +} + +// decode image to YCbCr format +static int stbi__decode_jpeg_image(stbi__jpeg *j) +{ + int m; + for (m = 0; m < 4; m++) { + j->img_comp[m].raw_data = NULL; + j->img_comp[m].raw_coeff = NULL; + } + j->restart_interval = 0; + if (!stbi__decode_jpeg_header(j, STBI__SCAN_load)) return 0; + m = stbi__get_marker(j); + while (!stbi__EOI(m)) { + if (stbi__SOS(m)) { + if (!stbi__process_scan_header(j)) return 0; + if (!stbi__parse_entropy_coded_data(j)) return 0; + if (j->marker == STBI__MARKER_none ) { + // handle 0s at the end of image data from IP Kamera 9060 + while (!stbi__at_eof(j->s)) { + int x = stbi__get8(j->s); + if (x == 255) { + j->marker = stbi__get8(j->s); + break; + } + } + // if we reach eof without hitting a marker, stbi__get_marker() below will fail and we'll eventually return 0 + } + } else if (stbi__DNL(m)) { + int Ld = stbi__get16be(j->s); + stbi__uint32 NL = stbi__get16be(j->s); + if (Ld != 4) return stbi__err("bad DNL len", "Corrupt JPEG"); + if (NL != j->s->img_y) return stbi__err("bad DNL height", "Corrupt JPEG"); + } else { + if (!stbi__process_marker(j, m)) return 0; + } + m = stbi__get_marker(j); + } + if (j->progressive) + stbi__jpeg_finish(j); + return 1; +} + +// static jfif-centered resampling (across block boundaries) + +typedef stbi_uc *(*resample_row_func)(stbi_uc *out, stbi_uc *in0, stbi_uc *in1, + int w, int hs); + +#define stbi__div4(x) ((stbi_uc) ((x) >> 2)) + +static stbi_uc *resample_row_1(stbi_uc *out, stbi_uc *in_near, stbi_uc *in_far, int w, int hs) +{ + STBI_NOTUSED(out); + STBI_NOTUSED(in_far); + STBI_NOTUSED(w); + STBI_NOTUSED(hs); + return in_near; +} + +static stbi_uc* stbi__resample_row_v_2(stbi_uc *out, stbi_uc *in_near, stbi_uc *in_far, int w, int hs) +{ + // need to generate two samples vertically for every one in input + int i; + STBI_NOTUSED(hs); + for (i=0; i < w; ++i) + out[i] = stbi__div4(3*in_near[i] + in_far[i] + 2); + return out; +} + +static stbi_uc* stbi__resample_row_h_2(stbi_uc *out, stbi_uc *in_near, stbi_uc *in_far, int w, int hs) +{ + // need to generate two samples horizontally for every one in input + int i; + stbi_uc *input = in_near; + + if (w == 1) { + // if only one sample, can't do any interpolation + out[0] = out[1] = input[0]; + return out; + } + + out[0] = input[0]; + out[1] = stbi__div4(input[0]*3 + input[1] + 2); + for (i=1; i < w-1; ++i) { + int n = 3*input[i]+2; + out[i*2+0] = stbi__div4(n+input[i-1]); + out[i*2+1] = stbi__div4(n+input[i+1]); + } + out[i*2+0] = stbi__div4(input[w-2]*3 + input[w-1] + 2); + out[i*2+1] = input[w-1]; + + STBI_NOTUSED(in_far); + STBI_NOTUSED(hs); + + return out; +} + +#define stbi__div16(x) ((stbi_uc) ((x) >> 4)) + +static stbi_uc *stbi__resample_row_hv_2(stbi_uc *out, stbi_uc *in_near, stbi_uc *in_far, int w, int hs) +{ + // need to generate 2x2 samples for every one in input + int i,t0,t1; + if (w == 1) { + out[0] = out[1] = stbi__div4(3*in_near[0] + in_far[0] + 2); + return out; + } + + t1 = 3*in_near[0] + in_far[0]; + out[0] = stbi__div4(t1+2); + for (i=1; i < w; ++i) { + t0 = t1; + t1 = 3*in_near[i]+in_far[i]; + out[i*2-1] = stbi__div16(3*t0 + t1 + 8); + out[i*2 ] = stbi__div16(3*t1 + t0 + 8); + } + out[w*2-1] = stbi__div4(t1+2); + + STBI_NOTUSED(hs); + + return out; +} + +#if defined(STBI_SSE2) || defined(STBI_NEON) +static stbi_uc *stbi__resample_row_hv_2_simd(stbi_uc *out, stbi_uc *in_near, stbi_uc *in_far, int w, int hs) +{ + // need to generate 2x2 samples for every one in input + int i=0,t0,t1; + + if (w == 1) { + out[0] = out[1] = stbi__div4(3*in_near[0] + in_far[0] + 2); + return out; + } + + t1 = 3*in_near[0] + in_far[0]; + // process groups of 8 pixels for as long as we can. + // note we can't handle the last pixel in a row in this loop + // because we need to handle the filter boundary conditions. + for (; i < ((w-1) & ~7); i += 8) { +#if defined(STBI_SSE2) + // load and perform the vertical filtering pass + // this uses 3*x + y = 4*x + (y - x) + __m128i zero = _mm_setzero_si128(); + __m128i farb = _mm_loadl_epi64((__m128i *) (in_far + i)); + __m128i nearb = _mm_loadl_epi64((__m128i *) (in_near + i)); + __m128i farw = _mm_unpacklo_epi8(farb, zero); + __m128i nearw = _mm_unpacklo_epi8(nearb, zero); + __m128i diff = _mm_sub_epi16(farw, nearw); + __m128i nears = _mm_slli_epi16(nearw, 2); + __m128i curr = _mm_add_epi16(nears, diff); // current row + + // horizontal filter works the same based on shifted vers of current + // row. "prev" is current row shifted right by 1 pixel; we need to + // insert the previous pixel value (from t1). + // "next" is current row shifted left by 1 pixel, with first pixel + // of next block of 8 pixels added in. + __m128i prv0 = _mm_slli_si128(curr, 2); + __m128i nxt0 = _mm_srli_si128(curr, 2); + __m128i prev = _mm_insert_epi16(prv0, t1, 0); + __m128i next = _mm_insert_epi16(nxt0, 3*in_near[i+8] + in_far[i+8], 7); + + // horizontal filter, polyphase implementation since it's convenient: + // even pixels = 3*cur + prev = cur*4 + (prev - cur) + // odd pixels = 3*cur + next = cur*4 + (next - cur) + // note the shared term. + __m128i bias = _mm_set1_epi16(8); + __m128i curs = _mm_slli_epi16(curr, 2); + __m128i prvd = _mm_sub_epi16(prev, curr); + __m128i nxtd = _mm_sub_epi16(next, curr); + __m128i curb = _mm_add_epi16(curs, bias); + __m128i even = _mm_add_epi16(prvd, curb); + __m128i odd = _mm_add_epi16(nxtd, curb); + + // interleave even and odd pixels, then undo scaling. + __m128i int0 = _mm_unpacklo_epi16(even, odd); + __m128i int1 = _mm_unpackhi_epi16(even, odd); + __m128i de0 = _mm_srli_epi16(int0, 4); + __m128i de1 = _mm_srli_epi16(int1, 4); + + // pack and write output + __m128i outv = _mm_packus_epi16(de0, de1); + _mm_storeu_si128((__m128i *) (out + i*2), outv); +#elif defined(STBI_NEON) + // load and perform the vertical filtering pass + // this uses 3*x + y = 4*x + (y - x) + uint8x8_t farb = vld1_u8(in_far + i); + uint8x8_t nearb = vld1_u8(in_near + i); + int16x8_t diff = vreinterpretq_s16_u16(vsubl_u8(farb, nearb)); + int16x8_t nears = vreinterpretq_s16_u16(vshll_n_u8(nearb, 2)); + int16x8_t curr = vaddq_s16(nears, diff); // current row + + // horizontal filter works the same based on shifted vers of current + // row. "prev" is current row shifted right by 1 pixel; we need to + // insert the previous pixel value (from t1). + // "next" is current row shifted left by 1 pixel, with first pixel + // of next block of 8 pixels added in. + int16x8_t prv0 = vextq_s16(curr, curr, 7); + int16x8_t nxt0 = vextq_s16(curr, curr, 1); + int16x8_t prev = vsetq_lane_s16(t1, prv0, 0); + int16x8_t next = vsetq_lane_s16(3*in_near[i+8] + in_far[i+8], nxt0, 7); + + // horizontal filter, polyphase implementation since it's convenient: + // even pixels = 3*cur + prev = cur*4 + (prev - cur) + // odd pixels = 3*cur + next = cur*4 + (next - cur) + // note the shared term. + int16x8_t curs = vshlq_n_s16(curr, 2); + int16x8_t prvd = vsubq_s16(prev, curr); + int16x8_t nxtd = vsubq_s16(next, curr); + int16x8_t even = vaddq_s16(curs, prvd); + int16x8_t odd = vaddq_s16(curs, nxtd); + + // undo scaling and round, then store with even/odd phases interleaved + uint8x8x2_t o; + o.val[0] = vqrshrun_n_s16(even, 4); + o.val[1] = vqrshrun_n_s16(odd, 4); + vst2_u8(out + i*2, o); +#endif + + // "previous" value for next iter + t1 = 3*in_near[i+7] + in_far[i+7]; + } + + t0 = t1; + t1 = 3*in_near[i] + in_far[i]; + out[i*2] = stbi__div16(3*t1 + t0 + 8); + + for (++i; i < w; ++i) { + t0 = t1; + t1 = 3*in_near[i]+in_far[i]; + out[i*2-1] = stbi__div16(3*t0 + t1 + 8); + out[i*2 ] = stbi__div16(3*t1 + t0 + 8); + } + out[w*2-1] = stbi__div4(t1+2); + + STBI_NOTUSED(hs); + + return out; +} +#endif + +static stbi_uc *stbi__resample_row_generic(stbi_uc *out, stbi_uc *in_near, stbi_uc *in_far, int w, int hs) +{ + // resample with nearest-neighbor + int i,j; + STBI_NOTUSED(in_far); + for (i=0; i < w; ++i) + for (j=0; j < hs; ++j) + out[i*hs+j] = in_near[i]; + return out; +} + +// this is a reduced-precision calculation of YCbCr-to-RGB introduced +// to make sure the code produces the same results in both SIMD and scalar +#define stbi__float2fixed(x) (((int) ((x) * 4096.0f + 0.5f)) << 8) +static void stbi__YCbCr_to_RGB_row(stbi_uc *out, const stbi_uc *y, const stbi_uc *pcb, const stbi_uc *pcr, int count, int step) +{ + int i; + for (i=0; i < count; ++i) { + int y_fixed = (y[i] << 20) + (1<<19); // rounding + int r,g,b; + int cr = pcr[i] - 128; + int cb = pcb[i] - 128; + r = y_fixed + cr* stbi__float2fixed(1.40200f); + g = y_fixed + (cr*-stbi__float2fixed(0.71414f)) + ((cb*-stbi__float2fixed(0.34414f)) & 0xffff0000); + b = y_fixed + cb* stbi__float2fixed(1.77200f); + r >>= 20; + g >>= 20; + b >>= 20; + if ((unsigned) r > 255) { if (r < 0) r = 0; else r = 255; } + if ((unsigned) g > 255) { if (g < 0) g = 0; else g = 255; } + if ((unsigned) b > 255) { if (b < 0) b = 0; else b = 255; } + out[0] = (stbi_uc)r; + out[1] = (stbi_uc)g; + out[2] = (stbi_uc)b; + out[3] = 255; + out += step; + } +} + +#if defined(STBI_SSE2) || defined(STBI_NEON) +static void stbi__YCbCr_to_RGB_simd(stbi_uc *out, stbi_uc const *y, stbi_uc const *pcb, stbi_uc const *pcr, int count, int step) +{ + int i = 0; + +#ifdef STBI_SSE2 + // step == 3 is pretty ugly on the final interleave, and i'm not convinced + // it's useful in practice (you wouldn't use it for textures, for example). + // so just accelerate step == 4 case. + if (step == 4) { + // this is a fairly straightforward implementation and not super-optimized. + __m128i signflip = _mm_set1_epi8(-0x80); + __m128i cr_const0 = _mm_set1_epi16( (short) ( 1.40200f*4096.0f+0.5f)); + __m128i cr_const1 = _mm_set1_epi16( - (short) ( 0.71414f*4096.0f+0.5f)); + __m128i cb_const0 = _mm_set1_epi16( - (short) ( 0.34414f*4096.0f+0.5f)); + __m128i cb_const1 = _mm_set1_epi16( (short) ( 1.77200f*4096.0f+0.5f)); + __m128i y_bias = _mm_set1_epi8((char) (unsigned char) 128); + __m128i xw = _mm_set1_epi16(255); // alpha channel + + for (; i+7 < count; i += 8) { + // load + __m128i y_bytes = _mm_loadl_epi64((__m128i *) (y+i)); + __m128i cr_bytes = _mm_loadl_epi64((__m128i *) (pcr+i)); + __m128i cb_bytes = _mm_loadl_epi64((__m128i *) (pcb+i)); + __m128i cr_biased = _mm_xor_si128(cr_bytes, signflip); // -128 + __m128i cb_biased = _mm_xor_si128(cb_bytes, signflip); // -128 + + // unpack to short (and left-shift cr, cb by 8) + __m128i yw = _mm_unpacklo_epi8(y_bias, y_bytes); + __m128i crw = _mm_unpacklo_epi8(_mm_setzero_si128(), cr_biased); + __m128i cbw = _mm_unpacklo_epi8(_mm_setzero_si128(), cb_biased); + + // color transform + __m128i yws = _mm_srli_epi16(yw, 4); + __m128i cr0 = _mm_mulhi_epi16(cr_const0, crw); + __m128i cb0 = _mm_mulhi_epi16(cb_const0, cbw); + __m128i cb1 = _mm_mulhi_epi16(cbw, cb_const1); + __m128i cr1 = _mm_mulhi_epi16(crw, cr_const1); + __m128i rws = _mm_add_epi16(cr0, yws); + __m128i gwt = _mm_add_epi16(cb0, yws); + __m128i bws = _mm_add_epi16(yws, cb1); + __m128i gws = _mm_add_epi16(gwt, cr1); + + // descale + __m128i rw = _mm_srai_epi16(rws, 4); + __m128i bw = _mm_srai_epi16(bws, 4); + __m128i gw = _mm_srai_epi16(gws, 4); + + // back to byte, set up for transpose + __m128i brb = _mm_packus_epi16(rw, bw); + __m128i gxb = _mm_packus_epi16(gw, xw); + + // transpose to interleave channels + __m128i t0 = _mm_unpacklo_epi8(brb, gxb); + __m128i t1 = _mm_unpackhi_epi8(brb, gxb); + __m128i o0 = _mm_unpacklo_epi16(t0, t1); + __m128i o1 = _mm_unpackhi_epi16(t0, t1); + + // store + _mm_storeu_si128((__m128i *) (out + 0), o0); + _mm_storeu_si128((__m128i *) (out + 16), o1); + out += 32; + } + } +#endif + +#ifdef STBI_NEON + // in this version, step=3 support would be easy to add. but is there demand? + if (step == 4) { + // this is a fairly straightforward implementation and not super-optimized. + uint8x8_t signflip = vdup_n_u8(0x80); + int16x8_t cr_const0 = vdupq_n_s16( (short) ( 1.40200f*4096.0f+0.5f)); + int16x8_t cr_const1 = vdupq_n_s16( - (short) ( 0.71414f*4096.0f+0.5f)); + int16x8_t cb_const0 = vdupq_n_s16( - (short) ( 0.34414f*4096.0f+0.5f)); + int16x8_t cb_const1 = vdupq_n_s16( (short) ( 1.77200f*4096.0f+0.5f)); + + for (; i+7 < count; i += 8) { + // load + uint8x8_t y_bytes = vld1_u8(y + i); + uint8x8_t cr_bytes = vld1_u8(pcr + i); + uint8x8_t cb_bytes = vld1_u8(pcb + i); + int8x8_t cr_biased = vreinterpret_s8_u8(vsub_u8(cr_bytes, signflip)); + int8x8_t cb_biased = vreinterpret_s8_u8(vsub_u8(cb_bytes, signflip)); + + // expand to s16 + int16x8_t yws = vreinterpretq_s16_u16(vshll_n_u8(y_bytes, 4)); + int16x8_t crw = vshll_n_s8(cr_biased, 7); + int16x8_t cbw = vshll_n_s8(cb_biased, 7); + + // color transform + int16x8_t cr0 = vqdmulhq_s16(crw, cr_const0); + int16x8_t cb0 = vqdmulhq_s16(cbw, cb_const0); + int16x8_t cr1 = vqdmulhq_s16(crw, cr_const1); + int16x8_t cb1 = vqdmulhq_s16(cbw, cb_const1); + int16x8_t rws = vaddq_s16(yws, cr0); + int16x8_t gws = vaddq_s16(vaddq_s16(yws, cb0), cr1); + int16x8_t bws = vaddq_s16(yws, cb1); + + // undo scaling, round, convert to byte + uint8x8x4_t o; + o.val[0] = vqrshrun_n_s16(rws, 4); + o.val[1] = vqrshrun_n_s16(gws, 4); + o.val[2] = vqrshrun_n_s16(bws, 4); + o.val[3] = vdup_n_u8(255); + + // store, interleaving r/g/b/a + vst4_u8(out, o); + out += 8*4; + } + } +#endif + + for (; i < count; ++i) { + int y_fixed = (y[i] << 20) + (1<<19); // rounding + int r,g,b; + int cr = pcr[i] - 128; + int cb = pcb[i] - 128; + r = y_fixed + cr* stbi__float2fixed(1.40200f); + g = y_fixed + cr*-stbi__float2fixed(0.71414f) + ((cb*-stbi__float2fixed(0.34414f)) & 0xffff0000); + b = y_fixed + cb* stbi__float2fixed(1.77200f); + r >>= 20; + g >>= 20; + b >>= 20; + if ((unsigned) r > 255) { if (r < 0) r = 0; else r = 255; } + if ((unsigned) g > 255) { if (g < 0) g = 0; else g = 255; } + if ((unsigned) b > 255) { if (b < 0) b = 0; else b = 255; } + out[0] = (stbi_uc)r; + out[1] = (stbi_uc)g; + out[2] = (stbi_uc)b; + out[3] = 255; + out += step; + } +} +#endif + +// set up the kernels +static void stbi__setup_jpeg(stbi__jpeg *j) +{ + j->idct_block_kernel = stbi__idct_block; + j->YCbCr_to_RGB_kernel = stbi__YCbCr_to_RGB_row; + j->resample_row_hv_2_kernel = stbi__resample_row_hv_2; + +#ifdef STBI_SSE2 + if (stbi__sse2_available()) { + j->idct_block_kernel = stbi__idct_simd; + j->YCbCr_to_RGB_kernel = stbi__YCbCr_to_RGB_simd; + j->resample_row_hv_2_kernel = stbi__resample_row_hv_2_simd; + } +#endif + +#ifdef STBI_NEON + j->idct_block_kernel = stbi__idct_simd; + j->YCbCr_to_RGB_kernel = stbi__YCbCr_to_RGB_simd; + j->resample_row_hv_2_kernel = stbi__resample_row_hv_2_simd; +#endif +} + +// clean up the temporary component buffers +static void stbi__cleanup_jpeg(stbi__jpeg *j) +{ + stbi__free_jpeg_components(j, j->s->img_n, 0); +} + +typedef struct +{ + resample_row_func resample; + stbi_uc *line0,*line1; + int hs,vs; // expansion factor in each axis + int w_lores; // horizontal pixels pre-expansion + int ystep; // how far through vertical expansion we are + int ypos; // which pre-expansion row we're on +} stbi__resample; + +// fast 0..255 * 0..255 => 0..255 rounded multiplication +static stbi_uc stbi__blinn_8x8(stbi_uc x, stbi_uc y) +{ + unsigned int t = x*y + 128; + return (stbi_uc) ((t + (t >>8)) >> 8); +} + +static stbi_uc *load_jpeg_image(stbi__jpeg *z, int *out_x, int *out_y, int *comp, int req_comp) +{ + int n, decode_n, is_rgb; + z->s->img_n = 0; // make stbi__cleanup_jpeg safe + + // validate req_comp + if (req_comp < 0 || req_comp > 4) return stbi__errpuc("bad req_comp", "Internal error"); + + // load a jpeg image from whichever source, but leave in YCbCr format + if (!stbi__decode_jpeg_image(z)) { stbi__cleanup_jpeg(z); return NULL; } + + // determine actual number of components to generate + n = req_comp ? req_comp : z->s->img_n >= 3 ? 3 : 1; + + is_rgb = z->s->img_n == 3 && (z->rgb == 3 || (z->app14_color_transform == 0 && !z->jfif)); + + if (z->s->img_n == 3 && n < 3 && !is_rgb) + decode_n = 1; + else + decode_n = z->s->img_n; + + // resample and color-convert + { + int k; + unsigned int i,j; + stbi_uc *output; + stbi_uc *coutput[4]; + + stbi__resample res_comp[4]; + + for (k=0; k < decode_n; ++k) { + stbi__resample *r = &res_comp[k]; + + // allocate line buffer big enough for upsampling off the edges + // with upsample factor of 4 + z->img_comp[k].linebuf = (stbi_uc *) stbi__malloc(z->s->img_x + 3); + if (!z->img_comp[k].linebuf) { stbi__cleanup_jpeg(z); return stbi__errpuc("outofmem", "Out of memory"); } + + r->hs = z->img_h_max / z->img_comp[k].h; + r->vs = z->img_v_max / z->img_comp[k].v; + r->ystep = r->vs >> 1; + r->w_lores = (z->s->img_x + r->hs-1) / r->hs; + r->ypos = 0; + r->line0 = r->line1 = z->img_comp[k].data; + + if (r->hs == 1 && r->vs == 1) r->resample = resample_row_1; + else if (r->hs == 1 && r->vs == 2) r->resample = stbi__resample_row_v_2; + else if (r->hs == 2 && r->vs == 1) r->resample = stbi__resample_row_h_2; + else if (r->hs == 2 && r->vs == 2) r->resample = z->resample_row_hv_2_kernel; + else r->resample = stbi__resample_row_generic; + } + + // can't error after this so, this is safe + output = (stbi_uc *) stbi__malloc_mad3(n, z->s->img_x, z->s->img_y, 1); + if (!output) { stbi__cleanup_jpeg(z); return stbi__errpuc("outofmem", "Out of memory"); } + + // now go ahead and resample + for (j=0; j < z->s->img_y; ++j) { + stbi_uc *out = output + n * z->s->img_x * j; + for (k=0; k < decode_n; ++k) { + stbi__resample *r = &res_comp[k]; + int y_bot = r->ystep >= (r->vs >> 1); + coutput[k] = r->resample(z->img_comp[k].linebuf, + y_bot ? r->line1 : r->line0, + y_bot ? r->line0 : r->line1, + r->w_lores, r->hs); + if (++r->ystep >= r->vs) { + r->ystep = 0; + r->line0 = r->line1; + if (++r->ypos < z->img_comp[k].y) + r->line1 += z->img_comp[k].w2; + } + } + if (n >= 3) { + stbi_uc *y = coutput[0]; + if (z->s->img_n == 3) { + if (is_rgb) { + for (i=0; i < z->s->img_x; ++i) { + out[0] = y[i]; + out[1] = coutput[1][i]; + out[2] = coutput[2][i]; + out[3] = 255; + out += n; + } + } else { + z->YCbCr_to_RGB_kernel(out, y, coutput[1], coutput[2], z->s->img_x, n); + } + } else if (z->s->img_n == 4) { + if (z->app14_color_transform == 0) { // CMYK + for (i=0; i < z->s->img_x; ++i) { + stbi_uc m = coutput[3][i]; + out[0] = stbi__blinn_8x8(coutput[0][i], m); + out[1] = stbi__blinn_8x8(coutput[1][i], m); + out[2] = stbi__blinn_8x8(coutput[2][i], m); + out[3] = 255; + out += n; + } + } else if (z->app14_color_transform == 2) { // YCCK + z->YCbCr_to_RGB_kernel(out, y, coutput[1], coutput[2], z->s->img_x, n); + for (i=0; i < z->s->img_x; ++i) { + stbi_uc m = coutput[3][i]; + out[0] = stbi__blinn_8x8(255 - out[0], m); + out[1] = stbi__blinn_8x8(255 - out[1], m); + out[2] = stbi__blinn_8x8(255 - out[2], m); + out += n; + } + } else { // YCbCr + alpha? Ignore the fourth channel for now + z->YCbCr_to_RGB_kernel(out, y, coutput[1], coutput[2], z->s->img_x, n); + } + } else + for (i=0; i < z->s->img_x; ++i) { + out[0] = out[1] = out[2] = y[i]; + out[3] = 255; // not used if n==3 + out += n; + } + } else { + if (is_rgb) { + if (n == 1) + for (i=0; i < z->s->img_x; ++i) + *out++ = stbi__compute_y(coutput[0][i], coutput[1][i], coutput[2][i]); + else { + for (i=0; i < z->s->img_x; ++i, out += 2) { + out[0] = stbi__compute_y(coutput[0][i], coutput[1][i], coutput[2][i]); + out[1] = 255; + } + } + } else if (z->s->img_n == 4 && z->app14_color_transform == 0) { + for (i=0; i < z->s->img_x; ++i) { + stbi_uc m = coutput[3][i]; + stbi_uc r = stbi__blinn_8x8(coutput[0][i], m); + stbi_uc g = stbi__blinn_8x8(coutput[1][i], m); + stbi_uc b = stbi__blinn_8x8(coutput[2][i], m); + out[0] = stbi__compute_y(r, g, b); + out[1] = 255; + out += n; + } + } else if (z->s->img_n == 4 && z->app14_color_transform == 2) { + for (i=0; i < z->s->img_x; ++i) { + out[0] = stbi__blinn_8x8(255 - coutput[0][i], coutput[3][i]); + out[1] = 255; + out += n; + } + } else { + stbi_uc *y = coutput[0]; + if (n == 1) + for (i=0; i < z->s->img_x; ++i) out[i] = y[i]; + else + for (i=0; i < z->s->img_x; ++i) *out++ = y[i], *out++ = 255; + } + } + } + stbi__cleanup_jpeg(z); + *out_x = z->s->img_x; + *out_y = z->s->img_y; + if (comp) *comp = z->s->img_n >= 3 ? 3 : 1; // report original components, not output + return output; + } +} + +static void *stbi__jpeg_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri) +{ + unsigned char* result; + stbi__jpeg* j = (stbi__jpeg*) stbi__malloc(sizeof(stbi__jpeg)); + STBI_NOTUSED(ri); + j->s = s; + stbi__setup_jpeg(j); + result = load_jpeg_image(j, x,y,comp,req_comp); + STBI_FREE(j); + return result; +} + +static int stbi__jpeg_test(stbi__context *s) +{ + int r; + stbi__jpeg* j = (stbi__jpeg*)stbi__malloc(sizeof(stbi__jpeg)); + j->s = s; + stbi__setup_jpeg(j); + r = stbi__decode_jpeg_header(j, STBI__SCAN_type); + stbi__rewind(s); + STBI_FREE(j); + return r; +} + +static int stbi__jpeg_info_raw(stbi__jpeg *j, int *x, int *y, int *comp) +{ + if (!stbi__decode_jpeg_header(j, STBI__SCAN_header)) { + stbi__rewind( j->s ); + return 0; + } + if (x) *x = j->s->img_x; + if (y) *y = j->s->img_y; + if (comp) *comp = j->s->img_n >= 3 ? 3 : 1; + return 1; +} + +static int stbi__jpeg_info(stbi__context *s, int *x, int *y, int *comp) +{ + int result; + stbi__jpeg* j = (stbi__jpeg*) (stbi__malloc(sizeof(stbi__jpeg))); + j->s = s; + result = stbi__jpeg_info_raw(j, x, y, comp); + STBI_FREE(j); + return result; +} +#endif + +// public domain zlib decode v0.2 Sean Barrett 2006-11-18 +// simple implementation +// - all input must be provided in an upfront buffer +// - all output is written to a single output buffer (can malloc/realloc) +// performance +// - fast huffman + +#ifndef STBI_NO_ZLIB + +// fast-way is faster to check than jpeg huffman, but slow way is slower +#define STBI__ZFAST_BITS 9 // accelerate all cases in default tables +#define STBI__ZFAST_MASK ((1 << STBI__ZFAST_BITS) - 1) + +// zlib-style huffman encoding +// (jpegs packs from left, zlib from right, so can't share code) +typedef struct +{ + stbi__uint16 fast[1 << STBI__ZFAST_BITS]; + stbi__uint16 firstcode[16]; + int maxcode[17]; + stbi__uint16 firstsymbol[16]; + stbi_uc size[288]; + stbi__uint16 value[288]; +} stbi__zhuffman; + +stbi_inline static int stbi__bitreverse16(int n) +{ + n = ((n & 0xAAAA) >> 1) | ((n & 0x5555) << 1); + n = ((n & 0xCCCC) >> 2) | ((n & 0x3333) << 2); + n = ((n & 0xF0F0) >> 4) | ((n & 0x0F0F) << 4); + n = ((n & 0xFF00) >> 8) | ((n & 0x00FF) << 8); + return n; +} + +stbi_inline static int stbi__bit_reverse(int v, int bits) +{ + STBI_ASSERT(bits <= 16); + // to bit reverse n bits, reverse 16 and shift + // e.g. 11 bits, bit reverse and shift away 5 + return stbi__bitreverse16(v) >> (16-bits); +} + +static int stbi__zbuild_huffman(stbi__zhuffman *z, const stbi_uc *sizelist, int num) +{ + int i,k=0; + int code, next_code[16], sizes[17]; + + // DEFLATE spec for generating codes + memset(sizes, 0, sizeof(sizes)); + memset(z->fast, 0, sizeof(z->fast)); + for (i=0; i < num; ++i) + ++sizes[sizelist[i]]; + sizes[0] = 0; + for (i=1; i < 16; ++i) + if (sizes[i] > (1 << i)) + return stbi__err("bad sizes", "Corrupt PNG"); + code = 0; + for (i=1; i < 16; ++i) { + next_code[i] = code; + z->firstcode[i] = (stbi__uint16) code; + z->firstsymbol[i] = (stbi__uint16) k; + code = (code + sizes[i]); + if (sizes[i]) + if (code-1 >= (1 << i)) return stbi__err("bad codelengths","Corrupt PNG"); + z->maxcode[i] = code << (16-i); // preshift for inner loop + code <<= 1; + k += sizes[i]; + } + z->maxcode[16] = 0x10000; // sentinel + for (i=0; i < num; ++i) { + int s = sizelist[i]; + if (s) { + int c = next_code[s] - z->firstcode[s] + z->firstsymbol[s]; + stbi__uint16 fastv = (stbi__uint16) ((s << 9) | i); + z->size [c] = (stbi_uc ) s; + z->value[c] = (stbi__uint16) i; + if (s <= STBI__ZFAST_BITS) { + int j = stbi__bit_reverse(next_code[s],s); + while (j < (1 << STBI__ZFAST_BITS)) { + z->fast[j] = fastv; + j += (1 << s); + } + } + ++next_code[s]; + } + } + return 1; +} + +// zlib-from-memory implementation for PNG reading +// because PNG allows splitting the zlib stream arbitrarily, +// and it's annoying structurally to have PNG call ZLIB call PNG, +// we require PNG read all the IDATs and combine them into a single +// memory buffer + +typedef struct +{ + stbi_uc *zbuffer, *zbuffer_end; + int num_bits; + stbi__uint32 code_buffer; + + char *zout; + char *zout_start; + char *zout_end; + int z_expandable; + + stbi__zhuffman z_length, z_distance; +} stbi__zbuf; + +stbi_inline static stbi_uc stbi__zget8(stbi__zbuf *z) +{ + if (z->zbuffer >= z->zbuffer_end) return 0; + return *z->zbuffer++; +} + +static void stbi__fill_bits(stbi__zbuf *z) +{ + do { + STBI_ASSERT(z->code_buffer < (1U << z->num_bits)); + z->code_buffer |= (unsigned int) stbi__zget8(z) << z->num_bits; + z->num_bits += 8; + } while (z->num_bits <= 24); +} + +stbi_inline static unsigned int stbi__zreceive(stbi__zbuf *z, int n) +{ + unsigned int k; + if (z->num_bits < n) stbi__fill_bits(z); + k = z->code_buffer & ((1 << n) - 1); + z->code_buffer >>= n; + z->num_bits -= n; + return k; +} + +static int stbi__zhuffman_decode_slowpath(stbi__zbuf *a, stbi__zhuffman *z) +{ + int b,s,k; + // not resolved by fast table, so compute it the slow way + // use jpeg approach, which requires MSbits at top + k = stbi__bit_reverse(a->code_buffer, 16); + for (s=STBI__ZFAST_BITS+1; ; ++s) + if (k < z->maxcode[s]) + break; + if (s == 16) return -1; // invalid code! + // code size is s, so: + b = (k >> (16-s)) - z->firstcode[s] + z->firstsymbol[s]; + STBI_ASSERT(z->size[b] == s); + a->code_buffer >>= s; + a->num_bits -= s; + return z->value[b]; +} + +stbi_inline static int stbi__zhuffman_decode(stbi__zbuf *a, stbi__zhuffman *z) +{ + int b,s; + if (a->num_bits < 16) stbi__fill_bits(a); + b = z->fast[a->code_buffer & STBI__ZFAST_MASK]; + if (b) { + s = b >> 9; + a->code_buffer >>= s; + a->num_bits -= s; + return b & 511; + } + return stbi__zhuffman_decode_slowpath(a, z); +} + +static int stbi__zexpand(stbi__zbuf *z, char *zout, int n) // need to make room for n bytes +{ + char *q; + int cur, limit, old_limit; + z->zout = zout; + if (!z->z_expandable) return stbi__err("output buffer limit","Corrupt PNG"); + cur = (int) (z->zout - z->zout_start); + limit = old_limit = (int) (z->zout_end - z->zout_start); + while (cur + n > limit) + limit *= 2; + q = (char *) STBI_REALLOC_SIZED(z->zout_start, old_limit, limit); + STBI_NOTUSED(old_limit); + if (q == NULL) return stbi__err("outofmem", "Out of memory"); + z->zout_start = q; + z->zout = q + cur; + z->zout_end = q + limit; + return 1; +} + +static const int stbi__zlength_base[31] = { + 3,4,5,6,7,8,9,10,11,13, + 15,17,19,23,27,31,35,43,51,59, + 67,83,99,115,131,163,195,227,258,0,0 }; + +static const int stbi__zlength_extra[31]= +{ 0,0,0,0,0,0,0,0,1,1,1,1,2,2,2,2,3,3,3,3,4,4,4,4,5,5,5,5,0,0,0 }; + +static const int stbi__zdist_base[32] = { 1,2,3,4,5,7,9,13,17,25,33,49,65,97,129,193, +257,385,513,769,1025,1537,2049,3073,4097,6145,8193,12289,16385,24577,0,0}; + +static const int stbi__zdist_extra[32] = +{ 0,0,0,0,1,1,2,2,3,3,4,4,5,5,6,6,7,7,8,8,9,9,10,10,11,11,12,12,13,13}; + +static int stbi__parse_huffman_block(stbi__zbuf *a) +{ + char *zout = a->zout; + for(;;) { + int z = stbi__zhuffman_decode(a, &a->z_length); + if (z < 256) { + if (z < 0) return stbi__err("bad huffman code","Corrupt PNG"); // error in huffman codes + if (zout >= a->zout_end) { + if (!stbi__zexpand(a, zout, 1)) return 0; + zout = a->zout; + } + *zout++ = (char) z; + } else { + stbi_uc *p; + int len,dist; + if (z == 256) { + a->zout = zout; + return 1; + } + z -= 257; + len = stbi__zlength_base[z]; + if (stbi__zlength_extra[z]) len += stbi__zreceive(a, stbi__zlength_extra[z]); + z = stbi__zhuffman_decode(a, &a->z_distance); + if (z < 0) return stbi__err("bad huffman code","Corrupt PNG"); + dist = stbi__zdist_base[z]; + if (stbi__zdist_extra[z]) dist += stbi__zreceive(a, stbi__zdist_extra[z]); + if (zout - a->zout_start < dist) return stbi__err("bad dist","Corrupt PNG"); + if (zout + len > a->zout_end) { + if (!stbi__zexpand(a, zout, len)) return 0; + zout = a->zout; + } + p = (stbi_uc *) (zout - dist); + if (dist == 1) { // run of one byte; common in images. + stbi_uc v = *p; + if (len) { do *zout++ = v; while (--len); } + } else { + if (len) { do *zout++ = *p++; while (--len); } + } + } + } +} + +static int stbi__compute_huffman_codes(stbi__zbuf *a) +{ + static const stbi_uc length_dezigzag[19] = { 16,17,18,0,8,7,9,6,10,5,11,4,12,3,13,2,14,1,15 }; + stbi__zhuffman z_codelength; + stbi_uc lencodes[286+32+137];//padding for maximum single op + stbi_uc codelength_sizes[19]; + int i,n; + + int hlit = stbi__zreceive(a,5) + 257; + int hdist = stbi__zreceive(a,5) + 1; + int hclen = stbi__zreceive(a,4) + 4; + int ntot = hlit + hdist; + + memset(codelength_sizes, 0, sizeof(codelength_sizes)); + for (i=0; i < hclen; ++i) { + int s = stbi__zreceive(a,3); + codelength_sizes[length_dezigzag[i]] = (stbi_uc) s; + } + if (!stbi__zbuild_huffman(&z_codelength, codelength_sizes, 19)) return 0; + + n = 0; + while (n < ntot) { + int c = stbi__zhuffman_decode(a, &z_codelength); + if (c < 0 || c >= 19) return stbi__err("bad codelengths", "Corrupt PNG"); + if (c < 16) + lencodes[n++] = (stbi_uc) c; + else { + stbi_uc fill = 0; + if (c == 16) { + c = stbi__zreceive(a,2)+3; + if (n == 0) return stbi__err("bad codelengths", "Corrupt PNG"); + fill = lencodes[n-1]; + } else if (c == 17) + c = stbi__zreceive(a,3)+3; + else { + STBI_ASSERT(c == 18); + c = stbi__zreceive(a,7)+11; + } + if (ntot - n < c) return stbi__err("bad codelengths", "Corrupt PNG"); + memset(lencodes+n, fill, c); + n += c; + } + } + if (n != ntot) return stbi__err("bad codelengths","Corrupt PNG"); + if (!stbi__zbuild_huffman(&a->z_length, lencodes, hlit)) return 0; + if (!stbi__zbuild_huffman(&a->z_distance, lencodes+hlit, hdist)) return 0; + return 1; +} + +static int stbi__parse_uncompressed_block(stbi__zbuf *a) +{ + stbi_uc header[4]; + int len,nlen,k; + if (a->num_bits & 7) + stbi__zreceive(a, a->num_bits & 7); // discard + // drain the bit-packed data into header + k = 0; + while (a->num_bits > 0) { + header[k++] = (stbi_uc) (a->code_buffer & 255); // suppress MSVC run-time check + a->code_buffer >>= 8; + a->num_bits -= 8; + } + STBI_ASSERT(a->num_bits == 0); + // now fill header the normal way + while (k < 4) + header[k++] = stbi__zget8(a); + len = header[1] * 256 + header[0]; + nlen = header[3] * 256 + header[2]; + if (nlen != (len ^ 0xffff)) return stbi__err("zlib corrupt","Corrupt PNG"); + if (a->zbuffer + len > a->zbuffer_end) return stbi__err("read past buffer","Corrupt PNG"); + if (a->zout + len > a->zout_end) + if (!stbi__zexpand(a, a->zout, len)) return 0; + memcpy(a->zout, a->zbuffer, len); + a->zbuffer += len; + a->zout += len; + return 1; +} + +static int stbi__parse_zlib_header(stbi__zbuf *a) +{ + int cmf = stbi__zget8(a); + int cm = cmf & 15; + /* int cinfo = cmf >> 4; */ + int flg = stbi__zget8(a); + if ((cmf*256+flg) % 31 != 0) return stbi__err("bad zlib header","Corrupt PNG"); // zlib spec + if (flg & 32) return stbi__err("no preset dict","Corrupt PNG"); // preset dictionary not allowed in png + if (cm != 8) return stbi__err("bad compression","Corrupt PNG"); // DEFLATE required for png + // window = 1 << (8 + cinfo)... but who cares, we fully buffer output + return 1; +} + +static const stbi_uc stbi__zdefault_length[288] = +{ + 8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8, 8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8, + 8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8, 8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8, + 8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8, 8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8, + 8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8, 8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8, + 8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8, 9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9, + 9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9, 9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9, + 9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9, 9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9, + 9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9, 9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9, + 7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7, 7,7,7,7,7,7,7,7,8,8,8,8,8,8,8,8 +}; +static const stbi_uc stbi__zdefault_distance[32] = +{ + 5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5 +}; +/* +Init algorithm: +{ + int i; // use <= to match clearly with spec + for (i=0; i <= 143; ++i) stbi__zdefault_length[i] = 8; + for ( ; i <= 255; ++i) stbi__zdefault_length[i] = 9; + for ( ; i <= 279; ++i) stbi__zdefault_length[i] = 7; + for ( ; i <= 287; ++i) stbi__zdefault_length[i] = 8; + + for (i=0; i <= 31; ++i) stbi__zdefault_distance[i] = 5; +} +*/ + +static int stbi__parse_zlib(stbi__zbuf *a, int parse_header) +{ + int final, type; + if (parse_header) + if (!stbi__parse_zlib_header(a)) return 0; + a->num_bits = 0; + a->code_buffer = 0; + do { + final = stbi__zreceive(a,1); + type = stbi__zreceive(a,2); + if (type == 0) { + if (!stbi__parse_uncompressed_block(a)) return 0; + } else if (type == 3) { + return 0; + } else { + if (type == 1) { + // use fixed code lengths + if (!stbi__zbuild_huffman(&a->z_length , stbi__zdefault_length , 288)) return 0; + if (!stbi__zbuild_huffman(&a->z_distance, stbi__zdefault_distance, 32)) return 0; + } else { + if (!stbi__compute_huffman_codes(a)) return 0; + } + if (!stbi__parse_huffman_block(a)) return 0; + } + } while (!final); + return 1; +} + +static int stbi__do_zlib(stbi__zbuf *a, char *obuf, int olen, int exp, int parse_header) +{ + a->zout_start = obuf; + a->zout = obuf; + a->zout_end = obuf + olen; + a->z_expandable = exp; + + return stbi__parse_zlib(a, parse_header); +} + +STBIDEF char *stbi_zlib_decode_malloc_guesssize(const char *buffer, int len, int initial_size, int *outlen) +{ + stbi__zbuf a; + char *p = (char *) stbi__malloc(initial_size); + if (p == NULL) return NULL; + a.zbuffer = (stbi_uc *) buffer; + a.zbuffer_end = (stbi_uc *) buffer + len; + if (stbi__do_zlib(&a, p, initial_size, 1, 1)) { + if (outlen) *outlen = (int) (a.zout - a.zout_start); + return a.zout_start; + } else { + STBI_FREE(a.zout_start); + return NULL; + } +} + +STBIDEF char *stbi_zlib_decode_malloc(char const *buffer, int len, int *outlen) +{ + return stbi_zlib_decode_malloc_guesssize(buffer, len, 16384, outlen); +} + +STBIDEF char *stbi_zlib_decode_malloc_guesssize_headerflag(const char *buffer, int len, int initial_size, int *outlen, int parse_header) +{ + stbi__zbuf a; + char *p = (char *) stbi__malloc(initial_size); + if (p == NULL) return NULL; + a.zbuffer = (stbi_uc *) buffer; + a.zbuffer_end = (stbi_uc *) buffer + len; + if (stbi__do_zlib(&a, p, initial_size, 1, parse_header)) { + if (outlen) *outlen = (int) (a.zout - a.zout_start); + return a.zout_start; + } else { + STBI_FREE(a.zout_start); + return NULL; + } +} + +STBIDEF int stbi_zlib_decode_buffer(char *obuffer, int olen, char const *ibuffer, int ilen) +{ + stbi__zbuf a; + a.zbuffer = (stbi_uc *) ibuffer; + a.zbuffer_end = (stbi_uc *) ibuffer + ilen; + if (stbi__do_zlib(&a, obuffer, olen, 0, 1)) + return (int) (a.zout - a.zout_start); + else + return -1; +} + +STBIDEF char *stbi_zlib_decode_noheader_malloc(char const *buffer, int len, int *outlen) +{ + stbi__zbuf a; + char *p = (char *) stbi__malloc(16384); + if (p == NULL) return NULL; + a.zbuffer = (stbi_uc *) buffer; + a.zbuffer_end = (stbi_uc *) buffer+len; + if (stbi__do_zlib(&a, p, 16384, 1, 0)) { + if (outlen) *outlen = (int) (a.zout - a.zout_start); + return a.zout_start; + } else { + STBI_FREE(a.zout_start); + return NULL; + } +} + +STBIDEF int stbi_zlib_decode_noheader_buffer(char *obuffer, int olen, const char *ibuffer, int ilen) +{ + stbi__zbuf a; + a.zbuffer = (stbi_uc *) ibuffer; + a.zbuffer_end = (stbi_uc *) ibuffer + ilen; + if (stbi__do_zlib(&a, obuffer, olen, 0, 0)) + return (int) (a.zout - a.zout_start); + else + return -1; +} +#endif + +// public domain "baseline" PNG decoder v0.10 Sean Barrett 2006-11-18 +// simple implementation +// - only 8-bit samples +// - no CRC checking +// - allocates lots of intermediate memory +// - avoids problem of streaming data between subsystems +// - avoids explicit window management +// performance +// - uses stb_zlib, a PD zlib implementation with fast huffman decoding + +#ifndef STBI_NO_PNG +typedef struct +{ + stbi__uint32 length; + stbi__uint32 type; +} stbi__pngchunk; + +static stbi__pngchunk stbi__get_chunk_header(stbi__context *s) +{ + stbi__pngchunk c; + c.length = stbi__get32be(s); + c.type = stbi__get32be(s); + return c; +} + +static int stbi__check_png_header(stbi__context *s) +{ + static const stbi_uc png_sig[8] = { 137,80,78,71,13,10,26,10 }; + int i; + for (i=0; i < 8; ++i) + if (stbi__get8(s) != png_sig[i]) return stbi__err("bad png sig","Not a PNG"); + return 1; +} + +typedef struct +{ + stbi__context *s; + stbi_uc *idata, *expanded, *out; + int depth; +} stbi__png; + + +enum { + STBI__F_none=0, + STBI__F_sub=1, + STBI__F_up=2, + STBI__F_avg=3, + STBI__F_paeth=4, + // synthetic filters used for first scanline to avoid needing a dummy row of 0s + STBI__F_avg_first, + STBI__F_paeth_first +}; + +static stbi_uc first_row_filter[5] = +{ + STBI__F_none, + STBI__F_sub, + STBI__F_none, + STBI__F_avg_first, + STBI__F_paeth_first +}; + +static int stbi__paeth(int a, int b, int c) +{ + int p = a + b - c; + int pa = abs(p-a); + int pb = abs(p-b); + int pc = abs(p-c); + if (pa <= pb && pa <= pc) return a; + if (pb <= pc) return b; + return c; +} + +static const stbi_uc stbi__depth_scale_table[9] = { 0, 0xff, 0x55, 0, 0x11, 0,0,0, 0x01 }; + +// create the png data from post-deflated data +static int stbi__create_png_image_raw(stbi__png *a, stbi_uc *raw, stbi__uint32 raw_len, int out_n, stbi__uint32 x, stbi__uint32 y, int depth, int color) +{ + int bytes = (depth == 16? 2 : 1); + stbi__context *s = a->s; + stbi__uint32 i,j,stride = x*out_n*bytes; + stbi__uint32 img_len, img_width_bytes; + int k; + int img_n = s->img_n; // copy it into a local for later + + int output_bytes = out_n*bytes; + int filter_bytes = img_n*bytes; + int width = x; + + STBI_ASSERT(out_n == s->img_n || out_n == s->img_n+1); + a->out = (stbi_uc *) stbi__malloc_mad3(x, y, output_bytes, 0); // extra bytes to write off the end into + if (!a->out) return stbi__err("outofmem", "Out of memory"); + + if (!stbi__mad3sizes_valid(img_n, x, depth, 7)) return stbi__err("too large", "Corrupt PNG"); + img_width_bytes = (((img_n * x * depth) + 7) >> 3); + img_len = (img_width_bytes + 1) * y; + + // we used to check for exact match between raw_len and img_len on non-interlaced PNGs, + // but issue #276 reported a PNG in the wild that had extra data at the end (all zeros), + // so just check for raw_len < img_len always. + if (raw_len < img_len) return stbi__err("not enough pixels","Corrupt PNG"); + + for (j=0; j < y; ++j) { + stbi_uc *cur = a->out + stride*j; + stbi_uc *prior; + int filter = *raw++; + + if (filter > 4) + return stbi__err("invalid filter","Corrupt PNG"); + + if (depth < 8) { + STBI_ASSERT(img_width_bytes <= x); + cur += x*out_n - img_width_bytes; // store output to the rightmost img_len bytes, so we can decode in place + filter_bytes = 1; + width = img_width_bytes; + } + prior = cur - stride; // bugfix: need to compute this after 'cur +=' computation above + + // if first row, use special filter that doesn't sample previous row + if (j == 0) filter = first_row_filter[filter]; + + // handle first byte explicitly + for (k=0; k < filter_bytes; ++k) { + switch (filter) { + case STBI__F_none : cur[k] = raw[k]; break; + case STBI__F_sub : cur[k] = raw[k]; break; + case STBI__F_up : cur[k] = STBI__BYTECAST(raw[k] + prior[k]); break; + case STBI__F_avg : cur[k] = STBI__BYTECAST(raw[k] + (prior[k]>>1)); break; + case STBI__F_paeth : cur[k] = STBI__BYTECAST(raw[k] + stbi__paeth(0,prior[k],0)); break; + case STBI__F_avg_first : cur[k] = raw[k]; break; + case STBI__F_paeth_first: cur[k] = raw[k]; break; + } + } + + if (depth == 8) { + if (img_n != out_n) + cur[img_n] = 255; // first pixel + raw += img_n; + cur += out_n; + prior += out_n; + } else if (depth == 16) { + if (img_n != out_n) { + cur[filter_bytes] = 255; // first pixel top byte + cur[filter_bytes+1] = 255; // first pixel bottom byte + } + raw += filter_bytes; + cur += output_bytes; + prior += output_bytes; + } else { + raw += 1; + cur += 1; + prior += 1; + } + + // this is a little gross, so that we don't switch per-pixel or per-component + if (depth < 8 || img_n == out_n) { + int nk = (width - 1)*filter_bytes; + #define STBI__CASE(f) \ + case f: \ + for (k=0; k < nk; ++k) + switch (filter) { + // "none" filter turns into a memcpy here; make that explicit. + case STBI__F_none: memcpy(cur, raw, nk); break; + STBI__CASE(STBI__F_sub) { cur[k] = STBI__BYTECAST(raw[k] + cur[k-filter_bytes]); } break; + STBI__CASE(STBI__F_up) { cur[k] = STBI__BYTECAST(raw[k] + prior[k]); } break; + STBI__CASE(STBI__F_avg) { cur[k] = STBI__BYTECAST(raw[k] + ((prior[k] + cur[k-filter_bytes])>>1)); } break; + STBI__CASE(STBI__F_paeth) { cur[k] = STBI__BYTECAST(raw[k] + stbi__paeth(cur[k-filter_bytes],prior[k],prior[k-filter_bytes])); } break; + STBI__CASE(STBI__F_avg_first) { cur[k] = STBI__BYTECAST(raw[k] + (cur[k-filter_bytes] >> 1)); } break; + STBI__CASE(STBI__F_paeth_first) { cur[k] = STBI__BYTECAST(raw[k] + stbi__paeth(cur[k-filter_bytes],0,0)); } break; + } + #undef STBI__CASE + raw += nk; + } else { + STBI_ASSERT(img_n+1 == out_n); + #define STBI__CASE(f) \ + case f: \ + for (i=x-1; i >= 1; --i, cur[filter_bytes]=255,raw+=filter_bytes,cur+=output_bytes,prior+=output_bytes) \ + for (k=0; k < filter_bytes; ++k) + switch (filter) { + STBI__CASE(STBI__F_none) { cur[k] = raw[k]; } break; + STBI__CASE(STBI__F_sub) { cur[k] = STBI__BYTECAST(raw[k] + cur[k- output_bytes]); } break; + STBI__CASE(STBI__F_up) { cur[k] = STBI__BYTECAST(raw[k] + prior[k]); } break; + STBI__CASE(STBI__F_avg) { cur[k] = STBI__BYTECAST(raw[k] + ((prior[k] + cur[k- output_bytes])>>1)); } break; + STBI__CASE(STBI__F_paeth) { cur[k] = STBI__BYTECAST(raw[k] + stbi__paeth(cur[k- output_bytes],prior[k],prior[k- output_bytes])); } break; + STBI__CASE(STBI__F_avg_first) { cur[k] = STBI__BYTECAST(raw[k] + (cur[k- output_bytes] >> 1)); } break; + STBI__CASE(STBI__F_paeth_first) { cur[k] = STBI__BYTECAST(raw[k] + stbi__paeth(cur[k- output_bytes],0,0)); } break; + } + #undef STBI__CASE + + // the loop above sets the high byte of the pixels' alpha, but for + // 16 bit png files we also need the low byte set. we'll do that here. + if (depth == 16) { + cur = a->out + stride*j; // start at the beginning of the row again + for (i=0; i < x; ++i,cur+=output_bytes) { + cur[filter_bytes+1] = 255; + } + } + } + } + + // we make a separate pass to expand bits to pixels; for performance, + // this could run two scanlines behind the above code, so it won't + // intefere with filtering but will still be in the cache. + if (depth < 8) { + for (j=0; j < y; ++j) { + stbi_uc *cur = a->out + stride*j; + stbi_uc *in = a->out + stride*j + x*out_n - img_width_bytes; + // unpack 1/2/4-bit into a 8-bit buffer. allows us to keep the common 8-bit path optimal at minimal cost for 1/2/4-bit + // png guarante byte alignment, if width is not multiple of 8/4/2 we'll decode dummy trailing data that will be skipped in the later loop + stbi_uc scale = (color == 0) ? stbi__depth_scale_table[depth] : 1; // scale grayscale values to 0..255 range + + // note that the final byte might overshoot and write more data than desired. + // we can allocate enough data that this never writes out of memory, but it + // could also overwrite the next scanline. can it overwrite non-empty data + // on the next scanline? yes, consider 1-pixel-wide scanlines with 1-bit-per-pixel. + // so we need to explicitly clamp the final ones + + if (depth == 4) { + for (k=x*img_n; k >= 2; k-=2, ++in) { + *cur++ = scale * ((*in >> 4) ); + *cur++ = scale * ((*in ) & 0x0f); + } + if (k > 0) *cur++ = scale * ((*in >> 4) ); + } else if (depth == 2) { + for (k=x*img_n; k >= 4; k-=4, ++in) { + *cur++ = scale * ((*in >> 6) ); + *cur++ = scale * ((*in >> 4) & 0x03); + *cur++ = scale * ((*in >> 2) & 0x03); + *cur++ = scale * ((*in ) & 0x03); + } + if (k > 0) *cur++ = scale * ((*in >> 6) ); + if (k > 1) *cur++ = scale * ((*in >> 4) & 0x03); + if (k > 2) *cur++ = scale * ((*in >> 2) & 0x03); + } else if (depth == 1) { + for (k=x*img_n; k >= 8; k-=8, ++in) { + *cur++ = scale * ((*in >> 7) ); + *cur++ = scale * ((*in >> 6) & 0x01); + *cur++ = scale * ((*in >> 5) & 0x01); + *cur++ = scale * ((*in >> 4) & 0x01); + *cur++ = scale * ((*in >> 3) & 0x01); + *cur++ = scale * ((*in >> 2) & 0x01); + *cur++ = scale * ((*in >> 1) & 0x01); + *cur++ = scale * ((*in ) & 0x01); + } + if (k > 0) *cur++ = scale * ((*in >> 7) ); + if (k > 1) *cur++ = scale * ((*in >> 6) & 0x01); + if (k > 2) *cur++ = scale * ((*in >> 5) & 0x01); + if (k > 3) *cur++ = scale * ((*in >> 4) & 0x01); + if (k > 4) *cur++ = scale * ((*in >> 3) & 0x01); + if (k > 5) *cur++ = scale * ((*in >> 2) & 0x01); + if (k > 6) *cur++ = scale * ((*in >> 1) & 0x01); + } + if (img_n != out_n) { + int q; + // insert alpha = 255 + cur = a->out + stride*j; + if (img_n == 1) { + for (q=x-1; q >= 0; --q) { + cur[q*2+1] = 255; + cur[q*2+0] = cur[q]; + } + } else { + STBI_ASSERT(img_n == 3); + for (q=x-1; q >= 0; --q) { + cur[q*4+3] = 255; + cur[q*4+2] = cur[q*3+2]; + cur[q*4+1] = cur[q*3+1]; + cur[q*4+0] = cur[q*3+0]; + } + } + } + } + } else if (depth == 16) { + // force the image data from big-endian to platform-native. + // this is done in a separate pass due to the decoding relying + // on the data being untouched, but could probably be done + // per-line during decode if care is taken. + stbi_uc *cur = a->out; + stbi__uint16 *cur16 = (stbi__uint16*)cur; + + for(i=0; i < x*y*out_n; ++i,cur16++,cur+=2) { + *cur16 = (cur[0] << 8) | cur[1]; + } + } + + return 1; +} + +static int stbi__create_png_image(stbi__png *a, stbi_uc *image_data, stbi__uint32 image_data_len, int out_n, int depth, int color, int interlaced) +{ + int bytes = (depth == 16 ? 2 : 1); + int out_bytes = out_n * bytes; + stbi_uc *final; + int p; + if (!interlaced) + return stbi__create_png_image_raw(a, image_data, image_data_len, out_n, a->s->img_x, a->s->img_y, depth, color); + + // de-interlacing + final = (stbi_uc *) stbi__malloc_mad3(a->s->img_x, a->s->img_y, out_bytes, 0); + for (p=0; p < 7; ++p) { + int xorig[] = { 0,4,0,2,0,1,0 }; + int yorig[] = { 0,0,4,0,2,0,1 }; + int xspc[] = { 8,8,4,4,2,2,1 }; + int yspc[] = { 8,8,8,4,4,2,2 }; + int i,j,x,y; + // pass1_x[4] = 0, pass1_x[5] = 1, pass1_x[12] = 1 + x = (a->s->img_x - xorig[p] + xspc[p]-1) / xspc[p]; + y = (a->s->img_y - yorig[p] + yspc[p]-1) / yspc[p]; + if (x && y) { + stbi__uint32 img_len = ((((a->s->img_n * x * depth) + 7) >> 3) + 1) * y; + if (!stbi__create_png_image_raw(a, image_data, image_data_len, out_n, x, y, depth, color)) { + STBI_FREE(final); + return 0; + } + for (j=0; j < y; ++j) { + for (i=0; i < x; ++i) { + int out_y = j*yspc[p]+yorig[p]; + int out_x = i*xspc[p]+xorig[p]; + memcpy(final + out_y*a->s->img_x*out_bytes + out_x*out_bytes, + a->out + (j*x+i)*out_bytes, out_bytes); + } + } + STBI_FREE(a->out); + image_data += img_len; + image_data_len -= img_len; + } + } + a->out = final; + + return 1; +} + +static int stbi__compute_transparency(stbi__png *z, stbi_uc tc[3], int out_n) +{ + stbi__context *s = z->s; + stbi__uint32 i, pixel_count = s->img_x * s->img_y; + stbi_uc *p = z->out; + + // compute color-based transparency, assuming we've + // already got 255 as the alpha value in the output + STBI_ASSERT(out_n == 2 || out_n == 4); + + if (out_n == 2) { + for (i=0; i < pixel_count; ++i) { + p[1] = (p[0] == tc[0] ? 0 : 255); + p += 2; + } + } else { + for (i=0; i < pixel_count; ++i) { + if (p[0] == tc[0] && p[1] == tc[1] && p[2] == tc[2]) + p[3] = 0; + p += 4; + } + } + return 1; +} + +static int stbi__compute_transparency16(stbi__png *z, stbi__uint16 tc[3], int out_n) +{ + stbi__context *s = z->s; + stbi__uint32 i, pixel_count = s->img_x * s->img_y; + stbi__uint16 *p = (stbi__uint16*) z->out; + + // compute color-based transparency, assuming we've + // already got 65535 as the alpha value in the output + STBI_ASSERT(out_n == 2 || out_n == 4); + + if (out_n == 2) { + for (i = 0; i < pixel_count; ++i) { + p[1] = (p[0] == tc[0] ? 0 : 65535); + p += 2; + } + } else { + for (i = 0; i < pixel_count; ++i) { + if (p[0] == tc[0] && p[1] == tc[1] && p[2] == tc[2]) + p[3] = 0; + p += 4; + } + } + return 1; +} + +static int stbi__expand_png_palette(stbi__png *a, stbi_uc *palette, int len, int pal_img_n) +{ + stbi__uint32 i, pixel_count = a->s->img_x * a->s->img_y; + stbi_uc *p, *temp_out, *orig = a->out; + + p = (stbi_uc *) stbi__malloc_mad2(pixel_count, pal_img_n, 0); + if (p == NULL) return stbi__err("outofmem", "Out of memory"); + + // between here and free(out) below, exitting would leak + temp_out = p; + + if (pal_img_n == 3) { + for (i=0; i < pixel_count; ++i) { + int n = orig[i]*4; + p[0] = palette[n ]; + p[1] = palette[n+1]; + p[2] = palette[n+2]; + p += 3; + } + } else { + for (i=0; i < pixel_count; ++i) { + int n = orig[i]*4; + p[0] = palette[n ]; + p[1] = palette[n+1]; + p[2] = palette[n+2]; + p[3] = palette[n+3]; + p += 4; + } + } + STBI_FREE(a->out); + a->out = temp_out; + + STBI_NOTUSED(len); + + return 1; +} + +static int stbi__unpremultiply_on_load = 0; +static int stbi__de_iphone_flag = 0; + +STBIDEF void stbi_set_unpremultiply_on_load(int flag_true_if_should_unpremultiply) +{ + stbi__unpremultiply_on_load = flag_true_if_should_unpremultiply; +} + +STBIDEF void stbi_convert_iphone_png_to_rgb(int flag_true_if_should_convert) +{ + stbi__de_iphone_flag = flag_true_if_should_convert; +} + +static void stbi__de_iphone(stbi__png *z) +{ + stbi__context *s = z->s; + stbi__uint32 i, pixel_count = s->img_x * s->img_y; + stbi_uc *p = z->out; + + if (s->img_out_n == 3) { // convert bgr to rgb + for (i=0; i < pixel_count; ++i) { + stbi_uc t = p[0]; + p[0] = p[2]; + p[2] = t; + p += 3; + } + } else { + STBI_ASSERT(s->img_out_n == 4); + if (stbi__unpremultiply_on_load) { + // convert bgr to rgb and unpremultiply + for (i=0; i < pixel_count; ++i) { + stbi_uc a = p[3]; + stbi_uc t = p[0]; + if (a) { + stbi_uc half = a / 2; + p[0] = (p[2] * 255 + half) / a; + p[1] = (p[1] * 255 + half) / a; + p[2] = ( t * 255 + half) / a; + } else { + p[0] = p[2]; + p[2] = t; + } + p += 4; + } + } else { + // convert bgr to rgb + for (i=0; i < pixel_count; ++i) { + stbi_uc t = p[0]; + p[0] = p[2]; + p[2] = t; + p += 4; + } + } + } +} + +#define STBI__PNG_TYPE(a,b,c,d) (((unsigned) (a) << 24) + ((unsigned) (b) << 16) + ((unsigned) (c) << 8) + (unsigned) (d)) + +static int stbi__parse_png_file(stbi__png *z, int scan, int req_comp) +{ + stbi_uc palette[1024], pal_img_n=0; + stbi_uc has_trans=0, tc[3]; + stbi__uint16 tc16[3]; + stbi__uint32 ioff=0, idata_limit=0, i, pal_len=0; + int first=1,k,interlace=0, color=0, is_iphone=0; + stbi__context *s = z->s; + + z->expanded = NULL; + z->idata = NULL; + z->out = NULL; + + if (!stbi__check_png_header(s)) return 0; + + if (scan == STBI__SCAN_type) return 1; + + for (;;) { + stbi__pngchunk c = stbi__get_chunk_header(s); + switch (c.type) { + case STBI__PNG_TYPE('C','g','B','I'): + is_iphone = 1; + stbi__skip(s, c.length); + break; + case STBI__PNG_TYPE('I','H','D','R'): { + int comp,filter; + if (!first) return stbi__err("multiple IHDR","Corrupt PNG"); + first = 0; + if (c.length != 13) return stbi__err("bad IHDR len","Corrupt PNG"); + s->img_x = stbi__get32be(s); if (s->img_x > (1 << 24)) return stbi__err("too large","Very large image (corrupt?)"); + s->img_y = stbi__get32be(s); if (s->img_y > (1 << 24)) return stbi__err("too large","Very large image (corrupt?)"); + z->depth = stbi__get8(s); if (z->depth != 1 && z->depth != 2 && z->depth != 4 && z->depth != 8 && z->depth != 16) return stbi__err("1/2/4/8/16-bit only","PNG not supported: 1/2/4/8/16-bit only"); + color = stbi__get8(s); if (color > 6) return stbi__err("bad ctype","Corrupt PNG"); + if (color == 3 && z->depth == 16) return stbi__err("bad ctype","Corrupt PNG"); + if (color == 3) pal_img_n = 3; else if (color & 1) return stbi__err("bad ctype","Corrupt PNG"); + comp = stbi__get8(s); if (comp) return stbi__err("bad comp method","Corrupt PNG"); + filter= stbi__get8(s); if (filter) return stbi__err("bad filter method","Corrupt PNG"); + interlace = stbi__get8(s); if (interlace>1) return stbi__err("bad interlace method","Corrupt PNG"); + if (!s->img_x || !s->img_y) return stbi__err("0-pixel image","Corrupt PNG"); + if (!pal_img_n) { + s->img_n = (color & 2 ? 3 : 1) + (color & 4 ? 1 : 0); + if ((1 << 30) / s->img_x / s->img_n < s->img_y) return stbi__err("too large", "Image too large to decode"); + if (scan == STBI__SCAN_header) return 1; + } else { + // if paletted, then pal_n is our final components, and + // img_n is # components to decompress/filter. + s->img_n = 1; + if ((1 << 30) / s->img_x / 4 < s->img_y) return stbi__err("too large","Corrupt PNG"); + // if SCAN_header, have to scan to see if we have a tRNS + } + break; + } + + case STBI__PNG_TYPE('P','L','T','E'): { + if (first) return stbi__err("first not IHDR", "Corrupt PNG"); + if (c.length > 256*3) return stbi__err("invalid PLTE","Corrupt PNG"); + pal_len = c.length / 3; + if (pal_len * 3 != c.length) return stbi__err("invalid PLTE","Corrupt PNG"); + for (i=0; i < pal_len; ++i) { + palette[i*4+0] = stbi__get8(s); + palette[i*4+1] = stbi__get8(s); + palette[i*4+2] = stbi__get8(s); + palette[i*4+3] = 255; + } + break; + } + + case STBI__PNG_TYPE('t','R','N','S'): { + if (first) return stbi__err("first not IHDR", "Corrupt PNG"); + if (z->idata) return stbi__err("tRNS after IDAT","Corrupt PNG"); + if (pal_img_n) { + if (scan == STBI__SCAN_header) { s->img_n = 4; return 1; } + if (pal_len == 0) return stbi__err("tRNS before PLTE","Corrupt PNG"); + if (c.length > pal_len) return stbi__err("bad tRNS len","Corrupt PNG"); + pal_img_n = 4; + for (i=0; i < c.length; ++i) + palette[i*4+3] = stbi__get8(s); + } else { + if (!(s->img_n & 1)) return stbi__err("tRNS with alpha","Corrupt PNG"); + if (c.length != (stbi__uint32) s->img_n*2) return stbi__err("bad tRNS len","Corrupt PNG"); + has_trans = 1; + if (z->depth == 16) { + for (k = 0; k < s->img_n; ++k) tc16[k] = (stbi__uint16)stbi__get16be(s); // copy the values as-is + } else { + for (k = 0; k < s->img_n; ++k) tc[k] = (stbi_uc)(stbi__get16be(s) & 255) * stbi__depth_scale_table[z->depth]; // non 8-bit images will be larger + } + } + break; + } + + case STBI__PNG_TYPE('I','D','A','T'): { + if (first) return stbi__err("first not IHDR", "Corrupt PNG"); + if (pal_img_n && !pal_len) return stbi__err("no PLTE","Corrupt PNG"); + if (scan == STBI__SCAN_header) { s->img_n = pal_img_n; return 1; } + if ((int)(ioff + c.length) < (int)ioff) return 0; + if (ioff + c.length > idata_limit) { + stbi__uint32 idata_limit_old = idata_limit; + stbi_uc *p; + if (idata_limit == 0) idata_limit = c.length > 4096 ? c.length : 4096; + while (ioff + c.length > idata_limit) + idata_limit *= 2; + STBI_NOTUSED(idata_limit_old); + p = (stbi_uc *) STBI_REALLOC_SIZED(z->idata, idata_limit_old, idata_limit); if (p == NULL) return stbi__err("outofmem", "Out of memory"); + z->idata = p; + } + if (!stbi__getn(s, z->idata+ioff,c.length)) return stbi__err("outofdata","Corrupt PNG"); + ioff += c.length; + break; + } + + case STBI__PNG_TYPE('I','E','N','D'): { + stbi__uint32 raw_len, bpl; + if (first) return stbi__err("first not IHDR", "Corrupt PNG"); + if (scan != STBI__SCAN_load) return 1; + if (z->idata == NULL) return stbi__err("no IDAT","Corrupt PNG"); + // initial guess for decoded data size to avoid unnecessary reallocs + bpl = (s->img_x * z->depth + 7) / 8; // bytes per line, per component + raw_len = bpl * s->img_y * s->img_n /* pixels */ + s->img_y /* filter mode per row */; + z->expanded = (stbi_uc *) stbi_zlib_decode_malloc_guesssize_headerflag((char *) z->idata, ioff, raw_len, (int *) &raw_len, !is_iphone); + if (z->expanded == NULL) return 0; // zlib should set error + STBI_FREE(z->idata); z->idata = NULL; + if ((req_comp == s->img_n+1 && req_comp != 3 && !pal_img_n) || has_trans) + s->img_out_n = s->img_n+1; + else + s->img_out_n = s->img_n; + if (!stbi__create_png_image(z, z->expanded, raw_len, s->img_out_n, z->depth, color, interlace)) return 0; + if (has_trans) { + if (z->depth == 16) { + if (!stbi__compute_transparency16(z, tc16, s->img_out_n)) return 0; + } else { + if (!stbi__compute_transparency(z, tc, s->img_out_n)) return 0; + } + } + if (is_iphone && stbi__de_iphone_flag && s->img_out_n > 2) + stbi__de_iphone(z); + if (pal_img_n) { + // pal_img_n == 3 or 4 + s->img_n = pal_img_n; // record the actual colors we had + s->img_out_n = pal_img_n; + if (req_comp >= 3) s->img_out_n = req_comp; + if (!stbi__expand_png_palette(z, palette, pal_len, s->img_out_n)) + return 0; + } else if (has_trans) { + // non-paletted image with tRNS -> source image has (constant) alpha + ++s->img_n; + } + STBI_FREE(z->expanded); z->expanded = NULL; + return 1; + } + + default: + // if critical, fail + if (first) return stbi__err("first not IHDR", "Corrupt PNG"); + if ((c.type & (1 << 29)) == 0) { + #ifndef STBI_NO_FAILURE_STRINGS + // not threadsafe + static char invalid_chunk[] = "XXXX PNG chunk not known"; + invalid_chunk[0] = STBI__BYTECAST(c.type >> 24); + invalid_chunk[1] = STBI__BYTECAST(c.type >> 16); + invalid_chunk[2] = STBI__BYTECAST(c.type >> 8); + invalid_chunk[3] = STBI__BYTECAST(c.type >> 0); + #endif + return stbi__err(invalid_chunk, "PNG not supported: unknown PNG chunk type"); + } + stbi__skip(s, c.length); + break; + } + // end of PNG chunk, read and skip CRC + stbi__get32be(s); + } +} + +static void *stbi__do_png(stbi__png *p, int *x, int *y, int *n, int req_comp, stbi__result_info *ri) +{ + void *result=NULL; + if (req_comp < 0 || req_comp > 4) return stbi__errpuc("bad req_comp", "Internal error"); + if (stbi__parse_png_file(p, STBI__SCAN_load, req_comp)) { + if (p->depth < 8) + ri->bits_per_channel = 8; + else + ri->bits_per_channel = p->depth; + result = p->out; + p->out = NULL; + if (req_comp && req_comp != p->s->img_out_n) { + if (ri->bits_per_channel == 8) + result = stbi__convert_format((unsigned char *) result, p->s->img_out_n, req_comp, p->s->img_x, p->s->img_y); + else + result = stbi__convert_format16((stbi__uint16 *) result, p->s->img_out_n, req_comp, p->s->img_x, p->s->img_y); + p->s->img_out_n = req_comp; + if (result == NULL) return result; + } + *x = p->s->img_x; + *y = p->s->img_y; + if (n) *n = p->s->img_n; + } + STBI_FREE(p->out); p->out = NULL; + STBI_FREE(p->expanded); p->expanded = NULL; + STBI_FREE(p->idata); p->idata = NULL; + + return result; +} + +static void *stbi__png_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri) +{ + stbi__png p; + p.s = s; + return stbi__do_png(&p, x,y,comp,req_comp, ri); +} + +static int stbi__png_test(stbi__context *s) +{ + int r; + r = stbi__check_png_header(s); + stbi__rewind(s); + return r; +} + +static int stbi__png_info_raw(stbi__png *p, int *x, int *y, int *comp) +{ + if (!stbi__parse_png_file(p, STBI__SCAN_header, 0)) { + stbi__rewind( p->s ); + return 0; + } + if (x) *x = p->s->img_x; + if (y) *y = p->s->img_y; + if (comp) *comp = p->s->img_n; + return 1; +} + +static int stbi__png_info(stbi__context *s, int *x, int *y, int *comp) +{ + stbi__png p; + p.s = s; + return stbi__png_info_raw(&p, x, y, comp); +} + +static int stbi__png_is16(stbi__context *s) +{ + stbi__png p; + p.s = s; + if (!stbi__png_info_raw(&p, NULL, NULL, NULL)) + return 0; + if (p.depth != 16) { + stbi__rewind(p.s); + return 0; + } + return 1; +} +#endif + +// Microsoft/Windows BMP image + +#ifndef STBI_NO_BMP +static int stbi__bmp_test_raw(stbi__context *s) +{ + int r; + int sz; + if (stbi__get8(s) != 'B') return 0; + if (stbi__get8(s) != 'M') return 0; + stbi__get32le(s); // discard filesize + stbi__get16le(s); // discard reserved + stbi__get16le(s); // discard reserved + stbi__get32le(s); // discard data offset + sz = stbi__get32le(s); + r = (sz == 12 || sz == 40 || sz == 56 || sz == 108 || sz == 124); + return r; +} + +static int stbi__bmp_test(stbi__context *s) +{ + int r = stbi__bmp_test_raw(s); + stbi__rewind(s); + return r; +} + + +// returns 0..31 for the highest set bit +static int stbi__high_bit(unsigned int z) +{ + int n=0; + if (z == 0) return -1; + if (z >= 0x10000) n += 16, z >>= 16; + if (z >= 0x00100) n += 8, z >>= 8; + if (z >= 0x00010) n += 4, z >>= 4; + if (z >= 0x00004) n += 2, z >>= 2; + if (z >= 0x00002) n += 1, z >>= 1; + return n; +} + +static int stbi__bitcount(unsigned int a) +{ + a = (a & 0x55555555) + ((a >> 1) & 0x55555555); // max 2 + a = (a & 0x33333333) + ((a >> 2) & 0x33333333); // max 4 + a = (a + (a >> 4)) & 0x0f0f0f0f; // max 8 per 4, now 8 bits + a = (a + (a >> 8)); // max 16 per 8 bits + a = (a + (a >> 16)); // max 32 per 8 bits + return a & 0xff; +} + +// extract an arbitrarily-aligned N-bit value (N=bits) +// from v, and then make it 8-bits long and fractionally +// extend it to full full range. +static int stbi__shiftsigned(int v, int shift, int bits) +{ + static unsigned int mul_table[9] = { + 0, + 0xff/*0b11111111*/, 0x55/*0b01010101*/, 0x49/*0b01001001*/, 0x11/*0b00010001*/, + 0x21/*0b00100001*/, 0x41/*0b01000001*/, 0x81/*0b10000001*/, 0x01/*0b00000001*/, + }; + static unsigned int shift_table[9] = { + 0, 0,0,1,0,2,4,6,0, + }; + if (shift < 0) + v <<= -shift; + else + v >>= shift; + STBI_ASSERT(v >= 0 && v < 256); + v >>= (8-bits); + STBI_ASSERT(bits >= 0 && bits <= 8); + return (int) ((unsigned) v * mul_table[bits]) >> shift_table[bits]; +} + +typedef struct +{ + int bpp, offset, hsz; + unsigned int mr,mg,mb,ma, all_a; +} stbi__bmp_data; + +static void *stbi__bmp_parse_header(stbi__context *s, stbi__bmp_data *info) +{ + int hsz; + if (stbi__get8(s) != 'B' || stbi__get8(s) != 'M') return stbi__errpuc("not BMP", "Corrupt BMP"); + stbi__get32le(s); // discard filesize + stbi__get16le(s); // discard reserved + stbi__get16le(s); // discard reserved + info->offset = stbi__get32le(s); + info->hsz = hsz = stbi__get32le(s); + info->mr = info->mg = info->mb = info->ma = 0; + + if (hsz != 12 && hsz != 40 && hsz != 56 && hsz != 108 && hsz != 124) return stbi__errpuc("unknown BMP", "BMP type not supported: unknown"); + if (hsz == 12) { + s->img_x = stbi__get16le(s); + s->img_y = stbi__get16le(s); + } else { + s->img_x = stbi__get32le(s); + s->img_y = stbi__get32le(s); + } + if (stbi__get16le(s) != 1) return stbi__errpuc("bad BMP", "bad BMP"); + info->bpp = stbi__get16le(s); + if (hsz != 12) { + int compress = stbi__get32le(s); + if (compress == 1 || compress == 2) return stbi__errpuc("BMP RLE", "BMP type not supported: RLE"); + stbi__get32le(s); // discard sizeof + stbi__get32le(s); // discard hres + stbi__get32le(s); // discard vres + stbi__get32le(s); // discard colorsused + stbi__get32le(s); // discard max important + if (hsz == 40 || hsz == 56) { + if (hsz == 56) { + stbi__get32le(s); + stbi__get32le(s); + stbi__get32le(s); + stbi__get32le(s); + } + if (info->bpp == 16 || info->bpp == 32) { + if (compress == 0) { + if (info->bpp == 32) { + info->mr = 0xffu << 16; + info->mg = 0xffu << 8; + info->mb = 0xffu << 0; + info->ma = 0xffu << 24; + info->all_a = 0; // if all_a is 0 at end, then we loaded alpha channel but it was all 0 + } else { + info->mr = 31u << 10; + info->mg = 31u << 5; + info->mb = 31u << 0; + } + } else if (compress == 3) { + info->mr = stbi__get32le(s); + info->mg = stbi__get32le(s); + info->mb = stbi__get32le(s); + // not documented, but generated by photoshop and handled by mspaint + if (info->mr == info->mg && info->mg == info->mb) { + // ?!?!? + return stbi__errpuc("bad BMP", "bad BMP"); + } + } else + return stbi__errpuc("bad BMP", "bad BMP"); + } + } else { + int i; + if (hsz != 108 && hsz != 124) + return stbi__errpuc("bad BMP", "bad BMP"); + info->mr = stbi__get32le(s); + info->mg = stbi__get32le(s); + info->mb = stbi__get32le(s); + info->ma = stbi__get32le(s); + stbi__get32le(s); // discard color space + for (i=0; i < 12; ++i) + stbi__get32le(s); // discard color space parameters + if (hsz == 124) { + stbi__get32le(s); // discard rendering intent + stbi__get32le(s); // discard offset of profile data + stbi__get32le(s); // discard size of profile data + stbi__get32le(s); // discard reserved + } + } + } + return (void *) 1; +} + + +static void *stbi__bmp_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri) +{ + stbi_uc *out; + unsigned int mr=0,mg=0,mb=0,ma=0, all_a; + stbi_uc pal[256][4]; + int psize=0,i,j,width; + int flip_vertically, pad, target; + stbi__bmp_data info; + STBI_NOTUSED(ri); + + info.all_a = 255; + if (stbi__bmp_parse_header(s, &info) == NULL) + return NULL; // error code already set + + flip_vertically = ((int) s->img_y) > 0; + s->img_y = abs((int) s->img_y); + + mr = info.mr; + mg = info.mg; + mb = info.mb; + ma = info.ma; + all_a = info.all_a; + + if (info.hsz == 12) { + if (info.bpp < 24) + psize = (info.offset - 14 - 24) / 3; + } else { + if (info.bpp < 16) + psize = (info.offset - 14 - info.hsz) >> 2; + } + + s->img_n = ma ? 4 : 3; + if (req_comp && req_comp >= 3) // we can directly decode 3 or 4 + target = req_comp; + else + target = s->img_n; // if they want monochrome, we'll post-convert + + // sanity-check size + if (!stbi__mad3sizes_valid(target, s->img_x, s->img_y, 0)) + return stbi__errpuc("too large", "Corrupt BMP"); + + out = (stbi_uc *) stbi__malloc_mad3(target, s->img_x, s->img_y, 0); + if (!out) return stbi__errpuc("outofmem", "Out of memory"); + if (info.bpp < 16) { + int z=0; + if (psize == 0 || psize > 256) { STBI_FREE(out); return stbi__errpuc("invalid", "Corrupt BMP"); } + for (i=0; i < psize; ++i) { + pal[i][2] = stbi__get8(s); + pal[i][1] = stbi__get8(s); + pal[i][0] = stbi__get8(s); + if (info.hsz != 12) stbi__get8(s); + pal[i][3] = 255; + } + stbi__skip(s, info.offset - 14 - info.hsz - psize * (info.hsz == 12 ? 3 : 4)); + if (info.bpp == 1) width = (s->img_x + 7) >> 3; + else if (info.bpp == 4) width = (s->img_x + 1) >> 1; + else if (info.bpp == 8) width = s->img_x; + else { STBI_FREE(out); return stbi__errpuc("bad bpp", "Corrupt BMP"); } + pad = (-width)&3; + if (info.bpp == 1) { + for (j=0; j < (int) s->img_y; ++j) { + int bit_offset = 7, v = stbi__get8(s); + for (i=0; i < (int) s->img_x; ++i) { + int color = (v>>bit_offset)&0x1; + out[z++] = pal[color][0]; + out[z++] = pal[color][1]; + out[z++] = pal[color][2]; + if((--bit_offset) < 0) { + bit_offset = 7; + v = stbi__get8(s); + } + } + stbi__skip(s, pad); + } + } else { + for (j=0; j < (int) s->img_y; ++j) { + for (i=0; i < (int) s->img_x; i += 2) { + int v=stbi__get8(s),v2=0; + if (info.bpp == 4) { + v2 = v & 15; + v >>= 4; + } + out[z++] = pal[v][0]; + out[z++] = pal[v][1]; + out[z++] = pal[v][2]; + if (target == 4) out[z++] = 255; + if (i+1 == (int) s->img_x) break; + v = (info.bpp == 8) ? stbi__get8(s) : v2; + out[z++] = pal[v][0]; + out[z++] = pal[v][1]; + out[z++] = pal[v][2]; + if (target == 4) out[z++] = 255; + } + stbi__skip(s, pad); + } + } + } else { + int rshift=0,gshift=0,bshift=0,ashift=0,rcount=0,gcount=0,bcount=0,acount=0; + int z = 0; + int easy=0; + stbi__skip(s, info.offset - 14 - info.hsz); + if (info.bpp == 24) width = 3 * s->img_x; + else if (info.bpp == 16) width = 2*s->img_x; + else /* bpp = 32 and pad = 0 */ width=0; + pad = (-width) & 3; + if (info.bpp == 24) { + easy = 1; + } else if (info.bpp == 32) { + if (mb == 0xff && mg == 0xff00 && mr == 0x00ff0000 && ma == 0xff000000) + easy = 2; + } + if (!easy) { + if (!mr || !mg || !mb) { STBI_FREE(out); return stbi__errpuc("bad masks", "Corrupt BMP"); } + // right shift amt to put high bit in position #7 + rshift = stbi__high_bit(mr)-7; rcount = stbi__bitcount(mr); + gshift = stbi__high_bit(mg)-7; gcount = stbi__bitcount(mg); + bshift = stbi__high_bit(mb)-7; bcount = stbi__bitcount(mb); + ashift = stbi__high_bit(ma)-7; acount = stbi__bitcount(ma); + } + for (j=0; j < (int) s->img_y; ++j) { + if (easy) { + for (i=0; i < (int) s->img_x; ++i) { + unsigned char a; + out[z+2] = stbi__get8(s); + out[z+1] = stbi__get8(s); + out[z+0] = stbi__get8(s); + z += 3; + a = (easy == 2 ? stbi__get8(s) : 255); + all_a |= a; + if (target == 4) out[z++] = a; + } + } else { + int bpp = info.bpp; + for (i=0; i < (int) s->img_x; ++i) { + stbi__uint32 v = (bpp == 16 ? (stbi__uint32) stbi__get16le(s) : stbi__get32le(s)); + unsigned int a; + out[z++] = STBI__BYTECAST(stbi__shiftsigned(v & mr, rshift, rcount)); + out[z++] = STBI__BYTECAST(stbi__shiftsigned(v & mg, gshift, gcount)); + out[z++] = STBI__BYTECAST(stbi__shiftsigned(v & mb, bshift, bcount)); + a = (ma ? stbi__shiftsigned(v & ma, ashift, acount) : 255); + all_a |= a; + if (target == 4) out[z++] = STBI__BYTECAST(a); + } + } + stbi__skip(s, pad); + } + } + + // if alpha channel is all 0s, replace with all 255s + if (target == 4 && all_a == 0) + for (i=4*s->img_x*s->img_y-1; i >= 0; i -= 4) + out[i] = 255; + + if (flip_vertically) { + stbi_uc t; + for (j=0; j < (int) s->img_y>>1; ++j) { + stbi_uc *p1 = out + j *s->img_x*target; + stbi_uc *p2 = out + (s->img_y-1-j)*s->img_x*target; + for (i=0; i < (int) s->img_x*target; ++i) { + t = p1[i], p1[i] = p2[i], p2[i] = t; + } + } + } + + if (req_comp && req_comp != target) { + out = stbi__convert_format(out, target, req_comp, s->img_x, s->img_y); + if (out == NULL) return out; // stbi__convert_format frees input on failure + } + + *x = s->img_x; + *y = s->img_y; + if (comp) *comp = s->img_n; + return out; +} +#endif + +// Targa Truevision - TGA +// by Jonathan Dummer +#ifndef STBI_NO_TGA +// returns STBI_rgb or whatever, 0 on error +static int stbi__tga_get_comp(int bits_per_pixel, int is_grey, int* is_rgb16) +{ + // only RGB or RGBA (incl. 16bit) or grey allowed + if (is_rgb16) *is_rgb16 = 0; + switch(bits_per_pixel) { + case 8: return STBI_grey; + case 16: if(is_grey) return STBI_grey_alpha; + // fallthrough + case 15: if(is_rgb16) *is_rgb16 = 1; + return STBI_rgb; + case 24: // fallthrough + case 32: return bits_per_pixel/8; + default: return 0; + } +} + +static int stbi__tga_info(stbi__context *s, int *x, int *y, int *comp) +{ + int tga_w, tga_h, tga_comp, tga_image_type, tga_bits_per_pixel, tga_colormap_bpp; + int sz, tga_colormap_type; + stbi__get8(s); // discard Offset + tga_colormap_type = stbi__get8(s); // colormap type + if( tga_colormap_type > 1 ) { + stbi__rewind(s); + return 0; // only RGB or indexed allowed + } + tga_image_type = stbi__get8(s); // image type + if ( tga_colormap_type == 1 ) { // colormapped (paletted) image + if (tga_image_type != 1 && tga_image_type != 9) { + stbi__rewind(s); + return 0; + } + stbi__skip(s,4); // skip index of first colormap entry and number of entries + sz = stbi__get8(s); // check bits per palette color entry + if ( (sz != 8) && (sz != 15) && (sz != 16) && (sz != 24) && (sz != 32) ) { + stbi__rewind(s); + return 0; + } + stbi__skip(s,4); // skip image x and y origin + tga_colormap_bpp = sz; + } else { // "normal" image w/o colormap - only RGB or grey allowed, +/- RLE + if ( (tga_image_type != 2) && (tga_image_type != 3) && (tga_image_type != 10) && (tga_image_type != 11) ) { + stbi__rewind(s); + return 0; // only RGB or grey allowed, +/- RLE + } + stbi__skip(s,9); // skip colormap specification and image x/y origin + tga_colormap_bpp = 0; + } + tga_w = stbi__get16le(s); + if( tga_w < 1 ) { + stbi__rewind(s); + return 0; // test width + } + tga_h = stbi__get16le(s); + if( tga_h < 1 ) { + stbi__rewind(s); + return 0; // test height + } + tga_bits_per_pixel = stbi__get8(s); // bits per pixel + stbi__get8(s); // ignore alpha bits + if (tga_colormap_bpp != 0) { + if((tga_bits_per_pixel != 8) && (tga_bits_per_pixel != 16)) { + // when using a colormap, tga_bits_per_pixel is the size of the indexes + // I don't think anything but 8 or 16bit indexes makes sense + stbi__rewind(s); + return 0; + } + tga_comp = stbi__tga_get_comp(tga_colormap_bpp, 0, NULL); + } else { + tga_comp = stbi__tga_get_comp(tga_bits_per_pixel, (tga_image_type == 3) || (tga_image_type == 11), NULL); + } + if(!tga_comp) { + stbi__rewind(s); + return 0; + } + if (x) *x = tga_w; + if (y) *y = tga_h; + if (comp) *comp = tga_comp; + return 1; // seems to have passed everything +} + +static int stbi__tga_test(stbi__context *s) +{ + int res = 0; + int sz, tga_color_type; + stbi__get8(s); // discard Offset + tga_color_type = stbi__get8(s); // color type + if ( tga_color_type > 1 ) goto errorEnd; // only RGB or indexed allowed + sz = stbi__get8(s); // image type + if ( tga_color_type == 1 ) { // colormapped (paletted) image + if (sz != 1 && sz != 9) goto errorEnd; // colortype 1 demands image type 1 or 9 + stbi__skip(s,4); // skip index of first colormap entry and number of entries + sz = stbi__get8(s); // check bits per palette color entry + if ( (sz != 8) && (sz != 15) && (sz != 16) && (sz != 24) && (sz != 32) ) goto errorEnd; + stbi__skip(s,4); // skip image x and y origin + } else { // "normal" image w/o colormap + if ( (sz != 2) && (sz != 3) && (sz != 10) && (sz != 11) ) goto errorEnd; // only RGB or grey allowed, +/- RLE + stbi__skip(s,9); // skip colormap specification and image x/y origin + } + if ( stbi__get16le(s) < 1 ) goto errorEnd; // test width + if ( stbi__get16le(s) < 1 ) goto errorEnd; // test height + sz = stbi__get8(s); // bits per pixel + if ( (tga_color_type == 1) && (sz != 8) && (sz != 16) ) goto errorEnd; // for colormapped images, bpp is size of an index + if ( (sz != 8) && (sz != 15) && (sz != 16) && (sz != 24) && (sz != 32) ) goto errorEnd; + + res = 1; // if we got this far, everything's good and we can return 1 instead of 0 + +errorEnd: + stbi__rewind(s); + return res; +} + +// read 16bit value and convert to 24bit RGB +static void stbi__tga_read_rgb16(stbi__context *s, stbi_uc* out) +{ + stbi__uint16 px = (stbi__uint16)stbi__get16le(s); + stbi__uint16 fiveBitMask = 31; + // we have 3 channels with 5bits each + int r = (px >> 10) & fiveBitMask; + int g = (px >> 5) & fiveBitMask; + int b = px & fiveBitMask; + // Note that this saves the data in RGB(A) order, so it doesn't need to be swapped later + out[0] = (stbi_uc)((r * 255)/31); + out[1] = (stbi_uc)((g * 255)/31); + out[2] = (stbi_uc)((b * 255)/31); + + // some people claim that the most significant bit might be used for alpha + // (possibly if an alpha-bit is set in the "image descriptor byte") + // but that only made 16bit test images completely translucent.. + // so let's treat all 15 and 16bit TGAs as RGB with no alpha. +} + +static void *stbi__tga_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri) +{ + // read in the TGA header stuff + int tga_offset = stbi__get8(s); + int tga_indexed = stbi__get8(s); + int tga_image_type = stbi__get8(s); + int tga_is_RLE = 0; + int tga_palette_start = stbi__get16le(s); + int tga_palette_len = stbi__get16le(s); + int tga_palette_bits = stbi__get8(s); + int tga_x_origin = stbi__get16le(s); + int tga_y_origin = stbi__get16le(s); + int tga_width = stbi__get16le(s); + int tga_height = stbi__get16le(s); + int tga_bits_per_pixel = stbi__get8(s); + int tga_comp, tga_rgb16=0; + int tga_inverted = stbi__get8(s); + // int tga_alpha_bits = tga_inverted & 15; // the 4 lowest bits - unused (useless?) + // image data + unsigned char *tga_data; + unsigned char *tga_palette = NULL; + int i, j; + unsigned char raw_data[4] = {0}; + int RLE_count = 0; + int RLE_repeating = 0; + int read_next_pixel = 1; + STBI_NOTUSED(ri); + + // do a tiny bit of precessing + if ( tga_image_type >= 8 ) + { + tga_image_type -= 8; + tga_is_RLE = 1; + } + tga_inverted = 1 - ((tga_inverted >> 5) & 1); + + // If I'm paletted, then I'll use the number of bits from the palette + if ( tga_indexed ) tga_comp = stbi__tga_get_comp(tga_palette_bits, 0, &tga_rgb16); + else tga_comp = stbi__tga_get_comp(tga_bits_per_pixel, (tga_image_type == 3), &tga_rgb16); + + if(!tga_comp) // shouldn't really happen, stbi__tga_test() should have ensured basic consistency + return stbi__errpuc("bad format", "Can't find out TGA pixelformat"); + + // tga info + *x = tga_width; + *y = tga_height; + if (comp) *comp = tga_comp; + + if (!stbi__mad3sizes_valid(tga_width, tga_height, tga_comp, 0)) + return stbi__errpuc("too large", "Corrupt TGA"); + + tga_data = (unsigned char*)stbi__malloc_mad3(tga_width, tga_height, tga_comp, 0); + if (!tga_data) return stbi__errpuc("outofmem", "Out of memory"); + + // skip to the data's starting position (offset usually = 0) + stbi__skip(s, tga_offset ); + + if ( !tga_indexed && !tga_is_RLE && !tga_rgb16 ) { + for (i=0; i < tga_height; ++i) { + int row = tga_inverted ? tga_height -i - 1 : i; + stbi_uc *tga_row = tga_data + row*tga_width*tga_comp; + stbi__getn(s, tga_row, tga_width * tga_comp); + } + } else { + // do I need to load a palette? + if ( tga_indexed) + { + // any data to skip? (offset usually = 0) + stbi__skip(s, tga_palette_start ); + // load the palette + tga_palette = (unsigned char*)stbi__malloc_mad2(tga_palette_len, tga_comp, 0); + if (!tga_palette) { + STBI_FREE(tga_data); + return stbi__errpuc("outofmem", "Out of memory"); + } + if (tga_rgb16) { + stbi_uc *pal_entry = tga_palette; + STBI_ASSERT(tga_comp == STBI_rgb); + for (i=0; i < tga_palette_len; ++i) { + stbi__tga_read_rgb16(s, pal_entry); + pal_entry += tga_comp; + } + } else if (!stbi__getn(s, tga_palette, tga_palette_len * tga_comp)) { + STBI_FREE(tga_data); + STBI_FREE(tga_palette); + return stbi__errpuc("bad palette", "Corrupt TGA"); + } + } + // load the data + for (i=0; i < tga_width * tga_height; ++i) + { + // if I'm in RLE mode, do I need to get a RLE stbi__pngchunk? + if ( tga_is_RLE ) + { + if ( RLE_count == 0 ) + { + // yep, get the next byte as a RLE command + int RLE_cmd = stbi__get8(s); + RLE_count = 1 + (RLE_cmd & 127); + RLE_repeating = RLE_cmd >> 7; + read_next_pixel = 1; + } else if ( !RLE_repeating ) + { + read_next_pixel = 1; + } + } else + { + read_next_pixel = 1; + } + // OK, if I need to read a pixel, do it now + if ( read_next_pixel ) + { + // load however much data we did have + if ( tga_indexed ) + { + // read in index, then perform the lookup + int pal_idx = (tga_bits_per_pixel == 8) ? stbi__get8(s) : stbi__get16le(s); + if ( pal_idx >= tga_palette_len ) { + // invalid index + pal_idx = 0; + } + pal_idx *= tga_comp; + for (j = 0; j < tga_comp; ++j) { + raw_data[j] = tga_palette[pal_idx+j]; + } + } else if(tga_rgb16) { + STBI_ASSERT(tga_comp == STBI_rgb); + stbi__tga_read_rgb16(s, raw_data); + } else { + // read in the data raw + for (j = 0; j < tga_comp; ++j) { + raw_data[j] = stbi__get8(s); + } + } + // clear the reading flag for the next pixel + read_next_pixel = 0; + } // end of reading a pixel + + // copy data + for (j = 0; j < tga_comp; ++j) + tga_data[i*tga_comp+j] = raw_data[j]; + + // in case we're in RLE mode, keep counting down + --RLE_count; + } + // do I need to invert the image? + if ( tga_inverted ) + { + for (j = 0; j*2 < tga_height; ++j) + { + int index1 = j * tga_width * tga_comp; + int index2 = (tga_height - 1 - j) * tga_width * tga_comp; + for (i = tga_width * tga_comp; i > 0; --i) + { + unsigned char temp = tga_data[index1]; + tga_data[index1] = tga_data[index2]; + tga_data[index2] = temp; + ++index1; + ++index2; + } + } + } + // clear my palette, if I had one + if ( tga_palette != NULL ) + { + STBI_FREE( tga_palette ); + } + } + + // swap RGB - if the source data was RGB16, it already is in the right order + if (tga_comp >= 3 && !tga_rgb16) + { + unsigned char* tga_pixel = tga_data; + for (i=0; i < tga_width * tga_height; ++i) + { + unsigned char temp = tga_pixel[0]; + tga_pixel[0] = tga_pixel[2]; + tga_pixel[2] = temp; + tga_pixel += tga_comp; + } + } + + // convert to target component count + if (req_comp && req_comp != tga_comp) + tga_data = stbi__convert_format(tga_data, tga_comp, req_comp, tga_width, tga_height); + + // the things I do to get rid of an error message, and yet keep + // Microsoft's C compilers happy... [8^( + tga_palette_start = tga_palette_len = tga_palette_bits = + tga_x_origin = tga_y_origin = 0; + // OK, done + return tga_data; +} +#endif + +// ************************************************************************************************* +// Photoshop PSD loader -- PD by Thatcher Ulrich, integration by Nicolas Schulz, tweaked by STB + +#ifndef STBI_NO_PSD +static int stbi__psd_test(stbi__context *s) +{ + int r = (stbi__get32be(s) == 0x38425053); + stbi__rewind(s); + return r; +} + +static int stbi__psd_decode_rle(stbi__context *s, stbi_uc *p, int pixelCount) +{ + int count, nleft, len; + + count = 0; + while ((nleft = pixelCount - count) > 0) { + len = stbi__get8(s); + if (len == 128) { + // No-op. + } else if (len < 128) { + // Copy next len+1 bytes literally. + len++; + if (len > nleft) return 0; // corrupt data + count += len; + while (len) { + *p = stbi__get8(s); + p += 4; + len--; + } + } else if (len > 128) { + stbi_uc val; + // Next -len+1 bytes in the dest are replicated from next source byte. + // (Interpret len as a negative 8-bit int.) + len = 257 - len; + if (len > nleft) return 0; // corrupt data + val = stbi__get8(s); + count += len; + while (len) { + *p = val; + p += 4; + len--; + } + } + } + + return 1; +} + +static void *stbi__psd_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri, int bpc) +{ + int pixelCount; + int channelCount, compression; + int channel, i; + int bitdepth; + int w,h; + stbi_uc *out; + STBI_NOTUSED(ri); + + // Check identifier + if (stbi__get32be(s) != 0x38425053) // "8BPS" + return stbi__errpuc("not PSD", "Corrupt PSD image"); + + // Check file type version. + if (stbi__get16be(s) != 1) + return stbi__errpuc("wrong version", "Unsupported version of PSD image"); + + // Skip 6 reserved bytes. + stbi__skip(s, 6 ); + + // Read the number of channels (R, G, B, A, etc). + channelCount = stbi__get16be(s); + if (channelCount < 0 || channelCount > 16) + return stbi__errpuc("wrong channel count", "Unsupported number of channels in PSD image"); + + // Read the rows and columns of the image. + h = stbi__get32be(s); + w = stbi__get32be(s); + + // Make sure the depth is 8 bits. + bitdepth = stbi__get16be(s); + if (bitdepth != 8 && bitdepth != 16) + return stbi__errpuc("unsupported bit depth", "PSD bit depth is not 8 or 16 bit"); + + // Make sure the color mode is RGB. + // Valid options are: + // 0: Bitmap + // 1: Grayscale + // 2: Indexed color + // 3: RGB color + // 4: CMYK color + // 7: Multichannel + // 8: Duotone + // 9: Lab color + if (stbi__get16be(s) != 3) + return stbi__errpuc("wrong color format", "PSD is not in RGB color format"); + + // Skip the Mode Data. (It's the palette for indexed color; other info for other modes.) + stbi__skip(s,stbi__get32be(s) ); + + // Skip the image resources. (resolution, pen tool paths, etc) + stbi__skip(s, stbi__get32be(s) ); + + // Skip the reserved data. + stbi__skip(s, stbi__get32be(s) ); + + // Find out if the data is compressed. + // Known values: + // 0: no compression + // 1: RLE compressed + compression = stbi__get16be(s); + if (compression > 1) + return stbi__errpuc("bad compression", "PSD has an unknown compression format"); + + // Check size + if (!stbi__mad3sizes_valid(4, w, h, 0)) + return stbi__errpuc("too large", "Corrupt PSD"); + + // Create the destination image. + + if (!compression && bitdepth == 16 && bpc == 16) { + out = (stbi_uc *) stbi__malloc_mad3(8, w, h, 0); + ri->bits_per_channel = 16; + } else + out = (stbi_uc *) stbi__malloc(4 * w*h); + + if (!out) return stbi__errpuc("outofmem", "Out of memory"); + pixelCount = w*h; + + // Initialize the data to zero. + //memset( out, 0, pixelCount * 4 ); + + // Finally, the image data. + if (compression) { + // RLE as used by .PSD and .TIFF + // Loop until you get the number of unpacked bytes you are expecting: + // Read the next source byte into n. + // If n is between 0 and 127 inclusive, copy the next n+1 bytes literally. + // Else if n is between -127 and -1 inclusive, copy the next byte -n+1 times. + // Else if n is 128, noop. + // Endloop + + // The RLE-compressed data is preceeded by a 2-byte data count for each row in the data, + // which we're going to just skip. + stbi__skip(s, h * channelCount * 2 ); + + // Read the RLE data by channel. + for (channel = 0; channel < 4; channel++) { + stbi_uc *p; + + p = out+channel; + if (channel >= channelCount) { + // Fill this channel with default data. + for (i = 0; i < pixelCount; i++, p += 4) + *p = (channel == 3 ? 255 : 0); + } else { + // Read the RLE data. + if (!stbi__psd_decode_rle(s, p, pixelCount)) { + STBI_FREE(out); + return stbi__errpuc("corrupt", "bad RLE data"); + } + } + } + + } else { + // We're at the raw image data. It's each channel in order (Red, Green, Blue, Alpha, ...) + // where each channel consists of an 8-bit (or 16-bit) value for each pixel in the image. + + // Read the data by channel. + for (channel = 0; channel < 4; channel++) { + if (channel >= channelCount) { + // Fill this channel with default data. + if (bitdepth == 16 && bpc == 16) { + stbi__uint16 *q = ((stbi__uint16 *) out) + channel; + stbi__uint16 val = channel == 3 ? 65535 : 0; + for (i = 0; i < pixelCount; i++, q += 4) + *q = val; + } else { + stbi_uc *p = out+channel; + stbi_uc val = channel == 3 ? 255 : 0; + for (i = 0; i < pixelCount; i++, p += 4) + *p = val; + } + } else { + if (ri->bits_per_channel == 16) { // output bpc + stbi__uint16 *q = ((stbi__uint16 *) out) + channel; + for (i = 0; i < pixelCount; i++, q += 4) + *q = (stbi__uint16) stbi__get16be(s); + } else { + stbi_uc *p = out+channel; + if (bitdepth == 16) { // input bpc + for (i = 0; i < pixelCount; i++, p += 4) + *p = (stbi_uc) (stbi__get16be(s) >> 8); + } else { + for (i = 0; i < pixelCount; i++, p += 4) + *p = stbi__get8(s); + } + } + } + } + } + + // remove weird white matte from PSD + if (channelCount >= 4) { + if (ri->bits_per_channel == 16) { + for (i=0; i < w*h; ++i) { + stbi__uint16 *pixel = (stbi__uint16 *) out + 4*i; + if (pixel[3] != 0 && pixel[3] != 65535) { + float a = pixel[3] / 65535.0f; + float ra = 1.0f / a; + float inv_a = 65535.0f * (1 - ra); + pixel[0] = (stbi__uint16) (pixel[0]*ra + inv_a); + pixel[1] = (stbi__uint16) (pixel[1]*ra + inv_a); + pixel[2] = (stbi__uint16) (pixel[2]*ra + inv_a); + } + } + } else { + for (i=0; i < w*h; ++i) { + unsigned char *pixel = out + 4*i; + if (pixel[3] != 0 && pixel[3] != 255) { + float a = pixel[3] / 255.0f; + float ra = 1.0f / a; + float inv_a = 255.0f * (1 - ra); + pixel[0] = (unsigned char) (pixel[0]*ra + inv_a); + pixel[1] = (unsigned char) (pixel[1]*ra + inv_a); + pixel[2] = (unsigned char) (pixel[2]*ra + inv_a); + } + } + } + } + + // convert to desired output format + if (req_comp && req_comp != 4) { + if (ri->bits_per_channel == 16) + out = (stbi_uc *) stbi__convert_format16((stbi__uint16 *) out, 4, req_comp, w, h); + else + out = stbi__convert_format(out, 4, req_comp, w, h); + if (out == NULL) return out; // stbi__convert_format frees input on failure + } + + if (comp) *comp = 4; + *y = h; + *x = w; + + return out; +} +#endif + +// ************************************************************************************************* +// Softimage PIC loader +// by Tom Seddon +// +// See http://softimage.wiki.softimage.com/index.php/INFO:_PIC_file_format +// See http://ozviz.wasp.uwa.edu.au/~pbourke/dataformats/softimagepic/ + +#ifndef STBI_NO_PIC +static int stbi__pic_is4(stbi__context *s,const char *str) +{ + int i; + for (i=0; i<4; ++i) + if (stbi__get8(s) != (stbi_uc)str[i]) + return 0; + + return 1; +} + +static int stbi__pic_test_core(stbi__context *s) +{ + int i; + + if (!stbi__pic_is4(s,"\x53\x80\xF6\x34")) + return 0; + + for(i=0;i<84;++i) + stbi__get8(s); + + if (!stbi__pic_is4(s,"PICT")) + return 0; + + return 1; +} + +typedef struct +{ + stbi_uc size,type,channel; +} stbi__pic_packet; + +static stbi_uc *stbi__readval(stbi__context *s, int channel, stbi_uc *dest) +{ + int mask=0x80, i; + + for (i=0; i<4; ++i, mask>>=1) { + if (channel & mask) { + if (stbi__at_eof(s)) return stbi__errpuc("bad file","PIC file too short"); + dest[i]=stbi__get8(s); + } + } + + return dest; +} + +static void stbi__copyval(int channel,stbi_uc *dest,const stbi_uc *src) +{ + int mask=0x80,i; + + for (i=0;i<4; ++i, mask>>=1) + if (channel&mask) + dest[i]=src[i]; +} + +static stbi_uc *stbi__pic_load_core(stbi__context *s,int width,int height,int *comp, stbi_uc *result) +{ + int act_comp=0,num_packets=0,y,chained; + stbi__pic_packet packets[10]; + + // this will (should...) cater for even some bizarre stuff like having data + // for the same channel in multiple packets. + do { + stbi__pic_packet *packet; + + if (num_packets==sizeof(packets)/sizeof(packets[0])) + return stbi__errpuc("bad format","too many packets"); + + packet = &packets[num_packets++]; + + chained = stbi__get8(s); + packet->size = stbi__get8(s); + packet->type = stbi__get8(s); + packet->channel = stbi__get8(s); + + act_comp |= packet->channel; + + if (stbi__at_eof(s)) return stbi__errpuc("bad file","file too short (reading packets)"); + if (packet->size != 8) return stbi__errpuc("bad format","packet isn't 8bpp"); + } while (chained); + + *comp = (act_comp & 0x10 ? 4 : 3); // has alpha channel? + + for(y=0; ytype) { + default: + return stbi__errpuc("bad format","packet has bad compression type"); + + case 0: {//uncompressed + int x; + + for(x=0;xchannel,dest)) + return 0; + break; + } + + case 1://Pure RLE + { + int left=width, i; + + while (left>0) { + stbi_uc count,value[4]; + + count=stbi__get8(s); + if (stbi__at_eof(s)) return stbi__errpuc("bad file","file too short (pure read count)"); + + if (count > left) + count = (stbi_uc) left; + + if (!stbi__readval(s,packet->channel,value)) return 0; + + for(i=0; ichannel,dest,value); + left -= count; + } + } + break; + + case 2: {//Mixed RLE + int left=width; + while (left>0) { + int count = stbi__get8(s), i; + if (stbi__at_eof(s)) return stbi__errpuc("bad file","file too short (mixed read count)"); + + if (count >= 128) { // Repeated + stbi_uc value[4]; + + if (count==128) + count = stbi__get16be(s); + else + count -= 127; + if (count > left) + return stbi__errpuc("bad file","scanline overrun"); + + if (!stbi__readval(s,packet->channel,value)) + return 0; + + for(i=0;ichannel,dest,value); + } else { // Raw + ++count; + if (count>left) return stbi__errpuc("bad file","scanline overrun"); + + for(i=0;ichannel,dest)) + return 0; + } + left-=count; + } + break; + } + } + } + } + + return result; +} + +static void *stbi__pic_load(stbi__context *s,int *px,int *py,int *comp,int req_comp, stbi__result_info *ri) +{ + stbi_uc *result; + int i, x,y, internal_comp; + STBI_NOTUSED(ri); + + if (!comp) comp = &internal_comp; + + for (i=0; i<92; ++i) + stbi__get8(s); + + x = stbi__get16be(s); + y = stbi__get16be(s); + if (stbi__at_eof(s)) return stbi__errpuc("bad file","file too short (pic header)"); + if (!stbi__mad3sizes_valid(x, y, 4, 0)) return stbi__errpuc("too large", "PIC image too large to decode"); + + stbi__get32be(s); //skip `ratio' + stbi__get16be(s); //skip `fields' + stbi__get16be(s); //skip `pad' + + // intermediate buffer is RGBA + result = (stbi_uc *) stbi__malloc_mad3(x, y, 4, 0); + memset(result, 0xff, x*y*4); + + if (!stbi__pic_load_core(s,x,y,comp, result)) { + STBI_FREE(result); + result=0; + } + *px = x; + *py = y; + if (req_comp == 0) req_comp = *comp; + result=stbi__convert_format(result,4,req_comp,x,y); + + return result; +} + +static int stbi__pic_test(stbi__context *s) +{ + int r = stbi__pic_test_core(s); + stbi__rewind(s); + return r; +} +#endif + +// ************************************************************************************************* +// GIF loader -- public domain by Jean-Marc Lienher -- simplified/shrunk by stb + +#ifndef STBI_NO_GIF +typedef struct +{ + stbi__int16 prefix; + stbi_uc first; + stbi_uc suffix; +} stbi__gif_lzw; + +typedef struct +{ + int w,h; + stbi_uc *out; // output buffer (always 4 components) + stbi_uc *background; // The current "background" as far as a gif is concerned + stbi_uc *history; + int flags, bgindex, ratio, transparent, eflags; + stbi_uc pal[256][4]; + stbi_uc lpal[256][4]; + stbi__gif_lzw codes[8192]; + stbi_uc *color_table; + int parse, step; + int lflags; + int start_x, start_y; + int max_x, max_y; + int cur_x, cur_y; + int line_size; + int delay; +} stbi__gif; + +static int stbi__gif_test_raw(stbi__context *s) +{ + int sz; + if (stbi__get8(s) != 'G' || stbi__get8(s) != 'I' || stbi__get8(s) != 'F' || stbi__get8(s) != '8') return 0; + sz = stbi__get8(s); + if (sz != '9' && sz != '7') return 0; + if (stbi__get8(s) != 'a') return 0; + return 1; +} + +static int stbi__gif_test(stbi__context *s) +{ + int r = stbi__gif_test_raw(s); + stbi__rewind(s); + return r; +} + +static void stbi__gif_parse_colortable(stbi__context *s, stbi_uc pal[256][4], int num_entries, int transp) +{ + int i; + for (i=0; i < num_entries; ++i) { + pal[i][2] = stbi__get8(s); + pal[i][1] = stbi__get8(s); + pal[i][0] = stbi__get8(s); + pal[i][3] = transp == i ? 0 : 255; + } +} + +static int stbi__gif_header(stbi__context *s, stbi__gif *g, int *comp, int is_info) +{ + stbi_uc version; + if (stbi__get8(s) != 'G' || stbi__get8(s) != 'I' || stbi__get8(s) != 'F' || stbi__get8(s) != '8') + return stbi__err("not GIF", "Corrupt GIF"); + + version = stbi__get8(s); + if (version != '7' && version != '9') return stbi__err("not GIF", "Corrupt GIF"); + if (stbi__get8(s) != 'a') return stbi__err("not GIF", "Corrupt GIF"); + + stbi__g_failure_reason = ""; + g->w = stbi__get16le(s); + g->h = stbi__get16le(s); + g->flags = stbi__get8(s); + g->bgindex = stbi__get8(s); + g->ratio = stbi__get8(s); + g->transparent = -1; + + if (comp != 0) *comp = 4; // can't actually tell whether it's 3 or 4 until we parse the comments + + if (is_info) return 1; + + if (g->flags & 0x80) + stbi__gif_parse_colortable(s,g->pal, 2 << (g->flags & 7), -1); + + return 1; +} + +static int stbi__gif_info_raw(stbi__context *s, int *x, int *y, int *comp) +{ + stbi__gif* g = (stbi__gif*) stbi__malloc(sizeof(stbi__gif)); + if (!stbi__gif_header(s, g, comp, 1)) { + STBI_FREE(g); + stbi__rewind( s ); + return 0; + } + if (x) *x = g->w; + if (y) *y = g->h; + STBI_FREE(g); + return 1; +} + +static void stbi__out_gif_code(stbi__gif *g, stbi__uint16 code) +{ + stbi_uc *p, *c; + int idx; + + // recurse to decode the prefixes, since the linked-list is backwards, + // and working backwards through an interleaved image would be nasty + if (g->codes[code].prefix >= 0) + stbi__out_gif_code(g, g->codes[code].prefix); + + if (g->cur_y >= g->max_y) return; + + idx = g->cur_x + g->cur_y; + p = &g->out[idx]; + g->history[idx / 4] = 1; + + c = &g->color_table[g->codes[code].suffix * 4]; + if (c[3] > 128) { // don't render transparent pixels; + p[0] = c[2]; + p[1] = c[1]; + p[2] = c[0]; + p[3] = c[3]; + } + g->cur_x += 4; + + if (g->cur_x >= g->max_x) { + g->cur_x = g->start_x; + g->cur_y += g->step; + + while (g->cur_y >= g->max_y && g->parse > 0) { + g->step = (1 << g->parse) * g->line_size; + g->cur_y = g->start_y + (g->step >> 1); + --g->parse; + } + } +} + +static stbi_uc *stbi__process_gif_raster(stbi__context *s, stbi__gif *g) +{ + stbi_uc lzw_cs; + stbi__int32 len, init_code; + stbi__uint32 first; + stbi__int32 codesize, codemask, avail, oldcode, bits, valid_bits, clear; + stbi__gif_lzw *p; + + lzw_cs = stbi__get8(s); + if (lzw_cs > 12) return NULL; + clear = 1 << lzw_cs; + first = 1; + codesize = lzw_cs + 1; + codemask = (1 << codesize) - 1; + bits = 0; + valid_bits = 0; + for (init_code = 0; init_code < clear; init_code++) { + g->codes[init_code].prefix = -1; + g->codes[init_code].first = (stbi_uc) init_code; + g->codes[init_code].suffix = (stbi_uc) init_code; + } + + // support no starting clear code + avail = clear+2; + oldcode = -1; + + len = 0; + for(;;) { + if (valid_bits < codesize) { + if (len == 0) { + len = stbi__get8(s); // start new block + if (len == 0) + return g->out; + } + --len; + bits |= (stbi__int32) stbi__get8(s) << valid_bits; + valid_bits += 8; + } else { + stbi__int32 code = bits & codemask; + bits >>= codesize; + valid_bits -= codesize; + // @OPTIMIZE: is there some way we can accelerate the non-clear path? + if (code == clear) { // clear code + codesize = lzw_cs + 1; + codemask = (1 << codesize) - 1; + avail = clear + 2; + oldcode = -1; + first = 0; + } else if (code == clear + 1) { // end of stream code + stbi__skip(s, len); + while ((len = stbi__get8(s)) > 0) + stbi__skip(s,len); + return g->out; + } else if (code <= avail) { + if (first) { + return stbi__errpuc("no clear code", "Corrupt GIF"); + } + + if (oldcode >= 0) { + p = &g->codes[avail++]; + if (avail > 8192) { + return stbi__errpuc("too many codes", "Corrupt GIF"); + } + + p->prefix = (stbi__int16) oldcode; + p->first = g->codes[oldcode].first; + p->suffix = (code == avail) ? p->first : g->codes[code].first; + } else if (code == avail) + return stbi__errpuc("illegal code in raster", "Corrupt GIF"); + + stbi__out_gif_code(g, (stbi__uint16) code); + + if ((avail & codemask) == 0 && avail <= 0x0FFF) { + codesize++; + codemask = (1 << codesize) - 1; + } + + oldcode = code; + } else { + return stbi__errpuc("illegal code in raster", "Corrupt GIF"); + } + } + } +} + +// this function is designed to support animated gifs, although stb_image doesn't support it +// two back is the image from two frames ago, used for a very specific disposal format +static stbi_uc *stbi__gif_load_next(stbi__context *s, stbi__gif *g, int *comp, int req_comp, stbi_uc *two_back) +{ + int dispose; + int first_frame; + int pi; + int pcount; + + // on first frame, any non-written pixels get the background colour (non-transparent) + first_frame = 0; + if (g->out == 0) { + if (!stbi__gif_header(s, g, comp,0)) return 0; // stbi__g_failure_reason set by stbi__gif_header + g->out = (stbi_uc *) stbi__malloc(4 * g->w * g->h); + g->background = (stbi_uc *) stbi__malloc(4 * g->w * g->h); + g->history = (stbi_uc *) stbi__malloc(g->w * g->h); + if (g->out == 0) return stbi__errpuc("outofmem", "Out of memory"); + + // image is treated as "tranparent" at the start - ie, nothing overwrites the current background; + // background colour is only used for pixels that are not rendered first frame, after that "background" + // color refers to teh color that was there the previous frame. + memset( g->out, 0x00, 4 * g->w * g->h ); + memset( g->background, 0x00, 4 * g->w * g->h ); // state of the background (starts transparent) + memset( g->history, 0x00, g->w * g->h ); // pixels that were affected previous frame + first_frame = 1; + } else { + // second frame - how do we dispoase of the previous one? + dispose = (g->eflags & 0x1C) >> 2; + pcount = g->w * g->h; + + if ((dispose == 3) && (two_back == 0)) { + dispose = 2; // if I don't have an image to revert back to, default to the old background + } + + if (dispose == 3) { // use previous graphic + for (pi = 0; pi < pcount; ++pi) { + if (g->history[pi]) { + memcpy( &g->out[pi * 4], &two_back[pi * 4], 4 ); + } + } + } else if (dispose == 2) { + // restore what was changed last frame to background before that frame; + for (pi = 0; pi < pcount; ++pi) { + if (g->history[pi]) { + memcpy( &g->out[pi * 4], &g->background[pi * 4], 4 ); + } + } + } else { + // This is a non-disposal case eithe way, so just + // leave the pixels as is, and they will become the new background + // 1: do not dispose + // 0: not specified. + } + + // background is what out is after the undoing of the previou frame; + memcpy( g->background, g->out, 4 * g->w * g->h ); + } + + // clear my history; + memset( g->history, 0x00, g->w * g->h ); // pixels that were affected previous frame + + for (;;) { + int tag = stbi__get8(s); + switch (tag) { + case 0x2C: /* Image Descriptor */ + { + stbi__int32 x, y, w, h; + stbi_uc *o; + + x = stbi__get16le(s); + y = stbi__get16le(s); + w = stbi__get16le(s); + h = stbi__get16le(s); + if (((x + w) > (g->w)) || ((y + h) > (g->h))) + return stbi__errpuc("bad Image Descriptor", "Corrupt GIF"); + + g->line_size = g->w * 4; + g->start_x = x * 4; + g->start_y = y * g->line_size; + g->max_x = g->start_x + w * 4; + g->max_y = g->start_y + h * g->line_size; + g->cur_x = g->start_x; + g->cur_y = g->start_y; + + g->lflags = stbi__get8(s); + + if (g->lflags & 0x40) { + g->step = 8 * g->line_size; // first interlaced spacing + g->parse = 3; + } else { + g->step = g->line_size; + g->parse = 0; + } + + if (g->lflags & 0x80) { + stbi__gif_parse_colortable(s,g->lpal, 2 << (g->lflags & 7), g->eflags & 0x01 ? g->transparent : -1); + g->color_table = (stbi_uc *) g->lpal; + } else if (g->flags & 0x80) { + g->color_table = (stbi_uc *) g->pal; + } else + return stbi__errpuc("missing color table", "Corrupt GIF"); + + o = stbi__process_gif_raster(s, g); + if (o == NULL) return NULL; + + // if this was the first frame, + pcount = g->w * g->h; + if (first_frame && (g->bgindex > 0)) { + // if first frame, any pixel not drawn to gets the background color + for (pi = 0; pi < pcount; ++pi) { + if (g->history[pi] == 0) { + g->pal[g->bgindex][3] = 255; // just in case it was made transparent, undo that; It will be reset next frame if need be; + memcpy( &g->out[pi * 4], &g->pal[g->bgindex], 4 ); + } + } + } + + return o; + } + + case 0x21: // Comment Extension. + { + int len; + int ext = stbi__get8(s); + if (ext == 0xF9) { // Graphic Control Extension. + len = stbi__get8(s); + if (len == 4) { + g->eflags = stbi__get8(s); + g->delay = 10 * stbi__get16le(s); // delay - 1/100th of a second, saving as 1/1000ths. + + // unset old transparent + if (g->transparent >= 0) { + g->pal[g->transparent][3] = 255; + } + if (g->eflags & 0x01) { + g->transparent = stbi__get8(s); + if (g->transparent >= 0) { + g->pal[g->transparent][3] = 0; + } + } else { + // don't need transparent + stbi__skip(s, 1); + g->transparent = -1; + } + } else { + stbi__skip(s, len); + break; + } + } + while ((len = stbi__get8(s)) != 0) { + stbi__skip(s, len); + } + break; + } + + case 0x3B: // gif stream termination code + return (stbi_uc *) s; // using '1' causes warning on some compilers + + default: + return stbi__errpuc("unknown code", "Corrupt GIF"); + } + } +} + +static void *stbi__load_gif_main(stbi__context *s, int **delays, int *x, int *y, int *z, int *comp, int req_comp) +{ + if (stbi__gif_test(s)) { + int layers = 0; + stbi_uc *u = 0; + stbi_uc *out = 0; + stbi_uc *two_back = 0; + stbi__gif g; + int stride; + memset(&g, 0, sizeof(g)); + if (delays) { + *delays = 0; + } + + do { + u = stbi__gif_load_next(s, &g, comp, req_comp, two_back); + if (u == (stbi_uc *) s) u = 0; // end of animated gif marker + + if (u) { + *x = g.w; + *y = g.h; + ++layers; + stride = g.w * g.h * 4; + + if (out) { + out = (stbi_uc*) STBI_REALLOC( out, layers * stride ); + if (delays) { + *delays = (int*) STBI_REALLOC( *delays, sizeof(int) * layers ); + } + } else { + out = (stbi_uc*)stbi__malloc( layers * stride ); + if (delays) { + *delays = (int*) stbi__malloc( layers * sizeof(int) ); + } + } + memcpy( out + ((layers - 1) * stride), u, stride ); + if (layers >= 2) { + two_back = out - 2 * stride; + } + + if (delays) { + (*delays)[layers - 1U] = g.delay; + } + } + } while (u != 0); + + // free temp buffer; + STBI_FREE(g.out); + STBI_FREE(g.history); + STBI_FREE(g.background); + + // do the final conversion after loading everything; + if (req_comp && req_comp != 4) + out = stbi__convert_format(out, 4, req_comp, layers * g.w, g.h); + + *z = layers; + return out; + } else { + return stbi__errpuc("not GIF", "Image was not as a gif type."); + } +} + +static void *stbi__gif_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri) +{ + stbi_uc *u = 0; + stbi__gif g; + memset(&g, 0, sizeof(g)); + + u = stbi__gif_load_next(s, &g, comp, req_comp, 0); + if (u == (stbi_uc *) s) u = 0; // end of animated gif marker + if (u) { + *x = g.w; + *y = g.h; + + // moved conversion to after successful load so that the same + // can be done for multiple frames. + if (req_comp && req_comp != 4) + u = stbi__convert_format(u, 4, req_comp, g.w, g.h); + } + + // free buffers needed for multiple frame loading; + STBI_FREE(g.history); + STBI_FREE(g.background); + + return u; +} + +static int stbi__gif_info(stbi__context *s, int *x, int *y, int *comp) +{ + return stbi__gif_info_raw(s,x,y,comp); +} +#endif + +// ************************************************************************************************* +// Radiance RGBE HDR loader +// originally by Nicolas Schulz +#ifndef STBI_NO_HDR +static int stbi__hdr_test_core(stbi__context *s, const char *signature) +{ + int i; + for (i=0; signature[i]; ++i) + if (stbi__get8(s) != signature[i]) + return 0; + stbi__rewind(s); + return 1; +} + +static int stbi__hdr_test(stbi__context* s) +{ + int r = stbi__hdr_test_core(s, "#?RADIANCE\n"); + stbi__rewind(s); + if(!r) { + r = stbi__hdr_test_core(s, "#?RGBE\n"); + stbi__rewind(s); + } + return r; +} + +#define STBI__HDR_BUFLEN 1024 +static char *stbi__hdr_gettoken(stbi__context *z, char *buffer) +{ + int len=0; + char c = '\0'; + + c = (char) stbi__get8(z); + + while (!stbi__at_eof(z) && c != '\n') { + buffer[len++] = c; + if (len == STBI__HDR_BUFLEN-1) { + // flush to end of line + while (!stbi__at_eof(z) && stbi__get8(z) != '\n') + ; + break; + } + c = (char) stbi__get8(z); + } + + buffer[len] = 0; + return buffer; +} + +static void stbi__hdr_convert(float *output, stbi_uc *input, int req_comp) +{ + if ( input[3] != 0 ) { + float f1; + // Exponent + f1 = (float) ldexp(1.0f, input[3] - (int)(128 + 8)); + if (req_comp <= 2) + output[0] = (input[0] + input[1] + input[2]) * f1 / 3; + else { + output[0] = input[0] * f1; + output[1] = input[1] * f1; + output[2] = input[2] * f1; + } + if (req_comp == 2) output[1] = 1; + if (req_comp == 4) output[3] = 1; + } else { + switch (req_comp) { + case 4: output[3] = 1; /* fallthrough */ + case 3: output[0] = output[1] = output[2] = 0; + break; + case 2: output[1] = 1; /* fallthrough */ + case 1: output[0] = 0; + break; + } + } +} + +static float *stbi__hdr_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri) +{ + char buffer[STBI__HDR_BUFLEN]; + char *token; + int valid = 0; + int width, height; + stbi_uc *scanline; + float *hdr_data; + int len; + unsigned char count, value; + int i, j, k, c1,c2, z; + const char *headerToken; + STBI_NOTUSED(ri); + + // Check identifier + headerToken = stbi__hdr_gettoken(s,buffer); + if (strcmp(headerToken, "#?RADIANCE") != 0 && strcmp(headerToken, "#?RGBE") != 0) + return stbi__errpf("not HDR", "Corrupt HDR image"); + + // Parse header + for(;;) { + token = stbi__hdr_gettoken(s,buffer); + if (token[0] == 0) break; + if (strcmp(token, "FORMAT=32-bit_rle_rgbe") == 0) valid = 1; + } + + if (!valid) return stbi__errpf("unsupported format", "Unsupported HDR format"); + + // Parse width and height + // can't use sscanf() if we're not using stdio! + token = stbi__hdr_gettoken(s,buffer); + if (strncmp(token, "-Y ", 3)) return stbi__errpf("unsupported data layout", "Unsupported HDR format"); + token += 3; + height = (int) strtol(token, &token, 10); + while (*token == ' ') ++token; + if (strncmp(token, "+X ", 3)) return stbi__errpf("unsupported data layout", "Unsupported HDR format"); + token += 3; + width = (int) strtol(token, NULL, 10); + + *x = width; + *y = height; + + if (comp) *comp = 3; + if (req_comp == 0) req_comp = 3; + + if (!stbi__mad4sizes_valid(width, height, req_comp, sizeof(float), 0)) + return stbi__errpf("too large", "HDR image is too large"); + + // Read data + hdr_data = (float *) stbi__malloc_mad4(width, height, req_comp, sizeof(float), 0); + if (!hdr_data) + return stbi__errpf("outofmem", "Out of memory"); + + // Load image data + // image data is stored as some number of sca + if ( width < 8 || width >= 32768) { + // Read flat data + for (j=0; j < height; ++j) { + for (i=0; i < width; ++i) { + stbi_uc rgbe[4]; + main_decode_loop: + stbi__getn(s, rgbe, 4); + stbi__hdr_convert(hdr_data + j * width * req_comp + i * req_comp, rgbe, req_comp); + } + } + } else { + // Read RLE-encoded data + scanline = NULL; + + for (j = 0; j < height; ++j) { + c1 = stbi__get8(s); + c2 = stbi__get8(s); + len = stbi__get8(s); + if (c1 != 2 || c2 != 2 || (len & 0x80)) { + // not run-length encoded, so we have to actually use THIS data as a decoded + // pixel (note this can't be a valid pixel--one of RGB must be >= 128) + stbi_uc rgbe[4]; + rgbe[0] = (stbi_uc) c1; + rgbe[1] = (stbi_uc) c2; + rgbe[2] = (stbi_uc) len; + rgbe[3] = (stbi_uc) stbi__get8(s); + stbi__hdr_convert(hdr_data, rgbe, req_comp); + i = 1; + j = 0; + STBI_FREE(scanline); + goto main_decode_loop; // yes, this makes no sense + } + len <<= 8; + len |= stbi__get8(s); + if (len != width) { STBI_FREE(hdr_data); STBI_FREE(scanline); return stbi__errpf("invalid decoded scanline length", "corrupt HDR"); } + if (scanline == NULL) { + scanline = (stbi_uc *) stbi__malloc_mad2(width, 4, 0); + if (!scanline) { + STBI_FREE(hdr_data); + return stbi__errpf("outofmem", "Out of memory"); + } + } + + for (k = 0; k < 4; ++k) { + int nleft; + i = 0; + while ((nleft = width - i) > 0) { + count = stbi__get8(s); + if (count > 128) { + // Run + value = stbi__get8(s); + count -= 128; + if (count > nleft) { STBI_FREE(hdr_data); STBI_FREE(scanline); return stbi__errpf("corrupt", "bad RLE data in HDR"); } + for (z = 0; z < count; ++z) + scanline[i++ * 4 + k] = value; + } else { + // Dump + if (count > nleft) { STBI_FREE(hdr_data); STBI_FREE(scanline); return stbi__errpf("corrupt", "bad RLE data in HDR"); } + for (z = 0; z < count; ++z) + scanline[i++ * 4 + k] = stbi__get8(s); + } + } + } + for (i=0; i < width; ++i) + stbi__hdr_convert(hdr_data+(j*width + i)*req_comp, scanline + i*4, req_comp); + } + if (scanline) + STBI_FREE(scanline); + } + + return hdr_data; +} + +static int stbi__hdr_info(stbi__context *s, int *x, int *y, int *comp) +{ + char buffer[STBI__HDR_BUFLEN]; + char *token; + int valid = 0; + int dummy; + + if (!x) x = &dummy; + if (!y) y = &dummy; + if (!comp) comp = &dummy; + + if (stbi__hdr_test(s) == 0) { + stbi__rewind( s ); + return 0; + } + + for(;;) { + token = stbi__hdr_gettoken(s,buffer); + if (token[0] == 0) break; + if (strcmp(token, "FORMAT=32-bit_rle_rgbe") == 0) valid = 1; + } + + if (!valid) { + stbi__rewind( s ); + return 0; + } + token = stbi__hdr_gettoken(s,buffer); + if (strncmp(token, "-Y ", 3)) { + stbi__rewind( s ); + return 0; + } + token += 3; + *y = (int) strtol(token, &token, 10); + while (*token == ' ') ++token; + if (strncmp(token, "+X ", 3)) { + stbi__rewind( s ); + return 0; + } + token += 3; + *x = (int) strtol(token, NULL, 10); + *comp = 3; + return 1; +} +#endif // STBI_NO_HDR + +#ifndef STBI_NO_BMP +static int stbi__bmp_info(stbi__context *s, int *x, int *y, int *comp) +{ + void *p; + stbi__bmp_data info; + + info.all_a = 255; + p = stbi__bmp_parse_header(s, &info); + stbi__rewind( s ); + if (p == NULL) + return 0; + if (x) *x = s->img_x; + if (y) *y = s->img_y; + if (comp) *comp = info.ma ? 4 : 3; + return 1; +} +#endif + +#ifndef STBI_NO_PSD +static int stbi__psd_info(stbi__context *s, int *x, int *y, int *comp) +{ + int channelCount, dummy, depth; + if (!x) x = &dummy; + if (!y) y = &dummy; + if (!comp) comp = &dummy; + if (stbi__get32be(s) != 0x38425053) { + stbi__rewind( s ); + return 0; + } + if (stbi__get16be(s) != 1) { + stbi__rewind( s ); + return 0; + } + stbi__skip(s, 6); + channelCount = stbi__get16be(s); + if (channelCount < 0 || channelCount > 16) { + stbi__rewind( s ); + return 0; + } + *y = stbi__get32be(s); + *x = stbi__get32be(s); + depth = stbi__get16be(s); + if (depth != 8 && depth != 16) { + stbi__rewind( s ); + return 0; + } + if (stbi__get16be(s) != 3) { + stbi__rewind( s ); + return 0; + } + *comp = 4; + return 1; +} + +static int stbi__psd_is16(stbi__context *s) +{ + int channelCount, depth; + if (stbi__get32be(s) != 0x38425053) { + stbi__rewind( s ); + return 0; + } + if (stbi__get16be(s) != 1) { + stbi__rewind( s ); + return 0; + } + stbi__skip(s, 6); + channelCount = stbi__get16be(s); + if (channelCount < 0 || channelCount > 16) { + stbi__rewind( s ); + return 0; + } + (void) stbi__get32be(s); + (void) stbi__get32be(s); + depth = stbi__get16be(s); + if (depth != 16) { + stbi__rewind( s ); + return 0; + } + return 1; +} +#endif + +#ifndef STBI_NO_PIC +static int stbi__pic_info(stbi__context *s, int *x, int *y, int *comp) +{ + int act_comp=0,num_packets=0,chained,dummy; + stbi__pic_packet packets[10]; + + if (!x) x = &dummy; + if (!y) y = &dummy; + if (!comp) comp = &dummy; + + if (!stbi__pic_is4(s,"\x53\x80\xF6\x34")) { + stbi__rewind(s); + return 0; + } + + stbi__skip(s, 88); + + *x = stbi__get16be(s); + *y = stbi__get16be(s); + if (stbi__at_eof(s)) { + stbi__rewind( s); + return 0; + } + if ( (*x) != 0 && (1 << 28) / (*x) < (*y)) { + stbi__rewind( s ); + return 0; + } + + stbi__skip(s, 8); + + do { + stbi__pic_packet *packet; + + if (num_packets==sizeof(packets)/sizeof(packets[0])) + return 0; + + packet = &packets[num_packets++]; + chained = stbi__get8(s); + packet->size = stbi__get8(s); + packet->type = stbi__get8(s); + packet->channel = stbi__get8(s); + act_comp |= packet->channel; + + if (stbi__at_eof(s)) { + stbi__rewind( s ); + return 0; + } + if (packet->size != 8) { + stbi__rewind( s ); + return 0; + } + } while (chained); + + *comp = (act_comp & 0x10 ? 4 : 3); + + return 1; +} +#endif + +// ************************************************************************************************* +// Portable Gray Map and Portable Pixel Map loader +// by Ken Miller +// +// PGM: http://netpbm.sourceforge.net/doc/pgm.html +// PPM: http://netpbm.sourceforge.net/doc/ppm.html +// +// Known limitations: +// Does not support comments in the header section +// Does not support ASCII image data (formats P2 and P3) +// Does not support 16-bit-per-channel + +#ifndef STBI_NO_PNM + +static int stbi__pnm_test(stbi__context *s) +{ + char p, t; + p = (char) stbi__get8(s); + t = (char) stbi__get8(s); + if (p != 'P' || (t != '5' && t != '6')) { + stbi__rewind( s ); + return 0; + } + return 1; +} + +static void *stbi__pnm_load(stbi__context *s, int *x, int *y, int *comp, int req_comp, stbi__result_info *ri) +{ + stbi_uc *out; + STBI_NOTUSED(ri); + + if (!stbi__pnm_info(s, (int *)&s->img_x, (int *)&s->img_y, (int *)&s->img_n)) + return 0; + + *x = s->img_x; + *y = s->img_y; + if (comp) *comp = s->img_n; + + if (!stbi__mad3sizes_valid(s->img_n, s->img_x, s->img_y, 0)) + return stbi__errpuc("too large", "PNM too large"); + + out = (stbi_uc *) stbi__malloc_mad3(s->img_n, s->img_x, s->img_y, 0); + if (!out) return stbi__errpuc("outofmem", "Out of memory"); + stbi__getn(s, out, s->img_n * s->img_x * s->img_y); + + if (req_comp && req_comp != s->img_n) { + out = stbi__convert_format(out, s->img_n, req_comp, s->img_x, s->img_y); + if (out == NULL) return out; // stbi__convert_format frees input on failure + } + return out; +} + +static int stbi__pnm_isspace(char c) +{ + return c == ' ' || c == '\t' || c == '\n' || c == '\v' || c == '\f' || c == '\r'; +} + +static void stbi__pnm_skip_whitespace(stbi__context *s, char *c) +{ + for (;;) { + while (!stbi__at_eof(s) && stbi__pnm_isspace(*c)) + *c = (char) stbi__get8(s); + + if (stbi__at_eof(s) || *c != '#') + break; + + while (!stbi__at_eof(s) && *c != '\n' && *c != '\r' ) + *c = (char) stbi__get8(s); + } +} + +static int stbi__pnm_isdigit(char c) +{ + return c >= '0' && c <= '9'; +} + +static int stbi__pnm_getinteger(stbi__context *s, char *c) +{ + int value = 0; + + while (!stbi__at_eof(s) && stbi__pnm_isdigit(*c)) { + value = value*10 + (*c - '0'); + *c = (char) stbi__get8(s); + } + + return value; +} + +static int stbi__pnm_info(stbi__context *s, int *x, int *y, int *comp) +{ + int maxv, dummy; + char c, p, t; + + if (!x) x = &dummy; + if (!y) y = &dummy; + if (!comp) comp = &dummy; + + stbi__rewind(s); + + // Get identifier + p = (char) stbi__get8(s); + t = (char) stbi__get8(s); + if (p != 'P' || (t != '5' && t != '6')) { + stbi__rewind(s); + return 0; + } + + *comp = (t == '6') ? 3 : 1; // '5' is 1-component .pgm; '6' is 3-component .ppm + + c = (char) stbi__get8(s); + stbi__pnm_skip_whitespace(s, &c); + + *x = stbi__pnm_getinteger(s, &c); // read width + stbi__pnm_skip_whitespace(s, &c); + + *y = stbi__pnm_getinteger(s, &c); // read height + stbi__pnm_skip_whitespace(s, &c); + + maxv = stbi__pnm_getinteger(s, &c); // read max value + + if (maxv > 255) + return stbi__err("max value > 255", "PPM image not 8-bit"); + else + return 1; +} +#endif + +static int stbi__info_main(stbi__context *s, int *x, int *y, int *comp) +{ + #ifndef STBI_NO_JPEG + if (stbi__jpeg_info(s, x, y, comp)) return 1; + #endif + + #ifndef STBI_NO_PNG + if (stbi__png_info(s, x, y, comp)) return 1; + #endif + + #ifndef STBI_NO_GIF + if (stbi__gif_info(s, x, y, comp)) return 1; + #endif + + #ifndef STBI_NO_BMP + if (stbi__bmp_info(s, x, y, comp)) return 1; + #endif + + #ifndef STBI_NO_PSD + if (stbi__psd_info(s, x, y, comp)) return 1; + #endif + + #ifndef STBI_NO_PIC + if (stbi__pic_info(s, x, y, comp)) return 1; + #endif + + #ifndef STBI_NO_PNM + if (stbi__pnm_info(s, x, y, comp)) return 1; + #endif + + #ifndef STBI_NO_HDR + if (stbi__hdr_info(s, x, y, comp)) return 1; + #endif + + // test tga last because it's a crappy test! + #ifndef STBI_NO_TGA + if (stbi__tga_info(s, x, y, comp)) + return 1; + #endif + return stbi__err("unknown image type", "Image not of any known type, or corrupt"); +} + +static int stbi__is_16_main(stbi__context *s) +{ + #ifndef STBI_NO_PNG + if (stbi__png_is16(s)) return 1; + #endif + + #ifndef STBI_NO_PSD + if (stbi__psd_is16(s)) return 1; + #endif + + return 0; +} + +#ifndef STBI_NO_STDIO +STBIDEF int stbi_info(char const *filename, int *x, int *y, int *comp) +{ + FILE *f = stbi__fopen(filename, "rb"); + int result; + if (!f) return stbi__err("can't fopen", "Unable to open file"); + result = stbi_info_from_file(f, x, y, comp); + fclose(f); + return result; +} + +STBIDEF int stbi_info_from_file(FILE *f, int *x, int *y, int *comp) +{ + int r; + stbi__context s; + long pos = ftell(f); + stbi__start_file(&s, f); + r = stbi__info_main(&s,x,y,comp); + fseek(f,pos,SEEK_SET); + return r; +} + +STBIDEF int stbi_is_16_bit(char const *filename) +{ + FILE *f = stbi__fopen(filename, "rb"); + int result; + if (!f) return stbi__err("can't fopen", "Unable to open file"); + result = stbi_is_16_bit_from_file(f); + fclose(f); + return result; +} + +STBIDEF int stbi_is_16_bit_from_file(FILE *f) +{ + int r; + stbi__context s; + long pos = ftell(f); + stbi__start_file(&s, f); + r = stbi__is_16_main(&s); + fseek(f,pos,SEEK_SET); + return r; +} +#endif // !STBI_NO_STDIO + +STBIDEF int stbi_info_from_memory(stbi_uc const *buffer, int len, int *x, int *y, int *comp) +{ + stbi__context s; + stbi__start_mem(&s,buffer,len); + return stbi__info_main(&s,x,y,comp); +} + +STBIDEF int stbi_info_from_callbacks(stbi_io_callbacks const *c, void *user, int *x, int *y, int *comp) +{ + stbi__context s; + stbi__start_callbacks(&s, (stbi_io_callbacks *) c, user); + return stbi__info_main(&s,x,y,comp); +} + +STBIDEF int stbi_is_16_bit_from_memory(stbi_uc const *buffer, int len) +{ + stbi__context s; + stbi__start_mem(&s,buffer,len); + return stbi__is_16_main(&s); +} + +STBIDEF int stbi_is_16_bit_from_callbacks(stbi_io_callbacks const *c, void *user) +{ + stbi__context s; + stbi__start_callbacks(&s, (stbi_io_callbacks *) c, user); + return stbi__is_16_main(&s); +} + +#endif // STB_IMAGE_IMPLEMENTATION + +/* + revision history: + 2.19 (2018-02-11) fix warning + 2.18 (2018-01-30) fix warnings + 2.17 (2018-01-29) change sbti__shiftsigned to avoid clang -O2 bug + 1-bit BMP + *_is_16_bit api + avoid warnings + 2.16 (2017-07-23) all functions have 16-bit variants; + STBI_NO_STDIO works again; + compilation fixes; + fix rounding in unpremultiply; + optimize vertical flip; + disable raw_len validation; + documentation fixes + 2.15 (2017-03-18) fix png-1,2,4 bug; now all Imagenet JPGs decode; + warning fixes; disable run-time SSE detection on gcc; + uniform handling of optional "return" values; + thread-safe initialization of zlib tables + 2.14 (2017-03-03) remove deprecated STBI_JPEG_OLD; fixes for Imagenet JPGs + 2.13 (2016-11-29) add 16-bit API, only supported for PNG right now + 2.12 (2016-04-02) fix typo in 2.11 PSD fix that caused crashes + 2.11 (2016-04-02) allocate large structures on the stack + remove white matting for transparent PSD + fix reported channel count for PNG & BMP + re-enable SSE2 in non-gcc 64-bit + support RGB-formatted JPEG + read 16-bit PNGs (only as 8-bit) + 2.10 (2016-01-22) avoid warning introduced in 2.09 by STBI_REALLOC_SIZED + 2.09 (2016-01-16) allow comments in PNM files + 16-bit-per-pixel TGA (not bit-per-component) + info() for TGA could break due to .hdr handling + info() for BMP to shares code instead of sloppy parse + can use STBI_REALLOC_SIZED if allocator doesn't support realloc + code cleanup + 2.08 (2015-09-13) fix to 2.07 cleanup, reading RGB PSD as RGBA + 2.07 (2015-09-13) fix compiler warnings + partial animated GIF support + limited 16-bpc PSD support + #ifdef unused functions + bug with < 92 byte PIC,PNM,HDR,TGA + 2.06 (2015-04-19) fix bug where PSD returns wrong '*comp' value + 2.05 (2015-04-19) fix bug in progressive JPEG handling, fix warning + 2.04 (2015-04-15) try to re-enable SIMD on MinGW 64-bit + 2.03 (2015-04-12) extra corruption checking (mmozeiko) + stbi_set_flip_vertically_on_load (nguillemot) + fix NEON support; fix mingw support + 2.02 (2015-01-19) fix incorrect assert, fix warning + 2.01 (2015-01-17) fix various warnings; suppress SIMD on gcc 32-bit without -msse2 + 2.00b (2014-12-25) fix STBI_MALLOC in progressive JPEG + 2.00 (2014-12-25) optimize JPG, including x86 SSE2 & NEON SIMD (ryg) + progressive JPEG (stb) + PGM/PPM support (Ken Miller) + STBI_MALLOC,STBI_REALLOC,STBI_FREE + GIF bugfix -- seemingly never worked + STBI_NO_*, STBI_ONLY_* + 1.48 (2014-12-14) fix incorrectly-named assert() + 1.47 (2014-12-14) 1/2/4-bit PNG support, both direct and paletted (Omar Cornut & stb) + optimize PNG (ryg) + fix bug in interlaced PNG with user-specified channel count (stb) + 1.46 (2014-08-26) + fix broken tRNS chunk (colorkey-style transparency) in non-paletted PNG + 1.45 (2014-08-16) + fix MSVC-ARM internal compiler error by wrapping malloc + 1.44 (2014-08-07) + various warning fixes from Ronny Chevalier + 1.43 (2014-07-15) + fix MSVC-only compiler problem in code changed in 1.42 + 1.42 (2014-07-09) + don't define _CRT_SECURE_NO_WARNINGS (affects user code) + fixes to stbi__cleanup_jpeg path + added STBI_ASSERT to avoid requiring assert.h + 1.41 (2014-06-25) + fix search&replace from 1.36 that messed up comments/error messages + 1.40 (2014-06-22) + fix gcc struct-initialization warning + 1.39 (2014-06-15) + fix to TGA optimization when req_comp != number of components in TGA; + fix to GIF loading because BMP wasn't rewinding (whoops, no GIFs in my test suite) + add support for BMP version 5 (more ignored fields) + 1.38 (2014-06-06) + suppress MSVC warnings on integer casts truncating values + fix accidental rename of 'skip' field of I/O + 1.37 (2014-06-04) + remove duplicate typedef + 1.36 (2014-06-03) + convert to header file single-file library + if de-iphone isn't set, load iphone images color-swapped instead of returning NULL + 1.35 (2014-05-27) + various warnings + fix broken STBI_SIMD path + fix bug where stbi_load_from_file no longer left file pointer in correct place + fix broken non-easy path for 32-bit BMP (possibly never used) + TGA optimization by Arseny Kapoulkine + 1.34 (unknown) + use STBI_NOTUSED in stbi__resample_row_generic(), fix one more leak in tga failure case + 1.33 (2011-07-14) + make stbi_is_hdr work in STBI_NO_HDR (as specified), minor compiler-friendly improvements + 1.32 (2011-07-13) + support for "info" function for all supported filetypes (SpartanJ) + 1.31 (2011-06-20) + a few more leak fixes, bug in PNG handling (SpartanJ) + 1.30 (2011-06-11) + added ability to load files via callbacks to accomidate custom input streams (Ben Wenger) + removed deprecated format-specific test/load functions + removed support for installable file formats (stbi_loader) -- would have been broken for IO callbacks anyway + error cases in bmp and tga give messages and don't leak (Raymond Barbiero, grisha) + fix inefficiency in decoding 32-bit BMP (David Woo) + 1.29 (2010-08-16) + various warning fixes from Aurelien Pocheville + 1.28 (2010-08-01) + fix bug in GIF palette transparency (SpartanJ) + 1.27 (2010-08-01) + cast-to-stbi_uc to fix warnings + 1.26 (2010-07-24) + fix bug in file buffering for PNG reported by SpartanJ + 1.25 (2010-07-17) + refix trans_data warning (Won Chun) + 1.24 (2010-07-12) + perf improvements reading from files on platforms with lock-heavy fgetc() + minor perf improvements for jpeg + deprecated type-specific functions so we'll get feedback if they're needed + attempt to fix trans_data warning (Won Chun) + 1.23 fixed bug in iPhone support + 1.22 (2010-07-10) + removed image *writing* support + stbi_info support from Jetro Lauha + GIF support from Jean-Marc Lienher + iPhone PNG-extensions from James Brown + warning-fixes from Nicolas Schulz and Janez Zemva (i.stbi__err. Janez (U+017D)emva) + 1.21 fix use of 'stbi_uc' in header (reported by jon blow) + 1.20 added support for Softimage PIC, by Tom Seddon + 1.19 bug in interlaced PNG corruption check (found by ryg) + 1.18 (2008-08-02) + fix a threading bug (local mutable static) + 1.17 support interlaced PNG + 1.16 major bugfix - stbi__convert_format converted one too many pixels + 1.15 initialize some fields for thread safety + 1.14 fix threadsafe conversion bug + header-file-only version (#define STBI_HEADER_FILE_ONLY before including) + 1.13 threadsafe + 1.12 const qualifiers in the API + 1.11 Support installable IDCT, colorspace conversion routines + 1.10 Fixes for 64-bit (don't use "unsigned long") + optimized upsampling by Fabian "ryg" Giesen + 1.09 Fix format-conversion for PSD code (bad global variables!) + 1.08 Thatcher Ulrich's PSD code integrated by Nicolas Schulz + 1.07 attempt to fix C++ warning/errors again + 1.06 attempt to fix C++ warning/errors again + 1.05 fix TGA loading to return correct *comp and use good luminance calc + 1.04 default float alpha is 1, not 255; use 'void *' for stbi_image_free + 1.03 bugfixes to STBI_NO_STDIO, STBI_NO_HDR + 1.02 support for (subset of) HDR files, float interface for preferred access to them + 1.01 fix bug: possible bug in handling right-side up bmps... not sure + fix bug: the stbi__bmp_load() and stbi__tga_load() functions didn't work at all + 1.00 interface to zlib that skips zlib header + 0.99 correct handling of alpha in palette + 0.98 TGA loader by lonesock; dynamically add loaders (untested) + 0.97 jpeg errors on too large a file; also catch another malloc failure + 0.96 fix detection of invalid v value - particleman@mollyrocket forum + 0.95 during header scan, seek to markers in case of padding + 0.94 STBI_NO_STDIO to disable stdio usage; rename all #defines the same + 0.93 handle jpegtran output; verbose errors + 0.92 read 4,8,16,24,32-bit BMP files of several formats + 0.91 output 24-bit Windows 3.0 BMP files + 0.90 fix a few more warnings; bump version number to approach 1.0 + 0.61 bugfixes due to Marc LeBlanc, Christopher Lloyd + 0.60 fix compiling as c++ + 0.59 fix warnings: merge Dave Moore's -Wall fixes + 0.58 fix bug: zlib uncompressed mode len/nlen was wrong endian + 0.57 fix bug: jpg last huffman symbol before marker was >9 bits but less than 16 available + 0.56 fix bug: zlib uncompressed mode len vs. nlen + 0.55 fix bug: restart_interval not initialized to 0 + 0.54 allow NULL for 'int *comp' + 0.53 fix bug in png 3->4; speedup png decoding + 0.52 png handles req_comp=3,4 directly; minor cleanup; jpeg comments + 0.51 obey req_comp requests, 1-component jpegs return as 1-component, + on 'test' only check type, not whether we support this variant + 0.50 (2006-11-19) + first released version +*/ + + +/* +------------------------------------------------------------------------------ +This software is available under 2 licenses -- choose whichever you prefer. +------------------------------------------------------------------------------ +ALTERNATIVE A - MIT License +Copyright (c) 2017 Sean Barrett +Permission is hereby granted, free of charge, to any person obtaining a copy of +this software and associated documentation files (the "Software"), to deal in +the Software without restriction, including without limitation the rights to +use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies +of the Software, and to permit persons to whom the Software is furnished to do +so, subject to the following conditions: +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. +------------------------------------------------------------------------------ +ALTERNATIVE B - Public Domain (www.unlicense.org) +This is free and unencumbered software released into the public domain. +Anyone is free to copy, modify, publish, use, compile, sell, or distribute this +software, either in source code form or as a compiled binary, for any purpose, +commercial or non-commercial, and by any means. +In jurisdictions that recognize copyright laws, the author or authors of this +software dedicate any and all copyright interest in the software to the public +domain. We make this dedication for the benefit of the public at large and to +the detriment of our heirs and successors. We intend this dedication to be an +overt act of relinquishment in perpetuity of all present and future rights to +this software under copyright law. +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN +ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION +WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. +------------------------------------------------------------------------------ +*/ diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/stb_image_write.h b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/stb_image_write.h new file mode 100644 index 00000000000..c05e95812b9 --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/stb_image_write.h @@ -0,0 +1,1568 @@ +/* stb_image_write - v1.09 - public domain - http://nothings.org/stb/stb_image_write.h + writes out PNG/BMP/TGA/JPEG/HDR images to C stdio - Sean Barrett 2010-2015 + no warranty implied; use at your own risk + + Before #including, + + #define STB_IMAGE_WRITE_IMPLEMENTATION + + in the file that you want to have the implementation. + + Will probably not work correctly with strict-aliasing optimizations. + + If using a modern Microsoft Compiler, non-safe versions of CRT calls may cause + compilation warnings or even errors. To avoid this, also before #including, + + #define STBI_MSC_SECURE_CRT + +ABOUT: + + This header file is a library for writing images to C stdio. It could be + adapted to write to memory or a general streaming interface; let me know. + + The PNG output is not optimal; it is 20-50% larger than the file + written by a decent optimizing implementation; though providing a custom + zlib compress function (see STBIW_ZLIB_COMPRESS) can mitigate that. + This library is designed for source code compactness and simplicity, + not optimal image file size or run-time performance. + +BUILDING: + + You can #define STBIW_ASSERT(x) before the #include to avoid using assert.h. + You can #define STBIW_MALLOC(), STBIW_REALLOC(), and STBIW_FREE() to replace + malloc,realloc,free. + You can #define STBIW_MEMMOVE() to replace memmove() + You can #define STBIW_ZLIB_COMPRESS to use a custom zlib-style compress function + for PNG compression (instead of the builtin one), it must have the following signature: + unsigned char * my_compress(unsigned char *data, int data_len, int *out_len, int quality); + The returned data will be freed with STBIW_FREE() (free() by default), + so it must be heap allocated with STBIW_MALLOC() (malloc() by default), + +USAGE: + + There are five functions, one for each image file format: + + int stbi_write_png(char const *filename, int w, int h, int comp, const void *data, int stride_in_bytes); + int stbi_write_bmp(char const *filename, int w, int h, int comp, const void *data); + int stbi_write_tga(char const *filename, int w, int h, int comp, const void *data); + int stbi_write_jpg(char const *filename, int w, int h, int comp, const void *data, int quality); + int stbi_write_hdr(char const *filename, int w, int h, int comp, const float *data); + + void stbi_flip_vertically_on_write(int flag); // flag is non-zero to flip data vertically + + There are also five equivalent functions that use an arbitrary write function. You are + expected to open/close your file-equivalent before and after calling these: + + int stbi_write_png_to_func(stbi_write_func *func, void *context, int w, int h, int comp, const void *data, int stride_in_bytes); + int stbi_write_bmp_to_func(stbi_write_func *func, void *context, int w, int h, int comp, const void *data); + int stbi_write_tga_to_func(stbi_write_func *func, void *context, int w, int h, int comp, const void *data); + int stbi_write_hdr_to_func(stbi_write_func *func, void *context, int w, int h, int comp, const float *data); + int stbi_write_jpg_to_func(stbi_write_func *func, void *context, int x, int y, int comp, const void *data, int quality); + + where the callback is: + void stbi_write_func(void *context, void *data, int size); + + You can configure it with these global variables: + int stbi_write_tga_with_rle; // defaults to true; set to 0 to disable RLE + int stbi_write_png_compression_level; // defaults to 8; set to higher for more compression + int stbi_write_force_png_filter; // defaults to -1; set to 0..5 to force a filter mode + + + You can define STBI_WRITE_NO_STDIO to disable the file variant of these + functions, so the library will not use stdio.h at all. However, this will + also disable HDR writing, because it requires stdio for formatted output. + + Each function returns 0 on failure and non-0 on success. + + The functions create an image file defined by the parameters. The image + is a rectangle of pixels stored from left-to-right, top-to-bottom. + Each pixel contains 'comp' channels of data stored interleaved with 8-bits + per channel, in the following order: 1=Y, 2=YA, 3=RGB, 4=RGBA. (Y is + monochrome color.) The rectangle is 'w' pixels wide and 'h' pixels tall. + The *data pointer points to the first byte of the top-left-most pixel. + For PNG, "stride_in_bytes" is the distance in bytes from the first byte of + a row of pixels to the first byte of the next row of pixels. + + PNG creates output files with the same number of components as the input. + The BMP format expands Y to RGB in the file format and does not + output alpha. + + PNG supports writing rectangles of data even when the bytes storing rows of + data are not consecutive in memory (e.g. sub-rectangles of a larger image), + by supplying the stride between the beginning of adjacent rows. The other + formats do not. (Thus you cannot write a native-format BMP through the BMP + writer, both because it is in BGR order and because it may have padding + at the end of the line.) + + PNG allows you to set the deflate compression level by setting the global + variable 'stbi_write_png_compression_level' (it defaults to 8). + + HDR expects linear float data. Since the format is always 32-bit rgb(e) + data, alpha (if provided) is discarded, and for monochrome data it is + replicated across all three channels. + + TGA supports RLE or non-RLE compressed data. To use non-RLE-compressed + data, set the global variable 'stbi_write_tga_with_rle' to 0. + + JPEG does ignore alpha channels in input data; quality is between 1 and 100. + Higher quality looks better but results in a bigger image. + JPEG baseline (no JPEG progressive). + +CREDITS: + + + Sean Barrett - PNG/BMP/TGA + Baldur Karlsson - HDR + Jean-Sebastien Guay - TGA monochrome + Tim Kelsey - misc enhancements + Alan Hickman - TGA RLE + Emmanuel Julien - initial file IO callback implementation + Jon Olick - original jo_jpeg.cpp code + Daniel Gibson - integrate JPEG, allow external zlib + Aarni Koskela - allow choosing PNG filter + + bugfixes: + github:Chribba + Guillaume Chereau + github:jry2 + github:romigrou + Sergio Gonzalez + Jonas Karlsson + Filip Wasil + Thatcher Ulrich + github:poppolopoppo + Patrick Boettcher + github:xeekworx + Cap Petschulat + Simon Rodriguez + Ivan Tikhonov + github:ignotion + Adam Schackart + +LICENSE + + See end of file for license information. + +*/ + +#ifndef INCLUDE_STB_IMAGE_WRITE_H +#define INCLUDE_STB_IMAGE_WRITE_H + +// if STB_IMAGE_WRITE_STATIC causes problems, try defining STBIWDEF to 'inline' or 'static inline' +#ifndef STBIWDEF +#ifdef STB_IMAGE_WRITE_STATIC +#define STBIWDEF static +#else +#ifdef __cplusplus +#define STBIWDEF extern "C" +#else +#define STBIWDEF extern +#endif +#endif +#endif + +#ifndef STB_IMAGE_WRITE_STATIC // C++ forbids static forward declarations +extern int stbi_write_tga_with_rle; +extern int stbi_write_png_compression_level; +extern int stbi_write_force_png_filter; +#endif + +#ifndef STBI_WRITE_NO_STDIO +STBIWDEF int stbi_write_png(char const *filename, int w, int h, int comp, const void *data, int stride_in_bytes); +STBIWDEF int stbi_write_bmp(char const *filename, int w, int h, int comp, const void *data); +STBIWDEF int stbi_write_tga(char const *filename, int w, int h, int comp, const void *data); +STBIWDEF int stbi_write_hdr(char const *filename, int w, int h, int comp, const float *data); +STBIWDEF int stbi_write_jpg(char const *filename, int x, int y, int comp, const void *data, int quality); +#endif + +typedef void stbi_write_func(void *context, void *data, int size); + +STBIWDEF int stbi_write_png_to_func(stbi_write_func *func, void *context, int w, int h, int comp, const void *data, int stride_in_bytes); +STBIWDEF int stbi_write_bmp_to_func(stbi_write_func *func, void *context, int w, int h, int comp, const void *data); +STBIWDEF int stbi_write_tga_to_func(stbi_write_func *func, void *context, int w, int h, int comp, const void *data); +STBIWDEF int stbi_write_hdr_to_func(stbi_write_func *func, void *context, int w, int h, int comp, const float *data); +STBIWDEF int stbi_write_jpg_to_func(stbi_write_func *func, void *context, int x, int y, int comp, const void *data, int quality); + +STBIWDEF void stbi_flip_vertically_on_write(int flip_boolean); + +#endif//INCLUDE_STB_IMAGE_WRITE_H + +#ifdef STB_IMAGE_WRITE_IMPLEMENTATION + +#ifdef _WIN32 + #ifndef _CRT_SECURE_NO_WARNINGS + #define _CRT_SECURE_NO_WARNINGS + #endif + #ifndef _CRT_NONSTDC_NO_DEPRECATE + #define _CRT_NONSTDC_NO_DEPRECATE + #endif +#endif + +#ifndef STBI_WRITE_NO_STDIO +#include +#endif // STBI_WRITE_NO_STDIO + +#include +#include +#include +#include + +#if defined(STBIW_MALLOC) && defined(STBIW_FREE) && (defined(STBIW_REALLOC) || defined(STBIW_REALLOC_SIZED)) +// ok +#elif !defined(STBIW_MALLOC) && !defined(STBIW_FREE) && !defined(STBIW_REALLOC) && !defined(STBIW_REALLOC_SIZED) +// ok +#else +#error "Must define all or none of STBIW_MALLOC, STBIW_FREE, and STBIW_REALLOC (or STBIW_REALLOC_SIZED)." +#endif + +#ifndef STBIW_MALLOC +#define STBIW_MALLOC(sz) malloc(sz) +#define STBIW_REALLOC(p,newsz) realloc(p,newsz) +#define STBIW_FREE(p) free(p) +#endif + +#ifndef STBIW_REALLOC_SIZED +#define STBIW_REALLOC_SIZED(p,oldsz,newsz) STBIW_REALLOC(p,newsz) +#endif + + +#ifndef STBIW_MEMMOVE +#define STBIW_MEMMOVE(a,b,sz) memmove(a,b,sz) +#endif + + +#ifndef STBIW_ASSERT +#include +#define STBIW_ASSERT(x) assert(x) +#endif + +#define STBIW_UCHAR(x) (unsigned char) ((x) & 0xff) + +#ifdef STB_IMAGE_WRITE_STATIC +static int stbi__flip_vertically_on_write=0; +static int stbi_write_png_compression_level = 8; +static int stbi_write_tga_with_rle = 1; +static int stbi_write_force_png_filter = -1; +#else +int stbi_write_png_compression_level = 8; +int stbi__flip_vertically_on_write=0; +int stbi_write_tga_with_rle = 1; +int stbi_write_force_png_filter = -1; +#endif + +STBIWDEF void stbi_flip_vertically_on_write(int flag) +{ + stbi__flip_vertically_on_write = flag; +} + +typedef struct +{ + stbi_write_func *func; + void *context; +} stbi__write_context; + +// initialize a callback-based context +static void stbi__start_write_callbacks(stbi__write_context *s, stbi_write_func *c, void *context) +{ + s->func = c; + s->context = context; +} + +#ifndef STBI_WRITE_NO_STDIO + +static void stbi__stdio_write(void *context, void *data, int size) +{ + fwrite(data,1,size,(FILE*) context); +} + +static int stbi__start_write_file(stbi__write_context *s, const char *filename) +{ + FILE *f; +#ifdef STBI_MSC_SECURE_CRT + if (fopen_s(&f, filename, "wb")) + f = NULL; +#else + f = fopen(filename, "wb"); +#endif + stbi__start_write_callbacks(s, stbi__stdio_write, (void *) f); + return f != NULL; +} + +static void stbi__end_write_file(stbi__write_context *s) +{ + fclose((FILE *)s->context); +} + +#endif // !STBI_WRITE_NO_STDIO + +typedef unsigned int stbiw_uint32; +typedef int stb_image_write_test[sizeof(stbiw_uint32)==4 ? 1 : -1]; + +static void stbiw__writefv(stbi__write_context *s, const char *fmt, va_list v) +{ + while (*fmt) { + switch (*fmt++) { + case ' ': break; + case '1': { unsigned char x = STBIW_UCHAR(va_arg(v, int)); + s->func(s->context,&x,1); + break; } + case '2': { int x = va_arg(v,int); + unsigned char b[2]; + b[0] = STBIW_UCHAR(x); + b[1] = STBIW_UCHAR(x>>8); + s->func(s->context,b,2); + break; } + case '4': { stbiw_uint32 x = va_arg(v,int); + unsigned char b[4]; + b[0]=STBIW_UCHAR(x); + b[1]=STBIW_UCHAR(x>>8); + b[2]=STBIW_UCHAR(x>>16); + b[3]=STBIW_UCHAR(x>>24); + s->func(s->context,b,4); + break; } + default: + STBIW_ASSERT(0); + return; + } + } +} + +static void stbiw__writef(stbi__write_context *s, const char *fmt, ...) +{ + va_list v; + va_start(v, fmt); + stbiw__writefv(s, fmt, v); + va_end(v); +} + +static void stbiw__putc(stbi__write_context *s, unsigned char c) +{ + s->func(s->context, &c, 1); +} + +static void stbiw__write3(stbi__write_context *s, unsigned char a, unsigned char b, unsigned char c) +{ + unsigned char arr[3]; + arr[0] = a, arr[1] = b, arr[2] = c; + s->func(s->context, arr, 3); +} + +static void stbiw__write_pixel(stbi__write_context *s, int rgb_dir, int comp, int write_alpha, int expand_mono, unsigned char *d) +{ + unsigned char bg[3] = { 255, 0, 255}, px[3]; + int k; + + if (write_alpha < 0) + s->func(s->context, &d[comp - 1], 1); + + switch (comp) { + case 2: // 2 pixels = mono + alpha, alpha is written separately, so same as 1-channel case + case 1: + if (expand_mono) + stbiw__write3(s, d[0], d[0], d[0]); // monochrome bmp + else + s->func(s->context, d, 1); // monochrome TGA + break; + case 4: + if (!write_alpha) { + // composite against pink background + for (k = 0; k < 3; ++k) + px[k] = bg[k] + ((d[k] - bg[k]) * d[3]) / 255; + stbiw__write3(s, px[1 - rgb_dir], px[1], px[1 + rgb_dir]); + break; + } + /* FALLTHROUGH */ + case 3: + stbiw__write3(s, d[1 - rgb_dir], d[1], d[1 + rgb_dir]); + break; + } + if (write_alpha > 0) + s->func(s->context, &d[comp - 1], 1); +} + +static void stbiw__write_pixels(stbi__write_context *s, int rgb_dir, int vdir, int x, int y, int comp, void *data, int write_alpha, int scanline_pad, int expand_mono) +{ + stbiw_uint32 zero = 0; + int i,j, j_end; + + if (y <= 0) + return; + + if (stbi__flip_vertically_on_write) + vdir *= -1; + + if (vdir < 0) + j_end = -1, j = y-1; + else + j_end = y, j = 0; + + for (; j != j_end; j += vdir) { + for (i=0; i < x; ++i) { + unsigned char *d = (unsigned char *) data + (j*x+i)*comp; + stbiw__write_pixel(s, rgb_dir, comp, write_alpha, expand_mono, d); + } + s->func(s->context, &zero, scanline_pad); + } +} + +static int stbiw__outfile(stbi__write_context *s, int rgb_dir, int vdir, int x, int y, int comp, int expand_mono, void *data, int alpha, int pad, const char *fmt, ...) +{ + if (y < 0 || x < 0) { + return 0; + } else { + va_list v; + va_start(v, fmt); + stbiw__writefv(s, fmt, v); + va_end(v); + stbiw__write_pixels(s,rgb_dir,vdir,x,y,comp,data,alpha,pad, expand_mono); + return 1; + } +} + +static int stbi_write_bmp_core(stbi__write_context *s, int x, int y, int comp, const void *data) +{ + int pad = (-x*3) & 3; + return stbiw__outfile(s,-1,-1,x,y,comp,1,(void *) data,0,pad, + "11 4 22 4" "4 44 22 444444", + 'B', 'M', 14+40+(x*3+pad)*y, 0,0, 14+40, // file header + 40, x,y, 1,24, 0,0,0,0,0,0); // bitmap header +} + +STBIWDEF int stbi_write_bmp_to_func(stbi_write_func *func, void *context, int x, int y, int comp, const void *data) +{ + stbi__write_context s; + stbi__start_write_callbacks(&s, func, context); + return stbi_write_bmp_core(&s, x, y, comp, data); +} + +#ifndef STBI_WRITE_NO_STDIO +STBIWDEF int stbi_write_bmp(char const *filename, int x, int y, int comp, const void *data) +{ + stbi__write_context s; + if (stbi__start_write_file(&s,filename)) { + int r = stbi_write_bmp_core(&s, x, y, comp, data); + stbi__end_write_file(&s); + return r; + } else + return 0; +} +#endif //!STBI_WRITE_NO_STDIO + +static int stbi_write_tga_core(stbi__write_context *s, int x, int y, int comp, void *data) +{ + int has_alpha = (comp == 2 || comp == 4); + int colorbytes = has_alpha ? comp-1 : comp; + int format = colorbytes < 2 ? 3 : 2; // 3 color channels (RGB/RGBA) = 2, 1 color channel (Y/YA) = 3 + + if (y < 0 || x < 0) + return 0; + + if (!stbi_write_tga_with_rle) { + return stbiw__outfile(s, -1, -1, x, y, comp, 0, (void *) data, has_alpha, 0, + "111 221 2222 11", 0, 0, format, 0, 0, 0, 0, 0, x, y, (colorbytes + has_alpha) * 8, has_alpha * 8); + } else { + int i,j,k; + int jend, jdir; + + stbiw__writef(s, "111 221 2222 11", 0,0,format+8, 0,0,0, 0,0,x,y, (colorbytes + has_alpha) * 8, has_alpha * 8); + + if (stbi__flip_vertically_on_write) { + j = 0; + jend = y; + jdir = 1; + } else { + j = y-1; + jend = -1; + jdir = -1; + } + for (; j != jend; j += jdir) { + unsigned char *row = (unsigned char *) data + j * x * comp; + int len; + + for (i = 0; i < x; i += len) { + unsigned char *begin = row + i * comp; + int diff = 1; + len = 1; + + if (i < x - 1) { + ++len; + diff = memcmp(begin, row + (i + 1) * comp, comp); + if (diff) { + const unsigned char *prev = begin; + for (k = i + 2; k < x && len < 128; ++k) { + if (memcmp(prev, row + k * comp, comp)) { + prev += comp; + ++len; + } else { + --len; + break; + } + } + } else { + for (k = i + 2; k < x && len < 128; ++k) { + if (!memcmp(begin, row + k * comp, comp)) { + ++len; + } else { + break; + } + } + } + } + + if (diff) { + unsigned char header = STBIW_UCHAR(len - 1); + s->func(s->context, &header, 1); + for (k = 0; k < len; ++k) { + stbiw__write_pixel(s, -1, comp, has_alpha, 0, begin + k * comp); + } + } else { + unsigned char header = STBIW_UCHAR(len - 129); + s->func(s->context, &header, 1); + stbiw__write_pixel(s, -1, comp, has_alpha, 0, begin); + } + } + } + } + return 1; +} + +STBIWDEF int stbi_write_tga_to_func(stbi_write_func *func, void *context, int x, int y, int comp, const void *data) +{ + stbi__write_context s; + stbi__start_write_callbacks(&s, func, context); + return stbi_write_tga_core(&s, x, y, comp, (void *) data); +} + +#ifndef STBI_WRITE_NO_STDIO +STBIWDEF int stbi_write_tga(char const *filename, int x, int y, int comp, const void *data) +{ + stbi__write_context s; + if (stbi__start_write_file(&s,filename)) { + int r = stbi_write_tga_core(&s, x, y, comp, (void *) data); + stbi__end_write_file(&s); + return r; + } else + return 0; +} +#endif + +// ************************************************************************************************* +// Radiance RGBE HDR writer +// by Baldur Karlsson + +#define stbiw__max(a, b) ((a) > (b) ? (a) : (b)) + +void stbiw__linear_to_rgbe(unsigned char *rgbe, float *linear) +{ + int exponent; + float maxcomp = stbiw__max(linear[0], stbiw__max(linear[1], linear[2])); + + if (maxcomp < 1e-32f) { + rgbe[0] = rgbe[1] = rgbe[2] = rgbe[3] = 0; + } else { + float normalize = (float) frexp(maxcomp, &exponent) * 256.0f/maxcomp; + + rgbe[0] = (unsigned char)(linear[0] * normalize); + rgbe[1] = (unsigned char)(linear[1] * normalize); + rgbe[2] = (unsigned char)(linear[2] * normalize); + rgbe[3] = (unsigned char)(exponent + 128); + } +} + +void stbiw__write_run_data(stbi__write_context *s, int length, unsigned char databyte) +{ + unsigned char lengthbyte = STBIW_UCHAR(length+128); + STBIW_ASSERT(length+128 <= 255); + s->func(s->context, &lengthbyte, 1); + s->func(s->context, &databyte, 1); +} + +void stbiw__write_dump_data(stbi__write_context *s, int length, unsigned char *data) +{ + unsigned char lengthbyte = STBIW_UCHAR(length); + STBIW_ASSERT(length <= 128); // inconsistent with spec but consistent with official code + s->func(s->context, &lengthbyte, 1); + s->func(s->context, data, length); +} + +void stbiw__write_hdr_scanline(stbi__write_context *s, int width, int ncomp, unsigned char *scratch, float *scanline) +{ + unsigned char scanlineheader[4] = { 2, 2, 0, 0 }; + unsigned char rgbe[4]; + float linear[3]; + int x; + + scanlineheader[2] = (width&0xff00)>>8; + scanlineheader[3] = (width&0x00ff); + + /* skip RLE for images too small or large */ + if (width < 8 || width >= 32768) { + for (x=0; x < width; x++) { + switch (ncomp) { + case 4: /* fallthrough */ + case 3: linear[2] = scanline[x*ncomp + 2]; + linear[1] = scanline[x*ncomp + 1]; + linear[0] = scanline[x*ncomp + 0]; + break; + default: + linear[0] = linear[1] = linear[2] = scanline[x*ncomp + 0]; + break; + } + stbiw__linear_to_rgbe(rgbe, linear); + s->func(s->context, rgbe, 4); + } + } else { + int c,r; + /* encode into scratch buffer */ + for (x=0; x < width; x++) { + switch(ncomp) { + case 4: /* fallthrough */ + case 3: linear[2] = scanline[x*ncomp + 2]; + linear[1] = scanline[x*ncomp + 1]; + linear[0] = scanline[x*ncomp + 0]; + break; + default: + linear[0] = linear[1] = linear[2] = scanline[x*ncomp + 0]; + break; + } + stbiw__linear_to_rgbe(rgbe, linear); + scratch[x + width*0] = rgbe[0]; + scratch[x + width*1] = rgbe[1]; + scratch[x + width*2] = rgbe[2]; + scratch[x + width*3] = rgbe[3]; + } + + s->func(s->context, scanlineheader, 4); + + /* RLE each component separately */ + for (c=0; c < 4; c++) { + unsigned char *comp = &scratch[width*c]; + + x = 0; + while (x < width) { + // find first run + r = x; + while (r+2 < width) { + if (comp[r] == comp[r+1] && comp[r] == comp[r+2]) + break; + ++r; + } + if (r+2 >= width) + r = width; + // dump up to first run + while (x < r) { + int len = r-x; + if (len > 128) len = 128; + stbiw__write_dump_data(s, len, &comp[x]); + x += len; + } + // if there's a run, output it + if (r+2 < width) { // same test as what we break out of in search loop, so only true if we break'd + // find next byte after run + while (r < width && comp[r] == comp[x]) + ++r; + // output run up to r + while (x < r) { + int len = r-x; + if (len > 127) len = 127; + stbiw__write_run_data(s, len, comp[x]); + x += len; + } + } + } + } + } +} + +static int stbi_write_hdr_core(stbi__write_context *s, int x, int y, int comp, float *data) +{ + if (y <= 0 || x <= 0 || data == NULL) + return 0; + else { + // Each component is stored separately. Allocate scratch space for full output scanline. + unsigned char *scratch = (unsigned char *) STBIW_MALLOC(x*4); + int i, len; + char buffer[128]; + char header[] = "#?RADIANCE\n# Written by stb_image_write.h\nFORMAT=32-bit_rle_rgbe\n"; + s->func(s->context, header, sizeof(header)-1); + +#ifdef STBI_MSC_SECURE_CRT + len = sprintf_s(buffer, "EXPOSURE= 1.0000000000000\n\n-Y %d +X %d\n", y, x); +#else + len = sprintf(buffer, "EXPOSURE= 1.0000000000000\n\n-Y %d +X %d\n", y, x); +#endif + s->func(s->context, buffer, len); + + for(i=0; i < y; i++) + stbiw__write_hdr_scanline(s, x, comp, scratch, data + comp*x*(stbi__flip_vertically_on_write ? y-1-i : i)*x); + STBIW_FREE(scratch); + return 1; + } +} + +STBIWDEF int stbi_write_hdr_to_func(stbi_write_func *func, void *context, int x, int y, int comp, const float *data) +{ + stbi__write_context s; + stbi__start_write_callbacks(&s, func, context); + return stbi_write_hdr_core(&s, x, y, comp, (float *) data); +} + +#ifndef STBI_WRITE_NO_STDIO +STBIWDEF int stbi_write_hdr(char const *filename, int x, int y, int comp, const float *data) +{ + stbi__write_context s; + if (stbi__start_write_file(&s,filename)) { + int r = stbi_write_hdr_core(&s, x, y, comp, (float *) data); + stbi__end_write_file(&s); + return r; + } else + return 0; +} +#endif // STBI_WRITE_NO_STDIO + + +////////////////////////////////////////////////////////////////////////////// +// +// PNG writer +// + +#ifndef STBIW_ZLIB_COMPRESS +// stretchy buffer; stbiw__sbpush() == vector<>::push_back() -- stbiw__sbcount() == vector<>::size() +#define stbiw__sbraw(a) ((int *) (a) - 2) +#define stbiw__sbm(a) stbiw__sbraw(a)[0] +#define stbiw__sbn(a) stbiw__sbraw(a)[1] + +#define stbiw__sbneedgrow(a,n) ((a)==0 || stbiw__sbn(a)+n >= stbiw__sbm(a)) +#define stbiw__sbmaybegrow(a,n) (stbiw__sbneedgrow(a,(n)) ? stbiw__sbgrow(a,n) : 0) +#define stbiw__sbgrow(a,n) stbiw__sbgrowf((void **) &(a), (n), sizeof(*(a))) + +#define stbiw__sbpush(a, v) (stbiw__sbmaybegrow(a,1), (a)[stbiw__sbn(a)++] = (v)) +#define stbiw__sbcount(a) ((a) ? stbiw__sbn(a) : 0) +#define stbiw__sbfree(a) ((a) ? STBIW_FREE(stbiw__sbraw(a)),0 : 0) + +static void *stbiw__sbgrowf(void **arr, int increment, int itemsize) +{ + int m = *arr ? 2*stbiw__sbm(*arr)+increment : increment+1; + void *p = STBIW_REALLOC_SIZED(*arr ? stbiw__sbraw(*arr) : 0, *arr ? (stbiw__sbm(*arr)*itemsize + sizeof(int)*2) : 0, itemsize * m + sizeof(int)*2); + STBIW_ASSERT(p); + if (p) { + if (!*arr) ((int *) p)[1] = 0; + *arr = (void *) ((int *) p + 2); + stbiw__sbm(*arr) = m; + } + return *arr; +} + +static unsigned char *stbiw__zlib_flushf(unsigned char *data, unsigned int *bitbuffer, int *bitcount) +{ + while (*bitcount >= 8) { + stbiw__sbpush(data, STBIW_UCHAR(*bitbuffer)); + *bitbuffer >>= 8; + *bitcount -= 8; + } + return data; +} + +static int stbiw__zlib_bitrev(int code, int codebits) +{ + int res=0; + while (codebits--) { + res = (res << 1) | (code & 1); + code >>= 1; + } + return res; +} + +static unsigned int stbiw__zlib_countm(unsigned char *a, unsigned char *b, int limit) +{ + int i; + for (i=0; i < limit && i < 258; ++i) + if (a[i] != b[i]) break; + return i; +} + +static unsigned int stbiw__zhash(unsigned char *data) +{ + stbiw_uint32 hash = data[0] + (data[1] << 8) + (data[2] << 16); + hash ^= hash << 3; + hash += hash >> 5; + hash ^= hash << 4; + hash += hash >> 17; + hash ^= hash << 25; + hash += hash >> 6; + return hash; +} + +#define stbiw__zlib_flush() (out = stbiw__zlib_flushf(out, &bitbuf, &bitcount)) +#define stbiw__zlib_add(code,codebits) \ + (bitbuf |= (code) << bitcount, bitcount += (codebits), stbiw__zlib_flush()) +#define stbiw__zlib_huffa(b,c) stbiw__zlib_add(stbiw__zlib_bitrev(b,c),c) +// default huffman tables +#define stbiw__zlib_huff1(n) stbiw__zlib_huffa(0x30 + (n), 8) +#define stbiw__zlib_huff2(n) stbiw__zlib_huffa(0x190 + (n)-144, 9) +#define stbiw__zlib_huff3(n) stbiw__zlib_huffa(0 + (n)-256,7) +#define stbiw__zlib_huff4(n) stbiw__zlib_huffa(0xc0 + (n)-280,8) +#define stbiw__zlib_huff(n) ((n) <= 143 ? stbiw__zlib_huff1(n) : (n) <= 255 ? stbiw__zlib_huff2(n) : (n) <= 279 ? stbiw__zlib_huff3(n) : stbiw__zlib_huff4(n)) +#define stbiw__zlib_huffb(n) ((n) <= 143 ? stbiw__zlib_huff1(n) : stbiw__zlib_huff2(n)) + +#define stbiw__ZHASH 16384 + +#endif // STBIW_ZLIB_COMPRESS + +unsigned char * stbi_zlib_compress(unsigned char *data, int data_len, int *out_len, int quality) +{ +#ifdef STBIW_ZLIB_COMPRESS + // user provided a zlib compress implementation, use that + return STBIW_ZLIB_COMPRESS(data, data_len, out_len, quality); +#else // use builtin + static unsigned short lengthc[] = { 3,4,5,6,7,8,9,10,11,13,15,17,19,23,27,31,35,43,51,59,67,83,99,115,131,163,195,227,258, 259 }; + static unsigned char lengtheb[]= { 0,0,0,0,0,0,0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4, 5, 5, 5, 5, 0 }; + static unsigned short distc[] = { 1,2,3,4,5,7,9,13,17,25,33,49,65,97,129,193,257,385,513,769,1025,1537,2049,3073,4097,6145,8193,12289,16385,24577, 32768 }; + static unsigned char disteb[] = { 0,0,0,0,1,1,2,2,3,3,4,4,5,5,6,6,7,7,8,8,9,9,10,10,11,11,12,12,13,13 }; + unsigned int bitbuf=0; + int i,j, bitcount=0; + unsigned char *out = NULL; + unsigned char ***hash_table = (unsigned char***) STBIW_MALLOC(stbiw__ZHASH * sizeof(char**)); + if (hash_table == NULL) + return NULL; + if (quality < 5) quality = 5; + + stbiw__sbpush(out, 0x78); // DEFLATE 32K window + stbiw__sbpush(out, 0x5e); // FLEVEL = 1 + stbiw__zlib_add(1,1); // BFINAL = 1 + stbiw__zlib_add(1,2); // BTYPE = 1 -- fixed huffman + + for (i=0; i < stbiw__ZHASH; ++i) + hash_table[i] = NULL; + + i=0; + while (i < data_len-3) { + // hash next 3 bytes of data to be compressed + int h = stbiw__zhash(data+i)&(stbiw__ZHASH-1), best=3; + unsigned char *bestloc = 0; + unsigned char **hlist = hash_table[h]; + int n = stbiw__sbcount(hlist); + for (j=0; j < n; ++j) { + if (hlist[j]-data > i-32768) { // if entry lies within window + int d = stbiw__zlib_countm(hlist[j], data+i, data_len-i); + if (d >= best) best=d,bestloc=hlist[j]; + } + } + // when hash table entry is too long, delete half the entries + if (hash_table[h] && stbiw__sbn(hash_table[h]) == 2*quality) { + STBIW_MEMMOVE(hash_table[h], hash_table[h]+quality, sizeof(hash_table[h][0])*quality); + stbiw__sbn(hash_table[h]) = quality; + } + stbiw__sbpush(hash_table[h],data+i); + + if (bestloc) { + // "lazy matching" - check match at *next* byte, and if it's better, do cur byte as literal + h = stbiw__zhash(data+i+1)&(stbiw__ZHASH-1); + hlist = hash_table[h]; + n = stbiw__sbcount(hlist); + for (j=0; j < n; ++j) { + if (hlist[j]-data > i-32767) { + int e = stbiw__zlib_countm(hlist[j], data+i+1, data_len-i-1); + if (e > best) { // if next match is better, bail on current match + bestloc = NULL; + break; + } + } + } + } + + if (bestloc) { + int d = (int) (data+i - bestloc); // distance back + STBIW_ASSERT(d <= 32767 && best <= 258); + for (j=0; best > lengthc[j+1]-1; ++j); + stbiw__zlib_huff(j+257); + if (lengtheb[j]) stbiw__zlib_add(best - lengthc[j], lengtheb[j]); + for (j=0; d > distc[j+1]-1; ++j); + stbiw__zlib_add(stbiw__zlib_bitrev(j,5),5); + if (disteb[j]) stbiw__zlib_add(d - distc[j], disteb[j]); + i += best; + } else { + stbiw__zlib_huffb(data[i]); + ++i; + } + } + // write out final bytes + for (;i < data_len; ++i) + stbiw__zlib_huffb(data[i]); + stbiw__zlib_huff(256); // end of block + // pad with 0 bits to byte boundary + while (bitcount) + stbiw__zlib_add(0,1); + + for (i=0; i < stbiw__ZHASH; ++i) + (void) stbiw__sbfree(hash_table[i]); + STBIW_FREE(hash_table); + + { + // compute adler32 on input + unsigned int s1=1, s2=0; + int blocklen = (int) (data_len % 5552); + j=0; + while (j < data_len) { + for (i=0; i < blocklen; ++i) s1 += data[j+i], s2 += s1; + s1 %= 65521, s2 %= 65521; + j += blocklen; + blocklen = 5552; + } + stbiw__sbpush(out, STBIW_UCHAR(s2 >> 8)); + stbiw__sbpush(out, STBIW_UCHAR(s2)); + stbiw__sbpush(out, STBIW_UCHAR(s1 >> 8)); + stbiw__sbpush(out, STBIW_UCHAR(s1)); + } + *out_len = stbiw__sbn(out); + // make returned pointer freeable + STBIW_MEMMOVE(stbiw__sbraw(out), out, *out_len); + return (unsigned char *) stbiw__sbraw(out); +#endif // STBIW_ZLIB_COMPRESS +} + +static unsigned int stbiw__crc32(unsigned char *buffer, int len) +{ + static unsigned int crc_table[256] = + { + 0x00000000, 0x77073096, 0xEE0E612C, 0x990951BA, 0x076DC419, 0x706AF48F, 0xE963A535, 0x9E6495A3, + 0x0eDB8832, 0x79DCB8A4, 0xE0D5E91E, 0x97D2D988, 0x09B64C2B, 0x7EB17CBD, 0xE7B82D07, 0x90BF1D91, + 0x1DB71064, 0x6AB020F2, 0xF3B97148, 0x84BE41DE, 0x1ADAD47D, 0x6DDDE4EB, 0xF4D4B551, 0x83D385C7, + 0x136C9856, 0x646BA8C0, 0xFD62F97A, 0x8A65C9EC, 0x14015C4F, 0x63066CD9, 0xFA0F3D63, 0x8D080DF5, + 0x3B6E20C8, 0x4C69105E, 0xD56041E4, 0xA2677172, 0x3C03E4D1, 0x4B04D447, 0xD20D85FD, 0xA50AB56B, + 0x35B5A8FA, 0x42B2986C, 0xDBBBC9D6, 0xACBCF940, 0x32D86CE3, 0x45DF5C75, 0xDCD60DCF, 0xABD13D59, + 0x26D930AC, 0x51DE003A, 0xC8D75180, 0xBFD06116, 0x21B4F4B5, 0x56B3C423, 0xCFBA9599, 0xB8BDA50F, + 0x2802B89E, 0x5F058808, 0xC60CD9B2, 0xB10BE924, 0x2F6F7C87, 0x58684C11, 0xC1611DAB, 0xB6662D3D, + 0x76DC4190, 0x01DB7106, 0x98D220BC, 0xEFD5102A, 0x71B18589, 0x06B6B51F, 0x9FBFE4A5, 0xE8B8D433, + 0x7807C9A2, 0x0F00F934, 0x9609A88E, 0xE10E9818, 0x7F6A0DBB, 0x086D3D2D, 0x91646C97, 0xE6635C01, + 0x6B6B51F4, 0x1C6C6162, 0x856530D8, 0xF262004E, 0x6C0695ED, 0x1B01A57B, 0x8208F4C1, 0xF50FC457, + 0x65B0D9C6, 0x12B7E950, 0x8BBEB8EA, 0xFCB9887C, 0x62DD1DDF, 0x15DA2D49, 0x8CD37CF3, 0xFBD44C65, + 0x4DB26158, 0x3AB551CE, 0xA3BC0074, 0xD4BB30E2, 0x4ADFA541, 0x3DD895D7, 0xA4D1C46D, 0xD3D6F4FB, + 0x4369E96A, 0x346ED9FC, 0xAD678846, 0xDA60B8D0, 0x44042D73, 0x33031DE5, 0xAA0A4C5F, 0xDD0D7CC9, + 0x5005713C, 0x270241AA, 0xBE0B1010, 0xC90C2086, 0x5768B525, 0x206F85B3, 0xB966D409, 0xCE61E49F, + 0x5EDEF90E, 0x29D9C998, 0xB0D09822, 0xC7D7A8B4, 0x59B33D17, 0x2EB40D81, 0xB7BD5C3B, 0xC0BA6CAD, + 0xEDB88320, 0x9ABFB3B6, 0x03B6E20C, 0x74B1D29A, 0xEAD54739, 0x9DD277AF, 0x04DB2615, 0x73DC1683, + 0xE3630B12, 0x94643B84, 0x0D6D6A3E, 0x7A6A5AA8, 0xE40ECF0B, 0x9309FF9D, 0x0A00AE27, 0x7D079EB1, + 0xF00F9344, 0x8708A3D2, 0x1E01F268, 0x6906C2FE, 0xF762575D, 0x806567CB, 0x196C3671, 0x6E6B06E7, + 0xFED41B76, 0x89D32BE0, 0x10DA7A5A, 0x67DD4ACC, 0xF9B9DF6F, 0x8EBEEFF9, 0x17B7BE43, 0x60B08ED5, + 0xD6D6A3E8, 0xA1D1937E, 0x38D8C2C4, 0x4FDFF252, 0xD1BB67F1, 0xA6BC5767, 0x3FB506DD, 0x48B2364B, + 0xD80D2BDA, 0xAF0A1B4C, 0x36034AF6, 0x41047A60, 0xDF60EFC3, 0xA867DF55, 0x316E8EEF, 0x4669BE79, + 0xCB61B38C, 0xBC66831A, 0x256FD2A0, 0x5268E236, 0xCC0C7795, 0xBB0B4703, 0x220216B9, 0x5505262F, + 0xC5BA3BBE, 0xB2BD0B28, 0x2BB45A92, 0x5CB36A04, 0xC2D7FFA7, 0xB5D0CF31, 0x2CD99E8B, 0x5BDEAE1D, + 0x9B64C2B0, 0xEC63F226, 0x756AA39C, 0x026D930A, 0x9C0906A9, 0xEB0E363F, 0x72076785, 0x05005713, + 0x95BF4A82, 0xE2B87A14, 0x7BB12BAE, 0x0CB61B38, 0x92D28E9B, 0xE5D5BE0D, 0x7CDCEFB7, 0x0BDBDF21, + 0x86D3D2D4, 0xF1D4E242, 0x68DDB3F8, 0x1FDA836E, 0x81BE16CD, 0xF6B9265B, 0x6FB077E1, 0x18B74777, + 0x88085AE6, 0xFF0F6A70, 0x66063BCA, 0x11010B5C, 0x8F659EFF, 0xF862AE69, 0x616BFFD3, 0x166CCF45, + 0xA00AE278, 0xD70DD2EE, 0x4E048354, 0x3903B3C2, 0xA7672661, 0xD06016F7, 0x4969474D, 0x3E6E77DB, + 0xAED16A4A, 0xD9D65ADC, 0x40DF0B66, 0x37D83BF0, 0xA9BCAE53, 0xDEBB9EC5, 0x47B2CF7F, 0x30B5FFE9, + 0xBDBDF21C, 0xCABAC28A, 0x53B39330, 0x24B4A3A6, 0xBAD03605, 0xCDD70693, 0x54DE5729, 0x23D967BF, + 0xB3667A2E, 0xC4614AB8, 0x5D681B02, 0x2A6F2B94, 0xB40BBE37, 0xC30C8EA1, 0x5A05DF1B, 0x2D02EF8D + }; + + unsigned int crc = ~0u; + int i; + for (i=0; i < len; ++i) + crc = (crc >> 8) ^ crc_table[buffer[i] ^ (crc & 0xff)]; + return ~crc; +} + +#define stbiw__wpng4(o,a,b,c,d) ((o)[0]=STBIW_UCHAR(a),(o)[1]=STBIW_UCHAR(b),(o)[2]=STBIW_UCHAR(c),(o)[3]=STBIW_UCHAR(d),(o)+=4) +#define stbiw__wp32(data,v) stbiw__wpng4(data, (v)>>24,(v)>>16,(v)>>8,(v)); +#define stbiw__wptag(data,s) stbiw__wpng4(data, s[0],s[1],s[2],s[3]) + +static void stbiw__wpcrc(unsigned char **data, int len) +{ + unsigned int crc = stbiw__crc32(*data - len - 4, len+4); + stbiw__wp32(*data, crc); +} + +static unsigned char stbiw__paeth(int a, int b, int c) +{ + int p = a + b - c, pa = abs(p-a), pb = abs(p-b), pc = abs(p-c); + if (pa <= pb && pa <= pc) return STBIW_UCHAR(a); + if (pb <= pc) return STBIW_UCHAR(b); + return STBIW_UCHAR(c); +} + +// @OPTIMIZE: provide an option that always forces left-predict or paeth predict +static void stbiw__encode_png_line(unsigned char *pixels, int stride_bytes, int width, int height, int y, int n, int filter_type, signed char *line_buffer) +{ + static int mapping[] = { 0,1,2,3,4 }; + static int firstmap[] = { 0,1,0,5,6 }; + int *mymap = (y != 0) ? mapping : firstmap; + int i; + int type = mymap[filter_type]; + unsigned char *z = pixels + stride_bytes * (stbi__flip_vertically_on_write ? height-1-y : y); + int signed_stride = stbi__flip_vertically_on_write ? -stride_bytes : stride_bytes; + for (i = 0; i < n; ++i) { + switch (type) { + case 0: line_buffer[i] = z[i]; break; + case 1: line_buffer[i] = z[i]; break; + case 2: line_buffer[i] = z[i] - z[i-signed_stride]; break; + case 3: line_buffer[i] = z[i] - (z[i-signed_stride]>>1); break; + case 4: line_buffer[i] = (signed char) (z[i] - stbiw__paeth(0,z[i-signed_stride],0)); break; + case 5: line_buffer[i] = z[i]; break; + case 6: line_buffer[i] = z[i]; break; + } + } + for (i=n; i < width*n; ++i) { + switch (type) { + case 0: line_buffer[i] = z[i]; break; + case 1: line_buffer[i] = z[i] - z[i-n]; break; + case 2: line_buffer[i] = z[i] - z[i-signed_stride]; break; + case 3: line_buffer[i] = z[i] - ((z[i-n] + z[i-signed_stride])>>1); break; + case 4: line_buffer[i] = z[i] - stbiw__paeth(z[i-n], z[i-signed_stride], z[i-signed_stride-n]); break; + case 5: line_buffer[i] = z[i] - (z[i-n]>>1); break; + case 6: line_buffer[i] = z[i] - stbiw__paeth(z[i-n], 0,0); break; + } + } +} + +unsigned char *stbi_write_png_to_mem(unsigned char *pixels, int stride_bytes, int x, int y, int n, int *out_len) +{ + int force_filter = stbi_write_force_png_filter; + int ctype[5] = { -1, 0, 4, 2, 6 }; + unsigned char sig[8] = { 137,80,78,71,13,10,26,10 }; + unsigned char *out,*o, *filt, *zlib; + signed char *line_buffer; + int j,zlen; + + if (stride_bytes == 0) + stride_bytes = x * n; + + if (force_filter >= 5) { + force_filter = -1; + } + + filt = (unsigned char *) STBIW_MALLOC((x*n+1) * y); if (!filt) return 0; + line_buffer = (signed char *) STBIW_MALLOC(x * n); if (!line_buffer) { STBIW_FREE(filt); return 0; } + for (j=0; j < y; ++j) { + int filter_type; + if (force_filter > -1) { + filter_type = force_filter; + stbiw__encode_png_line(pixels, stride_bytes, x, y, j, n, force_filter, line_buffer); + } else { // Estimate the best filter by running through all of them: + int best_filter = 0, best_filter_val = 0x7fffffff, est, i; + for (filter_type = 0; filter_type < 5; filter_type++) { + stbiw__encode_png_line(pixels, stride_bytes, x, y, j, n, filter_type, line_buffer); + + // Estimate the entropy of the line using this filter; the less, the better. + est = 0; + for (i = 0; i < x*n; ++i) { + est += abs((signed char) line_buffer[i]); + } + if (est < best_filter_val) { + best_filter_val = est; + best_filter = filter_type; + } + } + if (filter_type != best_filter) { // If the last iteration already got us the best filter, don't redo it + stbiw__encode_png_line(pixels, stride_bytes, x, y, j, n, best_filter, line_buffer); + filter_type = best_filter; + } + } + // when we get here, filter_type contains the filter type, and line_buffer contains the data + filt[j*(x*n+1)] = (unsigned char) filter_type; + STBIW_MEMMOVE(filt+j*(x*n+1)+1, line_buffer, x*n); + } + STBIW_FREE(line_buffer); + zlib = stbi_zlib_compress(filt, y*( x*n+1), &zlen, stbi_write_png_compression_level); + STBIW_FREE(filt); + if (!zlib) return 0; + + // each tag requires 12 bytes of overhead + out = (unsigned char *) STBIW_MALLOC(8 + 12+13 + 12+zlen + 12); + if (!out) return 0; + *out_len = 8 + 12+13 + 12+zlen + 12; + + o=out; + STBIW_MEMMOVE(o,sig,8); o+= 8; + stbiw__wp32(o, 13); // header length + stbiw__wptag(o, "IHDR"); + stbiw__wp32(o, x); + stbiw__wp32(o, y); + *o++ = 8; + *o++ = STBIW_UCHAR(ctype[n]); + *o++ = 0; + *o++ = 0; + *o++ = 0; + stbiw__wpcrc(&o,13); + + stbiw__wp32(o, zlen); + stbiw__wptag(o, "IDAT"); + STBIW_MEMMOVE(o, zlib, zlen); + o += zlen; + STBIW_FREE(zlib); + stbiw__wpcrc(&o, zlen); + + stbiw__wp32(o,0); + stbiw__wptag(o, "IEND"); + stbiw__wpcrc(&o,0); + + STBIW_ASSERT(o == out + *out_len); + + return out; +} + +#ifndef STBI_WRITE_NO_STDIO +STBIWDEF int stbi_write_png(char const *filename, int x, int y, int comp, const void *data, int stride_bytes) +{ + FILE *f; + int len; + unsigned char *png = stbi_write_png_to_mem((unsigned char *) data, stride_bytes, x, y, comp, &len); + if (png == NULL) return 0; +#ifdef STBI_MSC_SECURE_CRT + if (fopen_s(&f, filename, "wb")) + f = NULL; +#else + f = fopen(filename, "wb"); +#endif + if (!f) { STBIW_FREE(png); return 0; } + fwrite(png, 1, len, f); + fclose(f); + STBIW_FREE(png); + return 1; +} +#endif + +STBIWDEF int stbi_write_png_to_func(stbi_write_func *func, void *context, int x, int y, int comp, const void *data, int stride_bytes) +{ + int len; + unsigned char *png = stbi_write_png_to_mem((unsigned char *) data, stride_bytes, x, y, comp, &len); + if (png == NULL) return 0; + func(context, png, len); + STBIW_FREE(png); + return 1; +} + + +/* *************************************************************************** + * + * JPEG writer + * + * This is based on Jon Olick's jo_jpeg.cpp: + * public domain Simple, Minimalistic JPEG writer - http://www.jonolick.com/code.html + */ + +static const unsigned char stbiw__jpg_ZigZag[] = { 0,1,5,6,14,15,27,28,2,4,7,13,16,26,29,42,3,8,12,17,25,30,41,43,9,11,18, + 24,31,40,44,53,10,19,23,32,39,45,52,54,20,22,33,38,46,51,55,60,21,34,37,47,50,56,59,61,35,36,48,49,57,58,62,63 }; + +static void stbiw__jpg_writeBits(stbi__write_context *s, int *bitBufP, int *bitCntP, const unsigned short *bs) { + int bitBuf = *bitBufP, bitCnt = *bitCntP; + bitCnt += bs[1]; + bitBuf |= bs[0] << (24 - bitCnt); + while(bitCnt >= 8) { + unsigned char c = (bitBuf >> 16) & 255; + stbiw__putc(s, c); + if(c == 255) { + stbiw__putc(s, 0); + } + bitBuf <<= 8; + bitCnt -= 8; + } + *bitBufP = bitBuf; + *bitCntP = bitCnt; +} + +static void stbiw__jpg_DCT(float *d0p, float *d1p, float *d2p, float *d3p, float *d4p, float *d5p, float *d6p, float *d7p) { + float d0 = *d0p, d1 = *d1p, d2 = *d2p, d3 = *d3p, d4 = *d4p, d5 = *d5p, d6 = *d6p, d7 = *d7p; + float z1, z2, z3, z4, z5, z11, z13; + + float tmp0 = d0 + d7; + float tmp7 = d0 - d7; + float tmp1 = d1 + d6; + float tmp6 = d1 - d6; + float tmp2 = d2 + d5; + float tmp5 = d2 - d5; + float tmp3 = d3 + d4; + float tmp4 = d3 - d4; + + // Even part + float tmp10 = tmp0 + tmp3; // phase 2 + float tmp13 = tmp0 - tmp3; + float tmp11 = tmp1 + tmp2; + float tmp12 = tmp1 - tmp2; + + d0 = tmp10 + tmp11; // phase 3 + d4 = tmp10 - tmp11; + + z1 = (tmp12 + tmp13) * 0.707106781f; // c4 + d2 = tmp13 + z1; // phase 5 + d6 = tmp13 - z1; + + // Odd part + tmp10 = tmp4 + tmp5; // phase 2 + tmp11 = tmp5 + tmp6; + tmp12 = tmp6 + tmp7; + + // The rotator is modified from fig 4-8 to avoid extra negations. + z5 = (tmp10 - tmp12) * 0.382683433f; // c6 + z2 = tmp10 * 0.541196100f + z5; // c2-c6 + z4 = tmp12 * 1.306562965f + z5; // c2+c6 + z3 = tmp11 * 0.707106781f; // c4 + + z11 = tmp7 + z3; // phase 5 + z13 = tmp7 - z3; + + *d5p = z13 + z2; // phase 6 + *d3p = z13 - z2; + *d1p = z11 + z4; + *d7p = z11 - z4; + + *d0p = d0; *d2p = d2; *d4p = d4; *d6p = d6; +} + +static void stbiw__jpg_calcBits(int val, unsigned short bits[2]) { + int tmp1 = val < 0 ? -val : val; + val = val < 0 ? val-1 : val; + bits[1] = 1; + while(tmp1 >>= 1) { + ++bits[1]; + } + bits[0] = val & ((1<0)&&(DU[end0pos]==0); --end0pos) { + } + // end0pos = first element in reverse order !=0 + if(end0pos == 0) { + stbiw__jpg_writeBits(s, bitBuf, bitCnt, EOB); + return DU[0]; + } + for(i = 1; i <= end0pos; ++i) { + int startpos = i; + int nrzeroes; + unsigned short bits[2]; + for (; DU[i]==0 && i<=end0pos; ++i) { + } + nrzeroes = i-startpos; + if ( nrzeroes >= 16 ) { + int lng = nrzeroes>>4; + int nrmarker; + for (nrmarker=1; nrmarker <= lng; ++nrmarker) + stbiw__jpg_writeBits(s, bitBuf, bitCnt, M16zeroes); + nrzeroes &= 15; + } + stbiw__jpg_calcBits(DU[i], bits); + stbiw__jpg_writeBits(s, bitBuf, bitCnt, HTAC[(nrzeroes<<4)+bits[1]]); + stbiw__jpg_writeBits(s, bitBuf, bitCnt, bits); + } + if(end0pos != 63) { + stbiw__jpg_writeBits(s, bitBuf, bitCnt, EOB); + } + return DU[0]; +} + +static int stbi_write_jpg_core(stbi__write_context *s, int width, int height, int comp, const void* data, int quality) { + // Constants that don't pollute global namespace + static const unsigned char std_dc_luminance_nrcodes[] = {0,0,1,5,1,1,1,1,1,1,0,0,0,0,0,0,0}; + static const unsigned char std_dc_luminance_values[] = {0,1,2,3,4,5,6,7,8,9,10,11}; + static const unsigned char std_ac_luminance_nrcodes[] = {0,0,2,1,3,3,2,4,3,5,5,4,4,0,0,1,0x7d}; + static const unsigned char std_ac_luminance_values[] = { + 0x01,0x02,0x03,0x00,0x04,0x11,0x05,0x12,0x21,0x31,0x41,0x06,0x13,0x51,0x61,0x07,0x22,0x71,0x14,0x32,0x81,0x91,0xa1,0x08, + 0x23,0x42,0xb1,0xc1,0x15,0x52,0xd1,0xf0,0x24,0x33,0x62,0x72,0x82,0x09,0x0a,0x16,0x17,0x18,0x19,0x1a,0x25,0x26,0x27,0x28, + 0x29,0x2a,0x34,0x35,0x36,0x37,0x38,0x39,0x3a,0x43,0x44,0x45,0x46,0x47,0x48,0x49,0x4a,0x53,0x54,0x55,0x56,0x57,0x58,0x59, + 0x5a,0x63,0x64,0x65,0x66,0x67,0x68,0x69,0x6a,0x73,0x74,0x75,0x76,0x77,0x78,0x79,0x7a,0x83,0x84,0x85,0x86,0x87,0x88,0x89, + 0x8a,0x92,0x93,0x94,0x95,0x96,0x97,0x98,0x99,0x9a,0xa2,0xa3,0xa4,0xa5,0xa6,0xa7,0xa8,0xa9,0xaa,0xb2,0xb3,0xb4,0xb5,0xb6, + 0xb7,0xb8,0xb9,0xba,0xc2,0xc3,0xc4,0xc5,0xc6,0xc7,0xc8,0xc9,0xca,0xd2,0xd3,0xd4,0xd5,0xd6,0xd7,0xd8,0xd9,0xda,0xe1,0xe2, + 0xe3,0xe4,0xe5,0xe6,0xe7,0xe8,0xe9,0xea,0xf1,0xf2,0xf3,0xf4,0xf5,0xf6,0xf7,0xf8,0xf9,0xfa + }; + static const unsigned char std_dc_chrominance_nrcodes[] = {0,0,3,1,1,1,1,1,1,1,1,1,0,0,0,0,0}; + static const unsigned char std_dc_chrominance_values[] = {0,1,2,3,4,5,6,7,8,9,10,11}; + static const unsigned char std_ac_chrominance_nrcodes[] = {0,0,2,1,2,4,4,3,4,7,5,4,4,0,1,2,0x77}; + static const unsigned char std_ac_chrominance_values[] = { + 0x00,0x01,0x02,0x03,0x11,0x04,0x05,0x21,0x31,0x06,0x12,0x41,0x51,0x07,0x61,0x71,0x13,0x22,0x32,0x81,0x08,0x14,0x42,0x91, + 0xa1,0xb1,0xc1,0x09,0x23,0x33,0x52,0xf0,0x15,0x62,0x72,0xd1,0x0a,0x16,0x24,0x34,0xe1,0x25,0xf1,0x17,0x18,0x19,0x1a,0x26, + 0x27,0x28,0x29,0x2a,0x35,0x36,0x37,0x38,0x39,0x3a,0x43,0x44,0x45,0x46,0x47,0x48,0x49,0x4a,0x53,0x54,0x55,0x56,0x57,0x58, + 0x59,0x5a,0x63,0x64,0x65,0x66,0x67,0x68,0x69,0x6a,0x73,0x74,0x75,0x76,0x77,0x78,0x79,0x7a,0x82,0x83,0x84,0x85,0x86,0x87, + 0x88,0x89,0x8a,0x92,0x93,0x94,0x95,0x96,0x97,0x98,0x99,0x9a,0xa2,0xa3,0xa4,0xa5,0xa6,0xa7,0xa8,0xa9,0xaa,0xb2,0xb3,0xb4, + 0xb5,0xb6,0xb7,0xb8,0xb9,0xba,0xc2,0xc3,0xc4,0xc5,0xc6,0xc7,0xc8,0xc9,0xca,0xd2,0xd3,0xd4,0xd5,0xd6,0xd7,0xd8,0xd9,0xda, + 0xe2,0xe3,0xe4,0xe5,0xe6,0xe7,0xe8,0xe9,0xea,0xf2,0xf3,0xf4,0xf5,0xf6,0xf7,0xf8,0xf9,0xfa + }; + // Huffman tables + static const unsigned short YDC_HT[256][2] = { {0,2},{2,3},{3,3},{4,3},{5,3},{6,3},{14,4},{30,5},{62,6},{126,7},{254,8},{510,9}}; + static const unsigned short UVDC_HT[256][2] = { {0,2},{1,2},{2,2},{6,3},{14,4},{30,5},{62,6},{126,7},{254,8},{510,9},{1022,10},{2046,11}}; + static const unsigned short YAC_HT[256][2] = { + {10,4},{0,2},{1,2},{4,3},{11,4},{26,5},{120,7},{248,8},{1014,10},{65410,16},{65411,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {12,4},{27,5},{121,7},{502,9},{2038,11},{65412,16},{65413,16},{65414,16},{65415,16},{65416,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {28,5},{249,8},{1015,10},{4084,12},{65417,16},{65418,16},{65419,16},{65420,16},{65421,16},{65422,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {58,6},{503,9},{4085,12},{65423,16},{65424,16},{65425,16},{65426,16},{65427,16},{65428,16},{65429,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {59,6},{1016,10},{65430,16},{65431,16},{65432,16},{65433,16},{65434,16},{65435,16},{65436,16},{65437,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {122,7},{2039,11},{65438,16},{65439,16},{65440,16},{65441,16},{65442,16},{65443,16},{65444,16},{65445,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {123,7},{4086,12},{65446,16},{65447,16},{65448,16},{65449,16},{65450,16},{65451,16},{65452,16},{65453,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {250,8},{4087,12},{65454,16},{65455,16},{65456,16},{65457,16},{65458,16},{65459,16},{65460,16},{65461,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {504,9},{32704,15},{65462,16},{65463,16},{65464,16},{65465,16},{65466,16},{65467,16},{65468,16},{65469,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {505,9},{65470,16},{65471,16},{65472,16},{65473,16},{65474,16},{65475,16},{65476,16},{65477,16},{65478,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {506,9},{65479,16},{65480,16},{65481,16},{65482,16},{65483,16},{65484,16},{65485,16},{65486,16},{65487,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {1017,10},{65488,16},{65489,16},{65490,16},{65491,16},{65492,16},{65493,16},{65494,16},{65495,16},{65496,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {1018,10},{65497,16},{65498,16},{65499,16},{65500,16},{65501,16},{65502,16},{65503,16},{65504,16},{65505,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {2040,11},{65506,16},{65507,16},{65508,16},{65509,16},{65510,16},{65511,16},{65512,16},{65513,16},{65514,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {65515,16},{65516,16},{65517,16},{65518,16},{65519,16},{65520,16},{65521,16},{65522,16},{65523,16},{65524,16},{0,0},{0,0},{0,0},{0,0},{0,0}, + {2041,11},{65525,16},{65526,16},{65527,16},{65528,16},{65529,16},{65530,16},{65531,16},{65532,16},{65533,16},{65534,16},{0,0},{0,0},{0,0},{0,0},{0,0} + }; + static const unsigned short UVAC_HT[256][2] = { + {0,2},{1,2},{4,3},{10,4},{24,5},{25,5},{56,6},{120,7},{500,9},{1014,10},{4084,12},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {11,4},{57,6},{246,8},{501,9},{2038,11},{4085,12},{65416,16},{65417,16},{65418,16},{65419,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {26,5},{247,8},{1015,10},{4086,12},{32706,15},{65420,16},{65421,16},{65422,16},{65423,16},{65424,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {27,5},{248,8},{1016,10},{4087,12},{65425,16},{65426,16},{65427,16},{65428,16},{65429,16},{65430,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {58,6},{502,9},{65431,16},{65432,16},{65433,16},{65434,16},{65435,16},{65436,16},{65437,16},{65438,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {59,6},{1017,10},{65439,16},{65440,16},{65441,16},{65442,16},{65443,16},{65444,16},{65445,16},{65446,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {121,7},{2039,11},{65447,16},{65448,16},{65449,16},{65450,16},{65451,16},{65452,16},{65453,16},{65454,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {122,7},{2040,11},{65455,16},{65456,16},{65457,16},{65458,16},{65459,16},{65460,16},{65461,16},{65462,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {249,8},{65463,16},{65464,16},{65465,16},{65466,16},{65467,16},{65468,16},{65469,16},{65470,16},{65471,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {503,9},{65472,16},{65473,16},{65474,16},{65475,16},{65476,16},{65477,16},{65478,16},{65479,16},{65480,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {504,9},{65481,16},{65482,16},{65483,16},{65484,16},{65485,16},{65486,16},{65487,16},{65488,16},{65489,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {505,9},{65490,16},{65491,16},{65492,16},{65493,16},{65494,16},{65495,16},{65496,16},{65497,16},{65498,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {506,9},{65499,16},{65500,16},{65501,16},{65502,16},{65503,16},{65504,16},{65505,16},{65506,16},{65507,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {2041,11},{65508,16},{65509,16},{65510,16},{65511,16},{65512,16},{65513,16},{65514,16},{65515,16},{65516,16},{0,0},{0,0},{0,0},{0,0},{0,0},{0,0}, + {16352,14},{65517,16},{65518,16},{65519,16},{65520,16},{65521,16},{65522,16},{65523,16},{65524,16},{65525,16},{0,0},{0,0},{0,0},{0,0},{0,0}, + {1018,10},{32707,15},{65526,16},{65527,16},{65528,16},{65529,16},{65530,16},{65531,16},{65532,16},{65533,16},{65534,16},{0,0},{0,0},{0,0},{0,0},{0,0} + }; + static const int YQT[] = {16,11,10,16,24,40,51,61,12,12,14,19,26,58,60,55,14,13,16,24,40,57,69,56,14,17,22,29,51,87,80,62,18,22, + 37,56,68,109,103,77,24,35,55,64,81,104,113,92,49,64,78,87,103,121,120,101,72,92,95,98,112,100,103,99}; + static const int UVQT[] = {17,18,24,47,99,99,99,99,18,21,26,66,99,99,99,99,24,26,56,99,99,99,99,99,47,66,99,99,99,99,99,99, + 99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99}; + static const float aasf[] = { 1.0f * 2.828427125f, 1.387039845f * 2.828427125f, 1.306562965f * 2.828427125f, 1.175875602f * 2.828427125f, + 1.0f * 2.828427125f, 0.785694958f * 2.828427125f, 0.541196100f * 2.828427125f, 0.275899379f * 2.828427125f }; + + int row, col, i, k; + float fdtbl_Y[64], fdtbl_UV[64]; + unsigned char YTable[64], UVTable[64]; + + if(!data || !width || !height || comp > 4 || comp < 1) { + return 0; + } + + quality = quality ? quality : 90; + quality = quality < 1 ? 1 : quality > 100 ? 100 : quality; + quality = quality < 50 ? 5000 / quality : 200 - quality * 2; + + for(i = 0; i < 64; ++i) { + int uvti, yti = (YQT[i]*quality+50)/100; + YTable[stbiw__jpg_ZigZag[i]] = (unsigned char) (yti < 1 ? 1 : yti > 255 ? 255 : yti); + uvti = (UVQT[i]*quality+50)/100; + UVTable[stbiw__jpg_ZigZag[i]] = (unsigned char) (uvti < 1 ? 1 : uvti > 255 ? 255 : uvti); + } + + for(row = 0, k = 0; row < 8; ++row) { + for(col = 0; col < 8; ++col, ++k) { + fdtbl_Y[k] = 1 / (YTable [stbiw__jpg_ZigZag[k]] * aasf[row] * aasf[col]); + fdtbl_UV[k] = 1 / (UVTable[stbiw__jpg_ZigZag[k]] * aasf[row] * aasf[col]); + } + } + + // Write Headers + { + static const unsigned char head0[] = { 0xFF,0xD8,0xFF,0xE0,0,0x10,'J','F','I','F',0,1,1,0,0,1,0,1,0,0,0xFF,0xDB,0,0x84,0 }; + static const unsigned char head2[] = { 0xFF,0xDA,0,0xC,3,1,0,2,0x11,3,0x11,0,0x3F,0 }; + const unsigned char head1[] = { 0xFF,0xC0,0,0x11,8,(unsigned char)(height>>8),STBIW_UCHAR(height),(unsigned char)(width>>8),STBIW_UCHAR(width), + 3,1,0x11,0,2,0x11,1,3,0x11,1,0xFF,0xC4,0x01,0xA2,0 }; + s->func(s->context, (void*)head0, sizeof(head0)); + s->func(s->context, (void*)YTable, sizeof(YTable)); + stbiw__putc(s, 1); + s->func(s->context, UVTable, sizeof(UVTable)); + s->func(s->context, (void*)head1, sizeof(head1)); + s->func(s->context, (void*)(std_dc_luminance_nrcodes+1), sizeof(std_dc_luminance_nrcodes)-1); + s->func(s->context, (void*)std_dc_luminance_values, sizeof(std_dc_luminance_values)); + stbiw__putc(s, 0x10); // HTYACinfo + s->func(s->context, (void*)(std_ac_luminance_nrcodes+1), sizeof(std_ac_luminance_nrcodes)-1); + s->func(s->context, (void*)std_ac_luminance_values, sizeof(std_ac_luminance_values)); + stbiw__putc(s, 1); // HTUDCinfo + s->func(s->context, (void*)(std_dc_chrominance_nrcodes+1), sizeof(std_dc_chrominance_nrcodes)-1); + s->func(s->context, (void*)std_dc_chrominance_values, sizeof(std_dc_chrominance_values)); + stbiw__putc(s, 0x11); // HTUACinfo + s->func(s->context, (void*)(std_ac_chrominance_nrcodes+1), sizeof(std_ac_chrominance_nrcodes)-1); + s->func(s->context, (void*)std_ac_chrominance_values, sizeof(std_ac_chrominance_values)); + s->func(s->context, (void*)head2, sizeof(head2)); + } + + // Encode 8x8 macroblocks + { + static const unsigned short fillBits[] = {0x7F, 7}; + const unsigned char *imageData = (const unsigned char *)data; + int DCY=0, DCU=0, DCV=0; + int bitBuf=0, bitCnt=0; + // comp == 2 is grey+alpha (alpha is ignored) + int ofsG = comp > 2 ? 1 : 0, ofsB = comp > 2 ? 2 : 0; + int x, y, pos; + for(y = 0; y < height; y += 8) { + for(x = 0; x < width; x += 8) { + float YDU[64], UDU[64], VDU[64]; + for(row = y, pos = 0; row < y+8; ++row) { + for(col = x; col < x+8; ++col, ++pos) { + int p = (stbi__flip_vertically_on_write ? height-1-row : row)*width*comp + col*comp; + float r, g, b; + if(row >= height) { + p -= width*comp*(row+1 - height); + } + if(col >= width) { + p -= comp*(col+1 - width); + } + + r = imageData[p+0]; + g = imageData[p+ofsG]; + b = imageData[p+ofsB]; + YDU[pos]=+0.29900f*r+0.58700f*g+0.11400f*b-128; + UDU[pos]=-0.16874f*r-0.33126f*g+0.50000f*b; + VDU[pos]=+0.50000f*r-0.41869f*g-0.08131f*b; + } + } + + DCY = stbiw__jpg_processDU(s, &bitBuf, &bitCnt, YDU, fdtbl_Y, DCY, YDC_HT, YAC_HT); + DCU = stbiw__jpg_processDU(s, &bitBuf, &bitCnt, UDU, fdtbl_UV, DCU, UVDC_HT, UVAC_HT); + DCV = stbiw__jpg_processDU(s, &bitBuf, &bitCnt, VDU, fdtbl_UV, DCV, UVDC_HT, UVAC_HT); + } + } + + // Do the bit alignment of the EOI marker + stbiw__jpg_writeBits(s, &bitBuf, &bitCnt, fillBits); + } + + // EOI + stbiw__putc(s, 0xFF); + stbiw__putc(s, 0xD9); + + return 1; +} + +STBIWDEF int stbi_write_jpg_to_func(stbi_write_func *func, void *context, int x, int y, int comp, const void *data, int quality) +{ + stbi__write_context s; + stbi__start_write_callbacks(&s, func, context); + return stbi_write_jpg_core(&s, x, y, comp, (void *) data, quality); +} + + +#ifndef STBI_WRITE_NO_STDIO +STBIWDEF int stbi_write_jpg(char const *filename, int x, int y, int comp, const void *data, int quality) +{ + stbi__write_context s; + if (stbi__start_write_file(&s,filename)) { + int r = stbi_write_jpg_core(&s, x, y, comp, data, quality); + stbi__end_write_file(&s); + return r; + } else + return 0; +} +#endif + +#endif // STB_IMAGE_WRITE_IMPLEMENTATION + +/* Revision history + 1.09 (2018-02-11) + fix typo in zlib quality API, improve STB_I_W_STATIC in C++ + 1.08 (2018-01-29) + add stbi__flip_vertically_on_write, external zlib, zlib quality, choose PNG filter + 1.07 (2017-07-24) + doc fix + 1.06 (2017-07-23) + writing JPEG (using Jon Olick's code) + 1.05 ??? + 1.04 (2017-03-03) + monochrome BMP expansion + 1.03 ??? + 1.02 (2016-04-02) + avoid allocating large structures on the stack + 1.01 (2016-01-16) + STBIW_REALLOC_SIZED: support allocators with no realloc support + avoid race-condition in crc initialization + minor compile issues + 1.00 (2015-09-14) + installable file IO function + 0.99 (2015-09-13) + warning fixes; TGA rle support + 0.98 (2015-04-08) + added STBIW_MALLOC, STBIW_ASSERT etc + 0.97 (2015-01-18) + fixed HDR asserts, rewrote HDR rle logic + 0.96 (2015-01-17) + add HDR output + fix monochrome BMP + 0.95 (2014-08-17) + add monochrome TGA output + 0.94 (2014-05-31) + rename private functions to avoid conflicts with stb_image.h + 0.93 (2014-05-27) + warning fixes + 0.92 (2010-08-01) + casts to unsigned char to fix warnings + 0.91 (2010-07-17) + first public release + 0.90 first internal release +*/ + +/* +------------------------------------------------------------------------------ +This software is available under 2 licenses -- choose whichever you prefer. +------------------------------------------------------------------------------ +ALTERNATIVE A - MIT License +Copyright (c) 2017 Sean Barrett +Permission is hereby granted, free of charge, to any person obtaining a copy of +this software and associated documentation files (the "Software"), to deal in +the Software without restriction, including without limitation the rights to +use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies +of the Software, and to permit persons to whom the Software is furnished to do +so, subject to the following conditions: +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. +------------------------------------------------------------------------------ +ALTERNATIVE B - Public Domain (www.unlicense.org) +This is free and unencumbered software released into the public domain. +Anyone is free to copy, modify, publish, use, compile, sell, or distribute this +software, either in source code form or as a compiled binary, for any purpose, +commercial or non-commercial, and by any means. +In jurisdictions that recognize copyright laws, the author or authors of this +software dedicate any and all copyright interest in the software to the public +domain. We make this dedication for the benefit of the public at large and to +the detriment of our heirs and successors. We intend this dedication to be an +overt act of relinquishment in perpetuity of all present and future rights to +this software under copyright law. +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN +ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION +WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. +------------------------------------------------------------------------------ +*/ diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/tree.c b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/tree.c new file mode 100644 index 00000000000..67b6d431f6f --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/tree.c @@ -0,0 +1,139 @@ +#include +#include +#include "tree.h" +#include "utils.h" +#include "data.h" + +void change_leaves(tree *t, char *leaf_list) +{ + list *llist = get_paths(leaf_list); + char **leaves = (char **)list_to_array(llist); + int n = llist->size; + int i,j; + int found = 0; + for(i = 0; i < t->n; ++i){ + t->leaf[i] = 0; + for(j = 0; j < n; ++j){ + if (0==strcmp(t->name[i], leaves[j])){ + t->leaf[i] = 1; + ++found; + break; + } + } + } + fprintf(stderr, "Found %d leaves.\n", found); +} + +float get_hierarchy_probability(float *x, tree *hier, int c, int stride) +{ + float p = 1; + while(c >= 0){ + p = p * x[c*stride]; + c = hier->parent[c]; + } + return p; +} + +void hierarchy_predictions(float *predictions, int n, tree *hier, int only_leaves, int stride) +{ + int j; + for(j = 0; j < n; ++j){ + int parent = hier->parent[j]; + if(parent >= 0){ + predictions[j*stride] *= predictions[parent*stride]; + } + } + if(only_leaves){ + for(j = 0; j < n; ++j){ + if(!hier->leaf[j]) predictions[j*stride] = 0; + } + } +} + +int hierarchy_top_prediction(float *predictions, tree *hier, float thresh, int stride) +{ + float p = 1; + int group = 0; + int i; + while(1){ + float max = 0; + int max_i = 0; + + for(i = 0; i < hier->group_size[group]; ++i){ + int index = i + hier->group_offset[group]; + float val = predictions[(i + hier->group_offset[group])*stride]; + if(val > max){ + max_i = index; + max = val; + } + } + if(p*max > thresh){ + p = p*max; + group = hier->child[max_i]; + if(hier->child[max_i] < 0) return max_i; + } else if (group == 0){ + return max_i; + } else { + return hier->parent[hier->group_offset[group]]; + } + } + return 0; +} + +tree *read_tree(char *filename) +{ + tree t = {0}; + FILE *fp = fopen(filename, "r"); + + char *line; + int last_parent = -1; + int group_size = 0; + int groups = 0; + int n = 0; + while((line=fgetl(fp)) != 0){ + char *id = calloc(256, sizeof(char)); + int parent = -1; + sscanf(line, "%s %d", id, &parent); + t.parent = realloc(t.parent, (n+1)*sizeof(int)); + t.parent[n] = parent; + + t.child = realloc(t.child, (n+1)*sizeof(int)); + t.child[n] = -1; + + t.name = realloc(t.name, (n+1)*sizeof(char *)); + t.name[n] = id; + if(parent != last_parent){ + ++groups; + t.group_offset = realloc(t.group_offset, groups * sizeof(int)); + t.group_offset[groups - 1] = n - group_size; + t.group_size = realloc(t.group_size, groups * sizeof(int)); + t.group_size[groups - 1] = group_size; + group_size = 0; + last_parent = parent; + } + t.group = realloc(t.group, (n+1)*sizeof(int)); + t.group[n] = groups; + if (parent >= 0) { + t.child[parent] = groups; + } + ++n; + ++group_size; + } + ++groups; + t.group_offset = realloc(t.group_offset, groups * sizeof(int)); + t.group_offset[groups - 1] = n - group_size; + t.group_size = realloc(t.group_size, groups * sizeof(int)); + t.group_size[groups - 1] = group_size; + t.n = n; + t.groups = groups; + t.leaf = calloc(n, sizeof(int)); + int i; + for(i = 0; i < n; ++i) t.leaf[i] = 1; + for(i = 0; i < n; ++i) if(t.parent[i] >= 0) t.leaf[t.parent[i]] = 0; + + fclose(fp); + tree *tree_ptr = calloc(1, sizeof(tree)); + *tree_ptr = t; + //error(0); + return tree_ptr; +} diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/tree.h b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/tree.h new file mode 100644 index 00000000000..3802b8ead80 --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/tree.h @@ -0,0 +1,8 @@ +#ifndef TREE_H +#define TREE_H +#include "darknet.h" + +int hierarchy_top_prediction(float *predictions, tree *hier, float thresh, int stride); +float get_hierarchy_probability(float *x, tree *hier, int c, int stride); + +#endif diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/upsample_layer.c b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/upsample_layer.c new file mode 100644 index 00000000000..605f21f8ebd --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/upsample_layer.c @@ -0,0 +1,106 @@ +#include "upsample_layer.h" +#include "cuda.h" +#include "blas.h" + +#include + +layer make_upsample_layer(int batch, int w, int h, int c, int stride) +{ + layer l = {0}; + l.type = UPSAMPLE; + l.batch = batch; + l.w = w; + l.h = h; + l.c = c; + l.out_w = w*stride; + l.out_h = h*stride; + l.out_c = c; + if(stride < 0){ + stride = -stride; + l.reverse=1; + l.out_w = w/stride; + l.out_h = h/stride; + } + l.stride = stride; + l.outputs = l.out_w*l.out_h*l.out_c; + l.inputs = l.w*l.h*l.c; + l.delta = calloc(l.outputs*batch, sizeof(float)); + l.output = calloc(l.outputs*batch, sizeof(float));; + + l.forward = forward_upsample_layer; + l.backward = backward_upsample_layer; + #ifdef GPU + l.forward_gpu = forward_upsample_layer_gpu; + l.backward_gpu = backward_upsample_layer_gpu; + + l.delta_gpu = cuda_make_array(l.delta, l.outputs*batch); + l.output_gpu = cuda_make_array(l.output, l.outputs*batch); + #endif + if(l.reverse) fprintf(stderr, "downsample %2dx %4d x%4d x%4d -> %4d x%4d x%4d\n", stride, w, h, c, l.out_w, l.out_h, l.out_c); + else fprintf(stderr, "upsample %2dx %4d x%4d x%4d -> %4d x%4d x%4d\n", stride, w, h, c, l.out_w, l.out_h, l.out_c); + return l; +} + +void resize_upsample_layer(layer *l, int w, int h) +{ + l->w = w; + l->h = h; + l->out_w = w*l->stride; + l->out_h = h*l->stride; + if(l->reverse){ + l->out_w = w/l->stride; + l->out_h = h/l->stride; + } + l->outputs = l->out_w*l->out_h*l->out_c; + l->inputs = l->h*l->w*l->c; + l->delta = realloc(l->delta, l->outputs*l->batch*sizeof(float)); + l->output = realloc(l->output, l->outputs*l->batch*sizeof(float)); + +#ifdef GPU + cuda_free(l->output_gpu); + cuda_free(l->delta_gpu); + l->output_gpu = cuda_make_array(l->output, l->outputs*l->batch); + l->delta_gpu = cuda_make_array(l->delta, l->outputs*l->batch); +#endif + +} + +void forward_upsample_layer(const layer l, network net) +{ + fill_cpu(l.outputs*l.batch, 0, l.output, 1); + if(l.reverse){ + upsample_cpu(l.output, l.out_w, l.out_h, l.c, l.batch, l.stride, 0, l.scale, net.input); + }else{ + upsample_cpu(net.input, l.w, l.h, l.c, l.batch, l.stride, 1, l.scale, l.output); + } +} + +void backward_upsample_layer(const layer l, network net) +{ + if(l.reverse){ + upsample_cpu(l.delta, l.out_w, l.out_h, l.c, l.batch, l.stride, 1, l.scale, net.delta); + }else{ + upsample_cpu(net.delta, l.w, l.h, l.c, l.batch, l.stride, 0, l.scale, l.delta); + } +} + +#ifdef GPU +void forward_upsample_layer_gpu(const layer l, network net) +{ + fill_gpu(l.outputs*l.batch, 0, l.output_gpu, 1); + if(l.reverse){ + upsample_gpu(l.output_gpu, l.out_w, l.out_h, l.c, l.batch, l.stride, 0, l.scale, net.input_gpu); + }else{ + upsample_gpu(net.input_gpu, l.w, l.h, l.c, l.batch, l.stride, 1, l.scale, l.output_gpu); + } +} + +void backward_upsample_layer_gpu(const layer l, network net) +{ + if(l.reverse){ + upsample_gpu(l.delta_gpu, l.out_w, l.out_h, l.c, l.batch, l.stride, 1, l.scale, net.delta_gpu); + }else{ + upsample_gpu(net.delta_gpu, l.w, l.h, l.c, l.batch, l.stride, 0, l.scale, l.delta_gpu); + } +} +#endif diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/upsample_layer.h b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/upsample_layer.h new file mode 100644 index 00000000000..86790d10883 --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/upsample_layer.h @@ -0,0 +1,15 @@ +#ifndef UPSAMPLE_LAYER_H +#define UPSAMPLE_LAYER_H +#include "darknet.h" + +layer make_upsample_layer(int batch, int w, int h, int c, int stride); +void forward_upsample_layer(const layer l, network net); +void backward_upsample_layer(const layer l, network net); +void resize_upsample_layer(layer *l, int w, int h); + +#ifdef GPU +void forward_upsample_layer_gpu(const layer l, network net); +void backward_upsample_layer_gpu(const layer l, network net); +#endif + +#endif diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/utils.c b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/utils.c new file mode 100644 index 00000000000..4e4efc2c57e --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/utils.c @@ -0,0 +1,723 @@ +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#include "utils.h" + + +/* +// old timing. is it better? who knows!! +double get_wall_time() +{ + struct timeval time; + if (gettimeofday(&time,NULL)){ + return 0; + } + return (double)time.tv_sec + (double)time.tv_usec * .000001; +} +*/ + +double what_time_is_it_now() +{ + struct timespec now; + clock_gettime(CLOCK_REALTIME, &now); + return now.tv_sec + now.tv_nsec*1e-9; +} + +int *read_intlist(char *gpu_list, int *ngpus, int d) +{ + int *gpus = 0; + if(gpu_list){ + int len = strlen(gpu_list); + *ngpus = 1; + int i; + for(i = 0; i < len; ++i){ + if (gpu_list[i] == ',') ++*ngpus; + } + gpus = calloc(*ngpus, sizeof(int)); + for(i = 0; i < *ngpus; ++i){ + gpus[i] = atoi(gpu_list); + gpu_list = strchr(gpu_list, ',')+1; + } + } else { + gpus = calloc(1, sizeof(float)); + *gpus = d; + *ngpus = 1; + } + return gpus; +} + +int *read_map(char *filename) +{ + int n = 0; + int *map = 0; + char *str; + FILE *file = fopen(filename, "r"); + if(!file) file_error(filename); + while((str=fgetl(file))){ + ++n; + map = realloc(map, n*sizeof(int)); + map[n-1] = atoi(str); + } + return map; +} + +void sorta_shuffle(void *arr, size_t n, size_t size, size_t sections) +{ + size_t i; + for(i = 0; i < sections; ++i){ + size_t start = n*i/sections; + size_t end = n*(i+1)/sections; + size_t num = end-start; + shuffle(arr+(start*size), num, size); + } +} + +void shuffle(void *arr, size_t n, size_t size) +{ + size_t i; + void *swp = calloc(1, size); + for(i = 0; i < n-1; ++i){ + size_t j = i + rand()/(RAND_MAX / (n-i)+1); + memcpy(swp, arr+(j*size), size); + memcpy(arr+(j*size), arr+(i*size), size); + memcpy(arr+(i*size), swp, size); + } +} + +int *random_index_order(int min, int max) +{ + int *inds = calloc(max-min, sizeof(int)); + int i; + for(i = min; i < max; ++i){ + inds[i] = i; + } + for(i = min; i < max-1; ++i){ + int swap = inds[i]; + int index = i + rand()%(max-i); + inds[i] = inds[index]; + inds[index] = swap; + } + return inds; +} + +void del_arg(int argc, char **argv, int index) +{ + int i; + for(i = index; i < argc-1; ++i) argv[i] = argv[i+1]; + argv[i] = 0; +} + +int find_arg(int argc, char* argv[], char *arg) +{ + int i; + for(i = 0; i < argc; ++i) { + if(!argv[i]) continue; + if(0==strcmp(argv[i], arg)) { + del_arg(argc, argv, i); + return 1; + } + } + return 0; +} + +int find_int_arg(int argc, char **argv, char *arg, int def) +{ + int i; + for(i = 0; i < argc-1; ++i){ + if(!argv[i]) continue; + if(0==strcmp(argv[i], arg)){ + def = atoi(argv[i+1]); + del_arg(argc, argv, i); + del_arg(argc, argv, i); + break; + } + } + return def; +} + +float find_float_arg(int argc, char **argv, char *arg, float def) +{ + int i; + for(i = 0; i < argc-1; ++i){ + if(!argv[i]) continue; + if(0==strcmp(argv[i], arg)){ + def = atof(argv[i+1]); + del_arg(argc, argv, i); + del_arg(argc, argv, i); + break; + } + } + return def; +} + +char *find_char_arg(int argc, char **argv, char *arg, char *def) +{ + int i; + for(i = 0; i < argc-1; ++i){ + if(!argv[i]) continue; + if(0==strcmp(argv[i], arg)){ + def = argv[i+1]; + del_arg(argc, argv, i); + del_arg(argc, argv, i); + break; + } + } + return def; +} + + +char *basecfg(char *cfgfile) +{ + char *c = cfgfile; + char *next; + while((next = strchr(c, '/'))) + { + c = next+1; + } + c = copy_string(c); + next = strchr(c, '.'); + if (next) *next = 0; + return c; +} + +int alphanum_to_int(char c) +{ + return (c < 58) ? c - 48 : c-87; +} +char int_to_alphanum(int i) +{ + if (i == 36) return '.'; + return (i < 10) ? i + 48 : i + 87; +} + +void pm(int M, int N, float *A) +{ + int i,j; + for(i =0 ; i < M; ++i){ + printf("%d ", i+1); + for(j = 0; j < N; ++j){ + printf("%2.4f, ", A[i*N+j]); + } + printf("\n"); + } + printf("\n"); +} + +void find_replace(char *str, char *orig, char *rep, char *output) +{ + char buffer[4096] = {0}; + char *p; + + sprintf(buffer, "%s", str); + if(!(p = strstr(buffer, orig))){ // Is 'orig' even in 'str'? + sprintf(output, "%s", str); + return; + } + + *p = '\0'; + + sprintf(output, "%s%s%s", buffer, rep, p+strlen(orig)); +} + +float sec(clock_t clocks) +{ + return (float)clocks/CLOCKS_PER_SEC; +} + +void top_k(float *a, int n, int k, int *index) +{ + int i,j; + for(j = 0; j < k; ++j) index[j] = -1; + for(i = 0; i < n; ++i){ + int curr = i; + for(j = 0; j < k; ++j){ + if((index[j] < 0) || a[curr] > a[index[j]]){ + int swap = curr; + curr = index[j]; + index[j] = swap; + } + } + } +} + +void error(const char *s) +{ + perror(s); + assert(0); + exit(-1); +} + +unsigned char *read_file(char *filename) +{ + FILE *fp = fopen(filename, "rb"); + size_t size; + + fseek(fp, 0, SEEK_END); + size = ftell(fp); + fseek(fp, 0, SEEK_SET); + + unsigned char *text = calloc(size+1, sizeof(char)); + fread(text, 1, size, fp); + fclose(fp); + return text; +} + +void malloc_error() +{ + fprintf(stderr, "Malloc error\n"); + exit(-1); +} + +void file_error(char *s) +{ + fprintf(stderr, "Couldn't open file: %s\n", s); + exit(0); +} + +list *split_str(char *s, char delim) +{ + size_t i; + size_t len = strlen(s); + list *l = make_list(); + list_insert(l, s); + for(i = 0; i < len; ++i){ + if(s[i] == delim){ + s[i] = '\0'; + list_insert(l, &(s[i+1])); + } + } + return l; +} + +void strip(char *s) +{ + size_t i; + size_t len = strlen(s); + size_t offset = 0; + for(i = 0; i < len; ++i){ + char c = s[i]; + if(c==' '||c=='\t'||c=='\n') ++offset; + else s[i-offset] = c; + } + s[len-offset] = '\0'; +} + +void strip_char(char *s, char bad) +{ + size_t i; + size_t len = strlen(s); + size_t offset = 0; + for(i = 0; i < len; ++i){ + char c = s[i]; + if(c==bad) ++offset; + else s[i-offset] = c; + } + s[len-offset] = '\0'; +} + +void free_ptrs(void **ptrs, int n) +{ + int i; + for(i = 0; i < n; ++i) free(ptrs[i]); + free(ptrs); +} + +char *fgetl(FILE *fp) +{ + if(feof(fp)) return 0; + size_t size = 512; + char *line = malloc(size*sizeof(char)); + if(!fgets(line, size, fp)){ + free(line); + return 0; + } + + size_t curr = strlen(line); + + while((line[curr-1] != '\n') && !feof(fp)){ + if(curr == size-1){ + size *= 2; + line = realloc(line, size*sizeof(char)); + if(!line) { + printf("%ld\n", size); + malloc_error(); + } + } + size_t readsize = size-curr; + if(readsize > INT_MAX) readsize = INT_MAX-1; + fgets(&line[curr], readsize, fp); + curr = strlen(line); + } + if(line[curr-1] == '\n') line[curr-1] = '\0'; + + return line; +} + +int read_int(int fd) +{ + int n = 0; + int next = read(fd, &n, sizeof(int)); + if(next <= 0) return -1; + return n; +} + +void write_int(int fd, int n) +{ + int next = write(fd, &n, sizeof(int)); + if(next <= 0) error("read failed"); +} + +int read_all_fail(int fd, char *buffer, size_t bytes) +{ + size_t n = 0; + while(n < bytes){ + int next = read(fd, buffer + n, bytes-n); + if(next <= 0) return 1; + n += next; + } + return 0; +} + +int write_all_fail(int fd, char *buffer, size_t bytes) +{ + size_t n = 0; + while(n < bytes){ + size_t next = write(fd, buffer + n, bytes-n); + if(next <= 0) return 1; + n += next; + } + return 0; +} + +void read_all(int fd, char *buffer, size_t bytes) +{ + size_t n = 0; + while(n < bytes){ + int next = read(fd, buffer + n, bytes-n); + if(next <= 0) error("read failed"); + n += next; + } +} + +void write_all(int fd, char *buffer, size_t bytes) +{ + size_t n = 0; + while(n < bytes){ + size_t next = write(fd, buffer + n, bytes-n); + if(next <= 0) error("write failed"); + n += next; + } +} + + +char *copy_string(char *s) +{ + char *copy = malloc(strlen(s)+1); + strncpy(copy, s, strlen(s)+1); + return copy; +} + +list *parse_csv_line(char *line) +{ + list *l = make_list(); + char *c, *p; + int in = 0; + for(c = line, p = line; *c != '\0'; ++c){ + if(*c == '"') in = !in; + else if(*c == ',' && !in){ + *c = '\0'; + list_insert(l, copy_string(p)); + p = c+1; + } + } + list_insert(l, copy_string(p)); + return l; +} + +int count_fields(char *line) +{ + int count = 0; + int done = 0; + char *c; + for(c = line; !done; ++c){ + done = (*c == '\0'); + if(*c == ',' || done) ++count; + } + return count; +} + +float *parse_fields(char *line, int n) +{ + float *field = calloc(n, sizeof(float)); + char *c, *p, *end; + int count = 0; + int done = 0; + for(c = line, p = line; !done; ++c){ + done = (*c == '\0'); + if(*c == ',' || done){ + *c = '\0'; + field[count] = strtod(p, &end); + if(p == c) field[count] = nan(""); + if(end != c && (end != c-1 || *end != '\r')) field[count] = nan(""); //DOS file formats! + p = c+1; + ++count; + } + } + return field; +} + +float sum_array(float *a, int n) +{ + int i; + float sum = 0; + for(i = 0; i < n; ++i) sum += a[i]; + return sum; +} + +float mean_array(float *a, int n) +{ + return sum_array(a,n)/n; +} + +void mean_arrays(float **a, int n, int els, float *avg) +{ + int i; + int j; + memset(avg, 0, els*sizeof(float)); + for(j = 0; j < n; ++j){ + for(i = 0; i < els; ++i){ + avg[i] += a[j][i]; + } + } + for(i = 0; i < els; ++i){ + avg[i] /= n; + } +} + +void print_statistics(float *a, int n) +{ + float m = mean_array(a, n); + float v = variance_array(a, n); + printf("MSE: %.6f, Mean: %.6f, Variance: %.6f\n", mse_array(a, n), m, v); +} + +float variance_array(float *a, int n) +{ + int i; + float sum = 0; + float mean = mean_array(a, n); + for(i = 0; i < n; ++i) sum += (a[i] - mean)*(a[i]-mean); + float variance = sum/n; + return variance; +} + +int constrain_int(int a, int min, int max) +{ + if (a < min) return min; + if (a > max) return max; + return a; +} + +float constrain(float min, float max, float a) +{ + if (a < min) return min; + if (a > max) return max; + return a; +} + +float dist_array(float *a, float *b, int n, int sub) +{ + int i; + float sum = 0; + for(i = 0; i < n; i += sub) sum += pow(a[i]-b[i], 2); + return sqrt(sum); +} + +float mse_array(float *a, int n) +{ + int i; + float sum = 0; + for(i = 0; i < n; ++i) sum += a[i]*a[i]; + return sqrt(sum/n); +} + +void normalize_array(float *a, int n) +{ + int i; + float mu = mean_array(a,n); + float sigma = sqrt(variance_array(a,n)); + for(i = 0; i < n; ++i){ + a[i] = (a[i] - mu)/sigma; + } + mu = mean_array(a,n); + sigma = sqrt(variance_array(a,n)); +} + +void translate_array(float *a, int n, float s) +{ + int i; + for(i = 0; i < n; ++i){ + a[i] += s; + } +} + +float mag_array(float *a, int n) +{ + int i; + float sum = 0; + for(i = 0; i < n; ++i){ + sum += a[i]*a[i]; + } + return sqrt(sum); +} + +void scale_array(float *a, int n, float s) +{ + int i; + for(i = 0; i < n; ++i){ + a[i] *= s; + } +} + +int sample_array(float *a, int n) +{ + float sum = sum_array(a, n); + scale_array(a, n, 1./sum); + float r = rand_uniform(0, 1); + int i; + for(i = 0; i < n; ++i){ + r = r - a[i]; + if (r <= 0) return i; + } + return n-1; +} + +int max_int_index(int *a, int n) +{ + if(n <= 0) return -1; + int i, max_i = 0; + int max = a[0]; + for(i = 1; i < n; ++i){ + if(a[i] > max){ + max = a[i]; + max_i = i; + } + } + return max_i; +} + +int max_index(float *a, int n) +{ + if(n <= 0) return -1; + int i, max_i = 0; + float max = a[0]; + for(i = 1; i < n; ++i){ + if(a[i] > max){ + max = a[i]; + max_i = i; + } + } + return max_i; +} + +int int_index(int *a, int val, int n) +{ + int i; + for(i = 0; i < n; ++i){ + if(a[i] == val) return i; + } + return -1; +} + +int rand_int(int min, int max) +{ + if (max < min){ + int s = min; + min = max; + max = s; + } + int r = (rand()%(max - min + 1)) + min; + return r; +} + +// From http://en.wikipedia.org/wiki/Box%E2%80%93Muller_transform +float rand_normal() +{ + static int haveSpare = 0; + static double rand1, rand2; + + if(haveSpare) + { + haveSpare = 0; + return sqrt(rand1) * sin(rand2); + } + + haveSpare = 1; + + rand1 = rand() / ((double) RAND_MAX); + if(rand1 < 1e-100) rand1 = 1e-100; + rand1 = -2 * log(rand1); + rand2 = (rand() / ((double) RAND_MAX)) * TWO_PI; + + return sqrt(rand1) * cos(rand2); +} + +/* + float rand_normal() + { + int n = 12; + int i; + float sum= 0; + for(i = 0; i < n; ++i) sum += (float)rand()/RAND_MAX; + return sum-n/2.; + } + */ + +size_t rand_size_t() +{ + return ((size_t)(rand()&0xff) << 56) | + ((size_t)(rand()&0xff) << 48) | + ((size_t)(rand()&0xff) << 40) | + ((size_t)(rand()&0xff) << 32) | + ((size_t)(rand()&0xff) << 24) | + ((size_t)(rand()&0xff) << 16) | + ((size_t)(rand()&0xff) << 8) | + ((size_t)(rand()&0xff) << 0); +} + +float rand_uniform(float min, float max) +{ + if(max < min){ + float swap = min; + min = max; + max = swap; + } + return ((float)rand()/RAND_MAX * (max - min)) + min; +} + +float rand_scale(float s) +{ + float scale = rand_uniform(1, s); + if(rand()%2) return scale; + return 1./scale; +} + +float **one_hot_encode(float *a, int n, int k) +{ + int i; + float **t = calloc(n, sizeof(float*)); + for(i = 0; i < n; ++i){ + t[i] = calloc(k, sizeof(float)); + int index = (int)a[i]; + t[i][index] = 1; + } + return t; +} + diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/utils.h b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/utils.h new file mode 100644 index 00000000000..ef24da79888 --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/utils.h @@ -0,0 +1,53 @@ +#ifndef UTILS_H +#define UTILS_H +#include +#include +#include "darknet.h" +#include "list.h" + +#define TIME(a) \ + do { \ + double start = what_time_is_it_now(); \ + a; \ + printf("%s took: %f seconds\n", #a, what_time_is_it_now() - start); \ + } while (0) + +#define TWO_PI 6.2831853071795864769252866f + +double what_time_is_it_now(); +void shuffle(void *arr, size_t n, size_t size); +void sorta_shuffle(void *arr, size_t n, size_t size, size_t sections); +void free_ptrs(void **ptrs, int n); +int alphanum_to_int(char c); +char int_to_alphanum(int i); +int read_int(int fd); +void write_int(int fd, int n); +void read_all(int fd, char *buffer, size_t bytes); +void write_all(int fd, char *buffer, size_t bytes); +int read_all_fail(int fd, char *buffer, size_t bytes); +int write_all_fail(int fd, char *buffer, size_t bytes); +void find_replace(char *str, char *orig, char *rep, char *output); +void malloc_error(); +void file_error(char *s); +void strip(char *s); +void strip_char(char *s, char bad); +list *split_str(char *s, char delim); +char *fgetl(FILE *fp); +list *parse_csv_line(char *line); +char *copy_string(char *s); +int count_fields(char *line); +float *parse_fields(char *line, int n); +void translate_array(float *a, int n, float s); +float constrain(float min, float max, float a); +int constrain_int(int a, int min, int max); +float rand_scale(float s); +int rand_int(int min, int max); +void mean_arrays(float **a, int n, int els, float *avg); +float dist_array(float *a, float *b, int n, int sub); +float **one_hot_encode(float *a, int n, int k); +float sec(clock_t clocks); +void print_statistics(float *a, int n); +int int_index(int *a, int val, int n); + +#endif + diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/yolo_layer.c b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/yolo_layer.c new file mode 100644 index 00000000000..af714d46a3d --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/yolo_layer.c @@ -0,0 +1,374 @@ +#include "yolo_layer.h" +#include "activations.h" +#include "blas.h" +#include "box.h" +#include "cuda.h" +#include "utils.h" + +#include +#include +#include +#include + +layer make_yolo_layer(int batch, int w, int h, int n, int total, int *mask, int classes) +{ + int i; + layer l = {0}; + l.type = YOLO; + + l.n = n; + l.total = total; + l.batch = batch; + l.h = h; + l.w = w; + l.c = n*(classes + 4 + 1); + l.out_w = l.w; + l.out_h = l.h; + l.out_c = l.c; + l.classes = classes; + l.cost = calloc(1, sizeof(float)); + l.biases = calloc(total*2, sizeof(float)); + if(mask) l.mask = mask; + else{ + l.mask = calloc(n, sizeof(int)); + for(i = 0; i < n; ++i){ + l.mask[i] = i; + } + } + l.bias_updates = calloc(n*2, sizeof(float)); + l.outputs = h*w*n*(classes + 4 + 1); + l.inputs = l.outputs; + l.truths = 90*(4 + 1); + l.delta = calloc(batch*l.outputs, sizeof(float)); + l.output = calloc(batch*l.outputs, sizeof(float)); + for(i = 0; i < total*2; ++i){ + l.biases[i] = .5; + } + + l.forward = forward_yolo_layer; + l.backward = backward_yolo_layer; +#ifdef GPU + l.forward_gpu = forward_yolo_layer_gpu; + l.backward_gpu = backward_yolo_layer_gpu; + l.output_gpu = cuda_make_array(l.output, batch*l.outputs); + l.delta_gpu = cuda_make_array(l.delta, batch*l.outputs); +#endif + + fprintf(stderr, "detection\n"); + srand(0); + + return l; +} + +void resize_yolo_layer(layer *l, int w, int h) +{ + l->w = w; + l->h = h; + + l->outputs = h*w*l->n*(l->classes + 4 + 1); + l->inputs = l->outputs; + + l->output = realloc(l->output, l->batch*l->outputs*sizeof(float)); + l->delta = realloc(l->delta, l->batch*l->outputs*sizeof(float)); + +#ifdef GPU + cuda_free(l->delta_gpu); + cuda_free(l->output_gpu); + + l->delta_gpu = cuda_make_array(l->delta, l->batch*l->outputs); + l->output_gpu = cuda_make_array(l->output, l->batch*l->outputs); +#endif +} + +box get_yolo_box(float *x, float *biases, int n, int index, int i, int j, int lw, int lh, int w, int h, int stride) +{ + box b; + b.x = (i + x[index + 0*stride]) / lw; + b.y = (j + x[index + 1*stride]) / lh; + b.w = exp(x[index + 2*stride]) * biases[2*n] / w; + b.h = exp(x[index + 3*stride]) * biases[2*n+1] / h; + return b; +} + +float delta_yolo_box(box truth, float *x, float *biases, int n, int index, int i, int j, int lw, int lh, int w, int h, float *delta, float scale, int stride) +{ + box pred = get_yolo_box(x, biases, n, index, i, j, lw, lh, w, h, stride); + float iou = box_iou(pred, truth); + + float tx = (truth.x*lw - i); + float ty = (truth.y*lh - j); + float tw = log(truth.w*w / biases[2*n]); + float th = log(truth.h*h / biases[2*n + 1]); + + delta[index + 0*stride] = scale * (tx - x[index + 0*stride]); + delta[index + 1*stride] = scale * (ty - x[index + 1*stride]); + delta[index + 2*stride] = scale * (tw - x[index + 2*stride]); + delta[index + 3*stride] = scale * (th - x[index + 3*stride]); + return iou; +} + + +void delta_yolo_class(float *output, float *delta, int index, int class, int classes, int stride, float *avg_cat) +{ + int n; + if (delta[index]){ + delta[index + stride*class] = 1 - output[index + stride*class]; + if(avg_cat) *avg_cat += output[index + stride*class]; + return; + } + for(n = 0; n < classes; ++n){ + delta[index + stride*n] = ((n == class)?1 : 0) - output[index + stride*n]; + if(n == class && avg_cat) *avg_cat += output[index + stride*n]; + } +} + +static int entry_index(layer l, int batch, int location, int entry) +{ + int n = location / (l.w*l.h); + int loc = location % (l.w*l.h); + return batch*l.outputs + n*l.w*l.h*(4+l.classes+1) + entry*l.w*l.h + loc; +} + +void forward_yolo_layer(const layer l, network net) +{ + int i,j,b,t,n; + memcpy(l.output, net.input, l.outputs*l.batch*sizeof(float)); + +#ifndef GPU + for (b = 0; b < l.batch; ++b){ + for(n = 0; n < l.n; ++n){ + int index = entry_index(l, b, n*l.w*l.h, 0); + activate_array(l.output + index, 2*l.w*l.h, LOGISTIC); + index = entry_index(l, b, n*l.w*l.h, 4); + activate_array(l.output + index, (1+l.classes)*l.w*l.h, LOGISTIC); + } + } +#endif + + memset(l.delta, 0, l.outputs * l.batch * sizeof(float)); + if(!net.train) return; + float avg_iou = 0; + float recall = 0; + float recall75 = 0; + float avg_cat = 0; + float avg_obj = 0; + float avg_anyobj = 0; + int count = 0; + int class_count = 0; + *(l.cost) = 0; + for (b = 0; b < l.batch; ++b) { + for (j = 0; j < l.h; ++j) { + for (i = 0; i < l.w; ++i) { + for (n = 0; n < l.n; ++n) { + int box_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 0); + box pred = get_yolo_box(l.output, l.biases, l.mask[n], box_index, i, j, l.w, l.h, net.w, net.h, l.w*l.h); + float best_iou = 0; + int best_t = 0; + for(t = 0; t < l.max_boxes; ++t){ + box truth = float_to_box(net.truth + t*(4 + 1) + b*l.truths, 1); + if(!truth.x) break; + float iou = box_iou(pred, truth); + if (iou > best_iou) { + best_iou = iou; + best_t = t; + } + } + int obj_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 4); + avg_anyobj += l.output[obj_index]; + l.delta[obj_index] = 0 - l.output[obj_index]; + if (best_iou > l.ignore_thresh) { + l.delta[obj_index] = 0; + } + if (best_iou > l.truth_thresh) { + l.delta[obj_index] = 1 - l.output[obj_index]; + + int class = net.truth[best_t*(4 + 1) + b*l.truths + 4]; + if (l.map) class = l.map[class]; + int class_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 4 + 1); + delta_yolo_class(l.output, l.delta, class_index, class, l.classes, l.w*l.h, 0); + box truth = float_to_box(net.truth + best_t*(4 + 1) + b*l.truths, 1); + delta_yolo_box(truth, l.output, l.biases, l.mask[n], box_index, i, j, l.w, l.h, net.w, net.h, l.delta, (2-truth.w*truth.h), l.w*l.h); + } + } + } + } + for(t = 0; t < l.max_boxes; ++t){ + box truth = float_to_box(net.truth + t*(4 + 1) + b*l.truths, 1); + + if(!truth.x) break; + float best_iou = 0; + int best_n = 0; + i = (truth.x * l.w); + j = (truth.y * l.h); + box truth_shift = truth; + truth_shift.x = truth_shift.y = 0; + for(n = 0; n < l.total; ++n){ + box pred = {0}; + pred.w = l.biases[2*n]/net.w; + pred.h = l.biases[2*n+1]/net.h; + float iou = box_iou(pred, truth_shift); + if (iou > best_iou){ + best_iou = iou; + best_n = n; + } + } + + int mask_n = int_index(l.mask, best_n, l.n); + if(mask_n >= 0){ + int box_index = entry_index(l, b, mask_n*l.w*l.h + j*l.w + i, 0); + float iou = delta_yolo_box(truth, l.output, l.biases, best_n, box_index, i, j, l.w, l.h, net.w, net.h, l.delta, (2-truth.w*truth.h), l.w*l.h); + + int obj_index = entry_index(l, b, mask_n*l.w*l.h + j*l.w + i, 4); + avg_obj += l.output[obj_index]; + l.delta[obj_index] = 1 - l.output[obj_index]; + + int class = net.truth[t*(4 + 1) + b*l.truths + 4]; + if (l.map) class = l.map[class]; + int class_index = entry_index(l, b, mask_n*l.w*l.h + j*l.w + i, 4 + 1); + delta_yolo_class(l.output, l.delta, class_index, class, l.classes, l.w*l.h, &avg_cat); + + ++count; + ++class_count; + if(iou > .5) recall += 1; + if(iou > .75) recall75 += 1; + avg_iou += iou; + } + } + } + *(l.cost) = pow(mag_array(l.delta, l.outputs * l.batch), 2); + printf("Region %d Avg IOU: %f, Class: %f, Obj: %f, No Obj: %f, .5R: %f, .75R: %f, count: %d\n", net.index, avg_iou/count, avg_cat/class_count, avg_obj/count, avg_anyobj/(l.w*l.h*l.n*l.batch), recall/count, recall75/count, count); +} + +void backward_yolo_layer(const layer l, network net) +{ + axpy_cpu(l.batch*l.inputs, 1, l.delta, 1, net.delta, 1); +} + +void correct_yolo_boxes(detection *dets, int n, int w, int h, int netw, int neth, int relative) +{ + int i; + int new_w=0; + int new_h=0; + if (((float)netw/w) < ((float)neth/h)) { + new_w = netw; + new_h = (h * netw)/w; + } else { + new_h = neth; + new_w = (w * neth)/h; + } + for (i = 0; i < n; ++i){ + box b = dets[i].bbox; + b.x = (b.x - (netw - new_w)/2./netw) / ((float)new_w/netw); + b.y = (b.y - (neth - new_h)/2./neth) / ((float)new_h/neth); + b.w *= (float)netw/new_w; + b.h *= (float)neth/new_h; + if(!relative){ + b.x *= w; + b.w *= w; + b.y *= h; + b.h *= h; + } + dets[i].bbox = b; + } +} + +int yolo_num_detections(layer l, float thresh) +{ + int i, n; + int count = 0; + for (i = 0; i < l.w*l.h; ++i){ + for(n = 0; n < l.n; ++n){ + int obj_index = entry_index(l, 0, n*l.w*l.h + i, 4); + if(l.output[obj_index] > thresh){ + ++count; + } + } + } + return count; +} + +void avg_flipped_yolo(layer l) +{ + int i,j,n,z; + float *flip = l.output + l.outputs; + for (j = 0; j < l.h; ++j) { + for (i = 0; i < l.w/2; ++i) { + for (n = 0; n < l.n; ++n) { + for(z = 0; z < l.classes + 4 + 1; ++z){ + int i1 = z*l.w*l.h*l.n + n*l.w*l.h + j*l.w + i; + int i2 = z*l.w*l.h*l.n + n*l.w*l.h + j*l.w + (l.w - i - 1); + float swap = flip[i1]; + flip[i1] = flip[i2]; + flip[i2] = swap; + if(z == 0){ + flip[i1] = -flip[i1]; + flip[i2] = -flip[i2]; + } + } + } + } + } + for(i = 0; i < l.outputs; ++i){ + l.output[i] = (l.output[i] + flip[i])/2.; + } +} + +int get_yolo_detections(layer l, int w, int h, int netw, int neth, float thresh, int *map, int relative, detection *dets) +{ + int i,j,n; + float *predictions = l.output; + if (l.batch == 2) avg_flipped_yolo(l); + int count = 0; + for (i = 0; i < l.w*l.h; ++i){ + int row = i / l.w; + int col = i % l.w; + for(n = 0; n < l.n; ++n){ + int obj_index = entry_index(l, 0, n*l.w*l.h + i, 4); + float objectness = predictions[obj_index]; + if(objectness <= thresh) continue; + int box_index = entry_index(l, 0, n*l.w*l.h + i, 0); + dets[count].bbox = get_yolo_box(predictions, l.biases, l.mask[n], box_index, col, row, l.w, l.h, netw, neth, l.w*l.h); + dets[count].objectness = objectness; + dets[count].classes = l.classes; + for(j = 0; j < l.classes; ++j){ + int class_index = entry_index(l, 0, n*l.w*l.h + i, 4 + 1 + j); + float prob = objectness*predictions[class_index]; + dets[count].prob[j] = (prob > thresh) ? prob : 0; + } + ++count; + } + } + correct_yolo_boxes(dets, count, w, h, netw, neth, relative); + return count; +} + +#ifdef GPU + +void forward_yolo_layer_gpu(const layer l, network net) +{ + copy_gpu(l.batch*l.inputs, net.input_gpu, 1, l.output_gpu, 1); + int b, n; + for (b = 0; b < l.batch; ++b){ + for(n = 0; n < l.n; ++n){ + int index = entry_index(l, b, n*l.w*l.h, 0); + activate_array_gpu(l.output_gpu + index, 2*l.w*l.h, LOGISTIC); + index = entry_index(l, b, n*l.w*l.h, 4); + activate_array_gpu(l.output_gpu + index, (1+l.classes)*l.w*l.h, LOGISTIC); + } + } + if(!net.train || l.onlyforward){ + cuda_pull_array(l.output_gpu, l.output, l.batch*l.outputs); + return; + } + + cuda_pull_array(l.output_gpu, net.input, l.batch*l.inputs); + forward_yolo_layer(l, net); + cuda_push_array(l.delta_gpu, l.delta, l.batch*l.outputs); +} + +void backward_yolo_layer_gpu(const layer l, network net) +{ + axpy_gpu(l.batch*l.inputs, 1, l.delta_gpu, 1, net.delta_gpu, 1); +} +#endif + diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/yolo_layer.h b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/yolo_layer.h new file mode 100644 index 00000000000..d2a02432681 --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/darknet/src/yolo_layer.h @@ -0,0 +1,19 @@ +#ifndef YOLO_LAYER_H +#define YOLO_LAYER_H + +#include "darknet.h" +#include "layer.h" +#include "network.h" + +layer make_yolo_layer(int batch, int w, int h, int n, int total, int *mask, int classes); +void forward_yolo_layer(const layer l, network net); +void backward_yolo_layer(const layer l, network net); +void resize_yolo_layer(layer *l, int w, int h); +int yolo_num_detections(layer l, float thresh); + +#ifdef GPU +void forward_yolo_layer_gpu(const layer l, network net); +void backward_yolo_layer_gpu(layer l, network net); +#endif + +#endif diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/launch/yolo3.launch b/ros/src/computing/perception/detection/packages/yolo3_detector/launch/yolo3.launch new file mode 100644 index 00000000000..3d9ce713cc8 --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/launch/yolo3.launch @@ -0,0 +1,21 @@ + + + + + + + + + + + + + + + + + + + + + diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/package.xml b/ros/src/computing/perception/detection/packages/yolo3_detector/package.xml new file mode 100644 index 00000000000..57917d9aa7f --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/package.xml @@ -0,0 +1,25 @@ + + + yolo3_detector + 1.6.3 + Yolo3 image detector + Abraham Monrroy + BSD + catkin + + sensor_msgs + std_msgs + autoware_msgs + cv_bridge + image_transport + roscpp + + sensor_msgs + std_msgs + autoware_msgs + cv_bridge + image_transport + roscpp + + + diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/src/rect_class_score.h b/ros/src/computing/perception/detection/packages/yolo3_detector/src/rect_class_score.h new file mode 100644 index 00000000000..f3cc2bdada1 --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/src/rect_class_score.h @@ -0,0 +1,24 @@ +#ifndef RECTCLASSSCORE_H_ +#define RECTCLASSSCORE_H_ + +#include +#include + +template class RectClassScore +{ +public: + _Tp x, y, w, h; + _Tp score; + unsigned int class_type; + bool enabled; + + inline std::string toString() + { + std::ostringstream out; + out << class_type << "(x:" << x << ", y:" << y << ", w:" << w << ", h:" << h << ") =" << score; + return out.str(); + } +}; + + +#endif /* RECTCLASSSCORE_H_ */ diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/src/yolo3_detector.cpp b/ros/src/computing/perception/detection/packages/yolo3_detector/src/yolo3_detector.cpp new file mode 100644 index 00000000000..9d921506d38 --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/src/yolo3_detector.cpp @@ -0,0 +1,360 @@ +/* + * Copyright (c) 2018, Nagoya University + * All rights reserved. + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions are met: + * + * * Redistributions of source code must retain the above copyright notice, this + * list of conditions and the following disclaimer. + * + * * Redistributions in binary form must reproduce the above copyright notice, + * this list of conditions and the following disclaimer in the documentation + * and/or other materials provided with the distribution. + * + * * Neither the name of Autoware nor the names of its + * contributors may be used to endorse or promote products derived from + * this software without specific prior written permission. + * + * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" + * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE + * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE + * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE + * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL + * DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR + * SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER + * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, + * OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE + * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + ******************** + * v1.0: amc-nu (abrahammonrroy@yahoo.com) + * + * yolo3_node.cpp + * + * Created on: April 4th, 2018 + */ +#include "yolo3_detector.h" + +namespace darknet +{ + uint32_t Yolo3Detector::get_network_height() + { + return darknet_network_->h; + } + uint32_t Yolo3Detector::get_network_width() + { + return darknet_network_->w; + } + void Yolo3Detector::load(std::string& in_model_file, std::string& in_trained_file, double in_min_confidence, double in_nms_threshold) + { + min_confidence_ = in_min_confidence; + nms_threshold_ = in_nms_threshold; + darknet_network_ = parse_network_cfg(&in_model_file[0]); + load_weights(darknet_network_, &in_trained_file[0]); + set_batch_network(darknet_network_, 1); + + layer output_layer = darknet_network_->layers[darknet_network_->n - 1]; + darknet_boxes_.resize(output_layer.w * output_layer.h * output_layer.n); + } + + Yolo3Detector::~Yolo3Detector() + { + free_network(darknet_network_); + } + + std::vector< RectClassScore > Yolo3Detector::detect(image& in_darknet_image) + { + return forward(in_darknet_image); + } + + image Yolo3Detector::convert_image(const sensor_msgs::ImageConstPtr& msg) + { + if (msg->encoding != sensor_msgs::image_encodings::BGR8) + { + ROS_ERROR("Unsupported encoding"); + exit(-1); + } + + auto data = msg->data; + uint32_t height = msg->height, width = msg->width, offset = msg->step - 3 * width; + uint32_t i = 0, j = 0; + image im = make_image(width, height, 3); + + for (uint32_t line = height; line; line--) + { + for (uint32_t column = width; column; column--) + { + for (uint32_t channel = 0; channel < 3; channel++) + im.data[i + width * height * channel] = data[j++] / 255.; + i++; + } + j += offset; + } + + if (darknet_network_->w == (int) width && darknet_network_->h == (int) height) + { + return im; + } + image resized = resize_image(im, darknet_network_->w, darknet_network_->h); + free_image(im); + return resized; + } + + std::vector< RectClassScore > Yolo3Detector::forward(image& in_darknet_image) + { + float * in_data = in_darknet_image.data; + float *prediction = network_predict(darknet_network_, in_data); + layer output_layer = darknet_network_->layers[darknet_network_->n - 1]; + + output_layer.output = prediction; + int nboxes = 0; + int num_classes = output_layer.classes; + detection *darknet_detections = get_network_boxes(darknet_network_, darknet_network_->w, darknet_network_->h, min_confidence_, .5, NULL, 0, &nboxes); + + do_nms_sort(darknet_detections, nboxes, num_classes, nms_threshold_); + + std::vector< RectClassScore > detections; + + for (int i = 0; i < nboxes; i++) + { + int class_id = -1; + float score = 0.f; + //find the class + for(int j = 0; j < num_classes; ++j){ + if (darknet_detections[i].prob[j] >= min_confidence_){ + if (class_id < 0) { + class_id = j; + score = darknet_detections[i].prob[j]; + } + } + } + //if class found + if (class_id >= 0) + { + RectClassScore detection; + + detection.x = darknet_detections[i].bbox.x - darknet_detections[i].bbox.w/2; + detection.y = darknet_detections[i].bbox.y - darknet_detections[i].bbox.h/2; + detection.w = darknet_detections[i].bbox.w; + detection.h = darknet_detections[i].bbox.h; + detection.score = score; + detection.class_type = class_id; + //std::cout << detection.toString() << std::endl; + + detections.push_back(detection); + } + } + //std::cout << std::endl; + return detections; + } +} // namespace darknet + +/////////////////// + +void Yolo3DetectorNode::convert_rect_to_image_obj(std::vector< RectClassScore >& in_objects, autoware_msgs::image_obj& out_message, std::string in_class) +{ + for (unsigned int i = 0; i < in_objects.size(); ++i) + { + if ( (in_class == "car" + && (in_objects[i].class_type == Yolo3::CAR + || in_objects[i].class_type == Yolo3::BUS + || in_objects[i].class_type == Yolo3::TRUCK + || in_objects[i].class_type == Yolo3::MOTORBIKE + ) + ) || (in_class == "person" + && (in_objects[i].class_type == Yolo3::PERSON + || in_objects[i].class_type == Yolo3::BICYCLE + || in_objects[i].class_type == Yolo3::DOG + || in_objects[i].class_type == Yolo3::CAT + || in_objects[i].class_type == Yolo3::HORSE + ) + ) + ) + { + autoware_msgs::image_rect rect; + + rect.x = (in_objects[i].x /image_ratio_) - image_left_right_border_/image_ratio_; + rect.y = (in_objects[i].y /image_ratio_) - image_top_bottom_border_/image_ratio_; + rect.width = in_objects[i].w /image_ratio_; + rect.height = in_objects[i].h /image_ratio_; + if (in_objects[i].x < 0) + rect.x = 0; + if (in_objects[i].y < 0) + rect.y = 0; + if (in_objects[i].w < 0) + rect.width = 0; + if (in_objects[i].h < 0) + rect.height = 0; + + rect.score = in_objects[i].score; + + //std::cout << "x "<< rect.x<< " y " << rect.y << " w "<< rect.width << " h "<< rect.height<< " s " << rect.score << " c " << in_objects[i].class_type << std::endl; + + out_message.obj.push_back(rect); + + } + } +} + +void Yolo3DetectorNode::rgbgr_image(image& im) +{ + int i; + for(i = 0; i < im.w*im.h; ++i) + { + float swap = im.data[i]; + im.data[i] = im.data[i+im.w*im.h*2]; + im.data[i+im.w*im.h*2] = swap; + } +} + +image Yolo3DetectorNode::convert_ipl_to_image(const sensor_msgs::ImageConstPtr& msg) +{ + cv_bridge::CvImagePtr cv_image = cv_bridge::toCvCopy(msg, "bgr8");//toCvCopy(image_source, sensor_msgs::image_encodings::BGR8); + cv::Mat mat_image = cv_image->image; + + uint32_t network_input_width = yolo_detector_.get_network_width(); + uint32_t network_input_height = yolo_detector_.get_network_height(); + + uint32_t image_height = msg->height, + image_width = msg->width; + + IplImage ipl_image; + cv::Mat final_mat; + + if (network_input_width!=image_width + || network_input_height != image_height) + { + //final_mat = cv::Mat(network_input_width, network_input_height, CV_8UC3, cv::Scalar(0,0,0)); + image_ratio_ = (double ) network_input_width / (double)mat_image.cols; + + cv::resize(mat_image, final_mat, cv::Size(), image_ratio_, image_ratio_); + image_top_bottom_border_ = abs(final_mat.rows-network_input_height)/2; + image_left_right_border_ = abs(final_mat.cols-network_input_width)/2; + cv::copyMakeBorder(final_mat, final_mat, + image_top_bottom_border_, image_top_bottom_border_, + image_left_right_border_, image_left_right_border_, + cv::BORDER_CONSTANT, cv::Scalar(0,0,0)); + + } + else + final_mat = mat_image; + + ipl_image = final_mat; + + unsigned char *data = (unsigned char *)ipl_image.imageData; + int h = ipl_image.height; + int w = ipl_image.width; + int c = ipl_image.nChannels; + int step = ipl_image.widthStep; + int i, j, k; + + image darknet_image = make_image(w, h, c); + + for(i = 0; i < h; ++i){ + for(k= 0; k < c; ++k){ + for(j = 0; j < w; ++j){ + darknet_image.data[k*w*h + i*w + j] = data[i*step + j*c + k]/255.; + } + } + } + rgbgr_image(darknet_image); + return darknet_image; +} + +void Yolo3DetectorNode::image_callback(const sensor_msgs::ImageConstPtr& in_image_message) +{ + std::vector< RectClassScore > detections; + + darknet_image_ = convert_ipl_to_image(in_image_message); + + detections = yolo_detector_.detect(darknet_image_); + + //Prepare Output message + autoware_msgs::image_obj output_car_message; + autoware_msgs::image_obj output_person_message; + output_car_message.header = in_image_message->header; + output_car_message.type = "car"; + + output_person_message.header = in_image_message->header; + output_person_message.type = "person"; + + convert_rect_to_image_obj(detections, output_car_message, "car"); + convert_rect_to_image_obj(detections, output_person_message, "person"); + + publisher_car_objects_.publish(output_car_message); + publisher_person_objects_.publish(output_person_message); + + free(darknet_image_.data); +} + +void Yolo3DetectorNode::config_cb(const autoware_msgs::ConfigSsd::ConstPtr& param) +{ + score_threshold_ = param->score_threshold; +} + + +void Yolo3DetectorNode::Run() +{ + //ROS STUFF + ros::NodeHandle private_node_handle("~");//to receive args + + //RECEIVE IMAGE TOPIC NAME + std::string image_raw_topic_str; + if (private_node_handle.getParam("image_raw_node", image_raw_topic_str)) + { + ROS_INFO("Setting image node to %s", image_raw_topic_str.c_str()); + } + else + { + ROS_INFO("No image node received, defaulting to /image_raw, you can use _image_raw_node:=YOUR_TOPIC"); + image_raw_topic_str = "/image_raw"; + } + + std::string network_definition_file; + std::string pretrained_model_file; + if (private_node_handle.getParam("network_definition_file", network_definition_file)) + { + ROS_INFO("Network Definition File (Config): %s", network_definition_file.c_str()); + } + else + { + ROS_INFO("No Network Definition File was received. Finishing execution."); + return; + } + if (private_node_handle.getParam("pretrained_model_file", pretrained_model_file)) + { + ROS_INFO("Pretrained Model File (Weights): %s", pretrained_model_file.c_str()); + } + else + { + ROS_INFO("No Pretrained Model File was received. Finishing execution."); + return; + } + + private_node_handle.param("score_threshold", score_threshold_, 0.5); + ROS_INFO("[%s] score_threshold: %f",__APP_NAME__, score_threshold_); + + private_node_handle.param("nms_threshold", nms_threshold_, 0.45); + ROS_INFO("[%s] nms_threshold: %f",__APP_NAME__, nms_threshold_); + + + ROS_INFO("Initializing Yolo3 on Darknet..."); + yolo_detector_.load(network_definition_file, pretrained_model_file, score_threshold_, nms_threshold_); + ROS_INFO("Initialization complete."); + + publisher_car_objects_ = node_handle_.advertise("/obj_car/image_obj", 1); + publisher_person_objects_ = node_handle_.advertise("/obj_person/image_obj", 1); + + ROS_INFO("Subscribing to... %s", image_raw_topic_str.c_str()); + subscriber_image_raw_ = node_handle_.subscribe(image_raw_topic_str, 1, &Yolo3DetectorNode::image_callback, this); + + std::string config_topic("/config"); + config_topic += "/Yolo3"; + subscriber_yolo_config_ = node_handle_.subscribe(config_topic, 1, &Yolo3DetectorNode::config_cb, this); + + ROS_INFO_STREAM( __APP_NAME__ << "" ); + + ros::spin(); + ROS_INFO("END Yolo3"); + +} diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/src/yolo3_detector.h b/ros/src/computing/perception/detection/packages/yolo3_detector/src/yolo3_detector.h new file mode 100644 index 00000000000..8420591819d --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/src/yolo3_detector.h @@ -0,0 +1,140 @@ +/* + * Copyright (c) 2018, Nagoya University + * All rights reserved. + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions are met: + * + * * Redistributions of source code must retain the above copyright notice, this + * list of conditions and the following disclaimer. + * + * * Redistributions in binary form must reproduce the above copyright notice, + * this list of conditions and the following disclaimer in the documentation + * and/or other materials provided with the distribution. + * + * * Neither the name of Autoware nor the names of its + * contributors may be used to endorse or promote products derived from + * this software without specific prior written permission. + * + * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" + * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE + * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE + * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE + * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL + * DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR + * SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER + * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, + * OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE + * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + ******************** + * v1.0: amc-nu (abrahammonrroy@yahoo.com) + * + * yolo3_node.cpp + * + * Created on: April 4th, 2018 + */ +#ifndef DARKNET_YOLO3_H +#define DARKNET_YOLO3_H + +#define __APP_NAME__ "yolo3_detector" + +#include +#include +#include +#include + +#include +#include + +#include + +#include +#include + +#include + +#include + +extern "C" +{ +#undef __cplusplus +#include "box.h" +#include "image.h" +#include "network.h" +#include "detection_layer.h" +#include "parser.h" +#include "region_layer.h" +#include "utils.h" +#include "image.h" +#define __cplusplus +} + +namespace Yolo3 +{ + enum YoloDetectorClasses//using coco for default cfg and weights + { + PERSON, BICYCLE, CAR, MOTORBIKE, AEROPLANE, BUS, TRAIN, TRUCK, BOAT, TRAFFIC_LIGHT, + FIRE_HYDRANT, STOP_SIGN, PARKING_METER, BENCH, BIRD, CAT, DOG, HORSE, SHEEP, COW, + ELEPHANT, BEAR, ZEBRA, GIRAFFE, BACKPACK, UMBRELLA, HANDBAG, TIE, SUITCASE, FRISBEE, + SKIS, SNOWBOARD, SPORTS_BALL, KITE, BASEBALL_BAT, BASEBALL_GLOVE, SKATEBOARD, SURFBOARD, TENNIS_RACKET, BOTTLE, + WINE_GLASS, CUP, FORK, KNIFE, SPOON, BOWL, BANANA, APPLE, SANDWICH, ORANGE, + BROCCOLI, CARROT, HOT_DOG, PIZZA, DONUT, CAKE, CHAIR, SOFA, POTTEDPLANT, BED, + DININGTABLE, TOILET, TVMONITOR, LAPTOP, MOUSE, REMOTE, KEYBOARD, CELL_PHONE, MICROWAVE, OVEN, + TOASTER, SINK, REFRIGERATOR, BOOK, CLOCK, VASE, SCISSORS, TEDDY_BEAR, HAIR_DRIER, TOOTHBRUSH, + }; +} + +namespace darknet { + class Yolo3Detector { + private: + double min_confidence_, nms_threshold_; + network* darknet_network_; + std::vector darknet_boxes_; + std::vector > forward(image &in_darknet_image); + public: + Yolo3Detector() {} + + void load(std::string &in_model_file, std::string &in_trained_file, double in_min_confidence, + double in_nms_threshold); + + ~Yolo3Detector(); + + image convert_image(const sensor_msgs::ImageConstPtr &in_image_msg); + + std::vector > detect(image &in_darknet_image); + + uint32_t get_network_width(); + + uint32_t get_network_height(); + + + }; +} // namespace darknet + +class Yolo3DetectorNode { + ros::Subscriber subscriber_image_raw_; + ros::Subscriber subscriber_yolo_config_; + ros::Publisher publisher_car_objects_; + ros::Publisher publisher_person_objects_; + ros::NodeHandle node_handle_; + + darknet::Yolo3Detector yolo_detector_; + + image darknet_image_ = {}; + + float score_threshold_; + float nms_threshold_; + double image_ratio_;//resdize ratio used to fit input image to network input size + uint32_t image_top_bottom_border_;//black strips added to the input image to maintain aspect ratio while resizing it to fit the network input size + uint32_t image_left_right_border_; + + void convert_rect_to_image_obj(std::vector< RectClassScore >& in_objects, autoware_msgs::image_obj& out_message, std::string in_class); + void rgbgr_image(image& im); + image convert_ipl_to_image(const sensor_msgs::ImageConstPtr& msg); + void image_callback(const sensor_msgs::ImageConstPtr& in_image_message); + void config_cb(const autoware_msgs::ConfigSsd::ConstPtr& param); +public: + void Run(); +}; + +#endif // DARKNET_YOLO3_H diff --git a/ros/src/computing/perception/detection/packages/yolo3_detector/src/yolo3_node.cpp b/ros/src/computing/perception/detection/packages/yolo3_detector/src/yolo3_node.cpp new file mode 100644 index 00000000000..c529b3af46f --- /dev/null +++ b/ros/src/computing/perception/detection/packages/yolo3_detector/src/yolo3_node.cpp @@ -0,0 +1,46 @@ +/* + * Copyright (c) 2018, Nagoya University + * All rights reserved. + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions are met: + * + * * Redistributions of source code must retain the above copyright notice, this + * list of conditions and the following disclaimer. + * + * * Redistributions in binary form must reproduce the above copyright notice, + * this list of conditions and the following disclaimer in the documentation + * and/or other materials provided with the distribution. + * + * * Neither the name of Autoware nor the names of its + * contributors may be used to endorse or promote products derived from + * this software without specific prior written permission. + * + * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" + * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE + * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE + * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE + * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL + * DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR + * SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER + * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, + * OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE + * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + ******************** + * v1.0: amc-nu (abrahammonrroy@yahoo.com) + * + * Created on: April 4th, 2018 + */ + +#include "yolo3_detector.h" + +int main(int argc, char **argv) +{ + ros::init(argc, argv, "yolo3"); + + Yolo3DetectorNode app; + + app.Run(); + + return 0; +} diff --git a/ros/src/util/packages/runtime_manager/scripts/computing.yaml b/ros/src/util/packages/runtime_manager/scripts/computing.yaml index 4a945dfade7..3336040504d 100755 --- a/ros/src/util/packages/runtime_manager/scripts/computing.yaml +++ b/ros/src/util/packages/runtime_manager/scripts/computing.yaml @@ -180,6 +180,11 @@ subs : cmd : roslaunch cv_tracker yolo2.launch param: yolo2 + - name : yolo3_wa + desc : Yolo3 Image based detector + cmd : roslaunch yolo3_detector yolo3.launch + param: yolo3 + - name : range_fusion desc : range_fusion desc sample cmd : roslaunch cv_tracker ranging.launch @@ -3958,3 +3963,130 @@ params : cmd_param : dash : '' delim : ':=' + + - name : yolo2 + vars : + - name : score_threshold + desc : Detections with a confidence value larger than this value will be displayed. + label : Score Threshold + min : 0.0 + max : 1.0 + v : 0.5 + cmd_param : + dash : '' + delim : ':=' + - name : nms_threshold + desc : Non-Maximum suppresion area threshold ratio to merge proposals. + label : NMS Threshold + min : 0.0 + max : 1.0 + v : 0.45 + cmd_param : + dash : '' + delim : ':=' + - name : image_src + desc : Image topic to be fed to the network for detection + label : image_src + kind : str + v : /image_raw + cmd_param : + dash : '' + delim : ':=' + - name : network_definition_file + desc : Configuration file defining the network + label : 'network_definition (cfg)' + kind : path + v : $(echo $HOME)/darknet/cfg/yolo.cfg + cmd_param: + dash : '' + delim : ':=' + - name : pretrained_model_file + desc : Pre-trained model + label : 'pre_trained_model (weights)' + kind : path + v : $(echo $HOME)/darknet/data/yolo.weights + cmd_param: + dash : '' + delim : ':=' + - name : gpu_device_id + desc : gpu_device_id desc sample + label : gpu_device_id + min : 0 + max : 10 + v : 0 + cmd_param : + dash : '' + delim : ':=' + - name : camera_id + desc : camera_id desc sample + kind : menu + label : Camera ID + choices_type : str + v : '' + cmd_param : + dash : '' + delim : ':=' + + + - name : yolo3 + vars : + - name : score_threshold + desc : Detections with a confidence value larger than this value will be displayed. + label : Score Threshold + min : 0.0 + max : 1.0 + v : 0.5 + cmd_param : + dash : '' + delim : ':=' + - name : nms_threshold + desc : Non-Maximum suppresion area threshold ratio to merge proposals. + label : NMS Threshold + min : 0.0 + max : 1.0 + v : 0.45 + cmd_param : + dash : '' + delim : ':=' + - name : image_src + desc : Image topic to be fed to the network for detection + label : image_src + kind : str + v : /image_raw + cmd_param : + dash : '' + delim : ':=' + - name : network_definition_file + desc : Configuration file defining the network + label : 'network_definition (cfg)' + kind : path + v : $(rospack find yolo3_detector)/darknet/cfg/yolov3.cfg + cmd_param: + dash : '' + delim : ':=' + - name : pretrained_model_file + desc : Pre-trained model + label : 'pre_trained_model (weights)' + kind : path + v : $(rospack find yolo3_detector)/darknet/data/yolov3.weights + cmd_param: + dash : '' + delim : ':=' + - name : gpu_device_id + desc : gpu_device_id desc sample + label : gpu_device_id + min : 0 + max : 10 + v : 0 + cmd_param : + dash : '' + delim : ':=' + - name : camera_id + desc : camera_id desc sample + kind : menu + label : Camera ID + choices_type : str + v : '' + cmd_param : + dash : '' + delim : ':='