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| # AdelaiDet Model Zoo and Baselines | ||
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| ## Introduction | ||
| This file documents a collection of models trained with AdelaiDet in Nov, 2019. | ||
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| ## Models | ||
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| The inference time is measured on one 1080Ti based on the most recent commit on Detectron2 ([ffff8ac](https://github.com/facebookresearch/detectron2/commit/ffff8acc35ea88ad1cb1806ab0f00b4c1c5dbfd9)). | ||
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| More models will be released soon. Stay tuned. | ||
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| ### COCO Object Detecton Baselines with FCOS | ||
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| Name | box AP | download | ||
| --- |:---:|:---: | ||
| [FCOS_R_50_1x](configs/FCOS-Detection/R_50_1x.yaml) | 38.7 | [model](https://cloudstor.aarnet.edu.au/plus/s/glqFc13cCoEyHYy/download) | ||
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| ### COCO Instance Segmentation Baselines with [BlendMask](https://arxiv.org/abs/2001.00309) | ||
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| Model | Name |inference time (ms/im) | box AP | mask AP | download | ||
| --- |:---:|:---:|:---:|:---:|:---: | ||
| Mask R-CNN | [550_R_50_3x](configs/RCNN/550_R_50_FPN_3x.yaml) | 63 | 39.1 | 35.3 | | ||
| BlendMask | [550_R_50_3x](configs/BlendMask/550_R_50_3x.yaml) | 36 | 38.7 | 34.5 | [model](https://cloudstor.aarnet.edu.au/plus/s/R3Qintf7N8UCiIt/download) | ||
| Mask R-CNN | [R_50_1x](https://github.com/facebookresearch/detectron2/blob/master/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml) | 80 | 38.6 | 35.2 | | ||
| BlendMask | [R_50_1x](configs/BlendMask/R_50_1x.yaml) | 73 | 39.9 | 35.8 | [model](https://cloudstor.aarnet.edu.au/plus/s/zoxXPnr6Hw3OJgK/download) | ||
| Mask R-CNN | [R_50_3x](https://github.com/facebookresearch/detectron2/blob/master/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml) | 80 | 41.0 | 37.2 | | ||
| BlendMask | [R_50_3x](configs/BlendMask/R_50_3x.yaml) | 74 | 42.7 | 37.8 | [model](https://cloudstor.aarnet.edu.au/plus/s/ZnaInHFEKst6mvg/download) | ||
| Mask R-CNN | [R_101_3x](https://github.com/facebookresearch/detectron2/blob/master/configs/COCO-InstanceSegmentation/mask_rcnn_R_101_FPN_3x.yaml) | 100 | 42.9 | 38.6 | | ||
| BlendMask | [R_101_3x](configs/BlendMask/R_101_3x.yaml) | 94 | 44.8 | 39.5 | [model](https://cloudstor.aarnet.edu.au/plus/s/e4fXrliAcMtyEBy/download) | ||
| BlendMask | [R_101_dcni3_5x](configs/BlendMask/R_101_dcni3_5x.yaml) | 105 | 46.8 | 41.1 | [model](https://cloudstor.aarnet.edu.au/plus/s/vbnKnQtaGlw8TKv/download) | ||
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| ### COCO Panoptic Segmentation Baselines with BlendMask | ||
| Model | Name | PQ | PQ<sup>Th</sup> | PQ<sup>St</sup> | download | ||
| --- |:---:|:---:|:---:|:---:|:---: | ||
| Panoptic FPN | [R_50_3x](https://github.com/facebookresearch/detectron2/blob/master/configs/COCO-PanopticSegmentation/panoptic_fpn_R_50_3x.yaml) | 41.5 | 48.3 | 31.2 | | ||
| BlendMask | [R_50_3x](configs/BlendMask/Panoptic/R_50_3x.yaml) | 42.5 | 49.5 | 32.0 | [model](https://cloudstor.aarnet.edu.au/plus/s/oDgi0826JOJXCr5/download) | ||
| Panoptic FPN | [R_101_3x](https://github.com/facebookresearch/detectron2/blob/master/configs/COCO-InstanceSegmentation/panoptic_fpn_R_101_3x.yaml) | 43.0 | 49.7 | 32.9 | | ||
| BlendMask | [R_101_3x](configs/BlendMask/Panoptic/R_101_3x.yaml) | 44.3 | 51.6 | 33.2 | [model](https://cloudstor.aarnet.edu.au/plus/s/u6gZwj06MWDEkYe/download) | ||
| BlendMask | [R_101_dcni3_5x](configs/BlendMask/Panoptic/R_101_dcni3_5x.yaml) | 46.0 | 52.9 | 35.5 | [model](https://cloudstor.aarnet.edu.au/plus/s/Jwp41WEzDdrhWsN/download) | ||
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| ### Person in Context with BlendMask | ||
| Model | Name | box AP | mask AP | download | ||
| --- |:---:|:---:|:---:|:---: | ||
| BlendMask | [R_50_1x](configs/BlendMask/Person/R_50_1x.yaml) | 70.6 | 66.7 | [model](https://cloudstor.aarnet.edu.au/plus/s/nvpcKTFA5fsagc0/download) |
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| Original file line number | Diff line number | Diff line change |
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| from . import builtin # ensure the builtin datasets are registered | ||
| # from .dataset_mapper import DatasetMapperWithBasis | ||
| from .dataset_mapper import DatasetMapperWithBasis | ||
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| # __all__ = ["DatasetMapperWithBasis"] | ||
| __all__ = ["DatasetMapperWithBasis"] |
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| import copy | ||
| import numpy as np | ||
| import torch | ||
| from fvcore.common.file_io import PathManager | ||
| from PIL import Image | ||
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| from detectron2.data.dataset_mapper import DatasetMapper | ||
| from detectron2.data.detection_utils import SizeMismatchError | ||
| from detectron2.data import detection_utils as utils | ||
| from detectron2.data import transforms as T | ||
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| """ | ||
| This file contains the default mapping that's applied to "dataset dicts". | ||
| """ | ||
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| __all__ = ["DatasetMapperWithBasis"] | ||
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| class DatasetMapperWithBasis(DatasetMapper): | ||
| """ | ||
| This caller enables the default Detectron2 mapper to read an additional basis semantic label | ||
| """ | ||
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| def __init__(self, cfg, is_train=True): | ||
| super().__init__(cfg, is_train) | ||
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| # fmt: off | ||
| self.basis_loss_on = cfg.MODEL.BASIS_MODULE.LOSS_ON | ||
| self.ann_set = cfg.MODEL.BASIS_MODULE.ANN_SET | ||
| # fmt: on | ||
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| def __call__(self, dataset_dict): | ||
| """ | ||
| Args: | ||
| dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format. | ||
| Returns: | ||
| dict: a format that builtin models in detectron2 accept | ||
| """ | ||
| dataset_dict = copy.deepcopy(dataset_dict) # it will be modified by code below | ||
| # USER: Write your own image loading if it's not from a file | ||
| try: | ||
| image = utils.read_image(dataset_dict["file_name"], format=self.img_format) | ||
| except Exception as e: | ||
| print(dataset_dict["file_name"]) | ||
| print(e) | ||
| raise e | ||
| try: | ||
| utils.check_image_size(dataset_dict, image) | ||
| except SizeMismatchError as e: | ||
| expected_wh = (dataset_dict["width"], dataset_dict["height"]) | ||
| image_wh = (image.shape[1], image.shape[0]) | ||
| if (image_wh[1], image_wh[0]) == expected_wh: | ||
| print("transposing image {}".format(dataset_dict["file_name"])) | ||
| image = image.transpose(1, 0, 2) | ||
| else: | ||
| raise e | ||
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| if "annotations" not in dataset_dict or len(dataset_dict["annotations"]) == 0: | ||
| image, transforms = T.apply_transform_gens( | ||
| ([self.crop_gen] if self.crop_gen else []) + self.tfm_gens, image | ||
| ) | ||
| else: | ||
| # Crop around an instance if there are instances in the image. | ||
| # USER: Remove if you don't use cropping | ||
| if self.crop_gen: | ||
| crop_tfm = utils.gen_crop_transform_with_instance( | ||
| self.crop_gen.get_crop_size(image.shape[:2]), | ||
| image.shape[:2], | ||
| np.random.choice(dataset_dict["annotations"]), | ||
| ) | ||
| image = crop_tfm.apply_image(image) | ||
| image, transforms = T.apply_transform_gens(self.tfm_gens, image) | ||
| if self.crop_gen: | ||
| transforms = crop_tfm + transforms | ||
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| image_shape = image.shape[:2] # h, w | ||
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| # Pytorch's dataloader is efficient on torch.Tensor due to shared-memory, | ||
| # but not efficient on large generic data structures due to the use of pickle & mp.Queue. | ||
| # Therefore it's important to use torch.Tensor. | ||
| dataset_dict["image"] = torch.as_tensor(image.transpose(2, 0, 1).astype("float32")) | ||
| # Can use uint8 if it turns out to be slow some day | ||
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| # USER: Remove if you don't use pre-computed proposals. | ||
| if self.load_proposals: | ||
| utils.transform_proposals( | ||
| dataset_dict, image_shape, transforms, self.min_box_side_len, self.proposal_topk | ||
| ) | ||
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| if not self.is_train: | ||
| dataset_dict.pop("annotations", None) | ||
| dataset_dict.pop("sem_seg_file_name", None) | ||
| dataset_dict.pop("pano_seg_file_name", None) | ||
| return dataset_dict | ||
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| if "annotations" in dataset_dict: | ||
| # USER: Modify this if you want to keep them for some reason. | ||
| for anno in dataset_dict["annotations"]: | ||
| if not self.mask_on: | ||
| anno.pop("segmentation", None) | ||
| if not self.keypoint_on: | ||
| anno.pop("keypoints", None) | ||
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| # USER: Implement additional transformations if you have other types of data | ||
| annos = [ | ||
| utils.transform_instance_annotations( | ||
| obj, transforms, image_shape, keypoint_hflip_indices=self.keypoint_hflip_indices | ||
| ) | ||
| for obj in dataset_dict.pop("annotations") | ||
| if obj.get("iscrowd", 0) == 0 | ||
| ] | ||
| instances = utils.annotations_to_instances( | ||
| annos, image_shape, mask_format=self.mask_format | ||
| ) | ||
| # Create a tight bounding box from masks, useful when image is cropped | ||
| if self.crop_gen and instances.has("gt_masks"): | ||
| instances.gt_boxes = instances.gt_masks.get_bounding_boxes() | ||
| dataset_dict["instances"] = utils.filter_empty_instances(instances) | ||
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| # USER: Remove if you don't do semantic/panoptic segmentation. | ||
| if "sem_seg_file_name" in dataset_dict: | ||
| with PathManager.open(dataset_dict.pop("sem_seg_file_name"), "rb") as f: | ||
| sem_seg_gt = Image.open(f) | ||
| sem_seg_gt = np.asarray(sem_seg_gt, dtype="uint8") | ||
| sem_seg_gt = transforms.apply_segmentation(sem_seg_gt) | ||
| sem_seg_gt = torch.as_tensor(sem_seg_gt.astype("long")) | ||
| dataset_dict["sem_seg"] = sem_seg_gt | ||
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| if self.basis_loss_on and self.is_train: | ||
| # load basis supervisions | ||
| if self.ann_set == "coco": | ||
| basis_sem_path = dataset_dict["file_name"].replace('train2017', 'thing_train2017').replace('image/train', 'thing_train') | ||
| else: | ||
| basis_sem_path = dataset_dict["file_name"].replace('coco', 'lvis').replace('train2017', 'thing_train').replace('jpg', 'npz') | ||
| basis_sem_path = basis_sem_path.replace('jpg', 'npz') | ||
| basis_sem_gt = np.load(basis_sem_path)["mask"] | ||
| basis_sem_gt = transforms.apply_segmentation(basis_sem_gt) | ||
| basis_sem_gt = torch.as_tensor(basis_sem_gt.astype("long")) | ||
| dataset_dict["basis_sem"] = basis_sem_gt | ||
| return dataset_dict |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,3 +1,4 @@ | ||
| from .fpn import build_fcos_resnet_fpn_backbone | ||
| from .vovnet import build_vovnet_fpn_backbone, build_vovnet_backbone | ||
| from .dla import build_fcos_dla_fpn_backbone | ||
| from .resnet_lpf import build_resnet_lpf_backbone |
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