Change IR scale factor to 1.0 if tile size is too big (#2337)

* adaptive pooling 6 -> 1

* set IR scale factor to 1.0

* replace adaptive avg pool with gap

src/otx/algorithms/common/configs/training_base.py

@@ -398,7 +398,7 @@ class BaseTilingParameters(ParameterGroup):
             "to crash or result in out-of-memory errors. It is recommended to "
             "adjust the scale factor value carefully based on the available "
             "hardware resources and the needs of the application.",
-            default_value=2.0,
+            default_value=1.0,
             min_value=1.0,
             max_value=4.0,
             affects_outcome_of=ModelLifecycle.NONE,

src/otx/algorithms/detection/adapters/mmdet/models/detectors/custom_maskrcnn_tile_optimized.py

@@ -6,6 +6,7 @@
 
 import numpy as np
 import torch
+from mmcls.models.necks.gap import GlobalAveragePooling
 from mmcv.cnn import ConvModule
 from mmcv.runner import auto_fp16
 from mmdet.models.builder import DETECTORS
@@ -30,10 +31,15 @@ def __init__(self):
             nn.MaxPool2d(kernel_size=3, stride=2),
             ConvModule(192, 256, 3, padding=1, act_cfg=dict(type="ReLU")),
             nn.MaxPool2d(kernel_size=3, stride=2),
+            ConvModule(256, 256, 3, padding=1, act_cfg=dict(type="ReLU")),
+            nn.MaxPool2d(kernel_size=3, stride=2),
         )
-        self.avgpool = torch.nn.AdaptiveAvgPool2d((6, 6))
+        # NOTE: Original Adaptive Avg Pooling is replaced with Global Avg Pooling
+        # due to ONNX tracing issues: https://github.com/openvinotoolkit/training_extensions/pull/2337
+
+        self.gap = GlobalAveragePooling()
         self.classifier = torch.nn.Sequential(
-            torch.nn.Linear(256 * 6 * 6, 256),
+            torch.nn.Linear(256, 256),
             torch.nn.ReLU(inplace=True),
             torch.nn.Linear(256, 256),
             torch.nn.ReLU(inplace=True),
@@ -54,8 +60,7 @@ def forward(self, img: torch.Tensor) -> torch.Tensor:
             torch.Tensor: logits
         """
         x = self.features(img)
-        x = self.avgpool(x)
-        x = torch.flatten(x, 1)
+        x = self.gap(x)
         y = self.classifier(x)
         return y
 

src/otx/algorithms/detection/configs/instance_segmentation/configuration.yaml

@@ -592,7 +592,7 @@ tiling_parameters:
     header: OpenVINO IR Scale Factor
     description: The purpose of the scale parameter is to optimize the performance and efficiency of tiling in OpenVINO IR during inference. By controlling the increase in tile size and input size, the scale parameter allows for more efficient parallelization of the workload and improve the overall performance and efficiency of the inference process on OpenVINO.
     affects_outcome_of: TRAINING
-    default_value: 2.0
+    default_value: 1.0
     min_value: 1.0
     max_value: 4.0
     type: FLOAT
@@ -602,7 +602,7 @@ tiling_parameters:
       operator: AND
       rules: []
       type: UI_RULES
-    value: 2.0
+    value: 1.0
     visible_in_ui: true
     warning: null
 

src/otx/algorithms/detection/utils/data.py

@@ -442,7 +442,6 @@ def format_list_to_str(value_lists: list):
     return f"[{str_value[:-2]}]"
 
 
-# TODO [Eugene] please add unit test for this function
 def adaptive_tile_params(
     tiling_parameters: DetectionConfig.BaseTilingParameters, dataset: DatasetEntity, object_tile_ratio=0.03, rule="avg"
 ):

tests/unit/algorithms/detection/tiling/test_tiling_detection.py

@@ -35,6 +35,7 @@
     DEFAULT_ISEG_TEMPLATE_DIR,
     init_environment,
 )
+from otx.algorithms.detection.utils.data import adaptive_tile_params
 
 
 @DETECTORS.register_module(force=True)
@@ -428,3 +429,23 @@ def test_max_annotation(self, max_annotation=200):
             assert len(data["gt_bboxes"].data[0][0]) <= max_annotation
             assert len(data["gt_labels"].data[0][0]) <= max_annotation
             assert len(data["gt_masks"].data[0][0]) <= max_annotation
+
+    @e2e_pytest_unit
+    def test_adaptive_tile_parameters(self):
+        model_template = parse_model_template(os.path.join(DEFAULT_ISEG_TEMPLATE_DIR, "template.yaml"))
+        hp = create(model_template.hyper_parameters.data)
+
+        default_tile_size = hp.tiling_parameters.tile_size
+        default_tile_overlap = hp.tiling_parameters.tile_overlap
+        default_tile_max_number = hp.tiling_parameters.tile_max_number
+
+        adaptive_tile_params(hp.tiling_parameters, self.otx_dataset)
+
+        # check tile size is changed
+        assert hp.tiling_parameters.tile_size != default_tile_size
+
+        # check tile overlap is changed
+        assert hp.tiling_parameters.tile_overlap != default_tile_overlap
+
+        # check max output prediction size is changed
+        assert hp.tiling_parameters.tile_max_number != default_tile_max_number