Fix DEKR's replace_head & improve __repr__ for keypoints transforms (#…

…1227)

* YOLO-NAS Pose Estimation Experiment

* Added logging

* Remove test

* Tune recipe for L

* Tune recipe for S

* Tune optimizer params

* Lower LR

* Lower LR

* Added __repr__ for keypoint transforms to improve their printing in notebooks

* If OKS sigmas are not given explicitly, initialize with default 17 keypoints only if num_joints == 17, otherwise use default values and emit a warning

* Added recipe to train rescoring for yolo_nas_pose_l

* Added YOLO-NAS-POSE scores

* Added YOLO-NAS-POSE-M recipe

* Fine-tuning notebook for pose est

* Added lings to pretrained models

* Remove links to S & M models

* Update notebook

* Fix apply_sigmoid=True

* Increased eps to prevent divizion by zero

* Update notebook

* Adding predict

* Predict

* Predict

* Adding predict

* Adding predict

* Adding joint information to dataset configs

* Added makefile target recipe_accuracy_tests

* Remove temp files

* Rename variables for better clarity

* Move predict() related files to super_gradients.training.utils.predict

* Move predict() related files to super_gradients.training.utils.predict

* Rename file poses.py -> pose_estimation.py

* Rename joint_colors/joint_links -> edge_colors/edge_links

* Disable showing bounding box by default

* Allow passing edge & keypoints as None, in this case colors will be generated randomly

* Update docstrings

* Fix test

* Added unit tests to verify settings preprocesisng params from dataset works

* Added predict

* Added default prerprocessing settings for yolo-nas-pose

* Added default prerprocessing settings for yolo-nas-pose

* Added __repr__ to KeypointsImageToTensor

* _pad_image cannot work with pad_value that is tuple (r,g,b).
So we change the keypoint transforms defaults in config to use single scalar value

* _pad_image cannot work with pad_value that is tuple (r,g,b).
So we change the keypoint transforms defaults in config to use single scalar value

* _pad_image cannot work with pad_value that is tuple (r,g,b).
So we change the keypoint transforms defaults in config to use single scalar value

* Fix pad_value in keypoints transforms to accept single scalar value to make compatible with _pad_image

* Update signature of base YoloNasPose class (dropped arch_params)

* Simplify recipes to train YOLO-NAS-POSE

* Implement replace_head for dekr

* Implement replace_head for dekr

* Make more beautiful __repr__ implementation

* Change .format to string interpolation

src/super_gradients/training/metrics/pose_estimation_metrics.py

@@ -103,7 +103,14 @@ def __init__(
         self.greater_component_is_better = dict((k, True) for k in self.stats_names)
 
         if oks_sigmas is None:
-            oks_sigmas = np.array([0.26, 0.25, 0.25, 0.35, 0.35, 0.79, 0.79, 0.72, 0.72, 0.62, 0.62, 1.07, 1.07, 0.87, 0.87, 0.89, 0.89]) / 10.0
+            if num_joints == 17:
+                oks_sigmas = np.array([0.26, 0.25, 0.25, 0.35, 0.35, 0.79, 0.79, 0.72, 0.72, 0.62, 0.62, 1.07, 1.07, 0.87, 0.87, 0.89, 0.89]) / 10.0
+            else:
+                oks_sigmas = np.array([0.1] * num_joints)
+                logger.warning(
+                    f"Using default OKS sigmas of `0.1` for a custom dataset with {num_joints} joints. "
+                    f"To silence this warning, you may want to specify OKS sigmas explicitly as it has direct impact on the AP score."
+                )
 
         if len(oks_sigmas) != num_joints:
             raise ValueError(f"Length of oks_sigmas ({len(oks_sigmas)}) should be equal to num_joints {num_joints}")

src/super_gradients/training/models/pose_estimation_models/dekr_hrnet.py

@@ -328,22 +328,32 @@ def __init__(self, arch_params):
             setattr(self, "stage{}".format(i + 2), stage)
 
         # build head net
-        inp_channels = int(sum(self.stages_spec.NUM_CHANNELS[-1]))
-        config_heatmap = self.spec.HEAD_HEATMAP
-        config_offset = self.spec.HEAD_OFFSET
+        self.head_inp_channels = int(sum(self.stages_spec.NUM_CHANNELS[-1]))
+        self.config_heatmap = self.spec.HEAD_HEATMAP
+        self.config_offset = self.spec.HEAD_OFFSET
         self.num_joints = arch_params.num_classes
         self.num_offset = self.num_joints * 2
         self.num_joints_with_center = self.num_joints + 1
-        self.offset_prekpt = config_offset["NUM_CHANNELS_PERKPT"]
+        self.offset_prekpt = self.config_offset["NUM_CHANNELS_PERKPT"]
 
         offset_channels = self.num_joints * self.offset_prekpt
-        self.transition_heatmap = self._make_transition_for_head(inp_channels, config_heatmap["NUM_CHANNELS"])
-        self.transition_offset = self._make_transition_for_head(inp_channels, offset_channels)
-        self.head_heatmap = self._make_heatmap_head(config_heatmap)
-        self.offset_feature_layers, self.offset_final_layer = self._make_separete_regression_head(config_offset)
-        self.heatmap_activation = nn.Sigmoid() if config_heatmap["HEATMAP_APPLY_SIGMOID"] else nn.Identity()
+        self.transition_heatmap = self._make_transition_for_head(self.head_inp_channels, self.config_heatmap["NUM_CHANNELS"])
+        self.transition_offset = self._make_transition_for_head(self.head_inp_channels, offset_channels)
+        self.head_heatmap = self._make_heatmap_head(self.config_heatmap)
+        self.offset_feature_layers, self.offset_final_layer = self._make_separete_regression_head(self.config_offset)
+        self.heatmap_activation = nn.Sigmoid() if self.config_heatmap["HEATMAP_APPLY_SIGMOID"] else nn.Identity()
         self.init_weights()
 
+    def replace_head(self, new_num_classes: int):
+        self.num_joints = new_num_classes
+        self.num_offset = new_num_classes * 2
+        self.num_joints_with_center = new_num_classes + 1
+
+        offset_channels = self.num_joints * self.offset_prekpt
+        self.head_heatmap = self._make_heatmap_head(self.config_heatmap)
+        self.transition_offset = self._make_transition_for_head(self.head_inp_channels, offset_channels)
+        self.offset_feature_layers, self.offset_final_layer = self._make_separete_regression_head(self.config_offset)
+
     def _make_transition_for_head(self, inplanes: int, outplanes: int) -> nn.Module:
         transition_layer = [nn.Conv2d(inplanes, outplanes, 1, 1, 0, bias=False), nn.BatchNorm2d(outplanes), nn.ReLU(True)]
         return nn.Sequential(*transition_layer)

src/super_gradients/training/transforms/keypoint_transforms.py

@@ -71,6 +71,13 @@ def get_equivalent_preprocessing(self) -> List:
             preprocessing += t.get_equivalent_preprocessing()
         return preprocessing
 
+    def __repr__(self):
+        format_string = self.__class__.__name__ + "("
+        for t in self.transforms:
+            format_string += f"\t{repr(t)}"
+        format_string += "\n)"
+        return format_string
+
 
 @register_transform(Transforms.KeypointsImageToTensor)
 class KeypointsImageToTensor(KeypointTransform):
@@ -87,6 +94,9 @@ def get_equivalent_preprocessing(self) -> List:
             {Processings.ImagePermute: {"permutation": (2, 0, 1)}},
         ]
 
+    def __repr__(self):
+        return self.__class__.__name__ + f"(permutation={self.permutation})"
+
 
 @register_transform(Transforms.KeypointsImageStandardize)
 class KeypointsImageStandardize(KeypointTransform):
@@ -107,6 +117,9 @@ def __call__(self, image: np.ndarray, mask: np.ndarray, joints: np.ndarray, area
     def get_equivalent_preprocessing(self) -> List[Dict]:
         return [{Processings.StandardizeImage: {"max_value": self.max_value}}]
 
+    def __repr__(self):
+        return self.__class__.__name__ + f"(max_value={self.max_value})"
+
 
 @register_transform(Transforms.KeypointsImageNormalize)
 class KeypointsImageNormalize(KeypointTransform):
@@ -122,6 +135,9 @@ def __call__(self, image: np.ndarray, mask: np.ndarray, joints: np.ndarray, area
         image = (image - self.mean) / self.std
         return image, mask, joints, areas, bboxes
 
+    def __repr__(self):
+        return self.__class__.__name__ + f"(mean={self.mean}, std={self.std})"
+
     def get_equivalent_preprocessing(self) -> List:
         return [{Processings.NormalizeImage: {"mean": self.mean, "std": self.std}}]
 
@@ -143,6 +159,9 @@ def __init__(self, flip_index: List[int], prob: float = 0.5):
         self.flip_index = flip_index
         self.prob = prob
 
+    def __repr__(self):
+        return self.__class__.__name__ + f"(flip_index={self.flip_index}, prob={self.prob})"
+
     def __call__(self, image, mask, joints, areas: Optional[np.ndarray], bboxes: Optional[np.ndarray]):
         if image.shape[:2] != mask.shape[:2]:
             raise RuntimeError(f"Image shape ({image.shape[:2]}) does not match mask shape ({mask.shape[:2]}).")
@@ -218,6 +237,9 @@ def apply_to_bboxes(self, bboxes, rows):
     def get_equivalent_preprocessing(self) -> List:
         raise RuntimeError("KeypointsRandomHorizontalFlip does not have equivalent preprocessing.")
 
+    def __repr__(self):
+        return self.__class__.__name__ + f"(prob={self.prob})"
+
 
 @register_transform(Transforms.KeypointsLongestMaxSize)
 class KeypointsLongestMaxSize(KeypointTransform):
@@ -278,6 +300,13 @@ def apply_to_keypoints(cls, keypoints, scale):
     def apply_to_bboxes(cls, bboxes, scale):
         return bboxes * scale
 
+    def __repr__(self):
+        return (
+            self.__class__.__name__ + f"(max_height={self.max_height}, "
+            f"max_width={self.max_width}, "
+            f"interpolation={self.interpolation}, prob={self.prob})"
+        )
+
     def get_equivalent_preprocessing(self) -> List:
         return [{Processings.KeypointsLongestMaxSizeRescale: {"output_shape": (self.max_height, self.max_width)}}]
 
@@ -318,6 +347,14 @@ def __call__(self, image, mask, joints, areas: Optional[np.ndarray], bboxes: Opt
 
         return image, mask, joints, areas, bboxes
 
+    def __repr__(self):
+        return (
+            self.__class__.__name__ + f"(min_height={self.min_height}, "
+            f"min_width={self.min_width}, "
+            f"image_pad_value={self.image_pad_value}, "
+            f"mask_pad_value={self.mask_pad_value})"
+        )
+
     def get_equivalent_preprocessing(self) -> List:
         return [{Processings.KeypointsBottomRightPadding: {"output_shape": (self.min_height, self.min_width), "pad_value": self.image_pad_value}}]
 
@@ -353,6 +390,17 @@ def __init__(
         self.mask_pad_value = mask_pad_value
         self.prob = prob
 
+    def __repr__(self):
+        return (
+            self.__class__.__name__ + f"(max_rotation={self.max_rotation}, "
+            f"min_scale={self.min_scale}, "
+            f"max_scale={self.max_scale}, "
+            f"max_translate={self.max_translate}, "
+            f"image_pad_value={self.image_pad_value}, "
+            f"mask_pad_value={self.mask_pad_value}, "
+            f"prob={self.prob})"
+        )
+
     def _get_affine_matrix(self, img, angle, scale, dx, dy):
         """
 

tests/unit_tests/replace_head_test.py

@@ -30,6 +30,13 @@ def test_yolo_nas_replace_head(self):
             (_, pred_scores), _ = model.forward(input)
             self.assertEqual(pred_scores.size(2), 100)
 
+    def test_dekr_replace_head(self):
+        input = torch.randn(1, 3, 640, 640).to(self.device)
+        model = models.get(Models.DEKR_W32_NO_DC, num_classes=20, pretrained_weights="coco_pose").to(self.device).eval()
+        heatmap, offsets = model.forward(input)
+        self.assertEqual(heatmap.size(1), 20 + 1)
+        self.assertEqual(offsets.size(1), 20 * 2)
+
     def tearDown(self) -> None:
         if os.path.exists("~/.cache/torch/hub/"):
             shutil.rmtree("~/.cache/torch/hub/")