Merge pull request huggingface#9 from Superb-AI-Suite/develop

add num_nms parameters and set to 100

src/transformers/models/deta/image_processing_deta.py

@@ -1096,6 +1096,7 @@ def post_process_object_detection(
         threshold: float = 0.5,
         target_sizes: Union[TensorType, List[Tuple]] = None,
         nms_threshold: float = 0.7,
+        num_nms: int = 100,
     ):
         """
         Converts the output of [`DetaForObjectDetection`] into final bounding boxes in (top_left_x, top_left_y,
@@ -1111,6 +1112,8 @@ def post_process_object_detection(
                 (height, width) of each image in the batch. If left to None, predictions will not be resized.
             nms_threshold (`float`, *optional*, defaults to 0.7):
                 NMS threshold.
+            num_nms (`int`, *optional*, defaults to 100):
+                Number of objects threshold.
 
         Returns:
             `List[Dict]`: A list of dictionaries, each dictionary containing the scores, labels and boxes for an image
@@ -1158,7 +1161,7 @@ def post_process_object_detection(
             lbls = lbls[pre_topk]
 
             # apply NMS
-            keep_inds = batched_nms(box, score, lbls, nms_threshold)[:100]
+            keep_inds = batched_nms(box, score, lbls, nms_threshold)[:num_nms]
             score = score[keep_inds]
             lbls = lbls[keep_inds]
             box = box[keep_inds]

src/transformers/models/mask2former/image_processing_mask2former.py

@@ -1121,10 +1121,6 @@ def post_process_instance_segmentation(
             pred_scores = scores_per_image * mask_scores_per_image
             pred_classes = labels_per_image
 
-            mask_pred, pred_scores, pred_classes = remove_low_and_no_objects(
-                mask_pred, pred_scores, pred_classes, threshold, num_classes
-            )
-
             segmentation = torch.zeros((384, 384)) - 1
             if target_sizes is not None:
                 size = target_sizes[i] if isinstance(target_sizes[i], tuple) else target_sizes[i].cpu().tolist()

src/transformers/models/yolov6/image_processing_yolov6.py

@@ -1008,6 +1008,7 @@ def post_process_object_detection(
         target_sizes: Union[TensorType, List[Tuple]] = None,
         nms_threshold: float = 0.65,
         max_nms: int = 30000,
+        num_nms: int = 100,
     ):
         """
         Converts the raw output of [`Yolov6ForObjectDetection`] into final bounding boxes in (top_left_x, top_left_y,
@@ -1022,6 +1023,12 @@ def post_process_object_detection(
             target_sizes (`torch.Tensor` or `List[Tuple[int, int]]`, *optional*):
                 Tensor of shape `(batch_size, 2)` or list of tuples (`Tuple[int, int]`) containing the target size
                 `(height, width)` of each image in the batch. If unset, predictions will not be resized.
+            nms_threshold (`float`, *optional*):
+                NMS score threshold to keep duplicated object detection predictions.
+            max_nms (`int`, *optional*):
+                Number of maximum output for intermediate results.
+            num_nms (`int`, *optional*):
+                Number of final output after the nms results.
         Returns:
             `List[Dict]`: A list of dictionaries, each dictionary containing the scores, labels and boxes for an image
             in the batch as predicted by the model.
@@ -1067,7 +1074,7 @@ def post_process_object_detection(
                 score = score[indices]
 
             # apply NMS
-            keep_inds = nms(box, score, nms_threshold)[:300]
+            keep_inds = nms(box, score, nms_threshold)[:num_nms]
             score = score[keep_inds]
             lbls = lbls[keep_inds]
             box = box[keep_inds]

src/transformers/models/yolov6/modeling_yolov6.py

@@ -1077,7 +1077,7 @@ def _init_weights(self, module: Union[nn.Linear, nn.Conv2d, nn.LayerNorm]) -> No
             # cf https://github.com/pytorch/pytorch/pull/5617
             module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
             if module.bias is not None:
-                module.bias.data.zero_()        
+                module.bias.data.zero_()
         elif isinstance(module, nn.LayerNorm):
             module.bias.data.zero_()
             module.weight.data.fill_(1.0)