move comments to docstring

mmdet/datasets/pipelines/transforms.py

@@ -55,7 +55,9 @@ class Resize:
         keep_ratio (bool): Whether to keep the aspect ratio when resizing the
             image.
         bbox_clip_border (bool, optional): Whether to clip the objects outside
-            the border of the image. Defaults to True.
+            the border of the image. In some dataset like MOT17, the gt bboxes
+            are allowed to cross the border of images. Therefore, we don't
+            need to clip the gt bboxes in these cases. Defaults to True.
         backend (str): Image resize backend, choices are 'cv2' and 'pillow'.
             These two backends generates slightly different results. Defaults
             to 'cv2'.
@@ -239,9 +241,6 @@ def _resize_bboxes(self, results):
         """Resize bounding boxes with ``results['scale_factor']``."""
         for key in results.get('bbox_fields', []):
             bboxes = results[key] * results['scale_factor']
-            # In some dataset like MOT17, the gt bboxes are allowed to cross
-            # the border of images. Therefore, we don't need to clip the gt
-            # bboxes in these cases.
             if self.bbox_clip_border:
                 img_shape = results['img_shape']
                 bboxes[:, 0::2] = np.clip(bboxes[:, 0::2], 0, img_shape[1])
@@ -1986,7 +1985,9 @@ class Mosaic:
         min_bbox_size (int | float): The minimum pixel for filtering
             invalid bboxes after the mosaic pipeline. Default to 0.
         bbox_clip_border (bool, optional): Whether to clip the objects outside
-            the border of the image. Defaults to True.
+            the border of the image. In some dataset like MOT17, the gt bboxes
+            are allowed to cross the border of images. Therefore, we don't
+            need to clip the gt bboxes in these cases. Defaults to True.
         skip_filter (bool): Whether to skip filtering rules. If it
             is True, the filter rule will not be applied, and the
             `min_bbox_size` is invalid. Default to True.
@@ -2108,9 +2109,6 @@ def _mosaic_transform(self, results):
             mosaic_bboxes = np.concatenate(mosaic_bboxes, 0)
             mosaic_labels = np.concatenate(mosaic_labels, 0)
 
-            # In some dataset like MOT17, the gt bboxes are allowed to cross
-            # the border of images. Therefore, we don't need to clip the gt
-            # bboxes in these cases.
             if self.bbox_clip_border:
                 mosaic_bboxes[:, 0::2] = np.clip(mosaic_bboxes[:, 0::2], 0,
                                                  2 * self.img_scale[1])
@@ -2261,7 +2259,9 @@ class MixUp:
             threshold to filter bboxes. If max(h/w, w/h) larger than this
             value, the box will be removed. Default: 20.
         bbox_clip_border (bool, optional): Whether to clip the objects outside
-            the border of the image. Defaults to True.
+            the border of the image. In some dataset like MOT17, the gt bboxes
+            are allowed to cross the border of images. Therefore, we don't
+            need to clip the gt bboxes in these cases. Defaults to True.
         skip_filter (bool): Whether to skip filtering rules. If it
             is True, the filter rule will not be applied, and the
             `min_bbox_size` and `min_area_ratio` and `max_aspect_ratio`
@@ -2394,9 +2394,6 @@ def _mixup_transform(self, results):
         retrieve_gt_bboxes = retrieve_results['gt_bboxes']
         retrieve_gt_bboxes[:, 0::2] = retrieve_gt_bboxes[:, 0::2] * scale_ratio
         retrieve_gt_bboxes[:, 1::2] = retrieve_gt_bboxes[:, 1::2] * scale_ratio
-        # In some dataset like MOT17, the gt bboxes are allowed to cross
-        # the border of images. Therefore, we don't need to clip the gt
-        # bboxes in these cases.
         if self.bbox_clip_border:
             retrieve_gt_bboxes[:, 0::2] = np.clip(retrieve_gt_bboxes[:, 0::2],
                                                   0, origin_w)
@@ -2413,9 +2410,6 @@ def _mixup_transform(self, results):
             cp_retrieve_gt_bboxes[:, 0::2] - x_offset
         cp_retrieve_gt_bboxes[:, 1::2] = \
             cp_retrieve_gt_bboxes[:, 1::2] - y_offset
-        # In some dataset like MOT17, the gt bboxes are allowed to cross
-        # the border of images. Therefore, we don't need to clip the gt
-        # bboxes in these cases.
         if self.bbox_clip_border:
             cp_retrieve_gt_bboxes[:, 0::2] = np.clip(
                 cp_retrieve_gt_bboxes[:, 0::2], 0, target_w)
@@ -2512,7 +2506,9 @@ class RandomAffine:
             threshold to filter bboxes. If max(h/w, w/h) larger than this
             value, the box will be removed.
         bbox_clip_border (bool, optional): Whether to clip the objects outside
-            the border of the image. Defaults to True.
+            the border of the image. In some dataset like MOT17, the gt bboxes
+            are allowed to cross the border of images. Therefore, we don't
+            need to clip the gt bboxes in these cases. Defaults to True.
         skip_filter (bool): Whether to skip filtering rules. If it
             is True, the filter rule will not be applied, and the
             `min_bbox_size` and `min_area_ratio` and `max_aspect_ratio`
@@ -2604,9 +2600,6 @@ def __call__(self, results):
                 warp_bboxes = np.vstack(
                     (xs.min(1), ys.min(1), xs.max(1), ys.max(1))).T
 
-                # In some dataset like MOT17, the gt bboxes are allowed to
-                # cross the border of images. Therefore, we don't need to clip
-                # the gt bboxes in these cases.
                 if self.bbox_clip_border:
                     warp_bboxes[:, [0, 2]] = \
                         warp_bboxes[:, [0, 2]].clip(0, width)