Update docstrings of detection models regarding resizing strategy (#8385)

torchvision/models/detection/faster_rcnn.py

@@ -73,8 +73,12 @@ class FasterRCNN(GeneralizedRCNN):
             The backbone should return a single Tensor or and OrderedDict[Tensor].
         num_classes (int): number of output classes of the model (including the background).
             If box_predictor is specified, num_classes should be None.
-        min_size (int): minimum size of the image to be rescaled before feeding it to the backbone
-        max_size (int): maximum size of the image to be rescaled before feeding it to the backbone
+        min_size (int): Images are rescaled before feeding them to the backbone:
+            we attempt to preserve the aspect ratio and scale the shorter edge
+            to ``min_size``. If the resulting longer edge exceeds ``max_size``,
+            then downscale so that the longer edge does not exceed ``max_size``.
+            This may result in the shorter edge beeing lower than ``min_size``.
+        max_size (int): See ``min_size``.
         image_mean (Tuple[float, float, float]): mean values used for input normalization.
             They are generally the mean values of the dataset on which the backbone has been trained
             on

torchvision/models/detection/fcos.py

@@ -299,8 +299,12 @@ class FCOS(nn.Module):
             channels that each feature map has (and it should be the same for all feature maps).
             The backbone should return a single Tensor or an OrderedDict[Tensor].
         num_classes (int): number of output classes of the model (including the background).
-        min_size (int): minimum size of the image to be rescaled before feeding it to the backbone
-        max_size (int): maximum size of the image to be rescaled before feeding it to the backbone
+        min_size (int): Images are rescaled before feeding them to the backbone:
+            we attempt to preserve the aspect ratio and scale the shorter edge
+            to ``min_size``. If the resulting longer edge exceeds ``max_size``,
+            then downscale so that the longer edge does not exceed ``max_size``.
+            This may result in the shorter edge beeing lower than ``min_size``.
+        max_size (int): See ``min_size``.
         image_mean (Tuple[float, float, float]): mean values used for input normalization.
             They are generally the mean values of the dataset on which the backbone has been trained
             on

torchvision/models/detection/keypoint_rcnn.py

@@ -60,8 +60,12 @@ class KeypointRCNN(FasterRCNN):
             The backbone should return a single Tensor or and OrderedDict[Tensor].
         num_classes (int): number of output classes of the model (including the background).
             If box_predictor is specified, num_classes should be None.
-        min_size (int): minimum size of the image to be rescaled before feeding it to the backbone
-        max_size (int): maximum size of the image to be rescaled before feeding it to the backbone
+        min_size (int): Images are rescaled before feeding them to the backbone:
+            we attempt to preserve the aspect ratio and scale the shorter edge
+            to ``min_size``. If the resulting longer edge exceeds ``max_size``,
+            then downscale so that the longer edge does not exceed ``max_size``.
+            This may result in the shorter edge beeing lower than ``min_size``.
+        max_size (int): See ``min_size``.
         image_mean (Tuple[float, float, float]): mean values used for input normalization.
             They are generally the mean values of the dataset on which the backbone has been trained
             on

torchvision/models/detection/mask_rcnn.py

@@ -61,8 +61,12 @@ class MaskRCNN(FasterRCNN):
             The backbone should return a single Tensor or and OrderedDict[Tensor].
         num_classes (int): number of output classes of the model (including the background).
             If box_predictor is specified, num_classes should be None.
-        min_size (int): minimum size of the image to be rescaled before feeding it to the backbone
-        max_size (int): maximum size of the image to be rescaled before feeding it to the backbone
+        min_size (int): Images are rescaled before feeding them to the backbone:
+            we attempt to preserve the aspect ratio and scale the shorter edge
+            to ``min_size``. If the resulting longer edge exceeds ``max_size``,
+            then downscale so that the longer edge does not exceed ``max_size``.
+            This may result in the shorter edge beeing lower than ``min_size``.
+        max_size (int): See ``min_size``.
         image_mean (Tuple[float, float, float]): mean values used for input normalization.
             They are generally the mean values of the dataset on which the backbone has been trained
             on

torchvision/models/detection/retinanet.py

@@ -352,8 +352,12 @@ class RetinaNet(nn.Module):
             channels that each feature map has (and it should be the same for all feature maps).
             The backbone should return a single Tensor or an OrderedDict[Tensor].
         num_classes (int): number of output classes of the model (including the background).
-        min_size (int): minimum size of the image to be rescaled before feeding it to the backbone
-        max_size (int): maximum size of the image to be rescaled before feeding it to the backbone
+        min_size (int): Images are rescaled before feeding them to the backbone:
+            we attempt to preserve the aspect ratio and scale the shorter edge
+            to ``min_size``. If the resulting longer edge exceeds ``max_size``,
+            then downscale so that the longer edge does not exceed ``max_size``.
+            This may result in the shorter edge beeing lower than ``min_size``.
+        max_size (int): See ``min_size``.
         image_mean (Tuple[float, float, float]): mean values used for input normalization.
             They are generally the mean values of the dataset on which the backbone has been trained
             on