Add typing in GeneralizedRCNNTransform (#4369)

* Add types in transform.

* Trace on eval mode.

Co-authored-by: Francisco Massa <[email protected]>

Author: datumbox

torchvision/models/detection/transform.py

@@ -10,15 +10,13 @@
 
 
 @torch.jit.unused
-def _get_shape_onnx(image):
-    # type: (Tensor) -> Tensor
+def _get_shape_onnx(image: Tensor) -> Tensor:
     from torch.onnx import operators
     return operators.shape_as_tensor(image)[-2:]
 
 
 @torch.jit.unused
-def _fake_cast_onnx(v):
-    # type: (Tensor) -> float
+def _fake_cast_onnx(v: Tensor) -> float:
     # ONNX requires a tensor but here we fake its type for JIT.
     return v
 
@@ -74,7 +72,8 @@ class GeneralizedRCNNTransform(nn.Module):
     It returns a ImageList for the inputs, and a List[Dict[Tensor]] for the targets
     """
 
-    def __init__(self, min_size, max_size, image_mean, image_std, size_divisible=32, fixed_size=None):
+    def __init__(self, min_size: int, max_size: int, image_mean: List[float], image_std: List[float],
+                 size_divisible: int = 32, fixed_size: Optional[Tuple[int, int]] = None):
         super(GeneralizedRCNNTransform, self).__init__()
         if not isinstance(min_size, (list, tuple)):
             min_size = (min_size,)
@@ -86,10 +85,9 @@ def __init__(self, min_size, max_size, image_mean, image_std, size_divisible=32,
         self.fixed_size = fixed_size
 
     def forward(self,
-                images,       # type: List[Tensor]
-                targets=None  # type: Optional[List[Dict[str, Tensor]]]
-                ):
-        # type: (...) -> Tuple[ImageList, Optional[List[Dict[str, Tensor]]]]
+                images: List[Tensor],
+                targets: Optional[List[Dict[str, Tensor]]] = None
+                ) -> Tuple[ImageList, Optional[List[Dict[str, Tensor]]]]:
         images = [img for img in images]
         if targets is not None:
             # make a copy of targets to avoid modifying it in-place
@@ -126,7 +124,7 @@ def forward(self,
         image_list = ImageList(images, image_sizes_list)
         return image_list, targets
 
-    def normalize(self, image):
+    def normalize(self, image: Tensor) -> Tensor:
         if not image.is_floating_point():
             raise TypeError(
                 f"Expected input images to be of floating type (in range [0, 1]), "
@@ -137,8 +135,7 @@ def normalize(self, image):
         std = torch.as_tensor(self.image_std, dtype=dtype, device=device)
         return (image - mean[:, None, None]) / std[:, None, None]
 
-    def torch_choice(self, k):
-        # type: (List[int]) -> int
+    def torch_choice(self, k: List[int]) -> int:
         """
         Implements `random.choice` via torch ops so it can be compiled with
         TorchScript. Remove if https://github.com/pytorch/pytorch/issues/25803
@@ -175,8 +172,7 @@ def resize(self,
     # _onnx_batch_images() is an implementation of
     # batch_images() that is supported by ONNX tracing.
     @torch.jit.unused
-    def _onnx_batch_images(self, images, size_divisible=32):
-        # type: (List[Tensor], int) -> Tensor
+    def _onnx_batch_images(self, images: List[Tensor], size_divisible: int = 32) -> Tensor:
         max_size = []
         for i in range(images[0].dim()):
             max_size_i = torch.max(torch.stack([img.shape[i] for img in images]).to(torch.float32)).to(torch.int64)
@@ -197,16 +193,14 @@ def _onnx_batch_images(self, images, size_divisible=32):
 
         return torch.stack(padded_imgs)
 
-    def max_by_axis(self, the_list):
-        # type: (List[List[int]]) -> List[int]
+    def max_by_axis(self, the_list: List[List[int]]) -> List[int]:
         maxes = the_list[0]
         for sublist in the_list[1:]:
             for index, item in enumerate(sublist):
                 maxes[index] = max(maxes[index], item)
         return maxes
 
-    def batch_images(self, images, size_divisible=32):
-        # type: (List[Tensor], int) -> Tensor
+    def batch_images(self, images: List[Tensor], size_divisible: int = 32) -> Tensor:
         if torchvision._is_tracing():
             # batch_images() does not export well to ONNX
             # call _onnx_batch_images() instead
@@ -226,11 +220,10 @@ def batch_images(self, images, size_divisible=32):
         return batched_imgs
 
     def postprocess(self,
-                    result,               # type: List[Dict[str, Tensor]]
-                    image_shapes,         # type: List[Tuple[int, int]]
-                    original_image_sizes  # type: List[Tuple[int, int]]
-                    ):
-        # type: (...) -> List[Dict[str, Tensor]]
+                    result: List[Dict[str, Tensor]],
+                    image_shapes: List[Tuple[int, int]],
+                    original_image_sizes: List[Tuple[int, int]]
+                    ) -> List[Dict[str, Tensor]]:
         if self.training:
             return result
         for i, (pred, im_s, o_im_s) in enumerate(zip(result, image_shapes, original_image_sizes)):
@@ -247,7 +240,7 @@ def postprocess(self,
                 result[i]["keypoints"] = keypoints
         return result
 
-    def __repr__(self):
+    def __repr__(self) -> str:
         format_string = self.__class__.__name__ + '('
         _indent = '\n    '
         format_string += "{0}Normalize(mean={1}, std={2})".format(_indent, self.image_mean, self.image_std)
@@ -257,8 +250,7 @@ def __repr__(self):
         return format_string
 
 
-def resize_keypoints(keypoints, original_size, new_size):
-    # type: (Tensor, List[int], List[int]) -> Tensor
+def resize_keypoints(keypoints: Tensor, original_size: List[int], new_size: List[int]) -> Tensor:
     ratios = [
         torch.tensor(s, dtype=torch.float32, device=keypoints.device) /
         torch.tensor(s_orig, dtype=torch.float32, device=keypoints.device)
@@ -276,8 +268,7 @@ def resize_keypoints(keypoints, original_size, new_size):
     return resized_data
 
 
-def resize_boxes(boxes, original_size, new_size):
-    # type: (Tensor, List[int], List[int]) -> Tensor
+def resize_boxes(boxes: Tensor, original_size: List[int], new_size: List[int]) -> Tensor:
     ratios = [
         torch.tensor(s, dtype=torch.float32, device=boxes.device) /
         torch.tensor(s_orig, dtype=torch.float32, device=boxes.device)