[fbsync] Replaced ConvertImageDtype by ToDtype in reference scripts (#…

…7862)

Summary: (Note: this ignores all push blocking failures!)

Reviewed By: matteobettini

Differential Revision: D48900394

fbshipit-source-id: b4c93e890c8565bbba16211e3a7842a972b259a7

Co-authored-by: Nicolas Hug <nh.nicolas.hug@gmail.com>

references/classification/presets.py

@@ -61,7 +61,7 @@ def __init__(
 
         transforms.extend(
             [
-                T.ConvertImageDtype(torch.float),
+                T.ToDtype(torch.float, scale=True) if use_v2 else T.ConvertImageDtype(torch.float),
                 T.Normalize(mean=mean, std=std),
             ]
         )
@@ -106,7 +106,7 @@ def __init__(
             transforms.append(T.PILToTensor())
 
         transforms += [
-            T.ConvertImageDtype(torch.float),
+            T.ToDtype(torch.float, scale=True) if use_v2 else T.ConvertImageDtype(torch.float),
             T.Normalize(mean=mean, std=std),
         ]
 

references/detection/presets.py

@@ -73,7 +73,7 @@ def __init__(
             # Note: we could just convert to pure tensors even in v2.
             transforms += [T.ToImage() if use_v2 else T.PILToTensor()]
 
-        transforms += [T.ConvertImageDtype(torch.float)]
+        transforms += [T.ToDtype(torch.float, scale=True)]
 
         if use_v2:
             transforms += [
@@ -103,7 +103,7 @@ def __init__(self, backend="pil", use_v2=False):
         else:
             raise ValueError(f"backend can be 'datapoint', 'tensor' or 'pil', but got {backend}")
 
-        transforms += [T.ConvertImageDtype(torch.float)]
+        transforms += [T.ToDtype(torch.float, scale=True)]
 
         if use_v2:
             transforms += [T.ToPureTensor()]

references/detection/transforms.py

@@ -53,14 +53,17 @@ def forward(
         return image, target
 
 
-class ConvertImageDtype(nn.Module):
-    def __init__(self, dtype: torch.dtype) -> None:
+class ToDtype(nn.Module):
+    def __init__(self, dtype: torch.dtype, scale: bool = False) -> None:
         super().__init__()
         self.dtype = dtype
+        self.scale = scale
 
     def forward(
         self, image: Tensor, target: Optional[Dict[str, Tensor]] = None
     ) -> Tuple[Tensor, Optional[Dict[str, Tensor]]]:
+        if not self.scale:
+            return image.to(dtype=self.dtype), target
         image = F.convert_image_dtype(image, self.dtype)
         return image, target
 

references/segmentation/presets.py

@@ -60,7 +60,7 @@ def __init__(
             ]
         else:
             # No need to explicitly convert masks as they're magically int64 already
-            transforms += [T.ConvertImageDtype(torch.float)]
+            transforms += [T.ToDtype(torch.float, scale=True)]
 
         transforms += [T.Normalize(mean=mean, std=std)]
         if use_v2:
@@ -97,7 +97,7 @@ def __init__(
             transforms += [T.ToImage() if use_v2 else T.PILToTensor()]
 
         transforms += [
-            T.ConvertImageDtype(torch.float),
+            T.ToDtype(torch.float, scale=True),
             T.Normalize(mean=mean, std=std),
         ]
         if use_v2:

references/segmentation/transforms.py

@@ -81,11 +81,14 @@ def __call__(self, image, target):
         return image, target
 
 
-class ConvertImageDtype:
-    def __init__(self, dtype):
+class ToDtype:
+    def __init__(self, dtype, scale=False):
         self.dtype = dtype
+        self.scale = scale
 
     def __call__(self, image, target):
+        if not self.scale:
+            return image.to(dtype=self.dtype), target
         image = F.convert_image_dtype(image, self.dtype)
         return image, target
 

references/segmentation/v2_extras.py

@@ -78,6 +78,6 @@ def _coco_detection_masks_to_voc_segmentation_mask(self, target):
     def forward(self, image, target):
         segmentation_mask = self._coco_detection_masks_to_voc_segmentation_mask(target)
         if segmentation_mask is None:
-            segmentation_mask = torch.zeros(v2.functional.get_spatial_size(image), dtype=torch.uint8)
+            segmentation_mask = torch.zeros(v2.functional.get_size(image), dtype=torch.uint8)
 
         return image, datapoints.Mask(segmentation_mask)