make convert_image_dtype scriptable (#2485)

* make convert_image_dtype scriptable

* move convert dtype to functional_tensor since only works on tensors

* retain availability of convert_image_dtype in functional.py

* Update code and tests

* Replaced int by torch.dtype

* int -> torch.dtype and use F instead of F_t

* Update functional_tensor.py

Co-authored-by: vfdev-5 <[email protected]>

Author: nairbv

test/test_functional_tensor.py

@@ -744,6 +744,10 @@ def test_perspective(self):
                         batch_tensors, F.perspective, startpoints=spoints, endpoints=epoints, interpolation=0
                     )
 
+    def test_convert_image_dtype(self):
+        # TODO: add tests of CPU/CUDA on tensor and batch
+        pass
+
 
 @unittest.skipIf(not torch.cuda.is_available(), reason="Skip if no CUDA device")
 class CUDATester(Tester):

test/test_transforms.py

@@ -2,6 +2,7 @@
 import torch
 import torchvision.transforms as transforms
 import torchvision.transforms.functional as F
+import torchvision.transforms.functional_tensor as F_t
 from torch._utils_internal import get_file_path_2
 from numpy.testing import assert_array_almost_equal
 import unittest
@@ -544,13 +545,26 @@ def test_to_tensor(self):
         output = trans(img)
         self.assertTrue(np.allclose(input_data.numpy(), output.numpy()))
 
+    def test_max_value(self):
+        for dtype in int_dtypes():
+            self.assertEqual(F_t._max_value(dtype), torch.iinfo(dtype).max)
+
+        for dtype in float_dtypes():
+            self.assertGreater(F_t._max_value(dtype), torch.finfo(dtype).max)
+
     def test_convert_image_dtype_float_to_float(self):
         for input_dtype, output_dtypes in cycle_over(float_dtypes()):
             input_image = torch.tensor((0.0, 1.0), dtype=input_dtype)
             for output_dtype in output_dtypes:
                 with self.subTest(input_dtype=input_dtype, output_dtype=output_dtype):
                     transform = transforms.ConvertImageDtype(output_dtype)
+                    transform_script = torch.jit.script(F.convert_image_dtype)
+
                     output_image = transform(input_image)
+                    output_image_script = transform_script(input_image, output_dtype)
+
+                    script_diff = output_image_script - output_image
+                    self.assertLess(script_diff.abs().max(), 1e-6)
 
                     actual_min, actual_max = output_image.tolist()
                     desired_min, desired_max = 0.0, 1.0
@@ -564,6 +578,7 @@ def test_convert_image_dtype_float_to_int(self):
             for output_dtype in int_dtypes():
                 with self.subTest(input_dtype=input_dtype, output_dtype=output_dtype):
                     transform = transforms.ConvertImageDtype(output_dtype)
+                    transform_script = torch.jit.script(F.convert_image_dtype)
 
                     if (input_dtype == torch.float32 and output_dtype in (torch.int32, torch.int64)) or (
                             input_dtype == torch.float64 and output_dtype == torch.int64
@@ -572,6 +587,10 @@ def test_convert_image_dtype_float_to_int(self):
                             transform(input_image)
                     else:
                         output_image = transform(input_image)
+                        output_image_script = transform_script(input_image, output_dtype)
+
+                        script_diff = output_image_script - output_image
+                        self.assertLess(script_diff.abs().max(), 1e-6)
 
                         actual_min, actual_max = output_image.tolist()
                         desired_min, desired_max = 0, torch.iinfo(output_dtype).max
@@ -585,7 +604,13 @@ def test_convert_image_dtype_int_to_float(self):
             for output_dtype in float_dtypes():
                 with self.subTest(input_dtype=input_dtype, output_dtype=output_dtype):
                     transform = transforms.ConvertImageDtype(output_dtype)
+                    transform_script = torch.jit.script(F.convert_image_dtype)
+
                     output_image = transform(input_image)
+                    output_image_script = transform_script(input_image, output_dtype)
+
+                    script_diff = output_image_script - output_image
+                    self.assertLess(script_diff.abs().max(), 1e-6)
 
                     actual_min, actual_max = output_image.tolist()
                     desired_min, desired_max = 0.0, 1.0
@@ -604,7 +629,15 @@ def test_convert_image_dtype_int_to_int(self):
 
                 with self.subTest(input_dtype=input_dtype, output_dtype=output_dtype):
                     transform = transforms.ConvertImageDtype(output_dtype)
+                    transform_script = torch.jit.script(F.convert_image_dtype)
+
                     output_image = transform(input_image)
+                    output_image_script = transform_script(input_image, output_dtype)
+
+                    script_diff = output_image_script.float() - output_image.float()
+                    self.assertLess(
+                        script_diff.abs().max(), 1e-6, msg="{} vs {}".format(output_image_script, output_image)
+                    )
 
                     actual_min, actual_max = output_image.tolist()
                     desired_min, desired_max = 0, output_max

torchvision/transforms/functional.py

@@ -152,48 +152,10 @@ def convert_image_dtype(image: torch.Tensor, dtype: torch.dtype = torch.float) -
             overflow errors since the floating point ``dtype`` cannot store consecutive integers over the whole range
             of the integer ``dtype``.
     """
-    if image.dtype == dtype:
-        return image
-
-    if image.dtype.is_floating_point:
-        # float to float
-        if dtype.is_floating_point:
-            return image.to(dtype)
-
-        # float to int
-        if (image.dtype == torch.float32 and dtype in (torch.int32, torch.int64)) or (
-            image.dtype == torch.float64 and dtype == torch.int64
-        ):
-            msg = f"The cast from {image.dtype} to {dtype} cannot be performed safely."
-            raise RuntimeError(msg)
-
-        # https://github.com/pytorch/vision/pull/2078#issuecomment-612045321
-        # For data in the range 0-1, (float * 255).to(uint) is only 255
-        # when float is exactly 1.0.
-        # `max + 1 - epsilon` provides more evenly distributed mapping of
-        # ranges of floats to ints.
-        eps = 1e-3
-        result = image.mul(torch.iinfo(dtype).max + 1 - eps)
-        return result.to(dtype)
-    else:
-        # int to float
-        if dtype.is_floating_point:
-            max = torch.iinfo(image.dtype).max
-            image = image.to(dtype)
-            return image / max
-
-        # int to int
-        input_max = torch.iinfo(image.dtype).max
-        output_max = torch.iinfo(dtype).max
-
-        if input_max > output_max:
-            factor = (input_max + 1) // (output_max + 1)
-            image = image // factor
-            return image.to(dtype)
-        else:
-            factor = (output_max + 1) // (input_max + 1)
-            image = image.to(dtype)
-            return image * factor
+    if not isinstance(image, torch.Tensor):
+        raise TypeError('Input img should be Tensor Image')
+
+    return F_t.convert_image_dtype(image, dtype)
 
 
 def to_pil_image(pic, mode=None):

torchvision/transforms/functional_tensor.py

@@ -27,6 +27,101 @@ def _get_image_num_channels(img: Tensor) -> int:
     raise TypeError("Input ndim should be 2 or more. Got {}".format(img.ndim))
 
 
+def _max_value(dtype: torch.dtype) -> float:
+    # TODO: replace this method with torch.iinfo when it gets torchscript support.
+    # https://github.com/pytorch/pytorch/issues/41492
+
+    a = torch.tensor(2, dtype=dtype)
+    signed = 1 if torch.tensor(0, dtype=dtype).is_signed() else 0
+    bits = 1
+    max_value = torch.tensor(-signed, dtype=torch.long)
+    while True:
+        next_value = a.pow(bits - signed).sub(1)
+        if next_value > max_value:
+            max_value = next_value
+            bits *= 2
+        else:
+            return max_value.item()
+    return max_value.item()
+
+
+def convert_image_dtype(image: torch.Tensor, dtype: torch.dtype = torch.float) -> torch.Tensor:
+    """PRIVATE METHOD. Convert a tensor image to the given ``dtype`` and scale the values accordingly
+
+    .. warning::
+
+        Module ``transforms.functional_tensor`` is private and should not be used in user application.
+        Please, consider instead using methods from `transforms.functional` module.
+
+    Args:
+        image (torch.Tensor): Image to be converted
+        dtype (torch.dtype): Desired data type of the output
+
+    Returns:
+        (torch.Tensor): Converted image
+
+    .. note::
+
+        When converting from a smaller to a larger integer ``dtype`` the maximum values are **not** mapped exactly.
+        If converted back and forth, this mismatch has no effect.
+
+    Raises:
+        RuntimeError: When trying to cast :class:`torch.float32` to :class:`torch.int32` or :class:`torch.int64` as
+            well as for trying to cast :class:`torch.float64` to :class:`torch.int64`. These conversions might lead to
+            overflow errors since the floating point ``dtype`` cannot store consecutive integers over the whole range
+            of the integer ``dtype``.
+    """
+    if image.dtype == dtype:
+        return image
+
+    # TODO: replace with image.dtype.is_floating_point when torchscript supports it
+    if torch.empty(0, dtype=image.dtype).is_floating_point():
+
+        # TODO: replace with dtype.is_floating_point when torchscript supports it
+        if torch.tensor(0, dtype=dtype).is_floating_point():
+            return image.to(dtype)
+
+        # float to int
+        if (image.dtype == torch.float32 and dtype in (torch.int32, torch.int64)) or (
+            image.dtype == torch.float64 and dtype == torch.int64
+        ):
+            msg = f"The cast from {image.dtype} to {dtype} cannot be performed safely."
+            raise RuntimeError(msg)
+
+        # https://github.com/pytorch/vision/pull/2078#issuecomment-612045321
+        # For data in the range 0-1, (float * 255).to(uint) is only 255
+        # when float is exactly 1.0.
+        # `max + 1 - epsilon` provides more evenly distributed mapping of
+        # ranges of floats to ints.
+        eps = 1e-3
+        max_val = _max_value(dtype)
+        result = image.mul(max_val + 1.0 - eps)
+        return result.to(dtype)
+    else:
+        input_max = _max_value(image.dtype)
+        output_max = _max_value(dtype)
+
+        # int to float
+        # TODO: replace with dtype.is_floating_point when torchscript supports it
+        if torch.tensor(0, dtype=dtype).is_floating_point():
+            image = image.to(dtype)
+            return image / input_max
+
+        # int to int
+        if input_max > output_max:
+            # factor should be forced to int for torch jit script
+            # otherwise factor is a float and image // factor can produce different results
+            factor = int((input_max + 1) // (output_max + 1))
+            image = image // factor
+            return image.to(dtype)
+        else:
+            # factor should be forced to int for torch jit script
+            # otherwise factor is a float and image * factor can produce different results
+            factor = int((output_max + 1) // (input_max + 1))
+            image = image.to(dtype)
+            return image * factor
+
+
 def vflip(img: Tensor) -> Tensor:
     """PRIVATE METHOD. Vertically flip the given the Image Tensor.
 
@@ -302,13 +397,11 @@ def adjust_gamma(img: Tensor, gamma: float, gain: float = 1) -> Tensor:
     result = img
     dtype = img.dtype
     if not torch.is_floating_point(img):
-        result = result / 255.0
+        result = convert_image_dtype(result, torch.float32)
 
     result = (gain * result ** gamma).clamp(0, 1)
 
-    if result.dtype != dtype:
-        eps = 1e-3
-        result = (255 + 1.0 - eps) * result
+    result = convert_image_dtype(result, dtype)
     result = result.to(dtype)
     return result
 

torchvision/transforms/transforms.py

@@ -16,7 +16,6 @@
 
 from . import functional as F
 
-
 __all__ = ["Compose", "ToTensor", "PILToTensor", "ConvertImageDtype", "ToPILImage", "Normalize", "Resize", "Scale",
            "CenterCrop", "Pad", "Lambda", "RandomApply", "RandomChoice", "RandomOrder", "RandomCrop",
            "RandomHorizontalFlip", "RandomVerticalFlip", "RandomResizedCrop", "RandomSizedCrop", "FiveCrop", "TenCrop",
@@ -127,7 +126,7 @@ def __repr__(self):
         return self.__class__.__name__ + '()'
 
 
-class ConvertImageDtype:
+class ConvertImageDtype(torch.nn.Module):
     """Convert a tensor image to the given ``dtype`` and scale the values accordingly
 
     Args:
@@ -146,9 +145,10 @@ class ConvertImageDtype:
     """
 
     def __init__(self, dtype: torch.dtype) -> None:
+        super().__init__()
         self.dtype = dtype
 
-    def __call__(self, image: torch.Tensor) -> torch.Tensor:
+    def forward(self, image: torch.Tensor) -> torch.Tensor:
         return F.convert_image_dtype(image, self.dtype)