Scriptability checks for Tensor Transforms (#1690)

* scriptability checks

* tests upds

* linter upds

* linter upds

* upds

* tuple list changes

* linter updates

Author: surgan12

test/test_functional_tensor.py

@@ -1,34 +1,45 @@
 from __future__ import division
 import torch
+from torch import Tensor
 import torchvision.transforms as transforms
 import torchvision.transforms.functional_tensor as F_t
 import torchvision.transforms.functional as F
 import numpy as np
 import unittest
 import random
+from torch.jit.annotations import Optional, List, BroadcastingList2, Tuple
 
 
 class Tester(unittest.TestCase):
 
     def test_vflip(self):
+        script_vflip = torch.jit.script(F_t.vflip)
         img_tensor = torch.randn(3, 16, 16)
         img_tensor_clone = img_tensor.clone()
         vflipped_img = F_t.vflip(img_tensor)
         vflipped_img_again = F_t.vflip(vflipped_img)
         self.assertEqual(vflipped_img.shape, img_tensor.shape)
         self.assertTrue(torch.equal(img_tensor, vflipped_img_again))
         self.assertTrue(torch.equal(img_tensor, img_tensor_clone))
+        # scriptable function test
+        vflipped_img_script = script_vflip(img_tensor)
+        self.assertTrue(torch.equal(vflipped_img, vflipped_img_script))
 
     def test_hflip(self):
+        script_hflip = torch.jit.script(F_t.hflip)
         img_tensor = torch.randn(3, 16, 16)
         img_tensor_clone = img_tensor.clone()
         hflipped_img = F_t.hflip(img_tensor)
         hflipped_img_again = F_t.hflip(hflipped_img)
         self.assertEqual(hflipped_img.shape, img_tensor.shape)
         self.assertTrue(torch.equal(img_tensor, hflipped_img_again))
         self.assertTrue(torch.equal(img_tensor, img_tensor_clone))
+        # scriptable function test
+        hflipped_img_script = script_hflip(img_tensor)
+        self.assertTrue(torch.equal(hflipped_img, hflipped_img_script))
 
     def test_crop(self):
+        script_crop = torch.jit.script(F_t.crop)
         img_tensor = torch.randint(0, 255, (3, 16, 16), dtype=torch.uint8)
         img_tensor_clone = img_tensor.clone()
         top = random.randint(0, 15)
@@ -42,11 +53,18 @@ def test_crop(self):
         self.assertTrue(torch.equal(img_tensor, img_tensor_clone))
         self.assertTrue(torch.equal(img_cropped, (img_cropped_GT * 255).to(torch.uint8)),
                         "functional_tensor crop not working")
+        # scriptable function test
+        cropped_img_script = script_crop(img_tensor, top, left, height, width)
+        self.assertTrue(torch.equal(img_cropped, cropped_img_script))
 
     def test_adjustments(self):
-        fns = ((F.adjust_brightness, F_t.adjust_brightness),
-               (F.adjust_contrast, F_t.adjust_contrast),
-               (F.adjust_saturation, F_t.adjust_saturation))
+        script_adjust_brightness = torch.jit.script(F_t.adjust_brightness)
+        script_adjust_contrast = torch.jit.script(F_t.adjust_contrast)
+        script_adjust_saturation = torch.jit.script(F_t.adjust_saturation)
+
+        fns = ((F.adjust_brightness, F_t.adjust_brightness, script_adjust_brightness),
+               (F.adjust_contrast, F_t.adjust_contrast, script_adjust_contrast),
+               (F.adjust_saturation, F_t.adjust_saturation, script_adjust_saturation))
 
         for _ in range(20):
             channels = 3
@@ -60,11 +78,13 @@ def test_adjustments(self):
 
             factor = 3 * torch.rand(1)
             img_clone = img.clone()
-            for f, ft in fns:
+            for f, ft, sft in fns:
 
                 ft_img = ft(img, factor)
+                sft_img = sft(img, factor)
                 if not img.dtype.is_floating_point:
                     ft_img = ft_img.to(torch.float) / 255
+                    sft_img = sft_img.to(torch.float) / 255
 
                 img_pil = transforms.ToPILImage()(img)
                 f_img_pil = f(img_pil, factor)
@@ -73,28 +93,39 @@ def test_adjustments(self):
                 # F uses uint8 and F_t uses float, so there is a small
                 # difference in values caused by (at most 5) truncations.
                 max_diff = (ft_img - f_img).abs().max()
+                max_diff_scripted = (sft_img - f_img).abs().max()
                 self.assertLess(max_diff, 5 / 255 + 1e-5)
+                self.assertLess(max_diff_scripted, 5 / 255 + 1e-5)
                 self.assertTrue(torch.equal(img, img_clone))
 
     def test_rgb_to_grayscale(self):
+        script_rgb_to_grayscale = torch.jit.script(F_t.rgb_to_grayscale)
         img_tensor = torch.randint(0, 255, (3, 16, 16), dtype=torch.uint8)
         img_tensor_clone = img_tensor.clone()
         grayscale_tensor = F_t.rgb_to_grayscale(img_tensor).to(int)
         grayscale_pil_img = torch.tensor(np.array(F.to_grayscale(F.to_pil_image(img_tensor)))).to(int)
         max_diff = (grayscale_tensor - grayscale_pil_img).abs().max()
         self.assertLess(max_diff, 1.0001)
         self.assertTrue(torch.equal(img_tensor, img_tensor_clone))
+        # scriptable function test
+        grayscale_script = script_rgb_to_grayscale(img_tensor).to(int)
+        self.assertTrue(torch.equal(grayscale_script, grayscale_tensor))
 
     def test_center_crop(self):
+        script_center_crop = torch.jit.script(F_t.center_crop)
         img_tensor = torch.randint(0, 255, (1, 32, 32), dtype=torch.uint8)
         img_tensor_clone = img_tensor.clone()
         cropped_tensor = F_t.center_crop(img_tensor, [10, 10])
         cropped_pil_image = F.center_crop(transforms.ToPILImage()(img_tensor), [10, 10])
         cropped_pil_tensor = (transforms.ToTensor()(cropped_pil_image) * 255).to(torch.uint8)
         self.assertTrue(torch.equal(cropped_tensor, cropped_pil_tensor))
         self.assertTrue(torch.equal(img_tensor, img_tensor_clone))
+        # scriptable function test
+        cropped_script = script_center_crop(img_tensor, [10, 10])
+        self.assertTrue(torch.equal(cropped_script, cropped_tensor))
 
     def test_five_crop(self):
+        script_five_crop = torch.jit.script(F_t.five_crop)
         img_tensor = torch.randint(0, 255, (1, 32, 32), dtype=torch.uint8)
         img_tensor_clone = img_tensor.clone()
         cropped_tensor = F_t.five_crop(img_tensor, [10, 10])
@@ -110,8 +141,13 @@ def test_five_crop(self):
         self.assertTrue(torch.equal(cropped_tensor[4],
                                     (transforms.ToTensor()(cropped_pil_image[4]) * 255).to(torch.uint8)))
         self.assertTrue(torch.equal(img_tensor, img_tensor_clone))
+        # scriptable function test
+        cropped_script = script_five_crop(img_tensor, [10, 10])
+        for cropped_script_img, cropped_tensor_img in zip(cropped_script, cropped_tensor):
+            self.assertTrue(torch.equal(cropped_script_img, cropped_tensor_img))
 
     def test_ten_crop(self):
+        script_ten_crop = torch.jit.script(F_t.ten_crop)
         img_tensor = torch.randint(0, 255, (1, 32, 32), dtype=torch.uint8)
         img_tensor_clone = img_tensor.clone()
         cropped_tensor = F_t.ten_crop(img_tensor, [10, 10])
@@ -137,6 +173,10 @@ def test_ten_crop(self):
         self.assertTrue(torch.equal(cropped_tensor[9],
                                     (transforms.ToTensor()(cropped_pil_image[9]) * 255).to(torch.uint8)))
         self.assertTrue(torch.equal(img_tensor, img_tensor_clone))
+        # scriptable function test
+        cropped_script = script_ten_crop(img_tensor, [10, 10])
+        for cropped_script_img, cropped_tensor_img in zip(cropped_script, cropped_tensor):
+            self.assertTrue(torch.equal(cropped_script_img, cropped_tensor_img))
 
 
 if __name__ == '__main__':

torchvision/transforms/functional_tensor.py

@@ -1,38 +1,48 @@
+from __future__ import division
 import torch
 import torchvision.transforms.functional as F
+from torch import Tensor
+from torch.jit.annotations import Optional, List, BroadcastingList2, Tuple
 
 
-def vflip(img_tensor):
+def _is_tensor_a_torch_image(input):
+    return len(input.shape) == 3
+
+
+def vflip(img):
+    # type: (Tensor) -> Tensor
     """Vertically flip the given the Image Tensor.
 
     Args:
-        img_tensor (Tensor): Image Tensor to be flipped in the form [C, H, W].
+        img (Tensor): Image Tensor to be flipped in the form [C, H, W].
 
     Returns:
         Tensor:  Vertically flipped image Tensor.
     """
-    if not F._is_tensor_image(img_tensor):
+    if not _is_tensor_a_torch_image(img):
         raise TypeError('tensor is not a torch image.')
 
-    return img_tensor.flip(-2)
+    return img.flip(-2)
 
 
-def hflip(img_tensor):
+def hflip(img):
+    # type: (Tensor) -> Tensor
     """Horizontally flip the given the Image Tensor.
 
     Args:
-        img_tensor (Tensor): Image Tensor to be flipped in the form [C, H, W].
+        img (Tensor): Image Tensor to be flipped in the form [C, H, W].
 
     Returns:
         Tensor:  Horizontally flipped image Tensor.
     """
-    if not F._is_tensor_image(img_tensor):
+    if not _is_tensor_a_torch_image(img):
         raise TypeError('tensor is not a torch image.')
 
-    return img_tensor.flip(-1)
+    return img.flip(-1)
 
 
 def crop(img, top, left, height, width):
+    # type: (Tensor, int, int, int, int) -> Tensor
     """Crop the given Image Tensor.
 
     Args:
@@ -45,13 +55,14 @@ def crop(img, top, left, height, width):
     Returns:
         Tensor: Cropped image.
     """
-    if not F._is_tensor_image(img):
+    if not _is_tensor_a_torch_image(img):
         raise TypeError('tensor is not a torch image.')
 
     return img[..., top:top + height, left:left + width]
 
 
 def rgb_to_grayscale(img):
+    # type: (Tensor) -> Tensor
     """Convert the given RGB Image Tensor to Grayscale.
     For RGB to Grayscale conversion, ITU-R 601-2 luma transform is performed which
     is L = R * 0.2989 + G * 0.5870 + B * 0.1140
@@ -70,6 +81,7 @@ def rgb_to_grayscale(img):
 
 
 def adjust_brightness(img, brightness_factor):
+    # type: (Tensor, float) -> Tensor
     """Adjust brightness of an RGB image.
 
     Args:
@@ -81,13 +93,14 @@ def adjust_brightness(img, brightness_factor):
     Returns:
         Tensor: Brightness adjusted image.
     """
-    if not F._is_tensor_image(img):
+    if not _is_tensor_a_torch_image(img):
         raise TypeError('tensor is not a torch image.')
 
-    return _blend(img, 0, brightness_factor)
+    return _blend(img, torch.zeros_like(img), brightness_factor)
 
 
 def adjust_contrast(img, contrast_factor):
+    # type: (Tensor, float) -> Tensor
     """Adjust contrast of an RGB image.
 
     Args:
@@ -99,7 +112,7 @@ def adjust_contrast(img, contrast_factor):
     Returns:
         Tensor: Contrast adjusted image.
     """
-    if not F._is_tensor_image(img):
+    if not _is_tensor_a_torch_image(img):
         raise TypeError('tensor is not a torch image.')
 
     mean = torch.mean(rgb_to_grayscale(img).to(torch.float))
@@ -108,6 +121,7 @@ def adjust_contrast(img, contrast_factor):
 
 
 def adjust_saturation(img, saturation_factor):
+    # type: (Tensor, float) -> Tensor
     """Adjust color saturation of an RGB image.
 
     Args:
@@ -119,13 +133,14 @@ def adjust_saturation(img, saturation_factor):
     Returns:
         Tensor: Saturation adjusted image.
     """
-    if not F._is_tensor_image(img):
+    if not _is_tensor_a_torch_image(img):
         raise TypeError('tensor is not a torch image.')
 
     return _blend(img, rgb_to_grayscale(img), saturation_factor)
 
 
 def center_crop(img, output_size):
+    # type: (Tensor, BroadcastingList2[int]) -> Tensor
     """Crop the Image Tensor and resize it to desired size.
 
     Args:
@@ -136,7 +151,7 @@ def center_crop(img, output_size):
     Returns:
             Tensor: Cropped image.
     """
-    if not F._is_tensor_image(img):
+    if not _is_tensor_a_torch_image(img):
         raise TypeError('tensor is not a torch image.')
 
     _, image_width, image_height = img.size()
@@ -148,9 +163,10 @@ def center_crop(img, output_size):
 
 
 def five_crop(img, size):
+    # type: (Tensor, BroadcastingList2[int]) -> List[Tensor]
     """Crop the given Image Tensor into four corners and the central crop.
     .. Note::
-        This transform returns a tuple of Tensors and there may be a
+        This transform returns a List of Tensors and there may be a
         mismatch in the number of inputs and targets your ``Dataset`` returns.
 
     Args:
@@ -159,10 +175,10 @@ def five_crop(img, size):
            made.
 
     Returns:
-       tuple: tuple (tl, tr, bl, br, center)
+       List: List (tl, tr, bl, br, center)
                 Corresponding top left, top right, bottom left, bottom right and center crop.
     """
-    if not F._is_tensor_image(img):
+    if not _is_tensor_a_torch_image(img):
         raise TypeError('tensor is not a torch image.')
 
     assert len(size) == 2, "Please provide only two dimensions (h, w) for size."
@@ -179,14 +195,15 @@ def five_crop(img, size):
     br = crop(img, image_width - crop_width, image_height - crop_height, image_width, image_height)
     center = center_crop(img, (crop_height, crop_width))
 
-    return (tl, tr, bl, br, center)
+    return [tl, tr, bl, br, center]
 
 
 def ten_crop(img, size, vertical_flip=False):
+    # type: (Tensor, BroadcastingList2[int], bool) -> List[Tensor]
     """Crop the given Image Tensor into four corners and the central crop plus the
         flipped version of these (horizontal flipping is used by default).
     .. Note::
-        This transform returns a tuple of images and there may be a
+        This transform returns a List of images and there may be a
         mismatch in the number of inputs and targets your ``Dataset`` returns.
 
     Args:
@@ -196,11 +213,11 @@ def ten_crop(img, size, vertical_flip=False):
        vertical_flip (bool): Use vertical flipping instead of horizontal
 
     Returns:
-       tuple: tuple (tl, tr, bl, br, center, tl_flip, tr_flip, bl_flip, br_flip, center_flip)
+       List: List (tl, tr, bl, br, center, tl_flip, tr_flip, bl_flip, br_flip, center_flip)
                 Corresponding top left, top right, bottom left, bottom right and center crop
                 and same for the flipped image's tensor.
     """
-    if not F._is_tensor_image(img):
+    if not _is_tensor_a_torch_image(img):
         raise TypeError('tensor is not a torch image.')
 
     assert len(size) == 2, "Please provide only two dimensions (h, w) for size."
@@ -217,5 +234,6 @@ def ten_crop(img, size, vertical_flip=False):
 
 
 def _blend(img1, img2, ratio):
-    bound = 1 if img1.dtype.is_floating_point else 255
+    # type: (Tensor, Tensor, float) -> Tensor
+    bound = 1 if img1.dtype in [torch.half, torch.float32, torch.float64] else 255
     return (ratio * img1 + (1 - ratio) * img2).clamp(0, bound).to(img1.dtype)