Fixing the upper bound limit of random pixels in tests to 256. (#3136)

Reviewed By: datumbox

Differential Revision: D25396708

fbshipit-source-id: 7aa27cd76f135c0ccca6b8412fec9388b3df0919

test/fakedata_generation.py

@@ -21,7 +21,7 @@ def _encode(v):
         return torch.tensor(v, dtype=torch.int32).numpy().tobytes()[::-1]
 
     def _make_image_file(filename, num_images):
-        img = torch.randint(0, 255, size=(28 * 28 * num_images,), dtype=torch.uint8)
+        img = torch.randint(0, 256, size=(28 * 28 * num_images,), dtype=torch.uint8)
         with open(filename, "wb") as f:
             f.write(_encode(2051))  # magic header
             f.write(_encode(num_images))

test/test_datasets_samplers.py

@@ -29,7 +29,7 @@ def get_list_of_videos(num_videos=5, sizes=None, fps=None):
                 f = 5
             else:
                 f = fps[i]
-            data = torch.randint(0, 255, (size, 300, 400, 3), dtype=torch.uint8)
+            data = torch.randint(0, 256, (size, 300, 400, 3), dtype=torch.uint8)
             name = os.path.join(tmp_dir, "{}.mp4".format(i))
             names.append(name)
             io.write_video(name, data, fps=f)

test/test_datasets_video_utils.py

@@ -22,7 +22,7 @@ def get_list_of_videos(num_videos=5, sizes=None, fps=None):
                 f = 5
             else:
                 f = fps[i]
-            data = torch.randint(0, 255, (size, 300, 400, 3), dtype=torch.uint8)
+            data = torch.randint(0, 256, (size, 300, 400, 3), dtype=torch.uint8)
             name = os.path.join(tmp_dir, "{}.mp4".format(i))
             names.append(name)
             io.write_video(name, data, fps=f)

test/test_transforms_tensor.py

@@ -180,7 +180,7 @@ def test_center_crop(self):
         self._test_op(
             "center_crop", "CenterCrop", fn_kwargs=fn_kwargs, meth_kwargs=meth_kwargs
         )
-        tensor = torch.randint(0, 255, (3, 10, 10), dtype=torch.uint8, device=self.device)
+        tensor = torch.randint(0, 256, (3, 10, 10), dtype=torch.uint8, device=self.device)
         # Test torchscript of transforms.CenterCrop with size as int
         f = T.CenterCrop(size=5)
         scripted_fn = torch.jit.script(f)
@@ -294,7 +294,7 @@ def test_resize(self):
         self.assertEqual(y.shape[2], int(38 * 46 / 32))
 
         tensor, _ = self._create_data(height=34, width=36, device=self.device)
-        batch_tensors = torch.randint(0, 255, size=(4, 3, 44, 56), dtype=torch.uint8, device=self.device)
+        batch_tensors = torch.randint(0, 256, size=(4, 3, 44, 56), dtype=torch.uint8, device=self.device)
         script_fn = torch.jit.script(F.resize)
 
         for dt in [None, torch.float32, torch.float64]:
@@ -323,8 +323,8 @@ def test_resize(self):
             script_fn.save(os.path.join(tmp_dir, "t_resize.pt"))
 
     def test_resized_crop(self):
-        tensor = torch.randint(0, 255, size=(3, 44, 56), dtype=torch.uint8, device=self.device)
-        batch_tensors = torch.randint(0, 255, size=(4, 3, 44, 56), dtype=torch.uint8, device=self.device)
+        tensor = torch.randint(0, 256, size=(3, 44, 56), dtype=torch.uint8, device=self.device)
+        batch_tensors = torch.randint(0, 256, size=(4, 3, 44, 56), dtype=torch.uint8, device=self.device)
 
         for scale in [(0.7, 1.2), [0.7, 1.2]]:
             for ratio in [(0.75, 1.333), [0.75, 1.333]]:
@@ -341,8 +341,8 @@ def test_resized_crop(self):
             s_transform.save(os.path.join(tmp_dir, "t_resized_crop.pt"))
 
     def test_random_affine(self):
-        tensor = torch.randint(0, 255, size=(3, 44, 56), dtype=torch.uint8, device=self.device)
-        batch_tensors = torch.randint(0, 255, size=(4, 3, 44, 56), dtype=torch.uint8, device=self.device)
+        tensor = torch.randint(0, 256, size=(3, 44, 56), dtype=torch.uint8, device=self.device)
+        batch_tensors = torch.randint(0, 256, size=(4, 3, 44, 56), dtype=torch.uint8, device=self.device)
 
         for shear in [15, 10.0, (5.0, 10.0), [-15, 15], [-10.0, 10.0, -11.0, 11.0]]:
             for scale in [(0.7, 1.2), [0.7, 1.2]]:
@@ -363,8 +363,8 @@ def test_random_affine(self):
             s_transform.save(os.path.join(tmp_dir, "t_random_affine.pt"))
 
     def test_random_rotate(self):
-        tensor = torch.randint(0, 255, size=(3, 44, 56), dtype=torch.uint8, device=self.device)
-        batch_tensors = torch.randint(0, 255, size=(4, 3, 44, 56), dtype=torch.uint8, device=self.device)
+        tensor = torch.randint(0, 256, size=(3, 44, 56), dtype=torch.uint8, device=self.device)
+        batch_tensors = torch.randint(0, 256, size=(4, 3, 44, 56), dtype=torch.uint8, device=self.device)
 
         for center in [(0, 0), [10, 10], None, (56, 44)]:
             for expand in [True, False]:
@@ -383,8 +383,8 @@ def test_random_rotate(self):
             s_transform.save(os.path.join(tmp_dir, "t_random_rotate.pt"))
 
     def test_random_perspective(self):
-        tensor = torch.randint(0, 255, size=(3, 44, 56), dtype=torch.uint8, device=self.device)
-        batch_tensors = torch.randint(0, 255, size=(4, 3, 44, 56), dtype=torch.uint8, device=self.device)
+        tensor = torch.randint(0, 256, size=(3, 44, 56), dtype=torch.uint8, device=self.device)
+        batch_tensors = torch.randint(0, 256, size=(4, 3, 44, 56), dtype=torch.uint8, device=self.device)
 
         for distortion_scale in np.linspace(0.1, 1.0, num=20):
             for interpolation in [NEAREST, BILINEAR]: