Removed type from exception error (#2729)

Otherwise, torch jit scripted function raises exception on save

test/test_transforms_tensor.py

+import os
 import torch
 from torchvision import transforms as T
 from torchvision.transforms import functional as F
@@ -8,7 +9,7 @@ import numpy as np
 
 import unittest
 
-from common_utils import TransformsTester
+from common_utils import TransformsTester, get_tmp_dir
 
 
 class Tester(TransformsTester):
@@ -73,6 +74,9 @@ class Tester(TransformsTester):
         batch_tensors = self._create_data_batch(height=23, width=34, channels=3, num_samples=4, device=self.device)
         self._test_transform_vs_scripted_on_batch(f, scripted_fn, batch_tensors)
 
+        with get_tmp_dir() as tmp_dir:
+            scripted_fn.save(os.path.join(tmp_dir, "t_{}.pt".format(method)))
+
     def _test_op(self, func, method, fn_kwargs=None, meth_kwargs=None):
         self._test_functional_op(func, fn_kwargs)
         self._test_class_op(method, meth_kwargs)
@@ -188,6 +192,9 @@ class Tester(TransformsTester):
         scripted_fn = torch.jit.script(f)
         scripted_fn(tensor)
 
+        with get_tmp_dir() as tmp_dir:
+            scripted_fn.save(os.path.join(tmp_dir, "t_center_crop.pt"))
+
     def _test_op_list_output(self, func, method, out_length, fn_kwargs=None, meth_kwargs=None):
         if fn_kwargs is None:
             fn_kwargs = {}
@@ -231,6 +238,9 @@ class Tester(TransformsTester):
                 self.assertTrue(transformed_img.equal(transformed_batch[i, ...]),
                                 msg="{} vs {}".format(transformed_img, transformed_batch[i, ...]))
 
+        with get_tmp_dir() as tmp_dir:
+            scripted_fn.save(os.path.join(tmp_dir, "t_op_list_{}.pt".format(method)))
+
     def test_five_crop(self):
         fn_kwargs = meth_kwargs = {"size": (5,)}
         self._test_op_list_output(
@@ -294,6 +304,9 @@ class Tester(TransformsTester):
                     self._test_transform_vs_scripted(transform, s_transform, tensor)
                     self._test_transform_vs_scripted_on_batch(transform, s_transform, batch_tensors)
 
+        with get_tmp_dir() as tmp_dir:
+            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)
@@ -309,6 +322,9 @@ class Tester(TransformsTester):
                         self._test_transform_vs_scripted(transform, s_transform, tensor)
                         self._test_transform_vs_scripted_on_batch(transform, s_transform, batch_tensors)
 
+        with get_tmp_dir() as tmp_dir:
+            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)
@@ -327,6 +343,9 @@ class Tester(TransformsTester):
                             self._test_transform_vs_scripted(transform, s_transform, tensor)
                             self._test_transform_vs_scripted_on_batch(transform, s_transform, batch_tensors)
 
+        with get_tmp_dir() as tmp_dir:
+            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)
@@ -343,6 +362,9 @@ class Tester(TransformsTester):
                         self._test_transform_vs_scripted(transform, s_transform, tensor)
                         self._test_transform_vs_scripted_on_batch(transform, s_transform, batch_tensors)
 
+        with get_tmp_dir() as tmp_dir:
+            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)
@@ -358,6 +380,9 @@ class Tester(TransformsTester):
                 self._test_transform_vs_scripted(transform, s_transform, tensor)
                 self._test_transform_vs_scripted_on_batch(transform, s_transform, batch_tensors)
 
+        with get_tmp_dir() as tmp_dir:
+            s_transform.save(os.path.join(tmp_dir, "t_perspective.pt"))
+
     def test_to_grayscale(self):
 
         meth_kwargs = {"num_output_channels": 1}
@@ -388,6 +413,9 @@ class Tester(TransformsTester):
         self._test_transform_vs_scripted(fn, scripted_fn, tensor)
         self._test_transform_vs_scripted_on_batch(fn, scripted_fn, batch_tensors)
 
+        with get_tmp_dir() as tmp_dir:
+            scripted_fn.save(os.path.join(tmp_dir, "t_norm.pt"))
+
     def test_linear_transformation(self):
         c, h, w = 3, 24, 32
 
@@ -410,6 +438,9 @@ class Tester(TransformsTester):
         s_transformed_batch = scripted_fn(batch_tensors)
         self.assertTrue(transformed_batch.equal(s_transformed_batch))
 
+        with get_tmp_dir() as tmp_dir:
+            scripted_fn.save(os.path.join(tmp_dir, "t_norm.pt"))
+
     def test_compose(self):
         tensor, _ = self._create_data(26, 34, device=self.device)
         tensor = tensor.to(dtype=torch.float32) / 255.0

torchvision/transforms/functional_tensor.py

@@ -15,7 +15,7 @@ def _get_image_size(img: Tensor) -> List[int]:
     """Returns (w, h) of tensor image"""
     if _is_tensor_a_torch_image(img):
         return [img.shape[-1], img.shape[-2]]
-    raise TypeError("Unexpected type {}".format(type(img)))
+    raise TypeError("Unexpected input type")
 
 
 def _get_image_num_channels(img: Tensor) -> int:
@@ -24,7 +24,7 @@ def _get_image_num_channels(img: Tensor) -> int:
     elif img.ndim > 2:
         return img.shape[-3]
 
-    raise TypeError("Unexpected type {}".format(type(img)))
+    raise TypeError("Input ndim should be 2 or more. Got {}".format(img.ndim))
 
 
 def vflip(img: Tensor) -> Tensor:
@@ -223,7 +223,7 @@ def adjust_hue(img: Tensor, hue_factor: float) -> Tensor:
         raise ValueError('hue_factor ({}) is not in [-0.5, 0.5].'.format(hue_factor))
 
     if not (isinstance(img, torch.Tensor) and _is_tensor_a_torch_image(img)):
-        raise TypeError('img should be Tensor image. Got {}'.format(type(img)))
+        raise TypeError('Input img should be Tensor image')
 
     orig_dtype = img.dtype
     if img.dtype == torch.uint8:
@@ -294,7 +294,7 @@ def adjust_gamma(img: Tensor, gamma: float, gain: float = 1) -> Tensor:
     """
 
     if not isinstance(img, torch.Tensor):
-        raise TypeError('img should be a Tensor. Got {}'.format(type(img)))
+        raise TypeError('Input img should be a Tensor.')
 
     if gamma < 0:
         raise ValueError('Gamma should be a non-negative real number')
@@ -763,10 +763,10 @@ def _assert_grid_transform_inputs(
         coeffs: Optional[List[float]] = None,
 ):
     if not (isinstance(img, torch.Tensor) and _is_tensor_a_torch_image(img)):
-        raise TypeError("img should be Tensor Image. Got {}".format(type(img)))
+        raise TypeError("Input img should be Tensor Image")
 
     if matrix is not None and not isinstance(matrix, list):
-        raise TypeError("Argument matrix should be a list. Got {}".format(type(matrix)))
+        raise TypeError("Argument matrix should be a list")
 
     if matrix is not None and len(matrix) != 6:
         raise ValueError("Argument matrix should have 6 float values")
@@ -989,7 +989,7 @@ def perspective(
         Tensor: transformed image.
     """
     if not (isinstance(img, torch.Tensor) and _is_tensor_a_torch_image(img)):
-        raise TypeError('img should be Tensor Image. Got {}'.format(type(img)))
+        raise TypeError('Input img should be Tensor Image')
 
     _interpolation_modes = {
         0: "nearest",