Added CPU/CUDA and batch input for dtype conversion op (#2755)

* 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

* Added CPU/CUDA+batch tests

* Fixed tests according to review

Co-authored-by: Brian <nairbv@yahoo.com>

Author: vfdev-5

test/common_utils.py

@@ -369,3 +369,16 @@ def approxEqualTensorToPIL(self, tensor, pil_image, tol=1e-5, msg=None, agg_meth
             err < tol,
             msg="{}: err={}, tol={}: \n{}\nvs\n{}".format(msg, err, tol, tensor[0, :10, :10], pil_tensor[0, :10, :10])
         )
+
+
+def cycle_over(objs):
+    for idx, obj in enumerate(objs):
+        yield obj, objs[:idx] + objs[idx + 1:]
+
+
+def int_dtypes():
+    return torch.testing.integral_types()
+
+
+def float_dtypes():
+    return torch.testing.floating_types()

test/test_transforms.py

@@ -20,24 +20,11 @@
 except ImportError:
     stats = None
 
-GRACE_HOPPER = get_file_path_2(
-    os.path.dirname(os.path.abspath(__file__)), 'assets', 'grace_hopper_517x606.jpg')
-
-
-def cycle_over(objs):
-    objs = list(objs)
-    for idx, obj in enumerate(objs):
-        yield obj, objs[:idx] + objs[idx + 1:]
+from common_utils import cycle_over, int_dtypes, float_dtypes
 
 
-def int_dtypes():
-    yield from iter(
-        (torch.uint8, torch.int8, torch.int16, torch.short, torch.int32, torch.int, torch.int64, torch.long,)
-    )
-
-
-def float_dtypes():
-    yield from iter((torch.float32, torch.float, torch.float64, torch.double))
+GRACE_HOPPER = get_file_path_2(
+    os.path.dirname(os.path.abspath(__file__)), 'assets', 'grace_hopper_517x606.jpg')
 
 
 class Tester(unittest.TestCase):

test/test_transforms_tensor.py

@@ -9,7 +9,7 @@
 
 import unittest
 
-from common_utils import TransformsTester, get_tmp_dir
+from common_utils import TransformsTester, get_tmp_dir, int_dtypes, float_dtypes
 
 
 class Tester(TransformsTester):
@@ -27,26 +27,26 @@ def _test_functional_op(self, func, fn_kwargs):
         transformed_pil_img = f(pil_img, **fn_kwargs)
         self.compareTensorToPIL(transformed_tensor, transformed_pil_img)
 
-    def _test_transform_vs_scripted(self, transform, s_transform, tensor):
+    def _test_transform_vs_scripted(self, transform, s_transform, tensor, msg=None):
         torch.manual_seed(12)
         out1 = transform(tensor)
         torch.manual_seed(12)
         out2 = s_transform(tensor)
-        self.assertTrue(out1.equal(out2))
+        self.assertTrue(out1.equal(out2), msg=msg)
 
-    def _test_transform_vs_scripted_on_batch(self, transform, s_transform, batch_tensors):
+    def _test_transform_vs_scripted_on_batch(self, transform, s_transform, batch_tensors, msg=None):
         torch.manual_seed(12)
         transformed_batch = transform(batch_tensors)
 
         for i in range(len(batch_tensors)):
             img_tensor = batch_tensors[i, ...]
             torch.manual_seed(12)
             transformed_img = transform(img_tensor)
-            self.assertTrue(transformed_img.equal(transformed_batch[i, ...]))
+            self.assertTrue(transformed_img.equal(transformed_batch[i, ...]), msg=msg)
 
         torch.manual_seed(12)
         s_transformed_batch = s_transform(batch_tensors)
-        self.assertTrue(transformed_batch.equal(s_transformed_batch))
+        self.assertTrue(transformed_batch.equal(s_transformed_batch), msg=msg)
 
     def _test_class_op(self, method, meth_kwargs=None, test_exact_match=True, **match_kwargs):
         if meth_kwargs is None:
@@ -492,6 +492,32 @@ def test_random_erasing(self):
             self._test_transform_vs_scripted(fn, scripted_fn, tensor)
             self._test_transform_vs_scripted_on_batch(fn, scripted_fn, batch_tensors)
 
+    def test_convert_image_dtype(self):
+        tensor, _ = self._create_data(26, 34, device=self.device)
+        batch_tensors = torch.rand(4, 3, 44, 56, device=self.device)
+
+        for in_dtype in int_dtypes() + float_dtypes():
+            in_tensor = tensor.to(in_dtype)
+            in_batch_tensors = batch_tensors.to(in_dtype)
+            for out_dtype in int_dtypes() + float_dtypes():
+
+                fn = T.ConvertImageDtype(dtype=out_dtype)
+                scripted_fn = torch.jit.script(fn)
+
+                if (in_dtype == torch.float32 and out_dtype in (torch.int32, torch.int64)) or \
+                        (in_dtype == torch.float64 and out_dtype == torch.int64):
+                    with self.assertRaisesRegex(RuntimeError, r"cannot be performed safely"):
+                        self._test_transform_vs_scripted(fn, scripted_fn, in_tensor)
+                    with self.assertRaisesRegex(RuntimeError, r"cannot be performed safely"):
+                        self._test_transform_vs_scripted_on_batch(fn, scripted_fn, in_batch_tensors)
+                    continue
+
+                self._test_transform_vs_scripted(fn, scripted_fn, in_tensor)
+                self._test_transform_vs_scripted_on_batch(fn, scripted_fn, in_batch_tensors)
+
+        with get_tmp_dir() as tmp_dir:
+            scripted_fn.save(os.path.join(tmp_dir, "t_convert_dtype.pt"))
+
 
 @unittest.skipIf(not torch.cuda.is_available(), reason="Skip if no CUDA device")
 class CUDATester(Tester):