Unified Tensor/PIL crop (#2342)

* [WIP] Unified Tensor/PIL crop

* Fixed misplaced type annotation

* Fixed tests
- crop with padding
- other tests using mising private functions: _is_pil_image, _get_image_size

* Unified CenterCrop and F.center_crop
- sorted includes in transforms.py
- used py3 annotations

* Unified FiveCrop and F.five_crop

* Improved tests and docs

* Unified TenCrop and F.ten_crop

* Removed useless typing in functional_pil

* Updated code according to the review
- removed useless torch.jit.export
- added missing typing return type
- fixed F.F_pil._is_pil_image -> F._is_pil_image

* Removed useless torch.jit.export

* Improved code according to the review

Author: vfdev-5

test/test_transforms_tensor.py

@@ -99,6 +99,120 @@ def test_pad(self):
             "pad", "Pad", fn_kwargs=fn_kwargs, meth_kwargs=meth_kwargs
         )
 
+    def test_crop(self):
+        fn_kwargs = {"top": 2, "left": 3, "height": 4, "width": 5}
+        # Test transforms.RandomCrop with size and padding as tuple
+        meth_kwargs = {"size": (4, 5), "padding": (4, 4), "pad_if_needed": True, }
+        self._test_geom_op(
+            'crop', 'RandomCrop', fn_kwargs=fn_kwargs, meth_kwargs=meth_kwargs
+        )
+
+        tensor = torch.randint(0, 255, (3, 10, 10), dtype=torch.uint8)
+        # Test torchscript of transforms.RandomCrop with size as int
+        f = T.RandomCrop(size=5)
+        scripted_fn = torch.jit.script(f)
+        scripted_fn(tensor)
+
+        # Test torchscript of transforms.RandomCrop with size as [int, ]
+        f = T.RandomCrop(size=[5, ], padding=[2, ])
+        scripted_fn = torch.jit.script(f)
+        scripted_fn(tensor)
+
+        # Test torchscript of transforms.RandomCrop with size as list
+        f = T.RandomCrop(size=[6, 6])
+        scripted_fn = torch.jit.script(f)
+        scripted_fn(tensor)
+
+    def test_center_crop(self):
+        fn_kwargs = {"output_size": (4, 5)}
+        meth_kwargs = {"size": (4, 5), }
+        self._test_geom_op(
+            "center_crop", "CenterCrop", fn_kwargs=fn_kwargs, meth_kwargs=meth_kwargs
+        )
+        fn_kwargs = {"output_size": (5,)}
+        meth_kwargs = {"size": (5, )}
+        self._test_geom_op(
+            "center_crop", "CenterCrop", fn_kwargs=fn_kwargs, meth_kwargs=meth_kwargs
+        )
+        tensor = torch.randint(0, 255, (3, 10, 10), dtype=torch.uint8)
+        # Test torchscript of transforms.CenterCrop with size as int
+        f = T.CenterCrop(size=5)
+        scripted_fn = torch.jit.script(f)
+        scripted_fn(tensor)
+
+        # Test torchscript of transforms.CenterCrop with size as [int, ]
+        f = T.CenterCrop(size=[5, ])
+        scripted_fn = torch.jit.script(f)
+        scripted_fn(tensor)
+
+        # Test torchscript of transforms.CenterCrop with size as tuple
+        f = T.CenterCrop(size=(6, 6))
+        scripted_fn = torch.jit.script(f)
+        scripted_fn(tensor)
+
+    def _test_geom_op_list_output(self, func, method, out_length, fn_kwargs=None, meth_kwargs=None):
+        if fn_kwargs is None:
+            fn_kwargs = {}
+        if meth_kwargs is None:
+            meth_kwargs = {}
+        tensor, pil_img = self._create_data(height=20, width=20)
+        transformed_t_list = getattr(F, func)(tensor, **fn_kwargs)
+        transformed_p_list = getattr(F, func)(pil_img, **fn_kwargs)
+        self.assertEqual(len(transformed_t_list), len(transformed_p_list))
+        self.assertEqual(len(transformed_t_list), out_length)
+        for transformed_tensor, transformed_pil_img in zip(transformed_t_list, transformed_p_list):
+            self.compareTensorToPIL(transformed_tensor, transformed_pil_img)
+
+        scripted_fn = torch.jit.script(getattr(F, func))
+        transformed_t_list_script = scripted_fn(tensor.detach().clone(), **fn_kwargs)
+        self.assertEqual(len(transformed_t_list), len(transformed_t_list_script))
+        self.assertEqual(len(transformed_t_list_script), out_length)
+        for transformed_tensor, transformed_tensor_script in zip(transformed_t_list, transformed_t_list_script):
+            self.assertTrue(transformed_tensor.equal(transformed_tensor_script),
+                            msg="{} vs {}".format(transformed_tensor, transformed_tensor_script))
+
+        # test for class interface
+        f = getattr(T, method)(**meth_kwargs)
+        scripted_fn = torch.jit.script(f)
+        output = scripted_fn(tensor)
+        self.assertEqual(len(output), len(transformed_t_list_script))
+
+    def test_five_crop(self):
+        fn_kwargs = meth_kwargs = {"size": (5,)}
+        self._test_geom_op_list_output(
+            "five_crop", "FiveCrop", out_length=5, fn_kwargs=fn_kwargs, meth_kwargs=meth_kwargs
+        )
+        fn_kwargs = meth_kwargs = {"size": [5, ]}
+        self._test_geom_op_list_output(
+            "five_crop", "FiveCrop", out_length=5, fn_kwargs=fn_kwargs, meth_kwargs=meth_kwargs
+        )
+        fn_kwargs = meth_kwargs = {"size": (4, 5)}
+        self._test_geom_op_list_output(
+            "five_crop", "FiveCrop", out_length=5, fn_kwargs=fn_kwargs, meth_kwargs=meth_kwargs
+        )
+        fn_kwargs = meth_kwargs = {"size": [4, 5]}
+        self._test_geom_op_list_output(
+            "five_crop", "FiveCrop", out_length=5, fn_kwargs=fn_kwargs, meth_kwargs=meth_kwargs
+        )
+
+    def test_ten_crop(self):
+        fn_kwargs = meth_kwargs = {"size": (5,)}
+        self._test_geom_op_list_output(
+            "ten_crop", "TenCrop", out_length=10, fn_kwargs=fn_kwargs, meth_kwargs=meth_kwargs
+        )
+        fn_kwargs = meth_kwargs = {"size": [5, ]}
+        self._test_geom_op_list_output(
+            "ten_crop", "TenCrop", out_length=10, fn_kwargs=fn_kwargs, meth_kwargs=meth_kwargs
+        )
+        fn_kwargs = meth_kwargs = {"size": (4, 5)}
+        self._test_geom_op_list_output(
+            "ten_crop", "TenCrop", out_length=10, fn_kwargs=fn_kwargs, meth_kwargs=meth_kwargs
+        )
+        fn_kwargs = meth_kwargs = {"size": [4, 5]}
+        self._test_geom_op_list_output(
+            "ten_crop", "TenCrop", out_length=10, fn_kwargs=fn_kwargs, meth_kwargs=meth_kwargs
+        )
+
 
 if __name__ == '__main__':
     unittest.main()