def test_pad(device, dt, pad, config):
script_fn = torch.jit.script(F.pad)
tensor, pil_img = _create_data(7, 8, device=device)
batch_tensors = _create_data_batch(16, 18, num_samples=4, device=device)
if dt == torch.float16 and device == "cpu":
# skip float16 on CPU case
return
if dt is not None:
# This is a trivial cast to float of uint8 data to test all cases
tensor = tensor.to(dt)
batch_tensors = batch_tensors.to(dt)
pad_tensor = F_t.pad(tensor, pad, **config)
pad_pil_img = F_pil.pad(pil_img, pad, **config)
pad_tensor_8b = pad_tensor
# we need to cast to uint8 to compare with PIL image
if pad_tensor_8b.dtype != torch.uint8:
pad_tensor_8b = pad_tensor_8b.to(torch.uint8)
_assert_equal_tensor_to_pil(pad_tensor_8b, pad_pil_img, msg="{}, {}".format(pad, config))
if isinstance(pad, int):
script_pad = [pad, ]
else:
script_pad = pad
pad_tensor_script = script_fn(tensor, script_pad, **config)
assert_equal(pad_tensor, pad_tensor_script, msg="{}, {}".format(pad, config))
_test_fn_on_batch(batch_tensors, F.pad, padding=script_pad, **config)
def test_pad(self):
script_fn = torch.jit.script(F_t.pad)
tensor, pil_img = self._create_data(7, 8)
for pad in [1, [
1,
], [0, 1], (2, 2), [1, 0, 1, 2]]:
padding_mode = "constant"
for fill in [0, 10, 20]:
pad_tensor = F_t.pad(tensor,
pad,
fill=fill,
padding_mode=padding_mode)
pad_pil_img = F_pil.pad(pil_img,
pad,
fill=fill,
padding_mode=padding_mode)
self.compareTensorToPIL(pad_tensor,
pad_pil_img,
msg="{}, {}".format(pad, fill))
if isinstance(pad, int):
script_pad = [
pad,
]
else:
script_pad = pad
pad_tensor_script = script_fn(tensor,
script_pad,
fill=fill,
padding_mode=padding_mode)
self.assertTrue(pad_tensor.equal(pad_tensor_script),
msg="{}, {}".format(pad, fill))
def test_pad(self):
script_fn = torch.jit.script(F.pad)
tensor, pil_img = self._create_data(7, 8, device=self.device)
batch_tensors = self._create_data_batch(16, 18, num_samples=4, device=self.device)
for dt in [None, torch.float32, torch.float64, torch.float16]:
if dt == torch.float16 and torch.device(self.device).type == "cpu":
# skip float16 on CPU case
continue
if dt is not None:
# This is a trivial cast to float of uint8 data to test all cases
tensor = tensor.to(dt)
batch_tensors = batch_tensors.to(dt)
for pad in [2, [3, ], [0, 3], (3, 3), [4, 2, 4, 3]]:
configs = [
{"padding_mode": "constant", "fill": 0},
{"padding_mode": "constant", "fill": 10},
{"padding_mode": "constant", "fill": 20},
{"padding_mode": "edge"},
{"padding_mode": "reflect"},
{"padding_mode": "symmetric"},
]
for kwargs in configs:
pad_tensor = F_t.pad(tensor, pad, **kwargs)
pad_pil_img = F_pil.pad(pil_img, pad, **kwargs)
pad_tensor_8b = pad_tensor
# we need to cast to uint8 to compare with PIL image
if pad_tensor_8b.dtype != torch.uint8:
pad_tensor_8b = pad_tensor_8b.to(torch.uint8)
self.compareTensorToPIL(pad_tensor_8b, pad_pil_img, msg="{}, {}".format(pad, kwargs))
if isinstance(pad, int):
script_pad = [pad, ]
else:
script_pad = pad
pad_tensor_script = script_fn(tensor, script_pad, **kwargs)
self.assertTrue(pad_tensor.equal(pad_tensor_script), msg="{}, {}".format(pad, kwargs))
self._test_fn_on_batch(batch_tensors, F.pad, padding=script_pad, **kwargs)
with self.assertRaises(ValueError, msg="Padding can not be negative for symmetric padding_mode"):
F_t.pad(tensor, (-2, -3), padding_mode="symmetric")
def test_pad(self):
script_fn = torch.jit.script(F_t.pad)
tensor, pil_img = self._create_data(7, 8)
for dt in [None, torch.float32, torch.float64]:
if dt is not None:
# This is a trivial cast to float of uint8 data to test all cases
tensor = tensor.to(dt)
for pad in [2, [
3,
], [0, 3], (3, 3), [4, 2, 4, 3]]:
configs = [
{
"padding_mode": "constant",
"fill": 0
},
{
"padding_mode": "constant",
"fill": 10
},
{
"padding_mode": "constant",
"fill": 20
},
{
"padding_mode": "edge"
},
{
"padding_mode": "reflect"
},
]
for kwargs in configs:
pad_tensor = F_t.pad(tensor, pad, **kwargs)
pad_pil_img = F_pil.pad(pil_img, pad, **kwargs)
pad_tensor_8b = pad_tensor
# we need to cast to uint8 to compare with PIL image
if pad_tensor_8b.dtype != torch.uint8:
pad_tensor_8b = pad_tensor_8b.to(torch.uint8)
self.compareTensorToPIL(pad_tensor_8b,
pad_pil_img,
msg="{}, {}".format(pad, kwargs))
if isinstance(pad, int):
script_pad = [
pad,
]
else:
script_pad = pad
pad_tensor_script = script_fn(tensor, script_pad, **kwargs)
self.assertTrue(pad_tensor.equal(pad_tensor_script),
msg="{}, {}".format(pad, kwargs))
def pad_image_tensor(img: torch.Tensor,
padding: Union[int, List[int]],
fill: Union[int, float] = 0,
padding_mode: str = "constant") -> torch.Tensor:
num_channels, height, width = img.shape[-3:]
extra_dims = img.shape[:-3]
padded_image = _FT.pad(img=img.view(-1, num_channels, height, width),
padding=padding,
fill=fill,
padding_mode=padding_mode)
new_height, new_width = padded_image.shape[-2:]
return padded_image.view(extra_dims +
(num_channels, new_height, new_width))
