def _test_class_op(transform_cls, device, channels=3, meth_kwargs=None, test_exact_match=True, **match_kwargs):
meth_kwargs = meth_kwargs or {}
# test for class interface
f = transform_cls(**meth_kwargs)
scripted_fn = torch.jit.script(f)
tensor, pil_img = _create_data(26, 34, channels, device=device)
# set seed to reproduce the same transformation for tensor and PIL image
torch.manual_seed(12)
transformed_tensor = f(tensor)
torch.manual_seed(12)
transformed_pil_img = f(pil_img)
if test_exact_match:
_assert_equal_tensor_to_pil(transformed_tensor, transformed_pil_img, **match_kwargs)
else:
_assert_approx_equal_tensor_to_pil(transformed_tensor.float(), transformed_pil_img, **match_kwargs)
torch.manual_seed(12)
transformed_tensor_script = scripted_fn(tensor)
assert_equal(transformed_tensor, transformed_tensor_script)
batch_tensors = _create_data_batch(height=23, width=34, channels=channels, num_samples=4, device=device)
_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, f"t_{transform_cls.__name__}.pt"))
def test_rgb2hsv(device):
scripted_fn = torch.jit.script(F_t._rgb2hsv)
shape = (3, 150, 100)
for _ in range(10):
rgb_img = torch.rand(*shape, dtype=torch.float, device=device)
hsv_img = F_t._rgb2hsv(rgb_img)
ft_hsv_img = hsv_img.permute(1, 2, 0).flatten(0, 1)
r, g, b, = rgb_img.unbind(dim=-3)
r = r.flatten().cpu().numpy()
g = g.flatten().cpu().numpy()
b = b.flatten().cpu().numpy()
hsv = []
for r1, g1, b1 in zip(r, g, b):
hsv.append(colorsys.rgb_to_hsv(r1, g1, b1))
colorsys_img = torch.tensor(hsv, dtype=torch.float32, device=device)
ft_hsv_img_h, ft_hsv_img_sv = torch.split(ft_hsv_img, [1, 2], dim=1)
colorsys_img_h, colorsys_img_sv = torch.split(colorsys_img, [1, 2], dim=1)
max_diff_h = ((colorsys_img_h * 2 * math.pi).sin() - (ft_hsv_img_h * 2 * math.pi).sin()).abs().max()
max_diff_sv = (colorsys_img_sv - ft_hsv_img_sv).abs().max()
max_diff = max(max_diff_h, max_diff_sv)
assert max_diff < 1e-5
s_hsv_img = scripted_fn(rgb_img)
torch.testing.assert_close(hsv_img, s_hsv_img, rtol=1e-5, atol=1e-7)
batch_tensors = _create_data_batch(120, 100, num_samples=4, device=device).float()
_test_fn_on_batch(batch_tensors, F_t._rgb2hsv)
def check_functional_vs_PIL_vs_scripted(fn, fn_pil, fn_t, config, device, dtype, tol=2.0 + 1e-10, agg_method="max"):
script_fn = torch.jit.script(fn)
torch.manual_seed(15)
tensor, pil_img = _create_data(26, 34, device=device)
batch_tensors = _create_data_batch(16, 18, num_samples=4, device=device)
if dtype is not None:
tensor = F.convert_image_dtype(tensor, dtype)
batch_tensors = F.convert_image_dtype(batch_tensors, dtype)
out_fn_t = fn_t(tensor, **config)
out_pil = fn_pil(pil_img, **config)
out_scripted = script_fn(tensor, **config)
assert out_fn_t.dtype == out_scripted.dtype
assert out_fn_t.size()[1:] == out_pil.size[::-1]
rbg_tensor = out_fn_t
if out_fn_t.dtype != torch.uint8:
rbg_tensor = F.convert_image_dtype(out_fn_t, torch.uint8)
# Check that max difference does not exceed 2 in [0, 255] range
# Exact matching is not possible due to incompatibility convert_image_dtype and PIL results
_assert_approx_equal_tensor_to_pil(rbg_tensor.float(), out_pil, tol=tol, agg_method=agg_method)
atol = 1e-6
if out_fn_t.dtype == torch.uint8 and "cuda" in torch.device(device).type:
atol = 1.0
assert out_fn_t.allclose(out_scripted, atol=atol)
# FIXME: fn will be scripted again in _test_fn_on_batch. We could avoid that.
_test_fn_on_batch(batch_tensors, fn, scripted_fn_atol=atol, **config)
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_ten_crop(device):
script_ten_crop = torch.jit.script(F.ten_crop)
img_tensor, pil_img = _create_data(32, 34, device=device)
cropped_pil_images = F.ten_crop(pil_img, [10, 11])
cropped_tensors = F.ten_crop(img_tensor, [10, 11])
for i in range(10):
_assert_equal_tensor_to_pil(cropped_tensors[i], cropped_pil_images[i])
cropped_tensors = script_ten_crop(img_tensor, [10, 11])
for i in range(10):
_assert_equal_tensor_to_pil(cropped_tensors[i], cropped_pil_images[i])
batch_tensors = _create_data_batch(16, 18, num_samples=4, device=device)
tuple_transformed_batches = F.ten_crop(batch_tensors, [10, 11])
for i in range(len(batch_tensors)):
img_tensor = batch_tensors[i, ...]
tuple_transformed_imgs = F.ten_crop(img_tensor, [10, 11])
assert len(tuple_transformed_imgs) == len(tuple_transformed_batches)
for j in range(len(tuple_transformed_imgs)):
true_transformed_img = tuple_transformed_imgs[j]
transformed_img = tuple_transformed_batches[j][i, ...]
assert_equal(true_transformed_img, transformed_img)
# scriptable function test
s_tuple_transformed_batches = script_ten_crop(batch_tensors, [10, 11])
for transformed_batch, s_transformed_batch in zip(tuple_transformed_batches, s_tuple_transformed_batches):
assert_equal(transformed_batch, s_transformed_batch)
def test_x_crop(fn, method, out_length, size, device):
meth_kwargs = fn_kwargs = {"size": size}
scripted_fn = torch.jit.script(fn)
tensor, pil_img = _create_data(height=20, width=20, device=device)
transformed_t_list = fn(tensor, **fn_kwargs)
transformed_p_list = fn(pil_img, **fn_kwargs)
assert len(transformed_t_list) == len(transformed_p_list)
assert len(transformed_t_list) == out_length
for transformed_tensor, transformed_pil_img in zip(transformed_t_list, transformed_p_list):
_assert_equal_tensor_to_pil(transformed_tensor, transformed_pil_img)
transformed_t_list_script = scripted_fn(tensor.detach().clone(), **fn_kwargs)
assert len(transformed_t_list) == len(transformed_t_list_script)
assert len(transformed_t_list_script) == out_length
for transformed_tensor, transformed_tensor_script in zip(transformed_t_list, transformed_t_list_script):
assert_equal(transformed_tensor, transformed_tensor_script)
# test for class interface
fn = method(**meth_kwargs)
scripted_fn = torch.jit.script(fn)
output = scripted_fn(tensor)
assert len(output) == len(transformed_t_list_script)
# test on batch of tensors
batch_tensors = _create_data_batch(height=23, width=34, channels=3, num_samples=4, device=device)
torch.manual_seed(12)
transformed_batch_list = fn(batch_tensors)
for i in range(len(batch_tensors)):
img_tensor = batch_tensors[i, ...]
torch.manual_seed(12)
transformed_img_list = fn(img_tensor)
for transformed_img, transformed_batch in zip(transformed_img_list, transformed_batch_list):
assert_equal(transformed_img, transformed_batch[i, ...])
def test_batches(self, device, dt):
if dt == torch.float16 and device == "cpu":
# skip float16 on CPU case
return
batch_tensors = _create_data_batch(26, 36, num_samples=4, device=device)
if dt is not None:
batch_tensors = batch_tensors.to(dtype=dt)
_test_fn_on_batch(
batch_tensors, F.affine, angle=-43, translate=[-3, 4], scale=1.2, shear=[4.0, 5.0]
)
def test_rotate_batch(self, device, dt):
if dt == torch.float16 and device == "cpu":
# skip float16 on CPU case
return
batch_tensors = _create_data_batch(26, 36, num_samples=4, device=device)
if dt is not None:
batch_tensors = batch_tensors.to(dtype=dt)
center = (20, 22)
_test_fn_on_batch(
batch_tensors, F.rotate, angle=32, interpolation=NEAREST, expand=True, center=center
)
def test_hflip(device):
script_hflip = torch.jit.script(F.hflip)
img_tensor, pil_img = _create_data(16, 18, device=device)
hflipped_img = F.hflip(img_tensor)
hflipped_pil_img = F.hflip(pil_img)
_assert_equal_tensor_to_pil(hflipped_img, hflipped_pil_img)
# scriptable function test
hflipped_img_script = script_hflip(img_tensor)
assert_equal(hflipped_img, hflipped_img_script)
batch_tensors = _create_data_batch(16, 18, num_samples=4, device=device)
_test_fn_on_batch(batch_tensors, F.hflip)
def test_resize(device, dt, size, max_size, interpolation):
if dt == torch.float16 and device == "cpu":
# skip float16 on CPU case
return
if max_size is not None and isinstance(size, Sequence) and len(size) != 1:
return # unsupported
torch.manual_seed(12)
script_fn = torch.jit.script(F.resize)
tensor, pil_img = _create_data(26, 36, device=device)
batch_tensors = _create_data_batch(16, 18, num_samples=4, device=device)
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)
resized_tensor = F.resize(tensor, size=size, interpolation=interpolation, max_size=max_size)
resized_pil_img = F.resize(pil_img, size=size, interpolation=interpolation, max_size=max_size)
assert resized_tensor.size()[1:] == resized_pil_img.size[::-1]
if interpolation not in [NEAREST, ]:
# We can not check values if mode = NEAREST, as results are different
# E.g. resized_tensor = [[a, a, b, c, d, d, e, ...]]
# E.g. resized_pil_img = [[a, b, c, c, d, e, f, ...]]
resized_tensor_f = resized_tensor
# we need to cast to uint8 to compare with PIL image
if resized_tensor_f.dtype == torch.uint8:
resized_tensor_f = resized_tensor_f.to(torch.float)
# Pay attention to high tolerance for MAE
_assert_approx_equal_tensor_to_pil(resized_tensor_f, resized_pil_img, tol=8.0)
if isinstance(size, int):
script_size = [size, ]
else:
script_size = size
resize_result = script_fn(
tensor, size=script_size, interpolation=interpolation, max_size=max_size
)
assert_equal(resized_tensor, resize_result)
_test_fn_on_batch(
batch_tensors, F.resize, size=script_size, interpolation=interpolation, max_size=max_size
)
def test_crop(device, top, left, height, width):
script_crop = torch.jit.script(F.crop)
img_tensor, pil_img = _create_data(16, 18, device=device)
pil_img_cropped = F.crop(pil_img, top, left, height, width)
img_tensor_cropped = F.crop(img_tensor, top, left, height, width)
_assert_equal_tensor_to_pil(img_tensor_cropped, pil_img_cropped)
img_tensor_cropped = script_crop(img_tensor, top, left, height, width)
_assert_equal_tensor_to_pil(img_tensor_cropped, pil_img_cropped)
batch_tensors = _create_data_batch(16, 18, num_samples=4, device=device)
_test_fn_on_batch(batch_tensors, F.crop, top=top, left=left, height=height, width=width)
def test_rgb_to_grayscale(device, num_output_channels):
script_rgb_to_grayscale = torch.jit.script(F.rgb_to_grayscale)
img_tensor, pil_img = _create_data(32, 34, device=device)
gray_pil_image = F.rgb_to_grayscale(pil_img, num_output_channels=num_output_channels)
gray_tensor = F.rgb_to_grayscale(img_tensor, num_output_channels=num_output_channels)
_assert_approx_equal_tensor_to_pil(gray_tensor.float(), gray_pil_image, tol=1.0 + 1e-10, agg_method="max")
s_gray_tensor = script_rgb_to_grayscale(img_tensor, num_output_channels=num_output_channels)
assert_equal(s_gray_tensor, gray_tensor)
batch_tensors = _create_data_batch(16, 18, num_samples=4, device=device)
_test_fn_on_batch(batch_tensors, F.rgb_to_grayscale, num_output_channels=num_output_channels)
def test_center_crop(device):
script_center_crop = torch.jit.script(F.center_crop)
img_tensor, pil_img = _create_data(32, 34, device=device)
cropped_pil_image = F.center_crop(pil_img, [10, 11])
cropped_tensor = F.center_crop(img_tensor, [10, 11])
_assert_equal_tensor_to_pil(cropped_tensor, cropped_pil_image)
cropped_tensor = script_center_crop(img_tensor, [10, 11])
_assert_equal_tensor_to_pil(cropped_tensor, cropped_pil_image)
batch_tensors = _create_data_batch(16, 18, num_samples=4, device=device)
_test_fn_on_batch(batch_tensors, F.center_crop, output_size=[10, 11])
def test_perspective_batch(device, dims_and_points, dt):
if dt == torch.float16 and device == "cpu":
# skip float16 on CPU case
return
data_dims, (spoints, epoints) = dims_and_points
batch_tensors = _create_data_batch(*data_dims, num_samples=4, device=device)
if dt is not None:
batch_tensors = batch_tensors.to(dtype=dt)
# Ignore the equivalence between scripted and regular function on float16 cuda. The pixels at
# the border may be entirely different due to small rounding errors.
scripted_fn_atol = -1 if (dt == torch.float16 and device == "cuda") else 1e-8
_test_fn_on_batch(
batch_tensors, F.perspective, scripted_fn_atol=scripted_fn_atol,
startpoints=spoints, endpoints=epoints, interpolation=NEAREST
)
def test_resized_crop(device, mode):
# test values of F.resized_crop in several cases:
# 1) resize to the same size, crop to the same size => should be identity
tensor, _ = _create_data(26, 36, device=device)
out_tensor = F.resized_crop(tensor, top=0, left=0, height=26, width=36, size=[26, 36], interpolation=mode)
assert_equal(tensor, out_tensor, msg="{} vs {}".format(out_tensor[0, :5, :5], tensor[0, :5, :5]))
# 2) resize by half and crop a TL corner
tensor, _ = _create_data(26, 36, device=device)
out_tensor = F.resized_crop(tensor, top=0, left=0, height=20, width=30, size=[10, 15], interpolation=NEAREST)
expected_out_tensor = tensor[:, :20:2, :30:2]
assert_equal(
expected_out_tensor,
out_tensor,
msg="{} vs {}".format(expected_out_tensor[0, :10, :10], out_tensor[0, :10, :10]),
)
batch_tensors = _create_data_batch(26, 36, num_samples=4, device=device)
_test_fn_on_batch(
batch_tensors, F.resized_crop, top=1, left=2, height=20, width=30, size=[10, 15], interpolation=NEAREST
)
def test_hsv2rgb(device):
scripted_fn = torch.jit.script(F_t._hsv2rgb)
shape = (3, 100, 150)
for _ in range(10):
hsv_img = torch.rand(*shape, dtype=torch.float, device=device)
rgb_img = F_t._hsv2rgb(hsv_img)
ft_img = rgb_img.permute(1, 2, 0).flatten(0, 1)
h, s, v, = hsv_img.unbind(0)
h = h.flatten().cpu().numpy()
s = s.flatten().cpu().numpy()
v = v.flatten().cpu().numpy()
rgb = []
for h1, s1, v1 in zip(h, s, v):
rgb.append(colorsys.hsv_to_rgb(h1, s1, v1))
colorsys_img = torch.tensor(rgb, dtype=torch.float32, device=device)
torch.testing.assert_close(ft_img, colorsys_img, rtol=0.0, atol=1e-5)
s_rgb_img = scripted_fn(hsv_img)
torch.testing.assert_close(rgb_img, s_rgb_img)
batch_tensors = _create_data_batch(120, 100, num_samples=4, device=device).float()
_test_fn_on_batch(batch_tensors, F_t._hsv2rgb)
def _test_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 = {}
fn = getattr(F, func)
scripted_fn = torch.jit.script(fn)
tensor, pil_img = _create_data(height=20, width=20, device=self.device)
transformed_t_list = fn(tensor, **fn_kwargs)
transformed_p_list = fn(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):
_assert_equal_tensor_to_pil(transformed_tensor,
transformed_pil_img)
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):
assert_equal(
transformed_tensor,
transformed_tensor_script,
msg="{} vs {}".format(transformed_tensor,
transformed_tensor_script),
)
# test for class interface
fn = getattr(T, method)(**meth_kwargs)
scripted_fn = torch.jit.script(fn)
output = scripted_fn(tensor)
self.assertEqual(len(output), len(transformed_t_list_script))
# test on batch of tensors
batch_tensors = _create_data_batch(height=23,
width=34,
channels=3,
num_samples=4,
device=self.device)
torch.manual_seed(12)
transformed_batch_list = fn(batch_tensors)
for i in range(len(batch_tensors)):
img_tensor = batch_tensors[i, ...]
torch.manual_seed(12)
transformed_img_list = fn(img_tensor)
for transformed_img, transformed_batch in zip(
transformed_img_list, transformed_batch_list):
assert_equal(
transformed_img,
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)))
