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Unverified Commit 026991b1 authored by Philip Meier's avatar Philip Meier Committed by GitHub
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Reduce sample inputs for prototype transform kernels (#6714) 

* pad_image_tensor

* pad_mask and pad_bounding_box

* resize_{image_tensor, mask, bounding_box}

* center_crop_{image_tensor, mask}

* {five, ten}_crop_image_tensor

* crop_{image_tensor, mask}

* convert_color_space_image_tensor

* affine_{image_tensor, mask, bounding_box}

* rotate_{image_tensor, mask}

* gaussian_blur_image_tensor

* cleanup
parent e3941afc
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Showing with 309 additions and 153 deletions
test/prototype_common_utils.py
View file @ 026991b1
...@@ -28,6 +28,7 @@ __all__ = [ ...@@ -28,6 +28,7 @@ __all__ = [
"assert_close", "assert_close",
"assert_equal", "assert_equal",
"ArgsKwargs", "ArgsKwargs",
"VALID_EXTRA_DIMS",
"make_image_loaders", "make_image_loaders",
"make_image", "make_image",
"make_images", "make_images",
...@@ -201,7 +202,10 @@ def _parse_image_size(size, *, name="size"): ...@@ -201,7 +202,10 @@ def _parse_image_size(size, *, name="size"):
) )
DEFAULT_EXTRA_DIMS = ((), (0,), (4,), (2, 3), (5, 0), (0, 5)) VALID_EXTRA_DIMS = ((), (4,), (2, 3))
DEGENERATE_BATCH_DIMS = ((0,), (5, 0), (0, 5))
DEFAULT_EXTRA_DIMS = (*VALID_EXTRA_DIMS, *DEGENERATE_BATCH_DIMS)
def from_loader(loader_fn): def from_loader(loader_fn):
......
test/prototype_transforms_dispatcher_infos.py
View file @ 026991b1
...@@ -63,17 +63,40 @@ class DispatcherInfo: ...@@ -63,17 +63,40 @@ class DispatcherInfo:
yield args_kwargs yield args_kwargs
def xfail_python_scalar_arg_jit(name, *, reason=None): def xfail_jit_python_scalar_arg(name, *, reason=None):
reason = reason or f"Python scalar int or float for `{name}` is not supported when scripting" reason = reason or f"Python scalar int or float for `{name}` is not supported when scripting"
return TestMark( return TestMark(
("TestDispatchers", "test_scripted_smoke"), ("TestDispatchers", "test_scripted_smoke"),
pytest.mark.xfail(reason=reason), pytest.mark.xfail(reason=reason),
condition=lambda args_kwargs: isinstance(args_kwargs.kwargs[name], (int, float)), condition=lambda args_kwargs: isinstance(args_kwargs.kwargs.get(name), (int, float)),
) )
def xfail_integer_size_jit(name="size"): def xfail_jit_integer_size(name="size"):
return xfail_python_scalar_arg_jit(name, reason=f"Integer `{name}` is not supported when scripting.") return xfail_jit_python_scalar_arg(name, reason=f"Integer `{name}` is not supported when scripting.")
def xfail_jit_tuple_instead_of_list(name, *, reason=None):
reason = reason or f"Passing a tuple instead of a list for `{name}` is not supported when scripting"
return TestMark(
("TestDispatchers", "test_scripted_smoke"),
pytest.mark.xfail(reason=reason),
condition=lambda args_kwargs: isinstance(args_kwargs.kwargs.get(name), tuple),
)
def is_list_of_ints(args_kwargs):
fill = args_kwargs.kwargs.get("fill")
return isinstance(fill, list) and any(isinstance(scalar_fill, int) for scalar_fill in fill)
def xfail_jit_list_of_ints(name, *, reason=None):
reason = reason or f"Passing a list of integers for `{name}` is not supported when scripting"
return TestMark(
("TestDispatchers", "test_scripted_smoke"),
pytest.mark.xfail(reason=reason),
condition=is_list_of_ints,
)
skip_dispatch_feature = TestMark( skip_dispatch_feature = TestMark(
...@@ -123,7 +146,7 @@ DISPATCHER_INFOS = [ ...@@ -123,7 +146,7 @@ DISPATCHER_INFOS = [
}, },
pil_kernel_info=PILKernelInfo(F.resize_image_pil), pil_kernel_info=PILKernelInfo(F.resize_image_pil),
test_marks=[ test_marks=[
xfail_integer_size_jit(), xfail_jit_integer_size(),
], ],
), ),
DispatcherInfo( DispatcherInfo(
...@@ -136,7 +159,10 @@ DISPATCHER_INFOS = [ ...@@ -136,7 +159,10 @@ DISPATCHER_INFOS = [
pil_kernel_info=PILKernelInfo(F.affine_image_pil), pil_kernel_info=PILKernelInfo(F.affine_image_pil),
test_marks=[ test_marks=[
xfail_dispatch_pil_if_fill_sequence_needs_broadcast, xfail_dispatch_pil_if_fill_sequence_needs_broadcast,
xfail_python_scalar_arg_jit("shear"), xfail_jit_python_scalar_arg("shear"),
xfail_jit_tuple_instead_of_list("fill"),
# TODO: check if this is a regression since it seems that should be supported if `int` is ok
xfail_jit_list_of_ints("fill"),
], ],
), ),
DispatcherInfo( DispatcherInfo(
...@@ -156,6 +182,11 @@ DISPATCHER_INFOS = [ ...@@ -156,6 +182,11 @@ DISPATCHER_INFOS = [
features.Mask: F.rotate_mask, features.Mask: F.rotate_mask,
}, },
pil_kernel_info=PILKernelInfo(F.rotate_image_pil), pil_kernel_info=PILKernelInfo(F.rotate_image_pil),
test_marks=[
xfail_jit_tuple_instead_of_list("fill"),
# TODO: check if this is a regression since it seems that should be supported if `int` is ok
xfail_jit_list_of_ints("fill"),
],
), ),
DispatcherInfo( DispatcherInfo(
F.crop, F.crop,
...@@ -194,7 +225,12 @@ DISPATCHER_INFOS = [ ...@@ -194,7 +225,12 @@ DISPATCHER_INFOS = [
), ),
condition=lambda args_kwargs: fill_sequence_needs_broadcast(args_kwargs) condition=lambda args_kwargs: fill_sequence_needs_broadcast(args_kwargs)
and args_kwargs.kwargs.get("padding_mode", "constant") == "constant", and args_kwargs.kwargs.get("padding_mode", "constant") == "constant",
) ),
xfail_jit_python_scalar_arg("padding"),
xfail_jit_tuple_instead_of_list("padding"),
xfail_jit_tuple_instead_of_list("fill"),
# TODO: check if this is a regression since it seems that should be supported if `int` is ok
xfail_jit_list_of_ints("fill"),
], ],
), ),
DispatcherInfo( DispatcherInfo(
...@@ -227,7 +263,7 @@ DISPATCHER_INFOS = [ ...@@ -227,7 +263,7 @@ DISPATCHER_INFOS = [
}, },
pil_kernel_info=PILKernelInfo(F.center_crop_image_pil), pil_kernel_info=PILKernelInfo(F.center_crop_image_pil),
test_marks=[ test_marks=[
xfail_integer_size_jit("output_size"), xfail_jit_integer_size("output_size"),
], ],
), ),
DispatcherInfo( DispatcherInfo(
...@@ -237,8 +273,8 @@ DISPATCHER_INFOS = [ ...@@ -237,8 +273,8 @@ DISPATCHER_INFOS = [
}, },
pil_kernel_info=PILKernelInfo(F.gaussian_blur_image_pil), pil_kernel_info=PILKernelInfo(F.gaussian_blur_image_pil),
test_marks=[ test_marks=[
xfail_python_scalar_arg_jit("kernel_size"), xfail_jit_python_scalar_arg("kernel_size"),
xfail_python_scalar_arg_jit("sigma"), xfail_jit_python_scalar_arg("sigma"),
], ],
), ),
DispatcherInfo( DispatcherInfo(
...@@ -335,7 +371,7 @@ DISPATCHER_INFOS = [ ...@@ -335,7 +371,7 @@ DISPATCHER_INFOS = [
}, },
pil_kernel_info=PILKernelInfo(F.five_crop_image_pil), pil_kernel_info=PILKernelInfo(F.five_crop_image_pil),
test_marks=[ test_marks=[
xfail_integer_size_jit(), xfail_jit_integer_size(),
skip_dispatch_feature, skip_dispatch_feature,
], ],
), ),
...@@ -345,7 +381,7 @@ DISPATCHER_INFOS = [ ...@@ -345,7 +381,7 @@ DISPATCHER_INFOS = [
features.Image: F.ten_crop_image_tensor, features.Image: F.ten_crop_image_tensor,
}, },
test_marks=[ test_marks=[
xfail_integer_size_jit(), xfail_jit_integer_size(),
skip_dispatch_feature, skip_dispatch_feature,
], ],
pil_kernel_info=PILKernelInfo(F.ten_crop_image_pil), pil_kernel_info=PILKernelInfo(F.ten_crop_image_pil),
......
test/prototype_transforms_kernel_infos.py
View file @ 026991b1
...@@ -12,8 +12,16 @@ import torchvision.ops ...@@ -12,8 +12,16 @@ import torchvision.ops
import torchvision.prototype.transforms.functional as F import torchvision.prototype.transforms.functional as F
from _pytest.mark.structures import MarkDecorator from _pytest.mark.structures import MarkDecorator
from common_utils import cycle_over
from datasets_utils import combinations_grid from datasets_utils import combinations_grid
from prototype_common_utils import ArgsKwargs, make_bounding_box_loaders, make_image_loaders, make_mask_loaders from prototype_common_utils import (
ArgsKwargs,
make_bounding_box_loaders,
make_image_loader,
make_image_loaders,
make_mask_loaders,
VALID_EXTRA_DIMS,
)
from torchvision.prototype import features from torchvision.prototype import features
from torchvision.transforms.functional_tensor import _max_value as get_max_value from torchvision.transforms.functional_tensor import _max_value as get_max_value
...@@ -98,17 +106,40 @@ def mark_framework_limitation(test_id, reason): ...@@ -98,17 +106,40 @@ def mark_framework_limitation(test_id, reason):
return TestMark(test_id, pytest.mark.skip(reason=reason)) return TestMark(test_id, pytest.mark.skip(reason=reason))
def xfail_python_scalar_arg_jit(name, *, reason=None): def xfail_jit_python_scalar_arg(name, *, reason=None):
reason = reason or f"Python scalar int or float for `{name}` is not supported when scripting" reason = reason or f"Python scalar int or float for `{name}` is not supported when scripting"
return TestMark( return TestMark(
("TestKernels", "test_scripted_vs_eager"), ("TestKernels", "test_scripted_vs_eager"),
pytest.mark.xfail(reason=reason), pytest.mark.xfail(reason=reason),
condition=lambda args_kwargs: isinstance(args_kwargs.kwargs[name], (int, float)), condition=lambda args_kwargs: isinstance(args_kwargs.kwargs.get(name), (int, float)),
)
def xfail_jit_integer_size(name="size"):
return xfail_jit_python_scalar_arg(name, reason=f"Integer `{name}` is not supported when scripting.")
def xfail_jit_tuple_instead_of_list(name, *, reason=None):
reason = reason or f"Passing a tuple instead of a list for `{name}` is not supported when scripting"
return TestMark(
("TestKernels", "test_scripted_vs_eager"),
pytest.mark.xfail(reason=reason),
condition=lambda args_kwargs: isinstance(args_kwargs.kwargs.get(name), tuple),
) )
def xfail_integer_size_jit(name="size"): def is_list_of_ints(args_kwargs):
return xfail_python_scalar_arg_jit(name, reason=f"Integer `{name}` is not supported when scripting.") fill = args_kwargs.kwargs.get("fill")
return isinstance(fill, list) and any(isinstance(scalar_fill, int) for scalar_fill in fill)
def xfail_jit_list_of_ints(name, *, reason=None):
reason = reason or f"Passing a list of integers for `{name}` is not supported when scripting"
return TestMark(
("TestKernels", "test_scripted_vs_eager"),
pytest.mark.xfail(reason=reason),
condition=is_list_of_ints,
)
KERNEL_INFOS = [] KERNEL_INFOS = []
...@@ -173,15 +204,33 @@ def _get_resize_sizes(image_size): ...@@ -173,15 +204,33 @@ def _get_resize_sizes(image_size):
def sample_inputs_resize_image_tensor(): def sample_inputs_resize_image_tensor():
for image_loader in make_image_loaders(
sizes=["random"], color_spaces=[features.ColorSpace.RGB], dtypes=[torch.float32]
):
for size in _get_resize_sizes(image_loader.image_size):
yield ArgsKwargs(image_loader, size=size)
for image_loader, interpolation in itertools.product( for image_loader, interpolation in itertools.product(
make_image_loaders(dtypes=[torch.float32]), make_image_loaders(sizes=["random"], color_spaces=[features.ColorSpace.RGB]),
[ [
F.InterpolationMode.NEAREST, F.InterpolationMode.NEAREST,
F.InterpolationMode.BILINEAR,
F.InterpolationMode.BICUBIC, F.InterpolationMode.BICUBIC,
], ],
): ):
for size in _get_resize_sizes(image_loader.image_size): yield ArgsKwargs(image_loader, size=[min(image_loader.image_size) + 1], interpolation=interpolation)
yield ArgsKwargs(image_loader, size=size, interpolation=interpolation)
# We have a speed hack in place for nearest interpolation and single channel images (grayscale)
for image_loader in make_image_loaders(
sizes=["random"],
color_spaces=[features.ColorSpace.GRAY],
extra_dims=VALID_EXTRA_DIMS,
):
yield ArgsKwargs(
image_loader, size=[min(image_loader.image_size) + 1], interpolation=F.InterpolationMode.NEAREST
)
yield ArgsKwargs(make_image_loader(size=(11, 17)), size=20, max_size=25)
@pil_reference_wrapper @pil_reference_wrapper
...@@ -217,15 +266,14 @@ def reference_inputs_resize_image_tensor(): ...@@ -217,15 +266,14 @@ def reference_inputs_resize_image_tensor():
def sample_inputs_resize_bounding_box(): def sample_inputs_resize_bounding_box():
for bounding_box_loader in make_bounding_box_loaders(formats=[features.BoundingBoxFormat.XYXY]): for bounding_box_loader in make_bounding_box_loaders():
for size in _get_resize_sizes(bounding_box_loader.image_size): for size in _get_resize_sizes(bounding_box_loader.image_size):
yield ArgsKwargs(bounding_box_loader, size=size, image_size=bounding_box_loader.image_size) yield ArgsKwargs(bounding_box_loader, size=size, image_size=bounding_box_loader.image_size)
def sample_inputs_resize_mask(): def sample_inputs_resize_mask():
for mask_loader in make_mask_loaders(dtypes=[torch.uint8]): for mask_loader in make_mask_loaders(sizes=["random"], num_categories=["random"], num_objects=["random"]):
for size in _get_resize_sizes(mask_loader.shape[-2:]): yield ArgsKwargs(mask_loader, size=[min(mask_loader.shape[-2:]) + 1])
yield ArgsKwargs(mask_loader, size=size)
@pil_reference_wrapper @pil_reference_wrapper
...@@ -248,14 +296,14 @@ KERNEL_INFOS.extend( ...@@ -248,14 +296,14 @@ KERNEL_INFOS.extend(
reference_inputs_fn=reference_inputs_resize_image_tensor, reference_inputs_fn=reference_inputs_resize_image_tensor,
closeness_kwargs=DEFAULT_IMAGE_CLOSENESS_KWARGS, closeness_kwargs=DEFAULT_IMAGE_CLOSENESS_KWARGS,
test_marks=[ test_marks=[
xfail_integer_size_jit(), xfail_jit_integer_size(),
], ],
), ),
KernelInfo( KernelInfo(
F.resize_bounding_box, F.resize_bounding_box,
sample_inputs_fn=sample_inputs_resize_bounding_box, sample_inputs_fn=sample_inputs_resize_bounding_box,
test_marks=[ test_marks=[
xfail_integer_size_jit(), xfail_jit_integer_size(),
], ],
), ),
KernelInfo( KernelInfo(
...@@ -265,7 +313,7 @@ KERNEL_INFOS.extend( ...@@ -265,7 +313,7 @@ KERNEL_INFOS.extend(
reference_inputs_fn=reference_inputs_resize_mask, reference_inputs_fn=reference_inputs_resize_mask,
closeness_kwargs=DEFAULT_IMAGE_CLOSENESS_KWARGS, closeness_kwargs=DEFAULT_IMAGE_CLOSENESS_KWARGS,
test_marks=[ test_marks=[
xfail_integer_size_jit(), xfail_jit_integer_size(),
], ],
), ),
] ]
...@@ -290,28 +338,51 @@ def _diversify_affine_kwargs_types(affine_kwargs): ...@@ -290,28 +338,51 @@ def _diversify_affine_kwargs_types(affine_kwargs):
yield dict(affine_kwargs, shear=diverse_shear) yield dict(affine_kwargs, shear=diverse_shear)
def _full_affine_params(**partial_params):
partial_params.setdefault("angle", 0.0)
partial_params.setdefault("translate", [0.0, 0.0])
partial_params.setdefault("scale", 1.0)
partial_params.setdefault("shear", [0.0, 0.0])
partial_params.setdefault("center", None)
return partial_params
_DIVERSE_AFFINE_PARAMS = [
_full_affine_params(**{name: arg})
for name, args in [
("angle", [1.0, 2]),
("translate", [[1.0, 0.5], [1, 2], (1.0, 0.5), (1, 2)]),
("scale", [0.5]),
("shear", [1.0, 2, [1.0], [2], (1.0,), (2,), [1.0, 0.5], [1, 2], (1.0, 0.5), (1, 2)]),
("center", [None, [1.0, 0.5], [1, 2], (1.0, 0.5), (1, 2)]),
]
for arg in args
]
def sample_inputs_affine_image_tensor(): def sample_inputs_affine_image_tensor():
for image_loader, interpolation_mode, center in itertools.product( make_affine_image_loaders = functools.partial(
make_image_loaders(sizes=["random"], dtypes=[torch.float32]), make_image_loaders, sizes=["random"], color_spaces=[features.ColorSpace.RGB], dtypes=[torch.float32]
)
for image_loader, affine_params in itertools.product(make_affine_image_loaders(), _DIVERSE_AFFINE_PARAMS):
yield ArgsKwargs(image_loader, **affine_params)
for image_loader in make_affine_image_loaders():
fills = [None, 0.5]
if image_loader.num_channels > 1:
fills.extend(vector_fill * image_loader.num_channels for vector_fill in [(0.5,), (1,), [0.5], [1]])
for fill in fills:
yield ArgsKwargs(image_loader, **_full_affine_params(), fill=fill)
for image_loader, interpolation in itertools.product(
make_affine_image_loaders(),
[ [
F.InterpolationMode.NEAREST, F.InterpolationMode.NEAREST,
F.InterpolationMode.BILINEAR, F.InterpolationMode.BILINEAR,
], ],
[None, (0, 0)],
):
for fill in [None, 128.0, 128, [12.0], [0.5] * image_loader.num_channels]:
yield ArgsKwargs(
image_loader,
interpolation=interpolation_mode,
center=center,
fill=fill,
**_AFFINE_KWARGS[0],
)
for image_loader, affine_kwargs in itertools.product(
make_image_loaders(sizes=["random"], dtypes=[torch.float32]), _diversify_affine_kwargs_types(_AFFINE_KWARGS[0])
): ):
yield ArgsKwargs(image_loader, **affine_kwargs) yield ArgsKwargs(image_loader, **_full_affine_params(), fill=0)
def reference_inputs_affine_image_tensor(): def reference_inputs_affine_image_tensor():
...@@ -324,22 +395,14 @@ def reference_inputs_affine_image_tensor(): ...@@ -324,22 +395,14 @@ def reference_inputs_affine_image_tensor():
def sample_inputs_affine_bounding_box(): def sample_inputs_affine_bounding_box():
for bounding_box_loader in make_bounding_box_loaders(): for bounding_box_loader, affine_params in itertools.product(
yield ArgsKwargs( make_bounding_box_loaders(formats=[features.BoundingBoxFormat.XYXY]), _DIVERSE_AFFINE_PARAMS
bounding_box_loader,
format=bounding_box_loader.format,
image_size=bounding_box_loader.image_size,
**_AFFINE_KWARGS[0],
)
for bounding_box_loader, affine_kwargs in itertools.product(
make_bounding_box_loaders(), _diversify_affine_kwargs_types(_AFFINE_KWARGS[0])
): ):
yield ArgsKwargs( yield ArgsKwargs(
bounding_box_loader, bounding_box_loader,
format=bounding_box_loader.format, format=bounding_box_loader.format,
image_size=bounding_box_loader.image_size, image_size=bounding_box_loader.image_size,
**affine_kwargs, **affine_params,
) )
...@@ -423,16 +486,8 @@ def reference_inputs_affine_bounding_box(): ...@@ -423,16 +486,8 @@ def reference_inputs_affine_bounding_box():
def sample_inputs_affine_image_mask(): def sample_inputs_affine_image_mask():
for mask_loader, center in itertools.product( for mask_loader in make_mask_loaders(sizes=["random"], num_categories=["random"], num_objects=["random"]):
make_mask_loaders(sizes=["random"], dtypes=[torch.uint8]), yield ArgsKwargs(mask_loader, **_full_affine_params())
[None, (0, 0)],
):
yield ArgsKwargs(mask_loader, center=center, **_AFFINE_KWARGS[0])
for mask_loader, affine_kwargs in itertools.product(
make_mask_loaders(sizes=["random"], dtypes=[torch.uint8]), _diversify_affine_kwargs_types(_AFFINE_KWARGS[0])
):
yield ArgsKwargs(mask_loader, **affine_kwargs)
@pil_reference_wrapper @pil_reference_wrapper
...@@ -455,7 +510,12 @@ KERNEL_INFOS.extend( ...@@ -455,7 +510,12 @@ KERNEL_INFOS.extend(
reference_fn=pil_reference_wrapper(F.affine_image_pil), reference_fn=pil_reference_wrapper(F.affine_image_pil),
reference_inputs_fn=reference_inputs_affine_image_tensor, reference_inputs_fn=reference_inputs_affine_image_tensor,
closeness_kwargs=DEFAULT_IMAGE_CLOSENESS_KWARGS, closeness_kwargs=DEFAULT_IMAGE_CLOSENESS_KWARGS,
test_marks=[xfail_python_scalar_arg_jit("shear")], test_marks=[
xfail_jit_python_scalar_arg("shear"),
xfail_jit_tuple_instead_of_list("fill"),
# TODO: check if this is a regression since it seems that should be supported if `int` is ok
xfail_jit_list_of_ints("fill"),
],
), ),
KernelInfo( KernelInfo(
F.affine_bounding_box, F.affine_bounding_box,
...@@ -464,7 +524,7 @@ KERNEL_INFOS.extend( ...@@ -464,7 +524,7 @@ KERNEL_INFOS.extend(
reference_inputs_fn=reference_inputs_affine_bounding_box, reference_inputs_fn=reference_inputs_affine_bounding_box,
closeness_kwargs=dict(atol=1, rtol=0), closeness_kwargs=dict(atol=1, rtol=0),
test_marks=[ test_marks=[
xfail_python_scalar_arg_jit("shear"), xfail_jit_python_scalar_arg("shear"),
], ],
), ),
KernelInfo( KernelInfo(
...@@ -473,7 +533,9 @@ KERNEL_INFOS.extend( ...@@ -473,7 +533,9 @@ KERNEL_INFOS.extend(
reference_fn=reference_affine_mask, reference_fn=reference_affine_mask,
reference_inputs_fn=reference_inputs_resize_mask, reference_inputs_fn=reference_inputs_resize_mask,
closeness_kwargs=DEFAULT_IMAGE_CLOSENESS_KWARGS, closeness_kwargs=DEFAULT_IMAGE_CLOSENESS_KWARGS,
test_marks=[xfail_python_scalar_arg_jit("shear")], test_marks=[
xfail_jit_python_scalar_arg("shear"),
],
), ),
] ]
) )
...@@ -514,15 +576,21 @@ KERNEL_INFOS.append( ...@@ -514,15 +576,21 @@ KERNEL_INFOS.append(
def sample_inputs_convert_color_space_image_tensor(): def sample_inputs_convert_color_space_image_tensor():
color_spaces = set(features.ColorSpace) - {features.ColorSpace.OTHER} color_spaces = list(set(features.ColorSpace) - {features.ColorSpace.OTHER})
for image_loader in make_image_loaders(sizes=["random"], color_spaces=color_spaces, constant_alpha=True):
old_color_space = image_loader.color_space for old_color_space, new_color_space in cycle_over(color_spaces):
for params in combinations_grid(new_color_space=color_spaces - {old_color_space}, copy=(True, False)): for image_loader in make_image_loaders(sizes=["random"], color_spaces=[old_color_space], constant_alpha=True):
yield ArgsKwargs(image_loader, old_color_space=old_color_space, **params) yield ArgsKwargs(image_loader, old_color_space=old_color_space, new_color_space=new_color_space)
for color_space in color_spaces:
for image_loader in make_image_loaders(
sizes=["random"], color_spaces=[color_space], dtypes=[torch.float32], constant_alpha=True
):
yield ArgsKwargs(image_loader, old_color_space=color_space, new_color_space=color_space, copy=False)
@pil_reference_wrapper @pil_reference_wrapper
def reference_convert_color_space_image_tensor(image_pil, old_color_space, new_color_space, copy): def reference_convert_color_space_image_tensor(image_pil, old_color_space, new_color_space, copy=True):
color_space_pil = features.ColorSpace.from_pil_mode(image_pil.mode) color_space_pil = features.ColorSpace.from_pil_mode(image_pil.mode)
if color_space_pil != old_color_space: if color_space_pil != old_color_space:
raise pytest.UsageError( raise pytest.UsageError(
...@@ -600,25 +668,30 @@ _ROTATE_ANGLES = [-87, 15, 90] ...@@ -600,25 +668,30 @@ _ROTATE_ANGLES = [-87, 15, 90]
def sample_inputs_rotate_image_tensor(): def sample_inputs_rotate_image_tensor():
for image_loader, params in itertools.product( make_rotate_image_loaders = functools.partial(
make_image_loaders(sizes=["random"], dtypes=[torch.float32]), make_image_loaders, sizes=["random"], color_spaces=[features.ColorSpace.RGB], dtypes=[torch.float32]
combinations_grid( )
interpolation=[F.InterpolationMode.NEAREST, F.InterpolationMode.BILINEAR],
expand=[True, False], for image_loader in make_rotate_image_loaders():
center=[None, (0, 0)], yield ArgsKwargs(image_loader, angle=15.0, expand=True)
),
for image_loader, center in itertools.product(
make_rotate_image_loaders(), [None, [1.0, 0.5], [1, 2], (1.0, 0.5), (1, 2)]
): ):
if params["center"] is not None and params["expand"]: yield ArgsKwargs(image_loader, angle=15.0, center=center)
# Otherwise this will emit a warning and ignore center anyway
continue
for fill in [None, 0.5, [0.5] * image_loader.num_channels]: for image_loader in make_rotate_image_loaders():
yield ArgsKwargs( fills = [None, 0.5]
image_loader, if image_loader.num_channels > 1:
angle=_ROTATE_ANGLES[0], fills.extend(vector_fill * image_loader.num_channels for vector_fill in [(0.5,), (1,), [0.5], [1]])
fill=fill, for fill in fills:
**params, yield ArgsKwargs(image_loader, angle=15.0, fill=fill)
)
for image_loader, interpolation in itertools.product(
make_rotate_image_loaders(),
[F.InterpolationMode.NEAREST, F.InterpolationMode.BILINEAR],
):
yield ArgsKwargs(image_loader, angle=15.0, fill=0)
def reference_inputs_rotate_image_tensor(): def reference_inputs_rotate_image_tensor():
...@@ -637,22 +710,8 @@ def sample_inputs_rotate_bounding_box(): ...@@ -637,22 +710,8 @@ def sample_inputs_rotate_bounding_box():
def sample_inputs_rotate_mask(): def sample_inputs_rotate_mask():
for image_loader, params in itertools.product( for mask_loader in make_mask_loaders(sizes=["random"], num_categories=["random"], num_objects=["random"]):
make_image_loaders(sizes=["random"], dtypes=[torch.uint8]), yield ArgsKwargs(mask_loader, angle=15.0)
combinations_grid(
expand=[True, False],
center=[None, (0, 0)],
),
):
if params["center"] is not None and params["expand"]:
# Otherwise this will emit a warning and ignore center anyway
continue
yield ArgsKwargs(
image_loader,
angle=_ROTATE_ANGLES[0],
**params,
)
@pil_reference_wrapper @pil_reference_wrapper
...@@ -673,6 +732,11 @@ KERNEL_INFOS.extend( ...@@ -673,6 +732,11 @@ KERNEL_INFOS.extend(
reference_fn=pil_reference_wrapper(F.rotate_image_pil), reference_fn=pil_reference_wrapper(F.rotate_image_pil),
reference_inputs_fn=reference_inputs_rotate_image_tensor, reference_inputs_fn=reference_inputs_rotate_image_tensor,
closeness_kwargs=DEFAULT_IMAGE_CLOSENESS_KWARGS, closeness_kwargs=DEFAULT_IMAGE_CLOSENESS_KWARGS,
test_marks=[
xfail_jit_tuple_instead_of_list("fill"),
# TODO: check if this is a regression since it seems that should be supported if `int` is ok
xfail_jit_list_of_ints("fill"),
],
), ),
KernelInfo( KernelInfo(
F.rotate_bounding_box, F.rotate_bounding_box,
...@@ -692,7 +756,16 @@ _CROP_PARAMS = combinations_grid(top=[-8, 0, 9], left=[-8, 0, 9], height=[12, 20 ...@@ -692,7 +756,16 @@ _CROP_PARAMS = combinations_grid(top=[-8, 0, 9], left=[-8, 0, 9], height=[12, 20
def sample_inputs_crop_image_tensor(): def sample_inputs_crop_image_tensor():
for image_loader, params in itertools.product(make_image_loaders(), [_CROP_PARAMS[0], _CROP_PARAMS[-1]]): for image_loader, params in itertools.product(
make_image_loaders(sizes=[(16, 17)], color_spaces=[features.ColorSpace.RGB], dtypes=[torch.float32]),
[
dict(top=4, left=3, height=7, width=8),
dict(top=-1, left=3, height=7, width=8),
dict(top=4, left=-1, height=7, width=8),
dict(top=4, left=3, height=17, width=8),
dict(top=4, left=3, height=7, width=18),
],
):
yield ArgsKwargs(image_loader, **params) yield ArgsKwargs(image_loader, **params)
...@@ -709,8 +782,8 @@ def sample_inputs_crop_bounding_box(): ...@@ -709,8 +782,8 @@ def sample_inputs_crop_bounding_box():
def sample_inputs_crop_mask(): def sample_inputs_crop_mask():
for mask_loader, params in itertools.product(make_mask_loaders(), [_CROP_PARAMS[0], _CROP_PARAMS[-1]]): for mask_loader in make_mask_loaders(sizes=[(16, 17)], num_categories=["random"], num_objects=["random"]):
yield ArgsKwargs(mask_loader, **params) yield ArgsKwargs(mask_loader, top=4, left=3, height=7, width=8)
def reference_inputs_crop_mask(): def reference_inputs_crop_mask():
...@@ -829,12 +902,34 @@ _PAD_PARAMS = combinations_grid( ...@@ -829,12 +902,34 @@ _PAD_PARAMS = combinations_grid(
def sample_inputs_pad_image_tensor(): def sample_inputs_pad_image_tensor():
for image_loader, params in itertools.product(make_image_loaders(sizes=["random"]), _PAD_PARAMS): make_pad_image_loaders = functools.partial(
fills = [None, 128.0, 128, [12.0]] make_image_loaders, sizes=["random"], color_spaces=[features.ColorSpace.RGB], dtypes=[torch.float32]
if params["padding_mode"] == "constant": )
fills.append([12.0 + c for c in range(image_loader.num_channels)])
for image_loader, padding in itertools.product(
make_pad_image_loaders(),
[1, (1,), (1, 2), (1, 2, 3, 4), [1], [1, 2], [1, 2, 3, 4]],
):
yield ArgsKwargs(image_loader, padding=padding)
for image_loader in make_pad_image_loaders():
fills = [None, 0.5]
if image_loader.num_channels > 1:
fills.extend(vector_fill * image_loader.num_channels for vector_fill in [(0.5,), (1,), [0.5], [1]])
for fill in fills: for fill in fills:
yield ArgsKwargs(image_loader, fill=fill, **params) yield ArgsKwargs(image_loader, padding=[1], fill=fill)
for image_loader, padding_mode in itertools.product(
# We branch for non-constant padding and integer inputs
make_pad_image_loaders(dtypes=[torch.uint8]),
["constant", "symmetric", "edge", "reflect"],
):
yield ArgsKwargs(image_loader, padding=[1], padding_mode=padding_mode)
# `torch.nn.functional.pad` does not support symmetric padding, and thus we have a custom implementation. Besides
# negative padding, this is already handled by the inputs above.
for image_loader in make_pad_image_loaders():
yield ArgsKwargs(image_loader, padding=[-1], padding_mode="symmetric")
def reference_inputs_pad_image_tensor(): def reference_inputs_pad_image_tensor():
...@@ -848,18 +943,21 @@ def reference_inputs_pad_image_tensor(): ...@@ -848,18 +943,21 @@ def reference_inputs_pad_image_tensor():
def sample_inputs_pad_bounding_box(): def sample_inputs_pad_bounding_box():
for bounding_box_loader, params in itertools.product(make_bounding_box_loaders(), _PAD_PARAMS): for bounding_box_loader, padding in itertools.product(
if params["padding_mode"] != "constant": make_bounding_box_loaders(), [1, (1,), (1, 2), (1, 2, 3, 4), [1], [1, 2], [1, 2, 3, 4]]
continue ):
yield ArgsKwargs( yield ArgsKwargs(
bounding_box_loader, format=bounding_box_loader.format, image_size=bounding_box_loader.image_size, **params bounding_box_loader,
format=bounding_box_loader.format,
image_size=bounding_box_loader.image_size,
padding=padding,
padding_mode="constant",
) )
def sample_inputs_pad_mask(): def sample_inputs_pad_mask():
for image_loader, fill, params in itertools.product(make_mask_loaders(sizes=["random"]), [None, 127], _PAD_PARAMS): for mask_loader in make_mask_loaders(sizes=["random"], num_categories=["random"], num_objects=["random"]):
yield ArgsKwargs(image_loader, fill=fill, **params) yield ArgsKwargs(mask_loader, padding=[1])
def reference_inputs_pad_mask(): def reference_inputs_pad_mask():
...@@ -875,10 +973,21 @@ KERNEL_INFOS.extend( ...@@ -875,10 +973,21 @@ KERNEL_INFOS.extend(
reference_fn=pil_reference_wrapper(F.pad_image_pil), reference_fn=pil_reference_wrapper(F.pad_image_pil),
reference_inputs_fn=reference_inputs_pad_image_tensor, reference_inputs_fn=reference_inputs_pad_image_tensor,
closeness_kwargs=DEFAULT_IMAGE_CLOSENESS_KWARGS, closeness_kwargs=DEFAULT_IMAGE_CLOSENESS_KWARGS,
test_marks=[
xfail_jit_python_scalar_arg("padding"),
xfail_jit_tuple_instead_of_list("padding"),
xfail_jit_tuple_instead_of_list("fill"),
# TODO: check if this is a regression since it seems that should be supported if `int` is ok
xfail_jit_list_of_ints("fill"),
],
), ),
KernelInfo( KernelInfo(
F.pad_bounding_box, F.pad_bounding_box,
sample_inputs_fn=sample_inputs_pad_bounding_box, sample_inputs_fn=sample_inputs_pad_bounding_box,
test_marks=[
xfail_jit_python_scalar_arg("padding"),
xfail_jit_tuple_instead_of_list("padding"),
],
), ),
KernelInfo( KernelInfo(
F.pad_mask, F.pad_mask,
...@@ -1045,7 +1154,13 @@ _CENTER_CROP_OUTPUT_SIZES = [[4, 3], [42, 70], [4], 3, (5, 2), (6,)] ...@@ -1045,7 +1154,13 @@ _CENTER_CROP_OUTPUT_SIZES = [[4, 3], [42, 70], [4], 3, (5, 2), (6,)]
def sample_inputs_center_crop_image_tensor(): def sample_inputs_center_crop_image_tensor():
for image_loader, output_size in itertools.product( for image_loader, output_size in itertools.product(
make_image_loaders(sizes=_CENTER_CROP_IMAGE_SIZES), _CENTER_CROP_OUTPUT_SIZES make_image_loaders(sizes=[(16, 17)], color_spaces=[features.ColorSpace.RGB], dtypes=[torch.float32]),
[
# valid `output_size` types for which cropping is applied to both dimensions
*[5, (4,), (2, 3), [6], [3, 2]],
# `output_size`'s for which at least one dimension needs to be padded
*[[4, 18], [17, 5], [17, 18]],
],
): ):
yield ArgsKwargs(image_loader, output_size=output_size) yield ArgsKwargs(image_loader, output_size=output_size)
...@@ -1068,10 +1183,9 @@ def sample_inputs_center_crop_bounding_box(): ...@@ -1068,10 +1183,9 @@ def sample_inputs_center_crop_bounding_box():
def sample_inputs_center_crop_mask(): def sample_inputs_center_crop_mask():
for mask_loader, output_size in itertools.product( for mask_loader in make_mask_loaders(sizes=["random"], num_categories=["random"], num_objects=["random"]):
make_mask_loaders(sizes=_CENTER_CROP_IMAGE_SIZES), _CENTER_CROP_OUTPUT_SIZES height, width = mask_loader.shape[-2:]
): yield ArgsKwargs(mask_loader, output_size=(height // 2, width // 2))
yield ArgsKwargs(mask_loader, output_size=output_size)
def reference_inputs_center_crop_mask(): def reference_inputs_center_crop_mask():
...@@ -1090,14 +1204,14 @@ KERNEL_INFOS.extend( ...@@ -1090,14 +1204,14 @@ KERNEL_INFOS.extend(
reference_inputs_fn=reference_inputs_center_crop_image_tensor, reference_inputs_fn=reference_inputs_center_crop_image_tensor,
closeness_kwargs=DEFAULT_IMAGE_CLOSENESS_KWARGS, closeness_kwargs=DEFAULT_IMAGE_CLOSENESS_KWARGS,
test_marks=[ test_marks=[
xfail_integer_size_jit("output_size"), xfail_jit_integer_size("output_size"),
], ],
), ),
KernelInfo( KernelInfo(
F.center_crop_bounding_box, F.center_crop_bounding_box,
sample_inputs_fn=sample_inputs_center_crop_bounding_box, sample_inputs_fn=sample_inputs_center_crop_bounding_box,
test_marks=[ test_marks=[
xfail_integer_size_jit("output_size"), xfail_jit_integer_size("output_size"),
], ],
), ),
KernelInfo( KernelInfo(
...@@ -1107,7 +1221,7 @@ KERNEL_INFOS.extend( ...@@ -1107,7 +1221,7 @@ KERNEL_INFOS.extend(
reference_inputs_fn=reference_inputs_center_crop_mask, reference_inputs_fn=reference_inputs_center_crop_mask,
closeness_kwargs=DEFAULT_IMAGE_CLOSENESS_KWARGS, closeness_kwargs=DEFAULT_IMAGE_CLOSENESS_KWARGS,
test_marks=[ test_marks=[
xfail_integer_size_jit("output_size"), xfail_jit_integer_size("output_size"),
], ],
), ),
] ]
...@@ -1115,18 +1229,21 @@ KERNEL_INFOS.extend( ...@@ -1115,18 +1229,21 @@ KERNEL_INFOS.extend(
def sample_inputs_gaussian_blur_image_tensor(): def sample_inputs_gaussian_blur_image_tensor():
for image_loader, params in itertools.product( make_gaussian_blur_image_loaders = functools.partial(
make_image_loaders( make_image_loaders,
sizes=["random"], sizes=["random"],
# FIXME: kernel should support arbitrary batch sizes color_spaces=[features.ColorSpace.RGB],
extra_dims=[(), (4,)], # FIXME: kernel should support arbitrary batch sizes
), extra_dims=[(), (4,)],
combinations_grid( )
kernel_size=[(3, 3), [3, 3], 5],
sigma=[None, (3.0, 3.0), [2.0, 2.0], 4.0, [1.5], (3.14,)], for image_loader, kernel_size in itertools.product(make_gaussian_blur_image_loaders(), [5, (3, 3), [3, 3]]):
), yield ArgsKwargs(image_loader, kernel_size=kernel_size)
for image_loader, sigma in itertools.product(
make_gaussian_blur_image_loaders(), [None, (3.0, 3.0), [2.0, 2.0], 4.0, [1.5], (3.14,)]
): ):
yield ArgsKwargs(image_loader, **params) yield ArgsKwargs(image_loader, kernel_size=5, sigma=sigma)
KERNEL_INFOS.append( KERNEL_INFOS.append(
...@@ -1135,8 +1252,8 @@ KERNEL_INFOS.append( ...@@ -1135,8 +1252,8 @@ KERNEL_INFOS.append(
sample_inputs_fn=sample_inputs_gaussian_blur_image_tensor, sample_inputs_fn=sample_inputs_gaussian_blur_image_tensor,
closeness_kwargs=DEFAULT_IMAGE_CLOSENESS_KWARGS, closeness_kwargs=DEFAULT_IMAGE_CLOSENESS_KWARGS,
test_marks=[ test_marks=[
xfail_python_scalar_arg_jit("kernel_size"), xfail_jit_python_scalar_arg("kernel_size"),
xfail_python_scalar_arg_jit("sigma"), xfail_jit_python_scalar_arg("sigma"),
], ],
) )
) )
...@@ -1518,7 +1635,9 @@ def _get_five_ten_crop_image_size(size): ...@@ -1518,7 +1635,9 @@ def _get_five_ten_crop_image_size(size):
def sample_inputs_five_crop_image_tensor(): def sample_inputs_five_crop_image_tensor():
for size in _FIVE_TEN_CROP_SIZES: for size in _FIVE_TEN_CROP_SIZES:
for image_loader in make_image_loaders(sizes=[_get_five_ten_crop_image_size(size)]): for image_loader in make_image_loaders(
sizes=[_get_five_ten_crop_image_size(size)], color_spaces=[features.ColorSpace.RGB], dtypes=[torch.float32]
):
yield ArgsKwargs(image_loader, size=size) yield ArgsKwargs(image_loader, size=size)
...@@ -1530,7 +1649,9 @@ def reference_inputs_five_crop_image_tensor(): ...@@ -1530,7 +1649,9 @@ def reference_inputs_five_crop_image_tensor():
def sample_inputs_ten_crop_image_tensor(): def sample_inputs_ten_crop_image_tensor():
for size, vertical_flip in itertools.product(_FIVE_TEN_CROP_SIZES, [False, True]): for size, vertical_flip in itertools.product(_FIVE_TEN_CROP_SIZES, [False, True]):
for image_loader in make_image_loaders(sizes=[_get_five_ten_crop_image_size(size)]): for image_loader in make_image_loaders(
sizes=[_get_five_ten_crop_image_size(size)], color_spaces=[features.ColorSpace.RGB], dtypes=[torch.float32]
):
yield ArgsKwargs(image_loader, size=size, vertical_flip=vertical_flip) yield ArgsKwargs(image_loader, size=size, vertical_flip=vertical_flip)
...@@ -1548,7 +1669,7 @@ KERNEL_INFOS.extend( ...@@ -1548,7 +1669,7 @@ KERNEL_INFOS.extend(
reference_fn=pil_reference_wrapper(F.five_crop_image_pil), reference_fn=pil_reference_wrapper(F.five_crop_image_pil),
reference_inputs_fn=reference_inputs_five_crop_image_tensor, reference_inputs_fn=reference_inputs_five_crop_image_tensor,
test_marks=[ test_marks=[
xfail_integer_size_jit(), xfail_jit_integer_size(),
mark_framework_limitation(("TestKernels", "test_batched_vs_single"), "Custom batching needed."), mark_framework_limitation(("TestKernels", "test_batched_vs_single"), "Custom batching needed."),
], ],
closeness_kwargs=DEFAULT_IMAGE_CLOSENESS_KWARGS, closeness_kwargs=DEFAULT_IMAGE_CLOSENESS_KWARGS,
...@@ -1559,7 +1680,7 @@ KERNEL_INFOS.extend( ...@@ -1559,7 +1680,7 @@ KERNEL_INFOS.extend(
reference_fn=pil_reference_wrapper(F.ten_crop_image_pil), reference_fn=pil_reference_wrapper(F.ten_crop_image_pil),
reference_inputs_fn=reference_inputs_ten_crop_image_tensor, reference_inputs_fn=reference_inputs_ten_crop_image_tensor,
test_marks=[ test_marks=[
xfail_integer_size_jit(), xfail_jit_integer_size(),
mark_framework_limitation(("TestKernels", "test_batched_vs_single"), "Custom batching needed."), mark_framework_limitation(("TestKernels", "test_batched_vs_single"), "Custom batching needed."),
], ],
closeness_kwargs=DEFAULT_IMAGE_CLOSENESS_KWARGS, closeness_kwargs=DEFAULT_IMAGE_CLOSENESS_KWARGS,
......
torchvision/transforms/functional_tensor.py
View file @ 026991b1
...@@ -755,12 +755,7 @@ def gaussian_blur(img: Tensor, kernel_size: List[int], sigma: List[float]) -> Te ...@@ -755,12 +755,7 @@ def gaussian_blur(img: Tensor, kernel_size: List[int], sigma: List[float]) -> Te
kernel = _get_gaussian_kernel2d(kernel_size, sigma, dtype=dtype, device=img.device) kernel = _get_gaussian_kernel2d(kernel_size, sigma, dtype=dtype, device=img.device)
kernel = kernel.expand(img.shape[-3], 1, kernel.shape[0], kernel.shape[1]) kernel = kernel.expand(img.shape[-3], 1, kernel.shape[0], kernel.shape[1])
img, need_cast, need_squeeze, out_dtype = _cast_squeeze_in( img, need_cast, need_squeeze, out_dtype = _cast_squeeze_in(img, [kernel.dtype])
img,
[
kernel.dtype,
],
)
# padding = (left, right, top, bottom) # padding = (left, right, top, bottom)
padding = [kernel_size[0] // 2, kernel_size[0] // 2, kernel_size[1] // 2, kernel_size[1] // 2] padding = [kernel_size[0] // 2, kernel_size[0] // 2, kernel_size[1] // 2, kernel_size[1] // 2]
......
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