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Unverified Commit 50b77fa7 authored by Philip Meier's avatar Philip Meier Committed by GitHub
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use non-random images for interpolation kernels for testing (#6977) 

* use non-random images for interpolation kernels for testing

* use real image rather than artificial

* cleanup
parent 346f6dd9
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Showing with 72 additions and 96 deletions
test/prototype_common_utils.py
View file @ 50b77fa7
......@@ -3,6 +3,7 @@
import collections.abc
import dataclasses
import functools
import pathlib
from collections import defaultdict
from typing import Callable, Optional, Sequence, Tuple, Union
......@@ -14,7 +15,7 @@ from datasets_utils import combinations_grid
from torch.nn.functional import one_hot
from torch.testing._comparison import assert_equal as _assert_equal, BooleanPair, NonePair, NumberPair, TensorLikePair
from torchvision.prototype import features
from torchvision.prototype.transforms.functional import to_image_tensor
from torchvision.prototype.transforms.functional import convert_dtype_image_tensor, to_image_tensor
from torchvision.transforms.functional_tensor import _max_value as get_max_value
__all__ = [
......@@ -69,6 +70,7 @@ class ImagePair(TensorLikePair):
self._compare_attributes(actual, expected)
actual, expected = self._equalize_attributes(actual, expected)
actual, expected = self._promote_for_comparison(actual, expected)
abs_diff = torch.abs(actual - expected)
if self.allowed_percentage_diff is not None:
......@@ -313,6 +315,42 @@ def make_image_loaders(
make_images = from_loaders(make_image_loaders)
def make_image_loader_for_interpolation(size="random", *, color_space=features.ColorSpace.RGB, dtype=torch.uint8):
size = _parse_spatial_size(size)
num_channels = get_num_channels(color_space)
def fn(shape, dtype, device):
height, width = shape[-2:]
image_pil = (
PIL.Image.open(pathlib.Path(__file__).parent / "assets" / "encode_jpeg" / "grace_hopper_517x606.jpg")
.resize((width, height))
.convert(
{
features.ColorSpace.GRAY: "L",
features.ColorSpace.GRAY_ALPHA: "LA",
features.ColorSpace.RGB: "RGB",
features.ColorSpace.RGB_ALPHA: "RGBA",
}[color_space]
)
)
image_tensor = convert_dtype_image_tensor(to_image_tensor(image_pil).to(device=device), dtype=dtype)
return features.Image(image_tensor, color_space=color_space)
return ImageLoader(fn, shape=(num_channels, *size), dtype=dtype, color_space=color_space)
def make_image_loaders_for_interpolation(
sizes=((233, 147),),
color_spaces=(features.ColorSpace.RGB,),
dtypes=(torch.uint8,),
):
for params in combinations_grid(size=sizes, color_space=color_spaces, dtype=dtypes):
yield make_image_loader_for_interpolation(**params)
@dataclasses.dataclass
class BoundingBoxLoader(TensorLoader):
format: features.BoundingBoxFormat
......
test/prototype_transforms_kernel_infos.py
View file @ 50b77fa7
......@@ -19,6 +19,7 @@ from prototype_common_utils import (
make_bounding_box_loaders,
make_image_loader,
make_image_loaders,
make_image_loaders_for_interpolation,
make_mask_loaders,
make_video_loaders,
mark_framework_limitation,
......@@ -287,7 +288,7 @@ def reference_resize_image_tensor(*args, **kwargs):
def reference_inputs_resize_image_tensor():
for image_loader, interpolation in itertools.product(
make_image_loaders(extra_dims=[()], dtypes=[torch.uint8]),
make_image_loaders_for_interpolation(),
[
F.InterpolationMode.NEAREST,
F.InterpolationMode.NEAREST_EXACT,
......@@ -319,17 +320,6 @@ def sample_inputs_resize_mask():
yield ArgsKwargs(mask_loader, size=[min(mask_loader.shape[-2:]) + 1])
@pil_reference_wrapper
def reference_resize_mask(*args, **kwargs):
return F.resize_image_pil(*args, interpolation=F.InterpolationMode.NEAREST, **kwargs)
def reference_inputs_resize_mask():
for mask_loader in make_mask_loaders(extra_dims=[()], num_objects=[1]):
for size in _get_resize_sizes(mask_loader.shape[-2:]):
yield ArgsKwargs(mask_loader, size=size)
def sample_inputs_resize_video():
for video_loader in make_video_loaders(sizes=["random"], num_frames=["random"]):
yield ArgsKwargs(video_loader, size=[min(video_loader.shape[-2:]) + 1])
......@@ -369,11 +359,9 @@ KERNEL_INFOS.extend(
reference_inputs_fn=reference_inputs_resize_image_tensor,
float32_vs_uint8=True,
closeness_kwargs={
# TODO: investigate
**pil_reference_pixel_difference(110, agg_method="mean"),
**pil_reference_pixel_difference(10, agg_method="mean"),
**cuda_vs_cpu_pixel_difference(),
# TODO: investigate
**float32_vs_uint8_pixel_difference(50),
**float32_vs_uint8_pixel_difference(1, agg_method="mean"),
},
test_marks=[
xfail_jit_python_scalar_arg("size"),
......@@ -391,9 +379,6 @@ KERNEL_INFOS.extend(
KernelInfo(
F.resize_mask,
sample_inputs_fn=sample_inputs_resize_mask,
reference_fn=reference_resize_mask,
reference_inputs_fn=reference_inputs_resize_mask,
float32_vs_uint8=True,
closeness_kwargs=pil_reference_pixel_difference(10),
test_marks=[
xfail_jit_python_scalar_arg("size"),
......@@ -501,9 +486,7 @@ def sample_inputs_affine_image_tensor():
def reference_inputs_affine_image_tensor():
for image_loader, affine_kwargs in itertools.product(
make_image_loaders(extra_dims=[()], dtypes=[torch.uint8]), _AFFINE_KWARGS
):
for image_loader, affine_kwargs in itertools.product(make_image_loaders_for_interpolation(), _AFFINE_KWARGS):
yield ArgsKwargs(
image_loader,
interpolation=F.InterpolationMode.NEAREST,
......@@ -617,18 +600,6 @@ def sample_inputs_affine_mask():
yield ArgsKwargs(mask_loader, **_full_affine_params())
@pil_reference_wrapper
def reference_affine_mask(*args, **kwargs):
return F.affine_image_pil(*args, interpolation=F.InterpolationMode.NEAREST, **kwargs)
def reference_inputs_resize_mask():
for mask_loader, affine_kwargs in itertools.product(
make_mask_loaders(extra_dims=[()], num_objects=[1]), _AFFINE_KWARGS
):
yield ArgsKwargs(mask_loader, **affine_kwargs)
def sample_inputs_affine_video():
for video_loader in make_video_loaders(sizes=["random"], num_frames=["random"]):
yield ArgsKwargs(video_loader, **_full_affine_params())
......@@ -665,10 +636,6 @@ KERNEL_INFOS.extend(
KernelInfo(
F.affine_mask,
sample_inputs_fn=sample_inputs_affine_mask,
reference_fn=reference_affine_mask,
reference_inputs_fn=reference_inputs_resize_mask,
closeness_kwargs=pil_reference_pixel_difference(10),
float32_vs_uint8=True,
test_marks=[
xfail_jit_python_scalar_arg("shear"),
],
......@@ -870,9 +837,7 @@ def sample_inputs_rotate_image_tensor():
def reference_inputs_rotate_image_tensor():
for image_loader, angle in itertools.product(
make_image_loaders(extra_dims=[()], dtypes=[torch.uint8]), _ROTATE_ANGLES
):
for image_loader, angle in itertools.product(make_image_loaders_for_interpolation(), _ROTATE_ANGLES):
yield ArgsKwargs(image_loader, angle=angle)
......@@ -891,16 +856,6 @@ def sample_inputs_rotate_mask():
yield ArgsKwargs(mask_loader, angle=15.0)
@pil_reference_wrapper
def reference_rotate_mask(*args, **kwargs):
return F.rotate_image_pil(*args, interpolation=F.InterpolationMode.NEAREST, **kwargs)
def reference_inputs_rotate_mask():
for mask_loader, angle in itertools.product(make_mask_loaders(extra_dims=[()], num_objects=[1]), _ROTATE_ANGLES):
yield ArgsKwargs(mask_loader, angle=angle)
def sample_inputs_rotate_video():
for video_loader in make_video_loaders(sizes=["random"], num_frames=["random"]):
yield ArgsKwargs(video_loader, angle=15.0)
......@@ -914,8 +869,7 @@ KERNEL_INFOS.extend(
reference_fn=pil_reference_wrapper(F.rotate_image_pil),
reference_inputs_fn=reference_inputs_rotate_image_tensor,
float32_vs_uint8=True,
# TODO: investigate
closeness_kwargs=pil_reference_pixel_difference(110, agg_method="mean"),
closeness_kwargs=pil_reference_pixel_difference(1, agg_method="mean"),
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
......@@ -929,10 +883,6 @@ KERNEL_INFOS.extend(
KernelInfo(
F.rotate_mask,
sample_inputs_fn=sample_inputs_rotate_mask,
reference_fn=reference_rotate_mask,
reference_inputs_fn=reference_inputs_rotate_mask,
float32_vs_uint8=True,
closeness_kwargs=pil_reference_pixel_difference(10),
),
KernelInfo(
F.rotate_video,
......@@ -1058,7 +1008,7 @@ def reference_resized_crop_image_tensor(*args, **kwargs):
def reference_inputs_resized_crop_image_tensor():
for image_loader, interpolation, params in itertools.product(
make_image_loaders(extra_dims=[()], dtypes=[torch.uint8]),
make_image_loaders_for_interpolation(),
[
F.InterpolationMode.NEAREST,
F.InterpolationMode.NEAREST_EXACT,
......@@ -1089,13 +1039,6 @@ def sample_inputs_resized_crop_mask():
yield ArgsKwargs(mask_loader, **_RESIZED_CROP_PARAMS[0])
def reference_inputs_resized_crop_mask():
for mask_loader, params in itertools.product(
make_mask_loaders(extra_dims=[()], num_objects=[1]), _RESIZED_CROP_PARAMS
):
yield ArgsKwargs(mask_loader, **params)
def sample_inputs_resized_crop_video():
for video_loader in make_video_loaders(sizes=["random"], num_frames=["random"]):
yield ArgsKwargs(video_loader, **_RESIZED_CROP_PARAMS[0])
......@@ -1110,11 +1053,9 @@ KERNEL_INFOS.extend(
reference_inputs_fn=reference_inputs_resized_crop_image_tensor,
float32_vs_uint8=True,
closeness_kwargs={
# TODO: investigate
**pil_reference_pixel_difference(60, agg_method="mean"),
**cuda_vs_cpu_pixel_difference(),
# TODO: investigate
**float32_vs_uint8_pixel_difference(50),
**pil_reference_pixel_difference(3, agg_method="mean"),
**float32_vs_uint8_pixel_difference(3, agg_method="mean"),
},
),
KernelInfo(
......@@ -1124,10 +1065,6 @@ KERNEL_INFOS.extend(
KernelInfo(
F.resized_crop_mask,
sample_inputs_fn=sample_inputs_resized_crop_mask,
reference_fn=pil_reference_wrapper(F.resized_crop_image_pil),
reference_inputs_fn=reference_inputs_resized_crop_mask,
float32_vs_uint8=True,
closeness_kwargs=pil_reference_pixel_difference(10),
),
KernelInfo(
F.resized_crop_video,
......@@ -1298,12 +1235,24 @@ def sample_inputs_perspective_image_tensor():
def reference_inputs_perspective_image_tensor():
for image_loader, coefficients in itertools.product(
make_image_loaders(extra_dims=[()], dtypes=[torch.uint8]), _PERSPECTIVE_COEFFS
for image_loader, coefficients, interpolation in itertools.product(
make_image_loaders_for_interpolation(),
_PERSPECTIVE_COEFFS,
[
F.InterpolationMode.NEAREST,
F.InterpolationMode.BILINEAR,
],
):
# FIXME: PIL kernel doesn't support sequences of length 1 if the number of channels is larger. Shouldn't it?
for fill in get_fills(num_channels=image_loader.num_channels, dtype=image_loader.dtype):
yield ArgsKwargs(image_loader, None, None, fill=fill, coefficients=coefficients)
yield ArgsKwargs(
image_loader,
startpoints=None,
endpoints=None,
interpolation=interpolation,
fill=fill,
coefficients=coefficients,
)
def sample_inputs_perspective_bounding_box():
......@@ -1339,8 +1288,7 @@ KERNEL_INFOS.extend(
reference_inputs_fn=reference_inputs_perspective_image_tensor,
float32_vs_uint8=float32_vs_uint8_fill_adapter,
closeness_kwargs={
# TODO: investigate
**pil_reference_pixel_difference(160, agg_method="mean"),
**pil_reference_pixel_difference(2, agg_method="mean"),
**cuda_vs_cpu_pixel_difference(),
**float32_vs_uint8_pixel_difference(),
},
......@@ -1381,7 +1329,7 @@ def sample_inputs_elastic_image_tensor():
def reference_inputs_elastic_image_tensor():
for image_loader, interpolation in itertools.product(
make_image_loaders(extra_dims=[()], dtypes=[torch.uint8]),
make_image_loaders_for_interpolation(),
[
F.InterpolationMode.NEAREST,
F.InterpolationMode.BILINEAR,
......@@ -1409,12 +1357,6 @@ def sample_inputs_elastic_mask():
yield ArgsKwargs(mask_loader, displacement=displacement)
def reference_inputs_elastic_mask():
for mask_loader in make_mask_loaders(extra_dims=[()], num_objects=[1]):
displacement = _get_elastic_displacement(mask_loader.shape[-2:])
yield ArgsKwargs(mask_loader, displacement=displacement)
def sample_inputs_elastic_video():
for video_loader in make_video_loaders(sizes=["random"], num_frames=["random"]):
displacement = _get_elastic_displacement(video_loader.shape[-2:])
......@@ -1426,11 +1368,12 @@ KERNEL_INFOS.extend(
KernelInfo(
F.elastic_image_tensor,
sample_inputs_fn=sample_inputs_elastic_image_tensor,
reference_fn=pil_reference_wrapper(F.elastic_image_pil),
reference_inputs_fn=reference_inputs_elastic_image_tensor,
float32_vs_uint8=float32_vs_uint8_fill_adapter,
# TODO: investigate
closeness_kwargs=float32_vs_uint8_pixel_difference(60, agg_method="mean"),
closeness_kwargs={
**float32_vs_uint8_pixel_difference(6, agg_method="mean"),
**cuda_vs_cpu_pixel_difference(),
},
),
KernelInfo(
F.elastic_bounding_box,
......@@ -1439,15 +1382,11 @@ KERNEL_INFOS.extend(
KernelInfo(
F.elastic_mask,
sample_inputs_fn=sample_inputs_elastic_mask,
reference_fn=pil_reference_wrapper(F.elastic_image_pil),
reference_inputs_fn=reference_inputs_elastic_mask,
float32_vs_uint8=True,
# TODO: investigate
closeness_kwargs=pil_reference_pixel_difference(80, agg_method="mean"),
),
KernelInfo(
F.elastic_video,
sample_inputs_fn=sample_inputs_elastic_video,
closeness_kwargs=cuda_vs_cpu_pixel_difference(),
),
]
)
......@@ -2089,8 +2028,7 @@ KERNEL_INFOS.extend(
reference_inputs_fn=reference_inputs_adjust_hue_image_tensor,
float32_vs_uint8=True,
closeness_kwargs={
# TODO: investigate
**pil_reference_pixel_difference(20),
**pil_reference_pixel_difference(2, agg_method="mean"),
**float32_vs_uint8_pixel_difference(),
},
),
......
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