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Unverified Commit 5178a2e2 authored by Philip Meier's avatar Philip Meier Committed by GitHub
Browse files

[PoC] refactor transforms v2 tests (#7562) 


Co-authored-by: default avatarNicolas Hug <nh.nicolas.hug@gmail.com>
Co-authored-by: default avatarNicolas Hug <contact@nicolas-hug.com>
parent 17d50fc6
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Showing with 743 additions and 210 deletions
test/common_utils.py
View file @ 5178a2e2
......@@ -7,9 +7,11 @@ import itertools
import os
import pathlib
import random
import re
import shutil
import sys
import tempfile
import warnings
from collections import defaultdict
from subprocess import CalledProcessError, check_output, STDOUT
from typing import Callable, Sequence, Tuple, Union
......@@ -880,3 +882,23 @@ def assert_run_python_script(source_code):
raise RuntimeError(f"script errored with output:\n{e.output.decode()}")
if out != b"":
raise AssertionError(out.decode())
@contextlib.contextmanager
def assert_no_warnings():
# The name `catch_warnings` is a misnomer as the context manager does **not** catch any warnings, but rather scopes
# the warning filters. All changes that are made to the filters while in this context, will be reset upon exit.
with warnings.catch_warnings():
warnings.simplefilter("error")
yield
@contextlib.contextmanager
def ignore_jit_no_profile_information_warning():
# Calling a scripted object often triggers a warning like
# `UserWarning: operator() profile_node %$INT1 : int[] = prim::profile_ivalue($INT2) does not have profile information`
# with varying `INT1` and `INT2`. Since these are uninteresting for us and only clutter the test summary, we ignore
# them.
with warnings.catch_warnings():
warnings.filterwarnings("ignore", message=re.escape("operator() profile_node %"), category=UserWarning)
yield
test/test_transforms_v2.py
View file @ 5178a2e2
......@@ -1711,8 +1711,6 @@ def test_antialias_warning():
tensor_video = torch.randint(0, 256, size=(2, 3, 10, 10), dtype=torch.uint8)
match = "The default value of the antialias parameter"
with pytest.warns(UserWarning, match=match):
transforms.Resize((20, 20))(tensor_img)
with pytest.warns(UserWarning, match=match):
transforms.RandomResizedCrop((20, 20))(tensor_img)
with pytest.warns(UserWarning, match=match):
......@@ -1722,18 +1720,6 @@ def test_antialias_warning():
with pytest.warns(UserWarning, match=match):
transforms.RandomResize(10, 20)(tensor_img)
with pytest.warns(UserWarning, match=match):
transforms.functional.resize(tensor_img, (20, 20))
with pytest.warns(UserWarning, match=match):
transforms.functional.resize_image_tensor(tensor_img, (20, 20))
with pytest.warns(UserWarning, match=match):
transforms.functional.resize(tensor_video, (20, 20))
with pytest.warns(UserWarning, match=match):
transforms.functional.resize_video(tensor_video, (20, 20))
with pytest.warns(UserWarning, match=match):
datapoints.Image(tensor_img).resize((20, 20))
with pytest.warns(UserWarning, match=match):
datapoints.Image(tensor_img).resized_crop(0, 0, 10, 10, (20, 20))
......@@ -1744,27 +1730,17 @@ def test_antialias_warning():
with warnings.catch_warnings():
warnings.simplefilter("error")
transforms.Resize((20, 20))(pil_img)
transforms.RandomResizedCrop((20, 20))(pil_img)
transforms.ScaleJitter((20, 20))(pil_img)
transforms.RandomShortestSize((20, 20))(pil_img)
transforms.RandomResize(10, 20)(pil_img)
transforms.functional.resize(pil_img, (20, 20))
transforms.Resize((20, 20), antialias=True)(tensor_img)
transforms.RandomResizedCrop((20, 20), antialias=True)(tensor_img)
transforms.ScaleJitter((20, 20), antialias=True)(tensor_img)
transforms.RandomShortestSize((20, 20), antialias=True)(tensor_img)
transforms.RandomResize(10, 20, antialias=True)(tensor_img)
transforms.functional.resize(tensor_img, (20, 20), antialias=True)
transforms.functional.resize_image_tensor(tensor_img, (20, 20), antialias=True)
transforms.functional.resize(tensor_video, (20, 20), antialias=True)
transforms.functional.resize_video(tensor_video, (20, 20), antialias=True)
datapoints.Image(tensor_img).resize((20, 20), antialias=True)
datapoints.Image(tensor_img).resized_crop(0, 0, 10, 10, (20, 20), antialias=True)
datapoints.Video(tensor_video).resize((20, 20), antialias=True)
datapoints.Video(tensor_video).resized_crop(0, 0, 10, 10, (20, 20), antialias=True)
......
test/test_transforms_v2_refactored.py 0 → 100644
View file @ 5178a2e2
This diff is collapsed.
test/transforms_v2_dispatcher_infos.py
View file @ 5178a2e2
......@@ -148,19 +148,6 @@ DISPATCHER_INFOS = [
},
pil_kernel_info=PILKernelInfo(F.horizontal_flip_image_pil, kernel_name="horizontal_flip_image_pil"),
),
DispatcherInfo(
F.resize,
kernels={
datapoints.Image: F.resize_image_tensor,
datapoints.Video: F.resize_video,
datapoints.BoundingBox: F.resize_bounding_box,
datapoints.Mask: F.resize_mask,
},
pil_kernel_info=PILKernelInfo(F.resize_image_pil),
test_marks=[
xfail_jit_python_scalar_arg("size"),
],
),
DispatcherInfo(
F.affine,
kernels={
......
test/transforms_v2_kernel_infos.py
View file @ 5178a2e2
......@@ -238,179 +238,6 @@ KERNEL_INFOS.extend(
)
def _get_resize_sizes(spatial_size):
height, width = spatial_size
length = max(spatial_size)
yield length
yield [length]
yield (length,)
new_height = int(height * 0.75)
new_width = int(width * 1.25)
yield [new_height, new_width]
yield height, width
def sample_inputs_resize_image_tensor():
for image_loader in make_image_loaders(sizes=["random"], color_spaces=["RGB"], dtypes=[torch.float32]):
for size in _get_resize_sizes(image_loader.spatial_size):
yield ArgsKwargs(image_loader, size=size)
for image_loader, interpolation in itertools.product(
make_image_loaders(sizes=["random"], color_spaces=["RGB"]),
[F.InterpolationMode.NEAREST, F.InterpolationMode.BILINEAR],
):
yield ArgsKwargs(image_loader, size=[min(image_loader.spatial_size) + 1], interpolation=interpolation)
yield ArgsKwargs(make_image_loader(size=(11, 17)), size=20, max_size=25)
def sample_inputs_resize_image_tensor_bicubic():
for image_loader, interpolation in itertools.product(
make_image_loaders(sizes=["random"], color_spaces=["RGB"]), [F.InterpolationMode.BICUBIC]
):
yield ArgsKwargs(image_loader, size=[min(image_loader.spatial_size) + 1], interpolation=interpolation)
@pil_reference_wrapper
def reference_resize_image_tensor(*args, **kwargs):
if not kwargs.pop("antialias", False) and kwargs.get("interpolation", F.InterpolationMode.BILINEAR) in {
F.InterpolationMode.BILINEAR,
F.InterpolationMode.BICUBIC,
}:
raise pytest.UsageError("Anti-aliasing is always active in PIL")
return F.resize_image_pil(*args, **kwargs)
def reference_inputs_resize_image_tensor():
for image_loader, interpolation in itertools.product(
make_image_loaders_for_interpolation(),
[
F.InterpolationMode.NEAREST,
F.InterpolationMode.NEAREST_EXACT,
F.InterpolationMode.BILINEAR,
F.InterpolationMode.BICUBIC,
],
):
for size in _get_resize_sizes(image_loader.spatial_size):
yield ArgsKwargs(
image_loader,
size=size,
interpolation=interpolation,
antialias=interpolation
in {
F.InterpolationMode.BILINEAR,
F.InterpolationMode.BICUBIC,
},
)
def sample_inputs_resize_bounding_box():
for bounding_box_loader in make_bounding_box_loaders():
for size in _get_resize_sizes(bounding_box_loader.spatial_size):
yield ArgsKwargs(bounding_box_loader, spatial_size=bounding_box_loader.spatial_size, size=size)
def sample_inputs_resize_mask():
for mask_loader in make_mask_loaders(sizes=["random"], num_categories=["random"], num_objects=["random"]):
yield ArgsKwargs(mask_loader, size=[min(mask_loader.shape[-2:]) + 1])
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])
def reference_resize_bounding_box(bounding_box, *, spatial_size, size, max_size=None):
old_height, old_width = spatial_size
new_height, new_width = F._geometry._compute_resized_output_size(spatial_size, size=size, max_size=max_size)
if (old_height, old_width) == (new_height, new_width):
return bounding_box, (old_height, old_width)
affine_matrix = np.array(
[
[new_width / old_width, 0, 0],
[0, new_height / old_height, 0],
],
dtype="float64" if bounding_box.dtype == torch.float64 else "float32",
)
expected_bboxes = reference_affine_bounding_box_helper(
bounding_box,
format=bounding_box.format,
spatial_size=(new_height, new_width),
affine_matrix=affine_matrix,
)
return expected_bboxes, (new_height, new_width)
def reference_inputs_resize_bounding_box():
for bounding_box_loader in make_bounding_box_loaders(extra_dims=((), (4,))):
for size in _get_resize_sizes(bounding_box_loader.spatial_size):
yield ArgsKwargs(bounding_box_loader, size=size, spatial_size=bounding_box_loader.spatial_size)
KERNEL_INFOS.extend(
[
KernelInfo(
F.resize_image_tensor,
sample_inputs_fn=sample_inputs_resize_image_tensor,
reference_fn=reference_resize_image_tensor,
reference_inputs_fn=reference_inputs_resize_image_tensor,
float32_vs_uint8=True,
closeness_kwargs={
**pil_reference_pixel_difference(10, mae=True),
**cuda_vs_cpu_pixel_difference(),
**float32_vs_uint8_pixel_difference(1, mae=True),
},
test_marks=[
xfail_jit_python_scalar_arg("size"),
],
),
KernelInfo(
F.resize_image_tensor,
sample_inputs_fn=sample_inputs_resize_image_tensor_bicubic,
reference_fn=reference_resize_image_tensor,
reference_inputs_fn=reference_inputs_resize_image_tensor,
float32_vs_uint8=True,
closeness_kwargs={
**pil_reference_pixel_difference(10, mae=True),
**cuda_vs_cpu_pixel_difference(atol=30),
**float32_vs_uint8_pixel_difference(1, mae=True),
},
test_marks=[
xfail_jit_python_scalar_arg("size"),
],
),
KernelInfo(
F.resize_bounding_box,
sample_inputs_fn=sample_inputs_resize_bounding_box,
reference_fn=reference_resize_bounding_box,
reference_inputs_fn=reference_inputs_resize_bounding_box,
closeness_kwargs={
(("TestKernels", "test_against_reference"), torch.int64, "cpu"): dict(atol=1, rtol=0),
},
test_marks=[
xfail_jit_python_scalar_arg("size"),
],
),
KernelInfo(
F.resize_mask,
sample_inputs_fn=sample_inputs_resize_mask,
closeness_kwargs=pil_reference_pixel_difference(10),
test_marks=[
xfail_jit_python_scalar_arg("size"),
],
),
KernelInfo(
F.resize_video,
sample_inputs_fn=sample_inputs_resize_video,
closeness_kwargs=cuda_vs_cpu_pixel_difference(),
),
]
)
_AFFINE_KWARGS = combinations_grid(
angle=[-87, 15, 90],
translate=[(5, 5), (-5, -5)],
......
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