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Unverified Commit 0e496155 authored by Philip Meier's avatar Philip Meier Committed by GitHub
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port affine tests (#7708)

parent 22d981f4
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test/test_transforms_v2.py
View file @ 0e496155
......@@ -668,130 +668,6 @@ class TestRandomRotation:
assert out_img.spatial_size == out_bbox.spatial_size
class TestRandomAffine:
def test_assertions(self):
with pytest.raises(ValueError, match="is a single number, it must be positive"):
transforms.RandomAffine(-0.7)
for d in [[-0.7], [-0.7, 0, 0.7]]:
with pytest.raises(ValueError, match="degrees should be a sequence of length 2"):
transforms.RandomAffine(d)
with pytest.raises(TypeError, match="Got inappropriate fill arg"):
transforms.RandomAffine(12, fill="abc")
with pytest.raises(TypeError, match="Got inappropriate fill arg"):
transforms.RandomAffine(12, fill="abc")
for kwargs in [
{"center": 12},
{"translate": 12},
{"scale": 12},
]:
with pytest.raises(TypeError, match="should be a sequence of length"):
transforms.RandomAffine(12, **kwargs)
for kwargs in [{"center": [1, 2, 3]}, {"translate": [1, 2, 3]}, {"scale": [1, 2, 3]}]:
with pytest.raises(ValueError, match="should be a sequence of length"):
transforms.RandomAffine(12, **kwargs)
with pytest.raises(ValueError, match="translation values should be between 0 and 1"):
transforms.RandomAffine(12, translate=[-1.0, 2.0])
with pytest.raises(ValueError, match="scale values should be positive"):
transforms.RandomAffine(12, scale=[-1.0, 2.0])
with pytest.raises(ValueError, match="is a single number, it must be positive"):
transforms.RandomAffine(12, shear=-10)
for s in [[-0.7], [-0.7, 0, 0.7]]:
with pytest.raises(ValueError, match="shear should be a sequence of length 2"):
transforms.RandomAffine(12, shear=s)
@pytest.mark.parametrize("degrees", [23, [0, 45], (0, 45)])
@pytest.mark.parametrize("translate", [None, [0.1, 0.2]])
@pytest.mark.parametrize("scale", [None, [0.7, 1.2]])
@pytest.mark.parametrize("shear", [None, 2.0, [5.0, 15.0], [1.0, 2.0, 3.0, 4.0]])
def test__get_params(self, degrees, translate, scale, shear, mocker):
image = mocker.MagicMock(spec=datapoints.Image)
image.num_channels = 3
image.spatial_size = (24, 32)
h, w = image.spatial_size
transform = transforms.RandomAffine(degrees, translate=translate, scale=scale, shear=shear)
params = transform._get_params([image])
if not isinstance(degrees, (list, tuple)):
assert -degrees <= params["angle"] <= degrees
else:
assert degrees[0] <= params["angle"] <= degrees[1]
if translate is not None:
w_max = int(round(translate[0] * w))
h_max = int(round(translate[1] * h))
assert -w_max <= params["translate"][0] <= w_max
assert -h_max <= params["translate"][1] <= h_max
else:
assert params["translate"] == (0, 0)
if scale is not None:
assert scale[0] <= params["scale"] <= scale[1]
else:
assert params["scale"] == 1.0
if shear is not None:
if isinstance(shear, float):
assert -shear <= params["shear"][0] <= shear
assert params["shear"][1] == 0.0
elif len(shear) == 2:
assert shear[0] <= params["shear"][0] <= shear[1]
assert params["shear"][1] == 0.0
else:
assert shear[0] <= params["shear"][0] <= shear[1]
assert shear[2] <= params["shear"][1] <= shear[3]
else:
assert params["shear"] == (0, 0)
@pytest.mark.parametrize("degrees", [23, [0, 45], (0, 45)])
@pytest.mark.parametrize("translate", [None, [0.1, 0.2]])
@pytest.mark.parametrize("scale", [None, [0.7, 1.2]])
@pytest.mark.parametrize("shear", [None, 2.0, [5.0, 15.0], [1.0, 2.0, 3.0, 4.0]])
@pytest.mark.parametrize("fill", [0, [1, 2, 3], (2, 3, 4)])
@pytest.mark.parametrize("center", [None, [2.0, 3.0]])
def test__transform(self, degrees, translate, scale, shear, fill, center, mocker):
interpolation = InterpolationMode.BILINEAR
transform = transforms.RandomAffine(
degrees,
translate=translate,
scale=scale,
shear=shear,
interpolation=interpolation,
fill=fill,
center=center,
)
if isinstance(degrees, (tuple, list)):
assert transform.degrees == [float(degrees[0]), float(degrees[1])]
else:
assert transform.degrees == [float(-degrees), float(degrees)]
fn = mocker.patch("torchvision.transforms.v2.functional.affine")
inpt = mocker.MagicMock(spec=datapoints.Image)
inpt.num_channels = 3
inpt.spatial_size = (24, 32)
# vfdev-5, Feature Request: let's store params as Transform attribute
# This could be also helpful for users
# Otherwise, we can mock transform._get_params
torch.manual_seed(12)
_ = transform(inpt)
torch.manual_seed(12)
params = transform._get_params([inpt])
fill = transforms._utils._convert_fill_arg(fill)
fn.assert_called_once_with(inpt, **params, interpolation=interpolation, fill=fill, center=center)
class TestRandomCrop:
def test_assertions(self):
with pytest.raises(ValueError, match="Please provide only two dimensions"):
......
test/test_transforms_v2_functional.py
View file @ 0e496155
......@@ -665,77 +665,6 @@ def _compute_affine_matrix(angle_, translate_, scale_, shear_, center_):
return true_matrix
@pytest.mark.parametrize("device", cpu_and_cuda())
def test_correctness_affine_bounding_box_on_fixed_input(device):
# Check transformation against known expected output
format = datapoints.BoundingBoxFormat.XYXY
spatial_size = (64, 64)
in_boxes = [
[20, 25, 35, 45],
[50, 5, 70, 22],
[spatial_size[1] // 2 - 10, spatial_size[0] // 2 - 10, spatial_size[1] // 2 + 10, spatial_size[0] // 2 + 10],
[1, 1, 5, 5],
]
in_boxes = torch.tensor(in_boxes, dtype=torch.float64, device=device)
# Tested parameters
angle = 63
scale = 0.89
dx = 0.12
dy = 0.23
# Expected bboxes computed using albumentations:
# from albumentations.augmentations.geometric.functional import bbox_shift_scale_rotate
# from albumentations.augmentations.geometric.functional import normalize_bbox, denormalize_bbox
# expected_bboxes = []
# for in_box in in_boxes:
# n_in_box = normalize_bbox(in_box, *spatial_size)
# n_out_box = bbox_shift_scale_rotate(n_in_box, -angle, scale, dx, dy, *spatial_size)
# out_box = denormalize_bbox(n_out_box, *spatial_size)
# expected_bboxes.append(out_box)
expected_bboxes = [
(24.522435977922218, 34.375689508290854, 46.443125279998114, 54.3516575015695),
(54.88288587110401, 50.08453280875634, 76.44484547743795, 72.81332520036864),
(27.709526487041554, 34.74952648704156, 51.650473512958435, 58.69047351295844),
(48.56528888843238, 9.611532109828834, 53.35347829361575, 14.39972151501221),
]
expected_bboxes = clamp_bounding_box(
datapoints.BoundingBox(expected_bboxes, format="XYXY", spatial_size=spatial_size)
).tolist()
output_boxes = F.affine_bounding_box(
in_boxes,
format=format,
spatial_size=spatial_size,
angle=angle,
translate=(dx * spatial_size[1], dy * spatial_size[0]),
scale=scale,
shear=(0, 0),
)
torch.testing.assert_close(output_boxes.tolist(), expected_bboxes)
@pytest.mark.parametrize("device", cpu_and_cuda())
def test_correctness_affine_segmentation_mask_on_fixed_input(device):
# Check transformation against known expected output and CPU/CUDA devices
# Create a fixed input segmentation mask with 2 square masks
# in top-left, bottom-left corners
mask = torch.zeros(1, 32, 32, dtype=torch.long, device=device)
mask[0, 2:10, 2:10] = 1
mask[0, 32 - 9 : 32 - 3, 3:9] = 2
# Rotate 90 degrees and scale
expected_mask = torch.rot90(mask, k=-1, dims=(-2, -1))
expected_mask = torch.nn.functional.interpolate(expected_mask[None, :].float(), size=(64, 64), mode="nearest")
expected_mask = expected_mask[0, :, 16 : 64 - 16, 16 : 64 - 16].long()
out_mask = F.affine_mask(mask, 90, [0.0, 0.0], 64.0 / 32.0, [0.0, 0.0])
torch.testing.assert_close(out_mask, expected_mask)
@pytest.mark.parametrize("angle", range(-90, 90, 56))
@pytest.mark.parametrize("expand, center", [(True, None), (False, None), (False, (12, 14))])
def test_correctness_rotate_bounding_box(angle, expand, center):
......@@ -950,18 +879,6 @@ def test_correctness_crop_bounding_box(device, format, top, left, height, width,
torch.testing.assert_close(output_spatial_size, spatial_size)
@pytest.mark.parametrize("device", cpu_and_cuda())
def test_correctness_horizontal_flip_segmentation_mask_on_fixed_input(device):
mask = torch.zeros((3, 3, 3), dtype=torch.long, device=device)
mask[:, :, 0] = 1
out_mask = F.horizontal_flip_mask(mask)
expected_mask = torch.zeros((3, 3, 3), dtype=torch.long, device=device)
expected_mask[:, :, -1] = 1
torch.testing.assert_close(out_mask, expected_mask)
@pytest.mark.parametrize("device", cpu_and_cuda())
def test_correctness_vertical_flip_segmentation_mask_on_fixed_input(device):
mask = torch.zeros((3, 3, 3), dtype=torch.long, device=device)
......
test/test_transforms_v2_refactored.py
View file @ 0e496155
import contextlib
import inspect
import math
import re
from typing import get_type_hints
from unittest import mock
......@@ -25,6 +26,8 @@ from common_utils import (
)
from torch.testing import assert_close
from torchvision import datapoints
from torchvision.transforms._functional_tensor import _max_value as get_max_value
from torchvision.transforms.functional import pil_modes_mapping
from torchvision.transforms.v2 import functional as F
......@@ -162,7 +165,7 @@ def _check_dispatcher_dispatch(dispatcher, kernel, input, *args, **kwargs):
if isinstance(input, datapoints._datapoint.Datapoint):
# Due to our complex dispatch architecture for datapoints, we cannot spy on the kernel directly,
# but rather have to patch the `Datapoint.__F` attribute to contain the spied on kernel.
spy = mock.MagicMock(wraps=kernel)
spy = mock.MagicMock(wraps=kernel, name=kernel.__name__)
with mock.patch.object(F, kernel.__name__, spy):
# Due to Python's name mangling, the `Datapoint.__F` attribute is only accessible from inside the class.
# Since that is not the case here, we need to prefix f"_{cls.__name__}"
......@@ -473,10 +476,9 @@ class TestResize:
)
@pytest.mark.parametrize(
"dtype_and_make_mask", [(torch.uint8, make_segmentation_mask), (torch.bool, make_detection_mask)]
("dtype", "make_mask"), [(torch.uint8, make_segmentation_mask), (torch.bool, make_detection_mask)]
)
def test_kernel_mask(self, dtype_and_make_mask):
dtype, make_mask = dtype_and_make_mask
def test_kernel_mask(self, dtype, make_mask):
check_kernel(F.resize_mask, make_mask(dtype=dtype), size=self.OUTPUT_SIZES[-1])
def test_kernel_video(self):
......@@ -744,7 +746,7 @@ class TestHorizontalFlip:
@pytest.mark.parametrize("dtype", [torch.float32, torch.uint8])
@pytest.mark.parametrize("device", cpu_and_cuda())
def test_kernel_image_tensor(self, dtype, device):
check_kernel(F.horizontal_flip_image_tensor, self._make_input(torch.Tensor))
check_kernel(F.horizontal_flip_image_tensor, self._make_input(torch.Tensor, dtype=dtype, device=device))
@pytest.mark.parametrize("format", list(datapoints.BoundingBoxFormat))
@pytest.mark.parametrize("dtype", [torch.float32, torch.int64])
......@@ -785,16 +787,16 @@ class TestHorizontalFlip:
@pytest.mark.parametrize(
("input_type", "kernel"),
[
(torch.Tensor, F.resize_image_tensor),
(PIL.Image.Image, F.resize_image_pil),
(datapoints.Image, F.resize_image_tensor),
(datapoints.BoundingBox, F.resize_bounding_box),
(datapoints.Mask, F.resize_mask),
(datapoints.Video, F.resize_video),
(torch.Tensor, F.horizontal_flip_image_tensor),
(PIL.Image.Image, F.horizontal_flip_image_pil),
(datapoints.Image, F.horizontal_flip_image_tensor),
(datapoints.BoundingBox, F.horizontal_flip_bounding_box),
(datapoints.Mask, F.horizontal_flip_mask),
(datapoints.Video, F.horizontal_flip_video),
],
)
def test_dispatcher_signature(self, kernel, input_type):
check_dispatcher_signatures_match(F.resize, kernel=kernel, input_type=input_type)
check_dispatcher_signatures_match(F.horizontal_flip, kernel=kernel, input_type=input_type)
@pytest.mark.parametrize(
"input_type",
......@@ -860,3 +862,442 @@ class TestHorizontalFlip:
output = transform(input)
assert_equal(output, input)
class TestAffine:
def _make_input(
self, input_type, *, dtype=None, device="cpu", spatial_size=(17, 11), mask_type="segmentation", **kwargs
):
if input_type in {torch.Tensor, PIL.Image.Image, datapoints.Image}:
input = make_image(size=spatial_size, dtype=dtype or torch.uint8, device=device, **kwargs)
if input_type is torch.Tensor:
input = input.as_subclass(torch.Tensor)
elif input_type is PIL.Image.Image:
input = F.to_image_pil(input)
elif input_type is datapoints.BoundingBox:
kwargs.setdefault("format", datapoints.BoundingBoxFormat.XYXY)
input = make_bounding_box(
dtype=dtype or torch.float32,
device=device,
spatial_size=spatial_size,
**kwargs,
)
elif input_type is datapoints.Mask:
if mask_type == "segmentation":
make_mask = make_segmentation_mask
default_dtype = torch.uint8
elif mask_type == "detection":
make_mask = make_detection_mask
default_dtype = torch.bool
input = make_mask(size=spatial_size, dtype=dtype or default_dtype, device=device, **kwargs)
elif input_type is datapoints.Video:
input = make_video(size=spatial_size, dtype=dtype or torch.uint8, device=device, **kwargs)
return input
def _adapt_fill(self, value, *, dtype):
"""Adapt fill values in the range [0.0, 1.0] to the value range of the dtype"""
if value is None:
return value
max_value = get_max_value(dtype)
if isinstance(value, (int, float)):
return type(value)(value * max_value)
elif isinstance(value, (list, tuple)):
return type(value)(type(v)(v * max_value) for v in value)
else:
raise ValueError(f"fill should be an int or float, or a list or tuple of the former, but got '{value}'")
_EXHAUSTIVE_TYPE_AFFINE_KWARGS = dict(
# float, int
angle=[-10.9, 18],
# two-list of float, two-list of int, two-tuple of float, two-tuple of int
translate=[[6.3, -0.6], [1, -3], (16.6, -6.6), (-2, 4)],
# float
scale=[0.5],
# float, int,
# one-list of float, one-list of int, one-tuple of float, one-tuple of int
# two-list of float, two-list of int, two-tuple of float, two-tuple of int
shear=[35.6, 38, [-37.7], [-23], (5.3,), (-52,), [5.4, 21.8], [-47, 51], (-11.2, 36.7), (8, -53)],
# None
# two-list of float, two-list of int, two-tuple of float, two-tuple of int
center=[None, [1.2, 4.9], [-3, 1], (2.5, -4.7), (3, 2)],
)
# The special case for shear makes sure we pick a value that is supported while JIT scripting
_MINIMAL_AFFINE_KWARGS = {
k: vs[0] if k != "shear" else next(v for v in vs if isinstance(v, list))
for k, vs in _EXHAUSTIVE_TYPE_AFFINE_KWARGS.items()
}
_CORRECTNESS_AFFINE_KWARGS = {
k: [v for v in vs if v is None or isinstance(v, float) or (isinstance(v, list) and len(v) > 1)]
for k, vs in _EXHAUSTIVE_TYPE_AFFINE_KWARGS.items()
}
_EXHAUSTIVE_TYPE_FILLS = [
None,
1,
0.5,
[1],
[0.2],
(0,),
(0.7,),
[1, 0, 1],
[0.1, 0.2, 0.3],
(0, 1, 0),
(0.9, 0.234, 0.314),
]
_CORRECTNESS_FILL = [
v for v in _EXHAUSTIVE_TYPE_FILLS if v is None or isinstance(v, float) or (isinstance(v, list) and len(v) > 1)
]
_EXHAUSTIVE_TYPE_TRANSFORM_AFFINE_RANGES = dict(
degrees=[30, (-15, 20)],
translate=[None, (0.5, 0.5)],
scale=[None, (0.75, 1.25)],
shear=[None, (12, 30, -17, 5), 10, (-5, 12)],
)
_CORRECTNESS_TRANSFORM_AFFINE_RANGES = {
k: next(v for v in vs if v is not None) for k, vs in _EXHAUSTIVE_TYPE_TRANSFORM_AFFINE_RANGES.items()
}
def _check_kernel(self, kernel, input, *args, **kwargs):
kwargs_ = self._MINIMAL_AFFINE_KWARGS.copy()
kwargs_.update(kwargs)
check_kernel(kernel, input, *args, **kwargs_)
@pytest.mark.parametrize(
("param", "value"),
[
(param, value)
for param, values in [
("angle", _EXHAUSTIVE_TYPE_AFFINE_KWARGS["angle"]),
("translate", _EXHAUSTIVE_TYPE_AFFINE_KWARGS["translate"]),
("shear", _EXHAUSTIVE_TYPE_AFFINE_KWARGS["shear"]),
("center", _EXHAUSTIVE_TYPE_AFFINE_KWARGS["center"]),
("interpolation", [transforms.InterpolationMode.NEAREST, transforms.InterpolationMode.BILINEAR]),
("fill", _EXHAUSTIVE_TYPE_FILLS),
]
for value in values
],
)
@pytest.mark.parametrize("dtype", [torch.float32, torch.uint8])
@pytest.mark.parametrize("device", cpu_and_cuda())
def test_kernel_image_tensor(self, param, value, dtype, device):
if param == "fill":
value = self._adapt_fill(value, dtype=dtype)
self._check_kernel(
F.affine_image_tensor,
self._make_input(torch.Tensor, dtype=dtype, device=device),
**{param: value},
check_scripted_vs_eager=not (param in {"shear", "fill"} and isinstance(value, (int, float))),
check_cuda_vs_cpu=dict(atol=1, rtol=0)
if dtype is torch.uint8 and param == "interpolation" and value is transforms.InterpolationMode.BILINEAR
else True,
)
@pytest.mark.parametrize(
("param", "value"),
[
(param, value)
for param, values in [
("angle", _EXHAUSTIVE_TYPE_AFFINE_KWARGS["angle"]),
("translate", _EXHAUSTIVE_TYPE_AFFINE_KWARGS["translate"]),
("shear", _EXHAUSTIVE_TYPE_AFFINE_KWARGS["shear"]),
("center", _EXHAUSTIVE_TYPE_AFFINE_KWARGS["center"]),
]
for value in values
],
)
@pytest.mark.parametrize("format", list(datapoints.BoundingBoxFormat))
@pytest.mark.parametrize("dtype", [torch.float32, torch.int64])
@pytest.mark.parametrize("device", cpu_and_cuda())
def test_kernel_bounding_box(self, param, value, format, dtype, device):
bounding_box = self._make_input(datapoints.BoundingBox, format=format, dtype=dtype, device=device)
self._check_kernel(
F.affine_bounding_box,
self._make_input(datapoints.BoundingBox, format=format, dtype=dtype, device=device),
format=format,
spatial_size=bounding_box.spatial_size,
**{param: value},
check_scripted_vs_eager=not (param == "shear" and isinstance(value, (int, float))),
)
@pytest.mark.parametrize("mask_type", ["segmentation", "detection"])
def test_kernel_mask(self, mask_type):
check_kernel(
F.affine_mask, self._make_input(datapoints.Mask, mask_type=mask_type), **self._MINIMAL_AFFINE_KWARGS
)
def test_kernel_video(self):
check_kernel(F.affine_video, self._make_input(datapoints.Video), **self._MINIMAL_AFFINE_KWARGS)
@pytest.mark.parametrize(
("input_type", "kernel"),
[
(torch.Tensor, F.affine_image_tensor),
(PIL.Image.Image, F.affine_image_pil),
(datapoints.Image, F.affine_image_tensor),
(datapoints.BoundingBox, F.affine_bounding_box),
(datapoints.Mask, F.affine_mask),
(datapoints.Video, F.affine_video),
],
)
def test_dispatcher(self, kernel, input_type):
check_dispatcher(F.affine, kernel, self._make_input(input_type), **self._MINIMAL_AFFINE_KWARGS)
@pytest.mark.parametrize(
("input_type", "kernel"),
[
(torch.Tensor, F.affine_image_tensor),
(PIL.Image.Image, F.affine_image_pil),
(datapoints.Image, F.affine_image_tensor),
(datapoints.BoundingBox, F.affine_bounding_box),
(datapoints.Mask, F.affine_mask),
(datapoints.Video, F.affine_video),
],
)
def test_dispatcher_signature(self, kernel, input_type):
check_dispatcher_signatures_match(F.affine, kernel=kernel, input_type=input_type)
@pytest.mark.parametrize(
"input_type",
[torch.Tensor, PIL.Image.Image, datapoints.Image, datapoints.BoundingBox, datapoints.Mask, datapoints.Video],
)
@pytest.mark.parametrize("device", cpu_and_cuda())
def test_transform(self, input_type, device):
input = self._make_input(input_type, device=device)
check_transform(transforms.RandomAffine, input, **self._CORRECTNESS_TRANSFORM_AFFINE_RANGES)
@pytest.mark.parametrize("angle", _CORRECTNESS_AFFINE_KWARGS["angle"])
@pytest.mark.parametrize("translate", _CORRECTNESS_AFFINE_KWARGS["translate"])
@pytest.mark.parametrize("scale", _CORRECTNESS_AFFINE_KWARGS["scale"])
@pytest.mark.parametrize("shear", _CORRECTNESS_AFFINE_KWARGS["shear"])
@pytest.mark.parametrize("center", _CORRECTNESS_AFFINE_KWARGS["center"])
@pytest.mark.parametrize(
"interpolation", [transforms.InterpolationMode.NEAREST, transforms.InterpolationMode.BILINEAR]
)
@pytest.mark.parametrize("fill", _CORRECTNESS_FILL)
def test_functional_image_correctness(self, angle, translate, scale, shear, center, interpolation, fill):
image = self._make_input(torch.Tensor, dtype=torch.uint8, device="cpu")
fill = self._adapt_fill(fill, dtype=torch.uint8)
actual = F.affine(
image,
angle=angle,
translate=translate,
scale=scale,
shear=shear,
center=center,
interpolation=interpolation,
fill=fill,
)
expected = F.to_image_tensor(
F.affine(
F.to_image_pil(image),
angle=angle,
translate=translate,
scale=scale,
shear=shear,
center=center,
interpolation=interpolation,
fill=fill,
)
)
mae = (actual.float() - expected.float()).abs().mean()
assert mae < 2 if interpolation is transforms.InterpolationMode.NEAREST else 8
@pytest.mark.parametrize("center", _CORRECTNESS_AFFINE_KWARGS["center"])
@pytest.mark.parametrize(
"interpolation", [transforms.InterpolationMode.NEAREST, transforms.InterpolationMode.BILINEAR]
)
@pytest.mark.parametrize("fill", _CORRECTNESS_FILL)
@pytest.mark.parametrize("seed", list(range(5)))
def test_transform_image_correctness(self, center, interpolation, fill, seed):
image = self._make_input(torch.Tensor, dtype=torch.uint8, device="cpu")
fill = self._adapt_fill(fill, dtype=torch.uint8)
transform = transforms.RandomAffine(
**self._CORRECTNESS_TRANSFORM_AFFINE_RANGES, center=center, interpolation=interpolation, fill=fill
)
torch.manual_seed(seed)
actual = transform(image)
torch.manual_seed(seed)
expected = F.to_image_tensor(transform(F.to_image_pil(image)))
mae = (actual.float() - expected.float()).abs().mean()
assert mae < 2 if interpolation is transforms.InterpolationMode.NEAREST else 8
def _compute_affine_matrix(self, *, angle, translate, scale, shear, center):
rot = math.radians(angle)
cx, cy = center
tx, ty = translate
sx, sy = [math.radians(s) for s in ([shear, 0.0] if isinstance(shear, (int, float)) else shear)]
c_matrix = np.array([[1, 0, cx], [0, 1, cy], [0, 0, 1]])
t_matrix = np.array([[1, 0, tx], [0, 1, ty], [0, 0, 1]])
c_matrix_inv = np.linalg.inv(c_matrix)
rs_matrix = np.array(
[
[scale * math.cos(rot), -scale * math.sin(rot), 0],
[scale * math.sin(rot), scale * math.cos(rot), 0],
[0, 0, 1],
]
)
shear_x_matrix = np.array([[1, -math.tan(sx), 0], [0, 1, 0], [0, 0, 1]])
shear_y_matrix = np.array([[1, 0, 0], [-math.tan(sy), 1, 0], [0, 0, 1]])
rss_matrix = np.matmul(rs_matrix, np.matmul(shear_y_matrix, shear_x_matrix))
true_matrix = np.matmul(t_matrix, np.matmul(c_matrix, np.matmul(rss_matrix, c_matrix_inv)))
return true_matrix
def _reference_affine_bounding_box(self, bounding_box, *, angle, translate, scale, shear, center):
if center is None:
center = [s * 0.5 for s in bounding_box.spatial_size[::-1]]
affine_matrix = self._compute_affine_matrix(
angle=angle, translate=translate, scale=scale, shear=shear, center=center
)
affine_matrix = affine_matrix[:2, :]
expected_bboxes = reference_affine_bounding_box_helper(
bounding_box,
format=bounding_box.format,
spatial_size=bounding_box.spatial_size,
affine_matrix=affine_matrix,
)
return expected_bboxes
@pytest.mark.parametrize("format", list(datapoints.BoundingBoxFormat))
@pytest.mark.parametrize("angle", _CORRECTNESS_AFFINE_KWARGS["angle"])
@pytest.mark.parametrize("translate", _CORRECTNESS_AFFINE_KWARGS["translate"])
@pytest.mark.parametrize("scale", _CORRECTNESS_AFFINE_KWARGS["scale"])
@pytest.mark.parametrize("shear", _CORRECTNESS_AFFINE_KWARGS["shear"])
@pytest.mark.parametrize("center", _CORRECTNESS_AFFINE_KWARGS["center"])
def test_functional_bounding_box_correctness(self, format, angle, translate, scale, shear, center):
bounding_box = self._make_input(datapoints.BoundingBox, format=format)
actual = F.affine(
bounding_box,
angle=angle,
translate=translate,
scale=scale,
shear=shear,
center=center,
)
expected = self._reference_affine_bounding_box(
bounding_box,
angle=angle,
translate=translate,
scale=scale,
shear=shear,
center=center,
)
torch.testing.assert_close(actual, expected)
@pytest.mark.parametrize("format", list(datapoints.BoundingBoxFormat))
@pytest.mark.parametrize("center", _CORRECTNESS_AFFINE_KWARGS["center"])
@pytest.mark.parametrize("seed", list(range(5)))
def test_transform_bounding_box_correctness(self, format, center, seed):
bounding_box = self._make_input(datapoints.BoundingBox, format=format)
transform = transforms.RandomAffine(**self._CORRECTNESS_TRANSFORM_AFFINE_RANGES, center=center)
torch.manual_seed(seed)
params = transform._get_params([bounding_box])
torch.manual_seed(seed)
actual = transform(bounding_box)
expected = self._reference_affine_bounding_box(bounding_box, **params, center=center)
torch.testing.assert_close(actual, expected)
@pytest.mark.parametrize("degrees", _EXHAUSTIVE_TYPE_TRANSFORM_AFFINE_RANGES["degrees"])
@pytest.mark.parametrize("translate", _EXHAUSTIVE_TYPE_TRANSFORM_AFFINE_RANGES["translate"])
@pytest.mark.parametrize("scale", _EXHAUSTIVE_TYPE_TRANSFORM_AFFINE_RANGES["scale"])
@pytest.mark.parametrize("shear", _EXHAUSTIVE_TYPE_TRANSFORM_AFFINE_RANGES["shear"])
@pytest.mark.parametrize("seed", list(range(10)))
def test_transform_get_params_bounds(self, degrees, translate, scale, shear, seed):
image = self._make_input(torch.Tensor)
height, width = F.get_spatial_size(image)
transform = transforms.RandomAffine(degrees=degrees, translate=translate, scale=scale, shear=shear)
torch.manual_seed(seed)
params = transform._get_params([image])
if isinstance(degrees, (int, float)):
assert -degrees <= params["angle"] <= degrees
else:
assert degrees[0] <= params["angle"] <= degrees[1]
if translate is not None:
width_max = int(round(translate[0] * width))
height_max = int(round(translate[1] * height))
assert -width_max <= params["translate"][0] <= width_max
assert -height_max <= params["translate"][1] <= height_max
else:
assert params["translate"] == (0, 0)
if scale is not None:
assert scale[0] <= params["scale"] <= scale[1]
else:
assert params["scale"] == 1.0
if shear is not None:
if isinstance(shear, (int, float)):
assert -shear <= params["shear"][0] <= shear
assert params["shear"][1] == 0.0
elif len(shear) == 2:
assert shear[0] <= params["shear"][0] <= shear[1]
assert params["shear"][1] == 0.0
elif len(shear) == 4:
assert shear[0] <= params["shear"][0] <= shear[1]
assert shear[2] <= params["shear"][1] <= shear[3]
else:
assert params["shear"] == (0, 0)
@pytest.mark.parametrize("param", ["degrees", "translate", "scale", "shear", "center"])
@pytest.mark.parametrize("value", [0, [0], [0, 0, 0]])
def test_transform_sequence_len_errors(self, param, value):
if param in {"degrees", "shear"} and not isinstance(value, list):
return
kwargs = {param: value}
if param != "degrees":
kwargs["degrees"] = 0
with pytest.raises(
ValueError if isinstance(value, list) else TypeError, match=f"{param} should be a sequence of length 2"
):
transforms.RandomAffine(**kwargs)
def test_transform_negative_degrees_error(self):
with pytest.raises(ValueError, match="If degrees is a single number, it must be positive"):
transforms.RandomAffine(degrees=-1)
@pytest.mark.parametrize("translate", [[-1, 0], [2, 0], [-1, 2]])
def test_transform_translate_range_error(self, translate):
with pytest.raises(ValueError, match="translation values should be between 0 and 1"):
transforms.RandomAffine(degrees=0, translate=translate)
@pytest.mark.parametrize("scale", [[-1, 0], [0, -1], [-1, -1]])
def test_transform_scale_range_error(self, scale):
with pytest.raises(ValueError, match="scale values should be positive"):
transforms.RandomAffine(degrees=0, scale=scale)
def test_transform_negative_shear_error(self):
with pytest.raises(ValueError, match="If shear is a single number, it must be positive"):
transforms.RandomAffine(degrees=0, shear=-1)
def test_transform_unknown_fill_error(self):
with pytest.raises(TypeError, match="Got inappropriate fill arg"):
transforms.RandomAffine(degrees=0, fill="fill")
test/transforms_v2_dispatcher_infos.py
View file @ 0e496155
......@@ -138,21 +138,6 @@ xfails_pil_if_fill_sequence_needs_broadcast = xfails_pil(
DISPATCHER_INFOS = [
DispatcherInfo(
F.affine,
kernels={
datapoints.Image: F.affine_image_tensor,
datapoints.Video: F.affine_video,
datapoints.BoundingBox: F.affine_bounding_box,
datapoints.Mask: F.affine_mask,
},
pil_kernel_info=PILKernelInfo(F.affine_image_pil),
test_marks=[
*xfails_pil_if_fill_sequence_needs_broadcast,
xfail_jit_python_scalar_arg("shear"),
xfail_jit_python_scalar_arg("fill"),
],
),
DispatcherInfo(
F.vertical_flip,
kernels={
......
test/transforms_v2_kernel_infos.py
View file @ 0e496155
import decimal
import functools
import itertools
import math
import numpy as np
import PIL.Image
......@@ -156,46 +155,6 @@ def xfail_jit_python_scalar_arg(name, *, reason=None):
KERNEL_INFOS = []
_AFFINE_KWARGS = combinations_grid(
angle=[-87, 15, 90],
translate=[(5, 5), (-5, -5)],
scale=[0.77, 1.27],
shear=[(12, 12), (0, 0)],
)
def _diversify_affine_kwargs_types(affine_kwargs):
angle = affine_kwargs["angle"]
for diverse_angle in [int(angle), float(angle)]:
yield dict(affine_kwargs, angle=diverse_angle)
shear = affine_kwargs["shear"]
for diverse_shear in [tuple(shear), list(shear), int(shear[0]), float(shear[0])]:
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 get_fills(*, num_channels, dtype):
yield None
......@@ -226,72 +185,6 @@ def float32_vs_uint8_fill_adapter(other_args, kwargs):
return other_args, dict(kwargs, fill=fill)
def sample_inputs_affine_image_tensor():
make_affine_image_loaders = functools.partial(
make_image_loaders, sizes=["random"], color_spaces=["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():
for fill in get_fills(num_channels=image_loader.num_channels, dtype=image_loader.dtype):
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.BILINEAR,
],
):
yield ArgsKwargs(image_loader, **_full_affine_params(), fill=0)
def reference_inputs_affine_image_tensor():
for image_loader, affine_kwargs in itertools.product(make_image_loaders_for_interpolation(), _AFFINE_KWARGS):
yield ArgsKwargs(
image_loader,
interpolation=F.InterpolationMode.NEAREST,
**affine_kwargs,
)
def sample_inputs_affine_bounding_box():
for bounding_box_loader, affine_params in itertools.product(
make_bounding_box_loaders(formats=[datapoints.BoundingBoxFormat.XYXY]), _DIVERSE_AFFINE_PARAMS
):
yield ArgsKwargs(
bounding_box_loader,
format=bounding_box_loader.format,
spatial_size=bounding_box_loader.spatial_size,
**affine_params,
)
def _compute_affine_matrix(angle, translate, scale, shear, center):
rot = math.radians(angle)
cx, cy = center
tx, ty = translate
sx, sy = [math.radians(sh_) for sh_ in shear]
c_matrix = np.array([[1, 0, cx], [0, 1, cy], [0, 0, 1]])
t_matrix = np.array([[1, 0, tx], [0, 1, ty], [0, 0, 1]])
c_matrix_inv = np.linalg.inv(c_matrix)
rs_matrix = np.array(
[
[scale * math.cos(rot), -scale * math.sin(rot), 0],
[scale * math.sin(rot), scale * math.cos(rot), 0],
[0, 0, 1],
]
)
shear_x_matrix = np.array([[1, -math.tan(sx), 0], [0, 1, 0], [0, 0, 1]])
shear_y_matrix = np.array([[1, 0, 0], [-math.tan(sy), 1, 0], [0, 0, 1]])
rss_matrix = np.matmul(rs_matrix, np.matmul(shear_y_matrix, shear_x_matrix))
true_matrix = np.matmul(t_matrix, np.matmul(c_matrix, np.matmul(rss_matrix, c_matrix_inv)))
return true_matrix
def reference_affine_bounding_box_helper(bounding_box, *, format, spatial_size, affine_matrix):
def transform(bbox, affine_matrix_, format_, spatial_size_):
# Go to float before converting to prevent precision loss in case of CXCYWH -> XYXY and W or H is 1
......@@ -342,81 +235,6 @@ def reference_affine_bounding_box_helper(bounding_box, *, format, spatial_size,
return expected_bboxes
def reference_affine_bounding_box(bounding_box, *, format, spatial_size, angle, translate, scale, shear, center=None):
if center is None:
center = [s * 0.5 for s in spatial_size[::-1]]
affine_matrix = _compute_affine_matrix(angle, translate, scale, shear, center)
affine_matrix = affine_matrix[:2, :]
expected_bboxes = reference_affine_bounding_box_helper(
bounding_box, format=format, spatial_size=spatial_size, affine_matrix=affine_matrix
)
return expected_bboxes
def reference_inputs_affine_bounding_box():
for bounding_box_loader, affine_kwargs in itertools.product(
make_bounding_box_loaders(extra_dims=[()]),
_AFFINE_KWARGS,
):
yield ArgsKwargs(
bounding_box_loader,
format=bounding_box_loader.format,
spatial_size=bounding_box_loader.spatial_size,
**affine_kwargs,
)
def sample_inputs_affine_mask():
for mask_loader in make_mask_loaders(sizes=["random"], num_categories=["random"], num_objects=["random"]):
yield ArgsKwargs(mask_loader, **_full_affine_params())
def sample_inputs_affine_video():
for video_loader in make_video_loaders(sizes=["random"], num_frames=["random"]):
yield ArgsKwargs(video_loader, **_full_affine_params())
KERNEL_INFOS.extend(
[
KernelInfo(
F.affine_image_tensor,
sample_inputs_fn=sample_inputs_affine_image_tensor,
reference_fn=pil_reference_wrapper(F.affine_image_pil),
reference_inputs_fn=reference_inputs_affine_image_tensor,
float32_vs_uint8=True,
closeness_kwargs=pil_reference_pixel_difference(10, mae=True),
test_marks=[
xfail_jit_python_scalar_arg("shear"),
xfail_jit_python_scalar_arg("fill"),
],
),
KernelInfo(
F.affine_bounding_box,
sample_inputs_fn=sample_inputs_affine_bounding_box,
reference_fn=reference_affine_bounding_box,
reference_inputs_fn=reference_inputs_affine_bounding_box,
test_marks=[
xfail_jit_python_scalar_arg("shear"),
],
),
KernelInfo(
F.affine_mask,
sample_inputs_fn=sample_inputs_affine_mask,
test_marks=[
xfail_jit_python_scalar_arg("shear"),
],
),
KernelInfo(
F.affine_video,
sample_inputs_fn=sample_inputs_affine_video,
),
]
)
def sample_inputs_convert_format_bounding_box():
formats = list(datapoints.BoundingBoxFormat)
for bounding_box_loader, new_format in itertools.product(make_bounding_box_loaders(formats=formats), formats):
......
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