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test_functional_tensor.py
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import colorsys
import itertools
import math
import os
from typing import Sequence
import numpy as np
import pytest
import torch
import torchvision.transforms as T
import torchvision.transforms.functional as F
import torchvision.transforms.functional_pil as F_pil
import torchvision.transforms.functional_tensor as F_t
from common_utils import (
cpu_and_gpu,
needs_cuda,
_create_data,
_create_data_batch,
_assert_equal_tensor_to_pil,
_assert_approx_equal_tensor_to_pil,
_test_fn_on_batch,
assert_equal,
)
from torchvision.transforms import InterpolationMode
NEAREST, BILINEAR, BICUBIC = InterpolationMode.NEAREST, InterpolationMode.BILINEAR, InterpolationMode.BICUBIC
@pytest.mark.parametrize("device", cpu_and_gpu())
@pytest.mark.parametrize("fn", [F.get_image_size, F.get_image_num_channels])
def test_image_sizes(device, fn):
script_F = torch.jit.script(fn)
img_tensor, pil_img = _create_data(16, 18, 3, device=device)
value_img = fn(img_tensor)
value_pil_img = fn(pil_img)
assert value_img == value_pil_img
value_img_script = script_F(img_tensor)
assert value_img == value_img_script
batch_tensors = _create_data_batch(16, 18, 3, num_samples=4, device=device)
value_img_batch = fn(batch_tensors)
assert value_img == value_img_batch
@needs_cuda
def test_scale_channel():
"""Make sure that _scale_channel gives the same results on CPU and GPU as
histc or bincount are used depending on the device.
"""
# TODO: when # https://github.com/pytorch/pytorch/issues/53194 is fixed,
# only use bincount and remove that test.
size = (1_000,)
img_chan = torch.randint(0, 256, size=size).to("cpu")
scaled_cpu = F_t._scale_channel(img_chan)
scaled_cuda = F_t._scale_channel(img_chan.to("cuda"))
assert_equal(scaled_cpu, scaled_cuda.to("cpu"))
class TestRotate:
ALL_DTYPES = [None, torch.float32, torch.float64, torch.float16]
scripted_rotate = torch.jit.script(F.rotate)
IMG_W = 26
@pytest.mark.parametrize("device", cpu_and_gpu())
@pytest.mark.parametrize("height, width", [(26, IMG_W), (32, IMG_W)])
@pytest.mark.parametrize(
"center",
[
None,
(int(IMG_W * 0.3), int(IMG_W * 0.4)),
[int(IMG_W * 0.5), int(IMG_W * 0.6)],
],
)
@pytest.mark.parametrize("dt", ALL_DTYPES)
@pytest.mark.parametrize("angle", range(-180, 180, 17))
@pytest.mark.parametrize("expand", [True, False])
@pytest.mark.parametrize(
"fill",
[
None,
[0, 0, 0],
(1, 2, 3),
[255, 255, 255],
[
1,
],
(2.0,),
],
)
@pytest.mark.parametrize("fn", [F.rotate, scripted_rotate])
def test_rotate(self, device, height, width, center, dt, angle, expand, fill, fn):
tensor, pil_img = _create_data(height, width, device=device)
if dt == torch.float16 and torch.device(device).type == "cpu":
# skip float16 on CPU case
return
if dt is not None:
tensor = tensor.to(dtype=dt)
f_pil = int(fill[0]) if fill is not None and len(fill) == 1 else fill
out_pil_img = F.rotate(pil_img, angle=angle, interpolation=NEAREST, expand=expand, center=center, fill=f_pil)
out_pil_tensor = torch.from_numpy(np.array(out_pil_img).transpose((2, 0, 1)))
out_tensor = fn(tensor, angle=angle, interpolation=NEAREST, expand=expand, center=center, fill=fill).cpu()
if out_tensor.dtype != torch.uint8:
out_tensor = out_tensor.to(torch.uint8)
assert out_tensor.shape == out_pil_tensor.shape, (
f"{(height, width, NEAREST, dt, angle, expand, center)}: " f"{out_tensor.shape} vs {out_pil_tensor.shape}"
)
num_diff_pixels = (out_tensor != out_pil_tensor).sum().item() / 3.0
ratio_diff_pixels = num_diff_pixels / out_tensor.shape[-1] / out_tensor.shape[-2]
# Tolerance : less than 3% of different pixels
assert ratio_diff_pixels < 0.03, (
f"{(height, width, NEAREST, dt, angle, expand, center, fill)}: "
f"{ratio_diff_pixels}\n{out_tensor[0, :7, :7]} vs \n"
f"{out_pil_tensor[0, :7, :7]}"
)
@pytest.mark.parametrize("device", cpu_and_gpu())
@pytest.mark.parametrize("dt", ALL_DTYPES)
def test_rotate_batch(self, device, dt):
if dt == torch.float16 and device == "cpu":
# skip float16 on CPU case
return
batch_tensors = _create_data_batch(26, 36, num_samples=4, device=device)
if dt is not None:
batch_tensors = batch_tensors.to(dtype=dt)
center = (20, 22)
_test_fn_on_batch(batch_tensors, F.rotate, angle=32, interpolation=NEAREST, expand=True, center=center)
def test_rotate_deprecation_resample(self):
tensor, _ = _create_data(26, 26)
# assert deprecation warning and non-BC
with pytest.warns(UserWarning, match=r"Argument resample is deprecated and will be removed"):
res1 = F.rotate(tensor, 45, resample=2)
res2 = F.rotate(tensor, 45, interpolation=BILINEAR)
assert_equal(res1, res2)
def test_rotate_interpolation_type(self):
tensor, _ = _create_data(26, 26)
# assert changed type warning
with pytest.warns(UserWarning, match=r"Argument interpolation should be of type InterpolationMode"):
res1 = F.rotate(tensor, 45, interpolation=2)
res2 = F.rotate(tensor, 45, interpolation=BILINEAR)
assert_equal(res1, res2)
class TestAffine:
ALL_DTYPES = [None, torch.float32, torch.float64, torch.float16]
scripted_affine = torch.jit.script(F.affine)
@pytest.mark.parametrize("device", cpu_and_gpu())
@pytest.mark.parametrize("height, width", [(26, 26), (32, 26)])
@pytest.mark.parametrize("dt", ALL_DTYPES)
def test_identity_map(self, device, height, width, dt):
# Tests on square and rectangular images
tensor, pil_img = _create_data(height, width, device=device)
if dt == torch.float16 and device == "cpu":
# skip float16 on CPU case
return
if dt is not None:
tensor = tensor.to(dtype=dt)
# 1) identity map
out_tensor = F.affine(tensor, angle=0, translate=[0, 0], scale=1.0, shear=[0.0, 0.0], interpolation=NEAREST)
assert_equal(tensor, out_tensor, msg="{} vs {}".format(out_tensor[0, :5, :5], tensor[0, :5, :5]))
out_tensor = self.scripted_affine(
tensor, angle=0, translate=[0, 0], scale=1.0, shear=[0.0, 0.0], interpolation=NEAREST
)
assert_equal(tensor, out_tensor, msg="{} vs {}".format(out_tensor[0, :5, :5], tensor[0, :5, :5]))
@pytest.mark.parametrize("device", cpu_and_gpu())
@pytest.mark.parametrize("height, width", [(26, 26)])
@pytest.mark.parametrize("dt", ALL_DTYPES)
@pytest.mark.parametrize(
"angle, config",
[
(90, {"k": 1, "dims": (-1, -2)}),
(45, None),
(30, None),
(-30, None),
(-45, None),
(-90, {"k": -1, "dims": (-1, -2)}),
(180, {"k": 2, "dims": (-1, -2)}),
],
)
@pytest.mark.parametrize("fn", [F.affine, scripted_affine])
def test_square_rotations(self, device, height, width, dt, angle, config, fn):
# 2) Test rotation
tensor, pil_img = _create_data(height, width, device=device)
if dt == torch.float16 and device == "cpu":
# skip float16 on CPU case
return
if dt is not None:
tensor = tensor.to(dtype=dt)
out_pil_img = F.affine(
pil_img, angle=angle, translate=[0, 0], scale=1.0, shear=[0.0, 0.0], interpolation=NEAREST
)
out_pil_tensor = torch.from_numpy(np.array(out_pil_img).transpose((2, 0, 1))).to(device)
out_tensor = fn(tensor, angle=angle, translate=[0, 0], scale=1.0, shear=[0.0, 0.0], interpolation=NEAREST)
if config is not None:
assert_equal(torch.rot90(tensor, **config), out_tensor)
if out_tensor.dtype != torch.uint8:
out_tensor = out_tensor.to(torch.uint8)
num_diff_pixels = (out_tensor != out_pil_tensor).sum().item() / 3.0
ratio_diff_pixels = num_diff_pixels / out_tensor.shape[-1] / out_tensor.shape[-2]
# Tolerance : less than 6% of different pixels
assert ratio_diff_pixels < 0.06, "{}\n{} vs \n{}".format(
ratio_diff_pixels, out_tensor[0, :7, :7], out_pil_tensor[0, :7, :7]
)
@pytest.mark.parametrize("device", cpu_and_gpu())
@pytest.mark.parametrize("height, width", [(32, 26)])
@pytest.mark.parametrize("dt", ALL_DTYPES)
@pytest.mark.parametrize("angle", [90, 45, 15, -30, -60, -120])
@pytest.mark.parametrize("fn", [F.affine, scripted_affine])
def test_rect_rotations(self, device, height, width, dt, angle, fn):
# Tests on rectangular images
tensor, pil_img = _create_data(height, width, device=device)
if dt == torch.float16 and device == "cpu":
# skip float16 on CPU case
return
if dt is not None:
tensor = tensor.to(dtype=dt)
out_pil_img = F.affine(
pil_img, angle=angle, translate=[0, 0], scale=1.0, shear=[0.0, 0.0], interpolation=NEAREST
)
out_pil_tensor = torch.from_numpy(np.array(out_pil_img).transpose((2, 0, 1)))
out_tensor = fn(tensor, angle=angle, translate=[0, 0], scale=1.0, shear=[0.0, 0.0], interpolation=NEAREST).cpu()
if out_tensor.dtype != torch.uint8:
out_tensor = out_tensor.to(torch.uint8)
num_diff_pixels = (out_tensor != out_pil_tensor).sum().item() / 3.0
ratio_diff_pixels = num_diff_pixels / out_tensor.shape[-1] / out_tensor.shape[-2]
# Tolerance : less than 3% of different pixels
assert ratio_diff_pixels < 0.03, "{}: {}\n{} vs \n{}".format(
angle, ratio_diff_pixels, out_tensor[0, :7, :7], out_pil_tensor[0, :7, :7]
)
@pytest.mark.parametrize("device", cpu_and_gpu())
@pytest.mark.parametrize("height, width", [(26, 26), (32, 26)])
@pytest.mark.parametrize("dt", ALL_DTYPES)
@pytest.mark.parametrize("t", [[10, 12], (-12, -13)])
@pytest.mark.parametrize("fn", [F.affine, scripted_affine])
def test_translations(self, device, height, width, dt, t, fn):
# 3) Test translation
tensor, pil_img = _create_data(height, width, device=device)
if dt == torch.float16 and device == "cpu":
# skip float16 on CPU case
return
if dt is not None:
tensor = tensor.to(dtype=dt)
out_pil_img = F.affine(pil_img, angle=0, translate=t, scale=1.0, shear=[0.0, 0.0], interpolation=NEAREST)
out_tensor = fn(tensor, angle=0, translate=t, scale=1.0, shear=[0.0, 0.0], interpolation=NEAREST)
if out_tensor.dtype != torch.uint8:
out_tensor = out_tensor.to(torch.uint8)
_assert_equal_tensor_to_pil(out_tensor, out_pil_img)
@pytest.mark.parametrize("device", cpu_and_gpu())
@pytest.mark.parametrize("height, width", [(26, 26), (32, 26)])
@pytest.mark.parametrize("dt", ALL_DTYPES)
@pytest.mark.parametrize(
"a, t, s, sh, f",
[
(45.5, [5, 6], 1.0, [0.0, 0.0], None),
(33, (5, -4), 1.0, [0.0, 0.0], [0, 0, 0]),
(45, [-5, 4], 1.2, [0.0, 0.0], (1, 2, 3)),
(33, (-4, -8), 2.0, [0.0, 0.0], [255, 255, 255]),
(
85,
(10, -10),
0.7,
[0.0, 0.0],
[
1,
],
),
(
0,
[0, 0],
1.0,
[
35.0,
],
(2.0,),
),
(-25, [0, 0], 1.2, [0.0, 15.0], None),
(-45, [-10, 0], 0.7, [2.0, 5.0], None),
(-45, [-10, -10], 1.2, [4.0, 5.0], None),
(-90, [0, 0], 1.0, [0.0, 0.0], None),
],
)
@pytest.mark.parametrize("fn", [F.affine, scripted_affine])
def test_all_ops(self, device, height, width, dt, a, t, s, sh, f, fn):
# 4) Test rotation + translation + scale + shear
tensor, pil_img = _create_data(height, width, device=device)
if dt == torch.float16 and device == "cpu":
# skip float16 on CPU case
return
if dt is not None:
tensor = tensor.to(dtype=dt)
f_pil = int(f[0]) if f is not None and len(f) == 1 else f
out_pil_img = F.affine(pil_img, angle=a, translate=t, scale=s, shear=sh, interpolation=NEAREST, fill=f_pil)
out_pil_tensor = torch.from_numpy(np.array(out_pil_img).transpose((2, 0, 1)))
out_tensor = fn(tensor, angle=a, translate=t, scale=s, shear=sh, interpolation=NEAREST, fill=f).cpu()
if out_tensor.dtype != torch.uint8:
out_tensor = out_tensor.to(torch.uint8)
num_diff_pixels = (out_tensor != out_pil_tensor).sum().item() / 3.0
ratio_diff_pixels = num_diff_pixels / out_tensor.shape[-1] / out_tensor.shape[-2]
# Tolerance : less than 5% (cpu), 6% (cuda) of different pixels
tol = 0.06 if device == "cuda" else 0.05
assert ratio_diff_pixels < tol, "{}: {}\n{} vs \n{}".format(
(NEAREST, a, t, s, sh, f), ratio_diff_pixels, out_tensor[0, :7, :7], out_pil_tensor[0, :7, :7]
)
@pytest.mark.parametrize("device", cpu_and_gpu())
@pytest.mark.parametrize("dt", ALL_DTYPES)
def test_batches(self, device, dt):
if dt == torch.float16 and device == "cpu":
# skip float16 on CPU case
return
batch_tensors = _create_data_batch(26, 36, num_samples=4, device=device)
if dt is not None:
batch_tensors = batch_tensors.to(dtype=dt)
_test_fn_on_batch(batch_tensors, F.affine, angle=-43, translate=[-3, 4], scale=1.2, shear=[4.0, 5.0])
@pytest.mark.parametrize("device", cpu_and_gpu())
def test_warnings(self, device):
tensor, pil_img = _create_data(26, 26, device=device)
# assert deprecation warning and non-BC
with pytest.warns(UserWarning, match=r"Argument resample is deprecated and will be removed"):
res1 = F.affine(tensor, 45, translate=[0, 0], scale=1.0, shear=[0.0, 0.0], resample=2)
res2 = F.affine(tensor, 45, translate=[0, 0], scale=1.0, shear=[0.0, 0.0], interpolation=BILINEAR)
assert_equal(res1, res2)
# assert changed type warning
with pytest.warns(UserWarning, match=r"Argument interpolation should be of type InterpolationMode"):
res1 = F.affine(tensor, 45, translate=[0, 0], scale=1.0, shear=[0.0, 0.0], interpolation=2)
res2 = F.affine(tensor, 45, translate=[0, 0], scale=1.0, shear=[0.0, 0.0], interpolation=BILINEAR)
assert_equal(res1, res2)
with pytest.warns(UserWarning, match=r"Argument fillcolor is deprecated and will be removed"):
res1 = F.affine(pil_img, 45, translate=[0, 0], scale=1.0, shear=[0.0, 0.0], fillcolor=10)
res2 = F.affine(pil_img, 45, translate=[0, 0], scale=1.0, shear=[0.0, 0.0], fill=10)
# we convert the PIL images to numpy as assert_equal doesn't work on PIL images.
assert_equal(np.asarray(res1), np.asarray(res2))
def _get_data_dims_and_points_for_perspective():
# Ideally we would parametrize independently over data dims and points, but
# we want to tests on some points that also depend on the data dims.
# Pytest doesn't support covariant parametrization, so we do it somewhat manually here.
data_dims = [(26, 34), (26, 26)]
points = [
[[[0, 0], [33, 0], [33, 25], [0, 25]], [[3, 2], [32, 3], [30, 24], [2, 25]]],
[[[3, 2], [32, 3], [30, 24], [2, 25]], [[0, 0], [33, 0], [33, 25], [0, 25]]],
[[[3, 2], [32, 3], [30, 24], [2, 25]], [[5, 5], [30, 3], [33, 19], [4, 25]]],
]
dims_and_points = list(itertools.product(data_dims, points))
# up to here, we could just have used 2 @parametrized.
# Down below is the covarariant part as the points depend on the data dims.
n = 10
for dim in data_dims:
points += [(dim, T.RandomPerspective.get_params(dim[1], dim[0], i / n)) for i in range(n)]
return dims_and_points
@pytest.mark.parametrize("device", cpu_and_gpu())
@pytest.mark.parametrize("dims_and_points", _get_data_dims_and_points_for_perspective())
@pytest.mark.parametrize("dt", [None, torch.float32, torch.float64, torch.float16])
@pytest.mark.parametrize(
"fill",
(
None,
[0, 0, 0],
[1, 2, 3],
[255, 255, 255],
[
1,
],
(2.0,),
),
)
@pytest.mark.parametrize("fn", [F.perspective, torch.jit.script(F.perspective)])
def test_perspective_pil_vs_tensor(device, dims_and_points, dt, fill, fn):
if dt == torch.float16 and device == "cpu":
# skip float16 on CPU case
return
data_dims, (spoints, epoints) = dims_and_points
tensor, pil_img = _create_data(*data_dims, device=device)
if dt is not None:
tensor = tensor.to(dtype=dt)
interpolation = NEAREST
fill_pil = int(fill[0]) if fill is not None and len(fill) == 1 else fill
out_pil_img = F.perspective(
pil_img, startpoints=spoints, endpoints=epoints, interpolation=interpolation, fill=fill_pil
)
out_pil_tensor = torch.from_numpy(np.array(out_pil_img).transpose((2, 0, 1)))
out_tensor = fn(tensor, startpoints=spoints, endpoints=epoints, interpolation=interpolation, fill=fill).cpu()
if out_tensor.dtype != torch.uint8:
out_tensor = out_tensor.to(torch.uint8)
num_diff_pixels = (out_tensor != out_pil_tensor).sum().item() / 3.0
ratio_diff_pixels = num_diff_pixels / out_tensor.shape[-1] / out_tensor.shape[-2]
# Tolerance : less than 5% of different pixels
assert ratio_diff_pixels < 0.05
@pytest.mark.parametrize("device", cpu_and_gpu())
@pytest.mark.parametrize("dims_and_points", _get_data_dims_and_points_for_perspective())
@pytest.mark.parametrize("dt", [None, torch.float32, torch.float64, torch.float16])
def test_perspective_batch(device, dims_and_points, dt):
if dt == torch.float16 and device == "cpu":
# skip float16 on CPU case
return
data_dims, (spoints, epoints) = dims_and_points
batch_tensors = _create_data_batch(*data_dims, num_samples=4, device=device)
if dt is not None:
batch_tensors = batch_tensors.to(dtype=dt)
# Ignore the equivalence between scripted and regular function on float16 cuda. The pixels at
# the border may be entirely different due to small rounding errors.
scripted_fn_atol = -1 if (dt == torch.float16 and device == "cuda") else 1e-8
_test_fn_on_batch(
batch_tensors,
F.perspective,
scripted_fn_atol=scripted_fn_atol,
startpoints=spoints,
endpoints=epoints,
interpolation=NEAREST,
)
def test_perspective_interpolation_warning():
# assert changed type warning
spoints = [[0, 0], [33, 0], [33, 25], [0, 25]]
epoints = [[3, 2], [32, 3], [30, 24], [2, 25]]
tensor = torch.randint(0, 256, (3, 26, 26))
with pytest.warns(UserWarning, match="Argument interpolation should be of type InterpolationMode"):
res1 = F.perspective(tensor, startpoints=spoints, endpoints=epoints, interpolation=2)
res2 = F.perspective(tensor, startpoints=spoints, endpoints=epoints, interpolation=BILINEAR)
assert_equal(res1, res2)
@pytest.mark.parametrize("device", cpu_and_gpu())
@pytest.mark.parametrize("dt", [None, torch.float32, torch.float64, torch.float16])
@pytest.mark.parametrize(
"size",
[
32,
26,
[
32,
],
[32, 32],
(32, 32),
[26, 35],
],
)
@pytest.mark.parametrize("max_size", [None, 34, 40, 1000])
@pytest.mark.parametrize("interpolation", [BILINEAR, BICUBIC, NEAREST])
def test_resize(device, dt, size, max_size, interpolation):
if dt == torch.float16 and device == "cpu":
# skip float16 on CPU case
return
if max_size is not None and isinstance(size, Sequence) and len(size) != 1:
return # unsupported
torch.manual_seed(12)
script_fn = torch.jit.script(F.resize)
tensor, pil_img = _create_data(26, 36, device=device)
batch_tensors = _create_data_batch(16, 18, num_samples=4, device=device)
if dt is not None:
# This is a trivial cast to float of uint8 data to test all cases
tensor = tensor.to(dt)
batch_tensors = batch_tensors.to(dt)
resized_tensor = F.resize(tensor, size=size, interpolation=interpolation, max_size=max_size)
resized_pil_img = F.resize(pil_img, size=size, interpolation=interpolation, max_size=max_size)
assert resized_tensor.size()[1:] == resized_pil_img.size[::-1]
if interpolation not in [
NEAREST,
]:
# We can not check values if mode = NEAREST, as results are different
# E.g. resized_tensor = [[a, a, b, c, d, d, e, ...]]
# E.g. resized_pil_img = [[a, b, c, c, d, e, f, ...]]
resized_tensor_f = resized_tensor
# we need to cast to uint8 to compare with PIL image
if resized_tensor_f.dtype == torch.uint8:
resized_tensor_f = resized_tensor_f.to(torch.float)
# Pay attention to high tolerance for MAE
_assert_approx_equal_tensor_to_pil(resized_tensor_f, resized_pil_img, tol=8.0)
if isinstance(size, int):
script_size = [
size,
]
else:
script_size = size
resize_result = script_fn(tensor, size=script_size, interpolation=interpolation, max_size=max_size)
assert_equal(resized_tensor, resize_result)
_test_fn_on_batch(batch_tensors, F.resize, size=script_size, interpolation=interpolation, max_size=max_size)
@pytest.mark.parametrize("device", cpu_and_gpu())
def test_resize_asserts(device):
tensor, pil_img = _create_data(26, 36, device=device)
# assert changed type warning
with pytest.warns(UserWarning, match=r"Argument interpolation should be of type InterpolationMode"):
res1 = F.resize(tensor, size=32, interpolation=2)
res2 = F.resize(tensor, size=32, interpolation=BILINEAR)
assert_equal(res1, res2)
for img in (tensor, pil_img):
exp_msg = "max_size should only be passed if size specifies the length of the smaller edge"
with pytest.raises(ValueError, match=exp_msg):
F.resize(img, size=(32, 34), max_size=35)
with pytest.raises(ValueError, match="max_size = 32 must be strictly greater"):
F.resize(img, size=32, max_size=32)
@pytest.mark.parametrize("device", cpu_and_gpu())
@pytest.mark.parametrize("dt", [None, torch.float32, torch.float64, torch.float16])
@pytest.mark.parametrize("size", [[96, 72], [96, 420], [420, 72]])
@pytest.mark.parametrize("interpolation", [BILINEAR, BICUBIC])
def test_resize_antialias(device, dt, size, interpolation):
if dt == torch.float16 and device == "cpu":
# skip float16 on CPU case
return
torch.manual_seed(12)
script_fn = torch.jit.script(F.resize)
tensor, pil_img = _create_data(320, 290, device=device)
if dt is not None:
# This is a trivial cast to float of uint8 data to test all cases
tensor = tensor.to(dt)
resized_tensor = F.resize(tensor, size=size, interpolation=interpolation, antialias=True)
resized_pil_img = F.resize(pil_img, size=size, interpolation=interpolation)
assert resized_tensor.size()[1:] == resized_pil_img.size[::-1]
resized_tensor_f = resized_tensor
# we need to cast to uint8 to compare with PIL image
if resized_tensor_f.dtype == torch.uint8:
resized_tensor_f = resized_tensor_f.to(torch.float)
_assert_approx_equal_tensor_to_pil(resized_tensor_f, resized_pil_img, tol=0.5, msg=f"{size}, {interpolation}, {dt}")
accepted_tol = 1.0 + 1e-5
if interpolation == BICUBIC:
# this overall mean value to make the tests pass
# High value is mostly required for test cases with
# downsampling and upsampling where we can not exactly
# match PIL implementation.
accepted_tol = 15.0
_assert_approx_equal_tensor_to_pil(
resized_tensor_f, resized_pil_img, tol=accepted_tol, agg_method="max", msg=f"{size}, {interpolation}, {dt}"
)
if isinstance(size, int):
script_size = [
size,
]
else:
script_size = size
resize_result = script_fn(tensor, size=script_size, interpolation=interpolation, antialias=True)
assert_equal(resized_tensor, resize_result)
@needs_cuda
@pytest.mark.parametrize("interpolation", [BILINEAR, BICUBIC])
def test_assert_resize_antialias(interpolation):
# Checks implementation on very large scales
# and catch TORCH_CHECK inside interpolate_aa_kernels.cu
torch.manual_seed(12)
tensor, pil_img = _create_data(1000, 1000, device="cuda")
with pytest.raises(RuntimeError, match=r"Max supported scale factor is"):
F.resize(tensor, size=(5, 5), interpolation=interpolation, antialias=True)
@pytest.mark.parametrize("device", cpu_and_gpu())
@pytest.mark.parametrize("dt", [torch.float32, torch.float64, torch.float16])
@pytest.mark.parametrize("size", [[10, 7], [10, 42], [42, 7]])
@pytest.mark.parametrize("interpolation", [BILINEAR, BICUBIC])
def test_interpolate_antialias_backward(device, dt, size, interpolation):
if dt == torch.float16 and device == "cpu":
# skip float16 on CPU case
return
torch.manual_seed(12)
if interpolation == BILINEAR:
forward_op = torch.ops.torchvision._interpolate_bilinear2d_aa
backward_op = torch.ops.torchvision._interpolate_bilinear2d_aa_backward
elif interpolation == BICUBIC:
forward_op = torch.ops.torchvision._interpolate_bicubic2d_aa
backward_op = torch.ops.torchvision._interpolate_bicubic2d_aa_backward
class F(torch.autograd.Function):
@staticmethod
def forward(ctx, i):
result = forward_op(i, size, False)
ctx.save_for_backward(i, result)
return result
@staticmethod
def backward(ctx, grad_output):
i, result = ctx.saved_tensors
ishape = i.shape
oshape = result.shape[2:]
return backward_op(grad_output, oshape, ishape, False)
x = (torch.rand(1, 32, 29, 3, dtype=torch.double, device=device).permute(0, 3, 1, 2).requires_grad_(True),)
assert torch.autograd.gradcheck(F.apply, x, eps=1e-8, atol=1e-6, rtol=1e-6, fast_mode=False)
x = (torch.rand(1, 3, 32, 29, dtype=torch.double, device=device, requires_grad=True),)
assert torch.autograd.gradcheck(F.apply, x, eps=1e-8, atol=1e-6, rtol=1e-6, fast_mode=False)
def check_functional_vs_PIL_vs_scripted(
fn, fn_pil, fn_t, config, device, dtype, channels=3, tol=2.0 + 1e-10, agg_method="max"
):
script_fn = torch.jit.script(fn)
torch.manual_seed(15)
tensor, pil_img = _create_data(26, 34, channels=channels, device=device)
batch_tensors = _create_data_batch(16, 18, num_samples=4, channels=channels, device=device)
if dtype is not None:
tensor = F.convert_image_dtype(tensor, dtype)
batch_tensors = F.convert_image_dtype(batch_tensors, dtype)
out_fn_t = fn_t(tensor, **config)
out_pil = fn_pil(pil_img, **config)
out_scripted = script_fn(tensor, **config)
assert out_fn_t.dtype == out_scripted.dtype
assert out_fn_t.size()[1:] == out_pil.size[::-1]
rbg_tensor = out_fn_t
if out_fn_t.dtype != torch.uint8:
rbg_tensor = F.convert_image_dtype(out_fn_t, torch.uint8)
# Check that max difference does not exceed 2 in [0, 255] range
# Exact matching is not possible due to incompatibility convert_image_dtype and PIL results
_assert_approx_equal_tensor_to_pil(rbg_tensor.float(), out_pil, tol=tol, agg_method=agg_method)
atol = 1e-6
if out_fn_t.dtype == torch.uint8 and "cuda" in torch.device(device).type:
atol = 1.0
assert out_fn_t.allclose(out_scripted, atol=atol)
# FIXME: fn will be scripted again in _test_fn_on_batch. We could avoid that.
_test_fn_on_batch(batch_tensors, fn, scripted_fn_atol=atol, **config)
@pytest.mark.parametrize("device", cpu_and_gpu())
@pytest.mark.parametrize("dtype", (None, torch.float32, torch.float64))
@pytest.mark.parametrize("config", [{"brightness_factor": f} for f in (0.1, 0.5, 1.0, 1.34, 2.5)])
@pytest.mark.parametrize("channels", [1, 3])
def test_adjust_brightness(device, dtype, config, channels):
check_functional_vs_PIL_vs_scripted(
F.adjust_brightness,
F_pil.adjust_brightness,
F_t.adjust_brightness,
config,
device,
dtype,
channels,
)
@pytest.mark.parametrize("device", cpu_and_gpu())
@pytest.mark.parametrize("dtype", (None, torch.float32, torch.float64))
@pytest.mark.parametrize("channels", [1, 3])
def test_invert(device, dtype, channels):
check_functional_vs_PIL_vs_scripted(
F.invert, F_pil.invert, F_t.invert, {}, device, dtype, channels, tol=1.0, agg_method="max"
)
@pytest.mark.parametrize("device", cpu_and_gpu())
@pytest.mark.parametrize("config", [{"bits": bits} for bits in range(0, 8)])
@pytest.mark.parametrize("channels", [1, 3])
def test_posterize(device, config, channels):
check_functional_vs_PIL_vs_scripted(
F.posterize,
F_pil.posterize,
F_t.posterize,
config,
device,
dtype=None,
channels=channels,
tol=1.0,
agg_method="max",
)
@pytest.mark.parametrize("device", cpu_and_gpu())
@pytest.mark.parametrize("config", [{"threshold": threshold} for threshold in [0, 64, 128, 192, 255]])
@pytest.mark.parametrize("channels", [1, 3])
def test_solarize1(device, config, channels):
check_functional_vs_PIL_vs_scripted(
F.solarize,
F_pil.solarize,
F_t.solarize,
config,
device,
dtype=None,
channels=channels,
tol=1.0,
agg_method="max",
)
@pytest.mark.parametrize("device", cpu_and_gpu())
@pytest.mark.parametrize("dtype", (torch.float32, torch.float64))
@pytest.mark.parametrize("config", [{"threshold": threshold} for threshold in [0.0, 0.25, 0.5, 0.75, 1.0]])
@pytest.mark.parametrize("channels", [1, 3])
def test_solarize2(device, dtype, config, channels):
check_functional_vs_PIL_vs_scripted(
F.solarize,
lambda img, threshold: F_pil.solarize(img, 255 * threshold),
F_t.solarize,
config,
device,
dtype,
channels,
tol=1.0,
agg_method="max",
)
@pytest.mark.parametrize("device", cpu_and_gpu())
@pytest.mark.parametrize("dtype", (None, torch.float32, torch.float64))
@pytest.mark.parametrize("config", [{"sharpness_factor": f} for f in [0.2, 0.5, 1.0, 1.5, 2.0]])
@pytest.mark.parametrize("channels", [1, 3])
def test_adjust_sharpness(device, dtype, config, channels):
check_functional_vs_PIL_vs_scripted(
F.adjust_sharpness,
F_pil.adjust_sharpness,
F_t.adjust_sharpness,
config,
device,
dtype,
channels,
)
@pytest.mark.parametrize("device", cpu_and_gpu())
@pytest.mark.parametrize("dtype", (None, torch.float32, torch.float64))
@pytest.mark.parametrize("channels", [1, 3])
def test_autocontrast(device, dtype, channels):
check_functional_vs_PIL_vs_scripted(
F.autocontrast, F_pil.autocontrast, F_t.autocontrast, {}, device, dtype, channels, tol=1.0, agg_method="max"
)
@pytest.mark.parametrize("device", cpu_and_gpu())
@pytest.mark.parametrize("channels", [1, 3])
def test_equalize(device, channels):
torch.use_deterministic_algorithms(False)
check_functional_vs_PIL_vs_scripted(
F.equalize,
F_pil.equalize,
F_t.equalize,
{},
device,
dtype=None,
channels=channels,
tol=1.0,
agg_method="max",
)
@pytest.mark.parametrize("device", cpu_and_gpu())
@pytest.mark.parametrize("dtype", (None, torch.float32, torch.float64))
@pytest.mark.parametrize("config", [{"contrast_factor": f} for f in [0.2, 0.5, 1.0, 1.5, 2.0]])
@pytest.mark.parametrize("channels", [1, 3])
def test_adjust_contrast(device, dtype, config, channels):
check_functional_vs_PIL_vs_scripted(
F.adjust_contrast, F_pil.adjust_contrast, F_t.adjust_contrast, config, device, dtype, channels
)
@pytest.mark.parametrize("device", cpu_and_gpu())
@pytest.mark.parametrize("dtype", (None, torch.float32, torch.float64))
@pytest.mark.parametrize("config", [{"saturation_factor": f} for f in [0.5, 0.75, 1.0, 1.5, 2.0]])
@pytest.mark.parametrize("channels", [1, 3])
def test_adjust_saturation(device, dtype, config, channels):
check_functional_vs_PIL_vs_scripted(
F.adjust_saturation, F_pil.adjust_saturation, F_t.adjust_saturation, config, device, dtype, channels
)
@pytest.mark.parametrize("device", cpu_and_gpu())
@pytest.mark.parametrize("dtype", (None, torch.float32, torch.float64))
@pytest.mark.parametrize("config", [{"hue_factor": f} for f in [-0.45, -0.25, 0.0, 0.25, 0.45]])
@pytest.mark.parametrize("channels", [1, 3])
def test_adjust_hue(device, dtype, config, channels):
check_functional_vs_PIL_vs_scripted(
F.adjust_hue, F_pil.adjust_hue, F_t.adjust_hue, config, device, dtype, channels, tol=16.1, agg_method="max"
)
@pytest.mark.parametrize("device", cpu_and_gpu())
@pytest.mark.parametrize("dtype", (None, torch.float32, torch.float64))
@pytest.mark.parametrize("config", [{"gamma": g1, "gain": g2} for g1, g2 in zip([0.8, 1.0, 1.2], [0.7, 1.0, 1.3])])
@pytest.mark.parametrize("channels", [1, 3])
def test_adjust_gamma(device, dtype, config, channels):
check_functional_vs_PIL_vs_scripted(
F.adjust_gamma,
F_pil.adjust_gamma,
F_t.adjust_gamma,
config,
device,
dtype,
channels,
)
@pytest.mark.parametrize("device", cpu_and_gpu())
@pytest.mark.parametrize("dt", [None, torch.float32, torch.float64, torch.float16])
@pytest.mark.parametrize(
"pad",
[
2,
[
3,
],
[0, 3],
(3, 3),
[4, 2, 4, 3],
],
)
@pytest.mark.parametrize(
"config",
[
{"padding_mode": "constant", "fill": 0},
{"padding_mode": "constant", "fill": 10},
{"padding_mode": "constant", "fill": 20},
{"padding_mode": "edge"},
{"padding_mode": "reflect"},
{"padding_mode": "symmetric"},
],
)
def test_pad(device, dt, pad, config):
script_fn = torch.jit.script(F.pad)
tensor, pil_img = _create_data(7, 8, device=device)
batch_tensors = _create_data_batch(16, 18, num_samples=4, device=device)
if dt == torch.float16 and device == "cpu":
# skip float16 on CPU case
return
if dt is not None:
# This is a trivial cast to float of uint8 data to test all cases
tensor = tensor.to(dt)
batch_tensors = batch_tensors.to(dt)
pad_tensor = F_t.pad(tensor, pad, **config)
pad_pil_img = F_pil.pad(pil_img, pad, **config)
pad_tensor_8b = pad_tensor
# we need to cast to uint8 to compare with PIL image
if pad_tensor_8b.dtype != torch.uint8:
pad_tensor_8b = pad_tensor_8b.to(torch.uint8)
_assert_equal_tensor_to_pil(pad_tensor_8b, pad_pil_img, msg="{}, {}".format(pad, config))
if isinstance(pad, int):
script_pad = [
pad,
]
else:
script_pad = pad
pad_tensor_script = script_fn(tensor, script_pad, **config)
assert_equal(pad_tensor, pad_tensor_script, msg="{}, {}".format(pad, config))
_test_fn_on_batch(batch_tensors, F.pad, padding=script_pad, **config)
@pytest.mark.parametrize("device", cpu_and_gpu())
@pytest.mark.parametrize("mode", [NEAREST, BILINEAR, BICUBIC])
def test_resized_crop(device, mode):
# test values of F.resized_crop in several cases:
# 1) resize to the same size, crop to the same size => should be identity
tensor, _ = _create_data(26, 36, device=device)
out_tensor = F.resized_crop(tensor, top=0, left=0, height=26, width=36, size=[26, 36], interpolation=mode)
assert_equal(tensor, out_tensor, msg="{} vs {}".format(out_tensor[0, :5, :5], tensor[0, :5, :5]))
# 2) resize by half and crop a TL corner
tensor, _ = _create_data(26, 36, device=device)
out_tensor = F.resized_crop(tensor, top=0, left=0, height=20, width=30, size=[10, 15], interpolation=NEAREST)
expected_out_tensor = tensor[:, :20:2, :30:2]
assert_equal(
expected_out_tensor,
out_tensor,
msg="{} vs {}".format(expected_out_tensor[0, :10, :10], out_tensor[0, :10, :10]),
)
batch_tensors = _create_data_batch(26, 36, num_samples=4, device=device)
_test_fn_on_batch(
batch_tensors, F.resized_crop, top=1, left=2, height=20, width=30, size=[10, 15], interpolation=NEAREST
)
@pytest.mark.parametrize("device", cpu_and_gpu())
@pytest.mark.parametrize(
"func, args",
[
(F_t.get_image_size, ()),
(F_t.vflip, ()),
(F_t.hflip, ()),
(F_t.crop, (1, 2, 4, 5)),
(F_t.adjust_brightness, (0.0,)),
(F_t.adjust_contrast, (1.0,)),
(F_t.adjust_hue, (-0.5,)),
(F_t.adjust_saturation, (2.0,)),
(
F_t.pad,
(
[
2,
],
2,
"constant",
),
),