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test_functional_tensor.py
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test_functional_tensor.py
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import colorsys
import itertools
import math
import os
import warnings
from functools import partial
from typing import Sequence
import numpy as np
import PIL.Image
import pytest
import torch
import torchvision.transforms as T
import torchvision.transforms._functional_pil as F_pil
import torchvision.transforms._functional_tensor as F_t
import torchvision.transforms.functional as F
from common_utils import (
_assert_approx_equal_tensor_to_pil,
_assert_equal_tensor_to_pil,
_create_data,
_create_data_batch,
_test_fn_on_batch,
assert_equal,
cpu_and_gpu,
needs_cuda,
)
from torchvision.transforms import InterpolationMode
NEAREST, NEAREST_EXACT, BILINEAR, BICUBIC = (
InterpolationMode.NEAREST,
InterpolationMode.NEAREST_EXACT,
InterpolationMode.BILINEAR,
InterpolationMode.BICUBIC,
)
@pytest.mark.parametrize("device", cpu_and_gpu())
@pytest.mark.parametrize("fn", [F.get_image_size, F.get_image_num_channels, F.get_dimensions])
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", [(7, 33), (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, 34))
@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)}: {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_interpolation_type(self):
tensor, _ = _create_data(26, 26)
res1 = F.rotate(tensor, 45, interpolation=PIL.Image.BILINEAR)
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=f"{out_tensor[0, :5, :5]} vs {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=f"{out_tensor[0, :5, :5]} vs {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
@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])
@pytest.mark.parametrize("center", [None, [0, 0]])
def test_rect_rotations(self, device, height, width, dt, angle, fn, center):
# 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, center=center
)
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, center=center
).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
@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
@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_interpolation_type(self, device):
tensor, pil_img = _create_data(26, 26, device=device)
res1 = F.affine(tensor, 45, translate=[0, 0], scale=1.0, shear=[0.0, 0.0], interpolation=PIL.Image.BILINEAR)
res2 = F.affine(tensor, 45, translate=[0, 0], scale=1.0, shear=[0.0, 0.0], interpolation=BILINEAR)
assert_equal(res1, 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_type():
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))
res1 = F.perspective(tensor, startpoints=spoints, endpoints=epoints, interpolation=PIL.Image.BILINEAR)
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, NEAREST_EXACT])
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, antialias=True)
resized_pil_img = F.resize(pil_img, size=size, interpolation=interpolation, max_size=max_size, antialias=True)
assert resized_tensor.size()[1:] == resized_pil_img.size[::-1]
if interpolation != 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=3.0)
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, antialias=True)
assert_equal(resized_tensor, resize_result)
_test_fn_on_batch(
batch_tensors, F.resize, size=script_size, interpolation=interpolation, max_size=max_size, antialias=True
)
@pytest.mark.parametrize("device", cpu_and_gpu())
def test_resize_asserts(device):
tensor, pil_img = _create_data(26, 36, device=device)
res1 = F.resize(tensor, size=32, interpolation=PIL.Image.BILINEAR)
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, antialias=True)
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)
def test_resize_antialias_default_warning():
img = torch.randint(0, 256, size=(3, 44, 56), dtype=torch.uint8)
match = "The default value of the antialias"
with pytest.warns(UserWarning, match=match):
F.resize(img, size=(20, 20))
with pytest.warns(UserWarning, match=match):
F.resized_crop(img, 0, 0, 10, 10, size=(20, 20))
# For modes that aren't bicubic or bilinear, don't throw a warning
with warnings.catch_warnings():
warnings.simplefilter("error")
F.resize(img, size=(20, 20), interpolation=NEAREST)
F.resized_crop(img, 0, 0, 10, 10, size=(20, 20), interpolation=NEAREST)
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(
("dtype", "threshold"),
[
*[
(dtype, threshold)
for dtype, threshold in itertools.product(
[torch.float32, torch.float16],
[0.0, 0.25, 0.5, 0.75, 1.0],
)
],
*[(torch.uint8, threshold) for threshold in [0, 64, 128, 192, 255]],
*[(torch.int64, threshold) for threshold in [0, 2**32, 2**63 - 1]],
],
)
@pytest.mark.parametrize("device", cpu_and_gpu())
def test_solarize_threshold_within_bound(threshold, dtype, device):
make_img = torch.rand if dtype.is_floating_point else partial(torch.randint, 0, torch.iinfo(dtype).max)
img = make_img((3, 12, 23), dtype=dtype, device=device)
F_t.solarize(img, threshold)
@pytest.mark.parametrize(
("dtype", "threshold"),
[
(torch.float32, 1.5),
(torch.float16, 1.5),
(torch.uint8, 260),
(torch.int64, 2**64),
],
)
@pytest.mark.parametrize("device", cpu_and_gpu())
def test_solarize_threshold_above_bound(threshold, dtype, device):
make_img = torch.rand if dtype.is_floating_point else partial(torch.randint, 0, torch.iinfo(dtype).max)
img = make_img((3, 12, 23), dtype=dtype, device=device)
with pytest.raises(TypeError, match="Threshold should be less than bound of img."):
F_t.solarize(img, threshold)
@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("dtype", (None, torch.float32, torch.float64))
@pytest.mark.parametrize("channels", [1, 3])
def test_autocontrast_equal_minmax(device, dtype, channels):
a = _create_data_batch(32, 32, num_samples=1, channels=channels, device=device)
a = a / 2.0 + 0.3
assert (F.autocontrast(a)[0] == F.autocontrast(a[0])).all()
a[0, 0] = 0.7
assert (F.autocontrast(a)[0] == F.autocontrast(a[0])).all()
@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": 20.2},
{"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=f"{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=f"{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, NEAREST_EXACT, 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, antialias=True
)
assert_equal(tensor, out_tensor, msg=f"{out_tensor[0, :5, :5]} vs {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=f"{expected_out_tensor[0, :10, :10]} vs {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_dimensions, ()),
(F_t.get_image_size, ()),
(F_t.get_image_num_channels, ()),
(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")),
(F_t.resize, ([10, 11],)),
(F_t.perspective, ([0.2])),
(F_t.gaussian_blur, ((2, 2), (0.7, 0.5))),
(F_t.invert, ()),
(F_t.posterize, (0,)),
(F_t.solarize, (0.3,)),
(F_t.adjust_sharpness, (0.3,)),
(F_t.autocontrast, ()),
(F_t.equalize, ()),
],
)
def test_assert_image_tensor(device, func, args):
shape = (100,)
tensor = torch.rand(*shape, dtype=torch.float, device=device)
with pytest.raises(Exception, match=r"Tensor is not a torch image."):
func(tensor, *args)
@pytest.mark.parametrize("device", cpu_and_gpu())
def test_vflip(device):