2246 lines
81 KiB
Python
2246 lines
81 KiB
Python
import math
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import os
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import random
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import re
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import sys
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from functools import partial
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import numpy as np
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import pytest
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import torch
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import torchvision.transforms as transforms
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import torchvision.transforms._functional_tensor as F_t
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import torchvision.transforms.functional as F
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from PIL import Image
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from torch._utils_internal import get_file_path_2
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try:
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import accimage
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except ImportError:
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accimage = None
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try:
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from scipy import stats
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except ImportError:
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stats = None
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from common_utils import assert_equal, cycle_over, float_dtypes, int_dtypes
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GRACE_HOPPER = get_file_path_2(
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os.path.dirname(os.path.abspath(__file__)), "assets", "encode_jpeg", "grace_hopper_517x606.jpg"
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)
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def _get_grayscale_test_image(img, fill=None):
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img = img.convert("L")
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fill = (fill[0],) if isinstance(fill, tuple) else fill
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return img, fill
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class TestConvertImageDtype:
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@pytest.mark.parametrize("input_dtype, output_dtype", cycle_over(float_dtypes()))
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def test_float_to_float(self, input_dtype, output_dtype):
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input_image = torch.tensor((0.0, 1.0), dtype=input_dtype)
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transform = transforms.ConvertImageDtype(output_dtype)
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transform_script = torch.jit.script(F.convert_image_dtype)
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output_image = transform(input_image)
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output_image_script = transform_script(input_image, output_dtype)
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torch.testing.assert_close(output_image_script, output_image, rtol=0.0, atol=1e-6)
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actual_min, actual_max = output_image.tolist()
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desired_min, desired_max = 0.0, 1.0
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assert abs(actual_min - desired_min) < 1e-7
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assert abs(actual_max - desired_max) < 1e-7
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@pytest.mark.parametrize("input_dtype", float_dtypes())
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@pytest.mark.parametrize("output_dtype", int_dtypes())
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def test_float_to_int(self, input_dtype, output_dtype):
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input_image = torch.tensor((0.0, 1.0), dtype=input_dtype)
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transform = transforms.ConvertImageDtype(output_dtype)
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transform_script = torch.jit.script(F.convert_image_dtype)
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if (input_dtype == torch.float32 and output_dtype in (torch.int32, torch.int64)) or (
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input_dtype == torch.float64 and output_dtype == torch.int64
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):
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with pytest.raises(RuntimeError):
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transform(input_image)
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else:
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output_image = transform(input_image)
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output_image_script = transform_script(input_image, output_dtype)
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torch.testing.assert_close(output_image_script, output_image, rtol=0.0, atol=1e-6)
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actual_min, actual_max = output_image.tolist()
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desired_min, desired_max = 0, torch.iinfo(output_dtype).max
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assert actual_min == desired_min
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assert actual_max == desired_max
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@pytest.mark.parametrize("input_dtype", int_dtypes())
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@pytest.mark.parametrize("output_dtype", float_dtypes())
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def test_int_to_float(self, input_dtype, output_dtype):
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input_image = torch.tensor((0, torch.iinfo(input_dtype).max), dtype=input_dtype)
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transform = transforms.ConvertImageDtype(output_dtype)
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transform_script = torch.jit.script(F.convert_image_dtype)
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output_image = transform(input_image)
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output_image_script = transform_script(input_image, output_dtype)
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torch.testing.assert_close(output_image_script, output_image, rtol=0.0, atol=1e-6)
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actual_min, actual_max = output_image.tolist()
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desired_min, desired_max = 0.0, 1.0
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assert abs(actual_min - desired_min) < 1e-7
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assert actual_min >= desired_min
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assert abs(actual_max - desired_max) < 1e-7
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assert actual_max <= desired_max
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@pytest.mark.parametrize("input_dtype, output_dtype", cycle_over(int_dtypes()))
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def test_dtype_int_to_int(self, input_dtype, output_dtype):
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input_max = torch.iinfo(input_dtype).max
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input_image = torch.tensor((0, input_max), dtype=input_dtype)
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output_max = torch.iinfo(output_dtype).max
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transform = transforms.ConvertImageDtype(output_dtype)
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transform_script = torch.jit.script(F.convert_image_dtype)
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output_image = transform(input_image)
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output_image_script = transform_script(input_image, output_dtype)
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torch.testing.assert_close(
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output_image_script,
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output_image,
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rtol=0.0,
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atol=1e-6,
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msg=f"{output_image_script} vs {output_image}",
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)
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actual_min, actual_max = output_image.tolist()
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desired_min, desired_max = 0, output_max
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# see https://github.com/pytorch/vision/pull/2078#issuecomment-641036236 for details
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if input_max >= output_max:
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error_term = 0
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else:
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error_term = 1 - (torch.iinfo(output_dtype).max + 1) // (torch.iinfo(input_dtype).max + 1)
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assert actual_min == desired_min
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assert actual_max == (desired_max + error_term)
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@pytest.mark.parametrize("input_dtype, output_dtype", cycle_over(int_dtypes()))
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def test_int_to_int_consistency(self, input_dtype, output_dtype):
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input_max = torch.iinfo(input_dtype).max
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input_image = torch.tensor((0, input_max), dtype=input_dtype)
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output_max = torch.iinfo(output_dtype).max
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if output_max <= input_max:
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return
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transform = transforms.ConvertImageDtype(output_dtype)
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inverse_transfrom = transforms.ConvertImageDtype(input_dtype)
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output_image = inverse_transfrom(transform(input_image))
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actual_min, actual_max = output_image.tolist()
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desired_min, desired_max = 0, input_max
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assert actual_min == desired_min
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assert actual_max == desired_max
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@pytest.mark.skipif(accimage is None, reason="accimage not available")
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class TestAccImage:
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def test_accimage_to_tensor(self):
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trans = transforms.PILToTensor()
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expected_output = trans(Image.open(GRACE_HOPPER).convert("RGB"))
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output = trans(accimage.Image(GRACE_HOPPER))
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torch.testing.assert_close(output, expected_output)
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def test_accimage_pil_to_tensor(self):
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trans = transforms.PILToTensor()
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expected_output = trans(Image.open(GRACE_HOPPER).convert("RGB"))
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output = trans(accimage.Image(GRACE_HOPPER))
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assert expected_output.size() == output.size()
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torch.testing.assert_close(output, expected_output)
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def test_accimage_resize(self):
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trans = transforms.Compose(
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[
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transforms.Resize(256, interpolation=Image.LINEAR),
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transforms.PILToTensor(),
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transforms.ConvertImageDtype(dtype=torch.float),
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]
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)
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# Checking if Compose, Resize and ToTensor can be printed as string
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trans.__repr__()
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expected_output = trans(Image.open(GRACE_HOPPER).convert("RGB"))
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output = trans(accimage.Image(GRACE_HOPPER))
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assert expected_output.size() == output.size()
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assert np.abs((expected_output - output).mean()) < 1e-3
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assert (expected_output - output).var() < 1e-5
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# note the high absolute tolerance
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torch.testing.assert_close(output.numpy(), expected_output.numpy(), rtol=1e-5, atol=5e-2)
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def test_accimage_crop(self):
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trans = transforms.Compose(
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[transforms.CenterCrop(256), transforms.PILToTensor(), transforms.ConvertImageDtype(dtype=torch.float)]
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)
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# Checking if Compose, CenterCrop and ToTensor can be printed as string
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trans.__repr__()
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expected_output = trans(Image.open(GRACE_HOPPER).convert("RGB"))
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output = trans(accimage.Image(GRACE_HOPPER))
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assert expected_output.size() == output.size()
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torch.testing.assert_close(output, expected_output)
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class TestToTensor:
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@pytest.mark.parametrize("channels", [1, 3, 4])
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def test_to_tensor(self, channels):
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height, width = 4, 4
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trans = transforms.ToTensor()
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np_rng = np.random.RandomState(0)
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input_data = torch.ByteTensor(channels, height, width).random_(0, 255).float().div_(255)
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img = transforms.ToPILImage()(input_data)
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output = trans(img)
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torch.testing.assert_close(output, input_data)
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ndarray = np_rng.randint(low=0, high=255, size=(height, width, channels)).astype(np.uint8)
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output = trans(ndarray)
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expected_output = ndarray.transpose((2, 0, 1)) / 255.0
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torch.testing.assert_close(output.numpy(), expected_output, check_dtype=False)
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ndarray = np_rng.rand(height, width, channels).astype(np.float32)
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output = trans(ndarray)
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expected_output = ndarray.transpose((2, 0, 1))
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torch.testing.assert_close(output.numpy(), expected_output, check_dtype=False)
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# separate test for mode '1' PIL images
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input_data = torch.ByteTensor(1, height, width).bernoulli_()
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img = transforms.ToPILImage()(input_data.mul(255)).convert("1")
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output = trans(img)
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torch.testing.assert_close(input_data, output, check_dtype=False)
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def test_to_tensor_errors(self):
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height, width = 4, 4
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trans = transforms.ToTensor()
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np_rng = np.random.RandomState(0)
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with pytest.raises(TypeError):
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trans(np_rng.rand(1, height, width).tolist())
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with pytest.raises(ValueError):
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trans(np_rng.rand(height))
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with pytest.raises(ValueError):
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trans(np_rng.rand(1, 1, height, width))
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@pytest.mark.parametrize("dtype", [torch.float16, torch.float, torch.double])
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def test_to_tensor_with_other_default_dtypes(self, dtype):
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np_rng = np.random.RandomState(0)
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current_def_dtype = torch.get_default_dtype()
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t = transforms.ToTensor()
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np_arr = np_rng.randint(0, 255, (32, 32, 3), dtype=np.uint8)
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img = Image.fromarray(np_arr)
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torch.set_default_dtype(dtype)
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res = t(img)
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assert res.dtype == dtype, f"{res.dtype} vs {dtype}"
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torch.set_default_dtype(current_def_dtype)
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@pytest.mark.parametrize("channels", [1, 3, 4])
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def test_pil_to_tensor(self, channels):
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height, width = 4, 4
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trans = transforms.PILToTensor()
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np_rng = np.random.RandomState(0)
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input_data = torch.ByteTensor(channels, height, width).random_(0, 255)
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img = transforms.ToPILImage()(input_data)
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output = trans(img)
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torch.testing.assert_close(input_data, output)
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input_data = np_rng.randint(low=0, high=255, size=(height, width, channels)).astype(np.uint8)
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img = transforms.ToPILImage()(input_data)
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output = trans(img)
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expected_output = input_data.transpose((2, 0, 1))
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torch.testing.assert_close(output.numpy(), expected_output)
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input_data = torch.as_tensor(np_rng.rand(channels, height, width).astype(np.float32))
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img = transforms.ToPILImage()(input_data) # CHW -> HWC and (* 255).byte()
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output = trans(img) # HWC -> CHW
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expected_output = (input_data * 255).byte()
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torch.testing.assert_close(output, expected_output)
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# separate test for mode '1' PIL images
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input_data = torch.ByteTensor(1, height, width).bernoulli_()
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img = transforms.ToPILImage()(input_data.mul(255)).convert("1")
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output = trans(img).view(torch.uint8).bool().to(torch.uint8)
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torch.testing.assert_close(input_data, output)
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def test_pil_to_tensor_errors(self):
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height, width = 4, 4
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trans = transforms.PILToTensor()
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np_rng = np.random.RandomState(0)
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with pytest.raises(TypeError):
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trans(np_rng.rand(1, height, width).tolist())
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with pytest.raises(TypeError):
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trans(np_rng.rand(1, height, width))
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def test_randomresized_params():
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height = random.randint(24, 32) * 2
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width = random.randint(24, 32) * 2
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img = torch.ones(3, height, width)
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to_pil_image = transforms.ToPILImage()
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img = to_pil_image(img)
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size = 100
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epsilon = 0.05
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min_scale = 0.25
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for _ in range(10):
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scale_min = max(round(random.random(), 2), min_scale)
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scale_range = (scale_min, scale_min + round(random.random(), 2))
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aspect_min = max(round(random.random(), 2), epsilon)
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aspect_ratio_range = (aspect_min, aspect_min + round(random.random(), 2))
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randresizecrop = transforms.RandomResizedCrop(size, scale_range, aspect_ratio_range, antialias=True)
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i, j, h, w = randresizecrop.get_params(img, scale_range, aspect_ratio_range)
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aspect_ratio_obtained = w / h
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assert (
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min(aspect_ratio_range) - epsilon <= aspect_ratio_obtained
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and aspect_ratio_obtained <= max(aspect_ratio_range) + epsilon
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) or aspect_ratio_obtained == 1.0
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assert isinstance(i, int)
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assert isinstance(j, int)
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assert isinstance(h, int)
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assert isinstance(w, int)
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@pytest.mark.parametrize(
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"height, width",
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[
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# height, width
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# square image
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(28, 28),
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(27, 27),
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# rectangular image: h < w
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(28, 34),
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(29, 35),
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# rectangular image: h > w
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(34, 28),
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(35, 29),
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],
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)
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@pytest.mark.parametrize(
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"osize",
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[
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# single integer
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22,
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27,
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28,
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36,
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# single integer in tuple/list
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[
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22,
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],
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(27,),
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],
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)
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@pytest.mark.parametrize("max_size", (None, 37, 1000))
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def test_resize(height, width, osize, max_size):
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img = Image.new("RGB", size=(width, height), color=127)
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t = transforms.Resize(osize, max_size=max_size, antialias=True)
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result = t(img)
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msg = f"{height}, {width} - {osize} - {max_size}"
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osize = osize[0] if isinstance(osize, (list, tuple)) else osize
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# If size is an int, smaller edge of the image will be matched to this number.
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# i.e, if height > width, then image will be rescaled to (size * height / width, size).
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if height < width:
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exp_w, exp_h = (int(osize * width / height), osize) # (w, h)
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if max_size is not None and max_size < exp_w:
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exp_w, exp_h = max_size, int(max_size * exp_h / exp_w)
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assert result.size == (exp_w, exp_h), msg
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elif width < height:
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exp_w, exp_h = (osize, int(osize * height / width)) # (w, h)
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if max_size is not None and max_size < exp_h:
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exp_w, exp_h = int(max_size * exp_w / exp_h), max_size
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assert result.size == (exp_w, exp_h), msg
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else:
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exp_w, exp_h = (osize, osize) # (w, h)
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if max_size is not None and max_size < osize:
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exp_w, exp_h = max_size, max_size
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assert result.size == (exp_w, exp_h), msg
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|
|
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@pytest.mark.parametrize(
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"height, width",
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[
|
|
# height, width
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|
# square image
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(28, 28),
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(27, 27),
|
|
# rectangular image: h < w
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(28, 34),
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(29, 35),
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|
# rectangular image: h > w
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(34, 28),
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(35, 29),
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],
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)
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@pytest.mark.parametrize(
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"osize",
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[
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# two integers sequence output
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[22, 22],
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[22, 28],
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[22, 36],
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[27, 22],
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[36, 22],
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[28, 28],
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[28, 37],
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[37, 27],
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[37, 37],
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],
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)
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def test_resize_sequence_output(height, width, osize):
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img = Image.new("RGB", size=(width, height), color=127)
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oheight, owidth = osize
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t = transforms.Resize(osize, antialias=True)
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result = t(img)
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assert (owidth, oheight) == result.size
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def test_resize_antialias_error():
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osize = [37, 37]
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img = Image.new("RGB", size=(35, 29), color=127)
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with pytest.warns(UserWarning, match=r"Anti-alias option is always applied for PIL Image input"):
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t = transforms.Resize(osize, antialias=False)
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t(img)
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|
|
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@pytest.mark.parametrize("height, width", ((32, 64), (64, 32)))
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def test_resize_size_equals_small_edge_size(height, width):
|
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# Non-regression test for https://github.com/pytorch/vision/issues/5405
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# max_size used to be ignored if size == small_edge_size
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max_size = 40
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img = Image.new("RGB", size=(width, height), color=127)
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small_edge = min(height, width)
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t = transforms.Resize(small_edge, max_size=max_size, antialias=True)
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result = t(img)
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assert max(result.size) == max_size
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|
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def test_resize_equal_input_output_sizes():
|
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# Regression test for https://github.com/pytorch/vision/issues/7518
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height, width = 28, 27
|
|
img = Image.new("RGB", size=(width, height))
|
|
|
|
t = transforms.Resize((height, width), antialias=True)
|
|
result = t(img)
|
|
assert result is img
|
|
|
|
|
|
class TestPad:
|
|
@pytest.mark.parametrize("fill", [85, 85.0])
|
|
def test_pad(self, fill):
|
|
height = random.randint(10, 32) * 2
|
|
width = random.randint(10, 32) * 2
|
|
img = torch.ones(3, height, width, dtype=torch.uint8)
|
|
padding = random.randint(1, 20)
|
|
result = transforms.Compose(
|
|
[
|
|
transforms.ToPILImage(),
|
|
transforms.Pad(padding, fill=fill),
|
|
transforms.PILToTensor(),
|
|
]
|
|
)(img)
|
|
assert result.size(1) == height + 2 * padding
|
|
assert result.size(2) == width + 2 * padding
|
|
# check that all elements in the padded region correspond
|
|
# to the pad value
|
|
h_padded = result[:, :padding, :]
|
|
w_padded = result[:, :, :padding]
|
|
torch.testing.assert_close(h_padded, torch.full_like(h_padded, fill_value=fill), rtol=0.0, atol=0.0)
|
|
torch.testing.assert_close(w_padded, torch.full_like(w_padded, fill_value=fill), rtol=0.0, atol=0.0)
|
|
pytest.raises(ValueError, transforms.Pad(padding, fill=(1, 2)), transforms.ToPILImage()(img))
|
|
|
|
def test_pad_with_tuple_of_pad_values(self):
|
|
height = random.randint(10, 32) * 2
|
|
width = random.randint(10, 32) * 2
|
|
img = transforms.ToPILImage()(torch.ones(3, height, width))
|
|
|
|
padding = tuple(random.randint(1, 20) for _ in range(2))
|
|
output = transforms.Pad(padding)(img)
|
|
assert output.size == (width + padding[0] * 2, height + padding[1] * 2)
|
|
|
|
padding = [random.randint(1, 20) for _ in range(4)]
|
|
output = transforms.Pad(padding)(img)
|
|
assert output.size[0] == width + padding[0] + padding[2]
|
|
assert output.size[1] == height + padding[1] + padding[3]
|
|
|
|
# Checking if Padding can be printed as string
|
|
transforms.Pad(padding).__repr__()
|
|
|
|
def test_pad_with_non_constant_padding_modes(self):
|
|
"""Unit tests for edge, reflect, symmetric padding"""
|
|
img = torch.zeros(3, 27, 27).byte()
|
|
img[:, :, 0] = 1 # Constant value added to leftmost edge
|
|
img = transforms.ToPILImage()(img)
|
|
img = F.pad(img, 1, (200, 200, 200))
|
|
|
|
# pad 3 to all sidess
|
|
edge_padded_img = F.pad(img, 3, padding_mode="edge")
|
|
# First 6 elements of leftmost edge in the middle of the image, values are in order:
|
|
# edge_pad, edge_pad, edge_pad, constant_pad, constant value added to leftmost edge, 0
|
|
edge_middle_slice = np.asarray(edge_padded_img).transpose(2, 0, 1)[0][17][:6]
|
|
assert_equal(edge_middle_slice, np.asarray([200, 200, 200, 200, 1, 0], dtype=np.uint8))
|
|
assert transforms.PILToTensor()(edge_padded_img).size() == (3, 35, 35)
|
|
|
|
# Pad 3 to left/right, 2 to top/bottom
|
|
reflect_padded_img = F.pad(img, (3, 2), padding_mode="reflect")
|
|
# First 6 elements of leftmost edge in the middle of the image, values are in order:
|
|
# reflect_pad, reflect_pad, reflect_pad, constant_pad, constant value added to leftmost edge, 0
|
|
reflect_middle_slice = np.asarray(reflect_padded_img).transpose(2, 0, 1)[0][17][:6]
|
|
assert_equal(reflect_middle_slice, np.asarray([0, 0, 1, 200, 1, 0], dtype=np.uint8))
|
|
assert transforms.PILToTensor()(reflect_padded_img).size() == (3, 33, 35)
|
|
|
|
# Pad 3 to left, 2 to top, 2 to right, 1 to bottom
|
|
symmetric_padded_img = F.pad(img, (3, 2, 2, 1), padding_mode="symmetric")
|
|
# First 6 elements of leftmost edge in the middle of the image, values are in order:
|
|
# sym_pad, sym_pad, sym_pad, constant_pad, constant value added to leftmost edge, 0
|
|
symmetric_middle_slice = np.asarray(symmetric_padded_img).transpose(2, 0, 1)[0][17][:6]
|
|
assert_equal(symmetric_middle_slice, np.asarray([0, 1, 200, 200, 1, 0], dtype=np.uint8))
|
|
assert transforms.PILToTensor()(symmetric_padded_img).size() == (3, 32, 34)
|
|
|
|
# Check negative padding explicitly for symmetric case, since it is not
|
|
# implemented for tensor case to compare to
|
|
# Crop 1 to left, pad 2 to top, pad 3 to right, crop 3 to bottom
|
|
symmetric_padded_img_neg = F.pad(img, (-1, 2, 3, -3), padding_mode="symmetric")
|
|
symmetric_neg_middle_left = np.asarray(symmetric_padded_img_neg).transpose(2, 0, 1)[0][17][:3]
|
|
symmetric_neg_middle_right = np.asarray(symmetric_padded_img_neg).transpose(2, 0, 1)[0][17][-4:]
|
|
assert_equal(symmetric_neg_middle_left, np.asarray([1, 0, 0], dtype=np.uint8))
|
|
assert_equal(symmetric_neg_middle_right, np.asarray([200, 200, 0, 0], dtype=np.uint8))
|
|
assert transforms.PILToTensor()(symmetric_padded_img_neg).size() == (3, 28, 31)
|
|
|
|
def test_pad_raises_with_invalid_pad_sequence_len(self):
|
|
with pytest.raises(ValueError):
|
|
transforms.Pad(())
|
|
|
|
with pytest.raises(ValueError):
|
|
transforms.Pad((1, 2, 3))
|
|
|
|
with pytest.raises(ValueError):
|
|
transforms.Pad((1, 2, 3, 4, 5))
|
|
|
|
def test_pad_with_mode_F_images(self):
|
|
pad = 2
|
|
transform = transforms.Pad(pad)
|
|
|
|
img = Image.new("F", (10, 10))
|
|
padded_img = transform(img)
|
|
assert_equal(padded_img.size, [edge_size + 2 * pad for edge_size in img.size])
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"fn, trans, kwargs",
|
|
[
|
|
(F.invert, transforms.RandomInvert, {}),
|
|
(F.posterize, transforms.RandomPosterize, {"bits": 4}),
|
|
(F.solarize, transforms.RandomSolarize, {"threshold": 192}),
|
|
(F.adjust_sharpness, transforms.RandomAdjustSharpness, {"sharpness_factor": 2.0}),
|
|
(F.autocontrast, transforms.RandomAutocontrast, {}),
|
|
(F.equalize, transforms.RandomEqualize, {}),
|
|
(F.vflip, transforms.RandomVerticalFlip, {}),
|
|
(F.hflip, transforms.RandomHorizontalFlip, {}),
|
|
(partial(F.to_grayscale, num_output_channels=3), transforms.RandomGrayscale, {}),
|
|
],
|
|
)
|
|
@pytest.mark.parametrize("seed", range(10))
|
|
@pytest.mark.parametrize("p", (0, 1))
|
|
def test_randomness(fn, trans, kwargs, seed, p):
|
|
torch.manual_seed(seed)
|
|
img = transforms.ToPILImage()(torch.rand(3, 16, 18))
|
|
|
|
expected_transformed_img = fn(img, **kwargs)
|
|
randomly_transformed_img = trans(p=p, **kwargs)(img)
|
|
|
|
if p == 0:
|
|
assert randomly_transformed_img == img
|
|
elif p == 1:
|
|
assert randomly_transformed_img == expected_transformed_img
|
|
|
|
trans(**kwargs).__repr__()
|
|
|
|
|
|
def test_autocontrast_equal_minmax():
|
|
img_tensor = torch.tensor([[[10]], [[128]], [[245]]], dtype=torch.uint8).expand(3, 32, 32)
|
|
img_pil = F.to_pil_image(img_tensor)
|
|
|
|
img_tensor = F.autocontrast(img_tensor)
|
|
img_pil = F.autocontrast(img_pil)
|
|
torch.testing.assert_close(img_tensor, F.pil_to_tensor(img_pil))
|
|
|
|
|
|
class TestToPil:
|
|
def _get_1_channel_tensor_various_types():
|
|
img_data_float = torch.Tensor(1, 4, 4).uniform_()
|
|
expected_output = img_data_float.mul(255).int().float().div(255).numpy()
|
|
yield img_data_float, expected_output, "L"
|
|
|
|
img_data_byte = torch.ByteTensor(1, 4, 4).random_(0, 255)
|
|
expected_output = img_data_byte.float().div(255.0).numpy()
|
|
yield img_data_byte, expected_output, "L"
|
|
|
|
img_data_short = torch.ShortTensor(1, 4, 4).random_()
|
|
expected_output = img_data_short.numpy()
|
|
yield img_data_short, expected_output, "I;16" if sys.byteorder == "little" else "I;16B"
|
|
|
|
img_data_int = torch.IntTensor(1, 4, 4).random_()
|
|
expected_output = img_data_int.numpy()
|
|
yield img_data_int, expected_output, "I"
|
|
|
|
def _get_2d_tensor_various_types():
|
|
img_data_float = torch.Tensor(4, 4).uniform_()
|
|
expected_output = img_data_float.mul(255).int().float().div(255).numpy()
|
|
yield img_data_float, expected_output, "L"
|
|
|
|
img_data_byte = torch.ByteTensor(4, 4).random_(0, 255)
|
|
expected_output = img_data_byte.float().div(255.0).numpy()
|
|
yield img_data_byte, expected_output, "L"
|
|
|
|
img_data_short = torch.ShortTensor(4, 4).random_()
|
|
expected_output = img_data_short.numpy()
|
|
yield img_data_short, expected_output, "I;16" if sys.byteorder == "little" else "I;16B"
|
|
|
|
img_data_int = torch.IntTensor(4, 4).random_()
|
|
expected_output = img_data_int.numpy()
|
|
yield img_data_int, expected_output, "I"
|
|
|
|
@pytest.mark.parametrize("with_mode", [False, True])
|
|
@pytest.mark.parametrize("img_data, expected_output, expected_mode", _get_1_channel_tensor_various_types())
|
|
def test_1_channel_tensor_to_pil_image(self, with_mode, img_data, expected_output, expected_mode):
|
|
transform = transforms.ToPILImage(mode=expected_mode) if with_mode else transforms.ToPILImage()
|
|
to_tensor = transforms.ToTensor()
|
|
|
|
img = transform(img_data)
|
|
assert img.mode == expected_mode
|
|
torch.testing.assert_close(expected_output, to_tensor(img).numpy())
|
|
|
|
def test_1_channel_float_tensor_to_pil_image(self):
|
|
img_data = torch.Tensor(1, 4, 4).uniform_()
|
|
# 'F' mode for torch.FloatTensor
|
|
img_F_mode = transforms.ToPILImage(mode="F")(img_data)
|
|
assert img_F_mode.mode == "F"
|
|
torch.testing.assert_close(
|
|
np.array(Image.fromarray(img_data.squeeze(0).numpy(), mode="F")), np.array(img_F_mode)
|
|
)
|
|
|
|
@pytest.mark.parametrize("with_mode", [False, True])
|
|
@pytest.mark.parametrize(
|
|
"img_data, expected_mode",
|
|
[
|
|
(torch.Tensor(4, 4, 1).uniform_().numpy(), "L"),
|
|
(torch.ByteTensor(4, 4, 1).random_(0, 255).numpy(), "L"),
|
|
(torch.ShortTensor(4, 4, 1).random_().numpy(), "I;16" if sys.byteorder == "little" else "I;16B"),
|
|
(torch.IntTensor(4, 4, 1).random_().numpy(), "I"),
|
|
],
|
|
)
|
|
def test_1_channel_ndarray_to_pil_image(self, with_mode, img_data, expected_mode):
|
|
transform = transforms.ToPILImage(mode=expected_mode) if with_mode else transforms.ToPILImage()
|
|
img = transform(img_data)
|
|
assert img.mode == expected_mode
|
|
if np.issubdtype(img_data.dtype, np.floating):
|
|
img_data = (img_data * 255).astype(np.uint8)
|
|
# note: we explicitly convert img's dtype because pytorch doesn't support uint16
|
|
# and otherwise assert_close wouldn't be able to construct a tensor from the uint16 array
|
|
torch.testing.assert_close(img_data[:, :, 0], np.asarray(img).astype(img_data.dtype))
|
|
|
|
@pytest.mark.parametrize("expected_mode", [None, "LA"])
|
|
def test_2_channel_ndarray_to_pil_image(self, expected_mode):
|
|
img_data = torch.ByteTensor(4, 4, 2).random_(0, 255).numpy()
|
|
|
|
if expected_mode is None:
|
|
img = transforms.ToPILImage()(img_data)
|
|
assert img.mode == "LA" # default should assume LA
|
|
else:
|
|
img = transforms.ToPILImage(mode=expected_mode)(img_data)
|
|
assert img.mode == expected_mode
|
|
split = img.split()
|
|
for i in range(2):
|
|
torch.testing.assert_close(img_data[:, :, i], np.asarray(split[i]))
|
|
|
|
def test_2_channel_ndarray_to_pil_image_error(self):
|
|
img_data = torch.ByteTensor(4, 4, 2).random_(0, 255).numpy()
|
|
transforms.ToPILImage().__repr__()
|
|
|
|
# should raise if we try a mode for 4 or 1 or 3 channel images
|
|
with pytest.raises(ValueError, match=r"Only modes \['LA'\] are supported for 2D inputs"):
|
|
transforms.ToPILImage(mode="RGBA")(img_data)
|
|
with pytest.raises(ValueError, match=r"Only modes \['LA'\] are supported for 2D inputs"):
|
|
transforms.ToPILImage(mode="P")(img_data)
|
|
with pytest.raises(ValueError, match=r"Only modes \['LA'\] are supported for 2D inputs"):
|
|
transforms.ToPILImage(mode="RGB")(img_data)
|
|
|
|
@pytest.mark.parametrize("expected_mode", [None, "LA"])
|
|
def test_2_channel_tensor_to_pil_image(self, expected_mode):
|
|
img_data = torch.Tensor(2, 4, 4).uniform_()
|
|
expected_output = img_data.mul(255).int().float().div(255)
|
|
if expected_mode is None:
|
|
img = transforms.ToPILImage()(img_data)
|
|
assert img.mode == "LA" # default should assume LA
|
|
else:
|
|
img = transforms.ToPILImage(mode=expected_mode)(img_data)
|
|
assert img.mode == expected_mode
|
|
|
|
split = img.split()
|
|
for i in range(2):
|
|
torch.testing.assert_close(expected_output[i].numpy(), F.to_tensor(split[i]).squeeze(0).numpy())
|
|
|
|
def test_2_channel_tensor_to_pil_image_error(self):
|
|
img_data = torch.Tensor(2, 4, 4).uniform_()
|
|
|
|
# should raise if we try a mode for 4 or 1 or 3 channel images
|
|
with pytest.raises(ValueError, match=r"Only modes \['LA'\] are supported for 2D inputs"):
|
|
transforms.ToPILImage(mode="RGBA")(img_data)
|
|
with pytest.raises(ValueError, match=r"Only modes \['LA'\] are supported for 2D inputs"):
|
|
transforms.ToPILImage(mode="P")(img_data)
|
|
with pytest.raises(ValueError, match=r"Only modes \['LA'\] are supported for 2D inputs"):
|
|
transforms.ToPILImage(mode="RGB")(img_data)
|
|
|
|
@pytest.mark.parametrize("with_mode", [False, True])
|
|
@pytest.mark.parametrize("img_data, expected_output, expected_mode", _get_2d_tensor_various_types())
|
|
def test_2d_tensor_to_pil_image(self, with_mode, img_data, expected_output, expected_mode):
|
|
transform = transforms.ToPILImage(mode=expected_mode) if with_mode else transforms.ToPILImage()
|
|
to_tensor = transforms.ToTensor()
|
|
|
|
img = transform(img_data)
|
|
assert img.mode == expected_mode
|
|
torch.testing.assert_close(expected_output, to_tensor(img).numpy()[0])
|
|
|
|
@pytest.mark.parametrize("with_mode", [False, True])
|
|
@pytest.mark.parametrize(
|
|
"img_data, expected_mode",
|
|
[
|
|
(torch.Tensor(4, 4).uniform_().numpy(), "L"),
|
|
(torch.ByteTensor(4, 4).random_(0, 255).numpy(), "L"),
|
|
(torch.ShortTensor(4, 4).random_().numpy(), "I;16" if sys.byteorder == "little" else "I;16B"),
|
|
(torch.IntTensor(4, 4).random_().numpy(), "I"),
|
|
],
|
|
)
|
|
def test_2d_ndarray_to_pil_image(self, with_mode, img_data, expected_mode):
|
|
transform = transforms.ToPILImage(mode=expected_mode) if with_mode else transforms.ToPILImage()
|
|
img = transform(img_data)
|
|
assert img.mode == expected_mode
|
|
if np.issubdtype(img_data.dtype, np.floating):
|
|
img_data = (img_data * 255).astype(np.uint8)
|
|
np.testing.assert_allclose(img_data, img)
|
|
|
|
@pytest.mark.parametrize("expected_mode", [None, "RGB", "HSV", "YCbCr"])
|
|
def test_3_channel_tensor_to_pil_image(self, expected_mode):
|
|
img_data = torch.Tensor(3, 4, 4).uniform_()
|
|
expected_output = img_data.mul(255).int().float().div(255)
|
|
|
|
if expected_mode is None:
|
|
img = transforms.ToPILImage()(img_data)
|
|
assert img.mode == "RGB" # default should assume RGB
|
|
else:
|
|
img = transforms.ToPILImage(mode=expected_mode)(img_data)
|
|
assert img.mode == expected_mode
|
|
split = img.split()
|
|
for i in range(3):
|
|
torch.testing.assert_close(expected_output[i].numpy(), F.to_tensor(split[i]).squeeze(0).numpy())
|
|
|
|
def test_3_channel_tensor_to_pil_image_error(self):
|
|
img_data = torch.Tensor(3, 4, 4).uniform_()
|
|
error_message_3d = r"Only modes \['RGB', 'YCbCr', 'HSV'\] are supported for 3D inputs"
|
|
# should raise if we try a mode for 4 or 1 or 2 channel images
|
|
with pytest.raises(ValueError, match=error_message_3d):
|
|
transforms.ToPILImage(mode="RGBA")(img_data)
|
|
with pytest.raises(ValueError, match=error_message_3d):
|
|
transforms.ToPILImage(mode="P")(img_data)
|
|
with pytest.raises(ValueError, match=error_message_3d):
|
|
transforms.ToPILImage(mode="LA")(img_data)
|
|
|
|
with pytest.raises(ValueError, match=r"pic should be 2/3 dimensional. Got \d+ dimensions."):
|
|
transforms.ToPILImage()(torch.Tensor(1, 3, 4, 4).uniform_())
|
|
|
|
@pytest.mark.parametrize("expected_mode", [None, "RGB", "HSV", "YCbCr"])
|
|
def test_3_channel_ndarray_to_pil_image(self, expected_mode):
|
|
img_data = torch.ByteTensor(4, 4, 3).random_(0, 255).numpy()
|
|
|
|
if expected_mode is None:
|
|
img = transforms.ToPILImage()(img_data)
|
|
assert img.mode == "RGB" # default should assume RGB
|
|
else:
|
|
img = transforms.ToPILImage(mode=expected_mode)(img_data)
|
|
assert img.mode == expected_mode
|
|
split = img.split()
|
|
for i in range(3):
|
|
torch.testing.assert_close(img_data[:, :, i], np.asarray(split[i]))
|
|
|
|
def test_3_channel_ndarray_to_pil_image_error(self):
|
|
img_data = torch.ByteTensor(4, 4, 3).random_(0, 255).numpy()
|
|
|
|
# Checking if ToPILImage can be printed as string
|
|
transforms.ToPILImage().__repr__()
|
|
|
|
error_message_3d = r"Only modes \['RGB', 'YCbCr', 'HSV'\] are supported for 3D inputs"
|
|
# should raise if we try a mode for 4 or 1 or 2 channel images
|
|
with pytest.raises(ValueError, match=error_message_3d):
|
|
transforms.ToPILImage(mode="RGBA")(img_data)
|
|
with pytest.raises(ValueError, match=error_message_3d):
|
|
transforms.ToPILImage(mode="P")(img_data)
|
|
with pytest.raises(ValueError, match=error_message_3d):
|
|
transforms.ToPILImage(mode="LA")(img_data)
|
|
|
|
@pytest.mark.parametrize("expected_mode", [None, "RGBA", "CMYK", "RGBX"])
|
|
def test_4_channel_tensor_to_pil_image(self, expected_mode):
|
|
img_data = torch.Tensor(4, 4, 4).uniform_()
|
|
expected_output = img_data.mul(255).int().float().div(255)
|
|
|
|
if expected_mode is None:
|
|
img = transforms.ToPILImage()(img_data)
|
|
assert img.mode == "RGBA" # default should assume RGBA
|
|
else:
|
|
img = transforms.ToPILImage(mode=expected_mode)(img_data)
|
|
assert img.mode == expected_mode
|
|
|
|
split = img.split()
|
|
for i in range(4):
|
|
torch.testing.assert_close(expected_output[i].numpy(), F.to_tensor(split[i]).squeeze(0).numpy())
|
|
|
|
def test_4_channel_tensor_to_pil_image_error(self):
|
|
img_data = torch.Tensor(4, 4, 4).uniform_()
|
|
|
|
error_message_4d = r"Only modes \['RGBA', 'CMYK', 'RGBX'\] are supported for 4D inputs"
|
|
# should raise if we try a mode for 3 or 1 or 2 channel images
|
|
with pytest.raises(ValueError, match=error_message_4d):
|
|
transforms.ToPILImage(mode="RGB")(img_data)
|
|
with pytest.raises(ValueError, match=error_message_4d):
|
|
transforms.ToPILImage(mode="P")(img_data)
|
|
with pytest.raises(ValueError, match=error_message_4d):
|
|
transforms.ToPILImage(mode="LA")(img_data)
|
|
|
|
@pytest.mark.parametrize("expected_mode", [None, "RGBA", "CMYK", "RGBX"])
|
|
def test_4_channel_ndarray_to_pil_image(self, expected_mode):
|
|
img_data = torch.ByteTensor(4, 4, 4).random_(0, 255).numpy()
|
|
|
|
if expected_mode is None:
|
|
img = transforms.ToPILImage()(img_data)
|
|
assert img.mode == "RGBA" # default should assume RGBA
|
|
else:
|
|
img = transforms.ToPILImage(mode=expected_mode)(img_data)
|
|
assert img.mode == expected_mode
|
|
split = img.split()
|
|
for i in range(4):
|
|
torch.testing.assert_close(img_data[:, :, i], np.asarray(split[i]))
|
|
|
|
def test_4_channel_ndarray_to_pil_image_error(self):
|
|
img_data = torch.ByteTensor(4, 4, 4).random_(0, 255).numpy()
|
|
|
|
error_message_4d = r"Only modes \['RGBA', 'CMYK', 'RGBX'\] are supported for 4D inputs"
|
|
# should raise if we try a mode for 3 or 1 or 2 channel images
|
|
with pytest.raises(ValueError, match=error_message_4d):
|
|
transforms.ToPILImage(mode="RGB")(img_data)
|
|
with pytest.raises(ValueError, match=error_message_4d):
|
|
transforms.ToPILImage(mode="P")(img_data)
|
|
with pytest.raises(ValueError, match=error_message_4d):
|
|
transforms.ToPILImage(mode="LA")(img_data)
|
|
|
|
def test_ndarray_bad_types_to_pil_image(self):
|
|
trans = transforms.ToPILImage()
|
|
reg_msg = r"Input type \w+ is not supported"
|
|
with pytest.raises(TypeError, match=reg_msg):
|
|
trans(np.ones([4, 4, 1], np.int64))
|
|
with pytest.raises(TypeError, match=reg_msg):
|
|
trans(np.ones([4, 4, 1], np.uint16))
|
|
with pytest.raises(TypeError, match=reg_msg):
|
|
trans(np.ones([4, 4, 1], np.uint32))
|
|
|
|
with pytest.raises(ValueError, match=r"pic should be 2/3 dimensional. Got \d+ dimensions."):
|
|
transforms.ToPILImage()(np.ones([1, 4, 4, 3]))
|
|
with pytest.raises(ValueError, match=r"pic should not have > 4 channels. Got \d+ channels."):
|
|
transforms.ToPILImage()(np.ones([4, 4, 6]))
|
|
|
|
def test_tensor_bad_types_to_pil_image(self):
|
|
with pytest.raises(ValueError, match=r"pic should be 2/3 dimensional. Got \d+ dimensions."):
|
|
transforms.ToPILImage()(torch.ones(1, 3, 4, 4))
|
|
with pytest.raises(ValueError, match=r"pic should not have > 4 channels. Got \d+ channels."):
|
|
transforms.ToPILImage()(torch.ones(6, 4, 4))
|
|
|
|
|
|
def test_adjust_brightness():
|
|
x_shape = [2, 2, 3]
|
|
x_data = [0, 5, 13, 54, 135, 226, 37, 8, 234, 90, 255, 1]
|
|
x_np = np.array(x_data, dtype=np.uint8).reshape(x_shape)
|
|
x_pil = Image.fromarray(x_np, mode="RGB")
|
|
|
|
# test 0
|
|
y_pil = F.adjust_brightness(x_pil, 1)
|
|
y_np = np.array(y_pil)
|
|
torch.testing.assert_close(y_np, x_np)
|
|
|
|
# test 1
|
|
y_pil = F.adjust_brightness(x_pil, 0.5)
|
|
y_np = np.array(y_pil)
|
|
y_ans = [0, 2, 6, 27, 67, 113, 18, 4, 117, 45, 127, 0]
|
|
y_ans = np.array(y_ans, dtype=np.uint8).reshape(x_shape)
|
|
torch.testing.assert_close(y_np, y_ans)
|
|
|
|
# test 2
|
|
y_pil = F.adjust_brightness(x_pil, 2)
|
|
y_np = np.array(y_pil)
|
|
y_ans = [0, 10, 26, 108, 255, 255, 74, 16, 255, 180, 255, 2]
|
|
y_ans = np.array(y_ans, dtype=np.uint8).reshape(x_shape)
|
|
torch.testing.assert_close(y_np, y_ans)
|
|
|
|
|
|
def test_adjust_contrast():
|
|
x_shape = [2, 2, 3]
|
|
x_data = [0, 5, 13, 54, 135, 226, 37, 8, 234, 90, 255, 1]
|
|
x_np = np.array(x_data, dtype=np.uint8).reshape(x_shape)
|
|
x_pil = Image.fromarray(x_np, mode="RGB")
|
|
|
|
# test 0
|
|
y_pil = F.adjust_contrast(x_pil, 1)
|
|
y_np = np.array(y_pil)
|
|
torch.testing.assert_close(y_np, x_np)
|
|
|
|
# test 1
|
|
y_pil = F.adjust_contrast(x_pil, 0.5)
|
|
y_np = np.array(y_pil)
|
|
y_ans = [43, 45, 49, 70, 110, 156, 61, 47, 160, 88, 170, 43]
|
|
y_ans = np.array(y_ans, dtype=np.uint8).reshape(x_shape)
|
|
torch.testing.assert_close(y_np, y_ans)
|
|
|
|
# test 2
|
|
y_pil = F.adjust_contrast(x_pil, 2)
|
|
y_np = np.array(y_pil)
|
|
y_ans = [0, 0, 0, 22, 184, 255, 0, 0, 255, 94, 255, 0]
|
|
y_ans = np.array(y_ans, dtype=np.uint8).reshape(x_shape)
|
|
torch.testing.assert_close(y_np, y_ans)
|
|
|
|
|
|
def test_adjust_hue():
|
|
x_shape = [2, 2, 3]
|
|
x_data = [0, 5, 13, 54, 135, 226, 37, 8, 234, 90, 255, 1]
|
|
x_np = np.array(x_data, dtype=np.uint8).reshape(x_shape)
|
|
x_pil = Image.fromarray(x_np, mode="RGB")
|
|
|
|
with pytest.raises(ValueError):
|
|
F.adjust_hue(x_pil, -0.7)
|
|
F.adjust_hue(x_pil, 1)
|
|
|
|
# test 0: almost same as x_data but not exact.
|
|
# probably because hsv <-> rgb floating point ops
|
|
y_pil = F.adjust_hue(x_pil, 0)
|
|
y_np = np.array(y_pil)
|
|
y_ans = [0, 5, 13, 54, 139, 226, 35, 8, 234, 91, 255, 1]
|
|
y_ans = np.array(y_ans, dtype=np.uint8).reshape(x_shape)
|
|
torch.testing.assert_close(y_np, y_ans)
|
|
|
|
# test 1
|
|
y_pil = F.adjust_hue(x_pil, 0.25)
|
|
y_np = np.array(y_pil)
|
|
y_ans = [13, 0, 12, 224, 54, 226, 234, 8, 99, 1, 222, 255]
|
|
y_ans = np.array(y_ans, dtype=np.uint8).reshape(x_shape)
|
|
torch.testing.assert_close(y_np, y_ans)
|
|
|
|
# test 2
|
|
y_pil = F.adjust_hue(x_pil, -0.25)
|
|
y_np = np.array(y_pil)
|
|
y_ans = [0, 13, 2, 54, 226, 58, 8, 234, 152, 255, 43, 1]
|
|
y_ans = np.array(y_ans, dtype=np.uint8).reshape(x_shape)
|
|
torch.testing.assert_close(y_np, y_ans)
|
|
|
|
|
|
def test_adjust_sharpness():
|
|
x_shape = [4, 4, 3]
|
|
x_data = [
|
|
75,
|
|
121,
|
|
114,
|
|
105,
|
|
97,
|
|
107,
|
|
105,
|
|
32,
|
|
66,
|
|
111,
|
|
117,
|
|
114,
|
|
99,
|
|
104,
|
|
97,
|
|
0,
|
|
0,
|
|
65,
|
|
108,
|
|
101,
|
|
120,
|
|
97,
|
|
110,
|
|
100,
|
|
101,
|
|
114,
|
|
32,
|
|
86,
|
|
114,
|
|
121,
|
|
110,
|
|
105,
|
|
111,
|
|
116,
|
|
105,
|
|
115,
|
|
0,
|
|
0,
|
|
73,
|
|
32,
|
|
108,
|
|
111,
|
|
118,
|
|
101,
|
|
32,
|
|
121,
|
|
111,
|
|
117,
|
|
]
|
|
x_np = np.array(x_data, dtype=np.uint8).reshape(x_shape)
|
|
x_pil = Image.fromarray(x_np, mode="RGB")
|
|
|
|
# test 0
|
|
y_pil = F.adjust_sharpness(x_pil, 1)
|
|
y_np = np.array(y_pil)
|
|
torch.testing.assert_close(y_np, x_np)
|
|
|
|
# test 1
|
|
y_pil = F.adjust_sharpness(x_pil, 0.5)
|
|
y_np = np.array(y_pil)
|
|
y_ans = [
|
|
75,
|
|
121,
|
|
114,
|
|
105,
|
|
97,
|
|
107,
|
|
105,
|
|
32,
|
|
66,
|
|
111,
|
|
117,
|
|
114,
|
|
99,
|
|
104,
|
|
97,
|
|
30,
|
|
30,
|
|
74,
|
|
103,
|
|
96,
|
|
114,
|
|
97,
|
|
110,
|
|
100,
|
|
101,
|
|
114,
|
|
32,
|
|
81,
|
|
103,
|
|
108,
|
|
102,
|
|
101,
|
|
107,
|
|
116,
|
|
105,
|
|
115,
|
|
0,
|
|
0,
|
|
73,
|
|
32,
|
|
108,
|
|
111,
|
|
118,
|
|
101,
|
|
32,
|
|
121,
|
|
111,
|
|
117,
|
|
]
|
|
y_ans = np.array(y_ans, dtype=np.uint8).reshape(x_shape)
|
|
torch.testing.assert_close(y_np, y_ans)
|
|
|
|
# test 2
|
|
y_pil = F.adjust_sharpness(x_pil, 2)
|
|
y_np = np.array(y_pil)
|
|
y_ans = [
|
|
75,
|
|
121,
|
|
114,
|
|
105,
|
|
97,
|
|
107,
|
|
105,
|
|
32,
|
|
66,
|
|
111,
|
|
117,
|
|
114,
|
|
99,
|
|
104,
|
|
97,
|
|
0,
|
|
0,
|
|
46,
|
|
118,
|
|
111,
|
|
132,
|
|
97,
|
|
110,
|
|
100,
|
|
101,
|
|
114,
|
|
32,
|
|
95,
|
|
135,
|
|
146,
|
|
126,
|
|
112,
|
|
119,
|
|
116,
|
|
105,
|
|
115,
|
|
0,
|
|
0,
|
|
73,
|
|
32,
|
|
108,
|
|
111,
|
|
118,
|
|
101,
|
|
32,
|
|
121,
|
|
111,
|
|
117,
|
|
]
|
|
y_ans = np.array(y_ans, dtype=np.uint8).reshape(x_shape)
|
|
torch.testing.assert_close(y_np, y_ans)
|
|
|
|
# test 3
|
|
x_shape = [2, 2, 3]
|
|
x_data = [0, 5, 13, 54, 135, 226, 37, 8, 234, 90, 255, 1]
|
|
x_np = np.array(x_data, dtype=np.uint8).reshape(x_shape)
|
|
x_pil = Image.fromarray(x_np, mode="RGB")
|
|
x_th = torch.tensor(x_np.transpose(2, 0, 1))
|
|
y_pil = F.adjust_sharpness(x_pil, 2)
|
|
y_np = np.array(y_pil).transpose(2, 0, 1)
|
|
y_th = F.adjust_sharpness(x_th, 2)
|
|
torch.testing.assert_close(y_np, y_th.numpy())
|
|
|
|
|
|
def test_adjust_gamma():
|
|
x_shape = [2, 2, 3]
|
|
x_data = [0, 5, 13, 54, 135, 226, 37, 8, 234, 90, 255, 1]
|
|
x_np = np.array(x_data, dtype=np.uint8).reshape(x_shape)
|
|
x_pil = Image.fromarray(x_np, mode="RGB")
|
|
|
|
# test 0
|
|
y_pil = F.adjust_gamma(x_pil, 1)
|
|
y_np = np.array(y_pil)
|
|
torch.testing.assert_close(y_np, x_np)
|
|
|
|
# test 1
|
|
y_pil = F.adjust_gamma(x_pil, 0.5)
|
|
y_np = np.array(y_pil)
|
|
y_ans = [0, 35, 57, 117, 186, 241, 97, 45, 245, 152, 255, 16]
|
|
y_ans = np.array(y_ans, dtype=np.uint8).reshape(x_shape)
|
|
torch.testing.assert_close(y_np, y_ans)
|
|
|
|
# test 2
|
|
y_pil = F.adjust_gamma(x_pil, 2)
|
|
y_np = np.array(y_pil)
|
|
y_ans = [0, 0, 0, 11, 71, 201, 5, 0, 215, 31, 255, 0]
|
|
y_ans = np.array(y_ans, dtype=np.uint8).reshape(x_shape)
|
|
torch.testing.assert_close(y_np, y_ans)
|
|
|
|
|
|
def test_adjusts_L_mode():
|
|
x_shape = [2, 2, 3]
|
|
x_data = [0, 5, 13, 54, 135, 226, 37, 8, 234, 90, 255, 1]
|
|
x_np = np.array(x_data, dtype=np.uint8).reshape(x_shape)
|
|
x_rgb = Image.fromarray(x_np, mode="RGB")
|
|
|
|
x_l = x_rgb.convert("L")
|
|
assert F.adjust_brightness(x_l, 2).mode == "L"
|
|
assert F.adjust_saturation(x_l, 2).mode == "L"
|
|
assert F.adjust_contrast(x_l, 2).mode == "L"
|
|
assert F.adjust_hue(x_l, 0.4).mode == "L"
|
|
assert F.adjust_sharpness(x_l, 2).mode == "L"
|
|
assert F.adjust_gamma(x_l, 0.5).mode == "L"
|
|
|
|
|
|
def test_rotate():
|
|
x = np.zeros((100, 100, 3), dtype=np.uint8)
|
|
x[40, 40] = [255, 255, 255]
|
|
|
|
with pytest.raises(TypeError, match=r"img should be PIL Image"):
|
|
F.rotate(x, 10)
|
|
|
|
img = F.to_pil_image(x)
|
|
|
|
result = F.rotate(img, 45)
|
|
assert result.size == (100, 100)
|
|
r, c, ch = np.where(result)
|
|
assert all(x in r for x in [49, 50])
|
|
assert all(x in c for x in [36])
|
|
assert all(x in ch for x in [0, 1, 2])
|
|
|
|
result = F.rotate(img, 45, expand=True)
|
|
assert result.size == (142, 142)
|
|
r, c, ch = np.where(result)
|
|
assert all(x in r for x in [70, 71])
|
|
assert all(x in c for x in [57])
|
|
assert all(x in ch for x in [0, 1, 2])
|
|
|
|
result = F.rotate(img, 45, center=(40, 40))
|
|
assert result.size == (100, 100)
|
|
r, c, ch = np.where(result)
|
|
assert all(x in r for x in [40])
|
|
assert all(x in c for x in [40])
|
|
assert all(x in ch for x in [0, 1, 2])
|
|
|
|
result_a = F.rotate(img, 90)
|
|
result_b = F.rotate(img, -270)
|
|
|
|
assert_equal(np.array(result_a), np.array(result_b))
|
|
|
|
|
|
@pytest.mark.parametrize("mode", ["L", "RGB", "F"])
|
|
def test_rotate_fill(mode):
|
|
img = F.to_pil_image(np.ones((100, 100, 3), dtype=np.uint8) * 255, "RGB")
|
|
|
|
num_bands = len(mode)
|
|
wrong_num_bands = num_bands + 1
|
|
fill = 127
|
|
|
|
img_conv = img.convert(mode)
|
|
img_rot = F.rotate(img_conv, 45.0, fill=fill)
|
|
pixel = img_rot.getpixel((0, 0))
|
|
|
|
if not isinstance(pixel, tuple):
|
|
pixel = (pixel,)
|
|
assert pixel == tuple([fill] * num_bands)
|
|
|
|
with pytest.raises(ValueError):
|
|
F.rotate(img_conv, 45.0, fill=tuple([fill] * wrong_num_bands))
|
|
|
|
|
|
def test_gaussian_blur_asserts():
|
|
np_img = np.ones((100, 100, 3), dtype=np.uint8) * 255
|
|
img = F.to_pil_image(np_img, "RGB")
|
|
|
|
with pytest.raises(ValueError, match=r"If kernel_size is a sequence its length should be 2"):
|
|
F.gaussian_blur(img, [3])
|
|
with pytest.raises(ValueError, match=r"If kernel_size is a sequence its length should be 2"):
|
|
F.gaussian_blur(img, [3, 3, 3])
|
|
with pytest.raises(ValueError, match=r"Kernel size should be a tuple/list of two integers"):
|
|
transforms.GaussianBlur([3, 3, 3])
|
|
|
|
with pytest.raises(ValueError, match=r"kernel_size should have odd and positive integers"):
|
|
F.gaussian_blur(img, [4, 4])
|
|
with pytest.raises(ValueError, match=r"Kernel size value should be an odd and positive number"):
|
|
transforms.GaussianBlur([4, 4])
|
|
|
|
with pytest.raises(ValueError, match=r"kernel_size should have odd and positive integers"):
|
|
F.gaussian_blur(img, [-3, -3])
|
|
with pytest.raises(ValueError, match=r"Kernel size value should be an odd and positive number"):
|
|
transforms.GaussianBlur([-3, -3])
|
|
|
|
with pytest.raises(ValueError, match=r"If sigma is a sequence, its length should be 2"):
|
|
F.gaussian_blur(img, 3, [1, 1, 1])
|
|
with pytest.raises(ValueError, match=r"sigma should be a single number or a list/tuple with length 2"):
|
|
transforms.GaussianBlur(3, [1, 1, 1])
|
|
|
|
with pytest.raises(ValueError, match=r"sigma should have positive values"):
|
|
F.gaussian_blur(img, 3, -1.0)
|
|
with pytest.raises(ValueError, match=r"If sigma is a single number, it must be positive"):
|
|
transforms.GaussianBlur(3, -1.0)
|
|
|
|
with pytest.raises(TypeError, match=r"kernel_size should be int or a sequence of integers"):
|
|
F.gaussian_blur(img, "kernel_size_string")
|
|
with pytest.raises(ValueError, match=r"Kernel size should be a tuple/list of two integers"):
|
|
transforms.GaussianBlur("kernel_size_string")
|
|
|
|
with pytest.raises(TypeError, match=r"sigma should be either float or sequence of floats"):
|
|
F.gaussian_blur(img, 3, "sigma_string")
|
|
with pytest.raises(ValueError, match=r"sigma should be a single number or a list/tuple with length 2"):
|
|
transforms.GaussianBlur(3, "sigma_string")
|
|
|
|
|
|
def test_lambda():
|
|
trans = transforms.Lambda(lambda x: x.add(10))
|
|
x = torch.randn(10)
|
|
y = trans(x)
|
|
assert_equal(y, torch.add(x, 10))
|
|
|
|
trans = transforms.Lambda(lambda x: x.add_(10))
|
|
x = torch.randn(10)
|
|
y = trans(x)
|
|
assert_equal(y, x)
|
|
|
|
# Checking if Lambda can be printed as string
|
|
trans.__repr__()
|
|
|
|
|
|
def test_to_grayscale():
|
|
"""Unit tests for grayscale transform"""
|
|
|
|
x_shape = [2, 2, 3]
|
|
x_data = [0, 5, 13, 54, 135, 226, 37, 8, 234, 90, 255, 1]
|
|
x_np = np.array(x_data, dtype=np.uint8).reshape(x_shape)
|
|
x_pil = Image.fromarray(x_np, mode="RGB")
|
|
x_pil_2 = x_pil.convert("L")
|
|
gray_np = np.array(x_pil_2)
|
|
|
|
# Test Set: Grayscale an image with desired number of output channels
|
|
# Case 1: RGB -> 1 channel grayscale
|
|
trans1 = transforms.Grayscale(num_output_channels=1)
|
|
gray_pil_1 = trans1(x_pil)
|
|
gray_np_1 = np.array(gray_pil_1)
|
|
assert gray_pil_1.mode == "L", "mode should be L"
|
|
assert gray_np_1.shape == tuple(x_shape[0:2]), "should be 1 channel"
|
|
assert_equal(gray_np, gray_np_1)
|
|
|
|
# Case 2: RGB -> 3 channel grayscale
|
|
trans2 = transforms.Grayscale(num_output_channels=3)
|
|
gray_pil_2 = trans2(x_pil)
|
|
gray_np_2 = np.array(gray_pil_2)
|
|
assert gray_pil_2.mode == "RGB", "mode should be RGB"
|
|
assert gray_np_2.shape == tuple(x_shape), "should be 3 channel"
|
|
assert_equal(gray_np_2[:, :, 0], gray_np_2[:, :, 1])
|
|
assert_equal(gray_np_2[:, :, 1], gray_np_2[:, :, 2])
|
|
assert_equal(gray_np, gray_np_2[:, :, 0])
|
|
|
|
# Case 3: 1 channel grayscale -> 1 channel grayscale
|
|
trans3 = transforms.Grayscale(num_output_channels=1)
|
|
gray_pil_3 = trans3(x_pil_2)
|
|
gray_np_3 = np.array(gray_pil_3)
|
|
assert gray_pil_3.mode == "L", "mode should be L"
|
|
assert gray_np_3.shape == tuple(x_shape[0:2]), "should be 1 channel"
|
|
assert_equal(gray_np, gray_np_3)
|
|
|
|
# Case 4: 1 channel grayscale -> 3 channel grayscale
|
|
trans4 = transforms.Grayscale(num_output_channels=3)
|
|
gray_pil_4 = trans4(x_pil_2)
|
|
gray_np_4 = np.array(gray_pil_4)
|
|
assert gray_pil_4.mode == "RGB", "mode should be RGB"
|
|
assert gray_np_4.shape == tuple(x_shape), "should be 3 channel"
|
|
assert_equal(gray_np_4[:, :, 0], gray_np_4[:, :, 1])
|
|
assert_equal(gray_np_4[:, :, 1], gray_np_4[:, :, 2])
|
|
assert_equal(gray_np, gray_np_4[:, :, 0])
|
|
|
|
# Checking if Grayscale can be printed as string
|
|
trans4.__repr__()
|
|
|
|
|
|
@pytest.mark.parametrize("seed", range(10))
|
|
@pytest.mark.parametrize("p", (0, 1))
|
|
def test_random_apply(p, seed):
|
|
torch.manual_seed(seed)
|
|
random_apply_transform = transforms.RandomApply([transforms.RandomRotation((45, 50))], p=p)
|
|
img = transforms.ToPILImage()(torch.rand(3, 30, 40))
|
|
out = random_apply_transform(img)
|
|
if p == 0:
|
|
assert out == img
|
|
elif p == 1:
|
|
assert out != img
|
|
|
|
# Checking if RandomApply can be printed as string
|
|
random_apply_transform.__repr__()
|
|
|
|
|
|
@pytest.mark.parametrize("seed", range(10))
|
|
@pytest.mark.parametrize("proba_passthrough", (0, 1))
|
|
def test_random_choice(proba_passthrough, seed):
|
|
random.seed(seed) # RandomChoice relies on python builtin random.choice, not pytorch
|
|
|
|
random_choice_transform = transforms.RandomChoice(
|
|
[
|
|
lambda x: x, # passthrough
|
|
transforms.RandomRotation((45, 50)),
|
|
],
|
|
p=[proba_passthrough, 1 - proba_passthrough],
|
|
)
|
|
|
|
img = transforms.ToPILImage()(torch.rand(3, 30, 40))
|
|
out = random_choice_transform(img)
|
|
if proba_passthrough == 1:
|
|
assert out == img
|
|
elif proba_passthrough == 0:
|
|
assert out != img
|
|
|
|
# Checking if RandomChoice can be printed as string
|
|
random_choice_transform.__repr__()
|
|
|
|
|
|
@pytest.mark.skipif(stats is None, reason="scipy.stats not available")
|
|
def test_random_order():
|
|
random_state = random.getstate()
|
|
random.seed(42)
|
|
random_order_transform = transforms.RandomOrder([transforms.Resize(20, antialias=True), transforms.CenterCrop(10)])
|
|
img = transforms.ToPILImage()(torch.rand(3, 25, 25))
|
|
num_samples = 250
|
|
num_normal_order = 0
|
|
resize_crop_out = transforms.CenterCrop(10)(transforms.Resize(20, antialias=True)(img))
|
|
for _ in range(num_samples):
|
|
out = random_order_transform(img)
|
|
if out == resize_crop_out:
|
|
num_normal_order += 1
|
|
|
|
p_value = stats.binomtest(num_normal_order, num_samples, p=0.5).pvalue
|
|
random.setstate(random_state)
|
|
assert p_value > 0.0001
|
|
|
|
# Checking if RandomOrder can be printed as string
|
|
random_order_transform.__repr__()
|
|
|
|
|
|
def test_linear_transformation():
|
|
num_samples = 1000
|
|
x = torch.randn(num_samples, 3, 10, 10)
|
|
flat_x = x.view(x.size(0), x.size(1) * x.size(2) * x.size(3))
|
|
# compute principal components
|
|
sigma = torch.mm(flat_x.t(), flat_x) / flat_x.size(0)
|
|
u, s, _ = np.linalg.svd(sigma.numpy())
|
|
zca_epsilon = 1e-10 # avoid division by 0
|
|
d = torch.Tensor(np.diag(1.0 / np.sqrt(s + zca_epsilon)))
|
|
u = torch.Tensor(u)
|
|
principal_components = torch.mm(torch.mm(u, d), u.t())
|
|
mean_vector = torch.sum(flat_x, dim=0) / flat_x.size(0)
|
|
# initialize whitening matrix
|
|
whitening = transforms.LinearTransformation(principal_components, mean_vector)
|
|
# estimate covariance and mean using weak law of large number
|
|
num_features = flat_x.size(1)
|
|
cov = 0.0
|
|
mean = 0.0
|
|
for i in x:
|
|
xwhite = whitening(i)
|
|
xwhite = xwhite.view(1, -1).numpy()
|
|
cov += np.dot(xwhite, xwhite.T) / num_features
|
|
mean += np.sum(xwhite) / num_features
|
|
# if rtol for std = 1e-3 then rtol for cov = 2e-3 as std**2 = cov
|
|
torch.testing.assert_close(
|
|
cov / num_samples, np.identity(1), rtol=2e-3, atol=1e-8, check_dtype=False, msg="cov not close to 1"
|
|
)
|
|
torch.testing.assert_close(
|
|
mean / num_samples, 0, rtol=1e-3, atol=1e-8, check_dtype=False, msg="mean not close to 0"
|
|
)
|
|
|
|
# Checking if LinearTransformation can be printed as string
|
|
whitening.__repr__()
|
|
|
|
|
|
@pytest.mark.parametrize("dtype", int_dtypes())
|
|
def test_max_value(dtype):
|
|
|
|
assert F_t._max_value(dtype) == torch.iinfo(dtype).max
|
|
# remove float testing as it can lead to errors such as
|
|
# runtime error: 5.7896e+76 is outside the range of representable values of type 'float'
|
|
# for dtype in float_dtypes():
|
|
# self.assertGreater(F_t._max_value(dtype), torch.finfo(dtype).max)
|
|
|
|
|
|
@pytest.mark.xfail(
|
|
reason="torch.iinfo() is not supported by torchscript. See https://github.com/pytorch/pytorch/issues/41492."
|
|
)
|
|
def test_max_value_iinfo():
|
|
@torch.jit.script
|
|
def max_value(image: torch.Tensor) -> int:
|
|
return 1 if image.is_floating_point() else torch.iinfo(image.dtype).max
|
|
|
|
|
|
@pytest.mark.parametrize("should_vflip", [True, False])
|
|
@pytest.mark.parametrize("single_dim", [True, False])
|
|
def test_ten_crop(should_vflip, single_dim):
|
|
to_pil_image = transforms.ToPILImage()
|
|
h = random.randint(5, 25)
|
|
w = random.randint(5, 25)
|
|
crop_h = random.randint(1, h)
|
|
crop_w = random.randint(1, w)
|
|
if single_dim:
|
|
crop_h = min(crop_h, crop_w)
|
|
crop_w = crop_h
|
|
transform = transforms.TenCrop(crop_h, vertical_flip=should_vflip)
|
|
five_crop = transforms.FiveCrop(crop_h)
|
|
else:
|
|
transform = transforms.TenCrop((crop_h, crop_w), vertical_flip=should_vflip)
|
|
five_crop = transforms.FiveCrop((crop_h, crop_w))
|
|
|
|
img = to_pil_image(torch.FloatTensor(3, h, w).uniform_())
|
|
results = transform(img)
|
|
expected_output = five_crop(img)
|
|
|
|
# Checking if FiveCrop and TenCrop can be printed as string
|
|
transform.__repr__()
|
|
five_crop.__repr__()
|
|
|
|
if should_vflip:
|
|
vflipped_img = img.transpose(Image.FLIP_TOP_BOTTOM)
|
|
expected_output += five_crop(vflipped_img)
|
|
else:
|
|
hflipped_img = img.transpose(Image.FLIP_LEFT_RIGHT)
|
|
expected_output += five_crop(hflipped_img)
|
|
|
|
assert len(results) == 10
|
|
assert results == expected_output
|
|
|
|
|
|
@pytest.mark.parametrize("single_dim", [True, False])
|
|
def test_five_crop(single_dim):
|
|
to_pil_image = transforms.ToPILImage()
|
|
h = random.randint(5, 25)
|
|
w = random.randint(5, 25)
|
|
crop_h = random.randint(1, h)
|
|
crop_w = random.randint(1, w)
|
|
if single_dim:
|
|
crop_h = min(crop_h, crop_w)
|
|
crop_w = crop_h
|
|
transform = transforms.FiveCrop(crop_h)
|
|
else:
|
|
transform = transforms.FiveCrop((crop_h, crop_w))
|
|
|
|
img = torch.FloatTensor(3, h, w).uniform_()
|
|
|
|
results = transform(to_pil_image(img))
|
|
|
|
assert len(results) == 5
|
|
for crop in results:
|
|
assert crop.size == (crop_w, crop_h)
|
|
|
|
to_pil_image = transforms.ToPILImage()
|
|
tl = to_pil_image(img[:, 0:crop_h, 0:crop_w])
|
|
tr = to_pil_image(img[:, 0:crop_h, w - crop_w :])
|
|
bl = to_pil_image(img[:, h - crop_h :, 0:crop_w])
|
|
br = to_pil_image(img[:, h - crop_h :, w - crop_w :])
|
|
center = transforms.CenterCrop((crop_h, crop_w))(to_pil_image(img))
|
|
expected_output = (tl, tr, bl, br, center)
|
|
assert results == expected_output
|
|
|
|
|
|
@pytest.mark.parametrize("policy", transforms.AutoAugmentPolicy)
|
|
@pytest.mark.parametrize("fill", [None, 85, (128, 128, 128)])
|
|
@pytest.mark.parametrize("grayscale", [True, False])
|
|
def test_autoaugment(policy, fill, grayscale):
|
|
random.seed(42)
|
|
img = Image.open(GRACE_HOPPER)
|
|
if grayscale:
|
|
img, fill = _get_grayscale_test_image(img, fill)
|
|
transform = transforms.AutoAugment(policy=policy, fill=fill)
|
|
for _ in range(100):
|
|
img = transform(img)
|
|
transform.__repr__()
|
|
|
|
|
|
@pytest.mark.parametrize("num_ops", [1, 2, 3])
|
|
@pytest.mark.parametrize("magnitude", [7, 9, 11])
|
|
@pytest.mark.parametrize("fill", [None, 85, (128, 128, 128)])
|
|
@pytest.mark.parametrize("grayscale", [True, False])
|
|
def test_randaugment(num_ops, magnitude, fill, grayscale):
|
|
random.seed(42)
|
|
img = Image.open(GRACE_HOPPER)
|
|
if grayscale:
|
|
img, fill = _get_grayscale_test_image(img, fill)
|
|
transform = transforms.RandAugment(num_ops=num_ops, magnitude=magnitude, fill=fill)
|
|
for _ in range(100):
|
|
img = transform(img)
|
|
transform.__repr__()
|
|
|
|
|
|
@pytest.mark.parametrize("fill", [None, 85, (128, 128, 128)])
|
|
@pytest.mark.parametrize("num_magnitude_bins", [10, 13, 30])
|
|
@pytest.mark.parametrize("grayscale", [True, False])
|
|
def test_trivialaugmentwide(fill, num_magnitude_bins, grayscale):
|
|
random.seed(42)
|
|
img = Image.open(GRACE_HOPPER)
|
|
if grayscale:
|
|
img, fill = _get_grayscale_test_image(img, fill)
|
|
transform = transforms.TrivialAugmentWide(fill=fill, num_magnitude_bins=num_magnitude_bins)
|
|
for _ in range(100):
|
|
img = transform(img)
|
|
transform.__repr__()
|
|
|
|
|
|
@pytest.mark.parametrize("fill", [None, 85, (128, 128, 128)])
|
|
@pytest.mark.parametrize("severity", [1, 10])
|
|
@pytest.mark.parametrize("mixture_width", [1, 2])
|
|
@pytest.mark.parametrize("chain_depth", [-1, 2])
|
|
@pytest.mark.parametrize("all_ops", [True, False])
|
|
@pytest.mark.parametrize("grayscale", [True, False])
|
|
def test_augmix(fill, severity, mixture_width, chain_depth, all_ops, grayscale):
|
|
random.seed(42)
|
|
img = Image.open(GRACE_HOPPER)
|
|
if grayscale:
|
|
img, fill = _get_grayscale_test_image(img, fill)
|
|
transform = transforms.AugMix(
|
|
fill=fill, severity=severity, mixture_width=mixture_width, chain_depth=chain_depth, all_ops=all_ops
|
|
)
|
|
for _ in range(100):
|
|
img = transform(img)
|
|
transform.__repr__()
|
|
|
|
|
|
def test_random_crop():
|
|
height = random.randint(10, 32) * 2
|
|
width = random.randint(10, 32) * 2
|
|
oheight = random.randint(5, (height - 2) // 2) * 2
|
|
owidth = random.randint(5, (width - 2) // 2) * 2
|
|
img = torch.ones(3, height, width, dtype=torch.uint8)
|
|
result = transforms.Compose(
|
|
[
|
|
transforms.ToPILImage(),
|
|
transforms.RandomCrop((oheight, owidth)),
|
|
transforms.PILToTensor(),
|
|
]
|
|
)(img)
|
|
assert result.size(1) == oheight
|
|
assert result.size(2) == owidth
|
|
|
|
padding = random.randint(1, 20)
|
|
result = transforms.Compose(
|
|
[
|
|
transforms.ToPILImage(),
|
|
transforms.RandomCrop((oheight, owidth), padding=padding),
|
|
transforms.PILToTensor(),
|
|
]
|
|
)(img)
|
|
assert result.size(1) == oheight
|
|
assert result.size(2) == owidth
|
|
|
|
result = transforms.Compose(
|
|
[transforms.ToPILImage(), transforms.RandomCrop((height, width)), transforms.PILToTensor()]
|
|
)(img)
|
|
assert result.size(1) == height
|
|
assert result.size(2) == width
|
|
torch.testing.assert_close(result, img)
|
|
|
|
result = transforms.Compose(
|
|
[
|
|
transforms.ToPILImage(),
|
|
transforms.RandomCrop((height + 1, width + 1), pad_if_needed=True),
|
|
transforms.PILToTensor(),
|
|
]
|
|
)(img)
|
|
assert result.size(1) == height + 1
|
|
assert result.size(2) == width + 1
|
|
|
|
t = transforms.RandomCrop(33)
|
|
img = torch.ones(3, 32, 32)
|
|
with pytest.raises(ValueError, match=r"Required crop size .+ is larger than input image size .+"):
|
|
t(img)
|
|
|
|
|
|
def test_center_crop():
|
|
height = random.randint(10, 32) * 2
|
|
width = random.randint(10, 32) * 2
|
|
oheight = random.randint(5, (height - 2) // 2) * 2
|
|
owidth = random.randint(5, (width - 2) // 2) * 2
|
|
|
|
img = torch.ones(3, height, width, dtype=torch.uint8)
|
|
oh1 = (height - oheight) // 2
|
|
ow1 = (width - owidth) // 2
|
|
imgnarrow = img[:, oh1 : oh1 + oheight, ow1 : ow1 + owidth]
|
|
imgnarrow.fill_(0)
|
|
result = transforms.Compose(
|
|
[
|
|
transforms.ToPILImage(),
|
|
transforms.CenterCrop((oheight, owidth)),
|
|
transforms.PILToTensor(),
|
|
]
|
|
)(img)
|
|
assert result.sum() == 0
|
|
oheight += 1
|
|
owidth += 1
|
|
result = transforms.Compose(
|
|
[
|
|
transforms.ToPILImage(),
|
|
transforms.CenterCrop((oheight, owidth)),
|
|
transforms.PILToTensor(),
|
|
]
|
|
)(img)
|
|
sum1 = result.sum()
|
|
assert sum1 > 1
|
|
oheight += 1
|
|
owidth += 1
|
|
result = transforms.Compose(
|
|
[
|
|
transforms.ToPILImage(),
|
|
transforms.CenterCrop((oheight, owidth)),
|
|
transforms.PILToTensor(),
|
|
]
|
|
)(img)
|
|
sum2 = result.sum()
|
|
assert sum2 > 0
|
|
assert sum2 > sum1
|
|
|
|
|
|
@pytest.mark.parametrize("odd_image_size", (True, False))
|
|
@pytest.mark.parametrize("delta", (1, 3, 5))
|
|
@pytest.mark.parametrize("delta_width", (-2, -1, 0, 1, 2))
|
|
@pytest.mark.parametrize("delta_height", (-2, -1, 0, 1, 2))
|
|
def test_center_crop_2(odd_image_size, delta, delta_width, delta_height):
|
|
"""Tests when center crop size is larger than image size, along any dimension"""
|
|
|
|
# Since height is independent of width, we can ignore images with odd height and even width and vice-versa.
|
|
input_image_size = (random.randint(10, 32) * 2, random.randint(10, 32) * 2)
|
|
if odd_image_size:
|
|
input_image_size = (input_image_size[0] + 1, input_image_size[1] + 1)
|
|
|
|
delta_height *= delta
|
|
delta_width *= delta
|
|
|
|
img = torch.ones(3, *input_image_size, dtype=torch.uint8)
|
|
crop_size = (input_image_size[0] + delta_height, input_image_size[1] + delta_width)
|
|
|
|
# Test both transforms, one with PIL input and one with tensor
|
|
output_pil = transforms.Compose(
|
|
[transforms.ToPILImage(), transforms.CenterCrop(crop_size), transforms.PILToTensor()],
|
|
)(img)
|
|
assert output_pil.size()[1:3] == crop_size
|
|
|
|
output_tensor = transforms.CenterCrop(crop_size)(img)
|
|
assert output_tensor.size()[1:3] == crop_size
|
|
|
|
# Ensure output for PIL and Tensor are equal
|
|
assert_equal(
|
|
output_tensor,
|
|
output_pil,
|
|
msg=f"image_size: {input_image_size} crop_size: {crop_size}",
|
|
)
|
|
|
|
# Check if content in center of both image and cropped output is same.
|
|
center_size = (min(crop_size[0], input_image_size[0]), min(crop_size[1], input_image_size[1]))
|
|
crop_center_tl, input_center_tl = [0, 0], [0, 0]
|
|
for index in range(2):
|
|
if crop_size[index] > input_image_size[index]:
|
|
crop_center_tl[index] = (crop_size[index] - input_image_size[index]) // 2
|
|
else:
|
|
input_center_tl[index] = (input_image_size[index] - crop_size[index]) // 2
|
|
|
|
output_center = output_pil[
|
|
:,
|
|
crop_center_tl[0] : crop_center_tl[0] + center_size[0],
|
|
crop_center_tl[1] : crop_center_tl[1] + center_size[1],
|
|
]
|
|
|
|
img_center = img[
|
|
:,
|
|
input_center_tl[0] : input_center_tl[0] + center_size[0],
|
|
input_center_tl[1] : input_center_tl[1] + center_size[1],
|
|
]
|
|
|
|
assert_equal(output_center, img_center)
|
|
|
|
|
|
def test_color_jitter():
|
|
color_jitter = transforms.ColorJitter(2, 2, 2, 0.1)
|
|
|
|
x_shape = [2, 2, 3]
|
|
x_data = [0, 5, 13, 54, 135, 226, 37, 8, 234, 90, 255, 1]
|
|
x_np = np.array(x_data, dtype=np.uint8).reshape(x_shape)
|
|
x_pil = Image.fromarray(x_np, mode="RGB")
|
|
x_pil_2 = x_pil.convert("L")
|
|
|
|
for _ in range(10):
|
|
y_pil = color_jitter(x_pil)
|
|
assert y_pil.mode == x_pil.mode
|
|
|
|
y_pil_2 = color_jitter(x_pil_2)
|
|
assert y_pil_2.mode == x_pil_2.mode
|
|
|
|
# Checking if ColorJitter can be printed as string
|
|
color_jitter.__repr__()
|
|
|
|
|
|
@pytest.mark.parametrize("hue", [1, (-1, 1)])
|
|
def test_color_jitter_hue_out_of_bounds(hue):
|
|
with pytest.raises(ValueError, match=re.escape("hue values should be between (-0.5, 0.5)")):
|
|
transforms.ColorJitter(hue=hue)
|
|
|
|
|
|
@pytest.mark.parametrize("seed", range(10))
|
|
@pytest.mark.skipif(stats is None, reason="scipy.stats not available")
|
|
def test_random_erasing(seed):
|
|
torch.random.manual_seed(seed)
|
|
img = torch.ones(3, 128, 128)
|
|
|
|
t = transforms.RandomErasing(scale=(0.1, 0.1), ratio=(1 / 3, 3.0))
|
|
y, x, h, w, v = t.get_params(
|
|
img,
|
|
t.scale,
|
|
t.ratio,
|
|
[
|
|
t.value,
|
|
],
|
|
)
|
|
aspect_ratio = h / w
|
|
# Add some tolerance due to the rounding and int conversion used in the transform
|
|
tol = 0.05
|
|
assert 1 / 3 - tol <= aspect_ratio <= 3 + tol
|
|
|
|
# Make sure that h > w and h < w are equally likely (log-scale sampling)
|
|
aspect_ratios = []
|
|
random.seed(42)
|
|
trial = 1000
|
|
for _ in range(trial):
|
|
y, x, h, w, v = t.get_params(
|
|
img,
|
|
t.scale,
|
|
t.ratio,
|
|
[
|
|
t.value,
|
|
],
|
|
)
|
|
aspect_ratios.append(h / w)
|
|
|
|
count_bigger_then_ones = len([1 for aspect_ratio in aspect_ratios if aspect_ratio > 1])
|
|
p_value = stats.binomtest(count_bigger_then_ones, trial, p=0.5).pvalue
|
|
assert p_value > 0.0001
|
|
|
|
# Checking if RandomErasing can be printed as string
|
|
t.__repr__()
|
|
|
|
|
|
def test_random_rotation():
|
|
|
|
with pytest.raises(ValueError):
|
|
transforms.RandomRotation(-0.7)
|
|
|
|
with pytest.raises(ValueError):
|
|
transforms.RandomRotation([-0.7])
|
|
|
|
with pytest.raises(ValueError):
|
|
transforms.RandomRotation([-0.7, 0, 0.7])
|
|
|
|
t = transforms.RandomRotation(0, fill=None)
|
|
assert t.fill == 0
|
|
|
|
t = transforms.RandomRotation(10)
|
|
angle = t.get_params(t.degrees)
|
|
assert angle > -10 and angle < 10
|
|
|
|
t = transforms.RandomRotation((-10, 10))
|
|
angle = t.get_params(t.degrees)
|
|
assert -10 < angle < 10
|
|
|
|
# Checking if RandomRotation can be printed as string
|
|
t.__repr__()
|
|
|
|
t = transforms.RandomRotation((-10, 10), interpolation=Image.BILINEAR)
|
|
assert t.interpolation == transforms.InterpolationMode.BILINEAR
|
|
|
|
|
|
def test_random_rotation_error():
|
|
# assert fill being either a Sequence or a Number
|
|
with pytest.raises(TypeError):
|
|
transforms.RandomRotation(0, fill={})
|
|
|
|
|
|
def test_randomperspective():
|
|
for _ in range(10):
|
|
height = random.randint(24, 32) * 2
|
|
width = random.randint(24, 32) * 2
|
|
img = torch.ones(3, height, width)
|
|
to_pil_image = transforms.ToPILImage()
|
|
img = to_pil_image(img)
|
|
perp = transforms.RandomPerspective()
|
|
startpoints, endpoints = perp.get_params(width, height, 0.5)
|
|
tr_img = F.perspective(img, startpoints, endpoints)
|
|
tr_img2 = F.convert_image_dtype(F.pil_to_tensor(F.perspective(tr_img, endpoints, startpoints)))
|
|
tr_img = F.convert_image_dtype(F.pil_to_tensor(tr_img))
|
|
assert img.size[0] == width
|
|
assert img.size[1] == height
|
|
assert torch.nn.functional.mse_loss(
|
|
tr_img, F.convert_image_dtype(F.pil_to_tensor(img))
|
|
) + 0.3 > torch.nn.functional.mse_loss(tr_img2, F.convert_image_dtype(F.pil_to_tensor(img)))
|
|
|
|
|
|
@pytest.mark.parametrize("seed", range(10))
|
|
@pytest.mark.parametrize("mode", ["L", "RGB", "F"])
|
|
def test_randomperspective_fill(mode, seed):
|
|
torch.random.manual_seed(seed)
|
|
|
|
# assert fill being either a Sequence or a Number
|
|
with pytest.raises(TypeError):
|
|
transforms.RandomPerspective(fill={})
|
|
|
|
t = transforms.RandomPerspective(fill=None)
|
|
assert t.fill == 0
|
|
|
|
height = 100
|
|
width = 100
|
|
img = torch.ones(3, height, width)
|
|
to_pil_image = transforms.ToPILImage()
|
|
img = to_pil_image(img)
|
|
fill = 127
|
|
num_bands = len(mode)
|
|
|
|
img_conv = img.convert(mode)
|
|
perspective = transforms.RandomPerspective(p=1, fill=fill)
|
|
tr_img = perspective(img_conv)
|
|
pixel = tr_img.getpixel((0, 0))
|
|
|
|
if not isinstance(pixel, tuple):
|
|
pixel = (pixel,)
|
|
assert pixel == tuple([fill] * num_bands)
|
|
|
|
startpoints, endpoints = transforms.RandomPerspective.get_params(width, height, 0.5)
|
|
tr_img = F.perspective(img_conv, startpoints, endpoints, fill=fill)
|
|
pixel = tr_img.getpixel((0, 0))
|
|
|
|
if not isinstance(pixel, tuple):
|
|
pixel = (pixel,)
|
|
assert pixel == tuple([fill] * num_bands)
|
|
|
|
wrong_num_bands = num_bands + 1
|
|
with pytest.raises(ValueError):
|
|
F.perspective(img_conv, startpoints, endpoints, fill=tuple([fill] * wrong_num_bands))
|
|
|
|
|
|
@pytest.mark.skipif(stats is None, reason="scipy.stats not available")
|
|
def test_normalize():
|
|
def samples_from_standard_normal(tensor):
|
|
p_value = stats.kstest(list(tensor.view(-1)), "norm", args=(0, 1)).pvalue
|
|
return p_value > 0.0001
|
|
|
|
random_state = random.getstate()
|
|
random.seed(42)
|
|
for channels in [1, 3]:
|
|
img = torch.rand(channels, 10, 10)
|
|
mean = [img[c].mean() for c in range(channels)]
|
|
std = [img[c].std() for c in range(channels)]
|
|
normalized = transforms.Normalize(mean, std)(img)
|
|
assert samples_from_standard_normal(normalized)
|
|
random.setstate(random_state)
|
|
|
|
# Checking if Normalize can be printed as string
|
|
transforms.Normalize(mean, std).__repr__()
|
|
|
|
# Checking the optional in-place behaviour
|
|
tensor = torch.rand((1, 16, 16))
|
|
tensor_inplace = transforms.Normalize((0.5,), (0.5,), inplace=True)(tensor)
|
|
assert_equal(tensor, tensor_inplace)
|
|
|
|
|
|
@pytest.mark.parametrize("dtype1", [torch.float32, torch.float64])
|
|
@pytest.mark.parametrize("dtype2", [torch.int64, torch.float32, torch.float64])
|
|
def test_normalize_different_dtype(dtype1, dtype2):
|
|
img = torch.rand(3, 10, 10, dtype=dtype1)
|
|
mean = torch.tensor([1, 2, 3], dtype=dtype2)
|
|
std = torch.tensor([1, 2, 1], dtype=dtype2)
|
|
# checks that it doesn't crash
|
|
transforms.functional.normalize(img, mean, std)
|
|
|
|
|
|
def test_normalize_3d_tensor():
|
|
torch.manual_seed(28)
|
|
n_channels = 3
|
|
img_size = 10
|
|
mean = torch.rand(n_channels)
|
|
std = torch.rand(n_channels)
|
|
img = torch.rand(n_channels, img_size, img_size)
|
|
target = F.normalize(img, mean, std)
|
|
|
|
mean_unsqueezed = mean.view(-1, 1, 1)
|
|
std_unsqueezed = std.view(-1, 1, 1)
|
|
result1 = F.normalize(img, mean_unsqueezed, std_unsqueezed)
|
|
result2 = F.normalize(
|
|
img, mean_unsqueezed.repeat(1, img_size, img_size), std_unsqueezed.repeat(1, img_size, img_size)
|
|
)
|
|
torch.testing.assert_close(target, result1)
|
|
torch.testing.assert_close(target, result2)
|
|
|
|
|
|
class TestAffine:
|
|
@pytest.fixture(scope="class")
|
|
def input_img(self):
|
|
input_img = np.zeros((40, 40, 3), dtype=np.uint8)
|
|
for pt in [(16, 16), (20, 16), (20, 20)]:
|
|
for i in range(-5, 5):
|
|
for j in range(-5, 5):
|
|
input_img[pt[0] + i, pt[1] + j, :] = [255, 155, 55]
|
|
return input_img
|
|
|
|
def test_affine_translate_seq(self, input_img):
|
|
with pytest.raises(TypeError, match=r"Argument translate should be a sequence"):
|
|
F.affine(input_img, 10, translate=0, scale=1, shear=1)
|
|
|
|
@pytest.fixture(scope="class")
|
|
def pil_image(self, input_img):
|
|
return F.to_pil_image(input_img)
|
|
|
|
def _to_3x3_inv(self, inv_result_matrix):
|
|
result_matrix = np.zeros((3, 3))
|
|
result_matrix[:2, :] = np.array(inv_result_matrix).reshape((2, 3))
|
|
result_matrix[2, 2] = 1
|
|
return np.linalg.inv(result_matrix)
|
|
|
|
def _test_transformation(self, angle, translate, scale, shear, pil_image, input_img, center=None):
|
|
|
|
a_rad = math.radians(angle)
|
|
s_rad = [math.radians(sh_) for sh_ in shear]
|
|
cnt = [20, 20] if center is None else center
|
|
cx, cy = cnt
|
|
tx, ty = translate
|
|
sx, sy = s_rad
|
|
rot = a_rad
|
|
|
|
# 1) Check transformation matrix:
|
|
C = np.array([[1, 0, cx], [0, 1, cy], [0, 0, 1]])
|
|
T = np.array([[1, 0, tx], [0, 1, ty], [0, 0, 1]])
|
|
Cinv = np.linalg.inv(C)
|
|
|
|
RS = np.array(
|
|
[
|
|
[scale * math.cos(rot), -scale * math.sin(rot), 0],
|
|
[scale * math.sin(rot), scale * math.cos(rot), 0],
|
|
[0, 0, 1],
|
|
]
|
|
)
|
|
|
|
SHx = np.array([[1, -math.tan(sx), 0], [0, 1, 0], [0, 0, 1]])
|
|
|
|
SHy = np.array([[1, 0, 0], [-math.tan(sy), 1, 0], [0, 0, 1]])
|
|
|
|
RSS = np.matmul(RS, np.matmul(SHy, SHx))
|
|
|
|
true_matrix = np.matmul(T, np.matmul(C, np.matmul(RSS, Cinv)))
|
|
|
|
result_matrix = self._to_3x3_inv(
|
|
F._get_inverse_affine_matrix(center=cnt, angle=angle, translate=translate, scale=scale, shear=shear)
|
|
)
|
|
assert np.sum(np.abs(true_matrix - result_matrix)) < 1e-10
|
|
# 2) Perform inverse mapping:
|
|
true_result = np.zeros((40, 40, 3), dtype=np.uint8)
|
|
inv_true_matrix = np.linalg.inv(true_matrix)
|
|
for y in range(true_result.shape[0]):
|
|
for x in range(true_result.shape[1]):
|
|
# Same as for PIL:
|
|
# https://github.com/python-pillow/Pillow/blob/71f8ec6a0cfc1008076a023c0756542539d057ab/
|
|
# src/libImaging/Geometry.c#L1060
|
|
input_pt = np.array([x + 0.5, y + 0.5, 1.0])
|
|
res = np.floor(np.dot(inv_true_matrix, input_pt)).astype(int)
|
|
_x, _y = res[:2]
|
|
if 0 <= _x < input_img.shape[1] and 0 <= _y < input_img.shape[0]:
|
|
true_result[y, x, :] = input_img[_y, _x, :]
|
|
|
|
result = F.affine(pil_image, angle=angle, translate=translate, scale=scale, shear=shear, center=center)
|
|
assert result.size == pil_image.size
|
|
# Compute number of different pixels:
|
|
np_result = np.array(result)
|
|
n_diff_pixels = np.sum(np_result != true_result) / 3
|
|
# Accept 3 wrong pixels
|
|
error_msg = (
|
|
f"angle={angle}, translate={translate}, scale={scale}, shear={shear}\nn diff pixels={n_diff_pixels}\n"
|
|
)
|
|
assert n_diff_pixels < 3, error_msg
|
|
|
|
def test_transformation_discrete(self, pil_image, input_img):
|
|
# Test rotation
|
|
angle = 45
|
|
self._test_transformation(
|
|
angle=angle, translate=(0, 0), scale=1.0, shear=(0.0, 0.0), pil_image=pil_image, input_img=input_img
|
|
)
|
|
|
|
# Test rotation
|
|
angle = 45
|
|
self._test_transformation(
|
|
angle=angle,
|
|
translate=(0, 0),
|
|
scale=1.0,
|
|
shear=(0.0, 0.0),
|
|
pil_image=pil_image,
|
|
input_img=input_img,
|
|
center=[0, 0],
|
|
)
|
|
|
|
# Test translation
|
|
translate = [10, 15]
|
|
self._test_transformation(
|
|
angle=0.0, translate=translate, scale=1.0, shear=(0.0, 0.0), pil_image=pil_image, input_img=input_img
|
|
)
|
|
|
|
# Test scale
|
|
scale = 1.2
|
|
self._test_transformation(
|
|
angle=0.0, translate=(0.0, 0.0), scale=scale, shear=(0.0, 0.0), pil_image=pil_image, input_img=input_img
|
|
)
|
|
|
|
# Test shear
|
|
shear = [45.0, 25.0]
|
|
self._test_transformation(
|
|
angle=0.0, translate=(0.0, 0.0), scale=1.0, shear=shear, pil_image=pil_image, input_img=input_img
|
|
)
|
|
|
|
# Test shear with top-left as center
|
|
shear = [45.0, 25.0]
|
|
self._test_transformation(
|
|
angle=0.0,
|
|
translate=(0.0, 0.0),
|
|
scale=1.0,
|
|
shear=shear,
|
|
pil_image=pil_image,
|
|
input_img=input_img,
|
|
center=[0, 0],
|
|
)
|
|
|
|
@pytest.mark.parametrize("angle", range(-90, 90, 36))
|
|
@pytest.mark.parametrize("translate", range(-10, 10, 5))
|
|
@pytest.mark.parametrize("scale", [0.77, 1.0, 1.27])
|
|
@pytest.mark.parametrize("shear", range(-15, 15, 5))
|
|
def test_transformation_range(self, angle, translate, scale, shear, pil_image, input_img):
|
|
self._test_transformation(
|
|
angle=angle,
|
|
translate=(translate, translate),
|
|
scale=scale,
|
|
shear=(shear, shear),
|
|
pil_image=pil_image,
|
|
input_img=input_img,
|
|
)
|
|
|
|
|
|
def test_random_affine():
|
|
|
|
with pytest.raises(ValueError):
|
|
transforms.RandomAffine(-0.7)
|
|
with pytest.raises(ValueError):
|
|
transforms.RandomAffine([-0.7])
|
|
with pytest.raises(ValueError):
|
|
transforms.RandomAffine([-0.7, 0, 0.7])
|
|
with pytest.raises(TypeError):
|
|
transforms.RandomAffine([-90, 90], translate=2.0)
|
|
with pytest.raises(ValueError):
|
|
transforms.RandomAffine([-90, 90], translate=[-1.0, 1.0])
|
|
with pytest.raises(ValueError):
|
|
transforms.RandomAffine([-90, 90], translate=[-1.0, 0.0, 1.0])
|
|
|
|
with pytest.raises(ValueError):
|
|
transforms.RandomAffine([-90, 90], translate=[0.2, 0.2], scale=[0.0])
|
|
with pytest.raises(ValueError):
|
|
transforms.RandomAffine([-90, 90], translate=[0.2, 0.2], scale=[-1.0, 1.0])
|
|
with pytest.raises(ValueError):
|
|
transforms.RandomAffine([-90, 90], translate=[0.2, 0.2], scale=[0.5, -0.5])
|
|
with pytest.raises(ValueError):
|
|
transforms.RandomAffine([-90, 90], translate=[0.2, 0.2], scale=[0.5, 3.0, -0.5])
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with pytest.raises(ValueError):
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transforms.RandomAffine([-90, 90], translate=[0.2, 0.2], scale=[0.5, 0.5], shear=-7)
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with pytest.raises(ValueError):
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transforms.RandomAffine([-90, 90], translate=[0.2, 0.2], scale=[0.5, 0.5], shear=[-10])
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with pytest.raises(ValueError):
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transforms.RandomAffine([-90, 90], translate=[0.2, 0.2], scale=[0.5, 0.5], shear=[-10, 0, 10])
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with pytest.raises(ValueError):
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transforms.RandomAffine([-90, 90], translate=[0.2, 0.2], scale=[0.5, 0.5], shear=[-10, 0, 10, 0, 10])
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# assert fill being either a Sequence or a Number
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with pytest.raises(TypeError):
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transforms.RandomAffine(0, fill={})
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t = transforms.RandomAffine(0, fill=None)
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assert t.fill == 0
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x = np.zeros((100, 100, 3), dtype=np.uint8)
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img = F.to_pil_image(x)
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|
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t = transforms.RandomAffine(10, translate=[0.5, 0.3], scale=[0.7, 1.3], shear=[-10, 10, 20, 40])
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for _ in range(100):
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angle, translations, scale, shear = t.get_params(t.degrees, t.translate, t.scale, t.shear, img_size=img.size)
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assert -10 < angle < 10
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assert -img.size[0] * 0.5 <= translations[0] <= img.size[0] * 0.5
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assert -img.size[1] * 0.5 <= translations[1] <= img.size[1] * 0.5
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assert 0.7 < scale < 1.3
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assert -10 < shear[0] < 10
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assert -20 < shear[1] < 40
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|
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# Checking if RandomAffine can be printed as string
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t.__repr__()
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|
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t = transforms.RandomAffine(10, interpolation=transforms.InterpolationMode.BILINEAR)
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assert "bilinear" in t.__repr__()
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|
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t = transforms.RandomAffine(10, interpolation=Image.BILINEAR)
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assert t.interpolation == transforms.InterpolationMode.BILINEAR
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|
|
|
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def test_elastic_transformation():
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with pytest.raises(TypeError, match=r"alpha should be float or a sequence of floats"):
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transforms.ElasticTransform(alpha=True, sigma=2.0)
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with pytest.raises(TypeError, match=r"alpha should be a sequence of floats"):
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|
transforms.ElasticTransform(alpha=[1.0, True], sigma=2.0)
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|
with pytest.raises(ValueError, match=r"alpha is a sequence its length should be 2"):
|
|
transforms.ElasticTransform(alpha=[1.0, 0.0, 1.0], sigma=2.0)
|
|
|
|
with pytest.raises(TypeError, match=r"sigma should be float or a sequence of floats"):
|
|
transforms.ElasticTransform(alpha=2.0, sigma=True)
|
|
with pytest.raises(TypeError, match=r"sigma should be a sequence of floats"):
|
|
transforms.ElasticTransform(alpha=2.0, sigma=[1.0, True])
|
|
with pytest.raises(ValueError, match=r"sigma is a sequence its length should be 2"):
|
|
transforms.ElasticTransform(alpha=2.0, sigma=[1.0, 0.0, 1.0])
|
|
|
|
t = transforms.transforms.ElasticTransform(alpha=2.0, sigma=2.0, interpolation=Image.BILINEAR)
|
|
assert t.interpolation == transforms.InterpolationMode.BILINEAR
|
|
|
|
with pytest.raises(TypeError, match=r"fill should be int or float"):
|
|
transforms.ElasticTransform(alpha=1.0, sigma=1.0, fill={})
|
|
|
|
x = torch.randint(0, 256, (3, 32, 32), dtype=torch.uint8)
|
|
img = F.to_pil_image(x)
|
|
t = transforms.ElasticTransform(alpha=0.0, sigma=0.0)
|
|
transformed_img = t(img)
|
|
assert transformed_img == img
|
|
|
|
# Smoke test on PIL images
|
|
t = transforms.ElasticTransform(alpha=0.5, sigma=0.23)
|
|
transformed_img = t(img)
|
|
assert isinstance(transformed_img, Image.Image)
|
|
|
|
# Checking if ElasticTransform can be printed as string
|
|
t.__repr__()
|
|
|
|
|
|
def test_random_grayscale_with_grayscale_input():
|
|
transform = transforms.RandomGrayscale(p=1.0)
|
|
|
|
image_tensor = torch.randint(0, 256, (1, 16, 16), dtype=torch.uint8)
|
|
output_tensor = transform(image_tensor)
|
|
torch.testing.assert_close(output_tensor, image_tensor)
|
|
|
|
image_pil = F.to_pil_image(image_tensor)
|
|
output_pil = transform(image_pil)
|
|
torch.testing.assert_close(F.pil_to_tensor(output_pil), image_tensor)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
pytest.main([__file__])
|