dev(narugo): more unittests for new functions (fa5015b2) · Commits · git-mirror / Imgutils

imgutils/preprocess/torchvision.py

+2 −1

Original line number	Diff line number	Diff line
		@@ -409,7 +409,8 @@ def _parse_pad_to_size(obj):
		obj: PadToSize
		return {
		'size': list(obj.size),
		'background_color': obj.background_color,
		'background_color': (list(obj.background_color)
		if isinstance(obj.background_color, (list, tuple)) else obj.background_color),
		'interpolation': obj.interpolation.value,
		}

test/preprocess/test_torchvision.py

+119 −1

Original line number	Diff line number	Diff line
		@@ -5,7 +5,8 @@ import pytest
		from PIL import Image
		from hbutils.testing import tmatrix

		from imgutils.preprocess import NotParseTarget
		from imgutils.data import load_image, grid_transparent
		from imgutils.preprocess import NotParseTarget, create_pillow_transforms
		from imgutils.preprocess.torchvision import _get_interpolation_mode, create_torchvision_transforms, \
		parse_torchvision_transforms, register_torchvision_transform, register_torchvision_parse
		from test.testings import get_testfile
		@@ -235,3 +236,120 @@ class TestPreprocessPillow:
		'saturation': (0.0, 0.8),
		'type': 'color_jitter'
		})

		@skipUnless(_TORCHVISION_AVAILABLE, 'Torchvision required.')
		@pytest.mark.parametrize(*tmatrix({
		'json_': [
		{'type': 'pad_to_size', 'size': [512, 768], 'background_color': 'white', 'interpolation': 'nearest'},
		{'type': 'pad_to_size', 'size': [768, 512], 'background_color': 'gray', 'interpolation': 'lanczos'},
		{'type': 'pad_to_size', 'size': [512, 512], 'background_color': (0, 0, 255), 'interpolation': 'bicubic'},
		{'type': 'pad_to_size', 'size': [384, 512], 'background_color': 'blue', 'interpolation': 'box'},
		{'type': 'pad_to_size', 'size': [512, 512], 'background_color': (255, 255, 255),
		'interpolation': 'bilinear'},
		{'type': 'pad_to_size', 'size': [512, 512], 'background_color': 'blue', 'interpolation': 'lanczos'},
		{'type': 'pad_to_size', 'size': [768, 512], 'background_color': 'red', 'interpolation': 'nearest'},
		{'type': 'pad_to_size', 'size': [512, 512], 'background_color': 'gray', 'interpolation': 'hamming'},
		{'type': 'pad_to_size', 'size': [384, 384], 'background_color': (255, 0, 0, 128), 'interpolation': 'box'},
		{'type': 'pad_to_size', 'size': [384, 384], 'background_color': (0, 0, 255, 128),
		'interpolation': 'hamming'},
		{'type': 'pad_to_size', 'size': [512, 512], 'background_color': (0, 0, 255, 128),
		'interpolation': 'nearest'},
		{'type': 'pad_to_size', 'size': [384, 384], 'background_color': (0, 0, 255), 'interpolation': 'hamming'},
		{'type': 'pad_to_size', 'size': [384, 384], 'background_color': (255, 255, 255),
		'interpolation': 'hamming'},
		{'type': 'pad_to_size', 'size': [512, 512], 'background_color': (255, 0, 0, 128),
		'interpolation': 'hamming'},
		{'type': 'pad_to_size', 'size': [512, 512], 'background_color': 'white', 'interpolation': 'bilinear'},
		],
		'filename': [
		'nian_640_L.png',
		'nian_640_LA.png',
		'nian_640_RGB.png',
		'nian_640_RGBA.png',
		'png_640_m90.png',
		'png_640.png',
		'dori_640.png',
		]
		}, mode='matrix'))
		def test_align_pad_to_size(self, json_, filename, image_diff):
		src_image = load_image(get_testfile(filename), mode=None, force_background=None)
		tprocess = create_torchvision_transforms(json_)
		pprocess = create_pillow_transforms(json_)
		assert image_diff(
		grid_transparent(tprocess(src_image)),
		grid_transparent(pprocess(src_image)),
		throw_exception=False
		) < 1e-3

		@skipUnless(_TORCHVISION_AVAILABLE, 'Torchvision required.')
		@pytest.mark.parametrize(['json_from', 'json_to'], [
		({'type': 'pad_to_size', 'size': [512, 768], 'background_color': 'white', 'interpolation': 'nearest'},
		{'type': 'pad_to_size', 'size': [512, 768], 'background_color': 'white', 'interpolation': 'nearest'}),
		({'type': 'pad_to_size', 'size': [768, 512], 'background_color': 'gray', 'interpolation': 'lanczos'},
		{'type': 'pad_to_size', 'size': [768, 512], 'background_color': 'gray', 'interpolation': 'lanczos'}),
		({'type': 'pad_to_size', 'size': [512, 512], 'background_color': (0, 0, 255), 'interpolation': 'bicubic'},
		{'type': 'pad_to_size', 'size': [512, 512], 'background_color': [0, 0, 255], 'interpolation': 'bicubic'}),
		({'type': 'pad_to_size', 'size': [384, 512], 'background_color': 'blue', 'interpolation': 'box'},
		{'type': 'pad_to_size', 'size': [384, 512], 'background_color': 'blue', 'interpolation': 'box'}),
		({'type': 'pad_to_size', 'size': [512, 512], 'background_color': (255, 255, 255), 'interpolation': 'bilinear'},
		{'type': 'pad_to_size', 'size': [512, 512], 'background_color': [255, 255, 255], 'interpolation': 'bilinear'}),
		({'type': 'pad_to_size', 'size': [512, 512], 'background_color': 'blue', 'interpolation': 'lanczos'},
		{'type': 'pad_to_size', 'size': [512, 512], 'background_color': 'blue', 'interpolation': 'lanczos'}),
		({'type': 'pad_to_size', 'size': [768, 512], 'background_color': 'red', 'interpolation': 'nearest'},
		{'type': 'pad_to_size', 'size': [768, 512], 'background_color': 'red', 'interpolation': 'nearest'}),
		({'type': 'pad_to_size', 'size': [512, 512], 'background_color': 'gray', 'interpolation': 'hamming'},
		{'type': 'pad_to_size', 'size': [512, 512], 'background_color': 'gray', 'interpolation': 'hamming'}),
		({'type': 'pad_to_size', 'size': [384, 384], 'background_color': (255, 0, 0, 128), 'interpolation': 'box'},
		{'type': 'pad_to_size', 'size': [384, 384], 'background_color': [255, 0, 0, 128], 'interpolation': 'box'}),
		({'type': 'pad_to_size', 'size': [384, 384], 'background_color': (0, 0, 255, 128), 'interpolation': 'hamming'},
		{'type': 'pad_to_size', 'size': [384, 384], 'background_color': [0, 0, 255, 128], 'interpolation': 'hamming'}),
		({'type': 'pad_to_size', 'size': [512, 512], 'background_color': (0, 0, 255, 128), 'interpolation': 'nearest'},
		{'type': 'pad_to_size', 'size': [512, 512], 'background_color': [0, 0, 255, 128], 'interpolation': 'nearest'}),
		({'type': 'pad_to_size', 'size': [384, 384], 'background_color': (0, 0, 255), 'interpolation': 'hamming'},
		{'type': 'pad_to_size', 'size': [384, 384], 'background_color': [0, 0, 255], 'interpolation': 'hamming'}),
		({'type': 'pad_to_size', 'size': [384, 384], 'background_color': (255, 255, 255), 'interpolation': 'hamming'},
		{'type': 'pad_to_size', 'size': [384, 384], 'background_color': [255, 255, 255], 'interpolation': 'hamming'}),
		({'type': 'pad_to_size', 'size': [512, 512], 'background_color': (255, 0, 0, 128), 'interpolation': 'hamming'},
		{'type': 'pad_to_size', 'size': [512, 512], 'background_color': [255, 0, 0, 128], 'interpolation': 'hamming'}),
		({'type': 'pad_to_size', 'size': [512, 512], 'background_color': 'white', 'interpolation': 'bilinear'},
		{'type': 'pad_to_size', 'size': [512, 512], 'background_color': 'white', 'interpolation': 'bilinear'}),

		])
		def test_pad_to_size_to_json(self, json_from, json_to):
		assert parse_torchvision_transforms(create_torchvision_transforms(json_from)) == json_to

		@skipUnless(_TORCHVISION_AVAILABLE, 'Torchvision required.')
		@pytest.mark.parametrize(['json_', 'repr_text'], [
		({'type': 'pad_to_size', 'size': [512, 768], 'background_color': 'white', 'interpolation': 'nearest'},
		'PadToSize(size=(512, 768), interpolation=nearest, background_color=white)'),
		({'type': 'pad_to_size', 'size': [768, 512], 'background_color': 'gray', 'interpolation': 'lanczos'},
		'PadToSize(size=(768, 512), interpolation=lanczos, background_color=gray)'),
		({'type': 'pad_to_size', 'size': [512, 512], 'background_color': (0, 0, 255), 'interpolation': 'bicubic'},
		'PadToSize(size=(512, 512), interpolation=bicubic, background_color=(0, 0, 255))'),
		({'type': 'pad_to_size', 'size': [384, 512], 'background_color': 'blue', 'interpolation': 'box'},
		'PadToSize(size=(384, 512), interpolation=box, background_color=blue)'),
		({'type': 'pad_to_size', 'size': [512, 512], 'background_color': (255, 255, 255), 'interpolation': 'bilinear'},
		'PadToSize(size=(512, 512), interpolation=bilinear, background_color=(255, 255, 255))'),
		({'type': 'pad_to_size', 'size': [512, 512], 'background_color': 'blue', 'interpolation': 'lanczos'},
		'PadToSize(size=(512, 512), interpolation=lanczos, background_color=blue)'),
		({'type': 'pad_to_size', 'size': [768, 512], 'background_color': 'red', 'interpolation': 'nearest'},
		'PadToSize(size=(768, 512), interpolation=nearest, background_color=red)'),
		({'type': 'pad_to_size', 'size': [512, 512], 'background_color': 'gray', 'interpolation': 'hamming'},
		'PadToSize(size=(512, 512), interpolation=hamming, background_color=gray)'),
		({'type': 'pad_to_size', 'size': [384, 384], 'background_color': (255, 0, 0, 128), 'interpolation': 'box'},
		'PadToSize(size=(384, 384), interpolation=box, background_color=(255, 0, 0, 128))'),
		({'type': 'pad_to_size', 'size': [384, 384], 'background_color': (0, 0, 255, 128), 'interpolation': 'hamming'},
		'PadToSize(size=(384, 384), interpolation=hamming, background_color=(0, 0, 255, 128))'),
		({'type': 'pad_to_size', 'size': [512, 512], 'background_color': (0, 0, 255, 128), 'interpolation': 'nearest'},
		'PadToSize(size=(512, 512), interpolation=nearest, background_color=(0, 0, 255, 128))'),
		({'type': 'pad_to_size', 'size': [384, 384], 'background_color': (0, 0, 255), 'interpolation': 'hamming'},
		'PadToSize(size=(384, 384), interpolation=hamming, background_color=(0, 0, 255))'),
		({'type': 'pad_to_size', 'size': [384, 384], 'background_color': (255, 255, 255), 'interpolation': 'hamming'},
		'PadToSize(size=(384, 384), interpolation=hamming, background_color=(255, 255, 255))'),
		({'type': 'pad_to_size', 'size': [512, 512], 'background_color': (255, 0, 0, 128), 'interpolation': 'hamming'},
		'PadToSize(size=(512, 512), interpolation=hamming, background_color=(255, 0, 0, 128))'),
		({'type': 'pad_to_size', 'size': [512, 512], 'background_color': 'white', 'interpolation': 'bilinear'},
		'PadToSize(size=(512, 512), interpolation=bilinear, background_color=white)'),
		])
		def test_pad_to_size_repr_text(self, json_, repr_text):
		assert repr(create_torchvision_transforms(json_)) == repr_text