dev(narugo): alignment test passed (d11c6bd2) · Commits · git-mirror / Imgutils

test/preprocess/test_pillow_compose.py

+91 −1

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

		from imgutils.preprocess.pillow import PillowNormalize, PillowCompose, PillowResize, PillowMaybeToTensor, \
		PillowCenterCrop
		PillowCenterCrop, create_pillow_transforms
		from imgutils.preprocess.torchvision import create_torchvision_transforms
		from test.testings import get_testfile

		try:
		@@ -18,6 +19,71 @@ else:
		_TORCHVISION_AVAILABLE = True


		@pytest.fixture()
		def mobilenetv4_conv_large_e600_r384_in1k():
		return [{'antialias': True,
		'interpolation': 'bicubic',
		'max_size': None,
		'size': 404,
		'type': 'resize'},
		{'size': [384, 384], 'type': 'center_crop'},
		{'type': 'maybe_to_tensor'},
		{'mean': [0.48500001430511475, 0.4560000002384186, 0.4059999883174896],
		'std': [0.2290000021457672, 0.2240000069141388, 0.22499999403953552],
		'type': 'normalize'}]


		@pytest.fixture()
		def caformer_s36_sail_in1k_384():
		return [{'antialias': True,
		'interpolation': 'bicubic',
		'max_size': None,
		'size': 384,
		'type': 'resize'},
		{'size': [384, 384], 'type': 'center_crop'},
		{'type': 'maybe_to_tensor'},
		{'mean': [0.48500001430511475, 0.4560000002384186, 0.4059999883174896],
		'std': [0.2290000021457672, 0.2240000069141388, 0.22499999403953552],
		'type': 'normalize'}]


		@pytest.fixture()
		def beit_base_patch16_384_in22k_ft_in22k_in1k():
		return [{'antialias': True,
		'interpolation': 'bicubic',
		'max_size': None,
		'size': 384,
		'type': 'resize'},
		{'size': [384, 384], 'type': 'center_crop'},
		{'type': 'maybe_to_tensor'},
		{'mean': [0.5, 0.5, 0.5], 'std': [0.5, 0.5, 0.5], 'type': 'normalize'}]


		@pytest.fixture()
		def resnet101d_ra2_in1k():
		return [{'antialias': True,
		'interpolation': 'bicubic',
		'max_size': None,
		'size': 269,
		'type': 'resize'},
		{'size': [256, 256], 'type': 'center_crop'},
		{'type': 'maybe_to_tensor'},
		{'mean': [0.48500001430511475, 0.4560000002384186, 0.4059999883174896],
		'std': [0.2290000021457672, 0.2240000069141388, 0.22499999403953552],
		'type': 'normalize'}]


		@pytest.fixture()
		def meta_collect(mobilenetv4_conv_large_e600_r384_in1k, resnet101d_ra2_in1k, caformer_s36_sail_in1k_384,
		beit_base_patch16_384_in22k_ft_in22k_in1k):
		return {
		'mobilenetv4_conv_large.e600_r384_in1k': mobilenetv4_conv_large_e600_r384_in1k,
		'resnet101d.ra2_in1k': resnet101d_ra2_in1k,
		'caformer_s36.sail_in1k_384': caformer_s36_sail_in1k_384,
		'beit_base_patch16_384.in22k_ft_in22k_in1k': beit_base_patch16_384_in22k_ft_in22k_in1k,
		}


		def torchvision_maybetotensor():
		from torchvision.transforms import ToTensor
		class MaybeToTensor(ToTensor):
		@@ -125,3 +191,27 @@ PillowCompose(
		PillowNormalize(mean=[0.5 0.5 0.5], std=[0.5 0.5 0.5])
		)
		""").strip()

		@skipUnless(_TORCHVISION_AVAILABLE, 'Torchvision required.')
		@pytest.mark.parametrize(*tmatrix({
		'src_image': [
		'png_640.png',
		'png_640_m90.png',
		],
		'meta_name': [
		'mobilenetv4_conv_large.e600_r384_in1k',
		'caformer_s36.sail_in1k_384',
		'beit_base_patch16_384.in22k_ft_in22k_in1k',
		'resnet101d.ra2_in1k',
		]
		}))
		def test_compose_alignment(self, src_image, meta_name, meta_collect):
		image = Image.open(get_testfile(src_image))
		meta = meta_collect[meta_name]

		ptrans = create_pillow_transforms(meta)
		ttrans = create_torchvision_transforms(meta)
		np.testing.assert_array_almost_equal(
		ptrans(image),
		ttrans(image).numpy(),
		)