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import pytest |
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import torch |
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from torch.autograd import gradcheck |
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import kornia |
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from kornia.testing import _get_precision, assert_close, BaseTester |
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class TestRgbToLuv(BaseTester): |
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def test_smoke(self, device, dtype): |
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C, H, W = 3, 4, 5 |
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img = torch.rand(C, H, W, device=device, dtype=dtype) |
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assert isinstance(kornia.color.rgb_to_luv(img), torch.Tensor) |
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@pytest.mark.parametrize("shape", [(1, 3, 4, 4), (2, 3, 2, 4), (3, 3, 4, 1), (3, 2, 1)]) |
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def test_cardinality(self, device, dtype, shape): |
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img = torch.ones(shape, device=device, dtype=dtype) |
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assert kornia.color.rgb_to_luv(img).shape == shape |
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def test_exception(self, device, dtype): |
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with pytest.raises(TypeError): |
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assert kornia.color.rgb_to_luv([0.0]) |
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with pytest.raises(ValueError): |
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img = torch.ones(1, 1, device=device, dtype=dtype) |
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assert kornia.color.rgb_to_luv(img) |
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with pytest.raises(ValueError): |
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img = torch.ones(2, 1, 1, device=device, dtype=dtype) |
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assert kornia.color.rgb_to_luv(img) |
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def test_unit(self, device, dtype): |
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data = torch.tensor( |
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[ |
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[ |
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[0.0, 1.0, 0.69396782, 0.63581685, 0.09902618], |
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[0.59459005, 0.74215373, 0.89662376, 0.25920381, 0.89937686], |
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[0.29857584, 0.28139791, 0.16441015, 0.55507519, 0.06124221], |
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[0.40908658, 0.10261389, 0.01691456, 0.76006799, 0.32971736], |
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], |
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[ |
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[0.0, 1.0, 0.79009938, 0.91742945, 0.60044175], |
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[0.42812678, 0.18552390, 0.04186043, 0.38030245, 0.15420346], |
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[0.13552373, 0.53955473, 0.79102736, 0.49050815, 0.75271446], |
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[0.39861023, 0.80680277, 0.82823833, 0.54438462, 0.22063386], |
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], |
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[ |
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[0.0, 1.0, 0.84317145, 0.59529881, 0.15297393], |
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[0.59235313, 0.36617295, 0.34600773, 0.40304737, 0.61720451], |
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[0.46040250, 0.42006640, 0.54765106, 0.48982632, 0.13914755], |
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[0.58402964, 0.89597990, 0.98276161, 0.25019163, 0.69285921], |
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], |
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], |
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device=device, |
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dtype=dtype, |
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) |
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expected = torch.tensor( |
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[ |
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[ |
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[0.0, 100.0, 79.75208282, 86.38912964, 55.25164032], |
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[51.66668701, 43.81214523, 48.93865585, 39.03804398, 52.55152512], |
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[23.71140671, 52.38661957, 72.54607391, 53.89587402, 67.94892883], |
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[45.02897263, 75.98315430, 78.25762177, 61.85069656, 33.77972794], |
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], |
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[ |
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[-0.0, -0.00054950, -13.54032803, -35.42317200, -49.27433014], |
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[21.34596062, 94.13956451, 137.11340332, -14.69241238, 102.94833374], |
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[9.55611229, -30.01761436, -58.94236755, 9.83261871, -62.96137619], |
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[-1.55336237, -55.22497559, -56.21067810, 43.76751328, 1.46367633], |
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], |
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[ |
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[-0.0, 0.00766720, -13.74480152, 52.17128372, 60.92724228], |
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[-27.01125526, -1.72837746, 6.57535267, -7.83582020, -38.45543289], |
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[-50.89970779, 17.65329361, 36.54148102, 2.25501800, 78.93702698], |
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[-38.39783859, -31.71204376, -46.63606644, 50.16629410, -84.74416351], |
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], |
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], |
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device=device, |
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dtype=dtype, |
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) |
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tol_val: float = _get_precision(device, dtype) |
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assert_close(kornia.color.rgb_to_luv(data), expected, rtol=tol_val, atol=tol_val) |
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def test_forth_and_back(self, device, dtype): |
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data = torch.rand(3, 4, 5, device=device, dtype=dtype) |
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luv = kornia.color.rgb_to_luv |
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rgb = kornia.color.luv_to_rgb |
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data_out = luv(rgb(data)) |
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assert_close(data_out, data, rtol=1e-4, atol=1e-4) |
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@pytest.mark.grad |
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def test_gradcheck(self, device, dtype): |
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B, C, H, W = 2, 3, 4, 4 |
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img = torch.rand(B, C, H, W, device=device, dtype=torch.float64, requires_grad=True) |
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assert gradcheck(kornia.color.rgb_to_luv, (img,), raise_exception=True) |
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@pytest.mark.jit |
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def test_jit(self, device, dtype): |
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B, C, H, W = 2, 3, 4, 4 |
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img = torch.ones(B, C, H, W, device=device, dtype=dtype) |
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op = kornia.color.rgb_to_luv |
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op_jit = torch.jit.script(op) |
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assert_close(op(img), op_jit(img), rtol=1e-3, atol=1e-3) |
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@pytest.mark.nn |
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def test_module(self, device, dtype): |
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B, C, H, W = 2, 3, 4, 4 |
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img = torch.ones(B, C, H, W, device=device, dtype=dtype) |
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ops = kornia.color.RgbToLuv().to(device, dtype) |
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fcn = kornia.color.rgb_to_luv |
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assert_close(ops(img), fcn(img)) |
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class TestLuvToRgb(BaseTester): |
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def test_smoke(self, device, dtype): |
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C, H, W = 3, 4, 5 |
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img = torch.rand(C, H, W, device=device, dtype=dtype) |
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assert isinstance(kornia.color.luv_to_rgb(img), torch.Tensor) |
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@pytest.mark.parametrize("shape", [(1, 3, 4, 4), (2, 3, 2, 4), (3, 3, 4, 1), (3, 2, 1)]) |
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def test_cardinality(self, device, dtype, shape): |
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img = torch.ones(shape, device=device, dtype=dtype) |
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assert kornia.color.luv_to_rgb(img).shape == shape |
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def test_exception(self, device, dtype): |
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with pytest.raises(TypeError): |
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assert kornia.color.luv_to_rgb([0.0]) |
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with pytest.raises(ValueError): |
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img = torch.ones(1, 1, device=device, dtype=dtype) |
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assert kornia.color.luv_to_rgb(img) |
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with pytest.raises(ValueError): |
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img = torch.ones(2, 1, 1, device=device, dtype=dtype) |
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assert kornia.color.luv_to_rgb(img) |
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def test_unit(self, device, dtype): |
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data = torch.tensor( |
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[ |
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[ |
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[ |
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[50.21928787, 23.29810143, 14.98279190, 62.50927353, 72.78904724], |
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[70.86846924, 68.75330353, 52.81696701, 76.17090607, 88.63134003], |
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[46.87160873, 72.38699341, 37.71450806, 82.57386780, 74.79967499], |
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[77.33016968, 47.39180374, 61.76217651, 90.83254242, 86.96239471], |
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], |
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[ |
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[65.81327057, -3.69859719, 0.16971001, 14.86583614, -65.54960632], |
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[-41.03258133, -19.52661896, 64.16155243, -58.53935242, -71.78411102], |
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[112.05227661, -60.13330460, 43.07910538, -51.01456833, -58.25787354], |
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[-62.37575531, 50.88882065, -39.27450943, 17.00958824, -24.93779755], |
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], |
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[ |
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[-69.53346252, -73.34986877, -11.47461891, 66.73863220, 70.43983459], |
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[51.92737579, 58.77009583, 45.97863388, 24.44452858, 98.81991577], |
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[-7.60597992, 78.97976685, -69.31867218, 67.33953857, 14.28889370], |
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[92.31149292, -85.91405487, -32.83668518, -23.45091820, 69.99038696], |
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], |
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] |
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], |
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device=device, |
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dtype=dtype, |
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) |
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expected = torch.tensor( |
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[ |
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[ |
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[ |
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[0.78923208, 0.17048222, 0.14947766, 0.65528989, 0.07863078], |
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[0.41649094, 0.55222923, 0.72673196, 0.21939684, 0.34298307], |
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[0.82763243, 0.24021322, 0.58888060, 0.47255886, 0.16407511], |
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[0.30320778, 0.72233224, 0.21593384, 0.98893607, 0.71707106], |
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], |
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[ |
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[0.20532851, 0.13188709, 0.13879408, 0.59964627, 0.80721593], |
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[0.75411713, 0.70656943, 0.41770950, 0.82750136, 0.99659365], |
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[0.12436169, 0.79804462, 0.10958754, 0.89803618, 0.81000644], |
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[0.85726571, 0.17667055, 0.63285238, 0.85567462, 0.91538441], |
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], |
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[ |
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[0.73985511, 0.59308004, 0.21156698, 0.03804367, 0.32732114], |
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[0.42489606, 0.33011687, 0.12804756, 0.64905322, 0.25216782], |
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[0.41637793, 0.22158240, 0.63437861, 0.46121466, 0.68336427], |
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[0.06325728, 0.78878325, 0.74280596, 0.99514300, 0.47176042], |
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], |
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] |
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], |
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device=device, |
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dtype=dtype, |
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) |
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assert_close(kornia.color.luv_to_rgb(data), expected, rtol=1e-4, atol=1e-4) |
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def test_forth_and_back(self, device, dtype): |
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data = torch.rand(3, 4, 5, device=device, dtype=dtype) |
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luv = kornia.color.rgb_to_luv |
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rgb = kornia.color.luv_to_rgb |
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data_out = rgb(luv(data)) |
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assert_close(data_out, data, rtol=1e-4, atol=1e-4) |
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@pytest.mark.grad |
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def test_gradcheck(self, device, dtype): |
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B, C, H, W = 2, 3, 4, 4 |
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img = torch.rand(B, C, H, W, device=device, dtype=torch.float64, requires_grad=True) |
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img = kornia.color.rgb_to_luv(img) |
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assert gradcheck(kornia.color.luv_to_rgb, (img,), raise_exception=True) |
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@pytest.mark.jit |
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def test_jit(self, device, dtype): |
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B, C, H, W = 2, 3, 4, 4 |
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img = torch.ones(B, C, H, W, device=device, dtype=dtype) |
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op = kornia.color.luv_to_rgb |
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op_jit = torch.jit.script(op) |
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assert_close(op(img), op_jit(img)) |
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@pytest.mark.nn |
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def test_module(self, device, dtype): |
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B, C, H, W = 2, 3, 4, 4 |
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img = torch.ones(B, C, H, W, device=device, dtype=dtype) |
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ops = kornia.color.LuvToRgb().to(device, dtype) |
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fcn = kornia.color.luv_to_rgb |
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assert_close(ops(img), fcn(img)) |
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