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import math |
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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 assert_close, BaseTester |
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class TestRgbToHsv(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_hsv(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_hsv(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_hsv([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_hsv(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_hsv(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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[0.3944633, 0.8597369, 0.1670904, 0.2825457, 0.0953912], |
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[0.1251704, 0.8020709, 0.8933256, 0.9170977, 0.1497008], |
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[0.2711633, 0.1111478, 0.0783281, 0.2771807, 0.5487481], |
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[0.0086008, 0.8288748, 0.9647092, 0.8922020, 0.7614344], |
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[0.2898048, 0.1282895, 0.7621747, 0.5657831, 0.9918593], |
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], |
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[ |
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[0.5414237, 0.9962701, 0.8947155, 0.5900949, 0.9483274], |
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[0.0468036, 0.3933847, 0.8046577, 0.3640994, 0.0632100], |
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[0.6171775, 0.8624780, 0.4126036, 0.7600935, 0.7279997], |
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[0.4237089, 0.5365476, 0.5591233, 0.1523191, 0.1382165], |
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[0.8932794, 0.8517839, 0.7152701, 0.8983801, 0.5905426], |
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], |
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[ |
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[0.2869580, 0.4700376, 0.2743714, 0.8135023, 0.2229074], |
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[0.9306560, 0.3734594, 0.4566821, 0.7599275, 0.7557513], |
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[0.7415742, 0.6115875, 0.3317572, 0.0379378, 0.1315770], |
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[0.8692724, 0.0809556, 0.7767404, 0.8742208, 0.1522012], |
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[0.7708948, 0.4509611, 0.0481175, 0.2358997, 0.6900532], |
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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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[1.6519808, 1.31889750, 2.24879380, 3.58221600, 2.25095400], |
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[4.2816400, 0.04868213, 0.83454597, 5.53361700, 4.31957400], |
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[3.4185164, 2.79190370, 2.88832240, 1.74746920, 1.36192720], |
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[3.6837196, 0.63789610, 5.72131160, 5.26143740, 6.25968700], |
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[2.9292210, 2.56143520, 0.97840965, 1.57294110, 6.02352240], |
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], |
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[ |
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[0.4699935, 0.52820253, 0.81324730, 0.65267974, 0.89941100], |
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[0.9497089, 0.53438100, 0.48878422, 0.60298723, 0.91636120], |
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[0.6343409, 0.87112963, 0.81016120, 0.95008780, 0.81926220], |
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[0.9901055, 0.90233060, 0.42042294, 0.82927720, 0.81847864], |
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[0.6755719, 0.84938710, 0.93686795, 0.73741645, 0.40461043], |
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], |
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[ |
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[0.5414237, 0.99627006, 0.89471555, 0.81350225, 0.94832740], |
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[0.9306560, 0.80207086, 0.89332560, 0.91709770, 0.75575125], |
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[0.7415741, 0.86247796, 0.41260356, 0.76009345, 0.72799970], |
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[0.8692723, 0.82887480, 0.96470920, 0.89220200, 0.76143440], |
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[0.8932794, 0.85178390, 0.76217470, 0.89838010, 0.99185926], |
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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.rgb_to_hsv(data), expected) |
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def test_nan_rgb_to_hsv(self, device, dtype): |
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data = torch.zeros(3, 5, 5, device=device, dtype=dtype) |
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expected = torch.zeros_like(data) |
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assert_close(kornia.color.rgb_to_hsv(data), expected) |
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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_hsv, (img,), raise_exception=True) |
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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_hsv |
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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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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.RgbToHsv().to(device, dtype) |
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fcn = kornia.color.rgb_to_hsv |
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assert_close(ops(img), fcn(img)) |
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class TestHsvToRgb(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.hsv_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.hsv_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.hsv_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.hsv_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.hsv_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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[3.5433271, 5.6390061, 1.3766849, 2.5384088, 4.6848912], |
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[5.7209363, 5.3262630, 6.2059994, 4.1164689, 2.3872600], |
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[0.6370091, 3.6186798, 5.9170871, 2.8275447, 5.4289737], |
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[0.2751994, 1.6632686, 1.0049511, 0.7046204, 1.3791083], |
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[0.7863123, 4.4852505, 4.3064494, 2.5573561, 5.9083076], |
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], |
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[ |
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[0.5026655, 0.9453601, 0.5929778, 0.2632897, 0.4590443], |
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[0.6201433, 0.5610679, 0.9653260, 0.0830478, 0.5000827], |
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[0.6067343, 0.6422323, 0.6777940, 0.7705711, 0.6050767], |
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[0.5495264, 0.5573426, 0.4683768, 0.2268902, 0.2116482], |
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[0.6525245, 0.0022379, 0.4909980, 0.1682271, 0.6327152], |
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], |
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[ |
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[0.8471680, 0.9302199, 0.3265766, 0.7944570, 0.7038843], |
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[0.4833369, 0.2088473, 0.1169234, 0.4966302, 0.6448684], |
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[0.2713015, 0.5893380, 0.6015301, 0.6801558, 0.2322258], |
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[0.5704236, 0.6797268, 0.4755683, 0.4811209, 0.5317836], |
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[0.3236262, 0.0999796, 0.3614958, 0.5117705, 0.8194097], |
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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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[0.42132590, 0.93021995, 0.26564622, 0.58528465, 0.53384290], |
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[0.48333693, 0.20884734, 0.11692339, 0.45538613, 0.32238087], |
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[0.27130150, 0.21084610, 0.60153013, 0.15604737, 0.23222584], |
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[0.57042360, 0.45685310, 0.47556830, 0.48112088, 0.49611038], |
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[0.32362622, 0.09981924, 0.20394461, 0.42567685, 0.81940967], |
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], |
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[ |
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[0.68380290, 0.05082710, 0.32657660, 0.79445700, 0.38077020], |
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[0.18359877, 0.09166980, 0.00405421, 0.45823452, 0.64486840], |
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[0.20682439, 0.41690278, 0.19381660, 0.68015575, 0.09171140], |
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[0.33933756, 0.67972680, 0.46658220, 0.44541004, 0.53178360], |
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[0.27101707, 0.09975589, 0.18400209, 0.51177055, 0.30095676], |
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], |
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[ |
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[0.84716797, 0.59178180, 0.13292392, 0.67397410, 0.70388430], |
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[0.34453064, 0.19874583, 0.01237347, 0.49663020, 0.41256943], |
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[0.10669357, 0.58933800, 0.33635240, 0.52297890, 0.20633064], |
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[0.25696078, 0.30088606, 0.25282317, 0.37195927, 0.41923255], |
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[0.11245217, 0.09997964, 0.36149580, 0.46373847, 0.48655340], |
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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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f = kornia.color.hsv_to_rgb |
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assert_close(f(data), expected) |
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data[:, 0] += 2 * math.pi |
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assert_close(f(data), expected) |
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data[:, 0] -= 4 * math.pi |
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assert_close(f(data), expected) |
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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.hsv_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.hsv_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.HsvToRgb().to(device, dtype) |
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fcn = kornia.color.hsv_to_rgb |
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assert_close(ops(img), fcn(img)) |
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