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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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import kornia.testing as utils |
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from kornia.testing import assert_close |
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class TestBoxBlur: |
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def test_shape(self, device, dtype): |
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inp = torch.zeros(1, 3, 4, 4, device=device, dtype=dtype) |
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blur = kornia.filters.BoxBlur((3, 3)) |
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assert blur(inp).shape == (1, 3, 4, 4) |
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def test_shape_batch(self, device, dtype): |
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inp = torch.zeros(2, 6, 4, 4, device=device, dtype=dtype) |
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blur = kornia.filters.BoxBlur((3, 3)) |
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assert blur(inp).shape == (2, 6, 4, 4) |
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def test_kernel_3x3(self, device, dtype): |
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inp = torch.tensor( |
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[ |
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[ |
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[ |
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[1.0, 1.0, 1.0, 1.0, 1.0], |
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[1.0, 1.0, 1.0, 1.0, 1.0], |
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[1.0, 1.0, 1.0, 1.0, 1.0], |
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[2.0, 2.0, 2.0, 2.0, 2.0], |
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[2.0, 2.0, 2.0, 2.0, 2.0], |
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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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kernel_size = (3, 3) |
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actual = kornia.filters.box_blur(inp, kernel_size) |
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tol_val: float = utils._get_precision_by_name(device, 'xla', 1e-1, 1e-4) |
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assert_close(actual.sum(), torch.tensor(35.0).to(actual), rtol=tol_val, atol=tol_val) |
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def test_kernel_3x3_nonormalize(self, device, dtype): |
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inp = torch.tensor( |
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[ |
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[ |
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[ |
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[1.0, 1.0, 1.0, 1.0, 1.0], |
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[1.0, 1.0, 1.0, 1.0, 1.0], |
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[1.0, 1.0, 1.0, 1.0, 1.0], |
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[2.0, 2.0, 2.0, 2.0, 2.0], |
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[2.0, 2.0, 2.0, 2.0, 2.0], |
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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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kernel_size = (3, 3) |
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actual = kornia.filters.box_blur(inp, kernel_size, normalized=False) |
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tol_val: float = utils._get_precision_by_name(device, 'xla', 1e-1, 1e-4) |
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assert_close(actual.sum(), torch.tensor(35.0).to(actual), rtol=tol_val, atol=tol_val) |
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def test_kernel_5x5(self, device, dtype): |
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inp = torch.tensor( |
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[ |
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[ |
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[ |
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[1.0, 1.0, 1.0, 1.0, 1.0], |
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[1.0, 1.0, 1.0, 1.0, 1.0], |
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[1.0, 1.0, 1.0, 1.0, 1.0], |
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[2.0, 2.0, 2.0, 2.0, 2.0], |
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[2.0, 2.0, 2.0, 2.0, 2.0], |
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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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kernel_size = (5, 5) |
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expected = inp.sum((1, 2, 3)) / torch.mul(*kernel_size) |
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actual = kornia.filters.box_blur(inp, kernel_size) |
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tol_val: float = utils._get_precision_by_name(device, 'xla', 1e-1, 1e-4) |
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assert_close(actual[:, 0, 2, 2], expected, rtol=tol_val, atol=tol_val) |
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def test_kernel_5x5_batch(self, device, dtype): |
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batch_size = 3 |
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inp = torch.tensor( |
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[ |
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[ |
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[ |
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[1.0, 1.0, 1.0, 1.0, 1.0], |
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[1.0, 1.0, 1.0, 1.0, 1.0], |
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[1.0, 1.0, 1.0, 1.0, 1.0], |
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[2.0, 2.0, 2.0, 2.0, 2.0], |
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[2.0, 2.0, 2.0, 2.0, 2.0], |
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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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).repeat(batch_size, 1, 1, 1) |
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kernel_size = (5, 5) |
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expected = inp.sum((1, 2, 3)) / torch.mul(*kernel_size) |
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actual = kornia.filters.box_blur(inp, kernel_size) |
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tol_val: float = utils._get_precision_by_name(device, 'xla', 1e-1, 1e-4) |
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assert_close(actual[:, 0, 2, 2], expected, rtol=tol_val, atol=tol_val) |
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def test_noncontiguous(self, device, dtype): |
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batch_size = 3 |
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inp = torch.rand(3, 5, 5, device=device, dtype=dtype).expand(batch_size, -1, -1, -1) |
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kernel_size = (3, 3) |
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actual = kornia.filters.box_blur(inp, kernel_size) |
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assert_close(actual, actual) |
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def test_gradcheck(self, device, dtype): |
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batch_size, channels, height, width = 1, 2, 5, 4 |
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img = torch.rand(batch_size, channels, height, width, device=device, dtype=dtype) |
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img = utils.tensor_to_gradcheck_var(img) |
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assert gradcheck(kornia.filters.box_blur, (img, (3, 3)), raise_exception=True) |
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def test_jit(self, device, dtype): |
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op = kornia.filters.box_blur |
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op_script = torch.jit.script(op) |
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kernel_size = (3, 3) |
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img = torch.rand(2, 3, 4, 5, device=device, dtype=dtype) |
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actual = op_script(img, kernel_size) |
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expected = op(img, kernel_size) |
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assert_close(actual, expected) |
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def test_module(self, device, dtype): |
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op = kornia.filters.box_blur |
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op_module = kornia.filters.BoxBlur |
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kernel_size = (3, 3) |
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img = torch.rand(2, 3, 4, 5, device=device, dtype=dtype) |
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actual = op_module(kernel_size)(img) |
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expected = op(img, kernel_size) |
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assert_close(actual, expected) |
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