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212 lines
7.3 KiB
Python
212 lines
7.3 KiB
Python
# LICENSE HEADER MANAGED BY add-license-header
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#
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# Copyright 2018 Kornia Team
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import pytest
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import torch
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import kornia
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from testing.base import BaseTester
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class TestVflip(BaseTester):
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def smoke_test(self, device, dtype):
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f = kornia.geometry.transform.Vflip()
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repr = "Vflip()"
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assert str(f) == repr
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def test_vflip(self, device, dtype):
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f = kornia.geometry.transform.Vflip()
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input = torch.tensor([[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 1.0, 1.0]], device=device, dtype=dtype) # 3 x 3
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expected = torch.tensor(
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[[0.0, 1.0, 1.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]], device=device, dtype=dtype
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) # 3 x 3
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self.assert_close(f(input), expected)
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def test_batch_vflip(self, device, dtype):
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input = torch.tensor([[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 1.0, 1.0]], device=device, dtype=dtype) # 3 x 3
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input = input.repeat(2, 1, 1) # 2 x 3 x 3
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f = kornia.geometry.transform.Vflip()
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expected = torch.tensor(
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[[[0.0, 1.0, 1.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]], device=device, dtype=dtype
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) # 1 x 3 x 3
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expected = expected.repeat(2, 1, 1) # 2 x 3 x 3
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self.assert_close(f(input), expected)
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@pytest.mark.skip(reason="turn off all jit for a while")
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def test_jit(self, device, dtype):
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@torch.jit.script
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def op_script(data: torch.Tensor) -> torch.Tensor:
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return kornia.geometry.transform.vflip(data)
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input = torch.tensor([[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 1.0, 1.0]], device=device, dtype=dtype) # 3 x 3
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# Build jit trace
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op_trace = torch.jit.trace(op_script, (input,))
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# Create new inputs
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input = torch.tensor([[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [5.0, 5.0, 0.0]], device=device, dtype=dtype) # 3 x 3
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input = input.repeat(2, 1, 1) # 2 x 3 x 3
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expected = torch.tensor(
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[[[5.0, 5.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]], device=device, dtype=dtype
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) # 3 x 3
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expected = expected.repeat(2, 1, 1)
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actual = op_trace(input)
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self.assert_close(actual, expected)
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def test_gradcheck(self, device):
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input = torch.tensor([[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 1.0, 1.0]], device=device, dtype=torch.float64)
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self.gradcheck(kornia.geometry.transform.Vflip(), (input,))
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class TestHflip(BaseTester):
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def smoke_test(self, device, dtype):
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f = kornia.geometry.transform.Hflip()
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repr = "Hflip()"
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assert str(f) == repr
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def test_hflip(self, device, dtype):
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f = kornia.geometry.transform.Hflip()
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input = torch.tensor([[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 1.0, 1.0]], device=device, dtype=dtype) # 3 x 3
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expected = torch.tensor(
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[[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [1.0, 1.0, 0.0]], device=device, dtype=dtype
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) # 3 x 3
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self.assert_close(f(input), expected)
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def test_batch_hflip(self, device, dtype):
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input = torch.tensor(
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[[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 1.0, 1.0]], device=device, dtype=dtype
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) # 1 x 3 x 3
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input = input.repeat(2, 1, 1) # 2 x 3 x 3
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f = kornia.geometry.transform.Hflip()
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expected = torch.tensor(
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[[[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [1.0, 1.0, 0.0]]], device=device, dtype=dtype
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) # 3 x 3
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expected = expected.repeat(2, 1, 1) # 2 x 3 x 3
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self.assert_close(f(input), expected)
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@pytest.mark.skip(reason="turn off all jit for a while")
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def test_jit(self, device, dtype):
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@torch.jit.script
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def op_script(data: torch.Tensor) -> torch.Tensor:
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return kornia.geometry.transform.hflip(data)
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input = torch.tensor([[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 1.0, 1.0]], device=device, dtype=dtype) # 3 x 3
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# Build jit trace
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op_trace = torch.jit.trace(op_script, (input,))
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# Create new inputs
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input = torch.tensor([[0.0, 0.0, 0.0], [5.0, 5.0, 0.0], [0.0, 0.0, 0.0]], device=device, dtype=dtype) # 3 x 3
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input = input.repeat(2, 1, 1) # 2 x 3 x 3
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expected = torch.tensor(
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[[[0.0, 0.0, 0.0], [0.0, 5.0, 5.0], [0.0, 0.0, 0.0]]], device=device, dtype=dtype
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) # 3 x 3
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expected = expected.repeat(2, 1, 1)
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actual = op_trace(input)
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self.assert_close(actual, expected)
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def test_gradcheck(self, device):
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input = torch.tensor([[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 1.0, 1.0]], device=device, dtype=torch.float64)
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self.gradcheck(kornia.geometry.transform.Hflip(), (input,))
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class TestRot180(BaseTester):
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def smoke_test(self, device, dtype):
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f = kornia.geometry.transform.Rot180()
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repr = "Rot180()"
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assert str(f) == repr
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def test_rot180(self, device, dtype):
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f = kornia.geometry.transform.Rot180()
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input = torch.tensor([[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 1.0, 1.0]], device=device, dtype=dtype) # 3 x 3
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expected = torch.tensor(
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[[1.0, 1.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]], device=device, dtype=dtype
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) # 3 x 3
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self.assert_close(f(input), expected)
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def test_batch_rot180(self, device, dtype):
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input = torch.tensor([[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 1.0, 1.0]], device=device, dtype=dtype) # 3 x 3
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input = input.repeat(2, 1, 1) # 2 x 3 x 3
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f = kornia.geometry.transform.Rot180()
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expected = torch.tensor(
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[[1.0, 1.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]], device=device, dtype=dtype
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) # 1 x 3 x 3
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expected = expected.repeat(2, 1, 1) # 2 x 3 x 3
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self.assert_close(f(input), expected)
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@pytest.mark.skip(reason="turn off all jit for a while")
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def test_jit(self, device, dtype):
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@torch.jit.script
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def op_script(data: torch.Tensor) -> torch.Tensor:
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return kornia.geometry.transform.rot180(data)
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input = torch.tensor([[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 1.0, 1.0]], device=device, dtype=dtype) # 3 x 3
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# Build jit trace
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op_trace = torch.jit.trace(op_script, (input,))
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# Create new inputs
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input = torch.tensor([[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [5.0, 5.0, 0.0]], device=device, dtype=dtype) # 3 x 3
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input = input.repeat(2, 1, 1) # 2 x 3 x 3
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expected = torch.tensor(
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[[[0.0, 5.0, 5.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]], device=device, dtype=dtype
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) # 3 x 3
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expected = expected.repeat(2, 1, 1)
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actual = op_trace(input)
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self.assert_close(actual, expected)
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def test_gradcheck(self, device):
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input = torch.tensor(
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[[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 1.0, 1.0]], device=device, dtype=torch.float64
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) # 3 x 3
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self.gradcheck(kornia.geometry.transform.Rot180(), (input,))
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