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79 lines
2.8 KiB
Python
79 lines
2.8 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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from kornia.enhance.rescale import Rescale
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from testing.base import BaseTester
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class TestRescale(BaseTester):
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def test_smoke(self, device, dtype):
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r = Rescale(0.5).to(device, dtype)
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x = torch.ones(1, 3, 4, 4, device=device, dtype=dtype)
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assert isinstance(r(x), torch.Tensor)
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def test_cardinality(self, device, dtype):
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r = Rescale(2.0).to(device, dtype)
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x = torch.ones(2, 3, 5, 5, device=device, dtype=dtype)
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assert r(x).shape == x.shape
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def test_float_factor(self, device, dtype):
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r = Rescale(0.5).to(device, dtype)
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x = torch.ones(1, 1, 2, 2, device=device, dtype=dtype) * 4.0
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expected = torch.ones(1, 1, 2, 2, device=device, dtype=dtype) * 2.0
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self.assert_close(r(x), expected)
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def test_tensor_factor(self, device, dtype):
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factor = torch.tensor(3.0, device=device, dtype=dtype)
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r = Rescale(factor)
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x = torch.ones(1, 1, 2, 2, device=device, dtype=dtype) * 2.0
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expected = torch.ones(1, 1, 2, 2, device=device, dtype=dtype) * 6.0
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self.assert_close(r(x), expected)
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def test_zero_factor(self, device, dtype):
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r = Rescale(0.0).to(device, dtype)
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x = torch.rand(1, 3, 4, 4, device=device, dtype=dtype)
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expected = torch.zeros_like(x)
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self.assert_close(r(x), expected)
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def test_exception(self, device, dtype):
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with pytest.raises(TypeError):
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Rescale(torch.ones(2)) # not a 0-d tensor
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with pytest.raises(TypeError):
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Rescale([0.5]) # list is not valid either
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def test_gradcheck(self, device):
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factor = torch.tensor(2.0)
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r = Rescale(factor)
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x = torch.rand(1, 3, 4, 4, device=device, dtype=torch.float64, requires_grad=True)
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self.gradcheck(r, (x,))
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def test_module(self, device, dtype):
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r1 = Rescale(2.0).to(device, dtype)
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x = torch.rand(1, 3, 4, 4, device=device, dtype=dtype)
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self.assert_close(r1(x), x * 2.0)
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def test_dynamo(self, device, dtype, torch_optimizer):
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r = Rescale(0.5).to(device, dtype)
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x = torch.rand(1, 3, 4, 4, device=device, dtype=dtype)
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op = torch_optimizer(r)
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self.assert_close(op(x), r(x))
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