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53 lines
1.9 KiB
Python
53 lines
1.9 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.feature import SOSNet
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from testing.base import BaseTester
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class TestSOSNet(BaseTester):
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def test_shape(self, device):
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inp = torch.ones(1, 1, 32, 32, device=device)
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sosnet = SOSNet(pretrained=False).to(device)
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sosnet.eval() # batchnorm with size 1 is not allowed in train mode
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out = sosnet(inp)
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assert out.shape == (1, 128)
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def test_shape_batch(self, device):
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inp = torch.ones(16, 1, 32, 32, device=device)
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sosnet = SOSNet(pretrained=False).to(device)
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out = sosnet(inp)
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assert out.shape == (16, 128)
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@pytest.mark.skip("jacobian not well computed")
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def test_gradcheck(self, device):
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patches = torch.rand(2, 1, 32, 32, device=device, dtype=torch.float64)
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sosnet = SOSNet(pretrained=False).to(patches.device, patches.dtype)
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self.gradcheck(sosnet, (patches,), eps=1e-4, atol=1e-4)
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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, 1, 32, 32
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patches = torch.ones(B, C, H, W, device=device, dtype=dtype)
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model = SOSNet().to(patches.device, patches.dtype).eval()
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model_jit = torch.jit.script(SOSNet().to(patches.device, patches.dtype).eval())
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self.assert_close(model(patches), model_jit(patches))
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