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57 lines
1.9 KiB
Python
57 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.models.dexined import DexiNed
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from testing.base import BaseTester
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class TestDexiNed(BaseTester):
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def test_smoke(self, device, dtype):
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img = torch.rand(2, 3, 32, 32, device=device, dtype=dtype)
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net = DexiNed(pretrained=False).to(device, dtype)
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feat = net.get_features(img)
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assert len(feat) == 6
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out = net(img)
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assert out.shape == (2, 1, 32, 32)
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@pytest.mark.slow
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@pytest.mark.parametrize("data", ["dexined"], indirect=True)
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def test_inference(self, device, dtype, data):
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model = DexiNed(pretrained=False)
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model.load_state_dict(data, strict=True)
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model = model.to(device, dtype)
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model.eval()
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img = torch.tensor([[[[0.0, 255.0, 0.0], [0.0, 255.0, 0.0], [0.0, 255.0, 0.0]]]], device=device, dtype=dtype)
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img = img.repeat(1, 3, 1, 1)
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expect = torch.tensor(
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[[[[-0.3709, 0.0519, -0.2839], [0.0627, 0.6587, -0.1276], [-0.1840, -0.3917, -0.8240]]]],
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device=device,
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dtype=dtype,
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)
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out = model(img)
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self.assert_close(out, expect, atol=1e-3, rtol=1e-2)
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@pytest.mark.skip(reason="DexiNed do not compile with dynamo.")
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def test_dynamo(self, device, dtype, torch_optimizer): ...
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