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41 lines
1.4 KiB
Python
41 lines
1.4 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.vit_mobile import MobileViT
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from testing.base import BaseTester
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class TestMobileViT(BaseTester):
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@pytest.mark.parametrize("B", [1, 2])
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@pytest.mark.parametrize("image_size", [(256, 256)])
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@pytest.mark.parametrize("mode", ["xxs", "xs", "s"])
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@pytest.mark.parametrize("patch_size", [(2, 2)])
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def test_smoke(self, device, dtype, B, image_size, mode, patch_size):
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ih, iw = image_size
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channel = {"xxs": 320, "xs": 384, "s": 640}
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img = torch.rand(B, 3, ih, iw, device=device, dtype=dtype)
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mvit = MobileViT(mode=mode, patch_size=patch_size).to(device, dtype)
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out = mvit(img)
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assert isinstance(out, torch.Tensor)
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assert out.shape == (B, channel[mode], 8, 8)
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