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50 lines
1.9 KiB
Python
50 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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import kornia
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from testing.base import BaseTester
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class TestMeanAveragePrecision(BaseTester):
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def test_smoke(self, device, dtype):
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boxes = torch.tensor([[100, 50, 150, 100.0]], device=device, dtype=dtype)
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labels = torch.tensor([1], device=device, dtype=torch.long)
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scores = torch.tensor([0.7], device=device, dtype=dtype)
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gt_boxes = torch.tensor([[100, 50, 150, 100.0]], device=device, dtype=dtype)
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gt_labels = torch.tensor([1], device=device, dtype=torch.long)
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mean_ap = kornia.metrics.mean_average_precision([boxes], [labels], [scores], [gt_boxes], [gt_labels], 2)
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self.assert_close(mean_ap[0], torch.tensor(1.0, device=device, dtype=dtype))
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self.assert_close(mean_ap[1][1], 1.0)
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def test_raise(self, device, dtype):
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boxes = torch.tensor([[100, 50, 150, 100.0]], device=device, dtype=dtype)
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labels = torch.tensor([1], device=device, dtype=torch.long)
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scores = torch.tensor([0.7], device=device, dtype=dtype)
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gt_boxes = torch.tensor([[100, 50, 150, 100.0]], device=device, dtype=dtype)
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gt_labels = torch.tensor([1], device=device, dtype=torch.long)
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with pytest.raises(AssertionError):
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_ = kornia.metrics.mean_average_precision(boxes[0], [labels], [scores], [gt_boxes], [gt_labels], 2)
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