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import os.path as osp |
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import tempfile |
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from unittest import TestCase |
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import torch |
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from mmengine.fileio import load |
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from torch import Tensor |
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from mmdet.evaluation import DumpDetResults |
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from mmdet.structures.mask import encode_mask_results |
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class TestDumpResults(TestCase): |
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def test_init(self): |
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with self.assertRaisesRegex(ValueError, |
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'The output file must be a pkl file.'): |
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DumpDetResults(out_file_path='./results.json') |
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def test_process(self): |
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metric = DumpDetResults(out_file_path='./results.pkl') |
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data_samples = [dict(data=(Tensor([1, 2, 3]), Tensor([4, 5, 6])))] |
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metric.process(None, data_samples) |
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self.assertEqual(len(metric.results), 1) |
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self.assertEqual(metric.results[0]['data'][0].device, |
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torch.device('cpu')) |
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metric = DumpDetResults(out_file_path='./results.pkl') |
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masks = torch.zeros(10, 10, 4) |
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data_samples = [ |
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dict(pred_instances=dict(masks=masks), gt_instances=[]) |
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] |
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metric.process(None, data_samples) |
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self.assertEqual(len(metric.results), 1) |
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self.assertEqual(metric.results[0]['pred_instances']['masks'], |
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encode_mask_results(masks.numpy())) |
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self.assertNotIn('gt_instances', metric.results[0]) |
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def test_compute_metrics(self): |
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temp_dir = tempfile.TemporaryDirectory() |
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path = osp.join(temp_dir.name, 'results.pkl') |
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metric = DumpDetResults(out_file_path=path) |
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data_samples = [dict(data=(Tensor([1, 2, 3]), Tensor([4, 5, 6])))] |
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metric.process(None, data_samples) |
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metric.compute_metrics(metric.results) |
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self.assertTrue(osp.isfile(path)) |
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results = load(path) |
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self.assertEqual(len(results), 1) |
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self.assertEqual(results[0]['data'][0].device, torch.device('cpu')) |
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temp_dir.cleanup() |
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