mirror of https://github.com/open-mmlab/mmocr.git
86 lines
3.1 KiB
Python
86 lines
3.1 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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from unittest import TestCase
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import numpy as np
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import torch
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from mmengine.data import InstanceData
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from mmocr.data import TextDetDataSample
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class TestTextDetDataSample(TestCase):
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def _equal(self, a, b):
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if isinstance(a, (torch.Tensor, np.ndarray)):
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return (a == b).all()
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else:
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return a == b
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def test_init(self):
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meta_info = dict(
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img_size=[256, 256],
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scale_factor=np.array([1.5, 1.5]),
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img_shape=torch.rand(4))
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det_data_sample = TextDetDataSample(metainfo=meta_info)
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assert 'img_size' in det_data_sample
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self.assertListEqual(det_data_sample.img_size, [256, 256])
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self.assertListEqual(det_data_sample.get('img_size'), [256, 256])
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def test_setter(self):
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det_data_sample = TextDetDataSample()
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# test gt_instances
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gt_instances_data = dict(
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bboxes=torch.rand(4, 4),
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labels=torch.rand(4),
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masks=np.random.rand(4, 2, 2))
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gt_instances = InstanceData(**gt_instances_data)
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det_data_sample.gt_instances = gt_instances
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assert 'gt_instances' in det_data_sample
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assert self._equal(det_data_sample.gt_instances.bboxes,
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gt_instances_data['bboxes'])
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assert self._equal(det_data_sample.gt_instances.labels,
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gt_instances_data['labels'])
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assert self._equal(det_data_sample.gt_instances.masks,
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gt_instances_data['masks'])
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# test pred_instances
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pred_instances_data = dict(
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bboxes=torch.rand(2, 4),
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labels=torch.rand(2),
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masks=np.random.rand(2, 2, 2))
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pred_instances = InstanceData(**pred_instances_data)
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det_data_sample.pred_instances = pred_instances
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assert 'pred_instances' in det_data_sample
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assert self._equal(det_data_sample.pred_instances.bboxes,
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pred_instances_data['bboxes'])
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assert self._equal(det_data_sample.pred_instances.labels,
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pred_instances_data['labels'])
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assert self._equal(det_data_sample.pred_instances.masks,
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pred_instances_data['masks'])
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# test type error
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with self.assertRaises(AssertionError):
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det_data_sample.gt_instances = torch.rand(2, 4)
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with self.assertRaises(AssertionError):
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det_data_sample.pred_instances = torch.rand(2, 4)
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def test_deleter(self):
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gt_instances_data = dict(
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bboxes=torch.rand(4, 4),
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labels=torch.rand(4),
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masks=np.random.rand(4, 2, 2))
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det_data_sample = TextDetDataSample()
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gt_instances = InstanceData(data=gt_instances_data)
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det_data_sample.gt_instances = gt_instances
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assert 'gt_instances' in det_data_sample
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del det_data_sample.gt_instances
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assert 'gt_instances' not in det_data_sample
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det_data_sample.pred_instances = gt_instances
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assert 'pred_instances' in det_data_sample
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del det_data_sample.pred_instances
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assert 'pred_instances' not in det_data_sample
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