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from unittest import TestCase |
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import numpy as np |
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import pytest |
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import torch |
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from mmengine.structures import InstanceData, PixelData |
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from mmdet.structures import DetDataSample |
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def _equal(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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class TestDetDataSample(TestCase): |
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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 = DetDataSample(metainfo=meta_info) |
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assert 'img_size' in det_data_sample |
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assert det_data_sample.img_size == [256, 256] |
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assert det_data_sample.get('img_size') == [256, 256] |
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def test_setter(self): |
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det_data_sample = DetDataSample() |
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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 _equal(det_data_sample.gt_instances.bboxes, |
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gt_instances_data['bboxes']) |
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assert _equal(det_data_sample.gt_instances.labels, |
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gt_instances_data['labels']) |
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assert _equal(det_data_sample.gt_instances.masks, |
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gt_instances_data['masks']) |
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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 _equal(det_data_sample.pred_instances.bboxes, |
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pred_instances_data['bboxes']) |
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assert _equal(det_data_sample.pred_instances.labels, |
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pred_instances_data['labels']) |
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assert _equal(det_data_sample.pred_instances.masks, |
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pred_instances_data['masks']) |
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proposals_data = dict(bboxes=torch.rand(4, 4), labels=torch.rand(4)) |
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proposals = InstanceData(**proposals_data) |
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det_data_sample.proposals = proposals |
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assert 'proposals' in det_data_sample |
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assert _equal(det_data_sample.proposals.bboxes, |
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proposals_data['bboxes']) |
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assert _equal(det_data_sample.proposals.labels, |
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proposals_data['labels']) |
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ignored_instances_data = dict( |
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bboxes=torch.rand(4, 4), labels=torch.rand(4)) |
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ignored_instances = InstanceData(**ignored_instances_data) |
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det_data_sample.ignored_instances = ignored_instances |
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assert 'ignored_instances' in det_data_sample |
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assert _equal(det_data_sample.ignored_instances.bboxes, |
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ignored_instances_data['bboxes']) |
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assert _equal(det_data_sample.ignored_instances.labels, |
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ignored_instances_data['labels']) |
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gt_panoptic_seg_data = dict(panoptic_seg=torch.rand(5, 4)) |
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gt_panoptic_seg = PixelData(**gt_panoptic_seg_data) |
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det_data_sample.gt_panoptic_seg = gt_panoptic_seg |
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assert 'gt_panoptic_seg' in det_data_sample |
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assert _equal(det_data_sample.gt_panoptic_seg.panoptic_seg, |
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gt_panoptic_seg_data['panoptic_seg']) |
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pred_panoptic_seg_data = dict(panoptic_seg=torch.rand(5, 4)) |
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pred_panoptic_seg = PixelData(**pred_panoptic_seg_data) |
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det_data_sample.pred_panoptic_seg = pred_panoptic_seg |
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assert 'pred_panoptic_seg' in det_data_sample |
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assert _equal(det_data_sample.pred_panoptic_seg.panoptic_seg, |
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pred_panoptic_seg_data['panoptic_seg']) |
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gt_segm_seg_data = dict(segm_seg=torch.rand(5, 4, 2)) |
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gt_segm_seg = PixelData(**gt_segm_seg_data) |
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det_data_sample.gt_segm_seg = gt_segm_seg |
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assert 'gt_segm_seg' in det_data_sample |
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assert _equal(det_data_sample.gt_segm_seg.segm_seg, |
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gt_segm_seg_data['segm_seg']) |
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pred_segm_seg_data = dict(segm_seg=torch.rand(5, 4, 2)) |
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pred_segm_seg = PixelData(**pred_segm_seg_data) |
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det_data_sample.pred_segm_seg = pred_segm_seg |
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assert 'pred_segm_seg' in det_data_sample |
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assert _equal(det_data_sample.pred_segm_seg.segm_seg, |
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pred_segm_seg_data['segm_seg']) |
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with pytest.raises(AssertionError): |
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det_data_sample.pred_instances = torch.rand(2, 4) |
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with pytest.raises(AssertionError): |
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det_data_sample.pred_panoptic_seg = torch.rand(2, 4) |
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with pytest.raises(AssertionError): |
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det_data_sample.pred_sem_seg = 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 = DetDataSample() |
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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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pred_panoptic_seg_data = torch.rand(5, 4) |
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pred_panoptic_seg = PixelData(data=pred_panoptic_seg_data) |
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det_data_sample.pred_panoptic_seg = pred_panoptic_seg |
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assert 'pred_panoptic_seg' in det_data_sample |
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del det_data_sample.pred_panoptic_seg |
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assert 'pred_panoptic_seg' not in det_data_sample |
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pred_segm_seg_data = dict(segm_seg=torch.rand(5, 4, 2)) |
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pred_segm_seg = PixelData(**pred_segm_seg_data) |
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det_data_sample.pred_segm_seg = pred_segm_seg |
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assert 'pred_segm_seg' in det_data_sample |
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del det_data_sample.pred_segm_seg |
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assert 'pred_segm_seg' not in det_data_sample |
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