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import copy |
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import os.path as osp |
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import unittest |
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from mmcv.transforms import Compose |
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from mmdet.datasets.transforms import MultiBranch, RandomOrder |
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from mmdet.utils import register_all_modules |
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from .utils import construct_toy_data |
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register_all_modules() |
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class TestMultiBranch(unittest.TestCase): |
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def setUp(self): |
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"""Setup the model and optimizer which are used in every test method. |
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TestCase calls functions in this order: setUp() -> testMethod() -> |
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tearDown() -> cleanUp() |
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""" |
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data_prefix = osp.join(osp.dirname(__file__), '../../data') |
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img_path = osp.join(data_prefix, 'color.jpg') |
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seg_map = osp.join(data_prefix, 'gray.jpg') |
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self.meta_keys = ('img_id', 'img_path', 'ori_shape', 'img_shape', |
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'scale_factor', 'flip', 'flip_direction', |
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'homography_matrix') |
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self.results = { |
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'img_path': |
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img_path, |
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'img_id': |
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12345, |
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'img_shape': (300, 400), |
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'seg_map_path': |
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seg_map, |
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'instances': [{ |
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'bbox': [0, 0, 10, 20], |
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'bbox_label': 1, |
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'mask': [[0, 0, 0, 20, 10, 20, 10, 0]], |
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'ignore_flag': 0 |
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}, { |
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'bbox': [10, 10, 110, 120], |
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'bbox_label': 2, |
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'mask': [[10, 10, 110, 10, 110, 120, 110, 10]], |
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'ignore_flag': 0 |
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}, { |
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'bbox': [50, 50, 60, 80], |
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'bbox_label': 2, |
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'mask': [[50, 50, 60, 50, 60, 80, 50, 80]], |
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'ignore_flag': 1 |
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}] |
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} |
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self.branch_field = ['sup', 'sup_teacher', 'sup_student'] |
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self.weak_pipeline = [ |
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dict(type='ShearX'), |
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dict(type='PackDetInputs', meta_keys=self.meta_keys) |
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] |
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self.strong_pipeline = [ |
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dict(type='ShearX'), |
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dict(type='ShearY'), |
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dict(type='PackDetInputs', meta_keys=self.meta_keys) |
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] |
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self.labeled_pipeline = [ |
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dict(type='LoadImageFromFile'), |
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dict( |
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type='LoadAnnotations', |
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with_bbox=True, |
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with_mask=True, |
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with_seg=True), |
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dict(type='Resize', scale=(1333, 800), keep_ratio=True), |
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dict(type='RandomFlip', prob=0.5), |
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dict( |
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type='MultiBranch', |
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branch_field=self.branch_field, |
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sup_teacher=self.weak_pipeline, |
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sup_student=self.strong_pipeline), |
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] |
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self.unlabeled_pipeline = [ |
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dict(type='LoadImageFromFile'), |
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dict(type='Resize', scale=(1333, 800), keep_ratio=True), |
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dict(type='RandomFlip', prob=0.5), |
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dict( |
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type='MultiBranch', |
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branch_field=self.branch_field, |
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unsup_teacher=self.weak_pipeline, |
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unsup_student=self.strong_pipeline), |
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] |
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def test_transform(self): |
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labeled_pipeline = Compose(self.labeled_pipeline) |
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labeled_results = labeled_pipeline(copy.deepcopy(self.results)) |
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unlabeled_pipeline = Compose(self.unlabeled_pipeline) |
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unlabeled_results = unlabeled_pipeline(copy.deepcopy(self.results)) |
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sup_branches = ['sup_teacher', 'sup_student'] |
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for branch in sup_branches: |
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self.assertIn(branch, labeled_results['data_samples']) |
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self.assertIn('homography_matrix', |
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labeled_results['data_samples'][branch]) |
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self.assertIn('labels', |
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labeled_results['data_samples'][branch].gt_instances) |
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self.assertIn('bboxes', |
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labeled_results['data_samples'][branch].gt_instances) |
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self.assertIn('masks', |
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labeled_results['data_samples'][branch].gt_instances) |
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self.assertIn('gt_sem_seg', |
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labeled_results['data_samples'][branch]) |
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unsup_branches = ['unsup_teacher', 'unsup_student'] |
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for branch in unsup_branches: |
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self.assertIn(branch, unlabeled_results['data_samples']) |
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self.assertIn('homography_matrix', |
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unlabeled_results['data_samples'][branch]) |
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self.assertNotIn( |
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'labels', |
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unlabeled_results['data_samples'][branch].gt_instances) |
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self.assertNotIn( |
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'bboxes', |
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unlabeled_results['data_samples'][branch].gt_instances) |
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self.assertNotIn( |
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'masks', |
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unlabeled_results['data_samples'][branch].gt_instances) |
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self.assertNotIn('gt_sem_seg', |
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unlabeled_results['data_samples'][branch]) |
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def test_repr(self): |
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pipeline = [dict(type='PackDetInputs', meta_keys=())] |
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transform = MultiBranch( |
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branch_field=self.branch_field, sup=pipeline, unsup=pipeline) |
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self.assertEqual( |
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repr(transform), |
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("MultiBranch(branch_pipelines=['sup', 'unsup'])")) |
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class TestRandomOrder(unittest.TestCase): |
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def setUp(self): |
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"""Setup the model and optimizer which are used in every test method. |
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TestCase calls functions in this order: setUp() -> testMethod() -> |
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tearDown() -> cleanUp() |
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""" |
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self.results = construct_toy_data(poly2mask=True) |
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self.pipeline = [ |
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dict(type='Sharpness'), |
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dict(type='Contrast'), |
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dict(type='Brightness'), |
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dict(type='Rotate'), |
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dict(type='ShearX'), |
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dict(type='TranslateY') |
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] |
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def test_transform(self): |
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transform = RandomOrder(self.pipeline) |
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results = transform(copy.deepcopy(self.results)) |
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self.assertEqual(results['img_shape'], self.results['img_shape']) |
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self.assertEqual(results['gt_bboxes'].shape, |
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self.results['gt_bboxes'].shape) |
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self.assertEqual(results['gt_bboxes_labels'], |
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self.results['gt_bboxes_labels']) |
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self.assertEqual(results['gt_ignore_flags'], |
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self.results['gt_ignore_flags']) |
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self.assertEqual(results['gt_masks'].masks.shape, |
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self.results['gt_masks'].masks.shape) |
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self.assertEqual(results['gt_seg_map'].shape, |
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self.results['gt_seg_map'].shape) |
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def test_repr(self): |
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transform = RandomOrder(self.pipeline) |
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self.assertEqual( |
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repr(transform), ('RandomOrder(Sharpness, Contrast, ' |
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'Brightness, Rotate, ShearX, TranslateY, )')) |
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