# Copyright (c) OpenMMLab. All rights reserved. import os.path as osp import tempfile from unittest import TestCase, mock from unittest.mock import Mock, patch import mmcv import mmengine import numpy as np import torch from mmengine.structures import InstanceData from mmengine.utils import is_list_of from parameterized import parameterized from mmdet.apis import DetInferencer from mmdet.evaluation.functional import get_classes from mmdet.structures import DetDataSample class TestDetInferencer(TestCase): @mock.patch('mmengine.infer.infer._load_checkpoint', return_value=None) def test_init(self, mock): # init from metafile DetInferencer('rtmdet-t') # init from cfg DetInferencer('configs/yolox/yolox_tiny_8xb8-300e_coco.py') def assert_predictions_equal(self, preds1, preds2): for pred1, pred2 in zip(preds1, preds2): if 'bboxes' in pred1: self.assertTrue( np.allclose(pred1['bboxes'], pred2['bboxes'], 0.1)) if 'scores' in pred1: self.assertTrue( np.allclose(pred1['scores'], pred2['scores'], 0.1)) if 'labels' in pred1: self.assertTrue(np.allclose(pred1['labels'], pred2['labels'])) if 'panoptic_seg_path' in pred1: self.assertTrue( pred1['panoptic_seg_path'] == pred2['panoptic_seg_path']) @parameterized.expand([ 'rtmdet-t', 'mask-rcnn_r50_fpn_1x_coco', 'panoptic_fpn_r50_fpn_1x_coco' ]) def test_call(self, model): # single img img_path = 'tests/data/color.jpg' mock_load = Mock(return_value=None) with patch('mmengine.infer.infer._load_checkpoint', mock_load): inferencer = DetInferencer(model) # In the case of not loading the pretrained weight, the category # defaults to COCO 80, so it needs to be replaced. if model == 'panoptic_fpn_r50_fpn_1x_coco': inferencer.visualizer.dataset_meta = { 'classes': get_classes('coco_panoptic'), 'palette': 'random' } res_path = inferencer(img_path, return_vis=True) # ndarray img = mmcv.imread(img_path) res_ndarray = inferencer(img, return_vis=True) self.assert_predictions_equal(res_path['predictions'], res_ndarray['predictions']) self.assertIn('visualization', res_path) self.assertIn('visualization', res_ndarray) # multiple images img_paths = ['tests/data/color.jpg', 'tests/data/gray.jpg'] res_path = inferencer(img_paths, return_vis=True) # list of ndarray imgs = [mmcv.imread(p) for p in img_paths] res_ndarray = inferencer(imgs, return_vis=True) self.assert_predictions_equal(res_path['predictions'], res_ndarray['predictions']) self.assertIn('visualization', res_path) self.assertIn('visualization', res_ndarray) # img dir, test different batch sizes img_dir = 'tests/data/VOCdevkit/VOC2007/JPEGImages/' res_bs1 = inferencer(img_dir, batch_size=1, return_vis=True) res_bs3 = inferencer(img_dir, batch_size=3, return_vis=True) self.assert_predictions_equal(res_bs1['predictions'], res_bs3['predictions']) # There is a jitter operation when the mask is drawn, # so it cannot be asserted. if model == 'rtmdet-t': for res_bs1_vis, res_bs3_vis in zip(res_bs1['visualization'], res_bs3['visualization']): self.assertTrue(np.allclose(res_bs1_vis, res_bs3_vis)) @parameterized.expand([ 'rtmdet-t', 'mask-rcnn_r50_fpn_1x_coco', 'panoptic_fpn_r50_fpn_1x_coco' ]) def test_visualize(self, model): img_paths = ['tests/data/color.jpg', 'tests/data/gray.jpg'] mock_load = Mock(return_value=None) with patch('mmengine.infer.infer._load_checkpoint', mock_load): inferencer = DetInferencer(model) # In the case of not loading the pretrained weight, the category # defaults to COCO 80, so it needs to be replaced. if model == 'panoptic_fpn_r50_fpn_1x_coco': inferencer.visualizer.dataset_meta = { 'classes': get_classes('coco_panoptic'), 'palette': 'random' } with tempfile.TemporaryDirectory() as tmp_dir: inferencer(img_paths, out_dir=tmp_dir) for img_dir in ['color.jpg', 'gray.jpg']: self.assertTrue(osp.exists(osp.join(tmp_dir, 'vis', img_dir))) @parameterized.expand([ 'rtmdet-t', 'mask-rcnn_r50_fpn_1x_coco', 'panoptic_fpn_r50_fpn_1x_coco' ]) def test_postprocess(self, model): # return_datasamples img_path = 'tests/data/color.jpg' mock_load = Mock(return_value=None) with patch('mmengine.infer.infer._load_checkpoint', mock_load): inferencer = DetInferencer(model) # In the case of not loading the pretrained weight, the category # defaults to COCO 80, so it needs to be replaced. if model == 'panoptic_fpn_r50_fpn_1x_coco': inferencer.visualizer.dataset_meta = { 'classes': get_classes('coco_panoptic'), 'palette': 'random' } res = inferencer(img_path, return_datasamples=True) self.assertTrue(is_list_of(res['predictions'], DetDataSample)) with tempfile.TemporaryDirectory() as tmp_dir: res = inferencer(img_path, out_dir=tmp_dir, no_save_pred=False) dumped_res = mmengine.load( osp.join(tmp_dir, 'preds', 'color.json')) self.assertEqual(res['predictions'][0], dumped_res) @mock.patch('mmengine.infer.infer._load_checkpoint', return_value=None) def test_pred2dict(self, mock): data_sample = DetDataSample() data_sample.pred_instances = InstanceData() data_sample.pred_instances.bboxes = np.array([[0, 0, 1, 1]]) data_sample.pred_instances.labels = np.array([0]) data_sample.pred_instances.scores = torch.FloatTensor([0.9]) res = DetInferencer('rtmdet-t').pred2dict(data_sample) self.assertListAlmostEqual(res['bboxes'], [[0, 0, 1, 1]]) self.assertListAlmostEqual(res['labels'], [0]) self.assertListAlmostEqual(res['scores'], [0.9]) def assertListAlmostEqual(self, list1, list2, places=7): for i in range(len(list1)): if isinstance(list1[i], list): self.assertListAlmostEqual(list1[i], list2[i], places=places) else: self.assertAlmostEqual(list1[i], list2[i], places=places)