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962a057f4c
when adapt for mmlab, the original modules will be changed, which will make argument error when using mmdet original config and interface 1. add interface for removing adaptation for mmlab 2. register copy of mmlab module into easycv 3. bump internal version to 0.6.3.1 Link: https://code.alibaba-inc.com/pai-vision/EasyCV/codereview/10059050
144 lines
5.8 KiB
Python
144 lines
5.8 KiB
Python
# Copyright (c) Alibaba, Inc. and its affiliates.
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import os
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import unittest
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from inspect import signature
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import torch
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from mmcv.parallel import scatter_kwargs
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from tests.ut_config import (COCO_CLASSES, DET_DATA_SMALL_COCO_LOCAL,
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IMG_NORM_CFG_255)
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from easycv.apis.test import single_gpu_test
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from easycv.datasets import build_dataloader, build_dataset
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from easycv.models.builder import build_model
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from easycv.utils.config_tools import mmcv_config_fromfile
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from easycv.utils.mmlab_utils import (MM_REGISTRY, MMDET,
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dynamic_adapt_for_mmlab,
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remove_adapt_for_mmlab)
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class MMLabUtilTest(unittest.TestCase):
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def setUp(self):
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print(('Testing %s.%s' % (type(self).__name__, self._testMethodName)))
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def _get_model(self):
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config_path = 'configs/detection/mask_rcnn/mask_rcnn_r50_fpn.py'
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cfg = mmcv_config_fromfile(config_path)
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dynamic_adapt_for_mmlab(cfg)
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model = build_model(cfg.model)
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self.cfg = cfg
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return model
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def _get_dataset(self, mode='train'):
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if mode == 'train':
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pipeline = [
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dict(
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type='MMResize',
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img_scale=[(1333, 640), (1333, 672), (1333, 704),
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(1333, 736), (1333, 768), (1333, 800)],
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multiscale_mode='value',
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keep_ratio=True),
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dict(type='MMRandomFlip', flip_ratio=0.5),
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dict(type='MMNormalize', **IMG_NORM_CFG_255),
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dict(type='MMPad', size_divisor=32),
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dict(type='DefaultFormatBundle'),
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dict(
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type='Collect',
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keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks'],
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meta_keys=('filename', 'ori_filename', 'ori_shape',
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'ori_img_shape', 'img_shape', 'pad_shape',
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'scale_factor', 'flip', 'flip_direction',
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'img_norm_cfg'))
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]
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else:
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pipeline = [
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dict(
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type='MMMultiScaleFlipAug',
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img_scale=(1333, 800),
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flip=False,
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transforms=[
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dict(type='MMResize', keep_ratio=True),
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dict(type='MMRandomFlip'),
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dict(type='MMNormalize', **IMG_NORM_CFG_255),
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dict(type='MMPad', size_divisor=32),
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dict(type='ImageToTensor', keys=['img']),
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dict(
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type='Collect',
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keys=['img'],
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meta_keys=('filename', 'ori_filename', 'ori_shape',
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'ori_img_shape', 'img_shape',
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'pad_shape', 'scale_factor', 'flip',
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'flip_direction', 'img_norm_cfg')),
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])
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]
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data_root = DET_DATA_SMALL_COCO_LOCAL
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dataset_cfg = dict(
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type='DetDataset',
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data_source=dict(
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type='DetSourceCoco',
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ann_file=os.path.join(data_root,
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'instances_train2017_20.json'),
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img_prefix=os.path.join(data_root, 'train2017'),
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pipeline=[
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dict(type='LoadImageFromFile', to_float32=True),
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dict(
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type='LoadAnnotations', with_bbox=True, with_mask=True)
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],
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classes=COCO_CLASSES,
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filter_empty_gt=False,
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iscrowd=False),
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pipeline=pipeline)
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return build_dataset(dataset_cfg)
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def test_model_train(self):
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model = self._get_model()
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model = model.cuda()
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model.train()
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dataset = self._get_dataset()
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data_loader = build_dataloader(
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dataset, imgs_per_gpu=1, workers_per_gpu=1, num_gpus=1, dist=False)
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for i, data_batch in enumerate(data_loader):
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input_args, kwargs = scatter_kwargs(None, data_batch,
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[torch.cuda.current_device()])
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output = model(**kwargs[0], mode='train')
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self.assertEqual(len(output['loss_rpn_cls']), 5)
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self.assertEqual(len(output['loss_rpn_bbox']), 5)
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self.assertEqual(output['loss_cls'].shape, torch.Size([]))
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self.assertEqual(output['acc'].shape, torch.Size([1]))
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self.assertEqual(output['loss_bbox'].shape, torch.Size([]))
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self.assertEqual(output['loss_mask'].shape, torch.Size([1]))
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def test_model_test(self):
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model = self._get_model()
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model = model.cuda()
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dataset = self._get_dataset(mode='test')
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data_loader = build_dataloader(
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dataset, imgs_per_gpu=1, workers_per_gpu=1, num_gpus=1, dist=False)
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results = single_gpu_test(model, data_loader, mode='test')
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self.assertEqual(len(results['detection_boxes']), 20)
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self.assertEqual(len(results['detection_scores']), 20)
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self.assertEqual(len(results['detection_classes']), 20)
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self.assertEqual(len(results['detection_masks']), 20)
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self.assertEqual(len(results['img_metas']), 20)
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def test_reset(self):
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model = self._get_model()
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remove_adapt_for_mmlab(self.cfg)
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mmdet_registry = MM_REGISTRY[MMDET]
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for module, registry in mmdet_registry.items():
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for k, v in registry.module_dict.items():
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self.assertTrue('easycv' not in str(v))
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models = mmdet_registry['model']
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mask_rcnn = models.get('MaskRCNN')
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sig_str = str(signature(mask_rcnn.forward))
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self.assertTrue('mode' not in sig_str)
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if __name__ == '__main__':
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unittest.main()
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