mirror of https://github.com/alibaba/EasyCV.git
116 lines
3.5 KiB
Python
116 lines
3.5 KiB
Python
# Copyright (c) Alibaba, Inc. and its affiliates.
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import copy
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import logging
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import os
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import sys
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import tempfile
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import unittest
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from distutils.version import LooseVersion
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import torch
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from tests.ut_config import (PRETRAINED_MODEL_MAE, SMALL_IMAGENET_RAW_LOCAL,
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SSL_SMALL_IMAGENET_RAW)
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from easycv.file import io
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from easycv.utils.config_tools import mmcv_config_fromfile
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from easycv.utils.test_util import run_in_subprocess
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sys.path.append(os.path.dirname(os.path.realpath(__file__)))
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logging.basicConfig(level=logging.INFO)
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SSL_SMALL_IMAGENET_DATA_ROOT = SSL_SMALL_IMAGENET_RAW.rstrip('/') + '/'
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SMALL_IMAGENET_DATA_ROOT = SMALL_IMAGENET_RAW_LOCAL.rstrip('/') + '/'
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_COMMON_OPTIONS = {
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'checkpoint_config.interval': 1,
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'total_epochs': 1,
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'data.imgs_per_gpu': 8,
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}
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TRAIN_CONFIGS = [
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{
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'config_file':
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'configs/selfsup/mae/mae_vit_base_patch16_8xb64_400e.py',
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'cfg_options': {
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**_COMMON_OPTIONS, 'data.train.data_source.root':
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SSL_SMALL_IMAGENET_DATA_ROOT,
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'data.train.data_source.list_file':
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SSL_SMALL_IMAGENET_DATA_ROOT + 'train_20.txt'
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}
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},
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{
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'config_file':
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'benchmarks/selfsup/classification/imagenet/mae_vit_base_patch16_8xb64_100e_lrdecay065_fintune.py',
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'cfg_options': {
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**_COMMON_OPTIONS, 'data.train.data_source.root':
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SMALL_IMAGENET_DATA_ROOT + 'train/',
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'data.train.data_source.list_file':
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SMALL_IMAGENET_DATA_ROOT + 'meta/train_labeled_200.txt',
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'data.val.data_source.root':
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SMALL_IMAGENET_DATA_ROOT + 'validation/',
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'data.val.data_source.list_file':
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SMALL_IMAGENET_DATA_ROOT + 'meta/val_labeled_100.txt',
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'model.pretrained':
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PRETRAINED_MODEL_MAE
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}
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},
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]
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@unittest.skipIf(
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LooseVersion(torch.__version__) < LooseVersion('1.6.0'),
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'adapt fp16, not support torch.cuda.amp below 1.6.0 ')
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class MAETrainTest(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 tearDown(self):
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super().tearDown()
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def _base_train(self, train_cfgs):
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cfg_file = train_cfgs.pop('config_file')
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cfg_options = train_cfgs.pop('cfg_options', None)
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print(cfg_options)
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work_dir = train_cfgs.pop('work_dir', None)
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if not work_dir:
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work_dir = tempfile.TemporaryDirectory().name
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cfg = mmcv_config_fromfile(cfg_file)
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if cfg_options is not None:
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cfg.merge_from_dict(cfg_options)
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if 'eval_pipelines' in cfg:
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cfg.eval_pipelines[0].data = cfg.data.val
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tmp_cfg_file = tempfile.NamedTemporaryFile(suffix='.py').name
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cfg.dump(tmp_cfg_file)
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args_str = ' '.join(
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['='.join((str(k), str(v))) for k, v in train_cfgs.items()])
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cmd = 'python tools/train.py %s --fp16 --work_dir=%s %s' % \
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(tmp_cfg_file, work_dir, args_str)
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logging.info('run command: %s' % cmd)
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run_in_subprocess(cmd)
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output_files = io.listdir(work_dir)
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self.assertIn('epoch_1.pth', output_files)
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io.remove(work_dir)
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io.remove(tmp_cfg_file)
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def test_mae_pretrain(self):
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train_cfgs = copy.deepcopy(TRAIN_CONFIGS[0])
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self._base_train(train_cfgs)
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def test_mae_fintune(self):
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train_cfgs = copy.deepcopy(TRAIN_CONFIGS[1])
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self._base_train(train_cfgs)
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if __name__ == '__main__':
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unittest.main()
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