mirror of https://github.com/alibaba/EasyCV.git
140 lines
4.5 KiB
Python
140 lines
4.5 KiB
Python
# Copyright (c) Alibaba, Inc. and its affiliates.
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import copy
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import glob
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import json
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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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import torch
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from mmcv import Config
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from tests.ut_config import (DET_DATA_MANIFEST_OSS, DET_DATA_SMALL_COCO_LOCAL,
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PRETRAINED_MODEL_YOLOXS)
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from easycv.file import io
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from easycv.file.utils import get_oss_config
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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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SMALL_COCO_DATA_ROOT = DET_DATA_SMALL_COCO_LOCAL.rstrip('/') + '/'
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SMALL_COCO_ITAG_DATA_ROOT = DET_DATA_MANIFEST_OSS.rstrip('/') + '/'
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_COMMON_OPTIONS = {
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'checkpoint_config.interval': 1,
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'eval_config.interval': 1,
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'total_epochs': 1,
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'data.imgs_per_gpu': 8,
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'load_from': PRETRAINED_MODEL_YOLOXS,
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'optimizer.lr': 0.0
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}
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TRAIN_CONFIGS = [
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# itag test
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{
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'config_file':
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'configs/detection/yolox/yolox_s_8xb16_300e_coco_pai.py',
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'cfg_options': {
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**_COMMON_OPTIONS,
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'data.train.data_source.path':
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SMALL_COCO_ITAG_DATA_ROOT + 'train2017_20.manifest',
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'data.val.data_source.path':
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SMALL_COCO_ITAG_DATA_ROOT + 'val2017_20.manifest',
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}
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},
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{
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'config_file': 'configs/detection/yolox/yolox_s_8xb16_300e_coco.py',
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'cfg_options': {
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**_COMMON_OPTIONS, 'data.train.data_source.img_prefix':
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SMALL_COCO_DATA_ROOT + 'train2017',
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'data.val.data_source.img_prefix':
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SMALL_COCO_DATA_ROOT + 'val2017',
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'data.train.data_source.ann_file':
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SMALL_COCO_DATA_ROOT + 'instances_train2017_20.json',
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'data.val.data_source.ann_file':
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SMALL_COCO_DATA_ROOT + 'instances_val2017_20.json'
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}
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},
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]
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class YOLOXTrainTest(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 check_metric(self, work_dir):
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json_file = glob.glob(os.path.join(work_dir, '*.log.json'))
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with io.open(json_file[0], 'r') as f:
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content = f.readlines()
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res = json.loads(content[1])
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self.assertGreater(res['DetectionBoxes_Precision/mAP'], 0.4)
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self.assertGreater(res['DetectionBoxes_Precision/mAP@.50IOU'], 0.5)
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self.assertGreater(res['DetectionBoxes_Precision/mAP@.75IOU'], 0.4)
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def _base_train(self, train_cfgs, dist=False, dist_eval=False):
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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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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 = 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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cfg.eval_pipelines[0].data = dict(**cfg.data.val) # imgs_per_gpu=1
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cfg.eval_pipelines[0].dist_eval = dist_eval
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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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if dist:
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nproc_per_node = 2
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cmd = 'bash tools/dist_train.sh %s %s --launcher pytorch --work_dir=%s %s --fp16' % (
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tmp_cfg_file, nproc_per_node, work_dir, args_str)
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else:
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cmd = 'python tools/train.py %s --work_dir=%s %s --fp16' % (
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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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self.check_metric(work_dir)
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io.remove(work_dir)
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io.remove(tmp_cfg_file)
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def test_yolox_itag(self):
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train_cfgs = copy.deepcopy(TRAIN_CONFIGS[0])
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train_cfgs['cfg_options'].update(dict(oss_io_config=get_oss_config()))
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self._base_train(train_cfgs)
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def test_yolox_coco(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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@unittest.skipIf(torch.cuda.device_count() <= 1, 'distributed unittest')
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def test_yolox_itag_dist(self):
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train_cfgs = copy.deepcopy(TRAIN_CONFIGS[0])
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train_cfgs['cfg_options'].update(dict(oss_io_config=get_oss_config()))
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self._base_train(train_cfgs, dist_eval=True)
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if __name__ == '__main__':
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unittest.main()
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