def test_collect_env():
try:
import torch # noqa: F401
except ModuleNotFoundError:
pytest.skip('skipping tests that require PyTorch')
from mmcv.utils import collect_env
env_info = collect_env()
expected_keys = [
'sys.platform', 'Python', 'CUDA available', 'PyTorch',
'PyTorch compiling details', 'OpenCV', 'MMCV', 'MMCV Compiler',
'MMCV CUDA Compiler'
]
for key in expected_keys:
assert key in env_info
if env_info['CUDA available']:
for key in ['CUDA_HOME', 'NVCC']:
assert key in env_info
if sys.platform != 'win32':
assert 'GCC' in env_info
assert env_info['sys.platform'] == sys.platform
assert env_info['Python'] == sys.version.replace('\n', '')
assert env_info['MMCV'] == mmcv.__version__
[[1.0, 1.0, 3.0, 4.0, 0.5], [2.0, 2.0, 3.0, 4.0, 0.6],
[7.0, 7.0, 8.0, 8.0, 0.4]],
dtype=np.float32)
np_boxes2 = np.asarray(
[[0.0, 2.0, 2.0, 5.0, 0.3], [2.0, 1.0, 3.0, 3.0, 0.5],
[5.0, 5.0, 6.0, 7.0, 0.4]],
dtype=np.float32)
boxes1 = torch.from_numpy(np_boxes1)
boxes2 = torch.from_numpy(np_boxes2)
# test mmcv-full with CPU ops
box_iou_rotated(boxes1, boxes2)
# test mmcv-full with both CPU and CUDA ops
if torch.cuda.is_available():
boxes1 = boxes1.cuda()
boxes2 = boxes2.cuda()
box_iou_rotated(boxes1, boxes2)
if __name__ == '__main__':
print('Start checking the installation of mmcv-full ...')
check_installation()
print('mmcv-full has been installed successfully.\n')
env_info_dict = collect_env()
env_info = '\n'.join([(f'{k}: {v}') for k, v in env_info_dict.items()])
dash_line = '-' * 60 + '\n'
print('Environment info:\n' + dash_line + env_info + '\n' + dash_line)
def main():
args = parse_args()
cfg = Config.fromfile(args.config)
if args.cfg_options is not None:
cfg.merge_from_dict(args.cfg_options)
# set cudnn_benchmark
if cfg.get('cudnn_benchmark', False):
torch.backends.cudnn.benchmark = True
# work_dir is determined in this priority: CLI > segment in file > filename
if args.work_dir is not None:
# update configs according to CLI args if args.work_dir is not None
cfg.work_dir = args.work_dir
elif cfg.get('work_dir', None) is None:
# use config filename as default work_dir if cfg.work_dir is None
cfg.work_dir = osp.join('./work_dirs',
osp.splitext(osp.basename(args.config))[0])
if args.resume_from is not None:
cfg.resume_from = args.resume_from
if args.gpu_ids is not None:
cfg.gpu_ids = args.gpu_ids
else:
cfg.gpu_ids = range(1) if args.gpus is None else range(args.gpus)
# init distributed env first, since logger depends on the dist info.
if args.launcher == 'none':
distributed = False
else:
distributed = True
init_dist(args.launcher, **cfg.dist_params)
# re-set gpu_ids with distributed training mode
_, world_size = get_dist_info()
cfg.gpu_ids = range(world_size)
# create work_dir
mmcv.mkdir_or_exist(osp.abspath(cfg.work_dir))
# dump config
cfg.dump(osp.join(cfg.work_dir, osp.basename(args.config)))
# init the logger before other steps
timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime())
log_file = osp.join(cfg.work_dir, f'{timestamp}.log')
logger = get_root_logger(log_file=log_file, log_level=cfg.log_level)
# init the meta dict to record some important information such as
# environment info and seed, which will be logged
meta = dict()
# log env info
env_info_dict = collect_env()
env_info = '\n'.join([(f'{k}: {v}') for k, v in env_info_dict.items()])
dash_line = '-' * 60 + '\n'
logger.info('Environment info:\n' + dash_line + env_info + '\n' +
dash_line)
meta['env_info'] = env_info
meta['config'] = cfg.text
# log some basic info
logger.info(f'Distributed training: {distributed}')
logger.info(f'Config:\n{cfg.text}')
# set random seeds
if args.seed is not None:
logger.info(f'Set random seed to {args.seed}, '
f'deterministic: {args.deterministic}')
set_random_seed(args.seed, deterministic=args.deterministic)
cfg.seed = args.seed
meta['seed'] = args.seed
meta['exp_name'] = osp.basename(args.config)
model = build_reid(cfg.model)
datasets = [build_dataset(cfg.data.train)]
if cfg.checkpoint_config is not None:
# save project version in checkpoints as meta
cfg.checkpoint_config.meta = dict(reid_version=__version__)
train_reid(model,
datasets,
cfg,
distributed=distributed,
validate=(not args.no_validate),
timestamp=timestamp,
meta=meta)
