def test_collect_env():
env_info = collect_env()
assert env_info['PyTorch'] == torch.__version__
assert env_info['TorchVision'] == torchvision.__version__
assert env_info['OpenCV'] == cv2.__version__
assert env_info['MMCV'] == mmcv.__version__
assert env_info['MMPose'] == mmpose.__version__
def test_collect_env():
env_info = collect_env()
target_keys = [
'sys.platform', 'Python', 'CUDA available', 'GCC', 'PyTorch',
'PyTorch compiling details', 'TorchVision', 'OpenCV', 'MMCV', 'MMPose'
]
cuda_available = torch.cuda.is_available()
if cuda_available:
cuda_keys = ['CUDA_HOME', 'NVCC']
devices = defaultdict(list)
devices_dict = dict()
for k in range(torch.cuda.device_count()):
devices[torch.cuda.get_device_name(k)].append(str(k))
for name, devids in devices.items():
devices_dict['GPU ' + ','.join(devids)] = name
cuda_keys.append('GPU ' + ','.join(devids))
target_keys.extend(cuda_keys)
assert set(env_info.keys()) == set(target_keys)
assert env_info['sys.platform'] == sys.platform
assert env_info['Python'] == sys.version.replace('\n', '')
assert env_info['CUDA available'] == cuda_available
if cuda_available:
assert env_info['CUDA_HOME'] == CUDA_HOME
if CUDA_HOME is not None and osp.isdir(CUDA_HOME):
try:
nvcc = osp.join(CUDA_HOME, 'bin/nvcc')
nvcc = subprocess.check_output(
'"{}" -V | tail -n1'.format(nvcc), shell=True)
nvcc = nvcc.decode('utf-8').strip()
except subprocess.SubprocessError:
nvcc = 'Not Available'
assert env_info['NVCC'] == nvcc
for k, v in devices_dict.items():
assert env_info[k] == v
gcc = subprocess.check_output('gcc --version | head -n1', shell=True)
gcc = gcc.decode('utf-8').strip()
assert env_info['GCC'] == gcc
assert env_info['PyTorch'] == torch.__version__
assert env_info['PyTorch compiling details'] == get_build_config()
assert env_info['TorchVision'] == torchvision.__version__
assert env_info['OpenCV'] == cv2.__version__
assert env_info['MMCV'] == mmcv.__version__
assert env_info['MMPose'] == mmpose.__version__
def main():
args = parse_args()
cfg = Config.fromfile(args.config)
if args.options is not None:
cfg.merge_from_dict(args.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)
if args.autoscale_lr:
# apply the linear scaling rule (https://arxiv.org/abs/1706.02677)
cfg.optimizer['lr'] = cfg.optimizer['lr'] * len(cfg.gpu_ids) / 8
# 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)
# create work_dir
mmcv.mkdir_or_exist(osp.abspath(cfg.work_dir))
# 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
# log some basic info
logger.info(f'Distributed training: {distributed}')
logger.info(f'Config:\n{cfg.pretty_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
model = build_posenet(cfg.model)
datasets = [build_dataset(cfg.data.train)]
if len(cfg.workflow) == 2:
val_dataset = copy.deepcopy(cfg.data.val)
val_dataset.pipeline = cfg.data.train.pipeline
datasets.append(build_dataset(val_dataset))
if cfg.checkpoint_config is not None:
# save mmpose version, config file content
# checkpoints as meta data
cfg.checkpoint_config.meta = dict(
mmpose_version=__version__ + get_git_hash(digits=7),
config=cfg.pretty_text,
)
train_model(
model,
datasets,
cfg,
distributed=distributed,
validate=(not args.no_validate),
timestamp=timestamp,
meta=meta)
