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import warnings
warnings.filterwarnings("ignore", category=UserWarning)
import argparse
import os
import os.path as osp
import mmcv
import torch
from mmcv import DictAction
from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
from mmcv.runner import (get_dist_info, init_dist, load_checkpoint,
wrap_fp16_model)
from mogen.apis import multi_gpu_test, single_gpu_test
from mogen.datasets import build_dataloader, build_dataset
from mogen.models import build_architecture
def parse_args():
parser = argparse.ArgumentParser(description='mogen evaluation')
parser.add_argument('config', help='test config file path')
parser.add_argument('--work-dir',
help='the dir to save evaluation results')
parser.add_argument('checkpoint', help='checkpoint file')
parser.add_argument('--out', help='output result file')
parser.add_argument('--gpu_collect',
action='store_true',
help='whether to use gpu to collect results')
parser.add_argument('--tmpdir', help='tmp dir for writing some results')
parser.add_argument(
'--cfg-options',
nargs='+',
action=DictAction,
help='override some settings in the used config, the key-value pair '
'in xxx=yyy format will be merged into config file.')
parser.add_argument('--launcher',
choices=['none', 'pytorch', 'slurm', 'mpi'],
default='none',
help='job launcher')
parser.add_argument('--local_rank', type=int, default=0)
parser.add_argument('--device',
choices=['cpu', 'cuda'],
default='cuda',
help='device used for testing')
args = parser.parse_args()
if 'LOCAL_RANK' not in os.environ:
os.environ['LOCAL_RANK'] = str(args.local_rank)
return args
def main():
args = parse_args()
cfg = mmcv.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
cfg.data.test.test_mode = True
# 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)
# build the dataloader
dataset = build_dataset(cfg.data.test)
# the extra round_up data will be removed during gpu/cpu collect
data_loader = build_dataloader(dataset,
samples_per_gpu=cfg.data.samples_per_gpu,
workers_per_gpu=cfg.data.workers_per_gpu,
dist=distributed,
shuffle=False,
round_up=False)
# build the model and load checkpoint
model = build_architecture(cfg.model)
fp16_cfg = cfg.get('fp16', None)
if fp16_cfg is not None:
wrap_fp16_model(model)
load_checkpoint(model, args.checkpoint, map_location='cpu')
if not distributed:
if args.device == 'cpu':
model = model.cpu()
else:
model = MMDataParallel(model, device_ids=[0])
outputs = single_gpu_test(model, data_loader)
else:
model = MMDistributedDataParallel(
model.cuda(),
device_ids=[torch.cuda.current_device()],
broadcast_buffers=False)
outputs = multi_gpu_test(model, data_loader, args.tmpdir,
args.gpu_collect)
rank, _ = get_dist_info()
if rank == 0:
mmcv.mkdir_or_exist(osp.abspath(args.work_dir))
results = dataset.evaluate(outputs, args.work_dir)
for k, v in results.items():
print(f'\n{k} : {v:.4f}')
if args.out and rank == 0:
print(f'\nwriting results to {args.out}')
mmcv.dump(results, args.out)
if __name__ == '__main__':
main()
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