--- a
+++ b/ViTPose/tools/test.py
@@ -0,0 +1,184 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import argparse
+import os
+import os.path as osp
+import warnings
+
+import mmcv
+import torch
+from mmcv import Config, DictAction
+from mmcv.cnn import fuse_conv_bn
+from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
+from mmcv.runner import get_dist_info, init_dist, load_checkpoint
+
+from mmpose.apis import multi_gpu_test, single_gpu_test
+from mmpose.datasets import build_dataloader, build_dataset
+from mmpose.models import build_posenet
+from mmpose.utils import setup_multi_processes
+
+try:
+    from mmcv.runner import wrap_fp16_model
+except ImportError:
+    warnings.warn('auto_fp16 from mmpose will be deprecated from v0.15.0'
+                  'Please install mmcv>=1.1.4')
+    from mmpose.core import wrap_fp16_model
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description='mmpose test model')
+    parser.add_argument('config', help='test config file path')
+    parser.add_argument('checkpoint', help='checkpoint file')
+    parser.add_argument('--out', help='output result file')
+    parser.add_argument(
+        '--work-dir', help='the dir to save evaluation results')
+    parser.add_argument(
+        '--fuse-conv-bn',
+        action='store_true',
+        help='Whether to fuse conv and bn, this will slightly increase'
+        'the inference speed')
+    parser.add_argument(
+        '--gpu-id',
+        type=int,
+        default=0,
+        help='id of gpu to use '
+        '(only applicable to non-distributed testing)')
+    parser.add_argument(
+        '--eval',
+        default=None,
+        nargs='+',
+        help='evaluation metric, which depends on the dataset,'
+        ' e.g., "mAP" for MSCOCO')
+    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,
+        default={},
+        help='override some settings in the used config, the key-value pair '
+        'in xxx=yyy format will be merged into config file. For example, '
+        "'--cfg-options model.backbone.depth=18 model.backbone.with_cp=True'")
+    parser.add_argument(
+        '--launcher',
+        choices=['none', 'pytorch', 'slurm', 'mpi'],
+        default='none',
+        help='job launcher')
+    parser.add_argument('--local_rank', type=int, default=0)
+    args = parser.parse_args()
+    if 'LOCAL_RANK' not in os.environ:
+        os.environ['LOCAL_RANK'] = str(args.local_rank)
+    return args
+
+
+def merge_configs(cfg1, cfg2):
+    # Merge cfg2 into cfg1
+    # Overwrite cfg1 if repeated, ignore if value is None.
+    cfg1 = {} if cfg1 is None else cfg1.copy()
+    cfg2 = {} if cfg2 is None else cfg2
+    for k, v in cfg2.items():
+        if v:
+            cfg1[k] = v
+    return cfg1
+
+
+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 multi-process settings
+    setup_multi_processes(cfg)
+
+    # set cudnn_benchmark
+    if cfg.get('cudnn_benchmark', False):
+        torch.backends.cudnn.benchmark = True
+    cfg.model.pretrained = None
+    cfg.data.test.test_mode = 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])
+
+    mmcv.mkdir_or_exist(osp.abspath(cfg.work_dir))
+
+    # 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, dict(test_mode=True))
+    # step 1: give default values and override (if exist) from cfg.data
+    loader_cfg = {
+        **dict(seed=cfg.get('seed'), drop_last=False, dist=distributed),
+        **({} if torch.__version__ != 'parrots' else dict(
+               prefetch_num=2,
+               pin_memory=False,
+           )),
+        **dict((k, cfg.data[k]) for k in [
+                   'seed',
+                   'prefetch_num',
+                   'pin_memory',
+                   'persistent_workers',
+               ] if k in cfg.data)
+    }
+    # step2: cfg.data.test_dataloader has higher priority
+    test_loader_cfg = {
+        **loader_cfg,
+        **dict(shuffle=False, drop_last=False),
+        **dict(workers_per_gpu=cfg.data.get('workers_per_gpu', 1)),
+        **dict(samples_per_gpu=cfg.data.get('samples_per_gpu', 1)),
+        **cfg.data.get('test_dataloader', {})
+    }
+    data_loader = build_dataloader(dataset, **test_loader_cfg)
+
+    # build the model and load checkpoint
+    model = build_posenet(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 args.fuse_conv_bn:
+        model = fuse_conv_bn(model)
+
+    if not distributed:
+        model = MMDataParallel(model, device_ids=[args.gpu_id])
+        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()
+    eval_config = cfg.get('evaluation', {})
+    eval_config = merge_configs(eval_config, dict(metric=args.eval))
+
+    if rank == 0:
+        if args.out:
+            print(f'\nwriting results to {args.out}')
+            mmcv.dump(outputs, args.out)
+
+        results = dataset.evaluate(outputs, cfg.work_dir, **eval_config)
+        for k, v in sorted(results.items()):
+            print(f'{k}: {v}')
+
+
+if __name__ == '__main__':
+    main()