160 lines
5.1 KiB
Python
160 lines
5.1 KiB
Python
import argparse
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import os
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import os.path as osp
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import mmcv
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import torch
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from mmcv import Config, DictAction
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from mmcv.cnn import fuse_conv_bn
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from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
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from mmcv.runner import get_dist_info, init_dist, load_checkpoint
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from mmpose.apis import multi_gpu_test, single_gpu_test
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from mmpose.core import wrap_fp16_model
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from mmpose.datasets import build_dataloader, build_dataset
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from models import build_posenet
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import onnxruntime
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import onnx
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def parse_args():
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parser = argparse.ArgumentParser(description='mmpose test model')
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parser.add_argument('config', help='test config file path')
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parser.add_argument('checkpoint', help='checkpoint file')
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parser.add_argument('--out', help='output result file')
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parser.add_argument(
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'--fuse-conv-bn',
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action='store_true',
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help='Whether to fuse conv and bn, this will slightly increase'
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'the inference speed')
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parser.add_argument(
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'--eval',
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default=None,
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nargs='+',
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help='evaluation metric, which depends on the dataset,'
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' e.g., "mAP" for MSCOCO')
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parser.add_argument(
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'--gpu_collect',
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action='store_true',
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help='whether to use gpu to collect results')
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parser.add_argument('--tmpdir', help='tmp dir for writing some results')
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parser.add_argument(
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'--cfg-options',
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nargs='+',
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action=DictAction,
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default={},
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help='override some settings in the used config, the key-value pair '
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'in xxx=yyy format will be merged into config file. For example, '
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"'--cfg-options model.backbone.depth=18 model.backbone.with_cp=True'")
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parser.add_argument(
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'--launcher',
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choices=['none', 'pytorch', 'slurm', 'mpi'],
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default='none',
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help='job launcher')
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parser.add_argument('--local_rank', type=int, default=0)
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args = parser.parse_args()
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if 'LOCAL_RANK' not in os.environ:
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os.environ['LOCAL_RANK'] = str(args.local_rank)
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return args
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def merge_configs(cfg1, cfg2):
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# Merge cfg2 into cfg1
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# Overwrite cfg1 if repeated, ignore if value is None.
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cfg1 = {} if cfg1 is None else cfg1.copy()
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cfg2 = {} if cfg2 is None else cfg2
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for k, v in cfg2.items():
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if v:
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cfg1[k] = v
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return cfg1
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val_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(type='TopDownAffine'),
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dict(type='ToTensor'),
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dict(
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type='NormalizeTensor',
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mean=[0.485, 0.456, 0.406],
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std=[0.229, 0.224, 0.225]),
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dict(
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type='Collect',
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keys=[
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'img',
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],
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meta_keys=[
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'image_file', 'center', 'scale', 'rotation', 'bbox_score',
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'flip_pairs'
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]),
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]
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def main():
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args = parse_args()
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cfg = Config.fromfile(args.config)
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cfg.test_pipeline = val_pipeline
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if args.cfg_options is not None:
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cfg.merge_from_dict(args.cfg_options)
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# set cudnn_benchmark
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if cfg.get('cudnn_benchmark', False):
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torch.backends.cudnn.benchmark = True
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cfg.model.pretrained = None
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cfg.data.test.test_mode = True
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args.work_dir = osp.join('./work_dirs',
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osp.splitext(osp.basename(args.config))[0])
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mmcv.mkdir_or_exist(osp.abspath(args.work_dir))
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# init distributed env first, since logger depends on the dist info.
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if args.launcher == 'none':
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distributed = False
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else:
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distributed = True
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init_dist(args.launcher, **cfg.dist_params)
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# build the model and load checkpoint
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model = build_posenet(cfg.model)
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#print(model)
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fp16_cfg = cfg.get('fp16', None)
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if fp16_cfg is not None:
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wrap_fp16_model(model)
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#remove load checkpoint for testing temporarily
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load_checkpoint(model, args.checkpoint, map_location='cpu')
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# build the dataloader
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dataset = build_dataset(cfg.data.test, dict(test_mode=True))
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dataloader_setting = dict(
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samples_per_gpu=1,
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workers_per_gpu=cfg.data.get('workers_per_gpu', 1),
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dist=distributed,
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shuffle=False,
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drop_last=False)
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dataloader_setting = dict(dataloader_setting,
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**cfg.data.get('test_dataloader', {}))
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data_loader = build_dataloader(dataset, **dataloader_setting)
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if args.fuse_conv_bn:
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model = fuse_conv_bn(model)
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if not distributed:
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model = MMDataParallel(model, device_ids=[0])
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outputs = single_gpu_test(model, data_loader)
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else:
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model = MMDistributedDataParallel(
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model.cuda(),
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device_ids=[torch.cuda.current_device()],
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broadcast_buffers=False)
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outputs = multi_gpu_test(model, data_loader, args.tmpdir,
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args.gpu_collect)
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rank, _ = get_dist_info()
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eval_config = cfg.get('evaluation', {})
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eval_config = merge_configs(eval_config, dict(metric=args.eval))
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if rank == 0:
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if True:
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print(f'\nwriting results to {args.out}')
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mmcv.dump(outputs, 'out.json')
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print(dataset.evaluate(outputs, args.work_dir, **eval_config))
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if __name__ == '__main__':
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main()
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