usage: valid.py [-h] --cfg CFG ...
valid.py: error: the following arguments are required: --cfg

看下

你在调用valid.py 的命令行是什么样的？有没有加 --cfg 参数

parser.add_argument('--cfg',
help='experiment configure file name',
required=True,
type=str)

你的 valid.py 里，要求有 --cfg 参数的

from future import absolute_import
from future import division
from future import print_function

import argparse
import os
import sys
import stat
import pprint

import torch
import torch.backends.cudnn as cudnn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
import torchvision.transforms
import torch.multiprocessing
from tqdm import tqdm

import _init_paths
import models

from lib.config import cfg
from lib.config import update_config
from lib.core.inference import get_multi_stage_outputs
from lib.core.inference import aggregate_results
from lib.core.nms import pose_nms
from lib.core.match import match_pose_to_heatmap
from lib.dataset import make_test_dataloader
from lib.utils.utils import create_logger
from lib.utils.transforms import resize_align_multi_scale
from lib.utils.transforms import get_final_preds
from lib.utils.transforms import get_multi_scale_size
from lib.utils.rescore import rescore_valid

torch.multiprocessing.set_sharing_strategy('file_system')

def parse_args():
# 创建解析器
parser = argparse.ArgumentParser(description='Test keypoints network')
# general
# 实验配置文件名
# 给属性名之前加上“--”，就能将之变为可选参数
parser.add_argument('--cfg',
help='experiment configure file name',
required=True,
type=str)
# 使用命令行修改配置选项
parser.add_argument('opts',
help="Modify config options using the command-line",
default=None,
nargs=argparse.REMAINDER) # 命令行参数应当消耗的数目

args = parser.parse_args()  # parser中增加的属性内容都会在args实例中

return args

markdown format output

markdown形式输出

def _print_name_value(logger, name_value, full_arch_name):
names = name_value.keys()
values = name_value.values()
num_values = len(name_value)
logger.info(
'| Arch ' +
' '.join(['| {}'.format(name) for name in names]) +
' |'
) # {}表示占位符
logger.info('|---' * (num_values + 1) + '|')

if len(full_arch_name) > 15:
    full_arch_name = full_arch_name[:8] + '...'
logger.info(
    '| ' + full_arch_name + ' ' +
    ' '.join(['| {:.3f}'.format(value) for value in values]) +
    ' |'
)

def main():
args = parse_args()
update_config(cfg, args)

logger, final_output_dir, _ = create_logger(
    cfg, args.cfg, 'valid'
)

logger.info(pprint.pformat(args))
logger.info(cfg)

# cudnn related setting
cudnn.benchmark = cfg.CUDNN.BENCHMARK
torch.backends.cudnn.deterministic = cfg.CUDNN.DETERMINISTIC
torch.backends.cudnn.enabled = cfg.CUDNN.ENABLED
# eval()函数用于执行一个字符串表达式，并且返回该表达式的值(model新模型)
model = eval('models.' + cfg.MODEL.NAME + '.get_pose_net')(
    cfg, is_train=False
)

if cfg.TEST.MODEL_FILE:
    logger.info('=> loading model from {}'.format(cfg.TEST.MODEL_FILE))
    model.load_state_dict(torch.load(cfg.TEST.MODEL_FILE), strict=True)  # 讲训练好的模型参数(.pth)加载到模型中
else:
    model_state_file = os.path.join(
        final_output_dir, 'model_best.pth.tar'
    )  # 用于路径拼接文件路径
    logger.info('=> loading model from {}'.format(model_state_file))
    model.load_state_dict(torch.load(model_state_file))

model = torch.nn.DataParallel(model, device_ids=cfg.GPUS).cuda()  # 让模型使用 DataParallel 多gpu并行运行
model.eval()

data_loader, test_dataset = make_test_dataloader(cfg)
transforms = torchvision.transforms.Compose([
    torchvision.transforms.ToTensor(),
    torchvision.transforms.Normalize(
        mean=[0.485, 0.456, 0.406],
        std=[0.229, 0.224, 0.225]
    )
])

all_reg_preds = []
all_reg_scores = []

pbar = tqdm(total=len(test_dataset)) if cfg.TEST.LOG_PROGRESS else None
for i, images in enumerate(data_loader):
    assert 1 == images.size(0), 'Test batch size should be 1'
    image = images[0].cpu().numpy()

    # size at scale 1.0
    base_size, center, scale = get_multi_scale_size(
        image, cfg.DATASET.INPUT_SIZE, 1.0, 1.0
    )

    with torch.no_grad():
        heatmap_sum = 0
        poses = []

        for scale in sorted(cfg.TEST.SCALE_FACTOR, reverse=True):
            image_resized, center, scale_resized = resize_align_multi_scale(
                image, cfg.DATASET.INPUT_SIZE, scale, 1.0
            )

            image_resized = transforms(image_resized)
            image_resized = image_resized.unsqueeze(0).cuda()

            heatmap, posemap = get_multi_stage_outputs(
                cfg, model, image_resized, cfg.TEST.FLIP_TEST
            )
            heatmap_sum, poses = aggregate_results(
                cfg, heatmap_sum, poses, heatmap, posemap, scale
            )

        heatmap_avg = heatmap_sum / len(cfg.TEST.SCALE_FACTOR)
        poses, scores = pose_nms(cfg, heatmap_avg, poses)

        if len(scores) == 0:
            all_reg_preds.append([])
            all_reg_scores.append([])
        else:
            if cfg.TEST.MATCH_HMP:
                poses = match_pose_to_heatmap(cfg, poses, heatmap_avg)

            final_poses = get_final_preds(
                poses, center, scale_resized, base_size
            )
            if cfg.RESCORE.VALID:
                scores = rescore_valid(cfg, final_poses, scores)
            all_reg_preds.append(final_poses)
            all_reg_scores.append(scores)

    if cfg.TEST.LOG_PROGRESS:
        pbar.update()

sv_all_preds = [all_reg_preds]
sv_all_scores = [all_reg_scores]
sv_all_name = [cfg.NAME]

if cfg.TEST.LOG_PROGRESS:
    pbar.close()

for i in range(len(sv_all_preds)):
    print('Testing ' + sv_all_name[i])
    preds = sv_all_preds[i]
    scores = sv_all_scores[i]
    if cfg.RESCORE.GET_DATA:
        test_dataset.evaluate(
            cfg, preds, scores, final_output_dir, sv_all_name[i]
        )
        print('Generating dataset for rescorenet successfully')
    else:
        name_values, _ = test_dataset.evaluate(
            cfg, preds, scores, final_output_dir, sv_all_name[i]
        )

        if isinstance(name_values, list):
            for name_value in name_values:
                _print_name_value(logger, name_value, cfg.MODEL.NAME)
        else:
            _print_name_value(logger, name_values, cfg.MODEL.NAME)

if name == 'main':
main()