一、环境配置

requirements.txt ——该文件提供了需要的库

1.torch——进入官网：Start Locally | PyTorchAn open source machine learning framework that accelerates the path from research prototyping to production deployment.https://pytorch.org/get-started/locally/根据自己电脑配置下载符合自己的pytorch

conda install pytorch==1.8.0 torchvision==0.9.0 torchaudio==0.8.0 cudatoolkit=11.1 -c pytorch -c conda-forge

会遇到下载不了的情况——————换源

channels:
- https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/pytorch/
- https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
- https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/msys2/
- https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main/
- https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/
- defaults
show_channel_urls: true

2.pycocotools的安装（仅适用于windows）

通过在清华镜像源中找到适合自己的版本下载下来

镜像地址：Links for pycocotools-windows

在虚拟环境下安装：pip install 文件名

二、demo文件夹

picture_demo.py  输入图片关键点就会标注出来
web_demo.py  目前未学习

三、train文件夹

1._init_paths.py 用于实现路径的添加

2.train_Vgg19.py的学习（训练VGG19模型）

2.1从lib包中引用相关的模块

from lib.network.rtpose_vgg import get_model, use_vgg
from lib.datasets import coco, transforms, datasets
from lib.config import update_config

2.2Python中的argparse用法总结

Python中的argparse用法总结_Code小学僧的课后笔记-CSDN博客

def cli():parser = argparse.ArgumentParser(description=__doc__,formatter_class=argparse.ArgumentDefaultsHelpFormatter,)train_cli(parser)parser.add_argument('-o', '--output', default=None,help='output file')parser.add_argument('--stride-apply', default=1, type=int,help='apply and reset gradients every n batches')parser.add_argument('--epochs', default=75, type=int,help='number of epochs to train')parser.add_argument('--freeze-base', default=0, type=int,help='number of epochs to train with frozen base')parser.add_argument('--pre-lr', type=float, default=1e-4,help='pre learning rate')parser.add_argument('--update-batchnorm-runningstatistics',default=False, action='store_true',help='update batch norm running statistics')parser.add_argument('--square-edge', default=368, type=int,help='square edge of input images')parser.add_argument('--ema', default=1e-3, type=float,help='ema decay constant')parser.add_argument('--debug-without-plots', default=False, action='store_true',help='enable debug but dont plot')parser.add_argument('--disable-cuda', action='store_true',help='disable CUDA')                        parser.add_argument('--model_path', default='./network/weight/', type=str, metavar='DIR',help='path to where the model saved')                         args = parser.parse_args()# add args.deviceargs.device = torch.device('cpu')args.pin_memory = Falseif not args.disable_cuda and torch.cuda.is_available():args.device = torch.device('cuda')args.pin_memory = Truereturn args

获取模型VGG19

model = get_model('vgg19')

def get_model(trunk='vgg19'):"""Creates the whole CPM modelArgs:trunk: string, 'vgg19' or 'mobilenet'Returns: Module, the defined model"""blocks = {}# block0 is the preprocessing stageif trunk == 'vgg19':block0 = [{'conv1_1': [3, 64, 3, 1, 1]},{'conv1_2': [64, 64, 3, 1, 1]},{'pool1_stage1': [2, 2, 0]},{'conv2_1': [64, 128, 3, 1, 1]},{'conv2_2': [128, 128, 3, 1, 1]},{'pool2_stage1': [2, 2, 0]},{'conv3_1': [128, 256, 3, 1, 1]},{'conv3_2': [256, 256, 3, 1, 1]},{'conv3_3': [256, 256, 3, 1, 1]},{'conv3_4': [256, 256, 3, 1, 1]},{'pool3_stage1': [2, 2, 0]},{'conv4_1': [256, 512, 3, 1, 1]},{'conv4_2': [512, 512, 3, 1, 1]},{'conv4_3_CPM': [512, 256, 3, 1, 1]},{'conv4_4_CPM': [256, 128, 3, 1, 1]}]elif trunk == 'mobilenet':block0 = [{'conv_bn': [3, 32, 2]},  # out: 3, 32, 184, 184{'conv_dw1': [32, 64, 1]},  # out: 32, 64, 184, 184{'conv_dw2': [64, 128, 2]},  # out: 64, 128, 92, 92{'conv_dw3': [128, 128, 1]},  # out: 128, 256, 92, 92{'conv_dw4': [128, 256, 2]},  # out: 256, 256, 46, 46{'conv4_3_CPM': [256, 256, 1, 3, 1]},{'conv4_4_CPM': [256, 128, 1, 3, 1]}]# Stage 1blocks['block1_1'] = [{'conv5_1_CPM_L1': [128, 128, 3, 1, 1]},{'conv5_2_CPM_L1': [128, 128, 3, 1, 1]},{'conv5_3_CPM_L1': [128, 128, 3, 1, 1]},{'conv5_4_CPM_L1': [128, 512, 1, 1, 0]},{'conv5_5_CPM_L1': [512, 38, 1, 1, 0]}]blocks['block1_2'] = [{'conv5_1_CPM_L2': [128, 128, 3, 1, 1]},{'conv5_2_CPM_L2': [128, 128, 3, 1, 1]},{'conv5_3_CPM_L2': [128, 128, 3, 1, 1]},{'conv5_4_CPM_L2': [128, 512, 1, 1, 0]},{'conv5_5_CPM_L2': [512, 19, 1, 1, 0]}]# Stages 2 - 6for i in range(2, 7):blocks['block%d_1' % i] = [{'Mconv1_stage%d_L1' % i: [185, 128, 7, 1, 3]},{'Mconv2_stage%d_L1' % i: [128, 128, 7, 1, 3]},{'Mconv3_stage%d_L1' % i: [128, 128, 7, 1, 3]},{'Mconv4_stage%d_L1' % i: [128, 128, 7, 1, 3]},{'Mconv5_stage%d_L1' % i: [128, 128, 7, 1, 3]},{'Mconv6_stage%d_L1' % i: [128, 128, 1, 1, 0]},{'Mconv7_stage%d_L1' % i: [128, 38, 1, 1, 0]}]blocks['block%d_2' % i] = [{'Mconv1_stage%d_L2' % i: [185, 128, 7, 1, 3]},{'Mconv2_stage%d_L2' % i: [128, 128, 7, 1, 3]},{'Mconv3_stage%d_L2' % i: [128, 128, 7, 1, 3]},{'Mconv4_stage%d_L2' % i: [128, 128, 7, 1, 3]},{'Mconv5_stage%d_L2' % i: [128, 128, 7, 1, 3]},{'Mconv6_stage%d_L2' % i: [128, 128, 1, 1, 0]},{'Mconv7_stage%d_L2' % i: [128, 19, 1, 1, 0]}]models = {}if trunk == 'vgg19':print("Bulding VGG19")models['block0'] = make_vgg19_block(block0)for k, v in blocks.items():models[k] = make_stages(list(v))class rtpose_model(nn.Module):def __init__(self, model_dict):super(rtpose_model, self).__init__()self.model0 = model_dict['block0']self.model1_1 = model_dict['block1_1']self.model2_1 = model_dict['block2_1']self.model3_1 = model_dict['block3_1']self.model4_1 = model_dict['block4_1']self.model5_1 = model_dict['block5_1']self.model6_1 = model_dict['block6_1']self.model1_2 = model_dict['block1_2']self.model2_2 = model_dict['block2_2']self.model3_2 = model_dict['block3_2']self.model4_2 = model_dict['block4_2']self.model5_2 = model_dict['block5_2']self.model6_2 = model_dict['block6_2']self._initialize_weights_norm()def forward(self, x):saved_for_loss = []out1 = self.model0(x)out1_1 = self.model1_1(out1)out1_2 = self.model1_2(out1)out2 = torch.cat([out1_1, out1_2, out1], 1)saved_for_loss.append(out1_1)saved_for_loss.append(out1_2)out2_1 = self.model2_1(out2)out2_2 = self.model2_2(out2)out3 = torch.cat([out2_1, out2_2, out1], 1)saved_for_loss.append(out2_1)saved_for_loss.append(out2_2)out3_1 = self.model3_1(out3)out3_2 = self.model3_2(out3)out4 = torch.cat([out3_1, out3_2, out1], 1)saved_for_loss.append(out3_1)saved_for_loss.append(out3_2)out4_1 = self.model4_1(out4)out4_2 = self.model4_2(out4)out5 = torch.cat([out4_1, out4_2, out1], 1)saved_for_loss.append(out4_1)saved_for_loss.append(out4_2)out5_1 = self.model5_1(out5)out5_2 = self.model5_2(out5)out6 = torch.cat([out5_1, out5_2, out1], 1)saved_for_loss.append(out5_1)saved_for_loss.append(out5_2)out6_1 = self.model6_1(out6)out6_2 = self.model6_2(out6)saved_for_loss.append(out6_1)saved_for_loss.append(out6_2)return (out6_1, out6_2), saved_for_lossdef _initialize_weights_norm(self):for m in self.modules():if isinstance(m, nn.Conv2d):init.normal_(m.weight, std=0.01)if m.bias is not None:  # mobilenet conv2d doesn't add biasinit.constant_(m.bias, 0.0)# last layer of these block don't have Reluinit.normal_(self.model1_1[8].weight, std=0.01)init.normal_(self.model1_2[8].weight, std=0.01)init.normal_(self.model2_1[12].weight, std=0.01)init.normal_(self.model3_1[12].weight, std=0.01)init.normal_(self.model4_1[12].weight, std=0.01)init.normal_(self.model5_1[12].weight, std=0.01)init.normal_(self.model6_1[12].weight, std=0.01)init.normal_(self.model2_2[12].weight, std=0.01)init.normal_(self.model3_2[12].weight, std=0.01)init.normal_(self.model4_2[12].weight, std=0.01)init.normal_(self.model5_2[12].weight, std=0.01)init.normal_(self.model6_2[12].weight, std=0.01)model = rtpose_model(models)return model

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