代碼地址https://github.com/lshiwjx/2s-AGCN
本篇解析對(duì)應(yīng) 2s-AGCN/graph/ntu_rgb_d.py ，用于創(chuàng)建NTURGB-D對(duì)應(yīng)的圖結(jié)構(gòu)，以及2s-AGCN/main.py

import syssys.path.extend(['../']) from graph import toolsnum_node = 25#圖中節(jié)點(diǎn)總個(gè)數(shù) self_link = [(i, i) for i in range(num_node)]#為每個(gè)節(jié)點(diǎn)增加自環(huán)，使得聚合表征包含自身特征 #節(jié)點(diǎn)連接方式 inward_ori_index = [(1, 2), (2, 21), (3, 21), (4, 3), (5, 21), (6, 5), (7, 6),(8, 7), (9, 21), (10, 9), (11, 10), (12, 11), (13, 1),(14, 13), (15, 14), (16, 15), (17, 1), (18, 17), (19, 18),(20, 19), (22, 23), (23, 8), (24, 25), (25, 12)] #將1-->0 inward = [(i - 1, j - 1) for (i, j) in inward_ori_index] #構(gòu)建無(wú)向圖 outward = [(j, i) for (i, j) in inward] #定義鄰接列表 neighbor = inward + outwardclass Graph:def __init__(self, labeling_mode='spatial'):self.A = self.get_adjacency_matrix(labeling_mode)#構(gòu)建鄰接矩陣self.num_node = num_nodeself.self_link = self_linkself.inward = inwardself.outward = outwardself.neighbor = neighbordef get_adjacency_matrix(self, labeling_mode=None):if labeling_mode is None:return self.Aif labeling_mode == 'spatial':A = tools.get_spatial_graph(num_node, self_link, inward, outward)else:raise ValueError()return Aif __name__ == '__main__':import matplotlib.pyplot as pltimport os# os.environ['DISPLAY'] = 'localhost:11.0'A = Graph('spatial').get_adjacency_matrix()for i in A:plt.imshow(i, cmap='gray')plt.show()print(A)

下面是對(duì)2s-AGCN/main.py的解析

#!/usr/bin/env python from __future__ import print_functionimport argparse import inspect import os import pickle import random import shutil import time from collections import OrderedDictimport numpy as np # torch import torch import torch.backends.cudnn as cudnn import torch.nn as nn import torch.optim as optim import yaml from tensorboardX import SummaryWriter from torch.autograd import Variable from torch.optim.lr_scheduler import _LRScheduler from tqdm import tqdmclass GradualWarmupScheduler(_LRScheduler):def __init__(self, optimizer, total_epoch, after_scheduler=None):self.total_epoch = total_epochself.after_scheduler = after_schedulerself.finished = Falseself.last_epoch = -1super().__init__(optimizer)def get_lr(self):return [base_lr * (self.last_epoch + 1) / self.total_epoch for base_lr in self.base_lrs]def step(self, epoch=None, metric=None):if self.last_epoch >= self.total_epoch - 1:if metric is None:return self.after_scheduler.step(epoch)else:return self.after_scheduler.step(metric, epoch)else:return super(GradualWarmupScheduler, self).step(epoch)def init_seed(_):torch.cuda.manual_seed_all(1)torch.manual_seed(1)np.random.seed(1)random.seed(1)# torch.backends.cudnn.enabled = Falsetorch.backends.cudnn.deterministic = Truetorch.backends.cudnn.benchmark = Falsedef get_parser():# parameter priority: command line > config > default#在參數(shù)解析時(shí)，參數(shù)中并不區(qū)分字符‘-’和‘_’parser = argparse.ArgumentParser(description='Spatial Temporal Graph Convolution Network')parser.add_argument('--work-dir',default='./work_dir/temp',help='the work folder for storing results')parser.add_argument('-model_saved_name', default='')parser.add_argument('--config',default='./config/nturgbd-cross-view/test_bone.yaml',help='path to the configuration file')# processorparser.add_argument('--phase', default='train', help='must be train or test')parser.add_argument('--save-score',type=str2bool,default=False,help='if ture, the classification score will be stored')# visulize and debugparser.add_argument('--seed', type=int, default=1, help='random seed for pytorch')parser.add_argument('--log-interval',type=int,default=100,help='the interval for printing messages (#iteration)')parser.add_argument('--save-interval',type=int,default=2,help='the interval for storing models (#iteration)')parser.add_argument('--eval-interval',type=int,default=5,help='the interval for evaluating models (#iteration)')parser.add_argument('--print-log',type=str2bool,default=True,help='print logging or not')parser.add_argument('--show-topk',type=int,default=[1, 5],nargs='+',help='which Top K accuracy will be shown')# feederparser.add_argument('--feeder', default='feeder.feeder', help='data loader will be used')parser.add_argument('--num-worker',type=int,default=32,help='the number of worker for data loader')parser.add_argument('--train-feeder-args',default=dict(),help='the arguments of data loader for training')parser.add_argument('--test-feeder-args',default=dict(),help='the arguments of data loader for test')# modelparser.add_argument('--model', default=None, help='the model will be used')parser.add_argument('--model-args',type=dict,default=dict(),help='the arguments of model')parser.add_argument('--weights',default=None,help='the weights for network initialization')parser.add_argument('--ignore-weights',type=str,default=[],nargs='+',help='the name of weights which will be ignored in the initialization')# optimparser.add_argument('--base-lr', type=float, default=0.01, help='initial learning rate')parser.add_argument('--step',type=int,default=[20, 40, 60],nargs='+',help='the epoch where optimizer reduce the learning rate')parser.add_argument('--device',type=int,default=0,nargs='+',help='the indexes of GPUs for training or testing')parser.add_argument('--optimizer', default='SGD', help='type of optimizer')parser.add_argument('--nesterov', type=str2bool, default=False, help='use nesterov or not')parser.add_argument('--batch-size', type=int, default=256, help='training batch size')parser.add_argument('--test-batch-size', type=int, default=256, help='test batch size')parser.add_argument('--start-epoch',type=int,default=0,help='start training from which epoch')parser.add_argument('--num-epoch',type=int,default=80,help='stop training in which epoch')parser.add_argument('--weight-decay',type=float,default=0.0005,help='weight decay for optimizer')parser.add_argument('--only_train_part', default=False)parser.add_argument('--only_train_epoch', default=0)parser.add_argument('--warm_up_epoch', default=0)return parserclass Processor():""" Processor for Skeleton-based Action Recgnition"""def __init__(self, arg):self.arg = argself.save_arg()if arg.phase == 'train':if not arg.train_feeder_args['debug']:if os.path.isdir(arg.model_saved_name):print('log_dir: ', arg.model_saved_name, 'already exist')answer = input('delete it? y/n:')if answer == 'y':shutil.rmtree(arg.model_saved_name)print('Dir removed: ', arg.model_saved_name)input('Refresh the website of tensorboard by pressing any keys')else:print('Dir not removed: ', arg.model_saved_name)self.train_writer = SummaryWriter(os.path.join(arg.model_saved_name, 'train'), 'train')self.val_writer = SummaryWriter(os.path.join(arg.model_saved_name, 'val'), 'val')else:self.train_writer = self.val_writer = SummaryWriter(os.path.join(arg.model_saved_name, 'test'), 'test')self.global_step = 0self.load_model()self.load_optimizer()self.load_data()self.lr = self.arg.base_lrself.best_acc = 0def load_data(self):Feeder = import_class(self.arg.feeder)self.data_loader = dict()if self.arg.phase == 'train':self.data_loader['train'] = torch.utils.data.DataLoader(dataset=Feeder(**self.arg.train_feeder_args),batch_size=self.arg.batch_size,shuffle=True,num_workers=self.arg.num_worker,drop_last=True,worker_init_fn=init_seed)self.data_loader['test'] = torch.utils.data.DataLoader(dataset=Feeder(**self.arg.test_feeder_args),batch_size=self.arg.test_batch_size,shuffle=False,num_workers=self.arg.num_worker,drop_last=False,worker_init_fn=init_seed)def load_model(self):output_device = self.arg.device[0] if type(self.arg.device) is list else self.arg.deviceself.output_device = output_deviceModel = import_class(self.arg.model)shutil.copy2(inspect.getfile(Model), self.arg.work_dir)print(Model)self.model = Model(**self.arg.model_args).cuda(output_device)print(self.model)self.loss = nn.CrossEntropyLoss().cuda(output_device)if self.arg.weights:self.global_step = int(arg.weights[:-3].split('-')[-1])self.print_log('Load weights from {}.'.format(self.arg.weights))if '.pkl' in self.arg.weights:with open(self.arg.weights, 'r') as f:weights = pickle.load(f)else:weights = torch.load(self.arg.weights)weights = OrderedDict([[k.split('module.')[-1],v.cuda(output_device)] for k, v in weights.items()])keys = list(weights.keys())for w in self.arg.ignore_weights:for key in keys:if w in key:if weights.pop(key, None) is not None:self.print_log('Sucessfully Remove Weights: {}.'.format(key))else:self.print_log('Can Not Remove Weights: {}.'.format(key))try:self.model.load_state_dict(weights)except:state = self.model.state_dict()diff = list(set(state.keys()).difference(set(weights.keys())))print('Can not find these weights:')for d in diff:print(' ' + d)state.update(weights)self.model.load_state_dict(state)if type(self.arg.device) is list:if len(self.arg.device) > 1:self.model = nn.DataParallel(self.model,device_ids=self.arg.device,output_device=output_device)def load_optimizer(self):if self.arg.optimizer == 'SGD':self.optimizer = optim.SGD(self.model.parameters(),lr=self.arg.base_lr,momentum=0.9,nesterov=self.arg.nesterov,weight_decay=self.arg.weight_decay)elif self.arg.optimizer == 'Adam':self.optimizer = optim.Adam(self.model.parameters(),lr=self.arg.base_lr,weight_decay=self.arg.weight_decay)else:raise ValueError()lr_scheduler_pre = optim.lr_scheduler.MultiStepLR(self.optimizer, milestones=self.arg.step, gamma=0.1)self.lr_scheduler = GradualWarmupScheduler(self.optimizer, total_epoch=self.arg.warm_up_epoch,after_scheduler=lr_scheduler_pre)self.print_log('using warm up, epoch: {}'.format(self.arg.warm_up_epoch))def save_arg(self):# save argarg_dict = vars(self.arg)#返回對(duì)象object的屬性和屬性值的字典對(duì)象if not os.path.exists(self.arg.work_dir):os.makedirs(self.arg.work_dir)with open('{}/config.yaml'.format(self.arg.work_dir), 'w') as f:yaml.dump(arg_dict, f)#建立一個(gè)yaml文件，YAML 是專門用來(lái)寫配置文件的語(yǔ)言def adjust_learning_rate(self, epoch):#設(shè)置learning rateif self.arg.optimizer == 'SGD' or self.arg.optimizer == 'Adam':if epoch < self.arg.warm_up_epoch:lr = self.arg.base_lr * (epoch + 1) / self.arg.warm_up_epochelse:lr = self.arg.base_lr * (0.1 ** np.sum(epoch >= np.array(self.arg.step)))for param_group in self.optimizer.param_groups:param_group['lr'] = lrreturn lrelse:raise ValueError()def print_time(self):#時(shí)間信息localtime = time.asctime(time.localtime(time.time()))self.print_log("Local current time : " + localtime)def print_log(self, str, print_time=True):#日志if print_time:localtime = time.asctime(time.localtime(time.time()))str = "[ " + localtime + ' ] ' + strprint(str)if self.arg.print_log:with open('{}/log.txt'.format(self.arg.work_dir), 'a') as f:print(str, file=f)def record_time(self):self.cur_time = time.time()return self.cur_timedef split_time(self):split_time = time.time() - self.cur_timeself.record_time()return split_timedef train(self, epoch, save_model=False):#訓(xùn)練self.model.train()self.print_log('Training epoch: {}'.format(epoch + 1))loader = self.data_loader['train']self.adjust_learning_rate(epoch)# for name, param in self.model.named_parameters():# self.train_writer.add_histogram(name, param.clone().cpu().data.numpy(), epoch)loss_value = []self.train_writer.add_scalar('epoch', epoch, self.global_step)self.record_time()timer = dict(dataloader=0.001, model=0.001, statistics=0.001)process = tqdm(loader)if self.arg.only_train_part:if epoch > self.arg.only_train_epoch:print('only train part, require grad')for key, value in self.model.named_parameters():if 'PA' in key:value.requires_grad = True# print(key + '-require grad')else:print('only train part, do not require grad')for key, value in self.model.named_parameters():if 'PA' in key:value.requires_grad = False# print(key + '-not require grad')for batch_idx, (data, label, index) in enumerate(process):self.global_step += 1# get datadata = Variable(data.float().cuda(self.output_device), requires_grad=False)label = Variable(label.long().cuda(self.output_device), requires_grad=False)timer['dataloader'] += self.split_time()# forwardoutput = self.model(data)# if batch_idx == 0 and epoch == 0:# self.train_writer.add_graph(self.model, output)if isinstance(output, tuple):output, l1 = outputl1 = l1.mean()else:l1 = 0loss = self.loss(output, label) + l1# backwardself.optimizer.zero_grad()loss.backward()self.optimizer.step()loss_value.append(loss.data.item())timer['model'] += self.split_time()value, predict_label = torch.max(output.data, 1)acc = torch.mean((predict_label == label.data).float())self.train_writer.add_scalar('acc', acc, self.global_step)self.train_writer.add_scalar('loss', loss.data.item(), self.global_step)self.train_writer.add_scalar('loss_l1', l1, self.global_step)# self.train_writer.add_scalar('batch_time', process.iterable.last_duration, self.global_step)# statisticsself.lr = self.optimizer.param_groups[0]['lr']self.train_writer.add_scalar('lr', self.lr, self.global_step)# if self.global_step % self.arg.log_interval == 0:# self.print_log(# '\tBatch({}/{}) done. Loss: {:.4f} lr:{:.6f}'.format(# batch_idx, len(loader), loss.data[0], lr))timer['statistics'] += self.split_time()# statistics of time consumption and lossproportion = {k: '{:02d}%'.format(int(round(v * 100 / sum(timer.values()))))for k, v in timer.items()}self.print_log('\tMean training loss: {:.4f}.'.format(np.mean(loss_value)))self.print_log('\tTime consumption: [Data]{dataloader}, [Network]{model}'.format(**proportion))if save_model:state_dict = self.model.state_dict()weights = OrderedDict([[k.split('module.')[-1],v.cpu()] for k, v in state_dict.items()])torch.save(weights, self.arg.model_saved_name + '-' + str(epoch) + '-' + str(int(self.global_step)) + '.pt')def eval(self, epoch, save_score=False, loader_name=['test'], wrong_file=None, result_file=None):if wrong_file is not None:f_w = open(wrong_file, 'w')if result_file is not None:f_r = open(result_file, 'w')self.model.eval()self.print_log('Eval epoch: {}'.format(epoch + 1))for ln in loader_name:loss_value = []score_frag = []right_num_total = 0total_num = 0loss_total = 0step = 0process = tqdm(self.data_loader[ln])for batch_idx, (data, label, index) in enumerate(process):with torch.no_grad():data = Variable(data.float().cuda(self.output_device),requires_grad=False,volatile=True)label = Variable(label.long().cuda(self.output_device),requires_grad=False,volatile=True)output = self.model(data)if isinstance(output, tuple):output, l1 = outputl1 = l1.mean()else:l1 = 0loss = self.loss(output, label)score_frag.append(output.data.cpu().numpy())loss_value.append(loss.data.item())_, predict_label = torch.max(output.data, 1)step += 1if wrong_file is not None or result_file is not None:predict = list(predict_label.cpu().numpy())true = list(label.data.cpu().numpy())for i, x in enumerate(predict):if result_file is not None:f_r.write(str(x) + ',' + str(true[i]) + '\n')if x != true[i] and wrong_file is not None:f_w.write(str(index[i]) + ',' + str(x) + ',' + str(true[i]) + '\n')score = np.concatenate(score_frag)loss = np.mean(loss_value)accuracy = self.data_loader[ln].dataset.top_k(score, 1)if accuracy > self.best_acc:self.best_acc = accuracy# self.lr_scheduler.step(loss)print('Accuracy: ', accuracy, ' model: ', self.arg.model_saved_name)if self.arg.phase == 'train':self.val_writer.add_scalar('loss', loss, self.global_step)self.val_writer.add_scalar('loss_l1', l1, self.global_step)self.val_writer.add_scalar('acc', accuracy, self.global_step)score_dict = dict(zip(self.data_loader[ln].dataset.sample_name, score))self.print_log('\tMean {} loss of {} batches: {}.'.format(ln, len(self.data_loader[ln]), np.mean(loss_value)))for k in self.arg.show_topk:self.print_log('\tTop{}: {:.2f}%'.format(k, 100 * self.data_loader[ln].dataset.top_k(score, k)))if save_score:with open('{}/epoch{}_{}_score.pkl'.format(self.arg.work_dir, epoch + 1, ln), 'wb') as f:pickle.dump(score_dict, f)def start(self):if self.arg.phase == 'train':self.print_log('Parameters:\n{}\n'.format(str(vars(self.arg))))self.global_step = self.arg.start_epoch * len(self.data_loader['train']) / self.arg.batch_sizefor epoch in range(self.arg.start_epoch, self.arg.num_epoch):if self.lr < 1e-3:breaksave_model = ((epoch + 1) % self.arg.save_interval == 0) or (epoch + 1 == self.arg.num_epoch)self.train(epoch, save_model=save_model)self.eval(epoch,save_score=self.arg.save_score,loader_name=['test'])print('best accuracy: ', self.best_acc, ' model_name: ', self.arg.model_saved_name)elif self.arg.phase == 'test':if not self.arg.test_feeder_args['debug']:wf = self.arg.model_saved_name + '_wrong.txt'rf = self.arg.model_saved_name + '_right.txt'else:wf = rf = Noneif self.arg.weights is None:raise ValueError('Please appoint --weights.')self.arg.print_log = Falseself.print_log('Model: {}.'.format(self.arg.model))self.print_log('Weights: {}.'.format(self.arg.weights))self.eval(epoch=0, save_score=self.arg.save_score, loader_name=['test'], wrong_file=wf, result_file=rf)self.print_log('Done.\n')def str2bool(v):if v.lower() in ('yes', 'true', 't', 'y', '1'):return Trueelif v.lower() in ('no', 'false', 'f', 'n', '0'):return Falseelse:raise argparse.ArgumentTypeError('Boolean value expected.')def import_class(name):components = name.split('.')mod = __import__(components[0]) # import return modelfor comp in components[1:]:mod = getattr(mod, comp)return modif __name__ == '__main__':parser = get_parser()# load arg form config filep = parser.parse_args()if p.config is not None:with open(p.config, 'r') as f:default_arg = yaml.load(f)key = vars(p).keys()for k in default_arg.keys():if k not in key:print('WRONG ARG: {}'.format(k))assert (k in key)parser.set_defaults(**default_arg)arg = parser.parse_args()init_seed(0)processor = Processor(arg)processor.start()

總結(jié)

以上是生活随笔為你收集整理的GCN代码超详解析Two-stream adaptive graph convolutional network for Skeleton-Based Action Recognition（三）的全部?jī)?nèi)容，希望文章能夠幫你解決所遇到的問(wèn)題。

如果覺得生活随笔網(wǎng)站內(nèi)容還不錯(cuò)，歡迎將生活随笔推薦給好友。