简单易懂Pytorch实战实例VGG深度网络

Python  /  管理员 发布于 5年前   246

模型VGG，数据集cifar。对照这份代码走一遍，大概就知道整个pytorch的运行机制。

来源

定义模型：

'''VGG11/13/16/19 in Pytorch.'''import torchimport torch.nn as nnfrom torch.autograd import Variablecfg = {  'VGG11': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],  'VGG13': [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],  'VGG16': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M'],  'VGG19': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 256, 'M', 512, 512, 512, 512, 'M', 512, 512, 512, 512, 'M'],}# 模型需继承nn.Moduleclass VGG(nn.Module):# 初始化参数：  def __init__(self, vgg_name):    super(VGG, self).__init__()    self.features = self._make_layers(cfg[vgg_name])    self.classifier = nn.Linear(512, 10)# 模型计算时的前向过程，也就是按照这个过程进行计算  def forward(self, x):    out = self.features(x)    out = out.view(out.size(0), -1)    out = self.classifier(out)    return out  def _make_layers(self, cfg):    layers = []    in_channels = 3    for x in cfg:      if x == 'M':        layers += [nn.MaxPool2d(kernel_size=2, stride=2)]      else:        layers += [nn.Conv2d(in_channels, x, kernel_size=3, padding=1),  nn.BatchNorm2d(x),  nn.ReLU(inplace=True)]        in_channels = x    layers += [nn.AvgPool2d(kernel_size=1, stride=1)]    return nn.Sequential(*layers)# net = VGG('VGG11')# x = torch.randn(2,3,32,32)# print(net(Variable(x)).size())

定义训练过程：

'''Train CIFAR10 with PyTorch.'''from __future__ import print_functionimport torchimport torch.nn as nnimport torch.optim as optimimport torch.nn.functional as Fimport torch.backends.cudnn as cudnnimport torchvisionimport torchvision.transforms as transformsimport osimport argparsefrom models import *from utils import progress_barfrom torch.autograd import Variable# 获取参数parser = argparse.ArgumentParser(description='PyTorch CIFAR10 Training')parser.add_argument('--lr', default=0.1, type=float, help='learning rate')parser.add_argument('--resume', '-r', action='store_true', help='resume from checkpoint')args = parser.parse_args()use_cuda = torch.cuda.is_available()best_acc = 0 # best test accuracystart_epoch = 0 # start from epoch 0 or last checkpoint epoch# 获取数据集，并先进行预处理print('==> Preparing data..')# 图像预处理和增强transform_train = transforms.Compose([  transforms.RandomCrop(32, padding=4),  transforms.RandomHorizontalFlip(),  transforms.ToTensor(),  transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),])transform_test = transforms.Compose([  transforms.ToTensor(),  transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),])trainset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform_train)trainloader = torch.utils.data.DataLoader(trainset, batch_size=128, shuffle=True, num_workers=2)testset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transform_test)testloader = torch.utils.data.DataLoader(testset, batch_size=100, shuffle=False, num_workers=2)classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')# 继续训练模型或新建一个模型if args.resume:  # Load checkpoint.  print('==> Resuming from checkpoint..')  assert os.path.isdir('checkpoint'), 'Error: no checkpoint directory found!'  checkpoint = torch.load('./checkpoint/ckpt.t7')  net = checkpoint['net']  best_acc = checkpoint['acc']  start_epoch = checkpoint['epoch']else:  print('==> Building model..')  net = VGG('VGG16')  # net = ResNet18()  # net = PreActResNet18()  # net = GoogLeNet()  # net = DenseNet121()  # net = ResNeXt29_2x64d()  # net = MobileNet()  # net = MobileNetV2()  # net = DPN92()  # net = ShuffleNetG2()  # net = SENet18()# 如果GPU可用，使用GPUif use_cuda:  # move param and buffer to GPU  net.cuda()  # parallel use GPU  net = torch.nn.DataParallel(net, device_ids=range(torch.cuda.device_count()-1))  # speed up slightly  cudnn.benchmark = True# 定义度量和优化criterion = nn.CrossEntropyLoss()optimizer = optim.SGD(net.parameters(), lr=args.lr, momentum=0.9, weight_decay=5e-4)# 训练阶段def train(epoch):  print('\nEpoch: %d' % epoch)  # switch to train mode  net.train()  train_loss = 0  correct = 0  total = 0  # batch 数据  for batch_idx, (inputs, targets) in enumerate(trainloader):    # 将数据移到GPU上    if use_cuda:      inputs, targets = inputs.cuda(), targets.cuda()    # 先将optimizer梯度先置为0    optimizer.zero_grad()    # Variable表示该变量属于计算图的一部分，此处是图计算的开始处。图的leaf variable    inputs, targets = Variable(inputs), Variable(targets)    # 模型输出    outputs = net(inputs)    # 计算loss，图的终点处    loss = criterion(outputs, targets)    # 反向传播，计算梯度    loss.backward()    # 更新参数    optimizer.step()    # 注意如果你想统计loss，切勿直接使用loss相加，而是使用loss.data[0]。因为loss是计算图的一部分，如果你直接加loss，代表total loss同样属于模型一部分，那么图就越来越大    train_loss += loss.data[0]    # 数据统计    _, predicted = torch.max(outputs.data, 1)    total += targets.size(0)    correct += predicted.eq(targets.data).cpu().sum()    progress_bar(batch_idx, len(trainloader), 'Loss: %.3f | Acc: %.3f%% (%d/%d)'      % (train_loss/(batch_idx+1), 100.*correct/total, correct, total))# 测试阶段def test(epoch):  global best_acc  # 先切到测试模型  net.eval()  test_loss = 0  correct = 0  total = 0  for batch_idx, (inputs, targets) in enumerate(testloader):    if use_cuda:      inputs, targets = inputs.cuda(), targets.cuda()    inputs, targets = Variable(inputs, volatile=True), Variable(targets)    outputs = net(inputs)    loss = criterion(outputs, targets)    # loss is variable , if add it(+=loss) directly, there will be a bigger ang bigger graph.    test_loss += loss.data[0]    _, predicted = torch.max(outputs.data, 1)    total += targets.size(0)    correct += predicted.eq(targets.data).cpu().sum()    progress_bar(batch_idx, len(testloader), 'Loss: %.3f | Acc: %.3f%% (%d/%d)'      % (test_loss/(batch_idx+1), 100.*correct/total, correct, total))  # Save checkpoint.  # 保存模型  acc = 100.*correct/total  if acc > best_acc:    print('Saving..')    state = {      'net': net.module if use_cuda else net,      'acc': acc,      'epoch': epoch,    }    if not os.path.isdir('checkpoint'):      os.mkdir('checkpoint')    torch.save(state, './checkpoint/ckpt.t7')    best_acc = acc# 运行模型for epoch in range(start_epoch, start_epoch+200):  train(epoch)  test(epoch)  # 清除部分无用变量   torch.cuda.empty_cache()

运行：

新模型：
python main.py --lr=0.01
旧模型继续训练：
python main.py --resume --lr=0.01

一些utility：

'''Some helper functions for PyTorch, including:  - get_mean_and_std: calculate the mean and std value of dataset.  - msr_init: net parameter initialization.  - progress_bar: progress bar mimic xlua.progress.'''import osimport sysimport timeimport mathimport torch.nn as nnimport torch.nn.init as initdef get_mean_and_std(dataset):  '''Compute the mean and std value of dataset.'''  dataloader = torch.utils.data.DataLoader(dataset, batch_size=1, shuffle=True, num_workers=2)  mean = torch.zeros(3)  std = torch.zeros(3)  print('==> Computing mean and std..')  for inputs, targets in dataloader:    for i in range(3):      mean[i] += inputs[:,i,:,:].mean()      std[i] += inputs[:,i,:,:].std()  mean.div_(len(dataset))  std.div_(len(dataset))  return mean, stddef init_params(net):  '''Init layer parameters.'''  for m in net.modules():    if isinstance(m, nn.Conv2d):      init.kaiming_normal(m.weight, mode='fan_out')      if m.bias:        init.constant(m.bias, 0)    elif isinstance(m, nn.BatchNorm2d):      init.constant(m.weight, 1)      init.constant(m.bias, 0)    elif isinstance(m, nn.Linear):      init.normal(m.weight, std=1e-3)      if m.bias:        init.constant(m.bias, 0)_, term_width = os.popen('stty size', 'r').read().split()term_width = int(term_width)TOTAL_BAR_LENGTH = 65.last_time = time.time()begin_time = last_timedef progress_bar(current, total, msg=None):  global last_time, begin_time  if current == 0:    begin_time = time.time() # Reset for new bar.  cur_len = int(TOTAL_BAR_LENGTH*current/total)  rest_len = int(TOTAL_BAR_LENGTH - cur_len) - 1  sys.stdout.write(' [')  for i in range(cur_len):    sys.stdout.write('=')  sys.stdout.write('>')  for i in range(rest_len):    sys.stdout.write('.')  sys.stdout.write(']')  cur_time = time.time()  step_time = cur_time - last_time  last_time = cur_time  tot_time = cur_time - begin_time  L = []  L.append(' Step: %s' % format_time(step_time))  L.append(' | Tot: %s' % format_time(tot_time))  if msg:    L.append(' | ' + msg)  msg = ''.join(L)  sys.stdout.write(msg)  for i in range(term_width-int(TOTAL_BAR_LENGTH)-len(msg)-3):    sys.stdout.write(' ')  # Go back to the center of the bar.  for i in range(term_width-int(TOTAL_BAR_LENGTH/2)+2):    sys.stdout.write('\b')  sys.stdout.write(' %d/%d ' % (current+1, total))  if current < total-1:    sys.stdout.write('\r')  else:    sys.stdout.write('\n')  sys.stdout.flush()def format_time(seconds):  days = int(seconds / 3600/24)  seconds = seconds - days*3600*24  hours = int(seconds / 3600)  seconds = seconds - hours*3600  minutes = int(seconds / 60)  seconds = seconds - minutes*60  secondsf = int(seconds)  seconds = seconds - secondsf  millis = int(seconds*1000)  f = ''  i = 1  if days > 0:    f += str(days) + 'D'    i += 1  if hours > 0 and i <= 2:    f += str(hours) + 'h'    i += 1  if minutes > 0 and i <= 2:    f += str(minutes) + 'm'    i += 1  if secondsf > 0 and i <= 2:    f += str(secondsf) + 's'    i += 1  if millis > 0 and i <= 2:    f += str(millis) + 'ms'    i += 1  if f == '':    f = '0ms'  return f

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