torchvision包 包含了目前流行的数据集,模型结构和常用的图片转换工具。
torchvision.datasets中包含了以下数据集
MNIST
COCO(用于图像标注和目标检测)(Captioning and Detection)
LSUN Classification
ImageFolder
Imagenet-12
CIFAR10 and CIFAR100
STL10
torchvision.models
torchvision.models模块的 子模块中包含以下模型结构。
AlexNet
VGG
ResNet
SqueezeNet
DenseNet You can construct a model with random weights by calling its constructor:
pytorch torchvision transform
对PIL.Image进行变换
from __future__ import print_function import argparse #Python 命令行解析工具 import torch import torch.nn as nn import torch.nn.functional as F import torch.optim as optim from torchvision import datasets,transforms class Net(nn.Module): def __init__(self): super(Net,self).__init__() self.conv1 = nn.Conv2d(1,10,kernel_size=5) self.conv2 = nn.Conv2d(10,20,kernel_size=5) self.conv2_drop = nn.Dropout2d() self.fc1 = nn.Linear(320,50) self.fc2 = nn.Linear(50,10) def forward(self,x): x = F.relu(F.max_pool2d(self.conv1(x),2)) x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)),2)) x = x.view(-1,320) x = F.relu(self.fc1(x)) x = F.dropout(x,training=self.training) x = self.fc2(x) return F.log_softmax(x,dim=1) def train(args,model,device,train_loader,optimizer,epoch): model.train() for batch_idx,(data,target) in enumerate(train_loader): data,target = data.to(device),target.to(device) optimizer.zero_grad() output = model(data) loss = F.nll_loss(output,target) loss.backward() optimizer.step() if batch_idx % args.log_interval == 0: print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format( epoch,batch_idx * len(data),len(train_loader.dataset),100. * batch_idx / len(train_loader),loss.item())) def test(args,test_loader): model.eval() test_loss = 0 correct = 0 with torch.no_grad(): for data,target in test_loader: data,target.to(device) output = model(data) test_loss += F.nll_loss(output,target,size_average=False).item() # sum up batch loss pred = output.max(1,keepdim=True)[1] # get the index of the max log-probability correct += pred.eq(target.view_as(pred)).sum().item() test_loss /= len(test_loader.dataset) print('\nTest set: Average loss: {:.4f},Accuracy: {}/{} ({:.0f}%)\n'.format( test_loss,correct,len(test_loader.dataset),100. * correct / len(test_loader.dataset))) def main(): # Training settings parser = argparse.ArgumentParser(description='PyTorch MNIST Example') parser.add_argument('--batch-size',type=int,default=64,Metavar='N',help='input batch size for training (default: 64)') parser.add_argument('--test-batch-size',default=1000,help='input batch size for testing (default: 1000)') parser.add_argument('--epochs',default=10,help='number of epochs to train (default: 10)') parser.add_argument('--lr',type=float,default=0.01,Metavar='LR',help='learning rate (default: 0.01)') parser.add_argument('--momentum',default=0.5,Metavar='M',help='SGD momentum (default: 0.5)') parser.add_argument('--no-cuda',action='store_true',default=False,help='disables CUDA training') parser.add_argument('--seed',default=1,Metavar='S',help='random seed (default: 1)') parser.add_argument('--log-interval',help='how many batches to wait before logging training status') args = parser.parse_args() use_cuda = not args.no_cuda and torch.cuda.is_available() torch.manual_seed(args.seed) device = torch.device("cuda" if use_cuda else "cpu") kwargs = {'num_workers': 1,'pin_memory': True} if use_cuda else {} train_loader = torch.utils.data.DataLoader( datasets.MNIST('../data',train=True,download=True,transform=transforms.Compose([ transforms.ToTensor(),transforms.Normalize((0.1307,),(0.3081,)) ])),batch_size=args.batch_size,shuffle=True,**kwargs) test_loader = torch.utils.data.DataLoader( datasets.MNIST('../data',train=False,batch_size=args.test_batch_size,**kwargs) model = Net().to(device) optimizer = optim.SGD(model.parameters(),lr=args.lr,momentum=args.momentum) for epoch in range(1,args.epochs + 1): train(args,epoch) test(args,test_loader) if __name__ == '__main__': main()
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