pytorch使用多GPU训练MNIST

喜欢ヅ旅行 2022-05-16 11:08 216阅读 0赞

下面的代码参数没有调试，可能准确率不高，仅仅供参考代码格式。

import argparse
    import torch
    import torch.nn as nn
    import torch.optim as optim
    import torch.nn.functional as F
    from torchvision import  datasets
    from torchvision import transforms
    from torch.autograd import Variable
    import torch.utils.data.distributed
    
    import math
    import time
    
    import os
    
    os.environ["CUDA_VISIBLE_DEVICES"]="0, 1, 2, 3, 4, 5 ,6,7"
    
    lr = 0.001
    batch_size = 100
    epochs = 10
    test_batch_size=100
    momentum=0.5
    log_interval=100
    #输入数据变换操作
    transform_list = [
                    transforms.Resize(40),
                    transforms.RandomHorizontalFlip(),
                    transforms.RandomCrop(32),
                    transforms.ToTensor()
                    ]
    transform = transforms.Compose(transform_list)
    
    
    torch.manual_seed(2018)
    
    # Horovod: pin GPU to local rank.
    # torch.cuda.set_device(hvd.local_rank())
    torch.cuda.manual_seed(2018)
    
    kwargs = {
       'num_workers': 4, 'pin_memory': True}
    
    train_dataset = \
        datasets.MNIST('data-0', train=True, download=True,
                       transform=transforms.Compose([
                           transforms.ToTensor(),
                           transforms.Normalize((0.1307,), (0.3081,))
                       ]))
    
    
    train_loader = torch.utils.data.DataLoader(
        train_dataset, batch_size=batch_size, **kwargs)
    
    
    test_dataset = \
        datasets.MNIST('data-0', train=False, transform=transforms.Compose([
            transforms.ToTensor(),
            transforms.Normalize((0.1307,), (0.3081,))
        ]))
    
    test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=test_batch_size,
                                            **kwargs)
    
    def conv3x3(in_planes, out_planes, stride=1):
        """3x3 convolution with padding"""
        return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
                         padding=1, bias=False)
    
    
    # Residual Block
    class ResidualBlock(nn.Module):
        def __init__(self, in_channels, out_channels, stride=1, downsample=None):
            super(ResidualBlock, self).__init__()
            self.conv1 = conv3x3(in_channels, out_channels, stride)
            self.bn1 = nn.BatchNorm2d(out_channels)
            self.relu = nn.ReLU(inplace=True)
            self.conv2 = conv3x3(out_channels, out_channels)
            self.bn2 = nn.BatchNorm2d(out_channels)
            self.downsample = downsample
    
        def forward(self, x):
            residual = x
            out = self.conv1(x)
            out = self.bn1(out)
            out = self.relu(out)
            out = self.conv2(out)
            out = self.bn2(out)
            if self.downsample:
                residual = self.downsample(x)
            out += residual
            out = self.relu(out)
            return out
    
    
    # ResNet Module
    class ResNet(nn.Module):
        def __init__(self, block, layers, num_classes=10):
            super(ResNet, self).__init__()
            self.in_channels = 16
            self.conv = conv3x3(1, 16)
            self.bn = nn.BatchNorm2d(16)
            self.relu = nn.ReLU(inplace=True)
    
            self.layer1 = self.make_layer(block, 16, layers[0])
            self.layer2 = self.make_layer(block, 32, layers[1], 2)
            self.layer3 = self.make_layer(block, 64, layers[2], 2)
            self.layer4 = self.make_layer(block,128,layers[3])
            self.avg_pool = nn.AvgPool2d(7)
            self.fc = nn.Linear(128, num_classes)
    
        def make_layer(self, block, out_channels, blocks, stride=1):
            downsample = None
            if (stride != 1) or (self.in_channels != out_channels):
                downsample = nn.Sequential(
                    conv3x3(self.in_channels, out_channels, stride=stride),
                    nn.BatchNorm2d(out_channels))
            layers = []
            layers.append(block(self.in_channels, out_channels, stride, downsample))
            self.in_channels = out_channels
            for i in range(1, blocks):
                layers.append(block(out_channels, out_channels))
            return nn.Sequential(*layers)
    
        def forward(self, x):
            global  COUNTER
            out = self.conv(x)
            out = self.bn(out)
            out = self.relu(out)
            out = self.layer1(out)
            out = self.layer2(out)
            out = self.layer3(out)
            out = self.layer4(out)
            #print("###############",out.size())
            out = self.avg_pool(out)
            out = out.view(out.size(0), -1)
            out = self.fc(out)
            return out
    
    
    # model = ResNet(ResidualBlock, [2, 2, 2, 2])#.cuda()
    model = torch.nn.DataParallel(ResNet(ResidualBlock, [2, 2, 2, 2]),device_ids=[0,1,2,3,4,5,6,7]).cuda()
    
    # Horovod: scale learning rate by the number of GPUs.
    optimizer = optim.SGD(model.parameters(), lr=lr,
                          momentum=momentum)
    
    
    criterion = nn.CrossEntropyLoss()
    
    def train(epoch):
        model.train()
        for batch_idx, (data, target) in enumerate(train_loader):
            data, target = data.cuda(), target.cuda()
            data, target = Variable(data), Variable(target)
            optimizer.zero_grad()
            output = model(data)
            #loss = F.nll_loss(output, target)
            loss=criterion(output,target)
            loss.backward()
            optimizer.step()
            if batch_idx % log_interval == 0:
                print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
                    epoch, batch_idx * len(data), 0,
                    100. * batch_idx / len(train_loader), loss.item()))
    
    for epoch in range(1, epochs + 1):
        train(epoch)
    
    
    #测试过程
    # model.load_state_dict(torch.load('param_model.pkl'))
    # test()