Vgg11训练CIFA10数据集——pytorch实现

代码在kaggle上跑了60多分钟，精度85%

Sequential output shape:	 torch.Size([1, 64, 112, 112])
Sequential output shape:	 torch.Size([1, 128, 56, 56])
Sequential output shape:	 torch.Size([1, 256, 28, 28])
Sequential output shape:	 torch.Size([1, 512, 14, 14])
Sequential output shape:	 torch.Size([1, 512, 7, 7])
Sequential output shape:	 torch.Size([1, 25088])
Linear output shape:	 torch.Size([1, 4096])
ReLU output shape:	 torch.Size([1, 4096])
Dropout output shape:	 torch.Size([1, 4096])
Linear output shape:	 torch.Size([1, 4096])
ReLU output shape:	 torch.Size([1, 4096])
Dropout output shape:	 torch.Size([1, 4096])
Linear output shape:	 torch.Size([1, 10])

import os
import datetime
import torch
import torchvision
from torch import nn
from torch import optim
import torch.nn.functional as F
from torch.autograd import Variable
from torch.utils.data import DataLoader
from torchvision import transforms
import torchvision.models as models
from torchvision.utils import save_image
import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import norm
from PIL import Image
import time
import argparse

def try_gpu(i=0):
    """如果存在，则返回gpu(i)，否则返回cpu()"""
    if torch.cuda.device_count() >= i + 1:
        return torch.device(f'cuda:{i}')
    return torch.device('cpu')

batch_size = 64
path = './'
train_transform = transforms.Compose([
    transforms.RandomSizedCrop(224),# 随机剪切成227*227
    transforms.RandomHorizontalFlip(),# 随机水平翻转
    transforms.ToTensor(),
    transforms.Normalize(mean = [ 0.485, 0.456, 0.406 ],
                         std = [ 0.229, 0.224, 0.225 ]),
])
val_transform = transforms.Compose([
    transforms.Resize((224,224)),
    transforms.ToTensor(),
    transforms.Normalize(mean = [ 0.485, 0.456, 0.406 ],
                         std = [ 0.229, 0.224, 0.225 ]),
])

traindir = os.path.join(path, 'train')
valdir = os.path.join(path, 'val')

train_set = torchvision.datasets.CIFAR10(
    traindir, train=True, transform=train_transform, download=True)
valid_set = torchvision.datasets.CIFAR10(
    valdir, train=False, transform=val_transform, download=True)


train_loader = DataLoader(train_set, batch_size=batch_size, shuffle=True)
valid_loader = DataLoader(valid_set, batch_size=batch_size, shuffle=False)

dataloaders = {
    'train': train_loader,
    'valid': valid_loader,
#     'test': dataloader_test
    }

dataset_sizes = {
    'train': len(train_set),
    'valid': len(valid_set),
#     'test': len(test_set)
    }
print(dataset_sizes)


class Vgg11(nn.Module):
 
    def __init__(self):
        super().__init__()
        
        # vgg11的卷积通道变化
        conv_arch = ((1, 64), (1, 128), (2, 256), (2, 512), (2, 512))
        
        conv_blks = []
        in_channels = 3
        
        # 卷积部分
        for(num_convs, out_channels) in conv_arch:
            conv_blks.append(vgg_block(num_convs, in_channels, out_channels))
            in_channels = out_channels
        
        self.conv = nn.Sequential(*conv_blks)
        
        self.fc = nn.Sequential(
                        nn.Linear(out_channels * 7 * 7, 4096), nn.ReLU(),
                        nn.Dropout(p=0.5),
                        nn.Linear(4096, 4096), nn.ReLU(),
                        nn.Dropout(p=0.5),
                        nn.Linear(4096, 10))
        
        self.fn = nn.Flatten()
        
    def forward(self, x):
        out = self.conv(x)
        out = self.fn(out)
        out = self.fc(out)
        return out
    
# vgg块：num_convs个卷积层 + 1个最大汇聚层
def vgg_block(num_convs, in_channels, out_channels):
    layers = []
    for _ in range(num_convs):
        layers.append(nn.Conv2d(in_channels,out_channels,kernel_size=3,padding=1))
        layers.append(nn.ReLU())
        in_channels = out_channels
    layers.append(nn.MaxPool2d(kernel_size=2,stride=2))
    return nn.Sequential(*layers)
X = torch.randn(1, 3, 224, 224)
net = Vgg11()
for layer in net.conv:
    X=layer(X)
    print(layer.__class__.__name__,'output shape:\t',X.shape)
X=net.fn(X)
print(layer.__class__.__name__,'output shape:\t',X.shape)
for layer in net.fc:
    X=layer(X)
    print(layer.__class__.__name__,'output shape:\t',X.shape)

def train(model, criterion, optimizer, scheduler, device, num_epochs, dataloaders,dataset_sizes):
    model = model.to(device)
    print('training on ', device)
    since = time.time()

    best_model_wts = []
    best_acc = 0.0

    for epoch in range(num_epochs):
        # 训练模型
        s = time.time()
        model,train_epoch_acc,train_epoch_loss = train_model(
            model, criterion, optimizer, dataloaders['train'], dataset_sizes['train'], device)
        print('Epoch {}/{} - train Loss: {:.4f}  Acc: {:.4f}  Time:{:.1f}s'
            .format(epoch+1, num_epochs, train_epoch_loss, train_epoch_acc,time.time()-s))
        # 验证模型
        s = time.time()
        val_epoch_acc,val_epoch_loss = val_model(
            model, criterion, dataloaders['valid'], dataset_sizes['valid'], device)
        print('Epoch {}/{} - valid Loss: {:.4f}  Acc: {:.4f}  Time:{:.1f}s'
            .format(epoch+1, num_epochs, val_epoch_loss, val_epoch_acc,time.time()-s))
        # 每轮都记录最好的参数.
        if val_epoch_acc > best_acc:
            best_acc = val_epoch_acc
            best_model_wts = model.state_dict()
        # 优化器
#         if scheduler not in None:
#             scheduler.step()
        # 保存画图参数
        train_losses.append(train_epoch_loss.to('cpu'))
        train_acc.append(train_epoch_acc.to('cpu'))
        val_losses.append(val_epoch_loss.to('cpu'))
        val_acc.append(val_epoch_acc.to('cpu'))
        print()
    time_elapsed = time.time() - since
    print('Training complete in {:.0f}m {:.0f}s'.format(time_elapsed // 60, time_elapsed % 60))
    print('Best val Acc: {:4f}'.format(best_acc))
    # model.load_state_dict(best_model_wts)
    return model

def train_model(model, criterion, optimizer, dataloader, dataset_size,device):
    model.train()
    running_loss = 0.0
    running_corrects = 0
    for inputs,labels in dataloader:
        optimizer.zero_grad()
        # 输入的属性
        inputs = Variable(inputs.to(device))
        # 标签
        labels = Variable(labels.to(device))
        # 预测
        outputs = model(inputs)
        _,preds = torch.max(outputs.data,1)
        # 计算损失
        loss = criterion(outputs,labels)
        #梯度下降
        loss.backward()
        optimizer.step()

        running_loss += loss.data
        running_corrects += torch.sum(preds == labels.data)

    epoch_loss = running_loss / dataset_size
    epoch_acc = running_corrects / dataset_size
    
    return model,epoch_acc,epoch_loss

def val_model(model, criterion, dataloader, dataset_size, device):
    model.eval()
    running_loss = 0.0
    running_corrects = 0
    for (inputs,labels) in dataloader:
        # 输入的属性
        inputs = Variable(inputs.to(device))
        # 标签
        labels = Variable(labels.to(device))
        # 预测
        outputs = model(inputs)
        _,preds = torch.max(outputs.data,1)
        # 计算损失
        loss = criterion(outputs,labels)
        
        running_loss += loss.data
        running_corrects += torch.sum(preds == labels.data)

    epoch_loss = running_loss / dataset_size
    epoch_acc = running_corrects / dataset_size
    
    return epoch_acc,epoch_loss

val_losses,val_acc = [],[]
train_losses,train_acc = [],[]

lr,num_epochs = 0.01,10
model = Vgg11()
criterion = nn.CrossEntropyLoss()
# optimizer = optim.Adam(model.parameters(), lr=lr)
optimizer = optim.SGD(model.parameters(), lr=lr, momentum=0.9)
# scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=30, gamma=0.1)

model = train(model, criterion, optimizer, None ,
              try_gpu(), num_epochs, dataloaders, dataset_sizes)

plt.plot(range(1, len(train_losses)+1),train_losses, 'b', label='training loss')
plt.plot(range(1, len(val_losses)+1), val_losses, 'r', label='val loss')
plt.legend()

plt.plot(range(1,len(train_acc)+1),train_acc,'b--',label = 'train accuracy')
plt.plot(range(1,len(val_acc)+1),val_acc,'r--',label = 'val accuracy')
plt.legend()