# 方式一：保存加載整個(gè)state_dict（推薦）
# 保存
torch.save(model.state_dict(), PATH)
# 加載
model.load_state_dict(torch.load(PATH))
# 測(cè)試時(shí)不啟用 BatchNormalization 和 Dropout
model.eval()

# 方式二：保存加載整個(gè)模型
# 保存
torch.save(model, PATH)
# 加載
model = torch.load(PATH)
model.eval()

# 方式三：保存用于繼續(xù)訓(xùn)練的checkpoint或者多個(gè)模型
# 保存
torch.save({
? ? ? ? ? ? 'epoch': epoch,
? ? ? ? ? ? 'model_state_dict': model.state_dict(),
? ? ? ? ? ? ...
? ? ? ? ? ? }, PATH)
# 加載
checkpoint = torch.load(PATH)
start_epoch=checkpoint['epoch']
model.load_state_dict(checkpoint['model_state_dict'])
# 測(cè)試時(shí)
model.eval()
# 或者訓(xùn)練時(shí)
model.train()

# GPU上保存，CPU上加載
# 保存
torch.save(model.state_dict(), PATH)
# 加載
device = torch.device('cpu')
model.load_state_dict(torch.load(PATH, map_location=device))
# 如果是多gpu保存，需要去除關(guān)鍵字中的module，見第4部分

# GPU上保存，GPU上加載
# 保存
torch.save(model.state_dict(), PATH)
# 加載
device = torch.device("cuda")
model.load_state_dict(torch.load(PATH))
model.to(device)

# CPU上保存，GPU上加載
# 保存
torch.save(model.state_dict(), PATH)
# 加載
device = torch.device("cuda")
# 選擇希望使用的GPU
model.load_state_dict(torch.load(PATH, map_location="cuda:0")) ?
model.to(device)

# 打印模型的 state_dict
print("Model's state_dict:")
for param_tensor in model.state_dict():
? ? print(param_tensor, "\t", model.state_dict()[param_tensor].size())

#解決1：加載時(shí)將module去掉

# 創(chuàng)建一個(gè)不包含`module.`的新OrderedDict
from collections import OrderedDict
new_state_dict = OrderedDict()
for k, v in state_dict.items():
? ? name = k[7:] # 去掉 `module.`
? ? new_state_dict[name] = v
# 加載參數(shù)
model.load_state_dict(new_state_dict)

# 解決2：保存checkpoint時(shí)不保存module
torch.save(model.module.state_dict(), PATH)

'''本文件用于舉例說明pytorch保存和加載文件的方法''' 
import torch as torch
import torchvision as tv
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import torchvision.transforms as transforms
import os
  
# 參數(shù)聲明
batch_size = 32
epochs = 10
WORKERS = 0  # dataloder線程數(shù)
test_flag = False  # 測(cè)試標(biāo)志，True時(shí)加載保存好的模型進(jìn)行測(cè)試
ROOT = '/home/pxt/pytorch/cifar'  # MNIST數(shù)據(jù)集保存路徑
log_dir = '/home/pxt/pytorch/logs/cifar_model.pth'  # 模型保存路徑
# 加載MNIST數(shù)據(jù)集
transform = tv.transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])])
 
train_data = tv.datasets.CIFAR10(root=ROOT, train=True, download=True, transform=transform)
test_data = tv.datasets.CIFAR10(root=ROOT, train=False, download=False, transform=transform)
 
train_load = torch.utils.data.DataLoader(train_data, batch_size=batch_size, shuffle=True, num_workers=WORKERS)
test_load = torch.utils.data.DataLoader(test_data, batch_size=batch_size, shuffle=False, num_workers=WORKERS)
 
 
# 構(gòu)造模型
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, 3, padding=1)
        self.conv2 = nn.Conv2d(64, 128, 3, padding=1)
        self.conv3 = nn.Conv2d(128, 256, 3, padding=1)
        self.conv4 = nn.Conv2d(256, 256, 3, padding=1)
        self.pool = nn.MaxPool2d(2, 2)
        self.fc1 = nn.Linear(256 * 8 * 8, 1024)
        self.fc2 = nn.Linear(1024, 256)
        self.fc3 = nn.Linear(256, 10)
 
    def forward(self, x):
        x = F.relu(self.conv1(x))
        x = self.pool(F.relu(self.conv2(x)))
        x = F.relu(self.conv3(x))
        x = self.pool(F.relu(self.conv4(x)))
        x = x.view(-1, x.size()[1] * x.size()[2] * x.size()[3])
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x
  
model = Net().cpu()
 
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.01)
 
 
# 模型訓(xùn)練
def train(model, train_loader, epoch):
    model.train()
    train_loss = 0
    for i, data in enumerate(train_loader, 0):
        x, y = data
        x = x.cpu()
        y = y.cpu()
 
        optimizer.zero_grad()
        y_hat = model(x)
        loss = criterion(y_hat, y)
        loss.backward()
        optimizer.step()
        train_loss += loss
        print('正在進(jìn)行第{}個(gè)epoch中的第{}次循環(huán)'.format(epoch,i))
 
    loss_mean = train_loss / (i + 1)
    print('Train Epoch: {}\t Loss: {:.6f}'.format(epoch, loss_mean.item()))
 
 
# 模型測(cè)試
def test(model, test_loader):
    model.eval()
    test_loss = 0
    correct = 0
    with torch.no_grad():
        for i, data in enumerate(test_loader, 0):
            x, y = data
            x = x.cpu()
            y = y.cpu()
 
            optimizer.zero_grad()
            y_hat = model(x)
            test_loss += criterion(y_hat, y).item()
            pred = y_hat.max(1, keepdim=True)[1]
            correct += pred.eq(y.view_as(pred)).sum().item()
        test_loss /= (i + 1)
        print('Test set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
            test_loss, correct, len(test_data), 100. * correct / len(test_data)))
  
def main():
    # 如果test_flag=True,則加載已保存的模型并進(jìn)行測(cè)試，測(cè)試以后不進(jìn)行此模塊以后的步驟
    if test_flag:
        # 加載保存的模型直接進(jìn)行測(cè)試機(jī)驗(yàn)證
        checkpoint = torch.load(log_dir)
        model.load_state_dict(checkpoint['model'])
        optimizer.load_state_dict(checkpoint['optimizer'])
        start_epoch = checkpoint['epoch']
        test(model, test_load)
        return
 
    # 如果有保存的模型，則加載模型，并在其基礎(chǔ)上繼續(xù)訓(xùn)練
    if os.path.exists(log_dir):
        checkpoint = torch.load(log_dir)
        model.load_state_dict(checkpoint['model'])
        optimizer.load_state_dict(checkpoint['optimizer'])
        start_epoch = checkpoint['epoch']
        print('加載 epoch {} 成功！'.format(start_epoch))
    else:
        start_epoch = 0
        print('無保存了的模型，將從頭開始訓(xùn)練！')
 
    for epoch in range(start_epoch+1, epochs):
        train(model, train_load, epoch)
        test(model, test_load)
        # 保存模型
        state = {'model':model.state_dict(), 'optimizer':optimizer.state_dict(), 'epoch':epoch}
        torch.save(state, log_dir)
 
if __name__ == '__main__':
    main()