pytorch（二）——訓練自己的模型

from torch.autograd import Variable
from torchvision import transforms
from torch.utils.data import Dataset, DataLoader
from PIL import Image
root="E:/fashion_mnist/"

# -----------------ready the dataset--------------------------
def default_loader(path):
    return Image.open(path).convert('RGB')
class MyDataset(Dataset):
    def __init__(self, txt, transform=None, target_transform=None, loader=default_loader):
        fh = open(txt, 'r')
        imgs = []
        for line in fh:
            line = line.strip('\n')
            line = line.rstrip()
            words = line.split()
            if len(words)>2:
                words[0] = str((words[0]))+' '+str((words[1]))
                words[1] = words[2]
            print(len(words))

            imgs.append((words[0],int(words[1])))
            print((words[0],int(words[1])))
        self.imgs = imgs
        self.transform = transform
        self.target_transform = target_transform
        self.loader = loader

    def __getitem__(self, index):
        fn, label = self.imgs[index]
        img = self.loader(fn)
        if self.transform is not None:
            img = self.transform(img)
        return img,label

    def __len__(self):
        return len(self.imgs)


train_data=MyDataset(txt='/home/zyx/data/train1.txt', transform=transforms.ToTensor())
test_data=MyDataset(txt='/home/zyx/data/val1.txt', transform=transforms.ToTensor())
train_loader = DataLoader(dataset=train_data, batch_size=10, shuffle=True)
test_loader = DataLoader(dataset=test_data, batch_size=10)


#-----------------create the Net and training------------------------

class Net(torch.nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = torch.nn.Sequential(
            torch.nn.Conv2d(3, 32, 3, 1, 1),
            torch.nn.ReLU(),
            torch.nn.MaxPool2d(2))     #output shape(32,150,150)
        self.conv2 = torch.nn.Sequential(
            torch.nn.Conv2d(32, 64, 3, 1, 1),
            torch.nn.ReLU(),
            torch.nn.MaxPool2d(2)      #output shape(64,75,75)
        )
        self.conv3 = torch.nn.Sequential(
            torch.nn.Conv2d(64, 64, 3, 1, 1),
            torch.nn.ReLU(),
            torch.nn.MaxPool2d(2)      #output shape(64,27,27)
        )
        self.dense = torch.nn.Sequential(
            torch.nn.Linear(64 * 37 * 37, 5),
            torch.nn.ReLU(),
            torch.nn.Linear(5, 10)
        )

    def forward(self, x):
        conv1_out = self.conv1(x)
        conv2_out = self.conv2(conv1_out)
        conv3_out = self.conv3(conv2_out)
        res = conv3_out.view(conv3_out.size(0), -1)
        out = self.dense(res)
        return out


model = Net()
print(model)

optimizer = torch.optim.Adam(model.parameters())
loss_func = torch.nn.CrossEntropyLoss()

for epoch in range(5):
    print('epoch {}'.format(epoch + 1))
    # training-----------------------------
    train_loss = 0.
    train_acc = 0.
    for batch_x, batch_y in train_loader:
        batch_x, batch_y = Variable(batch_x), Variable(batch_y)
        out = model(batch_x)
        loss = loss_func(out, batch_y)
        train_loss += loss.item()
        pred = torch.max(out, 1)[1]
        train_correct = (pred == batch_y).sum()
        train_acc += train_correct.item()
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
    print('Train Loss: {:.6f}, Acc: {:.6f}'.format(train_loss / (len(
        train_data)), train_acc / (len(train_data))))

    # evaluation--------------------------------
    model.eval()
    eval_loss = 0.
    eval_acc = 0.
    for batch_x, batch_y in test_loader:
        batch_x, batch_y = Variable(batch_x, volatile=True), Variable(batch_y, volatile=True)
        out = model(batch_x)
        loss = loss_func(out, batch_y)
        eval_loss += loss.item()
        pred = torch.max(out, 1)[1]
        num_correct = (pred == batch_y).sum()
        eval_acc += num_correct.item()




    print('Test Loss: {:.6f}, Acc: {:.6f}'.format(eval_loss / (len(
        test_data)), eval_acc / (len(test_data))))

torch.save('/home/zyx/model', 'model.pkl')