from __future__ import print_function
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms, models
def train(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 % 100 == 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(model, device, test_loader):
model.eval()
test_loss = 0
correct = 0
with torch.no_grad():
for data, target in test_loader:
data, target = data.to(device), target.to(device)
output = model(data)
test_loss += F.nll_loss(output, target, reduction='sum').item() # sum up batch loss
pred = output.argmax(dim=1, keepdim=True) # 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():
# 0. Settings
torch.manual_seed(100)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
batch_size = 32
epochs = 20
lr = 0.01
momentum = 0.90
# 1. Dataset
dataset_train = datasets.CIFAR10('../data', train=True, download=True,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
]))
dataset_test = datasets.CIFAR10('../data', train=False, download=True,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
]))
train_loader = torch.utils.data.DataLoader(dataset_train, batch_size=batch_size, shuffle=True, num_workers=4)
test_loader = torch.utils.data.DataLoader(dataset_test, batch_size=batch_size, shuffle=True, num_workers=4)
# 2. Model
model = models.resnet18(pretrained=True, progress=True) # 从模型库中调用ResNet18
print(model)
# num_ftrs = model.fc.in_features
# model.fc = nn.Linear(num_ftrs, 10) # 得到新的模型,更改fc层
# model = model.to(device)
# print(model)
# 3. Optimizer
optimizer = optim.SGD(model.parameters(), lr=lr, momentum=momentum)
# 4. Training
for epoch in range(epochs):
train(model, device, train_loader, optimizer, epoch)
test(model, device, test_loader)
# save the current model
if (epoch + 1) % 10 == 0:
torch.save(model, "train_cnn_%3d.pt" % (epoch + 1))
if __name__ == '__main__':
main()
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