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train.py
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train.py
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import os
import argparse
import torch
import torchvision
import torch.nn as nn
import torch.optim as optim
import torchvision.transforms as transforms
from models.resnet_cifar import resnet18
# parser
parser = argparse.ArgumentParser(description='PyTorch CIFAR10 Training')
parser.add_argument('--epochs', default=50, type=int, metavar='N',
help='number of total epochs to run')
parser.add_argument('-b', '--batch_size', default=128, type=int,
metavar='N', help='mini-batch size (default: 128)')
parser.add_argument('--lr', '--learning_rate', default=0.01, type=float,
metavar='LR', help='initial learning rate')
parser.add_argument('--resume', action='store_true', default=True,
help='resume training')
args = parser.parse_args()
net = resnet18()
# Data
print('==> Preparing data..')
transform_train = transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
])
transform_test = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
])
trainset = torchvision.datasets.CIFAR10(root='/home/liyanyu/cifar/data', train=True, download=False,
transform=transform_train)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=128, shuffle=True, num_workers=4)
testset = torchvision.datasets.CIFAR10(root='/home/liyanyu/cifar/data', train=False, download=False,
transform=transform_test)
testloader = torch.utils.data.DataLoader(testset, batch_size=128, shuffle=False, num_workers=4)
classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')
NUM_CLASSES = 10
# Device
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
def main():
net.to(device)
if args.resume:
net.load_state_dict(torch.load('checkpoints/resnet_4bit_quantized_cifar10_acc_92.35.pt'))
torch.backends.cudnn.benchmark = True
criterion = nn.CrossEntropyLoss()
acc = 0
for epoch in range(args.epochs): # loop over the dataset multiple times
learning_rate = args.lr * (0.1 ** (epoch // 20))
optimizer = optim.SGD(net.parameters(), lr=learning_rate, momentum=0.9, weight_decay=1e-4)
running_loss = 0.0
print('learning rate:', learning_rate)
for i, data in enumerate(trainloader, 0):
# get the inputs; data is a list of [inputs, labels]
inputs, labels = data[0].to(device), data[1].to(device)
# zero the parameter gradients
optimizer.zero_grad()
# forward + backward + optimize
outputs = net(inputs)
# regularization for clipping a
# decay = 1e-4
# l2_reg = torch.tensor(0.0, requires_grad=True).cuda()
# for name, param in net.named_parameters():
# if '.a' in name:
# l2_reg = l2_reg + torch.norm(param)
loss = criterion(outputs, labels)
# loss = loss + decay * l2_reg
loss.backward()
optimizer.step()
# print statistics
running_loss += loss.item()
if i % 50 == 49: # print every 2000 mini-batches
print('[%d, %5d] loss: %.3f' %
(epoch + 1, i + 1, running_loss / 50))
running_loss = 0.0
# report test accuracy every epoch
total = 0
correct = 0
with torch.no_grad():
for data in testloader:
images, labels = data[0].to(device), data[1].to(device)
outputs = net(images)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
accuracy = 100 * float(correct) / float(total)
print('Accuracy on 10000 test images: %.2f %%' % accuracy)
if accuracy > acc:
acc = accuracy
path = 'checkpoints/{arch}_{type}_quantized_cifar10_acc_{prec1:.2f}.pt' \
.format(arch='resnet', type='4bit', prec1=acc)
torch.save(net.state_dict(), path)
print('Current best accuracy: %.2f %%' % acc)
print('Finished Training')
if __name__ == "__main__":
main()