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train_cifar10.py
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train_cifar10.py
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# -*- coding: utf-8 -*-
'''Train CIFAR10 with PyTorch.'''
from __future__ import print_function
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import torch.backends.cudnn as cudnn
import torchvision
import torchvision.transforms as transforms
import os
import argparse
from models import *
from utils import progress_bar
torch.backends.cudnn.benchmark = False
parser = argparse.ArgumentParser(description='PyTorch CIFAR10 Training')
parser.add_argument('--lr', default=0.1, type=float, help='learning rate')
parser.add_argument('--resume', '-r', action='store_true', help='resume from checkpoint')
parser.add_argument('--net', default='res18', help='choose from res18(def), res34, res50, vgg, densenet, mobilenet, shufflenet, efficientnet, resnext')
parser.add_argument('--fp16', action='store_true')
parser.add_argument('--epoch', default=50, type=int, help='learning rate')
parser.add_argument('--bs', default=128, type=int, help='batchsize')
args = parser.parse_args()
device = 'cuda' if torch.cuda.is_available() else 'cpu'
best_acc = 0 # best test accuracy
start_epoch = 0 # start from epoch 0 or last checkpoint epoch
# you might need to change lr along with bs for good accuracy.
bs = int(args.bs)
# 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='./data', train=True, download=True, transform=transform_train)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=bs, shuffle=True, num_workers=8)
testset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transform_test)
testloader = torch.utils.data.DataLoader(testset, batch_size=100, shuffle=False, num_workers=8)
classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')
# Model
print('==> Building model..')
# net = VGG('VGG19')
if args.net=='res18':
net = ResNet18()
elif args.net=='vgg':
net = VGG('VGG19')
elif args.net=='res34':
net = ResNet34()
elif args.net=='res50':
net = ResNet50()
elif args.net=='res101':
net = ResNet101()
# net = PreActResNet18()
# net = GoogLeNet()
elif args.net=='densenet':
net = DenseNet121()
elif args.net=='resnext':
net = ResNeXt29_4x64d()
# net = MobileNet()
elif args.net=='mobilenet':
net = MobileNetV2()
# net = DPN92()
elif args.net=='shufflenet':
net = ShuffleNetv2()
elif args.net=='efficientnet':
net = Efficientnet()
else:
print("{} not found").format(args.net)
# net = SENet18()
net = net.to(device)
if args.fp16:
from fp16util import network_to_half
net = network_to_half(net)
if device == 'cuda':
net = torch.nn.DataParallel(net) # make parallel
cudnn.benchmark = True
if args.resume:
# Load checkpoint.
print('==> Resuming from checkpoint..')
assert os.path.isdir('checkpoint'), 'Error: no checkpoint directory found!'
checkpoint = torch.load('./checkpoint/ckpt.t7')
net.load_state_dict(checkpoint['net'])
best_acc = checkpoint['acc']
start_epoch = checkpoint['epoch']
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=args.lr, momentum=0.9, weight_decay=1e-4)
# Training
def train(epoch):
print('\nEpoch: %d' % epoch)
net.train()
train_loss = 0
correct = 0
total = 0
for batch_idx, (inputs, targets) in enumerate(trainloader):
inputs, targets = inputs.to(device), targets.to(device)
optimizer.zero_grad()
outputs = net(inputs)
loss = criterion(outputs, targets)
loss.backward()
optimizer.step()
train_loss += loss.item()
_, predicted = outputs.max(1)
total += targets.size(0)
correct += predicted.eq(targets).sum().item()
progress_bar(batch_idx, len(trainloader), 'Loss: %.3f | Acc: %.3f%% (%d/%d)'
% (train_loss/(batch_idx+1), 100.*correct/total, correct, total))
return train_loss/(batch_idx+1)
def test(epoch):
global best_acc
net.eval()
test_loss = 0
correct = 0
total = 0
with torch.no_grad():
for batch_idx, (inputs, targets) in enumerate(testloader):
inputs, targets = inputs.to(device), targets.to(device)
outputs = net(inputs)
loss = criterion(outputs, targets)
test_loss += loss.item()
_, predicted = outputs.max(1)
total += targets.size(0)
correct += predicted.eq(targets).sum().item()
progress_bar(batch_idx, len(testloader), 'Loss: %.3f | Acc: %.3f%% (%d/%d)'
% (test_loss/(batch_idx+1), 100.*correct/total, correct, total))
# Save checkpoint.
acc = 100.*correct/total
if acc > best_acc:
print('Saving..')
state = {
'net': net.state_dict(),
'acc': acc,
'epoch': epoch,
}
if not os.path.isdir('checkpoint'):
os.mkdir('checkpoint')
if not args.fp16:
torch.save(state, './checkpoint/'+args.net+'-ckpt.tar')
else:
torch.save(state, './checkpoint/'+args.net+'-'+'FP16-ckpt.tar')
best_acc = acc
list_loss = []
for epoch in range(start_epoch, start_epoch+int(args.epoch)):
trainloss = train(epoch)
test(epoch)
list_loss.append(trainloss)
print(list_loss)
print(list_loss)