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@twmht
Created January 18, 2021 05:56
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'''Train CIFAR10 with PyTorch.'''
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
parser = argparse.ArgumentParser(description='PyTorch CIFAR10 Training')
parser.add_argument('--lr', default=0.1, type=float, help='learning rate')
parser.add_argument('--ngpu', default=4, type=float, help='learning rate')
parser.add_argument('--local_rank', default=0, type=int, help='learning rate')
parser.add_argument('--resume', '-r', action='store_true',
help='resume from checkpoint')
args = parser.parse_args()
world_size = args.ngpu
torch.distributed.init_process_group(
'nccl',
init_method='tcp://localhost:12355',
world_size=world_size,
rank=args.local_rank,
)
# seed = 133
# random.seed(seed)
# np.random.seed(seed)
# torch.cuda.manual_seed_all(seed)
# torch.backends.cudnn.deteministic = True
# torch.backends.cudnn.benchamrk = False
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
# 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=128, shuffle=True, num_workers=2)
sampler = torch.utils.data.distributed.DistributedSampler(
trainset,
num_replicas=args.ngpu,
rank=args.local_rank,
)
trainloader = torch.utils.data.DataLoader(
trainset,
batch_size=32,
num_workers=1, pin_memory=True, drop_last=True, shuffle=False, sampler=sampler)
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=2)
classes = ('plane', 'car', 'bird', 'cat', 'deer',
'dog', 'frog', 'horse', 'ship', 'truck')
# Model
print('==> Building model..')
# net = VGG('VGG19')
# net = ResNet18()
# net = PreActResNet18()
# net = GoogLeNet()
# net = DenseNet121()
# net = ResNeXt29_2x64d()
# net = MobileNet()
net = MobileNetV2()
# net = DPN92()
# net = ShuffleNetG2()
# net = SENet18()
# net = ShuffleNetV2(1)
# net = EfficientNetB0()
# net = RegNetX_200MF()
# net = SimpleDLA()
net = net.to(device)
# if device == 'cuda':
# net = torch.nn.DataParallel(net)
# cudnn.benchmark = True
# net = torch.nn.DataParallel(net)
if args.resume:
# Load checkpoint.
print('==> Resuming from checkpoint..')
assert os.path.isdir('checkpoint'), 'Error: no checkpoint directory found!'
checkpoint = torch.load('./checkpoint/ckpt.pth')
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=5e-4)
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=200)
# Training
def train(epoch):
print('\nEpoch: %d' % epoch)
net.train()
train_loss = 0
correct = 0
total = 0
sampler.set_epoch(epoch)
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()
if args.local_rank == 0:
progress_bar(batch_idx, len(trainloader), 'Loss: %.3f | Acc: %.3f%% (%d/%d)'
% (train_loss/(batch_idx+1), 100.*correct/total, correct, total))
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')
torch.save(state, './checkpoint/ckpt.pth')
best_acc = acc
for epoch in range(start_epoch, start_epoch+200):
train(epoch)
if args.local_rank == 0:
test(epoch)
# torch.distributed.barrier()
scheduler.step()
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