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main.py
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main.py
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'''Train 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 os.path as osp
import argparse
from models import *
from utils import progress_bar
from logger import CustomLogger
from apex import amp
import datetime
import yaml
parser = argparse.ArgumentParser(description='PyTorch Training Baseline')
# parser.add_argument('data', metavar='DIR', help='path to dataset')
parser.add_argument('--lr', default=0.1, type=float, help='learning rate')
parser.add_argument('--weight-decay', type=float, default=0.0005, help='weight decay')
parser.add_argument('--momentum', type=float, default=0.9, help='momentum')
parser.add_argument('--batch-train', type=int, default=128, help='batch size for train set')
parser.add_argument('--batch-test', type=int, default=100, help='batch size for test set')
parser.add_argument('--resume', '-r', action='store_true', help='resume from checkpoint')
parser.add_argument('--amp', action='store_true', help='train with amp')
parser.add_argument('--scheduler', action='store_true', help='train with scheduler')
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
# 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)),
])
# Data loading code
# traindir = os.path.join(args.data, 'train')
# valdir = os.path.join(args.data, 'val')
trainset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform_train)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=args.batch_train, shuffle=True, num_workers=2)
testset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transform_test)
testloader = torch.utils.data.DataLoader(testset, batch_size=args.batch_test, shuffle=False, num_workers=2)
# 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 = net.to(device)
if device == 'cuda':
net = torch.nn.DataParallel(net)
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.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=args.momentum, weight_decay=args.weight_decay)
if args.amp:
print('==> Operate amp')
net, optimizer = amp.initialize(net, optimizer, opt_level="O1")
if args.scheduler:
print('==> Operate scheduler')
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', factor=0.9, patience=1, min_lr=1e-10, verbose=True)
# logger
here = os.getcwd()
now = datetime.datetime.now()
args.out = now.strftime('%Y%m%d_%H%M%S.%f')
log_dir = osp.join(here, 'logs', args.out)
os.makedirs(log_dir)
logger = CustomLogger(out=log_dir)
# make dirs for the checkpoint
check_dir = osp.join(here, 'checkpoint', args.out)
os.makedirs(check_dir)
# for .yaml
args.dataset = ['CIFAR10']
args.optimizer = 'SGD'
args.model = 'ResNet18'
with open(osp.join(log_dir, 'config.yaml'), 'w') as f:
yaml.safe_dump(args.__dict__, f, default_flow_style=False)
# 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)
if args.amp:
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
else:
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))
# Save checkpoint. (every case)
print('Saving..')
torch.save(net.state_dict(), osp.join(check_dir, 'bengali_{}.pth'.format(epoch)))
logger.write(True, epoch, batch_idx, train_loss/(batch_idx+1), 100.*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. (only for the best case)
# 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
logger.write(False, epoch, batch_idx, test_loss/(batch_idx+1), 100.*correct/total)
return test_loss
# manually adjust learning rate
def adjust_learning_rate(optimizer, epoch, args):
"""Sets the learning rate to the initial LR decayed by 10"""
#lr = args.lr * (0.1 ** (epoch // 30))
if epoch == 0:
print('LR is set to {}'.format(args.lr))
if epoch == 30 or epoch == 60 or epoch == 80:
for param_group in optimizer.param_groups:
lr = param_group['lr'] * 0.1
param_group['lr'] = lr
print('LR is set to {}'.format(lr))
for epoch in range(start_epoch, start_epoch+100):
# adjust_learning_rate(optimizer, epoch, args)
train(epoch)
test_loss = test(epoch)
if args.scheduler:
scheduler.step(float(test_loss))