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main.py
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main.py
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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.models as models
import torchvision
import torchvision.transforms as transforms
from torch.utils.tensorboard import SummaryWriter
import os
import argparse
#from models import *
from utils import progress_bar
writer = SummaryWriter()
parser = argparse.ArgumentParser(description='PyTorch CIFAR10 Training')
parser.add_argument('--lr', default=0.01, type=float, help='learning rate')
parser.add_argument('--name', default='best', type=str, help='name of the file')
parser.add_argument('--epoch', default=100, type=int, help='num of epoch')
parser.add_argument('--resume', '-r', action='store_true',
help='resume from checkpoint')
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
print('==> Preparing data..')
transform_train = transforms.Compose([
transforms.RandomCrop(size=[224,224], padding=30),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
])
transform_test = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
])
dataset_train = torchvision.datasets.ImageFolder(root=os.getcwd() + '/dataset/train', transform=transform_train)
trainloader = torch.utils.data.DataLoader(dataset_train, batch_size=5, shuffle=True, num_workers=2)
dataset_test = torchvision.datasets.ImageFolder(root=os.getcwd() + '/dataset/test', transform=transform_test)
testloader = torch.utils.data.DataLoader(dataset_train, batch_size=5, shuffle=False, num_workers=2)
classes = ('Gingivitis', 'Normal')
print('==> Building model..')
net = models.resnet34()
num_ftrs = net.fc.in_features
net.fc = nn.Linear(num_ftrs, 2)
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/' + args.name + '.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
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))
writer.add_scalar('Loss/train', train_loss/(batch_idx+1), epoch)
writer.add_scalar('Accuracy/train', 100.*correct/total, epoch)
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))
writer.add_scalar('Loss/test', test_loss / (batch_idx + 1), epoch)
writer.add_scalar('Accuracy/test', 100. * correct / total, epoch)
# 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/' + args.name + '.pth')
best_acc = acc
for epoch in range(start_epoch, start_epoch + args.epoch):
train(epoch)
test(epoch)
scheduler.step()