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train.py
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train.py
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import os
import shutil
import argparse
import torch
import torch.nn as nn
import torchvision.models as models
from torchvision import transforms, utils
import torch.nn.functional as F
from dataset import loadedDataset
from model import LSTMModel
from utils import AverageMeter
parser = argparse.ArgumentParser(description = 'Training')
parser.add_argument('--model', default='./save_model/', type=str, help = 'path to model')
parser.add_argument('--arch', default = 'resnet50', help = 'model architecture')
parser.add_argument('--lstm-layers', default=2, type=int, help='number of lstm layers')
parser.add_argument('--hidden-size', default=512, type=int, help='output size of LSTM hidden layers')
parser.add_argument('--fc-size', default=1024, type=int, help='size of fully connected layer before LSTM')
parser.add_argument('--epochs', default=200, type=int, help='manual epoch number')
parser.add_argument('--lr', default=1e-4, type=float, help='initial learning rate')
parser.add_argument('--lr-step', default=100, type=float, help='learning rate decay frequency')
parser.add_argument('--batch-size', default=8, type=int, help='mini-batch size')
parser.add_argument('--workers', default=8, type=int, help='number of data loading workers')
args = parser.parse_args()
def save_checkpoint(state, is_best, filename='checkpoint.pth.tar'):
torch.save(state, os.path.join('./save_model/', filename))
if is_best:
shutil.copyfile(os.path.join('./save_model/', filename), './save_model/model_best.pth.tar')
def adjust_learning_rate(optimizer, epoch):
if not epoch % args.lr_step and epoch:
for param_group in optimizer.param_groups:
param_group['lr'] = param_group['lr'] * 0.1
return optimizer
def accuracy(output, target, topk=(1,)):
maxk = max(topk)
batch_size = target.size(0)
_, pred = output.topk(maxk, 1, True, True)
pred = pred.t()
correct = pred.eq(target.view(1, -1).expand_as(pred))
res = []
for k in topk:
correct_k = correct[:k].view(-1).float().sum(0, keepdim=True)
res.append(correct_k.mul_(100.0 / batch_size))
return res
def train(train_loader, model, criterion, optimizer, epoch):
losses = AverageMeter()
top1 = AverageMeter()
top5 = AverageMeter()
model.train() # switch to train mode
for i, (inputs, target, _) in enumerate(train_loader):
input_var = [input.cuda() for input in inputs]
target_var = target.cuda()
# compute output
output = model(input_var)
output = output[:, -1, :]
loss = criterion(output, target_var)
losses.update(loss.item(), 1)
# compute accuracy
prec1, prec5 = accuracy(output.data.cpu(), target, topk=(1, 5))
top1.update(prec1[0].item(), 1)
top5.update(prec5[0].item(), 1)
# zero the parameter gradients
optimizer.zero_grad()
# compute gradient
loss.backward()
optimizer.step()
print('Epoch: [{0}][{1}/{2}]\t'
'lr {lr:.5f}\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
'Top1 {top1.val:.3f} ({top1.avg:.3f})\t'
'Top5 {top5.val:.3f} ({top5.avg:.3f})'.format(
epoch, i, len(train_loader),
lr=optimizer.param_groups[-1]['lr'],
loss=losses,
top1=top1,
top5=top5))
def validate(val_loader, model, criterion):
losses = AverageMeter()
top1 = AverageMeter()
top5 = AverageMeter()
# switch to evaluate mode
model.eval()
for i, (inputs, target, _) in enumerate(val_loader):
input_var = [input.cuda() for input in inputs]
target_var = target.cuda()
# compute output
with torch.no_grad():
output = model(input_var)
output = output[:, -1, :]
loss = criterion(output, target_var)
losses.update(loss.item(), 1)
# compute accuracy
prec1, prec5 = accuracy(output.data.cpu(), target, topk=(1, 5))
top1.update(prec1[0].item(), 1)
top5.update(prec5[0].item(), 1)
print ('Test: [{0}/{1}]\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
'Top1 {top1.val:.3f} ({top1.avg:.3f})\t'
'Top5 {top5.val:.3f} ({top5.avg:.3f})'.format(
i, len(val_loader),
loss=losses,
top1=top1,
top5=top5))
return (top1.avg, top5.avg)
if __name__ == '__main__':
# Data Transform and data loading
traindir = './data/train/'
valdir = './data/valid/'
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.339, 0.224, 0.225])
transform = (transforms.Compose([
transforms.Resize(224),
transforms.CenterCrop(224),
transforms.ToTensor(),
normalize]
),
transforms.Compose([
transforms.Resize(224),
transforms.CenterCrop(224),
transforms.ToTensor()]
)
)
train_dataset = loadedDataset(traindir, transform)
val_dataset = loadedDataset(valdir, transform)
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=args.batch_size, shuffle=True,
num_workers=args.workers, pin_memory=True)
val_loader = torch.utils.data.DataLoader(
val_dataset,
batch_size=args.batch_size, shuffle=False,
num_workers=args.workers, pin_memory=True)
if os.path.exists(os.path.join(args.model, 'checkpoint.pth.tar')):
# load existing model
model_info = torch.load(os.path.join(args.model, 'checkpoint.pth.tar'))
print("==> loading existing model '{}' ".format(model_info['arch']))
original_model = models.__dict__[model_info['arch']](pretrained=False)
model = LSTMModel(original_model, model_info['arch'],
model_info['num_classes'], model_info['lstm_layers'], model_info['hidden_size'], model_info['fc_size'])
# print(model)
model.cuda()
model.load_state_dict(model_info['state_dict'])
best_prec = model_info['best_prec']
cur_epoch = model_info['epoch']
else:
if not os.path.isdir(args.model):
os.makedirs(args.model)
# load and create model
print("==> creating model '{}' ".format(args.arch))
original_model = models.__dict__[args.arch](pretrained=True)
model = LSTMModel(original_model, args.arch,
len(train_dataset.classes), args.lstm_layers, args.hidden_size, args.fc_size)
# print(model)
model.cuda()
cur_epoch = 0
# loss criterion and optimizer
criterion = nn.CrossEntropyLoss()
criterion = criterion.cuda()
optimizer = torch.optim.Adam([{'params': model.fc_pre.parameters()},
{'params': model.rnn.parameters()},
{'params': model.fc.parameters()}],
lr=args.lr)
best_prec = 0
# Training on epochs
for epoch in range(cur_epoch, args.epochs):
optimizer = adjust_learning_rate(optimizer, epoch)
print("---------------------------------------------------Training---------------------------------------------------")
# train on one epoch
train(train_loader, model, criterion, optimizer, epoch)
print("--------------------------------------------------Validation--------------------------------------------------")
# evaluate on validation set
prec1, prec5 = validate(val_loader, model, criterion)
print("------Validation Result------")
print(" Top1 accuracy: {prec: .2f} %".format(prec=prec1))
print(" Top5 accuracy: {prec: .2f} %".format(prec=prec5))
print("-----------------------------")
# remember best top1 accuracy and save checkpoint
is_best = prec1 > best_prec
best_prec = max(prec1, best_prec)
save_checkpoint({
'epoch': epoch + 1,
'arch': args.arch,
'num_classes': len(train_dataset.classes),
'lstm_layers': args.lstm_layers,
'hidden_size': args.hidden_size,
'fc_size': args.fc_size,
'state_dict': model.state_dict(),
'best_prec': best_prec,
'optimizer' : optimizer.state_dict(),}, is_best)