def main():
global args
args = parser.parse_args()
if args.save is '':
args.save = datetime.now().strftime('%Y-%m-%d_%H-%M-%S')
save_path = os.path.join(args.results_dir, args.save)
if not os.path.exists(save_path):
os.makedirs(save_path)
setup_logging(os.path.join(save_path, 'log.txt'))
checkpoint_file = os.path.join(save_path, 'checkpoint_epoch_%s.pth.tar')
logging.debug("run arguments: %s", args)
logging.info("using pretrained cnn %s", args.cnn)
cnn = resnet.__dict__[args.cnn](pretrained=True)
vocab = build_vocab()
model = CaptionModel(cnn, vocab,
embedding_size=args.embedding_size,
rnn_size=args.rnn_size,
num_layers=args.num_layers,
share_embedding_weights=args.share_weights)
train_data = get_iterator(get_coco_data(vocab, train=True),
batch_size=args.batch_size,
max_length=args.max_length,
shuffle=True,
num_workers=args.workers)
val_data = get_iterator(get_coco_data(vocab, train=False),
batch_size=args.eval_batch_size,
max_length=args.max_length,
shuffle=False,
num_workers=args.workers)
if 'cuda' in args.type:
cudnn.benchmark = True
model.cuda()
optimizer = select_optimizer(
args.optimizer, params=model.parameters(), lr=args.lr)
regime = lambda e: {'lr': args.lr * (args.lr_decay ** e),
'momentum': args.momentum,
'weight_decay': args.weight_decay}
model.finetune_cnn(False)
def forward(model, data, training=True, optimizer=None):
use_cuda = 'cuda' in args.type
loss = nn.CrossEntropyLoss()
perplexity = AverageMeter()
batch_time = AverageMeter()
data_time = AverageMeter()
if training:
model.train()
else:
model.eval()
end = time.time()
for i, (imgs, (captions, lengths)) in enumerate(data):
data_time.update(time.time() - end)
if use_cuda:
imgs = imgs.cuda()
captions = captions.cuda(async=True)
imgs = Variable(imgs, volatile=not training)
captions = Variable(captions, volatile=not training)
input_captions = captions[:-1]
target_captions = pack_padded_sequence(captions, lengths)[0]
pred, _ = model(imgs, input_captions, lengths)
err = loss(pred, target_captions)
perplexity.update(math.exp(err.data[0]))
if training:
optimizer.zero_grad()
err.backward()
clip_grad_norm(model.rnn.parameters(), args.grad_clip)
optimizer.step()
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if i % args.print_freq == 0:
logging.info('{phase} - Epoch: [{0}][{1}/{2}]\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Data {data_time.val:.3f} ({data_time.avg:.3f})\t'
'Perplexity {perp.val:.4f} ({perp.avg:.4f})'.format(
epoch, i, len(data),
phase='TRAINING' if training else 'EVALUATING',
batch_time=batch_time,
data_time=data_time, perp=perplexity))
return perplexity.avg
for epoch in range(args.start_epoch, args.epochs):
if epoch >= args.finetune_epoch:
model.finetune_cnn(True)
optimizer = adjust_optimizer(
optimizer, epoch, regime)
# Train
train_perp = forward(
model, train_data, training=True, optimizer=optimizer)
# Evaluate
val_perp = forward(model, val_data, training=False)
logging.info('\n Epoch: {0}\t'
'Training Perplexity {train_perp:.4f} \t'
'Validation Perplexity {val_perp:.4f} \n'
.format(epoch + 1, train_perp=train_perp, val_perp=val_perp))
model.save_checkpoint(checkpoint_file % (epoch + 1))
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Data {data_time.val:.3f} ({data_time.avg:.3f})\t'
'Perplexity {perp.val:.4f} ({perp.avg:.4f})'.format(
epoch,
i,
len(data),
phase='TRAINING' if training else 'EVALUATING',
batch_time=batch_time,
data_time=data_time,
perp=perplexity))
return perplexity.avg
for epoch in range(start_epoch, epochs):
if epoch >= finetune_epoch:
model.finetune_cnn(True)
optimizer = adjust_optimizer(optimizer, epoch, regime)
# Train
train_perp = forward(model,
trainDataLoader,
training=True,
optimizer=optimizer)
# Evaluate
val_perp = forward(model, valDataLoader, training=False)
logging.info('\n Epoch: {0}\t'
'Training Perplexity {train_perp:.4f} \t'
'Validation Perplexity {val_perp:.4f} \n'.format(
epoch + 1, train_perp=train_perp, val_perp=val_perp))
model.save_checkpoint(checkpoint_file % (epoch + 1))