def main():
args = parse_args()
# Set the GPU to use
torch.cuda.set_device(args.gpu)
annotations = osp.expanduser(args.annotations)
questions = osp.expanduser(args.questions)
vqa_loader = dataset.get_train_dataloader(annotations, questions,
args.images, args)
# We always use the vocab from the training set
vocab = vqa_loader.dataset.vocab
maps = {
"word_to_wid": vqa_loader.dataset.word_to_wid,
"wid_to_word": vqa_loader.dataset.wid_to_word,
"ans_to_aid": vqa_loader.dataset.ans_to_aid,
"aid_to_ans": vqa_loader.dataset.aid_to_ans,
}
val_loader = dataset.get_val_dataloader(osp.expanduser(
args.val_annotations),
osp.expanduser(args.val_questions),
args.val_images,
args,
maps=maps,
vocab=vocab,
shuffle=False)
arch = Models[args.arch].value
model = arch(len(vocab), output_dim=args.top_answer_limit)
if torch.cuda.is_available():
model.cuda()
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(),
lr=args.lr,
betas=tuple(args.betas),
weight_decay=args.weight_decay)
scheduler = lr_scheduler.StepLR(optimizer,
step_size=args.decay_interval,
gamma=args.lr_decay)
vis = visualize.Visualizer(args.port)
print("Beginning training")
print("#" * 80)
for epoch in range(args.start_epoch, args.epochs):
scheduler.step()
trainer.train(model,
vqa_loader,
criterion,
optimizer,
epoch,
args,
vis=vis)
trainer.evaluate(model, val_loader, criterion, epoch, args, vis=vis)
print("Training complete!")
def main():
args = parse_args()
# Set the GPU to use
torch.cuda.set_device(args.gpu)
transform = transforms.Compose([
transforms.Resize(256),
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
vqa_loader = dataset.get_train_dataloader(osp.expanduser(args.annotations),
osp.expanduser(args.questions),
args.images,
args,
raw_images=args.raw_images,
transforms=transform)
# We always use the vocab from the training set
vocab = vqa_loader.dataset.vocab
maps = {
"vocab": vocab,
"word_to_wid": vqa_loader.dataset.word_to_wid,
"wid_to_word": vqa_loader.dataset.wid_to_word,
"ans_to_aid": vqa_loader.dataset.ans_to_aid,
"aid_to_ans": vqa_loader.dataset.aid_to_ans,
}
val_transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
val_loader = dataset.get_val_dataloader(osp.expanduser(
args.val_annotations),
osp.expanduser(args.val_questions),
args.val_images,
args,
raw_images=args.raw_images,
maps=maps,
vocab=vocab,
shuffle=False,
transforms=val_transform)
arch = Models[args.arch].value
model = arch(len(vocab),
output_dim=args.top_answer_limit,
raw_images=args.raw_images)
if args.resume:
state = torch.load(args.resume)
model.load_state_dict(state["model"])
model.cuda()
criterion = nn.CrossEntropyLoss().cuda()
optimizer = optim.Adam(model.parameters(),
lr=args.lr,
betas=tuple(args.betas),
weight_decay=args.weight_decay)
scheduler = lr_scheduler.StepLR(optimizer,
step_size=args.decay_interval,
gamma=args.lr_decay)
if args.visualize:
vis = visualize.Visualizer(args.port)
else:
vis = None
print("Beginning training")
print("#" * 80)
for epoch in range(args.start_epoch, args.epochs):
scheduler.step()
trainer.train(model,
vqa_loader,
criterion,
optimizer,
epoch,
args,
vis=vis)
trainer.evaluate(model, val_loader, criterion, epoch, args, vis=vis)
print("Training complete!")
def main():
args = parse_args()
# Set the GPU to use
torch.cuda.set_device(args.gpu)
vqa_loader = dataset.get_train_dataloader(osp.expanduser(args.annotations),
osp.expanduser(args.questions),
args.images,
args,
raw_images=args.raw_images,
transforms=None)
# We always use the vocab from the training set
vocab = vqa_loader.dataset.vocab
maps = {
"vocab": vocab,
"word_to_wid": vqa_loader.dataset.word_to_wid,
"wid_to_word": vqa_loader.dataset.wid_to_word,
"ans_to_aid": vqa_loader.dataset.ans_to_aid,
"aid_to_ans": vqa_loader.dataset.aid_to_ans,
}
val_transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
val_loader = dataset.get_val_dataloader(osp.expanduser(
args.val_annotations),
osp.expanduser(args.val_questions),
args.val_images,
args,
raw_images=args.raw_images,
maps=maps,
vocab=vocab,
shuffle=False,
transforms=val_transform)
arch = Models[args.arch].value
model = arch(len(vocab),
output_dim=args.top_answer_limit,
raw_images=args.raw_images)
if args.resume:
state = torch.load(args.resume)
model.load_state_dict(state["model"])
else:
print(
"No trained model weights provided. Don't expect the answers to be meaningful."
)
if torch.cuda.is_available():
model.cuda()
with torch.no_grad():
results = evaluate(model, val_loader)
for k in results.keys():
results[k] = np.asarray(results[k])
acc = results[k].sum() / results[k].shape
print("Accuracy for {0} type answers: \t\t{1}".format(k, acc))