args.checkpoint_dir = os.path.join(args.checkpoint_dir,
args.time_id + '_' + args.identifier)
args.log_dir = os.path.join(args.log_dir,
args.time_id + '_' + args.identifier)
print(args.__dict__)
if not os.path.exists(args.checkpoint_dir) and args.log == True:
os.makedirs(args.checkpoint_dir)
os.makedirs(args.log_dir)
model_kwargs = {'vocab': load_vocab(args.vocab_json)}
shared_model = VqaLstmCnnAttentionModel(**model_kwargs)
if args.checkpoint_path != False:
print('Loading params from checkpoint: %s' % args.checkpoint_path)
shared_model.load_state_dict(checkpoint['state'])
shared_model.share_memory()
fgsm(0, args, shared_model, 0)
torch.cuda.empty_cache()
shared_nav_model.share_memory()
print('Loading navigation params from checkpoint: %s' %
args.nav_checkpoint_path)
shared_nav_model.load_state_dict(checkpoint['state'])
# Load answering model
print('Loading answering checkpoint from %s' % args.ans_checkpoint_path)
ans_checkpoint = torch.load(args.ans_checkpoint_path,
map_location={'cuda:0': 'cpu'})
ans_model_kwargs = {'vocab': load_vocab(args.vocab_json)}
shared_ans_model = VqaLstmCnnAttentionModel(**ans_model_kwargs)
shared_ans_model.share_memory()
print('Loading params from checkpoint: %s' % args.ans_checkpoint_path)
shared_ans_model.load_state_dict(ans_checkpoint['state'])
if args.mode == 'eval':
eval(0, args, shared_nav_model, shared_ans_model)
elif args.mode == 'train':
train(0, args, shared_nav_model, shared_ans_model)
else:
processes = []
print('Loading navigation params from checkpoint: %s' %
args.nav_checkpoint_path)
shared_nav_model.load_state_dict(checkpoint['state'])
# Load answering model
print('Loading answering checkpoint from %s' % args.ans_checkpoint_path)
ans_checkpoint = torch.load(
args.ans_checkpoint_path, map_location={
'cuda:0': 'cpu'
})
ans_model_kwargs = {'vocab': load_vocab(args.vocab_json)}
shared_ans_model = VqaLstmCnnAttentionModel(**ans_model_kwargs)
shared_ans_model.share_memory()
print('Loading params from checkpoint: %s' % args.ans_checkpoint_path)
shared_ans_model.load_state_dict(ans_checkpoint['state'])
if args.mode == 'eval':
eval(0, args, shared_nav_model, shared_ans_model)
elif args.mode == 'train':
train(0, args, shared_nav_model, shared_ans_model)
else:
processes = []