if __name__ == '__main__':
# get configs
args = get_eval_args()
print(args)
# load data
dm = DataManager(args)
# prepare model
model = init_model(args, dm)
model = model.cuda()
model.load_state_dict(torch.load(args.model_path))
# split data
_, _, test_data = dm.split()
test_loader = DataLoader(test_data,
batch_size=args.batch_size,
num_workers=args.num_workers,
collate_fn=partial(
collate_fn,
split='test',
padding_idx=dm.word2idx['<pad>']))
print('Start Video-Captioning Evaluation')
test_score = evaluate(0,
model,
test_loader,
dm,
maxlen=args.max_sent_len,
split='Test',
# load data
dm = DataManager(args)
# prepare model
model = init_model(args, dm)
model = model.cuda()
# prepare training
optimizer = Adam(lr=args.lr, params=model.parameters())
schedule = ExponentialLR(optimizer, args.lr_decay)
loss_fn = NLLLossWithLength(ignore_index=dm.word2idx['<pad>'])
# loss_fn = nn.NLLLoss(ignore_index=dm.word2idx['<pad>'])
# split data
train_data, val_data, test_data = dm.split()
train_loader = DataLoader(train_data, batch_size=args.batch_size, shuffle=True, num_workers=args.num_workers,
collate_fn=partial(collate_fn, split='train', padding_idx=dm.word2idx['<pad>']))
val_loader = DataLoader(val_data, batch_size=args.batch_size, num_workers=args.num_workers,
collate_fn=partial(collate_fn, split='val', padding_idx=dm.word2idx['<pad>']))
test_loader = DataLoader(test_data, batch_size=args.batch_size, num_workers=args.num_workers,
collate_fn=partial(collate_fn, split='test', padding_idx=dm.word2idx['<pad>']))
print('Start Video-Captioning Training')
best_cider = 0
best_epoch = -1
best_score = None
max_epoch = args.n_epoch
for i in range(max_epoch):
model.schedule_sample_prob = args.schedule_sample_prob + i * args.schedule_sample_ratio