Python attempt_load_model Examples

Example #1

    loss_criterion = loss_criterion.cuda()

torch.manual_seed(config['training']['random_seed'])
np.random.seed(config['training']['random_seed'])

model = models.SeqModel(
    src_vocab_size=src_vocab_size,
    tgt_vocab_size=tgt_vocab_size,
    pad_id_src=src['tok2id']['<pad>'],
    pad_id_tgt=tgt['tok2id']['<pad>'],
    config=config
)

logging.info('MODEL HAS %s params' %  model.count_params())
model, start_epoch = models.attempt_load_model(
    model=model,
    checkpoint_dir=working_dir)
if CUDA:
    model = model.cuda()

writer = SummaryWriter(working_dir)


if config['training']['optimizer'] == 'adam':
    lr = config['training']['learning_rate']
    optimizer = optim.Adam(model.parameters(), lr=lr)
elif config['training']['optimizer'] == 'sgd':
    lr = config['training']['learning_rate']
    optimizer = optim.SGD(model.parameters(), lr=lr)
else:
    raise NotImplementedError("Learning method not recommend for task")

Example #2

File: inference.py Project: shadowkiller33/delete_retrieve_generate

tgt_vocab_size = len(tgt['tok2id'])

torch.manual_seed(config['training']['random_seed'])
np.random.seed(config['training']['random_seed'])

model = models.SeqModel(
    src_vocab_size=src_vocab_size,
    tgt_vocab_size=tgt_vocab_size,
    pad_id_src=src['tok2id']['<pad>'],
    pad_id_tgt=tgt['tok2id']['<pad>'],
    config=config
)

logging.info('MODEL HAS %s params' %  model.count_params())
model, start_epoch = models.attempt_load_model(
    model=model,
    checkpoint_path=args.checkpoint)
if CUDA:
    model = model.cuda()

start = time.time()
model.eval()
dev_loss = evaluation.evaluate_lpp(
        model, src_test, tgt_test, config)

bleu, edit_distance, inputs, preds, golds, auxs = evaluation.inference_metrics(
    model, src_test, tgt_test, config)

with open(working_dir + '/auxs', 'w') as f:
    f.write('\n'.join(auxs) + '\n')
with open(working_dir + '/inputs', 'w') as f: