Python models.EncoderRNN() Examples

The following are 5 code examples of models.EncoderRNN(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module models , or try the search function .
Example #1
Source File: eval.py    From video-caption.pytorch with MIT License 6 votes vote down vote up 
def main(opt):
    dataset = VideoDataset(opt, "test")
    opt["vocab_size"] = dataset.get_vocab_size()
    opt["seq_length"] = dataset.max_len
    if opt["model"] == 'S2VTModel':
        model = S2VTModel(opt["vocab_size"], opt["max_len"], opt["dim_hidden"], opt["dim_word"],
                          rnn_dropout_p=opt["rnn_dropout_p"]).cuda()
    elif opt["model"] == "S2VTAttModel":
        encoder = EncoderRNN(opt["dim_vid"], opt["dim_hidden"], bidirectional=opt["bidirectional"],
                             input_dropout_p=opt["input_dropout_p"], rnn_dropout_p=opt["rnn_dropout_p"])
        decoder = DecoderRNN(opt["vocab_size"], opt["max_len"], opt["dim_hidden"], opt["dim_word"],
                             input_dropout_p=opt["input_dropout_p"],
                             rnn_dropout_p=opt["rnn_dropout_p"], bidirectional=opt["bidirectional"])
        model = S2VTAttModel(encoder, decoder).cuda()
    #model = nn.DataParallel(model)
    # Setup the model
    model.load_state_dict(torch.load(opt["saved_model"]))
    crit = utils.LanguageModelCriterion()

    test(model, crit, dataset, dataset.get_vocab(), opt)
Example #2
Source File: eval.py    From video-caption.pytorch with MIT License 5 votes vote down vote up 
def main(opt):
    dataset = VideoDataset(opt, "test")
    opt["vocab_size"] = dataset.get_vocab_size()
    opt["seq_length"] = dataset.max_len
    if opt['beam_size'] != 1:
        assert opt["batch_size"] == 1
    if opt["model"] == 'S2VTModel':
        model = S2VTModel(opt["vocab_size"], opt["max_len"], opt["dim_hidden"], opt["dim_word"], opt['dim_vid'],
                          n_layers=opt['num_layers'],
                          rnn_cell=opt['rnn_type'],
                          bidirectional=opt["bidirectional"],
                          rnn_dropout_p=opt["rnn_dropout_p"]).cuda()
    elif opt["model"] == "S2VTAttModel":
        encoder = EncoderRNN(opt["dim_vid"], opt["dim_hidden"],
                             n_layers=opt['num_layers'],
                             rnn_cell=opt['rnn_type'], bidirectional=opt["bidirectional"],
                             input_dropout_p=opt["input_dropout_p"], rnn_dropout_p=opt["rnn_dropout_p"])
        decoder = DecoderRNN(opt["vocab_size"], opt["max_len"], opt["dim_hidden"], opt["dim_word"],
                             n_layers=opt['num_layers'],
                             rnn_cell=opt['rnn_type'], input_dropout_p=opt["input_dropout_p"],
                             rnn_dropout_p=opt["rnn_dropout_p"], bidirectional=opt["bidirectional"])
        model = S2VTAttModel(encoder, decoder).cuda()
    model = nn.DataParallel(model)
    # Setup the model
    model.load_state_dict(torch.load(opt["saved_model"]))
    crit = utils.LanguageModelCriterion()

    test(model, crit, dataset, dataset.get_vocab(), opt)
Example #3
Source File: train.py    From video-caption.pytorch with MIT License 4 votes vote down vote up 
def main(opt):
    dataset = VideoDataset(opt, 'train')
    dataloader = DataLoader(dataset, batch_size=opt["batch_size"], shuffle=True)
    opt["vocab_size"] = dataset.get_vocab_size()
    if opt["model"] == 'S2VTModel':
        model = S2VTModel(
            opt["vocab_size"],
            opt["max_len"],
            opt["dim_hidden"],
            opt["dim_word"],
            opt['dim_vid'],
            rnn_cell=opt['rnn_type'],
            n_layers=opt['num_layers'],
            bidirectional=opt["bidirectional"],
            rnn_dropout_p=opt["rnn_dropout_p"]).cuda()
    elif opt["model"] == "S2VTAttModel":
        encoder = EncoderRNN(
            opt["dim_vid"],
            opt["dim_hidden"],
            n_layers=opt['num_layers'],
            bidirectional=opt["bidirectional"],
            input_dropout_p=opt["input_dropout_p"],
            rnn_cell=opt['rnn_type'],
            rnn_dropout_p=opt["rnn_dropout_p"])
        decoder = DecoderRNN(
            opt["vocab_size"],
            opt["max_len"],
            opt["dim_hidden"],
            opt["dim_word"],
            n_layers=opt['num_layers'],
            input_dropout_p=opt["input_dropout_p"],
            rnn_cell=opt['rnn_type'],
            rnn_dropout_p=opt["rnn_dropout_p"],
            bidirectional=opt["bidirectional"])
        model = S2VTAttModel(encoder, decoder).cuda()
    crit = utils.LanguageModelCriterion()
    rl_crit = utils.RewardCriterion()
    optimizer = optim.Adam(
        model.parameters(),
        lr=opt["learning_rate"],
        weight_decay=opt["weight_decay"])
    exp_lr_scheduler = optim.lr_scheduler.StepLR(
        optimizer,
        step_size=opt["learning_rate_decay_every"],
        gamma=opt["learning_rate_decay_rate"])

    train(dataloader, model, crit, optimizer, exp_lr_scheduler, opt, rl_crit)
Example #4
Source File: train.py    From video-caption.pytorch with MIT License 4 votes vote down vote up 
def main(opt):
    dataset = VideoDataset(opt, 'train')
    dataloader = DataLoader(dataset, batch_size=opt["batch_size"], shuffle=True)
    opt["vocab_size"] = dataset.get_vocab_size()
    if opt["model"] == 'S2VTModel':
        model = S2VTModel(
            opt["vocab_size"],
            opt["max_len"],
            opt["dim_hidden"],
            opt["dim_word"],
            opt['dim_vid'],
            rnn_cell=opt['rnn_type'],
            n_layers=opt['num_layers'],
            rnn_dropout_p=opt["rnn_dropout_p"])
    elif opt["model"] == "S2VTAttModel":
        encoder = EncoderRNN(
            opt["dim_vid"],
            opt["dim_hidden"],
            bidirectional=opt["bidirectional"],
            input_dropout_p=opt["input_dropout_p"],
            rnn_cell=opt['rnn_type'],
            rnn_dropout_p=opt["rnn_dropout_p"])
        decoder = DecoderRNN(
            opt["vocab_size"],
            opt["max_len"],
            opt["dim_hidden"],
            opt["dim_word"],
            input_dropout_p=opt["input_dropout_p"],
            rnn_cell=opt['rnn_type'],
            rnn_dropout_p=opt["rnn_dropout_p"],
            bidirectional=opt["bidirectional"])
        model = S2VTAttModel(encoder, decoder)
    model = model.cuda()
    crit = utils.LanguageModelCriterion()
    rl_crit = utils.RewardCriterion()
    optimizer = optim.Adam(
        model.parameters(),
        lr=opt["learning_rate"],
        weight_decay=opt["weight_decay"])
    exp_lr_scheduler = optim.lr_scheduler.StepLR(
        optimizer,
        step_size=opt["learning_rate_decay_every"],
        gamma=opt["learning_rate_decay_rate"])

    train(dataloader, model, crit, optimizer, exp_lr_scheduler, opt, rl_crit)
Example #5
Source File: train.py    From Sentiment-Analysis with Apache License 2.0 4 votes vote down vote up 
def main():
    voc = Lang('data/WORDMAP.json')
    print("voc.n_words: " + str(voc.n_words))

    train_data = SaDataset('train', voc)
    val_data = SaDataset('valid', voc)

    # Initialize encoder
    encoder = EncoderRNN(voc.n_words, hidden_size, encoder_n_layers, dropout)

    # Use appropriate device
    encoder = encoder.to(device)

    # Initialize optimizers
    print('Building optimizers ...')
    optimizer = optim.Adam(encoder.parameters(), lr=learning_rate)

    best_acc = 0
    epochs_since_improvement = 0

    # Epochs
    for epoch in range(start_epoch, epochs):
        # Decay learning rate if there is no improvement for 8 consecutive epochs, and terminate training after 20
        if epochs_since_improvement == 20:
            break
        if epochs_since_improvement > 0 and epochs_since_improvement % 8 == 0:
            adjust_learning_rate(optimizer, 0.8)

        # One epoch's training
        train(epoch, train_data, encoder, optimizer)

        # One epoch's validation
        val_acc, val_loss = valid(val_data, encoder)
        print('\n * ACCURACY - {acc:.3f}, LOSS - {loss:.3f}\n'.format(acc=val_acc, loss=val_loss))

        # Check if there was an improvement
        is_best = val_acc > best_acc
        best_acc = max(best_acc, val_acc)

        if not is_best:
            epochs_since_improvement += 1
            print("\nEpochs since last improvement: %d\n" % (epochs_since_improvement,))
        else:
            epochs_since_improvement = 0

        # Save checkpoint
        save_checkpoint(epoch, encoder, optimizer, val_acc, is_best)

        # Reshuffle samples
        np.random.shuffle(train_data.samples)
        np.random.shuffle(val_data.samples)