Python datasets.prepare_data() Examples

The following are 3 code examples of datasets.prepare_data(). 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 datasets , or try the search function .
Example #1
Source File: pretrain_DAMSM.py    From DM-GAN with MIT License 5 votes vote down vote up 
def evaluate(dataloader, cnn_model, rnn_model, batch_size):
    cnn_model.eval()
    rnn_model.eval()
    s_total_loss = 0
    w_total_loss = 0
    for step, data in enumerate(dataloader, 0):
        real_imgs, captions, cap_lens, \
                class_ids, keys = prepare_data(data)

        words_features, sent_code = cnn_model(real_imgs[-1])
        # nef = words_features.size(1)
        # words_features = words_features.view(batch_size, nef, -1)

        hidden = rnn_model.init_hidden(batch_size)
        words_emb, sent_emb = rnn_model(captions, cap_lens, hidden)

        w_loss0, w_loss1, attn = words_loss(words_features, words_emb, labels,
                                            cap_lens, class_ids, batch_size)
        w_total_loss += (w_loss0 + w_loss1).data

        s_loss0, s_loss1 = \
            sent_loss(sent_code, sent_emb, labels, class_ids, batch_size)
        s_total_loss += (s_loss0 + s_loss1).data

        if step == 50:
            break

    s_cur_loss = s_total_loss[0] / step
    w_cur_loss = w_total_loss[0] / step

    return s_cur_loss, w_cur_loss
Example #2
Source File: pretrain_DAMSM.py    From attn-gan with MIT License 5 votes vote down vote up 
def evaluate(dataloader, cnn_model, rnn_model, batch_size):
    cnn_model.eval()
    rnn_model.eval()
    s_total_loss = 0
    w_total_loss = 0
    for step, data in enumerate(dataloader, 0):
        real_imgs, captions, cap_lens, \
                class_ids, keys = prepare_data(data)

        words_features, sent_code = cnn_model(real_imgs[-1])
        # nef = words_features.size(1)
        # words_features = words_features.view(batch_size, nef, -1)

        hidden = rnn_model.init_hidden(batch_size)
        words_emb, sent_emb = rnn_model(captions, cap_lens, hidden)

        w_loss0, w_loss1, attn = words_loss(words_features, words_emb, labels,
                                            cap_lens, class_ids, batch_size)
        w_total_loss += (w_loss0 + w_loss1).data

        s_loss0, s_loss1 = \
            sent_loss(sent_code, sent_emb, labels, class_ids, batch_size)
        s_total_loss += (s_loss0 + s_loss1).data

        if step == 50:
            break

    s_cur_loss = s_total_loss[0] / step
    w_cur_loss = w_total_loss[0] / step

    return s_cur_loss, w_cur_loss
Example #3
Source File: pretrain_DAMSM.py    From AttnGAN with MIT License 5 votes vote down vote up 
def evaluate(dataloader, cnn_model, rnn_model, batch_size):
    cnn_model.eval()
    rnn_model.eval()
    s_total_loss = 0
    w_total_loss = 0
    for step, data in enumerate(dataloader, 0):
        real_imgs, captions, cap_lens, \
                class_ids, keys = prepare_data(data)

        words_features, sent_code = cnn_model(real_imgs[-1])
        # nef = words_features.size(1)
        # words_features = words_features.view(batch_size, nef, -1)

        hidden = rnn_model.init_hidden(batch_size)
        words_emb, sent_emb = rnn_model(captions, cap_lens, hidden)

        w_loss0, w_loss1, attn = words_loss(words_features, words_emb, labels,
                                            cap_lens, class_ids, batch_size)
        w_total_loss += (w_loss0 + w_loss1).data

        s_loss0, s_loss1 = \
            sent_loss(sent_code, sent_emb, labels, class_ids, batch_size)
        s_total_loss += (s_loss0 + s_loss1).data

        if step == 50:
            break

    s_cur_loss = s_total_loss[0] / step
    w_cur_loss = w_total_loss[0] / step

    return s_cur_loss, w_cur_loss