Python model.CNN_ENCODER Examples

The following are 3 code examples of model.CNN_ENCODER(). 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 model , 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 build_models():
    # build model ############################################################
    text_encoder = RNN_ENCODER(dataset.n_words, nhidden=cfg.TEXT.EMBEDDING_DIM)
    image_encoder = CNN_ENCODER(cfg.TEXT.EMBEDDING_DIM)
    labels = Variable(torch.LongTensor(range(batch_size)))
    start_epoch = 0
    if cfg.TRAIN.NET_E != '':
        state_dict = torch.load(cfg.TRAIN.NET_E)
        text_encoder.load_state_dict(state_dict)
        print('Load ', cfg.TRAIN.NET_E)
        #
        name = cfg.TRAIN.NET_E.replace('text_encoder', 'image_encoder')
        state_dict = torch.load(name)
        image_encoder.load_state_dict(state_dict)
        print('Load ', name)

        istart = cfg.TRAIN.NET_E.rfind('_') + 8
        iend = cfg.TRAIN.NET_E.rfind('.')
        start_epoch = cfg.TRAIN.NET_E[istart:iend]
        start_epoch = int(start_epoch) + 1
        print('start_epoch', start_epoch)
    if cfg.CUDA:
        text_encoder = text_encoder.cuda()
        image_encoder = image_encoder.cuda()
        labels = labels.cuda()

    return text_encoder, image_encoder, labels, start_epoch
Example #2
Source File: pretrain_DAMSM.py    From attn-gan with MIT License 5 votes vote down vote up 
def build_models():
    # build model ############################################################
    text_encoder = RNN_ENCODER(dataset.n_words, nhidden=cfg.TEXT.EMBEDDING_DIM)
    image_encoder = CNN_ENCODER(cfg.TEXT.EMBEDDING_DIM)
    labels = Variable(torch.LongTensor(range(batch_size)))
    start_epoch = 0
    if cfg.TRAIN.NET_E != '':
        state_dict = torch.load(cfg.TRAIN.NET_E)
        text_encoder.load_state_dict(state_dict)
        print('Load ', cfg.TRAIN.NET_E)
        #
        name = cfg.TRAIN.NET_E.replace('text_encoder', 'image_encoder')
        state_dict = torch.load(name)
        image_encoder.load_state_dict(state_dict)
        print('Load ', name)

        istart = cfg.TRAIN.NET_E.rfind('_') + 8
        iend = cfg.TRAIN.NET_E.rfind('.')
        start_epoch = cfg.TRAIN.NET_E[istart:iend]
        start_epoch = int(start_epoch) + 1
        print('start_epoch', start_epoch)
    if cfg.CUDA:
        text_encoder = text_encoder.cuda()
        image_encoder = image_encoder.cuda()
        labels = labels.cuda()

    return text_encoder, image_encoder, labels, start_epoch
Example #3
Source File: pretrain_DAMSM.py    From AttnGAN with MIT License 5 votes vote down vote up 
def build_models():
    # build model ############################################################
    text_encoder = RNN_ENCODER(dataset.n_words, nhidden=cfg.TEXT.EMBEDDING_DIM)
    image_encoder = CNN_ENCODER(cfg.TEXT.EMBEDDING_DIM)
    labels = Variable(torch.LongTensor(range(batch_size)))
    start_epoch = 0
    if cfg.TRAIN.NET_E != '':
        state_dict = torch.load(cfg.TRAIN.NET_E)
        text_encoder.load_state_dict(state_dict)
        print('Load ', cfg.TRAIN.NET_E)
        #
        name = cfg.TRAIN.NET_E.replace('text_encoder', 'image_encoder')
        state_dict = torch.load(name)
        image_encoder.load_state_dict(state_dict)
        print('Load ', name)

        istart = cfg.TRAIN.NET_E.rfind('_') + 8
        iend = cfg.TRAIN.NET_E.rfind('.')
        start_epoch = cfg.TRAIN.NET_E[istart:iend]
        start_epoch = int(start_epoch) + 1
        print('start_epoch', start_epoch)
    if cfg.CUDA:
        text_encoder = text_encoder.cuda()
        image_encoder = image_encoder.cuda()
        labels = labels.cuda()

    return text_encoder, image_encoder, labels, start_epoch