Python evaluation.encode_data() Examples

The following are 6 code examples of evaluation.encode_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 evaluation , or try the search function .
Example #1
Source File: train.py    From VSE-C with MIT License 6 votes vote down vote up 
def validate_caption_only(opt, val_loader, model):
    # compute the encoding for all the validation images and captions
    img_embs, cap_embs = encode_data(
        model, val_loader, opt.log_step, logging.info)

    # caption retrieval
    (r1, r5, r10, medr, meanr) = i2t_text_only(img_embs, cap_embs, measure=opt.measure)
    logging.info("Image to text: %.1f, %.1f, %.1f, %.1f, %.1f" %
                 (r1, r5, r10, medr, meanr))
    # sum of recalls to be used for early stopping
    currscore = r1 + r5 + r10

    # record metrics in tensorboard
    tb_logger.log_value('r1', r1, step=model.Eiters)
    tb_logger.log_value('r5', r5, step=model.Eiters)
    tb_logger.log_value('r10', r10, step=model.Eiters)
    tb_logger.log_value('medr', medr, step=model.Eiters)
    tb_logger.log_value('meanr', meanr, step=model.Eiters)
    tb_logger.log_value('rsum', currscore, step=model.Eiters)

    return currscore
Example #2
Source File: train.py    From VSE-C with MIT License 5 votes vote down vote up 
def validate(opt, val_loader, model):
    # compute the encoding for all the validation images and captions
    img_embs, cap_embs = encode_data(
        model, val_loader, opt.log_step, logging.info)

    # caption retrieval
    (r1, r5, r10, medr, meanr) = i2t(img_embs, cap_embs, measure=opt.measure)
    logging.info("Image to text: %.1f, %.1f, %.1f, %.1f, %.1f" %
                 (r1, r5, r10, medr, meanr))
    # image retrieval
    (r1i, r5i, r10i, medri, meanr) = t2i(
        img_embs, cap_embs, measure=opt.measure)
    logging.info("Text to image: %.1f, %.1f, %.1f, %.1f, %.1f" %
                 (r1i, r5i, r10i, medri, meanr))
    # sum of recalls to be used for early stopping
    currscore = r1 + r5 + r10 + r1i + r5i + r10i

    # record metrics in tensorboard
    tb_logger.log_value('r1', r1, step=model.Eiters)
    tb_logger.log_value('r5', r5, step=model.Eiters)
    tb_logger.log_value('r10', r10, step=model.Eiters)
    tb_logger.log_value('medr', medr, step=model.Eiters)
    tb_logger.log_value('meanr', meanr, step=model.Eiters)
    tb_logger.log_value('r1i', r1i, step=model.Eiters)
    tb_logger.log_value('r5i', r5i, step=model.Eiters)
    tb_logger.log_value('r10i', r10i, step=model.Eiters)
    tb_logger.log_value('medri', medri, step=model.Eiters)
    tb_logger.log_value('meanr', meanr, step=model.Eiters)
    tb_logger.log_value('rsum', currscore, step=model.Eiters)

    return currscore
Example #3
Source File: train.py    From vsepp with Apache License 2.0 5 votes vote down vote up 
def validate(opt, val_loader, model):
    # compute the encoding for all the validation images and captions
    img_embs, cap_embs = encode_data(
        model, val_loader, opt.log_step, logging.info)

    # caption retrieval
    (r1, r5, r10, medr, meanr) = i2t(img_embs, cap_embs, measure=opt.measure)
    logging.info("Image to text: %.1f, %.1f, %.1f, %.1f, %.1f" %
                 (r1, r5, r10, medr, meanr))
    # image retrieval
    (r1i, r5i, r10i, medri, meanr) = t2i(
        img_embs, cap_embs, measure=opt.measure)
    logging.info("Text to image: %.1f, %.1f, %.1f, %.1f, %.1f" %
                 (r1i, r5i, r10i, medri, meanr))
    # sum of recalls to be used for early stopping
    currscore = r1 + r5 + r10 + r1i + r5i + r10i

    # record metrics in tensorboard
    tb_logger.log_value('r1', r1, step=model.Eiters)
    tb_logger.log_value('r5', r5, step=model.Eiters)
    tb_logger.log_value('r10', r10, step=model.Eiters)
    tb_logger.log_value('medr', medr, step=model.Eiters)
    tb_logger.log_value('meanr', meanr, step=model.Eiters)
    tb_logger.log_value('r1i', r1i, step=model.Eiters)
    tb_logger.log_value('r5i', r5i, step=model.Eiters)
    tb_logger.log_value('r10i', r10i, step=model.Eiters)
    tb_logger.log_value('medri', medri, step=model.Eiters)
    tb_logger.log_value('meanr', meanr, step=model.Eiters)
    tb_logger.log_value('rsum', currscore, step=model.Eiters)

    return currscore
Example #4
Source File: train.py    From CAMP_iccv19 with Apache License 2.0 5 votes vote down vote up 
def validate(opt, val_loader, model, tb_logger):
    # compute the encoding for all the validation images and captions
    print("start validate")
    model.val_start()


    img_embs, cap_embs, cap_masks = encode_data(
        model, val_loader, opt.log_step, logging.info)

    # caption retrieval
    (i2t_r1, i2t_r5, i2t_r10, i2t_medr, i2t_meanr), (t2i_r1, t2i_r5, t2i_r10, t2i_medr, t2i_meanr) = i2t(img_embs, cap_embs, cap_masks, measure=opt.measure, model=model)
    logging.info("Image to text: %.1f, %.1f, %.1f, %.1f, %.1f" %
                 (i2t_r1, i2t_r5, i2t_r10, i2t_medr, i2t_meanr))
    # image retrieval
    #(r1i, r5i, r10i, medri, meanr) = t2i(
    #    img_embs, cap_embs, measure=opt.measure, model=model)
    logging.info("Text to image: %.1f, %.1f, %.1f, %.1f, %.1f" %
                 (t2i_r1, t2i_r5, t2i_r10, t2i_medr, t2i_meanr))
    # sum of recalls to be used for early stopping
    currscore = i2t_r1 + i2t_r5 + i2t_r10 + t2i_r1 + t2i_r5 + t2i_r10

    # record metrics in tensorboard
    tb_logger.log_value('i2t_r1', i2t_r1, step=model.Eiters)
    tb_logger.log_value('i2t_r5', i2t_r5, step=model.Eiters)
    tb_logger.log_value('i2t_r10', i2t_r10, step=model.Eiters)
    tb_logger.log_value('i2t_medr', i2t_medr, step=model.Eiters)
    tb_logger.log_value('i2t_meanr', i2t_meanr, step=model.Eiters)
    tb_logger.log_value('t2i_r1', t2i_r1, step=model.Eiters)
    tb_logger.log_value('t2i_r5', t2i_r5, step=model.Eiters)
    tb_logger.log_value('t2i_r10', t2i_r10, step=model.Eiters)
    tb_logger.log_value('t2i_medr', t2i_medr, step=model.Eiters)
    tb_logger.log_value('t2i_meanr', t2i_meanr, step=model.Eiters)
    tb_logger.log_value('rsum', currscore, step=model.Eiters)

    return currscore
Example #5
Source File: train.py    From SCAN with Apache License 2.0 4 votes vote down vote up 
def validate(opt, val_loader, model):
    # compute the encoding for all the validation images and captions
    img_embs, cap_embs, cap_lens = encode_data(
        model, val_loader, opt.log_step, logging.info)

    img_embs = numpy.array([img_embs[i] for i in range(0, len(img_embs), 5)])

    start = time.time()
    if opt.cross_attn == 't2i':
        sims = shard_xattn_t2i(img_embs, cap_embs, cap_lens, opt, shard_size=128)
    elif opt.cross_attn == 'i2t':
        sims = shard_xattn_i2t(img_embs, cap_embs, cap_lens, opt, shard_size=128)
    else:
        raise NotImplementedError
    end = time.time()
    print("calculate similarity time:", end-start)

    # caption retrieval
    (r1, r5, r10, medr, meanr) = i2t(img_embs, cap_embs, cap_lens, sims)
    logging.info("Image to text: %.1f, %.1f, %.1f, %.1f, %.1f" %
                 (r1, r5, r10, medr, meanr))
    # image retrieval
    (r1i, r5i, r10i, medri, meanr) = t2i(
        img_embs, cap_embs, cap_lens, sims)
    logging.info("Text to image: %.1f, %.1f, %.1f, %.1f, %.1f" %
                 (r1i, r5i, r10i, medri, meanr))
    # sum of recalls to be used for early stopping
    currscore = r1 + r5 + r10 + r1i + r5i + r10i

    # record metrics in tensorboard
    tb_logger.log_value('r1', r1, step=model.Eiters)
    tb_logger.log_value('r5', r5, step=model.Eiters)
    tb_logger.log_value('r10', r10, step=model.Eiters)
    tb_logger.log_value('medr', medr, step=model.Eiters)
    tb_logger.log_value('meanr', meanr, step=model.Eiters)
    tb_logger.log_value('r1i', r1i, step=model.Eiters)
    tb_logger.log_value('r5i', r5i, step=model.Eiters)
    tb_logger.log_value('r10i', r10i, step=model.Eiters)
    tb_logger.log_value('medri', medri, step=model.Eiters)
    tb_logger.log_value('meanr', meanr, step=model.Eiters)
    tb_logger.log_value('rsum', currscore, step=model.Eiters)

    return currscore
Example #6
Source File: test_modules.py    From CAMP_iccv19 with Apache License 2.0 4 votes vote down vote up 
def test_CAMP_model(config_path):
    print("OK!")
    logging.basicConfig(format='%(asctime)s %(message)s', level=logging.INFO)
    parser = argparse.ArgumentParser()
    #config_path = "./experiments/f30k_cross_attention/config_test.yaml"
    with open(config_path) as f:
        opt = yaml.load(f)
    opt = EasyDict(opt['common'])


    vocab = pickle.load(open(os.path.join(opt.vocab_path,
            '%s_vocab.pkl' % opt.data_name), 'rb'))
    opt.vocab_size = len(vocab)

    train_logger = LogCollector()

    print("----Start init model----")
    CAMP = model.CAMP(opt)
    CAMP.logger = train_logger

    if opt.resume is not None:
       ckp = torch.load(opt.resume)
       CAMP.load_state_dict(ckp["model"])

    CAMP.train_start()
    print("----Model init success----")

    """
    fake_img = torch.randn(16, 36, opt.img_dim)
    fake_text = torch.ones(16, 32).long()
    fake_lengths = torch.Tensor([32] * 16)
    fake_pos = torch.ones(16, 32).long()
    fake_ids = torch.ones(16).long()

    CAMP.train_emb(fake_img, fake_text, fake_lengths,
                   instance_ids=fake_ids)
    print("----Test train_emb success----")
    """
    
    train_loader, val_loader = data.get_loaders(
        opt.data_name, vocab, opt.crop_size, 128, 4, opt)

    test_loader = data.get_test_loader("test", opt.data_name, vocab, opt.crop_size, 128, 4, opt)

    CAMP.val_start()
    img_embs, cap_embs, cap_masks = encode_data(
        CAMP, test_loader, opt.log_step, logging.info)


    (r1, r5, r10, medr, meanr), (r1i, r5i, r10i, medri, meanri), score_matrix= i2t(img_embs, cap_embs, cap_masks, measure=opt.measure,
                                     model=CAMP, return_ranks=True)
    logging.info("Image to text: %.1f, %.1f, %.1f, %.1f, %.1f" %
                 (r1, r5, r10, medr, meanr))
    logging.info("Text to image: %.1f, %.1f, %.1f, %.1f, %.1f" %
                 (r1i, r5i, r10i, medri, meanri))