Python evaluation.encode_data方法代碼示例

本文整理匯總了Python中evaluation.encode_data方法的典型用法代碼示例。如果您正苦於以下問題：Python evaluation.encode_data方法的具體用法？Python evaluation.encode_data怎麽用？Python evaluation.encode_data使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類evaluation的用法示例。

在下文中一共展示了evaluation.encode_data方法的5個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。

示例1: validate_caption_only

# 需要導入模塊: import evaluation [as 別名]
# 或者: from evaluation import encode_data [as 別名]
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

開發者ID:ExplorerFreda，項目名稱:VSE-C，代碼行數:23，代碼來源:train.py

示例2: validate

# 需要導入模塊: import evaluation [as 別名]
# 或者: from evaluation import encode_data [as 別名]
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

開發者ID:ExplorerFreda，項目名稱:VSE-C，代碼行數:33，代碼來源:train.py

示例3: validate

# 需要導入模塊: import evaluation [as 別名]
# 或者: from evaluation import encode_data [as 別名]
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

開發者ID:ZihaoWang-CV，項目名稱:CAMP_iccv19，代碼行數:37，代碼來源:train.py

示例4: validate

# 需要導入模塊: import evaluation [as 別名]
# 或者: from evaluation import encode_data [as 別名]
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

開發者ID:kuanghuei，項目名稱:SCAN，代碼行數:45，代碼來源:train.py

示例5: test_CAMP_model

# 需要導入模塊: import evaluation [as 別名]
# 或者: from evaluation import encode_data [as 別名]
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))

開發者ID:ZihaoWang-CV，項目名稱:CAMP_iccv19，代碼行數:57，代碼來源:test_modules.py

注：本文中的evaluation.encode_data方法示例由純淨天空整理自Github/MSDocs等開源代碼及文檔管理平台，相關代碼片段篩選自各路編程大神貢獻的開源項目，源碼版權歸原作者所有，傳播和使用請參考對應項目的License；未經允許，請勿轉載。