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测试时为啥候选物品的embedding 不需要过Model模块的fc呢，也即MLP模块，而是直接使用物品的modalembedding或者说id embedding #7

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Jackie-gj opened this issue Dec 28, 2023 · 2 comments
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测试时为啥候选物品的embedding 不需要过Model模块的fc呢，也即MLP模块，而是直接使用物品的modalembedding或者说id embedding #7

Jackie-gj opened this issue Dec 28, 2023 · 2 comments

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@Jackie-gj
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    self.fc = MLP_Layers(word_embedding_dim=num_fc_ftr,
                         item_embedding_dim=args.embedding_dim,
                         layers=[args.embedding_dim] * (args.dnn_layer + 1),
                         drop_rate=args.drop_rate)这段代码在训练是对输入的embedding进行了转换，然后再与候选的正负样本计算相似度以及BCE损失， 在模型预测时，为啥是直接使用item_embeddings而不需要经过上面得MLP_Layers呢？
item_embeddings = item_embeddings.to(local_rank)
with torch.no_grad():
    eval_all_user = []
    item_rank = torch.Tensor(np.arange(item_num) + 1).to(local_rank)
    for data in eval_dl:
        user_ids, input_embs, log_mask, labels = data
        user_ids, input_embs, log_mask, labels = \
            user_ids.to(local_rank), input_embs.to(local_rank),\
            log_mask.to(local_rank), labels.to(local_rank).detach()
        prec_emb = model.module.user_encoder(input_embs, log_mask, local_rank)[:, -1].detach()
        scores = torch.matmul(prec_emb, item_embeddings.t()).squeeze(dim=-1).detach()
@zyuanat
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zyuanat commented Feb 2, 2024 •
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感谢提问，抱歉这么晚回复
所提到的item embedding其实是已经通过model模块的fc层的，在metics.py文件的get_item_embeddings()函数中的 “item_emb = model.module.fc(item_emb)”这一行：

def get_item_embeddings(model, item_image_embs, test_batch_size, args, local_rank):
    model.eval()
    item_dataset = Build_Id_Eval_Dataset(data=item_image_embs)
    item_dataloader = DataLoader(item_dataset, batch_size=test_batch_size,
                                 num_workers=args.num_workers, pin_memory=True)
    item_embeddings = []
    with torch.no_grad():
        for input_ids in item_dataloader:
            item_emb = input_ids.to(local_rank)
            item_emb = model.module.fc(item_emb)
            item_embeddings.extend(item_emb)
    return torch.stack(tensors=item_embeddings, dim=0).to(torch.device("cpu")).detach()

@Jackie-gj
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Jackie-gj commented Feb 23, 2024 via email

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@Jackie-gj @zyuanat