Comments (2)
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()
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