recommend/inference.py

import sys
sys.path.append('./')

import time
import torch
import numpy as np
from os import path
from model.LightGCN import LightGCN
from utils.parse_args import args
from utils.data_loader import EdgeListData
from utils.data_generator import build_user_seed_graph

args.device = 'cuda:7'
args.data_path = './user_seed_graph.txt'
args.pre_model_path = './model/LightGCN_pretrained.pt'

def run_inference(user_id=1):
    # 1. 实时生成user-seed交互图
    print("正在生成用户-种子交互文件...")
    build_user_seed_graph()

    # 2. 加载数据集
    print("正在加载数据集...")
    t_data_start = time.time()
    dataset = EdgeListData(args.data_path, args.data_path)
    t_data_end = time.time()

    # 3. 加载LightGCN模型
    print("正在加载模型参数...")
    pretrained_dict = torch.load(args.pre_model_path, map_location=args.device, weights_only=True)
    pretrained_dict['user_embedding'] = pretrained_dict['user_embedding'][:dataset.num_users]
    pretrained_dict['item_embedding'] = pretrained_dict['item_embedding'][:dataset.num_items]

    model = LightGCN(dataset, phase='vanilla').to(args.device)
    model.load_state_dict(pretrained_dict, strict=False)
    model.eval()

    # 4. 推理
    print(f"正在为用户 {user_id} 推理推荐结果...")
    t_infer_start = time.time()
    with torch.no_grad():
        user_emb, item_emb = model.generate()
        user_vec = user_emb[user_id].unsqueeze(0)
        scores = model.rating(user_vec, item_emb).squeeze(0)
        pred_item = torch.argmax(scores).item()
    t_infer_end = time.time()

    print(f"用户{user_id}下一个最可能点击的物品ID为: {pred_item}")
    print(f"加载数据集耗时: {t_data_end - t_data_start:.4f} 秒")
    print(f"推理耗时: {t_infer_end - t_infer_start:.4f} 秒")

if __name__ == "__main__":
    t_start = time.time()
    user_id = 1
    run_inference(user_id)
    t_end = time.time()
    print(f"脚本总耗时: {t_end - t_start:.4f} 秒")