v1_rnn_mini/run.py

#!/usr/bin/env python3
# Copyright (c) 2026 XingChina
# SPDX-License-Identifier: BSD-3-Clause
# 本代码采用 BSD 3-Clause 许可证，详见项目根目录的 LICENSE 文件。

import torch
import torch.nn as nn
import numpy as np
import json
import random

# 加载字符映射
with open("checkpoints_mengdie/char2idx.json", "r", encoding="utf-8") as f:
    char2idx = json.load(f)

idx2char = {int(v): k for k, v in char2idx.items()}
vocab_size = len(char2idx)

print(f"词表大小: {vocab_size}")
print(f"词表示例: {list(char2idx.items())[:10]}")

# 模型结构（必须与训练时一致！！！）
class TinyCharRNN(nn.Module):
    def __init__(self, vocab_size, hidden_size=32):
        super().__init__()
        self.embedding = nn.Embedding(vocab_size, hidden_size)
        self.rnn = nn.RNN(hidden_size, hidden_size, batch_first=True)
        self.fc = nn.Linear(hidden_size, vocab_size)
    def forward(self, x, hidden=None):
        x = self.embedding(x)
        out, hidden = self.rnn(x, hidden)
        out = self.fc(out)
        return out, hidden

# 加载模型
model = TinyCharRNN(vocab_size, hidden_size=32)
model.load_state_dict(torch.load("checkpoints_mengdie/mengdie_final.pth", map_location='cpu'))
model.eval()
print("春梦蝶猫娘模型加载成功！喵～\n")

def generate_response(prompt, length=150, temperature=0.8):
    """根据提示词生成猫娘的回应"""
    if not prompt:
        prompt = random.choice(list(char2idx.keys()))
    # 将prompt转为索引
    indices = []
    for ch in prompt:
        if ch in char2idx:
            indices.append(char2idx[ch])
        else:
            # 如果字符不在词表中，跳过（或者用空格代替）
            # 这里跳过，不影响生成
            continue
    if not indices:
        # 如果全部跳过，就用一个默认字符
        indices = [char2idx['你']]
    input_tensor = torch.tensor([indices])
    hidden = None
    result = list(prompt)  # 保留原始输入
    with torch.no_grad():
        for _ in range(length):
            logits, hidden = model(input_tensor, hidden)
            probs = torch.softmax(logits[0, -1] / temperature, dim=0).cpu().numpy()
            next_idx = np.random.choice(len(probs), p=probs)
            next_char = idx2char[next_idx]
            result.append(next_char)
            input_tensor = torch.tensor([[next_idx]])
    return ''.join(result)

print("你可以开始和春梦蝶聊天了！输入 'q' 退出。")
while True:
    user = input("\n你: ")
    if user.lower() == 'q':
        break
    # 使用用户输入的最后几个字符作为生成起点
    start = user[-5:] if len(user) >= 5 else user
    reply = generate_response(start, length=150, temperature=0.85)
    print(f"春梦蝶: {reply}")