119 lines
5.0 KiB
Python
119 lines
5.0 KiB
Python
from model import TransClassifier
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from transformers import AutoTokenizer
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from data_process import extract_json_data, Formatter
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import torch
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import json
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from typing import Dict, List, Optional
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import os
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import random
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import warnings
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warnings.filterwarnings("ignore")
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valid_keys = [
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"Core_Fear_Source", "Pain_Threshold", "Time_Window_Pressure", "Helplessness_Index",
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"Social_Shame", "Payer_Decision_Maker", "Hidden_Wealth_Proof", "Price_Sensitivity",
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"Sunk_Cost", "Compensatory_Spending", "Trust_Deficit", "Secret_Resistance", "Family_Sabotage",
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"Low_Self_Efficacy", "Attribution_Barrier", "Emotional_Trigger", "Ultimatum_Event", "Expectation_Bonus",
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"Competitor_Mindset", "Cognitive_Stage", "Follow_up_Priority", "Last_Interaction", "Referral_Potential"
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]
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ch_valid_keys = [
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"核心恐惧源", "疼痛阈值", "时间窗口压力", "无助指数",
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"社会羞耻感", "付款决策者", "隐藏财富证明", "价格敏感度",
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"沉没成本", "补偿性消费", "信任赤字", "秘密抵触情绪", "家庭破坏",
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"低自我效能感", "归因障碍", "情绪触发点", "最后通牒事件", "期望加成",
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"竞争者心态", "认知阶段", "跟进优先级", "最后互动时间", "推荐潜力"
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]
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all_keys = valid_keys + ["session_id", "label"]
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en2ch = {en:ch for en, ch in zip(valid_keys, ch_valid_keys)}
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d1_keys = valid_keys[:5]
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d2_keys = valid_keys[5:10]
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d3_keys = valid_keys[10:15]
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d4_keys = valid_keys[15:19]
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d5_keys = valid_keys[19:23]
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class InferenceEngine:
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def __init__(self, backbone_dir: str, ckpt_path: str = "best_ckpt.pth", device: str = "cuda"):
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self.backbone_dir = backbone_dir
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self.ckpt_path = ckpt_path
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self.device = device
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# 加载 tokenizer
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self.tokenizer = AutoTokenizer.from_pretrained(backbone_dir)
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print(f"Tokenizer loaded from {backbone_dir}")
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# 加载模型
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self.model = TransClassifier(backbone_dir, device)
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self.model.to(device)
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if self.ckpt_path:
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self.model.load_state_dict(torch.load(ckpt_path, map_location=device))
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print(f"Model loaded from {ckpt_path}")
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else:
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print("Warning: No checkpoint path provided. Using untrained model.")
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self.model.eval()
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print("Inference engine initialized successfully.")
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self.formatter = Formatter(en2ch)
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def inference_batch(self, json_list: List[str]) -> dict:
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"""
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批量推理函数，输入为 JSON 字符串列表，输出为包含转换概率的字典列表。为防止OOM，列表最大长度为8。
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请注意Json文件中的词条数必须大于等于10.
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"""
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# print(111111)
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assert len(json_list) <= 8, "单次输入json文件数量不可超过8。"
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id2feature = extract_json_data(json_list)
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print(json.dumps(id2feature ,indent=2 ,ensure_ascii=False))
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# id2feature
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message_list = []
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for id, feature in id2feature.items():
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messages = self.formatter.get_llm_prompt(feature)
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message_list.append(messages)
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inputs = self.tokenizer.apply_chat_template(
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message_list,
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tokenize=False,
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add_generation_prompt=True,
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enable_thinking=False
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)
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model_inputs = self.tokenizer(
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inputs,
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padding=True,
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truncation=True,
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max_length=2048,
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return_tensors="pt"
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).to(self.device)
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with torch.inference_mode():
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with torch.amp.autocast(device_type=self.device, dtype=torch.bfloat16):
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outputs = self.model(model_inputs)
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# 1. 计算分类标签（argmax）
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preds = torch.argmax(outputs, dim=1).cpu().numpy().tolist()
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# 2. 计算softmax概率（核心修正：转CPU、转numpy、转列表，解决Tensor序列化问题）
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outputs_float = outputs.float() # 转换为 float32 避免精度问题
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probs = torch.softmax(outputs_float, dim=1) # probs: [B, 2]
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# 转换为CPU的numpy数组，再转列表（每个样本对应2个类别的概率）
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probs = probs.cpu().numpy().tolist()
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probs = [p[1] for p in probs] # 只保留类别1的概率
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# 3. 计算置信度
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confidence = [abs(p - 0.5) * 2 for p in probs]
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# 返回格式：labels是每个样本的分类标签列表，probs是每个样本的类别概率列表，confidence是每个样本的置信度列表
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return {"labels": preds, "probs": probs, "confidence": confidence}
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def inference_sample(self, json_path: str) -> dict:
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"""
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单样本推理函数，输入为 JSON 字符串路径，输出为包含转换概率的字典。
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请注意Json文件中的词条数必须大于等于10.
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"""
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return self.inference_batch([json_path])
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if __name__ == "__main__":
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# 配置参数
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backbone_dir = "Qwen3-1.7B"
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ckpt_path = "best_ckpt.pth"
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device = "cuda"
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engine = InferenceEngine(backbone_dir, ckpt_path, device) |