predict.py
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import torch
import pandas as pd
import numpy as np
from torch.utils.data import DataLoader, TensorDataset
from tqdm import tqdm
import os
import sys
import json
import chardet # 导入 chardet
# 导入您定义的模型和模块
from MHA import MultiHeadAttentionLayer
from classifier import FinalClassifier
from BERT_CTM import BERT_CTM_Model
# 设置设备
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
def detect_file_encoding(file_path, num_bytes=10000):
"""
使用 chardet 检测文件的编码。
:param file_path: 文件路径
:param num_bytes: 用于检测的字节数
:return: 检测到的编码
"""
with open(file_path, 'rb') as f:
rawdata = f.read(num_bytes)
result = chardet.detect(rawdata)
encoding = result['encoding']
confidence = result['confidence']
print(f"Detected encoding: {encoding} with confidence {confidence}")
return encoding
def get_bert_ctm_embeddings(texts, bert_model_path, ctm_tokenizer_path, n_components=12, num_epochs=20):
# 创建BERT_CTM_Model实例
bert_ctm_model = BERT_CTM_Model(
bert_model_path=bert_model_path,
ctm_tokenizer_path=ctm_tokenizer_path,
n_components=n_components,
num_epochs=num_epochs
)
# 加载已保存的CTM模型
bert_ctm_model.load_model()
# 获取嵌入
embeddings = bert_ctm_model.get_bert_embeddings(texts)
return embeddings
def prepare_dataloader(features, batch_size):
tensor_x = torch.tensor(features, dtype=torch.float32)
dataset = TensorDataset(tensor_x)
return DataLoader(dataset, batch_size=batch_size, shuffle=False)
def predict(model_save_path, input_data_path, output_path, bert_model_path, ctm_tokenizer_path, stats_output_path,
batch_size=128,
num_classes=2):
try:
# 加载模型
# 修改这里，设置 weights_only=True 以消除 FutureWarning
checkpoint = torch.load(model_save_path, map_location=device, weights_only=False)
classifier_model = FinalClassifier(input_dim=768, num_classes=num_classes)
classifier_model.load_state_dict(checkpoint['classifier_model_state_dict'])
classifier_model.to(device)
classifier_model.eval()
attention_model = MultiHeadAttentionLayer(embed_size=768, num_heads=8)
attention_model.load_state_dict(checkpoint['attention_model_state_dict'])
attention_model.to(device)
attention_model.eval()
# 检测文件编码
encoding = detect_file_encoding(input_data_path)
# 读取输入数据
data = pd.read_csv(input_data_path, encoding=encoding)
texts = data['TEXT'].tolist()
# 生成嵌入
print("Generating embeddings...")
embeddings = get_bert_ctm_embeddings(texts, bert_model_path, ctm_tokenizer_path)
# 准备DataLoader
data_loader = prepare_dataloader(embeddings, batch_size)
# 存储预测结果
all_predictions = []
with torch.no_grad():
for batch in tqdm(data_loader, desc="Predicting"):
batch_x = batch[0].to(device)
batch_x = torch.mean(batch_x, dim=1)
attention_output = attention_model(batch_x, batch_x, batch_x)
outputs = classifier_model(attention_output)
outputs = torch.mean(outputs, dim=1)
_, predicted = torch.max(outputs, 1)
all_predictions.extend(predicted.cpu().numpy())
# 保存预测结果
data['Predicted_Label'] = all_predictions
data.to_csv(output_path, index=False, encoding='utf-8')
print(f"Predictions saved to {output_path}")
# 统计标签的个数和占比
label_counts = data['Predicted_Label'].value_counts()
total_count = len(data)
stats = {}
for label, count in label_counts.items():
label_name = "良好" if label == 0 else "不良"
percentage = (count / total_count) * 100
stats[label_name] = {
'count': count,
'percentage': f"{percentage:.2f}%"
}
print(f"Label: {label_name}, Count: {count}, Percentage: {percentage:.2f}%")
# 将统计信息保存到 JSON 文件
with open(stats_output_path, 'w', encoding='utf-8') as f:
json.dump(stats, f, ensure_ascii=False)
return True # 成功执行
except Exception as e:
print(f"Error during prediction: {e}")
return False # 执行失败
if __name__ == "__main__":
if len(sys.argv) != 3:
print("Usage: python using_example.py <input_data_path> <stats_output_path>")
sys.exit(1)
input_data_path = sys.argv[1]
stats_output_path = sys.argv[2]
# 定义路径
model_save_path = 'BCAT/final_model.pt'
output_path = 'BCAT/predictions.csv' # 保存预测结果的文件
bert_model_path = 'BCAT/bert_model'
ctm_tokenizer_path = 'BCAT/sentence_bert_model'
# 执行预测
success = predict(model_save_path, input_data_path, output_path, bert_model_path, ctm_tokenizer_path,
stats_output_path)
if success:
sys.exit(0) # 成功
else:
sys.exit(1) # 失败