111 lines
4.9 KiB
Python
111 lines
4.9 KiB
Python
from guangfufadian.cross_exp.exp_crossformer import Exp_crossformer # 根据实际路径导入
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from guangfufadian.utils.tools import StandardScaler
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import pickle,os,sys
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import torch
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import pandas as pd
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import torch
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import numpy as np
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# 获取当前工作目录
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current_directory = os.getcwd()
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current_directory = os.path.join(current_directory,'guangfufadian')
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# 设置参数
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class guangfufadian_Args:
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# 在这里定义模型参数
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data_dim = 14
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in_len = 192
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out_len = 96
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seg_len = 6
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win_size = 2
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factor = 10
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d_model = 256
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d_ff = 512
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n_heads = 4
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e_layers = 3
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dropout = 0.2
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use_multi_gpu = False
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use_gpu = True
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device_ids = [0] # 如果使用多 GPU，设置设备 ID
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batch_size = 32
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train_epochs = 100
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patience = 10
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num_workers = 4
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# root_path = '/home/xiazj/project_test/Crossformer-master/datasets/station08_utf8.csv' # 数据路径
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data_path = os.path.join(current_directory,'datasets/station08_utf8.csv') # 数据文件名
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checkpoints = os.path.join(current_directory,'checkpoints') # 模型保存路径
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learning_rate = 0.001 # 学习率
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data_split = 0.8 # 数据划分比例
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scale_statistic = pickle.load(open(os.path.join(checkpoints,'Crossformer_station08_il192_ol96_sl6_win2_fa10_dm256_nh4_el3_itr0/scale_statistic.pkl'), 'rb'))
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baseline = False
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gpu = 0
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# 实例化参数
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guangfufadian_args = guangfufadian_Args()
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class ModelInference:
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def __init__(self, model_path, args):
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# 初始化模型
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self.model_experiment = Exp_crossformer(args)
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self.model_experiment.model.load_state_dict(torch.load(model_path)) # 加载模型参数
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self.model_experiment.model.eval() # 设置模型为评估模式
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self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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self.model_experiment.model.to(self.device)
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self.scaler = StandardScaler(mean = args.scale_statistic['mean'], std = args.scale_statistic['std'])
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"""
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加载推理数据
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如果数据列发生变化，或者列不匹配，报错
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数据长度满足192，截取前192
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数据长度不满足192，前拼接满足数量级
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"""
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def load_pred_data(self,data):
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raw_data = data.values
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length = raw_data.shape[0]
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# 顺序，必须一致才能进行下一步
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check_list = ['date_time', 'nwp_globalirrad', 'nwp_directirrad', 'nwp_temperature',
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'nwp_humidity', 'nwp_windspeed', 'nwp_winddirection', 'nwp_pressure',
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'lmd_totalirrad', 'lmd_diffuseirrad', 'lmd_temperature', 'lmd_pressure',
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'lmd_winddirection', 'lmd_windspeed', 'power']
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columns_match = list(data.columns) == check_list
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if columns_match:
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if length > guangfufadian_args.in_len:
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input_data = raw_data[:guangfufadian_args.in_len, 1:].astype(np.float32)
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input_tensor = torch.tensor(input_data, dtype=torch.float32).unsqueeze(0) # 增加batch维度
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else:
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rows_to_add = guangfufadian_args.in_len - length
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first_row = raw_data[0]
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new_rows = [first_row] * rows_to_add # 复制第一行的值
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# 拼接新的行和原始数据
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input_data = new_rows + list(raw_data)
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input_data = np.array(input_data)
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input_data = input_data[:guangfufadian_args.in_len, 1:].astype(np.float32)
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input_tensor = torch.tensor(input_data, dtype=torch.float32).unsqueeze(0) # 增加batch维度
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return {'status':True, 'reason':input_tensor }
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else:
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print("文件不匹配，请检查上传文件与模版是否一致")
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return {'status':False, 'reason':'文件不匹配，请检查上传文件与模版是否一致'}
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# 数据归一化
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def preprocess_data(self, input_tensor):
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input_tensor = self.scaler.transform(input_tensor)
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return input_tensor.to(self.device)
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# 数据预测
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def predict(self, input_tensor):
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with torch.no_grad(): # 不计算梯度
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predictions = self.model_experiment._predict_batch(input_tensor)
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# return predictions.cpu().numpy() # 返回 NumPy 数组
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return predictions
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# 推理过程
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def run_inference(self, data):
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raw_data = self.load_pred_data(data)
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if raw_data['status'] == False:
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return {'status':False, 'reason':'文件不匹配，请检查上传文件与模版是否一致'}
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else:
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input_tensor = self.preprocess_data(raw_data['reason'])
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predictions = self.predict(input_tensor)
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predictions = self.scaler.inverse_transform(predictions)
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predictions = predictions.squeeze(0)
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predictions = predictions.cpu().numpy()
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return {'status':True, 'reason':predictions[:,-1:]} |