321 lines
16 KiB
Python
321 lines
16 KiB
Python
# -*- coding:utf-8 -*-
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# 该脚本用来加载torchscript模型，并执行单张图片推理
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import argparse
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import time
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from pathlib import Path
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import cv2
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import torch
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from numpy import random
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import sys
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sys.path.append(R"../0_segrnn")
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import argparse
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import os
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import torch
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from exp.exp_long_term_forecasting import Exp_Long_Term_Forecast
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from exp.exp_classification import Exp_Classification
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from utils.print_args import print_args
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from utils.tools import visual
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from models import SegRNN
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import torch.nn.functional as F
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import random
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import numpy as np
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def cal_accuracy(y_pred, y_true):
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return np.mean(y_pred == y_true)
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def cal_precision_recall(predictions, trues):
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TP = np.sum((predictions == 1) & (trues == 1))
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FP = np.sum((predictions == 1) & (trues == 0))
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FN = np.sum((predictions == 0) & (trues == 1))
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precision = TP / (TP + FP)
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recall = TP / (TP + FN)
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f1 = 2 * (precision * recall) / (precision + recall)
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return precision, recall, f1
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def segrnn_forward_ltf(self, x,pos_emb,hx):
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x = self.valueEmbedding(x.reshape(-1, self.seg_num_x, self.seg_len))
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# encoding
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_, hn = self.rnn(x, hx) # bc,n,d 1,bc,d
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h0 = F.max_pool1d(hn, kernel_size=1, stride=1)
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h1 = F.max_pool1d(hn, kernel_size=1, stride=1)
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h2 = F.max_pool1d(hn, kernel_size=1, stride=1)
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h3 = F.max_pool1d(hn, kernel_size=1, stride=1)
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hn = torch.cat([h0,h1,h2,h3], dim=2)
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hn = hn.view(1, -1, self.d_model)
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_, hy = self.rnn(pos_emb, hn) # bcm,1,d 1,bcm,d
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y = self.predict(hy).view(-1, self.enc_in, self.pred_len)
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y = y.permute(0, 2, 1)
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return y
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def segrnn_forward_cls(self, x,pos_emb,hx):
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# segment and embedding b,c,s -> bc,n,w -> bc,n,d
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x = self.valueEmbedding(x.reshape(-1, self.seg_num_x, self.seg_len))
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# encoding
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_, hn = self.rnn(x, hx) # bc,n,d 1,bc,d
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h0 = F.max_pool1d(hn, kernel_size=1, stride=1)
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h1 = F.max_pool1d(hn, kernel_size=1, stride=1)
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h2 = F.max_pool1d(hn, kernel_size=1, stride=1)
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h3 = F.max_pool1d(hn, kernel_size=1, stride=1)
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h4 = F.max_pool1d(hn, kernel_size=1, stride=1)
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h5 = F.max_pool1d(hn, kernel_size=1, stride=1)
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h6 = F.max_pool1d(hn, kernel_size=1, stride=1)
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h7 = F.max_pool1d(hn, kernel_size=1, stride=1)
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h8 = F.max_pool1d(hn, kernel_size=1, stride=1)
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# hn = hn.repeat(1, 1, self.seg_num_y)
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hn = torch.cat([h0,h1,h2,h3,h4,h5,h6,h7,h8], dim=2)
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hn = hn.view(1, -1, self.d_model)
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_, hy = self.rnn(pos_emb, hn) # bcm,1,d 1,bcm,d
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# 1,bcm,d -> 1,bcm,w -> b,c,s
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y = self.predict(hy).view(-1, self.enc_in, self.pred_len)
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# permute and denorm
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y = y.permute(0, 2, 1)
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output = y.reshape(y.shape[0], -1)
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# (batch_size, num_classes)
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output = self.projection(output)
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return output
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def infer(self, setting, test=0):
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# if test:
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# print('loading model')
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# self.model.load_state_dict(torch.load(os.path.join('../0_segrnn/checkpoints/' + setting, 'checkpoint.pth')))
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preds = []
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trues = []
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folder_path = './test_results/' + setting + '/'
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if not os.path.exists(folder_path):
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os.makedirs(folder_path)
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# self.model.eval()
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with torch.no_grad():
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if self.args.task_name == 'long_term_forecast':
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self.model = torch.jit.load(self.args.model_pt['ltf'])
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print('model load Done')
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test_data, test_loader = self._get_data(flag='test')
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for i, (batch_x, batch_y, batch_x_mark, batch_y_mark) in enumerate(test_loader):
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batch_x = batch_x.float().to(self.device)
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batch_y = batch_y.float().to(self.device)
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batch_x_mark = batch_x_mark.float().to(self.device)
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batch_y_mark = batch_y_mark.float().to(self.device)
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# decoder input
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dec_inp = torch.zeros_like(batch_y[:, -self.args.pred_len:, :]).float()
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dec_inp = torch.cat([batch_y[:, :self.args.label_len, :], dec_inp], dim=1).float().to(self.device)
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# #forward
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# outputs = self.model(batch_x, batch_x_mark, dec_inp, batch_y_mark)
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# #trace
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# SegRNN.Model.forward = segrnn_forward_ltf
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pos_emb = torch.load('../2_compile/fmodel/pos_emb_ltf.pth').cpu()
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seq_last = batch_x[:, -1:, :].detach()
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batch_x = (batch_x - seq_last).permute(0, 2, 1) # b,c,s
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hx = torch.zeros(1,7, 512, dtype=batch_x.dtype, device=batch_x.device)
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# traced_script_module = torch.jit.trace(self.model.cpu(),(batch_x.cpu(), pos_emb.cpu(), hx.cpu()))
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traced_out = self.model(batch_x, pos_emb, hx)
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#compare to outputs
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outputs = traced_out+seq_last
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f_dim = -1 if self.args.features == 'MS' else 0
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outputs = outputs[:, -self.args.pred_len:, :]
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batch_y = batch_y[:, -self.args.pred_len:, :].to(self.device)
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outputs = outputs.detach().cpu().numpy()
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batch_y = batch_y.detach().cpu().numpy()
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outputs = outputs[:, :, f_dim:]
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batch_y = batch_y[:, :, f_dim:]
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pred = outputs
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true = batch_y
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preds.append(pred)
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trues.append(true)
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input = (batch_x.permute(0, 2, 1)+seq_last).detach().cpu().numpy()
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gt = np.concatenate((input[0, :, -1], true[0, :, -1]), axis=0)
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pd = np.concatenate((input[0, :, -1], pred[0, :, -1]), axis=0)
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visual(gt, pd, os.path.join(folder_path, str(i) + '.pdf'))
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print('pt model infer fished,result saved in{}'.format(folder_path))
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break
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elif args.task_name == 'classification':
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self.model = torch.jit.load(self.args.model_pt['cls'])
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print('model load Done')
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test_data, test_loader = self._get_data(flag='TEST')
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for i, (batch_x, label, padding_mask) in enumerate(test_loader):
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batch_x = batch_x.float().to(self.device)
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padding_mask = padding_mask.float().to(self.device)
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label = label.to(self.device)
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#forward
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pos_emb = torch.load('../2_compile/fmodel/pos_emb_cls.pth')
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batch_x = batch_x.permute(0, 2, 1)
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hx = torch.zeros(1,61, 512, dtype=batch_x.dtype, device=batch_x.device)
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# traced_script_module = torch.jit.trace(self.model.cpu(),(batch_x.cpu(), pos_emb.cpu(), hx.cpu()))
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traced_out = self.model(batch_x.cpu(), pos_emb.cpu(), hx.cpu())
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outputs = traced_out
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probs = torch.nn.functional.softmax(outputs) # (total_samples, num_classes) est. prob. for each class and sample
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predictions = torch.argmax(probs, dim=1).cpu().numpy() # (total_samples,) int class index for each sample
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trues = label.flatten().cpu().numpy()
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print('pt model infer fished,result:')
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print('predictions:{},trues{}'.format(predictions,trues))
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break
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return
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if __name__ == '__main__':
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fix_seed = 2024
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random.seed(fix_seed)
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torch.manual_seed(fix_seed)
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np.random.seed(fix_seed)
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parser = argparse.ArgumentParser(description='TimesNet')
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# basic config
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parser.add_argument('--task_name', type=str, required=True, default='long_term_forecast',
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help='task name, options:[long_term_forecast, short_term_forecast, imputation, classification, anomaly_detection]')
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parser.add_argument('--is_training', type=int, required=True, default=1, help='status')
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parser.add_argument('--model_id', type=str, required=True, default='test', help='model id')
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parser.add_argument('--model', type=str, required=True, default='Autoformer',
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help='model name, options: [Autoformer, Transformer, TimesNet]')
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parser.add_argument('--model_pt', type=dict, default={'ltf':'../2_compile/fmodel/segrnn_ltf.pt','cls':'../2_compile/fmodel/segrnn_cls.pt'}, help='torchscript model path')
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# data loader
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parser.add_argument('--data', type=str, required=True, default='ETTm1', help='dataset type')
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parser.add_argument('--root_path', type=str, default='./data/ETT/', help='root path of the data file')
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parser.add_argument('--data_path', type=str, default='ETTh1.csv', help='data file')
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parser.add_argument('--features', type=str, default='M',
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help='forecasting task, options:[M, S, MS]; M:multivariate predict multivariate, S:univariate predict univariate, MS:multivariate predict univariate')
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parser.add_argument('--target', type=str, default='OT', help='target feature in S or MS task')
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parser.add_argument('--freq', type=str, default='h',
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help='freq for time features encoding, options:[s:secondly, t:minutely, h:hourly, d:daily, b:business days, w:weekly, m:monthly], you can also use more detailed freq like 15min or 3h')
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parser.add_argument('--checkpoints', type=str, default='./checkpoints/', help='location of model checkpoints')
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# forecasting task
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parser.add_argument('--seq_len', type=int, default=96, help='input sequence length')
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parser.add_argument('--label_len', type=int, default=48, help='start token length')
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parser.add_argument('--pred_len', type=int, default=96, help='prediction sequence length')
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parser.add_argument('--seasonal_patterns', type=str, default='Monthly', help='subset for M4')
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parser.add_argument('--inverse', action='store_true', help='inverse output data', default=False)
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# model define
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parser.add_argument('--expand', type=int, default=2, help='expansion factor for Mamba')
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parser.add_argument('--d_conv', type=int, default=4, help='conv kernel size for Mamba')
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parser.add_argument('--top_k', type=int, default=5, help='for TimesBlock')
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parser.add_argument('--num_kernels', type=int, default=6, help='for Inception')
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parser.add_argument('--enc_in', type=int, default=7, help='encoder input size')
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parser.add_argument('--dec_in', type=int, default=7, help='decoder input size')
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parser.add_argument('--c_out', type=int, default=7, help='output size')
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parser.add_argument('--d_model', type=int, default=512, help='dimension of model')
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parser.add_argument('--n_heads', type=int, default=8, help='num of heads')
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parser.add_argument('--e_layers', type=int, default=2, help='num of encoder layers')
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parser.add_argument('--d_layers', type=int, default=1, help='num of decoder layers')
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parser.add_argument('--d_ff', type=int, default=2048, help='dimension of fcn')
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parser.add_argument('--moving_avg', type=int, default=25, help='window size of moving average')
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parser.add_argument('--factor', type=int, default=1, help='attn factor')
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parser.add_argument('--distil', action='store_false',
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help='whether to use distilling in encoder, using this argument means not using distilling',
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default=True)
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parser.add_argument('--dropout', type=float, default=0.1, help='dropout')
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parser.add_argument('--embed', type=str, default='timeF',
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help='time features encoding, options:[timeF, fixed, learned]')
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parser.add_argument('--activation', type=str, default='gelu', help='activation')
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parser.add_argument('--output_attention', action='store_true', help='whether to output attention in ecoder')
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parser.add_argument('--channel_independence', type=int, default=1,
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help='0: channel dependence 1: channel independence for FreTS model')
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parser.add_argument('--decomp_method', type=str, default='moving_avg',
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help='method of series decompsition, only support moving_avg or dft_decomp')
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parser.add_argument('--use_norm', type=int, default=1, help='whether to use normalize; True 1 False 0')
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parser.add_argument('--down_sampling_layers', type=int, default=0, help='num of down sampling layers')
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parser.add_argument('--down_sampling_window', type=int, default=1, help='down sampling window size')
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parser.add_argument('--down_sampling_method', type=str, default=None,
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help='down sampling method, only support avg, max, conv')
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parser.add_argument('--seg_len', type=int, default=48,
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help='the length of segmen-wise iteration of SegRNN')
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# optimization
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parser.add_argument('--num_workers', type=int, default=10, help='data loader num workers')
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parser.add_argument('--itr', type=int, default=1, help='experiments times')
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parser.add_argument('--train_epochs', type=int, default=10, help='train epochs')
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parser.add_argument('--batch_size', type=int, default=32, help='batch size of train input data')
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parser.add_argument('--patience', type=int, default=3, help='early stopping patience')
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parser.add_argument('--learning_rate', type=float, default=0.0001, help='optimizer learning rate')
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parser.add_argument('--des', type=str, default='test', help='exp description')
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parser.add_argument('--loss', type=str, default='MSE', help='loss function')
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parser.add_argument('--lradj', type=str, default='type1', help='adjust learning rate')
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parser.add_argument('--use_amp', action='store_true', help='use automatic mixed precision training', default=False)
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# GPU
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parser.add_argument('--use_gpu', type=bool, default=False, help='use gpu')
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parser.add_argument('--gpu', type=int, default=0, help='gpu')
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parser.add_argument('--use_multi_gpu', action='store_true', help='use multiple gpus', default=False)
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parser.add_argument('--devices', type=str, default='0,1,2,3', help='device ids of multile gpus')
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# de-stationary projector params
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parser.add_argument('--p_hidden_dims', type=int, nargs='+', default=[128, 128],
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help='hidden layer dimensions of projector (List)')
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parser.add_argument('--p_hidden_layers', type=int, default=2, help='number of hidden layers in projector')
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# metrics (dtw)
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parser.add_argument('--use_dtw', type=bool, default=False,
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help='the controller of using dtw metric (dtw is time consuming, not suggested unless necessary)')
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args = parser.parse_args()
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print('Args in experiment:')
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print_args(args)
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if args.task_name == 'long_term_forecast':
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Exp_Long_Term_Forecast.infer = infer
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Exp = Exp_Long_Term_Forecast
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elif args.task_name == 'classification':
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Exp_Classification.infer = infer
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Exp = Exp_Classification
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else:
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Exp = Exp_Long_Term_Forecast
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if args.is_training:
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pass
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else:
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ii = 0
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exp = Exp(args) # set experiments
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setting = '{}_{}_{}_{}_ft{}_sl{}_ll{}_pl{}_dm{}_nh{}_el{}_dl{}_df{}_expand{}_dc{}_fc{}_eb{}_dt{}_{}_{}'.format(
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args.task_name,
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args.model_id,
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args.model,
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args.data,
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args.features,
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args.seq_len,
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args.label_len,
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args.pred_len,
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args.d_model,
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args.n_heads,
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args.e_layers,
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args.d_layers,
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args.d_ff,
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args.expand,
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args.d_conv,
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args.factor,
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args.embed,
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args.distil,
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args.des, ii)
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print('>>>>>>>infer : {}<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<'.format(setting))
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exp.infer(setting, test=1)
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