Torch.onnx.export error

[shushan2017]

from tqdm import tqdm
import torch
import pandas as pd
import numpy as np
import math
from torch import nn
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()

data = np.genfromtxt('c:/data.csv', delimiter=",")
data=scaler.fit_transform(data.reshape(-1,1)).flatten()
# In[ ]:
iw = 96
ow = 15
train=data

from torch.utils.data import DataLoader, Dataset

class windowDataset(Dataset):
    def __init__(self, y, input_window=80, output_window=20, stride=3):
        L = y.shape[0]
        num_samples = (L - input_window - output_window) // stride + 1
        X = np.zeros([input_window, num_samples])
        Y = np.zeros([output_window, num_samples])
        print(X.shape,y.shape)
        for i in np.arange(num_samples):
            start_x = stride*i
            end_x = start_x + input_window
            X[:,i] = y[start_x:end_x]

            start_y = stride*i + input_window
            end_y = start_y + output_window
            Y[:,i] = y[start_y:end_y]

        # size: [num_samples, input_window, 1]
        X = X.reshape(X.shape[1], X.shape[0], 1) 
        Y = Y.reshape(Y.shape[1], Y.shape[0], 1)
        self.x = X
        self.y = Y
        self.len = len(X)

    def __getitem__(self, i):
        return self.x[i], self.y[i]
    def __len__(self):
        return self.len


# In[ ]:
train_dataset = windowDataset(train, input_window=iw, output_window=ow, stride=2)
train_loader = DataLoader(train_dataset, batch_size=512)
# In[ ]:
from PatchTST import PatchTST
class PT_config():
    def __init__(self):
        self.seq_len=iw
        self.pred_len=ow
        self.individual=0
        self.enc_in=1
        self.e_layers=3
        self.n_heads= 16 
        self.d_model= 128 
        self.d_ff= 256 
        self.dropout =0.2
        self.fc_dropout= 0.2
        self.head_dropout= 0
        self.patch_len =16
        self.stride =8
        self.padding_patch='end'
        self.revin=1
        self.affine=0
        self.subtract_last=0
        self.decomposition=1
        self.kernel_size=25

# In[ ]:
# # 3. Train
#device = torch.device("cuda")
device = torch.device("cpu")
lr = 1e-4

class modelParam():
    def __init__(self,label):
        self.model=PatchTST(configs=PT_config()).to(device)
        self.optimizer=torch.optim.Adam(self.model.parameters(), lr=lr)

    def epoch(self,epoch):
        self.epoch=epoch
        return self.epoch

# In[ ]:
criterion = nn.MSELoss() #0.58
# In[ ]:
PT=modelParam('PT')
PTmodel=PT.model.to(device)
optimizer=PT.optimizer
epoch=PT.epoch(2) #训练次数
progress = tqdm(range(epoch))
PTmodel.train()
losses=[]
for i in progress:
    batchloss = 0.0
    for (inputs, outputs) in train_loader:
        optimizer.zero_grad()
        #'''
        result = PTmodel(inputs.float().to(device))
        loss = criterion(result, outputs.float().to(device))
        loss.backward()
        optimizer.step()
        batchloss += loss
    losses.append(batchloss.cpu().item())
    progress.set_description("PT- loss: {:0.6f}".format(batchloss.cpu().item() / len(train_loader)))

torch.save(PTmodel.to('cpu'), 'PTmodel.pth')

# In[ ]:
input_shape = (1, 96, 1)  
input_names = ["input"]  
output_names = ["output"]  


x = torch.randn(input_shape)

 
onnx_model_path = "PTmodel.onnx"  
dynamic_axes = {'input': {0: 'batch_size', 1: 'sequence_length', 2: 'input_dim'}, # 输入张量的动态维度axis
                'output': {0: 'batch_size', 1: 'sequence_length', 2: 'output_dim'}} # 输出张量的动态维度axis
torch.onnx.export(PTmodel, x, onnx_model_path, input_names=input_names, output_names=output_names, dynamic_axes=dynamic_axes)

data.csv

[yuqinie98]

please provide more details

[shushan2017]

please provide more details

Thank you for your prompt reply! I have modified my question and uploaded my code and test data. Could you please take a look at any bugs in my code? thank you

[yuqinie98]

Hi, somehow you delete the error hint, so sorry I cannot figure out a specific error here. If you want to get a more intuitive code to start with, maybe you could tried with this code: https://github.com/timeseriesAI/tsai/blob/main/tutorial_nbs/15_PatchTST_a_new_transformer_for_LTSF.ipynb.

[shushan2017]

thank you! tsai is ok!