Error analysing LSTM model using X-Cube-AI:INTERNAL ERROR : Unkonwn dimensions : CH

[Wangxingkun]

When I analyze the ONNX model in CubeAl,it says INTERNAL ERROR : Unkonwn dimensions : CH.The code of the model is simple as follows，input_size = 1 hidden_size = 1 output_size = 1 num_layers = 1
I don't know why and I will really appreciate any advice or workarounds. Thank you：

class LSTMNet(nn.Module):
 def __init__(self, input_size, hidden_size, output_size, num_layers=1)
 super(LSTMNet, self).__init__()
 self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=False) # batch_first=False
 self.fc = nn.Linear(hidden_size, output_size)

 def forward(self, x):
 h0 = torch.zeros(num_layers, x.size(1), hidden_size).to(x.device) 
 c0 = torch.zeros(num_layers, x.size(1), hidden_size).to(x.device)
 out, _ = self.lstm(x, (h0, c0))
 out = self.fc(out[-1, :, :]) 
 return out

[Wangxingkun]

And this is my complete training code, if you know the reason why please reply me, thank you !!!

class SequenceDataset(Dataset):
 def __init__(self, sequences, labels):
 self.sequences = sequences
 self.labels = labels

 def __len__(self:(
 return len(self.labels)

 def __getitem__(self, idx):
 return torch.Tensor(self.sequences[idx]), torch.Tensor(self.labels[idx])

class LSTMNet(nn.Module):
 def __init__(self, input_size, hidden_size, output_size, num_layers=1:(
 super(LSTMNet, self).__init__()
 self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=False) # batch_first=False
 self.fc = nn.Linear(hidden_size, output_size)

 def forward(self, x):
 h0 = torch.zeros(num_layers, x.size(1), hidden_size).to(x.device) 
 c0 = torch.zeros(num_layers, x.size(1), hidden_size).to(x.device)
 out, _ = self.lstm(x, (h0, c0))
 out = self.fc(out[-1, :, :]) 
 return out

input_size = 1 
hidden_size = 1 
output_size = 1
num_layers = 1
num_epochs = 120
batch_size = 32
learning_rate = 0.001

num_samples = 1000
sequence_length = 15
sequences = torch.rand(num_samples, sequence_length, input_size)
labels = torch.randint(0, 2, (num_samples, 1))

dataset = SequenceDataset(sequences.numpy(), labels.numpy())
train_loader = DataLoader(dataset, batch_size=batch_size, shuffle=True)

model = LSTMNet(input_size, hidden_size, output_size, num_layers)
criterion = nn.BCEWithLogitsLoss()
optimizer = optim.Adam(model.parameters(), lr=learning_rate)

model.train()
for epoch in range(num_epochs):
 for seqs, lbls in train_loader:
 seqs = seqs.permute(1, 0, 2) 
 outputs = model(seqs)
 loss = criterion(outputs, lbls)
 optimizer.zero_grad()
 loss.backward()
 optimizer.step()
 print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item():.4f}')

example_input = torch.rand(sequence_length, 1, input_size) 
torch.onnx.export(model, example_input, 'lstm_model.onnx',
 input_names=['input'],
 output_names=['output'],
 dynamic_axes={'input': {1: 'batch_size', 0: 'sequence_length'},
 'output': {0: 'batch_size'}})
print('Model exported as lstm_model.onnx')

[Julian E.]

Hello @Wangxingkun,

 

I am not an expert regarding pytorch so I will need my colleagues for a more complete answer. But they are in vacation. In the meantime, you can take a look at this code that works and should help you

 

import torch
import torch.nn as nn

class LSTM(nn.Module):
 def __init__(self, input_size, hidden_size, num_layers, num_output=1):
 super(LSTM, self).__init__()
 self.num_layers = num_layers
 self.input_size = input_size
 self.hidden_size = hidden_size
 self.num_output = num_output

 self.lstm = nn.LSTM(input_size=input_size, hidden_size=hidden_size,
 num_layers=num_layers, batch_first=False)

 self.fc = nn.Linear(hidden_size, num_output)

 def forward(self, x, device='cuda'):

 ula, (h_out, _) = self.lstm(x) 
 out = self.fc(h_out[-1]) 
 return out

# If not 1 : error
# other we can put anything
input_size = 10
hidden_size = 10
num_layers = 1
num_output = 10

model = LSTM(input_size, hidden_size, num_layers, num_output)

model.eval()

dummy_input = torch.randn(1, 128, input_size) # Batch size 1, Sequence length 128, Input size 10

onnx_file_path = "simple_lstml.onnx"
torch.onnx.export(model, dummy_input, onnx_file_path, 
 input_names=['input'], output_names=['output'])

 

Have a good day,

Julian

[Wangxingkun]

Thank you very much Julian, I have tried your code but it still says 'INTERNAL ERROR : Unkonwn dimensions : CH'.It seems that as long as there is an LSTM layer in the model, there will be the following error. If it is a fully connected layer, there will be no such error.But in my project, the LSTM layer is necessary, and I don't know how to fix it.It makes me a little frustrated.