Thanks for your reply.
Unfortunately, the input input tensor is [batch_size, channels, height, width] and I used permute to achieve it.
I used the same pipeline with other C++ network and it’s worked well. That’s why I asked this question. I can’t understand where the issue comes from. I supposed it may come from the transcription from the python network to c++ network but it works when I used torch.jit.load.
For the transcription, I used only trace. The .graph and the .code do not help me well.
It may come from dtype issue. I’ll investigate on this.