Initializing LSTM which is imported using ONNX

Hi,

I am training an LSTM for RL using Ray in Python. I would like to export this model using ONNX and afterwards import it in Matlab. As far as I have understood, I need to initialize the model in matlab after importing. However, I cannot find out the correct input shapes/formats in Matlab to make this work.

Minimum working example:

Python code to train LSTM:

import torch
import numpy as np
from ray.rllib.algorithms.ppo import PPOConfig
% Config Algorithm
algo = (
PPOConfig()
.env_runners(num_env_runners=1)
.resources(num_gpus=0)
.environment(env="CartPole-v1")
.training(model={"use_lstm": True})
.build()
)
% train for 2 episodes
for i in range(2):
    result = algo.train()
    
% get policiy
ppo_policy = algo.get_policy()

% batch size
B=1

% initialize LSTM input:
input_dict = {"obs":  torch.tensor(np.random.uniform(0, 1.0, size=(B,4)).astype(np.float32))}
state_batches = [torch.zeros((B,256), dtype=torch.float32),torch.zeros((B,256), dtype=torch.float32)]
seq_lens = torch.ones([B], dtype=int)

% apply LSTM to inputs
policy = algo.get_policy()
model = policy.model
print(model(input_dict, state=state_batches, seq_lens=seq_lens))

% save model to ONNX
ppo_policy.export_model('onnx14', onnx=14)

Code in Matlab:

% Import model from where I saved it
net = importNetworkFromONNX('path/to/onnx-model');

% input shapes
obs_size = [1,4];
state_size=[2,1,256];
seq_lens_size=[1];

% initialize input arrays
obs = dlarray(rand(obs_size),"BS");
state = dlarray(rand(state_size),"SBS");
seq_len = dlarray(rand(seq_lens_size),"SB");

% initialize net
net = initialize(net,obs,state,seq_len);

Error message:

I appreciate any help!

Best,

Andreas

[Nilesh]

Hello Andreas,

Have you tried asking your issue with ChatGPT.

[Andreas]

Yes, but without success so far.

Answer 1

[Joss Knight]

This code is suspect

% initialize input arrays
obs = dlarray(rand(obs_size),"BS");
state = dlarray(rand(state_size),"SBS");
seq_len = dlarray(rand(seq_lens_size),"SB");

% initialize net
net = initialize(net,obs,state,seq_len);

I think your network has a single input, so you need to pass a single input to initialize (along with the network), basically just some example input exactly like you want to pass to predict. I think you have two channels and a sequence length of 256? And one of your dimensions is Time so you need a T dimension. And I don't think you have any spatial dimensions, so no S labels. So you need something like

exampleInput = dlarray(rand(2,1,256),'CBT');
net = initialize(net, exampleInput);

Or if you prefer, a permutation of that like

exampleInput = dlarray(rand(256,2,1),'TCB');
net = initialize(net, exampleInput);

If this doesn't work, try running analyzeNetwork(net) to see where your inputs are and we can work out what to expect.

Answer 2

[Kaustab Pal]

Hi @Andreas,

It seems you want to determine the input dimension of your imported network. You can easily find this information using the analyzeNetwork function. This function provides an interactive visualization of the network architecture and detailed information, including:

• Layer types
• Sizes and formats of layer learnable parameters
• States and activations
• Total number of learnable parameters

The activation size of the topmost layer will give you the input dimension.

Additionally, when creating dlarray objects in MATLAB, you need to specify the format, which must follow this order:

• "S" (Spatial)
• "C" (Channel)
• "B" (Batch)
• "T" (Time)
• "U" (Unspecified)

For more details, you can refer to the following links:

1. analyzeNetwork Documentation: https://www.mathworks.com/help/deeplearning/ref/analyzenetwork.html#mw_bdd24886-fa03-4540-a111-391541a0a684
2. dlarray Documentation:: https://www.mathworks.com/help/deeplearning/ref/dlarray.html#d126e57736:~:text=When%20you%20create%20a%20formatted%20dlarray%20object%2C%20the%20software%20automatically%20permutes%20the%20dimensions%20such%20that%20the%20format%20has%20dimensions%20in%20this%20order%3A

Hope this helps.

[Joss Knight]

Just FYI, the formats do not have to follow that order.