!!IMPORTANT BUG 1st RELEASE!! The first release has a bug: does not permute input dimensions directly (CHW->HWC), so it has to be transposed before hand. The framework only works with grey images out of the box. Clone the current master branch of the repo to have an out of the box functionality for RGB images.

torch2cmsis

Library for converting a neural network developed in Pytorch to CMSIS Legacy API.

The idea is simple:

1. Build your network in PyTorch.
2. Add the CMSISConverter and run the steps

cm_converter = CMSISConverter(<cfiles_folder>, <pytorch_model>, <header file for weights>, <header_file_name_parameters>, <bits>, <compilation_string>
cm_converter.convert_model()

3. Now you have both header files with all the weights and parameters necessary for running the model with CMSIS
4. Once you have your model built with CMSIS, you can run: cm_converter.evaluate_cmsis(<execution_file>, <test_dataloader>) to check the accuracy.
5. You can check that the activations for an input of all Conv and Linear layers from CMSIS and PyTOrch match with cm_converter.sample_inference_checker(<execution_path_cfile>, <input>, draw=True)

NOTE: currently the automatic code generation is under development so you still have to create your network manually. IMPORTANT: read te section Main Caveats, because there are certain requirements to use this package

Installation

Just pip install torch2cmsis

If you clone the repo just pip install . in the main directory

Examples

In the folder examples you are going to find different explained examples on how to use torch2cmsis at different levels of configuration.

Clone the repo recursively to include CMSIS: git clone --recursive https://github.com/BCJuan/torch2cmsis.git

• MNIST Example:
  • Shows how to obtain quantized weights and parameters as header files ready to use for CMSIS models
  • How to test the CMSIS model
  • How to obtain basic logs to manually check activations

Development

Support matrix for differen architecture components and features.

• Layers:
  • Conv2d
  • Linear
  • MaxPool2d
  • AvgPool2d
  • DepthwiseConv2d
• Quantization
  • q7
  • q15
• Other matters:
  • Non square matrices
  • Concatenation
  • Element Wise Addition
  • Element Wise Product
• Automatic code generation
  • ...
• Adaptation to s8 scheme
  • ...

Main caveats for usage (IMPORTANT)

• Model needs a method which delivers the size of the interface between convolutions and the first fully connected layer. We need to solve this to make it automatic.
  • See the definition of self.conv_linear_interface_shape in CMSISConverter
• In the CMSIS network definition, save the output of your prediction (vector level, not class) as y_out.raw.
  • You can see an example at examples/mnist/cfiles7main.c, where save("logs/y_out.raw", y_out, sizeof(y_out));
• The first fully connected layer encountered inside the model is considered to be the interface between the convolution layers and fully connected ones.
  • See lines 283, 68 with the definition of self.conv_linear_interface_shape
  • You have to name it self.interface in the definition of the model.
• All layers must be on their own, that is, do not use containers such as nn.Sequential, nn.ModuleList or others. This is because the converter uses named_children for inspecting the graph and depending on how layers are defined the graph description might stop at the container.
• Dute to the need of internal evaluations of the network during the refinement of the Q.Q formats there are constraints for how the dataloaders serve the inputs and labels.
  • All the dataloaders used must give a tuple of (input, output) like:

  for inputs, labels in dataloader:
  ...
  

  • We are currently working to make this constraint go away

Contributing

There is a lot of work to do (as detailed in the Development section), so if you want to contribute do not doubt to reach me.