onnx-mlir-init-test

Links to get input images and postprocessed image

1. Unet
2. Alexnet
3. resnet
4. mobilenet-net

Steps to generate and run bin

1. follow instructions from onnx-mlir-docker to setup onnx-mlir-docker
2. create parent folder automation in path containerId:/workdir/
3. create folder with name onnx-mlir_files, llvm-IR_files/raw etc etc (check ref-for-docker-folder-structure for list of all folders`
4. use torchhub links to get proper input for all models.
5. Update <model>.main.cpp -> static float img_data[] = {<input data as 1D array>} [line 16] and shape[] [line 18]
6. if required use Netron to get input and output dimensions. Update img_data and input img dimension
7. run cd auto_scripts
8. run ./get_bin_abs.sh <filename> to copy files from local to docker and generate llvm IR, asm and then exec bin
9. to regenerate only bin run ./get_bin_abs.sh <filename> bin
10. this cmd will generate filename.output.txt file containing model output as 1D array.

TO-DO Use output array and get post processed image / data for all 4 models and comapre them.

Ref for docker folder structure

run ./delete_all_files_con.sh to delete all generated files from docker container and run ./delete_all_files_host.sh to delete all generated files from local

to read numpy bytes

save numpy bites in google colab

file_path = "/content/drive/My Drive/<model>.bin"  # Change the file path as needed
# np.save(file_path, tensor_data.numpy().tobytes())


with open(file_path, "wb") as f:
    # Write the data to the file
    f.write(tensor_data.numpy().tobytes())

copy .bin file and paste it in bin folder/ folder in which all excutable binary will be saved and excuted from.