yolov8n onnx to tflite without transpose ops

[codewarrior26]

Issue Type

Others

OS

Linux

onnx2tf version number

1.25.7

onnx version number

1.16.1

onnxruntime version number

1.18.1

onnxsim (onnx_simplifier) version number

0.4.33

tensorflow version number

2.17.0

Download URL for ONNX

https://drive.google.com/file/d/1aFK3NAkWutnOFFw2LWiun9JeYS8k0Qu6/view?usp=sharing

Parameter Replacement JSON

{
  "format_version": 1,
  "operations": [

    {
      "op_name": "wa/model.22/Concat",
      "param_target": "outputs",
      "param_name": "wa/model.22/Concat_output_0",
      "post_process_transpose_perm": [0, 3, 1, 2]
    }
   
  ]
}

Description

Purpose: Personal development

What: There are no errors occurring during the conversion from onnx to tflite, but I want to understand if there is a way to remove the one or more of (4) transpose operators in the resulting tflite graph during the conversion process.

How I used ultralytics library to convert yolov8n.pt model to tflite file using their export function. However, the final tflite file contains 4 transpose operators. It seems like ultralytics export function uses the onnx2tf tool for conversion, but it looks like they don't use --prf option. So, I want to use the onnx2tf to convert the yolov8n.onnx file to tflite float 32 by myself. So when I run !onnx2tf -i yolov8n.onnx, the conversion is successful, but I wanted to explore further if the 4 transpose ops can be avoided. So I wanted to understand how I could go about using the replacement.json file for achieving this.

For example: this is a part of the onnx graph (visualized on netron)

And for this part, the resulting tflite graph looks like:

where a transpose operator gets added between concatenation and reshape.

In order to avoid the above transpose operator, I attempted to use replace.json as stated above. But I'm pretty sure that won't help. I would really appreciate some help and guidance as to how I could approach avoiding the 4 transpose operators.

Why: If I can understand how to use replacement.json in this case, I could use that knowledge for conversion of similar models.