Discrepancies between Conv layer outputs of the ONNX model and converted TensorFlow models about onnx2tf

[PINTO0309]

Unfortunately, there is always an error of about 1e-4 due to differences in run-time calculation specifications.

If you can't tolerate that minor error, don't use tflite.

[SuhwanSong]

Unfortunately, there is always an error of about 1e-4 due to differences in run-time calculation specifications.

If you can't tolerate that minor error, don't use tflite.

Thanks :)

Sorry for bothering you but there is a discrepancy between the outputs of the TensorFlow model and the ONNX model.
Note that the outputs of TensorFlow Lite and TensorFlow models match.

Here's the new poc file
poc.zip

from os.path import join
import tensorflow
import onnxruntime
import numpy as np

import onnx2tf
from einops import rearrange


if __name__ == "__main__" :


    onnx_model_path = 'poc.onnx'
    tf_output_path = './tf_path'

    # Convert ONNX model into TensorFlow
    onnx2tf.convert(
        input_onnx_file_path=onnx_model_path,
        output_folder_path=tf_output_path,
        copy_onnx_input_output_names_to_tflite=True,
        non_verbose=True,
    )

    # input
    input_np = np.random.randn(1, 3, 224, 224).astype('f')

    # load and run onnx model
    ort_session = onnxruntime.InferenceSession(onnx_model_path)
    ort_output  = ort_session.run(None, {'x' : input_np})


    # Prepare input for TensorFlow models
    input_for_tf = rearrange(input_np, 'b c h w -> b h w c')

    interpreter = tensorflow.lite.Interpreter(join(tf_output_path, 'poc_float32.tflite'))
    input_details = interpreter.get_input_details()
    output_details = interpreter.get_output_details()

    interpreter.allocate_tensors()

    interpreter.set_tensor(input_details[0]['index'], input_for_tf)
    interpreter.invoke()
    output_data = interpreter.get_tensor(output_details[0]['index'])
    #print (output_data)


    # Load and run TensorFlow model
    tf_model = tensorflow.saved_model.load(tf_output_path)
    tf_output = tf_model(input_for_tf)


    # Compare ONNX and TensorFlow outputs
    if np.allclose(ort_output, tf_output, rtol=1e-03, atol=1e-04):
        print("Test Passed: ONNX and TensorFlow outputs match\n")
    else:
        print("Test Failed: ONNX and TensorFlow outputs differ\n")
        
    # Compare TFlite and TensorFlow outputs
    if np.allclose(output_data, tf_output, rtol=1e-03, atol=1e-04):
        print("Test Passed: TFlite and TensorFlow outputs match\n")
    else:
        print("Test Failed: TFlite and TensorFlow outputs differ\n")

[PINTO0309]

Sorry, but I don't understand the point you are trying to make. The final outputs of ONNX and TFLite match.

pip show onnx2tf

Name: onnx2tf
Version: 1.19.15

onnx2tf -i poc.onnx -cotof

poc_float32.tflite.zip