TensorflowUsage.md

This md will show you how to apply onnx-tool to a tensorflow-keras model

• tensorflow export to ONNX, then use onnx_tool

  import tensorflow
  from tensorflow import keras
  import onnx
  import tf2onnx
  import onnx_tool
  from onnx_tool import create_ndarray_f32
  
  temp_model_file = 'tmp.onnx'
  
  model = tensorflow.keras.applications.InceptionV3(
      include_top=True,
      weights="imagenet",
      input_tensor=None,
      input_shape=None,
      pooling=None,
      classes=1000,
      classifier_activation="softmax",
  )
  onnx_model = tf2onnx.convert.from_keras(model,
                  input_signature=None, opset=None, custom_ops=None,
                  custom_op_handlers=None, custom_rewriter=None,
                  inputs_as_nchw=None, outputs_as_nchw=None, extra_opset=None,
                  shape_override=None, target=None, large_model=False, output_path=None)
  if isinstance(onnx_model,(list,tuple)):
      onnxproto=onnx_model[0]
  onnx.save_model(onnxproto, temp_model_file)
  inputshape=(1, 299,299,3)
  dynamics_input={
      'input_1':create_ndarray_f32(inputshape)
  }
  onnx_tool.model_profile(temp_model_file,dynamic_shapes=dynamics_input)

• example code tfkeras_example.py