onnx是Facebook打造的AI中介軟體，但是Tensorflow官方不支援onnx，所以只能用onnx自己提供的方式從tensorflow嘗試轉換

Tensorflow模型轉onnx

Tensorflow轉onnx， onnx官方github上有提供轉換的方式，地址為https://github.com/onnx/tutorials/blob/master/tutorials/OnnxTensorflowExport.ipynb 。按連結中的步驟一步一步就能完成mnist的模型轉換，我也成功轉換出了mnist.onnx模型。但是在上面步驟中model = onnx.load('mnist.onnx')

onnx模型轉換為Tensorflow模型

上面提到按官網的教程從tensorflow轉換生成的onnx模型執行tf_rep = prepare(model)有問題。所以這裡我從網上下載的一個pytorch轉換的mnist onnx模型為實驗物件，實驗用的onnx下載地址：https://download.csdn.net/download/computerme/10448754
onnx模型轉換為Tensorflow模型的程式碼如下：

import onnx
import numpy as np
from onnx_tf.backend import prepare

model = onnx.load('./assets/mnist_model.onnx')
tf_rep = prepare(model)

img = np.load("./assets/image.npz")
output = tf_rep.run(img.reshape([1, 1,28,28]))

print("outpu mat: \n",output)
print("The digit is classified as ", np.argmax(output))

import
 
 tensorflow as tf
with tf.Session() as persisted_sess:
    print("load graph")
    persisted_sess.graph.as_default()
    tf.import_graph_def(tf_rep.predict_net.graph.as_graph_def(), name='')
    inp = persisted_sess.graph.get_tensor_by_name(
        tf_rep.predict_net.tensor_dict[tf_rep.predict_net.external_input[0]].name
    )
    out = persisted_sess.graph.get_tensor_by_name(
        tf_rep.predict_net.tensor_dict[tf_rep.predict_net.external_output[0]].name
    )
    res = persisted_sess.run(out, {inp: img.reshape([1, 1,28,28])})
    print(res)
    print("The digit is classified as ",np.argmax(res))

tf_rep.export_graph('tf.pb')

轉換完成後，需要對轉換出的tf.pb模型進行驗證，驗證方式如下：

import numpy as np
import tensorflow as tf
from tensorflow.python.platform import gfile

name = "tf.pb"

with tf.Session() as persisted_sess:
    print("load graph")
    with gfile.FastGFile(name, 'rb') as f:
        graph_def = tf.GraphDef()
        graph_def.ParseFromString(f.read())

    persisted_sess.graph.as_default()
    tf.import_graph_def(graph_def, name='')

    inp = persisted_sess.graph.get_tensor_by_name('0:0')
    out = persisted_sess.graph.get_tensor_by_name('LogSoftmax:0')
    #test = np.random.rand(1, 1, 28, 28).astype(np.float32)
    #feed_dict = {inp: test}

    img = np.load("./assets/image.npz")
    feed_dict = {inp: img.reshape([1, 1,28,28])}

    classification = persisted_sess.run(out, feed_dict)
    print(out)
    print(classification)