onnx与tensorflow格式的相互转换

onnx是Facebook打造的AI中间件，但是Tensorflow官方不支持onnx，所以只能用onnx自己提供的方式从tensorflow尝试转换

Tensorflow模型转onnx

Tensorflow转onnx， onnx官方github上有提供转换的方式，地址为https://github.com/onnx/tutorials/blob/master/tutorials/OnnxTensorflowExport.ipynb 。按链接中的步骤一步一步就能完成mnist的模型转换，我也成功转换出了mnist.onnx模型。但是在上面步骤中model = onnx.load('mnist.onnx')之后执行tf_rep = prepare(model)一直不成功。但是换成网上别人用pytorch转的mnist.onnx执行tf_rep = prepare(model)又完全是OK的，这个暂时还没找到原因在哪里。

onnx模型转换为Tensorflow模型

上面提到按官网的教程从tensorflow转换生成的onnx模型执行tf_rep = prepare(model)有问题。所以这里我从网上下载的一个pytorch转换的mnist onnx模型为实验对象，实验用的onnx下载地址：https://download.youkuaiyun.com/download/computerme/10448754
onnx模型转换为Tensorflow模型的代码如下：

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')

转换完成后，需要对转换出的tf.pb模型进行验证，验证方式如下：

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)

Reference:
https://github.com/onnx/onnx-tensorflow/issues/167