如何在张量流中验证优化模型

Question

I'm following a tutorial from codelabs . They use this script to optimize the model

python -m tensorflow.python.tools.optimize_for_inference \
  --input=tf_files/retrained_graph.pb \
  --output=tf_files/optimized_graph.pb \
  --input_names="input" \
  --output_names="final_result"

they verify the optimized_graph.pb using this script

python -m scripts.label_image \
    --graph=tf_files/optimized_graph.pb \
    --image=tf_files/flower_photos/daisy/3475870145_685a19116d.jpg

The problem is I try to use optimize_for_inference to my own code which is not for image classification.

Previously, before optimizing, I use this script to verify my model by test it to a sample data:

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

def load_graph(frozen_graph_filename):
    with tf.gfile.GFile(frozen_graph_filename, "rb") as f:
        graph_def = tf.GraphDef()
        graph_def.ParseFromString(f.read())

    with tf.Graph().as_default() as graph:
        tf.import_graph_def(graph_def, name="prefix")

    input_name = graph.get_operations()[0].name+':0'
    output_name = graph.get_operations()[-1].name+':0'

    return graph, input_name, output_name

def predict(model_path, input_data):
    # load tf graph
    tf_model,tf_input,tf_output = load_graph(model_path)

    x = tf_model.get_tensor_by_name(tf_input)
    y = tf_model.get_tensor_by_name(tf_output) 

    model_input = tf.train.Example(
        features=tf.train.Features(feature={
        "thisisinput": tf.train.Feature(float_list=tf.train.FloatList(value=input_data)),
    }))
    model_input = model_input.SerializeToString()

    num_outputs = 3
    predictions = np.zeros(num_outputs)
    with tf.Session(graph=tf_model) as sess:
        y_out = sess.run(y, feed_dict={x: [model_input]})
        predictions = y_out

    return predictions

if __name__=="__main__":
    input_data = [4.7,3.2,1.6,0.2] # my model recieve 4 inputs
    print(np.argmax(predict("not_optimized_model.pb",x)))

but after optimizing the model, my testing script doesn't work. It raises an error:

ValueError: Input 0 of node import/ParseExample/ParseExample was passed float from import/inputtensors:0 incompatible with expected string.

So my question is how to verify my model after optimizing the model? I can't use --image command like the tutorial.

Answer 1

I've solved the error by changing the placeholder's type with tf.float32 when exporting the model:

def my_serving_input_fn():
    input_data = {
        "featurename" : tf.placeholder(tf.float32, [None, 4], name='inputtensors')
    }
    return tf.estimator.export.ServingInputReceiver(input_data, input_data)

and then change the prediction function above to:

def predict(model_path, input_data):
    # load tf graph
    tf_model, tf_input, tf_output = load_graph(model_path)

    x = tf_model.get_tensor_by_name(tf_input)
    y = tf_model.get_tensor_by_name(tf_output) 

    num_outputs = 3
    predictions = np.zeros(num_outputs)
    with tf.Session(graph=tf_model) as sess:
        y_out = sess.run(y, feed_dict={x: [input_data]})
        predictions = y_out

    return predictions

After freezing the model, the prediction code above will be work. But unfortunately it raises another error when trying to load pb directly after exporting the model .