Forums - Generating .dlc from tensorflow checkpoint meta file

 

#!/usr/bin/env python

 

import os

import tensorflow as tf

 

class Network(object):

 

    def __init__(self, input_size, learning_rate=0.001):

 

        rows, cols, depth = input_size

 

        with tf.variable_scope('input_layer'):

            self.input_layer = tf.placeholder(tf.float32,

                shape=[None, rows, cols, depth],

                name='Network_Input')

 

        with tf.variable_scope('conv_1'):

            # First hidden layer

            conv_1 = tf.layers.conv2d(

                inputs=self.input_layer,

                filters=16,

                kernel_size=[8, 8],

                strides=(4, 4),

                padding='SAME',

                activation=tf.nn.relu

            )

 

        with tf.variable_scope('conv_2'):

            # Second hidden layer

            conv_2 = tf.layers.conv2d(

                inputs=conv_1,

                filters=32,

                kernel_size=[4, 4],

                strides=(2, 2),

                padding='SAME',

                activation=tf.nn.relu

            )

 

        with tf.variable_scope('conv_2_flat'):

            conv_2_flat = tf.contrib.layers.flatten(conv_2)

 

        with tf.variable_scope('dense_3'):

            # Third hidden layer

            dense_3 = tf.layers.dense(inputs=conv_2_flat, units=256, activation=tf.nn.relu)

 

        with tf.variable_scope('output_layer'):

            # Output layer

            self.output_layer = tf.layers.dense(inputs=dense_3, units=2, activation=None, name='Network_Output')

 

        # Do I need to specify shape?

        self.target = tf.placeholder(tf.float32, name='Network_Target')

 

        self.loss = tf.reduce_mean(tf.square(tf.subtract(self.output_layer, self.target)))

 

        self.optm = tf.train.RMSPropOptimizer(learning_rate=learning_rate).minimize(self.loss)

 

        init = tf.global_variables_initializer()

        self.saver = tf.train.Saver()

        self.sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))

        self.sess.run(init)

 

    def save(self, path):

        save_path_model = self.saver.save(self.sess, os.path.join(path, 'model.ckpt'))

        print('Model saved in file: {}'.format(save_path_model))

        return path

 

 

network = Network((64, 64, 12))

network.save('/home/ubuntu/Desktop/tensorflow/export')

➜  tensorflow snpe-tensorflow-to-dlc --graph ./export/model.ckpt.meta -i "input_layer/Network_Input" 64,64,12 --out_node output_layer/Network_Output/MatMul

2018-02-15 10:45:13.231736: I tensorflow/core/platform/cpu_feature_guard.cc:137] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.1 SSE4.2 AVX AVX2 FMA

2018-02-15 10:45:13,413 - 309 - WARNING - WARNING_TF_SCOPE_OP_NOT_CONSUMED: Operation (conv_2_flat_layer/Flatten/flatten/Shape) not consumed by converter: Shape.

2018-02-15 10:45:13,413 - 309 - WARNING - WARNING_TF_SCOPE_OP_NOT_CONSUMED: Operation (conv_2_flat_layer/Flatten/flatten/strided_slice) not consumed by converter: StridedSlice.

2018-02-15 10:45:13,413 - 309 - WARNING - WARNING_TF_SCOPE_OP_NOT_CONSUMED: Operation (conv_2_flat_layer/Flatten/flatten/Reshape/shape) not consumed by converter: Pack.

2018-02-15 10:45:13,413 - 309 - WARNING - WARNING_TF_SCOPE_OP_NOT_CONSUMED: Operation (output_layer/Network_Output/MatMul) not consumed by converter: MatMul.

2018-02-15 10:45:13,413 - 123 - ERROR - Conversion failed: ERROR_TF_OPERATION_NOT_MAPPED_TO_LAYER: Some operations in the Tensorflow graph were not resolved to a layer. You can use --allow_unconsumed_nodes for partial graph resolution!