NetworkExtractor.py

# Copyright (c) 2016, NVIDIA CORPORATION. All rights reserved.
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import os
import re
import numpy as np
import tensorflow as tf

from Config import Config
import objgraph

from tensorflow.python.ops import control_flow_ops
from tensorflow.python.training import moving_averages
from tensorflow.contrib import rnn


# tf.enable_eager_execution()

class NetworkExtractor:
    def __init__(self, device, model_name, num_actions, is_training):
        # tf.enable_eager_execution()
        self.device = device
        self.model_name = model_name
        self.num_actions = num_actions

        self.img_width = Config.IMAGE_WIDTH
        self.img_height = Config.IMAGE_HEIGHT
        self.img_channels = Config.STACKED_FRAMES

        self.learning_rate = Config.LEARNING_RATE_START
        self.beta = Config.BETA_START
        self.log_epsilon = Config.LOG_EPSILON

        self.Num_Of_Features = 23
        self.BN_EPSILON = 0.001
        self.BN_DECAY = 0.9997
        self.MOVING_AVARAGE_DECAY = 0.9997
        self.IS_TRAINING = is_training

        self.graph = tf.Graph()
        self.graph2 = tf.Graph()
        self.extractor = tf.load_op_library('./libRadiomicsGLCM.so').radiomics_glcm

        with self.graph2.as_default() as g2:
            with tf.device(self.device):
                self._create_graph2()
                self.sess2 = tf.Session(
                    graph=self.graph2,
                    config=tf.ConfigProto(
                        allow_soft_placement=True,
                        log_device_placement=True,
                        gpu_options=tf.GPUOptions(allow_growth=True)))

                self.sess2.run(tf.global_variables_initializer())
                # self.sess2.graph.finalize()
                # objgraph.show_growth()
                # objgraph.show_refs(self.graph2, filename='./graph2.png')

    def _create_graph2(self):
        self.x1 = tf.placeholder(tf.int32, shape=[None, self.img_height, self.img_width], name='X1')
        self.features = tf.placeholder(tf.float32, shape=[None, self.img_channels * self.Num_Of_Features],
                                       name='features')
        self.features = self.extractor(self.x1)


    def extract(self, x):
        features = self.sess2.run(self.features, feed_dict={self.x1: x})
        return features