text: import inspect
  import os
  
  import numpy as np
  import tensorflow.compat.v1 as tf
  tf.disable_v2_behavior()
  
  from src.utils.utils import gaussian_kernel
  
  VGG_MEAN = [103.939, 116.779, 123.68]
  
  
  class VGG16mo:
      def __init__(self, vgg16_npy_path=None):
          if vgg16_npy_path is None:
              path = inspect.getfile(VGG16mo)
              path = os.path.abspath(os.path.join(path, os.pardir))
              path = os.path.join(path, "vgg16partial.npy")
              vgg16_npy_path = path
              #print(path)
  
          self.data_dict = np.load(vgg16_npy_path, encoding='latin1', allow_pickle=True).item()
          #print("npy file loaded")
  
      def build(self, bgr):
          blue, green, red = tf.split(axis=3, num_or_size_splits=3, value=bgr)
          assert red.get_shape().as_list()[1:] == [224, 224, 1]
          assert green.get_shape().as_list()[1:] == [224, 224, 1]
          assert blue.get_shape().as_list()[1:] == [224, 224, 1]
          bgr = tf.concat(axis=3, values=[
              blue - VGG_MEAN[0],
              green - VGG_MEAN[1],
              red - VGG_MEAN[2],
          ])
          assert bgr.get_shape().as_list()[1:] == [224, 224, 3]
  
          self.conv1_1 = self.conv_layer(bgr, "conv1_1")
          self.conv1_2 = self.conv_layer(self.conv1_1, "conv1_2")
          self.pool1 = self.max_pool(self.conv1_2, 'pool1')
  
          self.conv2_1 = self.conv_layer(self.pool1, "conv2_1")
          self.conv2_2 = self.conv_layer(self.conv2_1, "conv2_2")
          self.pool2 = self.max_pool(self.conv2_2, 'pool2')
  
          self.conv3_1 = self.conv_layer(self.pool2, "conv3_1")
          self.conv3_2 = self.conv_layer(self.conv3_1, "conv3_2")
          self.conv3_3 = self.conv_layer(self.conv3_2, "conv3_3")
          self.pool3 = self.max_pool(self.conv3_3, 'pool3')
          self.pool3_1_s4 = self.max_pool_s4(self.conv3_1, 'pool3_1_s4')
          self.pool3_s4 = self.max_pool_s4(self.conv3_3, 'pool3_s4')
  
          self.conv4_1 = self.conv_layer(self.pool3, "conv4_1")
          self.conv4_2 = self.conv_layer(self.conv4_1, "conv4_2")
          self.conv4_3 = self.conv_layer(self.conv4_2, "conv4_3")
          self.pool4 = self.max_pool(self.conv4_3, 'pool4')
  
          self.conv5_1 = self.conv_layer(self.pool4, "conv5_1")
          self.pool5_1 = self.max_pool(self.conv5_1, 'pool5')
  
          self.data_dict = None
  
      def max_pool(self, bottom, name):
          return tf.nn.max_pool(bottom, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME', name=name)
  
      def max_pool_s4(self, bottom, name):
          return tf.nn.max_pool(bottom, ksize=[1, 2, 2, 1], strides=[1, 4, 4, 1], padding='SAME', name=name)
  
      def conv_layer(self, bottom, name):
          with tf.compat.v1.variable_scope(name):
              filt = self.get_conv_filter(name)
  
              conv = tf.nn.conv2d(bottom, filt, [1, 1, 1, 1], padding='SAME')
  
              conv_biases = self.get_bias(name)
              bias = tf.nn.bias_add(conv, conv_biases)
  
              relu = tf.nn.relu(bias)
              return relu
  
      def get_conv_filter(self, name):
          return tf.constant(self.data_dict[name][0], name="filter")
  
      def get_bias(self, name):
          return tf.constant(self.data_dict[name][1], name="biases")
  
      def get_fc_weight(self, name):
          return tf.constant(self.data_dict[name][0], name="weights")
  