dpn.py 文件源码

def _bn_relu_conv_block(input,
                        filters,
                        kernel=(3, 3),
                        stride=(1, 1),
                        weight_decay=5e-4):
  ''' Adds a Batchnorm-Relu-Conv block for DPN
  Args:
      input: input tensor
      filters: number of output filters
      kernel: convolution kernel size
      stride: stride of convolution
  Returns: a keras tensor
  '''
  channel_axis = -1

  x = slim.conv2d(input, filters, kernel, padding='SAME', stride=stride,
                  weights_regularizer=slim.l2_regularizer(weight_decay),
                  weights_initializer=tf.contrib.layers.xavier_initializer(),
                  biases_initializer=None)

  x = slim.batch_norm(x)
  x = tf.nn.relu(x)
  return x