dpn.py 文件源码

def _root_block(input,
                initial_conv_filters,
                weight_decay=5e-4,
                ksize=(7,7),
                is_pool=True):
  ''' Adds an initial conv block, with batch norm and relu for the DPN
  Args:
      input: input tensor
      initial_conv_filters: number of filters for initial conv block
      weight_decay: weight decay factor
  Returns: a keras tensor
  '''
  x = slim.conv2d(input,
                  initial_conv_filters,
                  ksize,
                  padding='SAME',
                  stride=(1, 1),
                  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)
  if is_pool:
    x = slim.max_pool2d(x, (3, 3), stride=(2, 2), padding='SAME')
  return x