common_layers.py 文件源码

def deconv_stride2_multistep(x,
                             nbr_steps,
                             output_filters,
                             name=None,
                             reuse=None):
  """Use a deconvolution to upsample x by 2**`nbr_steps`.

  Args:
    x: a `Tensor` with shape `[batch, spatial, depth]` or
     `[batch, spatial_1, spatial_2, depth]`
    nbr_steps: an int specifying the number of doubling upsample rounds to
     apply.
    output_filters: an int specifying the filter count for the deconvolutions
    name: a string
    reuse: a boolean

  Returns:
    a `Tensor` with shape `[batch, spatial * (2**nbr_steps), output_filters]` or
     `[batch, spatial_1 * (2**nbr_steps), spatial_2 * (2**nbr_steps),
       output_filters]`
  """
  with tf.variable_scope(
      name, default_name="deconv_stride2_multistep", values=[x], reuse=reuse):

    def deconv1d(cur, i):
      cur_shape = shape_list(cur)
      thicker = conv(
          cur,
          output_filters * 2, (1, 1),
          padding="SAME",
          activation=tf.nn.relu,
          name="deconv1d" + str(i))
      return tf.reshape(thicker,
                        [cur_shape[0], cur_shape[1] * 2, 1, output_filters])

    def deconv2d(cur, i):
      thicker = conv(
          cur,
          output_filters * 4, (1, 1),
          padding="SAME",
          activation=tf.nn.relu,
          name="deconv2d" + str(i))
      return tf.depth_to_space(thicker, 2)

    cur = x
    for i in xrange(nbr_steps):
      if cur.get_shape()[2] == 1:
        cur = deconv1d(cur, i)
      else:
        cur_dim = shape_list(cur)[2]
        if isinstance(cur_dim, int):
          if cur_dim == 1:
            cur = deconv1d(cur, i)
          else:
            cur = deconv2d(cur, i)
        else:
          cur = tf.cond(
              tf.equal(cur_dim, 1),
              lambda idx=i: deconv1d(cur, idx),
              lambda idx=i: deconv2d(cur, idx))
    return cur