def unpooling(input: Tensor,
argmax: Tensor,
output_shape: TensorShape,
name='unpooling'):
"""
unpooling層
:param input:
:param argmax:
:param output_shape:
:return:
次ノードへのTensor
"""
with tf.name_scope(name):
# inputを直線に並べる
input_stride = tf.reshape(input, [-1])
# argmaxを直線に並べる。1行1要素の2次元になる。
argmax_stride = tf.reshape(argmax, [-1, 1])
# outputの要素数
num_elem = output_shape.num_elements()
# inputをargmaxに従い、num_elemの1行Tensorに再配置
output_stride = tf.scatter_nd(argmax_stride, input_stride, [num_elem])
output = tf.reshape(output_stride, output_shape)
return output
def build(self, input_shape: tf.TensorShape):
# Input Size B x Hi x Wi x C x 2
# Reorder to B x C x Hi x Wi x 2
self.reordered_input_shape = [input_shape[0], input_shape[3], input_shape[1], input_shape[2], input_shape[4]]
presence_mask = tf.fill(self.reordered_input_shape[:-1], True)
presence_indices = tf.where(presence_mask)
self.presence_b = tf.expand_dims(presence_indices[:, 0], axis=-1)
self.presence_chw = presence_indices[:, 1:]
position_mask = tf.fill(self.reordered_input_shape, True)
position_indices = tf.where(position_mask)
self.position_b = tf.expand_dims(position_indices[:, 0], axis=-1)
self.position_chwd = position_indices[:, 1:]
# Size B x Hi x Wi x C x 2
self.input_size = tf.cast(input_shape.num_elements(), tf.int32) / 2
self.xy_offset = tf.repeat([[self.x_offset, self.y_offset]], repeats=[self.input_size], axis=0)
self.presence_shape = [input_shape[0], self.length, self.width, self.num_channels, input_shape[1], input_shape[2]]
self.presence_vals = tf.ones([self.input_size], dtype=tf.int32)
self.position_shape = [input_shape[0], self.length, self.width, self.num_channels, input_shape[1], input_shape[2], 2]
