def unpool(pool, ind, ksize=[1, 2, 2, 1], scope='unpool'):
with tf.variable_scope(scope):
input_shape = pool.get_shape().as_list()
output_shape = (input_shape[0], input_shape[1] * ksize[1], input_shape[2] * ksize[2], input_shape[3])
flat_input_size = np.prod(input_shape)
flat_output_shape = [output_shape[0], output_shape[1] * output_shape[2] * output_shape[3]]
pool_ = tf.reshape(pool, [flat_input_size])
batch_range = tf.reshape(tf.range(output_shape[0], dtype=ind.dtype), shape=[input_shape[0], 1, 1, 1])
b = tf.ones_like(ind) * batch_range
b = tf.reshape(b, [flat_input_size, 1])
ind_ = tf.reshape(ind, [flat_input_size, 1])
ind_ = tf.concat([b, ind_], 1)
ret = tf.scatter_nd(ind_, pool_, shape=flat_output_shape)
ret = tf.reshape(ret, output_shape)
return ret
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