def max_unpool(inputs, pooling_indices, output_shape=None, k_size=[1, 2, 2, 1]):
# NOTE! this function is based on the implementation by kwotsin in
# https://github.com/kwotsin/TensorFlow-ENet
# inputs has shape [batch_size, height, width, channels]
# pooling_indices: pooling indices of the previously max_pooled layer
# output_shape: what shape the returned tensor should have
pooling_indices = tf.cast(pooling_indices, tf.int32)
input_shape = tf.shape(inputs, out_type=tf.int32)
one_like_pooling_indices = tf.ones_like(pooling_indices, dtype=tf.int32)
batch_shape = tf.concat([[input_shape[0]], [1], [1], [1]], 0)
batch_range = tf.reshape(tf.range(input_shape[0], dtype=tf.int32), shape=batch_shape)
b = one_like_pooling_indices*batch_range
y = pooling_indices//(output_shape[2]*output_shape[3])
x = (pooling_indices//output_shape[3]) % output_shape[2]
feature_range = tf.range(output_shape[3], dtype=tf.int32)
f = one_like_pooling_indices*feature_range
inputs_size = tf.size(inputs)
indices = tf.transpose(tf.reshape(tf.stack([b, y, x, f]), [4, inputs_size]))
values = tf.reshape(inputs, [inputs_size])
ret = tf.scatter_nd(indices, values, output_shape)
return ret
# function for colorizing a label image:
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