def image_augmentation(x):
# img, noise
one = tf.fill([tf.shape(x)[0], 1], 1.)
zero = tf.fill([tf.shape(x)[0], 1], 0.)
transforms = tf.concat([one, zero, zero, zero, one, zero, zero, zero], axis=1)
rands = tf.concat([tf.truncated_normal([tf.shape(x)[0], 6], stddev=0.05), zero, zero], axis=1)
return images_transform(x, transforms + rands, interpolation='BILINEAR')
def call(self, x):
# img, noise
one = tf.fill([tf.shape(x[0])[0], 1], 1.)
zero = tf.fill([tf.shape(x[0])[0], 1], 0.)
transforms = tf.concat([one, zero, zero, zero, one, zero, zero, zero],
axis=1)
rands = tf.concat([
tf.truncated_normal([tf.shape(x[0])[0], 6], stddev=x[1]), zero,
zero
],
axis=1)
return images_transform(x[0],
transforms + rands,
interpolation='BILINEAR')
