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')