utilities.py

import tensorflow as tf
import cv2
import numpy as np

def PReLU(x, scope):
    # PReLU(x) = x if x > 0, alpha*x otherwise

    alpha = tf.get_variable(scope + "/alpha", shape=[1],
                initializer=tf.constant_initializer(0), dtype=tf.float32)

    output = tf.nn.relu(x) + alpha*(x - abs(x))*0.5

    return output

# function for 2D spatial dropout:
def spatial_dropout(x, drop_prob):
    # x is a tensor of shape [batch_size, height, width, channels]

    keep_prob = 1.0 - drop_prob
    input_shape = x.get_shape().as_list()

    batch_size = input_shape[0]
    channels = input_shape[3]

    # drop each channel with probability drop_prob:
    noise_shape = tf.constant(value=[batch_size, 1, 1, channels])
    x_drop = tf.nn.dropout(x, keep_prob, noise_shape=noise_shape)

    output = x_drop

    return output

# function for unpooling max_pool:
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:
def label_img_to_color(img):
    label_to_color = {
        0: [128, 64,128],
        1: [244, 35,232],
        2: [ 70, 70, 70],
        3: [102,102,156],
        4: [190,153,153],
        5: [153,153,153],
        6: [250,170, 30],
        7: [220,220,  0],
        8: [107,142, 35],
        9: [152,251,152],
        10: [ 70,130,180],
        11: [220, 20, 60],
        12: [255,  0,  0],
        13: [  0,  0,142],
        14: [  0,  0, 70],
        15: [  0, 60,100],
        16: [  0, 80,100],
        17: [  0,  0,230],
        18: [119, 11, 32],
        19: [81,  0, 81]
        }

    img_height, img_width = img.shape

    img_color = np.zeros((img_height, img_width, 3))
    for row in range(img_height):
        for col in range(img_width):
            label = img[row, col]

            img_color[row, col] = np.array(label_to_color[label])

    return img_color