shadowhunt/functions.py at main · 4-pm/shadowhunt · GitHub

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import tensorflow as tf

CLASSES = 1
COLORS = ['red']
SAMPLE_SIZE = (256, 256)
#OUTPUT_SIZE = (1080, 1920)

def input_layer():
    return tf.keras.layers.Input(shape=SAMPLE_SIZE + (3,))


def downsample_block(filters, size, batch_norm=True):
    initializer = tf.keras.initializers.GlorotNormal()

    result = tf.keras.Sequential()

    result.add(
        tf.keras.layers.Conv2D(filters, size, strides=2, padding='same',
                               kernel_initializer=initializer, use_bias=False))

    if batch_norm:
        result.add(tf.keras.layers.BatchNormalization())

    result.add(tf.keras.layers.LeakyReLU())
    return result


def upsample_block(filters, size, dropout=False):
    initializer = tf.keras.initializers.GlorotNormal()

    result = tf.keras.Sequential()

    result.add(
        tf.keras.layers.Conv2DTranspose(filters, size, strides=2, padding='same',
                                        kernel_initializer=initializer, use_bias=False))

    result.add(tf.keras.layers.BatchNormalization())

    if dropout:
        result.add(tf.keras.layers.Dropout(0.25))

    result.add(tf.keras.layers.ReLU())
    return result


def output_layer(size):
    initializer = tf.keras.initializers.GlorotNormal()
    return tf.keras.layers.Conv2DTranspose(CLASSES, size, strides=2, padding='same',
                                           kernel_initializer=initializer, activation='sigmoid')

downsample_stack = [
    downsample_block(64, 4, batch_norm=False),
    downsample_block(128, 4),
    downsample_block(256, 4),
    downsample_block(512, 4),
    downsample_block(512, 4),
    downsample_block(512, 4),
    downsample_block(512, 4),
]

upsample_stack = [
    upsample_block(512, 4, dropout=True),
    upsample_block(512, 4, dropout=True),
    upsample_block(512, 4, dropout=True),
    upsample_block(256, 4),
    upsample_block(128, 4),
    upsample_block(64, 4)
]


def create_layers():
    inp_layer = input_layer()

    out_layer = output_layer(4)
    x = inp_layer
    downsample_skips = []

    for block in downsample_stack:
        x = block(x)
        downsample_skips.append(x)

    downsample_skips = reversed(downsample_skips[:-1])

    for up_block, down_block in zip(upsample_stack, downsample_skips):
        x = up_block(x)
        x = tf.keras.layers.Concatenate()([x, down_block])

    out_layer = out_layer(x)

    return inp_layer, out_layer