CycleGAN-Tensorflow-2/module.py at master · agrimwood/CycleGAN-Tensorflow-2 · GitHub

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import tensorflow as tf
import tensorflow_addons as tfa
import tensorflow.keras as keras


# ==============================================================================
# =                                  networks                                  =
# ==============================================================================


def _get_norm_layer(norm):
    if norm == "none":
        return lambda: lambda x: x
    elif norm == "batch_norm":
        return keras.layers.BatchNormalization
    elif norm == "instance_norm":
        return tfa.layers.InstanceNormalization
    elif norm == "layer_norm":
        return keras.layers.LayerNormalization


def ResnetGenerator(
    input_shape=(256, 256, 3),
    output_channels=3,
    dim=64,
    n_downsamplings=2,
    n_blocks=9,
    norm="instance_norm",
):
    Norm = _get_norm_layer(norm)

    def _residual_block(x):
        dim = x.shape[-1]
        h = x

        h = tf.pad(h, [[0, 0], [1, 1], [1, 1], [0, 0]], mode="REFLECT")
        h = keras.layers.Conv2D(dim, 3, padding="valid", use_bias=False)(h)
        h = Norm()(h)
        h = tf.nn.relu(h)

        h = tf.pad(h, [[0, 0], [1, 1], [1, 1], [0, 0]], mode="REFLECT")
        h = keras.layers.Conv2D(dim, 3, padding="valid", use_bias=False)(h)
        h = Norm()(h)

        return keras.layers.add([x, h])

    # 0
    h = inputs = keras.Input(shape=input_shape)

    # 1
    h = tf.pad(h, [[0, 0], [3, 3], [3, 3], [0, 0]], mode="REFLECT")
    h = keras.layers.Conv2D(dim, 7, padding="valid", use_bias=False)(h)
    h = Norm()(h)
    h = tf.nn.relu(h)

    # 2
    for _ in range(n_downsamplings):
        dim *= 2
        h = keras.layers.Conv2D(dim, 3, strides=2, padding="same", use_bias=False)(h)
        h = Norm()(h)
        h = tf.nn.relu(h)

    # 3
    for _ in range(n_blocks):
        h = _residual_block(h)

    # 4
    for _ in range(n_downsamplings):
        dim //= 2
        h = keras.layers.Conv2DTranspose(
            dim, 3, strides=2, padding="same", use_bias=False
        )(h)
        h = Norm()(h)
        h = tf.nn.relu(h)

    # 5
    h = tf.pad(h, [[0, 0], [3, 3], [3, 3], [0, 0]], mode="REFLECT")
    h = keras.layers.Conv2D(output_channels, 7, padding="valid")(h)
    h = tf.tanh(h)

    return keras.Model(inputs=inputs, outputs=h)


def ConvDiscriminator(
    input_shape=(256, 256, 3), dim=64, n_downsamplings=3, norm="instance_norm"
):
    dim_ = dim
    Norm = _get_norm_layer(norm)

    # 0
    h = inputs = keras.Input(shape=input_shape)

    # 1
    h = keras.layers.Conv2D(dim, 4, strides=2, padding="same")(h)
    h = tf.nn.leaky_relu(h, alpha=0.2)

    for _ in range(n_downsamplings - 1):
        dim = min(dim * 2, dim_ * 8)
        h = keras.layers.Conv2D(dim, 4, strides=2, padding="same", use_bias=False)(h)
        h = Norm()(h)
        h = tf.nn.leaky_relu(h, alpha=0.2)

    # 2
    dim = min(dim * 2, dim_ * 8)
    h = keras.layers.Conv2D(dim, 4, strides=1, padding="same", use_bias=False)(h)
    h = Norm()(h)
    h = tf.nn.leaky_relu(h, alpha=0.2)

    # 3
    h = keras.layers.Conv2D(1, 4, strides=1, padding="same")(h)

    return keras.Model(inputs=inputs, outputs=h)


# ==============================================================================
# =                          learning rate scheduler                           =
# ==============================================================================


class LinearDecay(keras.optimizers.schedules.LearningRateSchedule):
    # if `step` < `step_decay`: use fixed learning rate
    # else: linearly decay the learning rate to zero

    def __init__(self, initial_learning_rate, total_steps, step_decay):
        super(LinearDecay, self).__init__()
        self._initial_learning_rate = initial_learning_rate
        self._steps = total_steps
        self._step_decay = step_decay
        self.current_learning_rate = tf.Variable(
            initial_value=initial_learning_rate, trainable=False, dtype=tf.float32
        )

    def __call__(self, step):
        self.current_learning_rate.assign(
            tf.cond(
                step >= self._step_decay,
                true_fn=lambda: self._initial_learning_rate
                * (
                    1 - 1 / (self._steps - self._step_decay) * (step - self._step_decay)
                ),
                false_fn=lambda: self._initial_learning_rate,
            )
        )
        return self.current_learning_rate