                   axis=None,
                   epsilon=1e-3,
                   center=True,
                   scale=True,
                   beta_initializer='zeros',
                   gamma_initializer='ones',
                   beta_regularizer=None,
                   gamma_regularizer=None,
                   beta_constraint=None,
                   gamma_constraint=None,
                   **kwargs):
 
</s> remove         super(InstanceNormalization, self).__init__(**kwargs)
 </s> add         super().__init__(**kwargs) </s> remove     Normalize the activations of the previous layer at each step,
    i.e. applies a transformation that maintains the mean activation
    close to 0 and the activation standard deviation close to 1.
    # Arguments
        axis: Integer, the axis that should be normalized
            (typically the features axis).
            For instance, after a `Conv2D` layer with
            `data_format="channels_first"`,
            set `axis=1` in `InstanceNormalization`.
            Setting `axis=None` will normalize all values in each instance of the batch.
            Axis 0 is the batch dimension. `axis` cannot be set to 0 to avoid errors.
        epsilon: Small float added to variance to avoid dividing by zero.
        center: If True, add offset of `beta` to normalized tensor.
            If False, `beta` is ignored.
        scale: If True, multiply by `gamma`.
            If False, `gamma` is not used.
            When the next layer is linear (also e.g. `nn.relu`),
            this can be disabled since the scaling
            will be done by the next layer.
        beta_initializer: Initializer for the beta weight.
        gamma_initializer: Initializer for the gamma weight.
        beta_regularizer: Optional regularizer for the beta weight.
        gamma_regularizer: Optional regularizer for the gamma weight.
        beta_constraint: Optional constraint for the beta weight.
        gamma_constraint: Optional constraint for the gamma weight.
    # Input shape
        Arbitrary. Use the keyword argument `input_shape`
        (tuple of integers, does not include the samples axis)
        when using this layer as the first layer in a model.
    # Output shape
        Same shape as input.
    # References
        - [Layer Normalization](https://arxiv.org/abs/1607.06450)
        - [Instance Normalization: The Missing Ingredient for Fast
                                   Stylization](https://arxiv.org/abs/1607.08022)
 </s> add     Normalize the activations of the previous layer at each step, i.e. applies a transformation
    that maintains the mean activation close to 0 and the activation standard deviation close to 1.

    Parameters
    ----------
    axis: int, optional
        The axis that should be normalized (typically the features axis). For instance, after a
        `Conv2D` layer with `data_format="channels_first"`, set `axis=1` in
        :class:`InstanceNormalization`. Setting `axis=None` will normalize all values in each
        instance of the batch. Axis 0 is the batch dimension. `axis` cannot be set to 0 to avoid
        errors. Default: ``None``
    epsilon: float, optional
        Small float added to variance to avoid dividing by zero. Default: `1e-3`
    center: bool, optional
        If ``True``, add offset of `beta` to normalized tensor. If ``False``, `beta` is ignored.
        Default: ``True``
    scale: bool, optional
        If ``True``, multiply by `gamma`. If ``False``, `gamma` is not used. When the next layer
        is linear (also e.g. `relu`), this can be disabled since the scaling will be done by
        the next layer. Default: ``True``
    beta_initializer: str, optional
        Initializer for the beta weight. Default: `"zeros"`
    gamma_initializer: str, optional
        Initializer for the gamma weight. Default: `"ones"`
    beta_regularizer: str, optional
        Optional regularizer for the beta weight. Default: ``None``
    gamma_regularizer: str, optional
        Optional regularizer for the gamma weight. Default: ``None``
    beta_constraint: float, optional
        Optional constraint for the beta weight. Default: ``None``
    gamma_constraint: float, optional
        Optional constraint for the gamma weight. Default: ``None``

    References
    ----------
        - Layer Normalization - https://arxiv.org/abs/1607.06450

        - Instance Normalization: The Missing Ingredient for Fast Stylization -
    https://arxiv.org/abs/1607.08022
 </s> remove class GroupNormalization(Layer):
    """ Group Normalization
        from: shoanlu GAN: https://github.com/shaoanlu/faceswap-GAN"""

    def __init__(self, axis=-1,
                 gamma_init='one', beta_init='zero',
                 gamma_regularizer=None, beta_regularizer=None,
                 epsilon=1e-6,
                 group=32,
                 data_format=None,
                 **kwargs):
        self.beta = None
        self.gamma = None
        super(GroupNormalization, self).__init__(**kwargs)

        self.axis = to_list(axis)
        self.gamma_init = initializers.get(gamma_init)
        self.beta_init = initializers.get(beta_init)
        self.gamma_regularizer = regularizers.get(gamma_regularizer)
        self.beta_regularizer = regularizers.get(beta_regularizer)
        self.epsilon = epsilon
        self.group = group
        self.data_format = K.normalize_data_format(data_format)

        self.supports_masking = True

    def build(self, input_shape):
        self.input_spec = [InputSpec(shape=input_shape)]
        shape = [1 for _ in input_shape]
        if self.data_format == 'channels_last':
            channel_axis = -1
            shape[channel_axis] = input_shape[channel_axis]
        elif self.data_format == 'channels_first':
            channel_axis = 1
            shape[channel_axis] = input_shape[channel_axis]
        # for i in self.axis:
        #    shape[i] = input_shape[i]
        self.gamma = self.add_weight(shape=shape,
                                     initializer=self.gamma_init,
                                     regularizer=self.gamma_regularizer,
                                     name='gamma')
        self.beta = self.add_weight(shape=shape,
                                    initializer=self.beta_init,
                                    regularizer=self.beta_regularizer,
                                    name='beta')
        self.built = True

    def call(self, inputs, mask=None):
        input_shape = K.int_shape(inputs)
        if len(input_shape) != 4 and len(input_shape) != 2:
            raise ValueError('Inputs should have rank ' +
                             str(4) + " or " + str(2) +
                             '; Received input shape:', str(input_shape))

        if len(input_shape) == 4:
            if self.data_format == 'channels_last':
                batch_size, height, width, channels = input_shape
                if batch_size is None:
                    batch_size = -1

                if channels < self.group:
                    raise ValueError('Input channels should be larger than group size' +
                                     '; Received input channels: ' + str(channels) +
                                     '; Group size: ' + str(self.group))

                var_x = K.reshape(inputs, (batch_size,
                                           height,
                                           width,
                                           self.group,
                                           channels // self.group))
                mean = K.mean(var_x, axis=[1, 2, 4], keepdims=True)
                std = K.sqrt(K.var(var_x, axis=[1, 2, 4], keepdims=True) + self.epsilon)
                var_x = (var_x - mean) / std

                var_x = K.reshape(var_x, (batch_size, height, width, channels))
                retval = self.gamma * var_x + self.beta
            elif self.data_format == 'channels_first':
                batch_size, channels, height, width = input_shape
                if batch_size is None:
                    batch_size = -1

                if channels < self.group:
                    raise ValueError('Input channels should be larger than group size' +
                                     '; Received input channels: ' + str(channels) +
                                     '; Group size: ' + str(self.group))

                var_x = K.reshape(inputs, (batch_size,
                                           self.group,
                                           channels // self.group,
                                           height,
                                           width))
                mean = K.mean(var_x, axis=[2, 3, 4], keepdims=True)
                std = K.sqrt(K.var(var_x, axis=[2, 3, 4], keepdims=True) + self.epsilon)
                var_x = (var_x - mean) / std

                var_x = K.reshape(var_x, (batch_size, channels, height, width))
                retval = self.gamma * var_x + self.beta

        elif len(input_shape) == 2:
            reduction_axes = list(range(0, len(input_shape)))
            del reduction_axes[0]
            batch_size, _ = input_shape
            if batch_size is None:
                batch_size = -1

            mean = K.mean(inputs, keepdims=True)
            std = K.sqrt(K.var(inputs, keepdims=True) + self.epsilon)
            var_x = (inputs - mean) / std

            retval = self.gamma * var_x + self.beta
        return retval

    def get_config(self):
        config = {'epsilon': self.epsilon,
                  'axis': self.axis,
                  'gamma_init': initializers.serialize(self.gamma_init),
                  'beta_init': initializers.serialize(self.beta_init),
                  'gamma_regularizer': regularizers.serialize(self.gamma_regularizer),
                  'beta_regularizer': regularizers.serialize(self.gamma_regularizer),
                  'group': self.group}
        base_config = super(GroupNormalization, self).get_config()
        return dict(list(base_config.items()) + list(config.items()))


# Update normalizations into Keras custom objects
 </s> add # Update normalization into Keras custom objects </s> remove         super(PixelShuffler, self).__init__(**kwargs)
 </s> add         super().__init__(**kwargs) </s> remove         self.size = conv_utils.normalize_tuple(size, 2, 'size')
 </s> add         self.size = conv_utils.normalize_tuple(size, 2, "size")
                 beta_initializer="zeros",
                 gamma_initializer="ones",
                   axis=None,
                   epsilon=1e-3,
                   center=True,
                   scale=True,
                 beta_initializer="zeros",
                 gamma_initializer="ones",
                 beta_initializer="zeros",
                 gamma_initializer="ones",
                   beta_regularizer=None,
                   gamma_regularizer=None,
                   beta_constraint=None,
                   gamma_constraint=None,
                   **kwargs):
keep keep keep keep replace replace keep keep keep keep keep
 Simple backend unit tests (#1020)

* Add simple backend tests for lib.model
* Document lib.model
* Fix GMSD Loss for AMD
* Remove obsolete code from lib.model 
lib/model/normalization.py