from keras.layers import Layer, InputSpec from keras import initializers, regularizers, constraints from keras import backend as K from keras_contrib import backend as KC class GroupNormalization(Layer): """Group normalization layer. Group Normalization divides the channels into groups and computes within each group the mean and variance for normalization. Group Normalization's computation is independent of batch sizes, and its accuracy is stable in a wide range of batch sizes. Relation to Layer Normalization: If the number of groups is set to 1, then this operation becomes identical to Layer Normalization. Relation to Instance Normalization: If the number of groups is set to the input dimension (number of groups is equal to number of channels), then this operation becomes identical to Instance Normalization. # Arguments groups: Integer, the number of groups for Group Normalization. Can be in the range [1, N] where N is the input dimension. The input dimension must be divisible by the number of groups. 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 `BatchNormalization`. 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 - [Group Normalization](https://arxiv.org/abs/1803.08494) """ def __init__(self, groups=32, axis=-1, epsilon=1e-5, center=True, scale=True, beta_initializer='zeros', gamma_initializer='ones', beta_regularizer=None, gamma_regularizer=None, beta_constraint=None, gamma_constraint=None, **kwargs): super(GroupNormalization, self).__init__(**kwargs) self.supports_masking = True self.groups = groups self.axis = axis self.epsilon = epsilon self.center = center self.scale = scale self.beta_initializer = initializers.get(beta_initializer) self.gamma_initializer = initializers.get(gamma_initializer) self.beta_regularizer = regularizers.get(beta_regularizer) self.gamma_regularizer = regularizers.get(gamma_regularizer) self.beta_constraint = constraints.get(beta_constraint) self.gamma_constraint = constraints.get(gamma_constraint) def build(self, input_shape): dim = input_shape[self.axis] if dim is None: raise ValueError('Axis ' + str(self.axis) + ' of ' 'input tensor should have a defined dimension ' 'but the layer received an input with shape ' + str(input_shape) + '.') if dim < self.groups: raise ValueError('Number of groups (' + str(self.groups) + ') cannot be ' 'more than the number of channels (' + str(dim) + ').') if dim % self.groups != 0: raise ValueError('Number of groups (' + str(self.groups) + ') must be a ' 'multiple of the number of channels (' + str(dim) + ').') self.input_spec = InputSpec(ndim=len(input_shape), axes={self.axis: dim}) shape = (dim,) if self.scale: self.gamma = self.add_weight(shape=shape, name='gamma', initializer=self.gamma_initializer, regularizer=self.gamma_regularizer, constraint=self.gamma_constraint) else: self.gamma = None if self.center: self.beta = self.add_weight(shape=shape, name='beta', initializer=self.beta_initializer, regularizer=self.beta_regularizer, constraint=self.beta_constraint) else: self.beta = None self.built = True def call(self, inputs, **kwargs): input_shape = K.int_shape(inputs) tensor_input_shape = K.shape(inputs) # Prepare broadcasting shape. reduction_axes = list(range(len(input_shape))) del reduction_axes[self.axis] broadcast_shape = [1] * len(input_shape) broadcast_shape[self.axis] = input_shape[self.axis] // self.groups broadcast_shape.insert(1, self.groups) reshape_group_shape = K.shape(inputs) group_axes = [reshape_group_shape[i] for i in range(len(input_shape))] group_axes[self.axis] = input_shape[self.axis] // self.groups group_axes.insert(1, self.groups) # reshape inputs to new group shape group_shape = [group_axes[0], self.groups] + group_axes[2:] group_shape = K.stack(group_shape) inputs = K.reshape(inputs, group_shape) group_reduction_axes = list(range(len(group_axes))) mean, variance = KC.moments(inputs, group_reduction_axes[2:], keep_dims=True) inputs = (inputs - mean) / (K.sqrt(variance + self.epsilon)) # prepare broadcast shape inputs = K.reshape(inputs, group_shape) outputs = inputs # In this case we must explicitly broadcast all parameters. if self.scale: broadcast_gamma = K.reshape(self.gamma, broadcast_shape) outputs = outputs * broadcast_gamma if self.center: broadcast_beta = K.reshape(self.beta, broadcast_shape) outputs = outputs + broadcast_beta # finally we reshape the output back to the input shape outputs = K.reshape(outputs, tensor_input_shape) return outputs def get_config(self): config = { 'groups': self.groups, 'axis': self.axis, 'epsilon': self.epsilon, 'center': self.center, 'scale': self.scale, 'beta_initializer': initializers.serialize(self.beta_initializer), 'gamma_initializer': initializers.serialize(self.gamma_initializer), 'beta_regularizer': regularizers.serialize(self.beta_regularizer), 'gamma_regularizer': regularizers.serialize(self.gamma_regularizer), 'beta_constraint': constraints.serialize(self.beta_constraint), 'gamma_constraint': constraints.serialize(self.gamma_constraint) } base_config = super(GroupNormalization, self).get_config() return dict(list(base_config.items()) + list(config.items())) def compute_output_shape(self, input_shape): return input_shape