MTRF/algorithms/softlearning/models/normalization.py [26:366]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - def register_keras_custom_object(cls): get_custom_objects()[cls.__name__] = cls return cls @register_keras_custom_object class GroupNormalization(tf.keras.layers.Layer): """Group normalization layer. Group Normalization divides the channels into groups and computes within each group the mean and variance for normalization. Empirically, its accuracy is more stable than batch norm in a wide range of small batch sizes, if learning rate is adjusted linearly with 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. 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. 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=2, 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 = tf.keras.initializers.get(beta_initializer) self.gamma_initializer = tf.keras.initializers.get(gamma_initializer) self.beta_regularizer = tf.keras.regularizers.get(beta_regularizer) self.gamma_regularizer = tf.keras.regularizers.get(gamma_regularizer) self.beta_constraint = tf.keras.constraints.get(beta_constraint) self.gamma_constraint = tf.keras.constraints.get(gamma_constraint) self._check_axis() def build(self, input_shape): self._check_if_input_shape_is_none(input_shape) self._set_number_of_groups_for_instance_norm(input_shape) self._check_size_of_dimensions(input_shape) self._create_input_spec(input_shape) self._add_gamma_weight(input_shape) self._add_beta_weight(input_shape) self.built = True super(GroupNormalization, self).build(input_shape) def call(self, inputs): input_shape = tf.keras.backend.int_shape(inputs) tensor_input_shape = tf.shape(inputs) reshaped_inputs, group_shape = self._reshape_into_groups( inputs, input_shape, tensor_input_shape) normalized_inputs = self._apply_normalization(reshaped_inputs, input_shape) outputs = tf.reshape(normalized_inputs, 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': tf.keras.initializers.serialize(self.beta_initializer), 'gamma_initializer': tf.keras.initializers.serialize(self.gamma_initializer), 'beta_regularizer': tf.keras.regularizers.serialize(self.beta_regularizer), 'gamma_regularizer': tf.keras.regularizers.serialize(self.gamma_regularizer), 'beta_constraint': tf.keras.constraints.serialize(self.beta_constraint), 'gamma_constraint': tf.keras.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 def _reshape_into_groups(self, inputs, input_shape, tensor_input_shape): group_shape = [tensor_input_shape[i] for i in range(len(input_shape))] group_shape[self.axis] = input_shape[self.axis] // self.groups group_shape.insert(1, self.groups) group_shape = tf.stack(group_shape) reshaped_inputs = tf.reshape(inputs, group_shape) return reshaped_inputs, group_shape def _apply_normalization(self, reshaped_inputs, input_shape): group_shape = tf.keras.backend.int_shape(reshaped_inputs) group_reduction_axes = list(range(len(group_shape))) # Remember the ordering of the tensor is [batch, group , steps]. Jump # the first 2 to calculate the variance and the mean mean, variance = tf.nn.moments( reshaped_inputs, group_reduction_axes[2:], keep_dims=True) gamma, beta = self._get_reshaped_weights(input_shape) normalized_inputs = tf.nn.batch_normalization( reshaped_inputs, mean=mean, variance=variance, scale=gamma, offset=beta, variance_epsilon=self.epsilon) return normalized_inputs def _get_reshaped_weights(self, input_shape): broadcast_shape = self._create_broadcast_shape(input_shape) gamma = None beta = None if self.scale: gamma = tf.reshape(self.gamma, broadcast_shape) if self.center: beta = tf.reshape(self.beta, broadcast_shape) return gamma, beta def _check_if_input_shape_is_none(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) + '.') def _set_number_of_groups_for_instance_norm(self, input_shape): dim = input_shape[self.axis] if self.groups == -1: self.groups = dim def _check_size_of_dimensions(self, input_shape): dim = input_shape[self.axis] 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) + ').') def _check_axis(self): if self.axis == 0: raise ValueError( "You are trying to normalize your batch axis. Do you want to " "use tf.layer.batch_normalization instead") def _create_input_spec(self, input_shape): dim = input_shape[self.axis] self.input_spec = tf.keras.layers.InputSpec( ndim=len(input_shape), axes={self.axis: dim}) def _add_gamma_weight(self, input_shape): dim = input_shape[self.axis] 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 def _add_beta_weight(self, input_shape): dim = input_shape[self.axis] shape = (dim,) 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 def _create_broadcast_shape(self, input_shape): broadcast_shape = [1] * len(input_shape) broadcast_shape[self.axis] = input_shape[self.axis] // self.groups broadcast_shape.insert(1, self.groups) return broadcast_shape @register_keras_custom_object class LayerNormalization(GroupNormalization): """Layer normalization layer. Layer Normalization is an specific case of ```GroupNormalization```since it normalizes all features of a layer. The Groupsize is 1. Empirically, its accuracy is more stable than batch norm in a wide range of small batch sizes, if learning rate is adjusted linearly with batch sizes. Arguments axis: Integer, the axis that should be normalized. 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. 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) """ def __init__(self, **kwargs): if "groups" in kwargs: logging.warning("The given value for groups will be overwritten.") kwargs["groups"] = 1 super(LayerNormalization, self).__init__(**kwargs) @register_keras_custom_object class InstanceNormalization(GroupNormalization): """Instance normalization layer. Instance Normalization is an specific case of ```GroupNormalization```since it normalizes all features of one channel. The Groupsize is equal to the channel size. Empirically, its accuracy is more stable than batch norm in a wide range of small batch sizes, if learning rate is adjusted linearly with batch sizes. Arguments axis: Integer, the axis that should be normalized. 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. 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 - [Instance Normalization: The Missing Ingredient for Fast Stylization] (https://arxiv.org/abs/1607.08022) """ def __init__(self, **kwargs): if "groups" in kwargs: logging.warning("The given value for groups will be overwritten.") kwargs["groups"] = -1 super(InstanceNormalization, self).__init__(**kwargs) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - MTRF/algorithms/softlearning/preprocessors/normalization.py [26:366]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - def register_keras_custom_object(cls): get_custom_objects()[cls.__name__] = cls return cls @register_keras_custom_object class GroupNormalization(tf.keras.layers.Layer): """Group normalization layer. Group Normalization divides the channels into groups and computes within each group the mean and variance for normalization. Empirically, its accuracy is more stable than batch norm in a wide range of small batch sizes, if learning rate is adjusted linearly with 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. 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. 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=2, 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 = tf.keras.initializers.get(beta_initializer) self.gamma_initializer = tf.keras.initializers.get(gamma_initializer) self.beta_regularizer = tf.keras.regularizers.get(beta_regularizer) self.gamma_regularizer = tf.keras.regularizers.get(gamma_regularizer) self.beta_constraint = tf.keras.constraints.get(beta_constraint) self.gamma_constraint = tf.keras.constraints.get(gamma_constraint) self._check_axis() def build(self, input_shape): self._check_if_input_shape_is_none(input_shape) self._set_number_of_groups_for_instance_norm(input_shape) self._check_size_of_dimensions(input_shape) self._create_input_spec(input_shape) self._add_gamma_weight(input_shape) self._add_beta_weight(input_shape) self.built = True super(GroupNormalization, self).build(input_shape) def call(self, inputs): input_shape = tf.keras.backend.int_shape(inputs) tensor_input_shape = tf.shape(inputs) reshaped_inputs, group_shape = self._reshape_into_groups( inputs, input_shape, tensor_input_shape) normalized_inputs = self._apply_normalization(reshaped_inputs, input_shape) outputs = tf.reshape(normalized_inputs, 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': tf.keras.initializers.serialize(self.beta_initializer), 'gamma_initializer': tf.keras.initializers.serialize(self.gamma_initializer), 'beta_regularizer': tf.keras.regularizers.serialize(self.beta_regularizer), 'gamma_regularizer': tf.keras.regularizers.serialize(self.gamma_regularizer), 'beta_constraint': tf.keras.constraints.serialize(self.beta_constraint), 'gamma_constraint': tf.keras.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 def _reshape_into_groups(self, inputs, input_shape, tensor_input_shape): group_shape = [tensor_input_shape[i] for i in range(len(input_shape))] group_shape[self.axis] = input_shape[self.axis] // self.groups group_shape.insert(1, self.groups) group_shape = tf.stack(group_shape) reshaped_inputs = tf.reshape(inputs, group_shape) return reshaped_inputs, group_shape def _apply_normalization(self, reshaped_inputs, input_shape): group_shape = tf.keras.backend.int_shape(reshaped_inputs) group_reduction_axes = list(range(len(group_shape))) # Remember the ordering of the tensor is [batch, group , steps]. Jump # the first 2 to calculate the variance and the mean mean, variance = tf.nn.moments( reshaped_inputs, group_reduction_axes[2:], keep_dims=True) gamma, beta = self._get_reshaped_weights(input_shape) normalized_inputs = tf.nn.batch_normalization( reshaped_inputs, mean=mean, variance=variance, scale=gamma, offset=beta, variance_epsilon=self.epsilon) return normalized_inputs def _get_reshaped_weights(self, input_shape): broadcast_shape = self._create_broadcast_shape(input_shape) gamma = None beta = None if self.scale: gamma = tf.reshape(self.gamma, broadcast_shape) if self.center: beta = tf.reshape(self.beta, broadcast_shape) return gamma, beta def _check_if_input_shape_is_none(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) + '.') def _set_number_of_groups_for_instance_norm(self, input_shape): dim = input_shape[self.axis] if self.groups == -1: self.groups = dim def _check_size_of_dimensions(self, input_shape): dim = input_shape[self.axis] 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) + ').') def _check_axis(self): if self.axis == 0: raise ValueError( "You are trying to normalize your batch axis. Do you want to " "use tf.layer.batch_normalization instead") def _create_input_spec(self, input_shape): dim = input_shape[self.axis] self.input_spec = tf.keras.layers.InputSpec( ndim=len(input_shape), axes={self.axis: dim}) def _add_gamma_weight(self, input_shape): dim = input_shape[self.axis] 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 def _add_beta_weight(self, input_shape): dim = input_shape[self.axis] shape = (dim,) 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 def _create_broadcast_shape(self, input_shape): broadcast_shape = [1] * len(input_shape) broadcast_shape[self.axis] = input_shape[self.axis] // self.groups broadcast_shape.insert(1, self.groups) return broadcast_shape @register_keras_custom_object class LayerNormalization(GroupNormalization): """Layer normalization layer. Layer Normalization is an specific case of ```GroupNormalization```since it normalizes all features of a layer. The Groupsize is 1. Empirically, its accuracy is more stable than batch norm in a wide range of small batch sizes, if learning rate is adjusted linearly with batch sizes. Arguments axis: Integer, the axis that should be normalized. 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. 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) """ def __init__(self, **kwargs): if "groups" in kwargs: logging.warning("The given value for groups will be overwritten.") kwargs["groups"] = 1 super(LayerNormalization, self).__init__(**kwargs) @register_keras_custom_object class InstanceNormalization(GroupNormalization): """Instance normalization layer. Instance Normalization is an specific case of ```GroupNormalization```since it normalizes all features of one channel. The Groupsize is equal to the channel size. Empirically, its accuracy is more stable than batch norm in a wide range of small batch sizes, if learning rate is adjusted linearly with batch sizes. Arguments axis: Integer, the axis that should be normalized. 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. 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 - [Instance Normalization: The Missing Ingredient for Fast Stylization] (https://arxiv.org/abs/1607.08022) """ def __init__(self, **kwargs): if "groups" in kwargs: logging.warning("The given value for groups will be overwritten.") kwargs["groups"] = -1 super(InstanceNormalization, self).__init__(**kwargs) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -