''' @Date : 2022.05.29 @Author : Jethro ''' import inspect from typing import List from tensorflow.keras import backend as K, Model, Input, optimizers from tensorflow.keras import layers from tensorflow.keras.layers import Activation, SpatialDropout1D, Lambda from tensorflow.keras.layers import Layer, Conv1D, Dense, BatchNormalization, LayerNormalization def is_power_of_two(num: int): return num != 0 and ((num & (num - 1)) == 0) def adjust_dilations(dilations: list): if all([is_power_of_two(i) for i in dilations]): return dilations else: new_dilations = [2 ** i for i in dilations] return new_dilations class ResidualBlock(Layer): def __init__(self, dilation_rate: int, nb_filters: int, kernel_size: int, padding: str, activation: str = 'relu', dropout_rate: float = 0, kernel_initializer: str = 'he_normal', use_batch_norm: bool = False, use_layer_norm: bool = False, use_weight_norm: bool = False, **kwargs): """Defines the residual block for the WaveNet TCN Args: x: The previous layer in the model training: boolean indicating whether the layer should behave in training mode or in inference mode dilation_rate: The dilation power of 2 we are using for this residual block nb_filters: The number of convolutional filters to use in this block kernel_size: The size of the convolutional kernel padding: The padding used in the convolutional layers, 'same' or 'causal'. activation: The final activation used in o = Activation(x + F(x)) dropout_rate: Float between 0 and 1. Fraction of the input units to drop. kernel_initializer: Initializer for the kernel weights matrix (Conv1D). use_batch_norm: Whether to use batch normalization in the residual layers or not. use_layer_norm: Whether to use layer normalization in the residual layers or not. use_weight_norm: Whether to use weight normalization in the residual layers or not. kwargs: Any initializers for Layer class. """ self.dilation_rate = dilation_rate self.nb_filters = nb_filters self.kernel_size = kernel_size self.padding = padding self.activation = activation self.dropout_rate = dropout_rate self.use_batch_norm = use_batch_norm self.use_layer_norm = use_layer_norm self.use_weight_norm = use_weight_norm self.kernel_initializer = kernel_initializer self.layers = [] self.layers_outputs = [] self.shape_match_conv = None self.res_output_shape = None self.final_activation = None super(ResidualBlock, self).__init__(**kwargs) def _build_layer(self, layer): """Helper function for building layer Args: layer: Appends layer to internal layer list and builds it based on the current output shape of ResidualBlocK. Updates current output shape. """ self.layers.append(layer) self.layers[-1].build(self.res_output_shape) self.res_output_shape = self.layers[-1].compute_output_shape(self.res_output_shape) def build(self, input_shape): with K.name_scope(self.name): # name scope used to make sure weights get unique names self.layers = [] self.res_output_shape = input_shape for k in range(2): name = 'conv1D_{}'.format(k) with K.name_scope(name): # name scope used to make sure weights get unique names conv = Conv1D( filters=self.nb_filters, kernel_size=self.kernel_size, dilation_rate=self.dilation_rate, padding=self.padding, name=name, kernel_initializer=self.kernel_initializer ) if self.use_weight_norm: from tensorflow_addons.layers import WeightNormalization # wrap it. WeightNormalization API is different than BatchNormalization or LayerNormalization. with K.name_scope('norm_{}'.format(k)): conv = WeightNormalization(conv) self._build_layer(conv) with K.name_scope('norm_{}'.format(k)): if self.use_batch_norm: self._build_layer(BatchNormalization()) elif self.use_layer_norm: self._build_layer(LayerNormalization()) elif self.use_weight_norm: pass # done above. self._build_layer(Activation(self.activation)) self._build_layer(SpatialDropout1D(rate=self.dropout_rate)) if self.nb_filters != input_shape[-1]: # 1x1 conv to match the shapes (channel dimension). name = 'matching_conv1D' with K.name_scope(name): # make and build this layer separately because it directly uses input_shape self.shape_match_conv = Conv1D(filters=self.nb_filters, kernel_size=1, padding='same', name=name, kernel_initializer=self.kernel_initializer) else: name = 'matching_identity' self.shape_match_conv = Lambda(lambda x: x, name=name) with K.name_scope(name): self.shape_match_conv.build(input_shape) self.res_output_shape = self.shape_match_conv.compute_output_shape(input_shape) self._build_layer(Activation(self.activation)) self.final_activation = Activation(self.activation) self.final_activation.build(self.res_output_shape) # probably isn't necessary # this is done to force Keras to add the layers in the list to self._layers for layer in self.layers: self.__setattr__(layer.name, layer) self.__setattr__(self.shape_match_conv.name, self.shape_match_conv) self.__setattr__(self.final_activation.name, self.final_activation) super(ResidualBlock, self).build(input_shape) # done to make sure self.built is set True def call(self, inputs, training=None): """ Returns: A tuple where the first element is the residual model tensor, and the second is the skip connection tensor. """ x = inputs self.layers_outputs = [x] for layer in self.layers: training_flag = 'training' in dict(inspect.signature(layer.call).parameters) x = layer(x, training=training) if training_flag else layer(x) self.layers_outputs.append(x) x2 = self.shape_match_conv(inputs) self.layers_outputs.append(x2) res_x = layers.add([x2, x]) self.layers_outputs.append(res_x) res_act_x = self.final_activation(res_x) self.layers_outputs.append(res_act_x) return [res_act_x, x] def compute_output_shape(self, input_shape): return [self.res_output_shape, self.res_output_shape] class TCN(Layer): """Creates a TCN layer. Input shape: A tensor of shape (batch_size, timesteps, input_dim). Args: nb_filters: The number of filters to use in the convolutional layers. Can be a list. kernel_size: The size of the kernel to use in each convolutional layer. dilations: The list of the dilations. Example is: [1, 2, 4, 8, 16, 32, 64]. nb_stacks : The number of stacks of residual blocks to use. padding: The padding to use in the convolutional layers, 'causal' or 'same'. use_skip_connections: Boolean. If we want to add skip connections from input to each residual blocK. return_sequences: Boolean. Whether to return the last output in the output sequence, or the full sequence. activation: The activation used in the residual blocks o = Activation(x + F(x)). dropout_rate: Float between 0 and 1. Fraction of the input units to drop. kernel_initializer: Initializer for the kernel weights matrix (Conv1D). use_batch_norm: Whether to use batch normalization in the residual layers or not. use_layer_norm: Whether to use layer normalization in the residual layers or not. use_weight_norm: Whether to use weight normalization in the residual layers or not. kwargs: Any other arguments for configuring parent class Layer. For example "name=str", Name of the model. Use unique names when using multiple TCN. Returns: A TCN layer. """ def __init__(self, nb_filters=64, kernel_size=3, nb_stacks=1, dilations=(1, 2, 4, 8, 16, 32), padding='causal', use_skip_connections=True, dropout_rate=0.0, return_sequences=False, activation='relu', kernel_initializer='he_normal', use_batch_norm=False, use_layer_norm=False, use_weight_norm=False, **kwargs): self.return_sequences = return_sequences self.dropout_rate = dropout_rate self.use_skip_connections = use_skip_connections self.dilations = dilations self.nb_stacks = nb_stacks self.kernel_size = kernel_size self.nb_filters = nb_filters self.activation = activation self.padding = padding self.kernel_initializer = kernel_initializer self.use_batch_norm = use_batch_norm self.use_layer_norm = use_layer_norm self.use_weight_norm = use_weight_norm self.skip_connections = [] self.residual_blocks = [] self.layers_outputs = [] self.build_output_shape = None self.slicer_layer = None # in case return_sequence=False self.output_slice_index = None # in case return_sequence=False self.padding_same_and_time_dim_unknown = False # edge case if padding='same' and time_dim = None if self.use_batch_norm + self.use_layer_norm + self.use_weight_norm > 1: raise ValueError('Only one normalization can be specified at once.') if isinstance(self.nb_filters, list): assert len(self.nb_filters) == len(self.dilations) if padding != 'causal' and padding != 'same': raise ValueError("Only 'causal' or 'same' padding are compatible for this layer.") # initialize parent class super(TCN, self).__init__(**kwargs) @property def receptive_field(self): return 1 + 2 * (self.kernel_size - 1) * self.nb_stacks * sum(self.dilations) def build(self, input_shape): # member to hold current output shape of the layer for building purposes self.build_output_shape = input_shape # list to hold all the member ResidualBlocks self.residual_blocks = [] total_num_blocks = self.nb_stacks * len(self.dilations) if not self.use_skip_connections: total_num_blocks += 1 # cheap way to do a false case for below for s in range(self.nb_stacks): for i, d in enumerate(self.dilations): res_block_filters = self.nb_filters[i] if isinstance(self.nb_filters, list) else self.nb_filters self.residual_blocks.append(ResidualBlock(dilation_rate=d, nb_filters=res_block_filters, kernel_size=self.kernel_size, padding=self.padding, activation=self.activation, dropout_rate=self.dropout_rate, use_batch_norm=self.use_batch_norm, use_layer_norm=self.use_layer_norm, use_weight_norm=self.use_weight_norm, kernel_initializer=self.kernel_initializer, name='residual_block_{}'.format(len(self.residual_blocks)))) # build newest residual block self.residual_blocks[-1].build(self.build_output_shape) self.build_output_shape = self.residual_blocks[-1].res_output_shape # this is done to force keras to add the layers in the list to self._layers for layer in self.residual_blocks: self.__setattr__(layer.name, layer) self.output_slice_index = None if self.padding == 'same': time = self.build_output_shape.as_list()[1] if time is not None: # if time dimension is defined. e.g. shape = (bs, 500, input_dim). self.output_slice_index = int(self.build_output_shape.as_list()[1] / 2) else: # It will known at call time. c.f. self.call. self.padding_same_and_time_dim_unknown = True else: self.output_slice_index = -1 # causal case. self.slicer_layer = Lambda(lambda tt: tt[:, self.output_slice_index, :]) def compute_output_shape(self, input_shape): """ Overridden in case keras uses it somewhere... no idea. Just trying to avoid future errors. """ if not self.built: self.build(input_shape) if not self.return_sequences: batch_size = self.build_output_shape[0] batch_size = batch_size.value if hasattr(batch_size, 'value') else batch_size nb_filters = self.build_output_shape[-1] return [batch_size, nb_filters] else: # Compatibility tensorflow 1.x return [v.value if hasattr(v, 'value') else v for v in self.build_output_shape] def call(self, inputs, training=None): x = inputs self.layers_outputs = [x] self.skip_connections = [] for layer in self.residual_blocks: try: x, skip_out = layer(x, training=training) except TypeError: # compatibility with tensorflow 1.x x, skip_out = layer(K.cast(x, 'float32'), training=training) self.skip_connections.append(skip_out) self.layers_outputs.append(x) if self.use_skip_connections: x = layers.add(self.skip_connections) self.layers_outputs.append(x) if not self.return_sequences: # case: time dimension is unknown. e.g. (bs, None, input_dim). if self.padding_same_and_time_dim_unknown: self.output_slice_index = K.shape(self.layers_outputs[-1])[1] // 2 x = self.slicer_layer(x) self.layers_outputs.append(x) return x def get_config(self): """ Returns the config of a the layer. This is used for saving and loading from a model :return: python dictionary with specs to rebuild layer """ config = super(TCN, self).get_config() config['nb_filters'] = self.nb_filters config['kernel_size'] = self.kernel_size config['nb_stacks'] = self.nb_stacks config['dilations'] = self.dilations config['padding'] = self.padding config['use_skip_connections'] = self.use_skip_connections config['dropout_rate'] = self.dropout_rate config['return_sequences'] = self.return_sequences config['activation'] = self.activation config['use_batch_norm'] = self.use_batch_norm config['use_layer_norm'] = self.use_layer_norm config['use_weight_norm'] = self.use_weight_norm config['kernel_initializer'] = self.kernel_initializer return config def compiled_tcn(num_feat, # type: int num_classes, # type: int nb_filters, # type: int kernel_size, # type: int dilations, # type: List[int] nb_stacks, # type: int max_len, # type: int output_len=1, # type: int padding='causal', # type: str use_skip_connections=False, # type: bool return_sequences=True, regression=False, # type: bool dropout_rate=0.05, # type: float name='tcn', # type: str, kernel_initializer='he_normal', # type: str, activation='relu', # type:str, opt='adam', lr=0.002, use_batch_norm=False, use_layer_norm=False, use_weight_norm=False): # type: (...) -> Model """Creates a compiled TCN model for a given task (i.e. regression or classification). Classification uses a sparse categorical loss. Please input class ids and not one-hot encodings. Args: num_feat: The number of features of your input, i.e. the last dimension of: (batch_size, timesteps, input_dim). num_classes: The size of the final dense layer, how many classes we are predicting. nb_filters: The number of filters to use in the convolutional layers. kernel_size: The size of the kernel to use in each convolutional layer. dilations: The list of the dilations. Example is: [1, 2, 4, 8, 16, 32, 64]. nb_stacks : The number of stacks of residual blocks to use. max_len: The maximum sequence length, use None if the sequence length is dynamic. padding: The padding to use in the convolutional layers. use_skip_connections: Boolean. If we want to add skip connections from input to each residual blocK. return_sequences: Boolean. Whether to return the last output in the output sequence, or the full sequence. regression: Whether the output should be continuous or discrete. dropout_rate: Float between 0 and 1. Fraction of the input units to drop. activation: The activation used in the residual blocks o = Activation(x + F(x)). name: Name of the model. Useful when having multiple TCN. kernel_initializer: Initializer for the kernel weights matrix (Conv1D). opt: Optimizer name. lr: Learning rate. use_batch_norm: Whether to use batch normalization in the residual layers or not. use_layer_norm: Whether to use layer normalization in the residual layers or not. use_weight_norm: Whether to use weight normalization in the residual layers or not. Returns: A compiled keras TCN. """ dilations = adjust_dilations(dilations) input_layer = Input(shape=(max_len, num_feat)) x = TCN(nb_filters, kernel_size, nb_stacks, dilations, padding, use_skip_connections, dropout_rate, return_sequences, activation, kernel_initializer, use_batch_norm, use_layer_norm, use_weight_norm, name=name)(input_layer) print('x.shape=', x.shape) def get_opt(): if opt == 'adam': return optimizers.Adam(lr=lr, clipnorm=1.) elif opt == 'rmsprop': return optimizers.RMSprop(lr=lr, clipnorm=1.) else: raise Exception('Only Adam and RMSProp are available here') if not regression: # classification x = Dense(num_classes)(x) x = Activation('softmax')(x) output_layer = x model = Model(input_layer, output_layer) # https://github.com/keras-team/keras/pull/11373 # It's now in Keras@master but still not available with pip. # TODO remove later. def accuracy(y_true, y_pred): # reshape in case it's in shape (num_samples, 1) instead of (num_samples,) if K.ndim(y_true) == K.ndim(y_pred): y_true = K.squeeze(y_true, -1) # convert dense predictions to labels y_pred_labels = K.argmax(y_pred, axis=-1) y_pred_labels = K.cast(y_pred_labels, K.floatx()) return K.cast(K.equal(y_true, y_pred_labels), K.floatx()) model.compile(get_opt(), loss='sparse_categorical_crossentropy', metrics=[accuracy]) else: # regression x = Dense(output_len)(x) x = Activation('linear')(x) output_layer = x model = Model(input_layer, output_layer) model.compile(get_opt(), loss='mean_squared_error') print('model.x = {}'.format(input_layer.shape)) print('model.y = {}'.format(output_layer.shape)) return model def tcn_full_summary(model: Model, expand_residual_blocks=True): import tensorflow as tf # 2.6.0-rc1, 2.5.0... versions = [int(v) for v in tf.__version__.split('-')[0].split('.')] if versions[0] <= 2 and versions[1] < 5: layers = model._layers.copy() # store existing layers model._layers.clear() # clear layers for i in range(len(layers)): if isinstance(layers[i], TCN): for layer in layers[i]._layers: if not isinstance(layer, ResidualBlock): if not hasattr(layer, '__iter__'): model._layers.append(layer) else: if expand_residual_blocks: for lyr in layer._layers: if not hasattr(lyr, '__iter__'): model._layers.append(lyr) else: model._layers.append(layer) else: model._layers.append(layers[i]) model.summary() # print summary # restore original layers model._layers.clear() [model._layers.append(lyr) for lyr in layers] else: print('WARNING: tcn_full_summary: Compatible with tensorflow 2.5.0 or below.')