############################################################################### # Copyright (C) 2020-2021 Habana Labs, Ltd. an Intel Company ############################################################################### # List of changes: # - Added Conv2D and Dense kernel and bias regularization # - Added densent general configuration to support densent121, densenet161, densenet169 import tensorflow as tf from tensorflow.keras.layers import Input, Concatenate, ZeroPadding2D from tensorflow.keras.layers import Dense, Dropout, Activation from tensorflow.keras.layers import Conv2D from tensorflow.keras.layers import AveragePooling2D, GlobalAveragePooling2D, MaxPooling2D from tensorflow.keras.layers import BatchNormalization from tensorflow.keras.models import Model def densenet_model(img_rows, img_cols, color_type=1, nb_dense_block=4, growth_rate=32, nb_filter=64,nb_layers=[6, 12, 24, 16], reduction=0.5, dropout_rate=0.0, weight_decay=1e-4, num_classes=None): ''' DenseNet 121 Model for Keras Model Schema is based on https://github.com/flyyufelix/DenseNet-Keras ImageNet Pretrained Weights Theano: https://drive.google.com/open?id=0Byy2AcGyEVxfMlRYb3YzV210VzQ TensorFlow: https://drive.google.com/open?id=0Byy2AcGyEVxfSTA4SHJVOHNuTXc # Arguments nb_dense_block: number of dense blocks to add to end growth_rate: number of filters to add per dense block nb_filter: initial number of filters reduction: reduction factor of transition blocks. dropout_rate: dropout rate weight_decay: weight decay factor classes: optional number of classes to classify images weights_path: path to pre-trained weights # Returns A Keras model instance. ''' eps = 1.1e-5 # compute compression factor compression = 1.0 - reduction # Handle Dimension Ordering for different backends global concat_axis concat_axis = 3 img_input = Input(shape=(img_rows, img_cols, color_type), name='data') # Initial convolution x = ZeroPadding2D((3, 3), name='conv1_zeropadding')(img_input) x = Conv2D(nb_filter, 7, strides=(2, 2), name='conv1', use_bias=False, kernel_regularizer=tf.keras.regularizers.l2(weight_decay), bias_regularizer=tf.keras.regularizers.l2(weight_decay))(x) x = BatchNormalization(epsilon=eps, axis=concat_axis, name='conv1_bn', beta_regularizer=tf.keras.regularizers.l2(weight_decay), gamma_regularizer=tf.keras.regularizers.l2(weight_decay))(x) # x = Scale(axis=concat_axis, name='conv1_scale')(x) x = Activation('relu', name='relu1')(x) x = ZeroPadding2D((1, 1), name='pool1_zeropadding')(x) x = MaxPooling2D((3, 3), strides=(2, 2), name='pool1')(x) # Add dense blocks for block_idx in range(nb_dense_block - 1): stage = block_idx + 2 x, nb_filter = dense_block(x, stage, nb_layers[block_idx], nb_filter, growth_rate, dropout_rate=dropout_rate, weight_decay=weight_decay) # Add transition_block x = transition_block(x, stage, nb_filter, compression=compression, dropout_rate=dropout_rate, weight_decay=weight_decay) nb_filter = int(nb_filter * compression) final_stage = stage + 1 x, nb_filter = dense_block(x, final_stage, nb_layers[-1], nb_filter, growth_rate, dropout_rate=dropout_rate, weight_decay=weight_decay) x = BatchNormalization(epsilon=eps, axis=concat_axis, name='conv' + str(final_stage) + '_blk_bn', beta_regularizer=tf.keras.regularizers.l2(weight_decay), gamma_regularizer=tf.keras.regularizers.l2(weight_decay))(x) # x = Scale(axis=concat_axis, name='conv'+str(final_stage)+'_blk_scale')(x) x = Activation('relu', name='relu' + str(final_stage) + '_blk')(x) x_fc = GlobalAveragePooling2D(name='pool' + str(final_stage))(x) x_fc = Dense(num_classes, kernel_regularizer=tf.keras.regularizers.l2(weight_decay), bias_regularizer=tf.keras.regularizers.l2(weight_decay), name='fc6')(x_fc) x_fc = Activation('softmax', name='prob')(x_fc) model = Model(img_input, x_fc, name='densenet') return model def conv_block(x, stage, branch, nb_filter, dropout_rate=None, weight_decay=1e-4): '''Apply BatchNorm, Relu, bottleneck 1x1 Conv2D, 3x3 Conv2D, and option dropout # Arguments x: input tensor stage: index for dense block branch: layer index within each dense block nb_filter: number of filters dropout_rate: dropout rate weight_decay: weight decay factor ''' eps = 1.1e-5 conv_name_base = 'conv' + str(stage) + '_' + str(branch) relu_name_base = 'relu' + str(stage) + '_' + str(branch) # 1x1 Convolution (Bottleneck layer) inter_channel = nb_filter * 4 x = BatchNormalization(epsilon=eps, axis=concat_axis, name=conv_name_base + '_x1_bn', beta_regularizer=tf.keras.regularizers.l2(weight_decay), gamma_regularizer=tf.keras.regularizers.l2(weight_decay))(x) # x = Scale(axis=concat_axis, name=conv_name_base+'_x1_scale')(x) x = Activation('relu', name=relu_name_base + '_x1')(x) x = Conv2D(inter_channel, 1, name=conv_name_base + '_x1', use_bias=False, kernel_regularizer=tf.keras.regularizers.l2(weight_decay), bias_regularizer=tf.keras.regularizers.l2(weight_decay))(x) if dropout_rate: x = Dropout(dropout_rate)(x) # 3x3 Convolution x = BatchNormalization(epsilon=eps, axis=concat_axis, name=conv_name_base + '_x2_bn', beta_regularizer=tf.keras.regularizers.l2(weight_decay), gamma_regularizer=tf.keras.regularizers.l2(weight_decay))(x) # x = Scale(axis=concat_axis, name=conv_name_base+'_x2_scale')(x) x = Activation('relu', name=relu_name_base + '_x2')(x) x = ZeroPadding2D((1, 1), name=conv_name_base + '_x2_zeropadding')(x) x = Conv2D(nb_filter, 3, name=conv_name_base + '_x2', use_bias=False, kernel_regularizer=tf.keras.regularizers.l2(weight_decay), bias_regularizer=tf.keras.regularizers.l2(weight_decay))(x) if dropout_rate: x = Dropout(dropout_rate)(x) return x def transition_block(x, stage, nb_filter, compression=1.0, dropout_rate=None, weight_decay=1E-4): ''' Apply BatchNorm, 1x1 Convolution, averagePooling, optional compression, dropout # Arguments x: input tensor stage: index for dense block nb_filter: number of filters compression: calculated as 1 - reduction. Reduces the number of feature maps in the transition block. dropout_rate: dropout rate weight_decay: weight decay factor ''' eps = 1.1e-5 conv_name_base = 'conv' + str(stage) + '_blk' relu_name_base = 'relu' + str(stage) + '_blk' pool_name_base = 'pool' + str(stage) x = BatchNormalization(epsilon=eps, axis=concat_axis, name=conv_name_base + '_bn', beta_regularizer=tf.keras.regularizers.l2(weight_decay), gamma_regularizer=tf.keras.regularizers.l2(weight_decay))(x) # x = Scale(axis=concat_axis, name=conv_name_base+'_scale')(x) x = Activation('relu', name=relu_name_base)(x) x = Conv2D(int(nb_filter * compression), 1, name=conv_name_base, use_bias=False, kernel_regularizer=tf.keras.regularizers.l2(weight_decay), bias_regularizer=tf.keras.regularizers.l2(weight_decay))(x) if dropout_rate: x = Dropout(dropout_rate)(x) x = AveragePooling2D((2, 2), strides=(2, 2), name=pool_name_base)(x) return x def dense_block(x, stage, nb_layers, nb_filter, growth_rate, dropout_rate=None, weight_decay=1e-4, grow_nb_filters=True): ''' Build a dense_block where the output of each conv_block is fed to subsequent ones # Arguments x: input tensor stage: index for dense block nb_layers: the number of layers of conv_block to append to the model. nb_filter: number of filters growth_rate: growth rate dropout_rate: dropout rate weight_decay: weight decay factor grow_nb_filters: flag to decide to allow number of filters to grow ''' eps = 1.1e-5 concat_feat = x for i in range(nb_layers): branch = i + 1 x = conv_block(concat_feat, stage, branch, growth_rate, dropout_rate, weight_decay) concat_feat = Concatenate()([concat_feat, x]) if grow_nb_filters: nb_filter += growth_rate return concat_feat, nb_filter