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###############################################################################
# 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
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