docker/bloom13b/Model-References/TensorFlow/computer_vision/densenet/densenet.py

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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