resnet_helpers.py 文件源码

def atrous_identity_block(kernel_size, filters, stage, block, weight_decay=0., atrous_rate=(2, 2), batch_momentum=0.99):
    '''The identity_block is the block that has no conv layer at shortcut
    # Arguments
        kernel_size: defualt 3, the kernel size of middle conv layer at main path
        filters: list of integers, the nb_filters of 3 conv layer at main path
        stage: integer, current stage label, used for generating layer names
        block: 'a','b'..., current block label, used for generating layer names
    '''
    def f(input_tensor):
        nb_filter1, nb_filter2, nb_filter3 = filters
        if K.image_data_format() == 'channels_last':
            bn_axis = 3
        else:
            bn_axis = 1
        conv_name_base = 'res' + str(stage) + block + '_branch'
        bn_name_base = 'bn' + str(stage) + block + '_branch'

        x = Conv2D(nb_filter1, (1, 1), name=conv_name_base + '2a', kernel_regularizer=l2(weight_decay))(input_tensor)
        x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2a', momentum=batch_momentum)(x)
        x = Activation('relu')(x)

        x = Conv2D(nb_filter2, (kernel_size, kernel_size), dilation_rate=atrous_rate,
                          padding='same', name=conv_name_base + '2b', kernel_regularizer=l2(weight_decay))(x)
        x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2b', momentum=batch_momentum)(x)
        x = Activation('relu')(x)

        x = Conv2D(nb_filter3, (1, 1), name=conv_name_base + '2c', kernel_regularizer=l2(weight_decay))(x)
        x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2c', momentum=batch_momentum)(x)

        x = Add()([x, input_tensor])
        x = Activation('relu')(x)
        return x
    return f