models/FCN.py

import os
from keras.models import Model
from keras.layers import Input
from keras.layers import Conv2D, MaxPooling2D, Dropout, UpSampling2D
from utils.BilinearUpSampling import BilinearUpSampling2D


def FCN_Vgg16_16s(input_shape=None, weight_decay=0., batch_momentum=0.9, batch_shape=None, classes=1):
    if batch_shape:
        img_input = Input(batch_shape=batch_shape)
        image_size = batch_shape[1:3]
    else:
        img_input = Input(shape=input_shape)
        image_size = input_shape[0:2]
    # Block 1
    x = Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv1', kernel_regularizer='l2')(img_input)
    x = Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv2', kernel_regularizer='l2')(x)
    x = MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool')(x)

    # Block 2
    x = Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv1', kernel_regularizer='l2')(x)
    x = Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv2', kernel_regularizer='l2')(x)
    x = MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool')(x)

    # Block 3
    x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv1', kernel_regularizer='l2')(x)
    x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv2', kernel_regularizer='l2')(x)
    x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv3', kernel_regularizer='l2')(x)
    x = MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool')(x)

    # Block 4
    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv1', kernel_regularizer='l2')(x)
    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv2', kernel_regularizer='l2')(x)
    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv3', kernel_regularizer='l2')(x)
    x = MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool')(x)

    # Block 5
    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv1', kernel_regularizer='l2')(x)
    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv2', kernel_regularizer='l2')(x)
    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv3', kernel_regularizer='l2')(x)

    # Convolutional layers transfered from fully-connected layers
    x = Conv2D(4096, (7, 7), activation='relu', padding='same', dilation_rate=(2, 2),
                      name='fc1', kernel_regularizer='l2')(x)
    x = Dropout(0.5)(x)
    x = Conv2D(4096, (1, 1), activation='relu', padding='same', name='fc2', kernel_regularizer='l2')(x)
    x = Dropout(0.5)(x)
    #classifying layer
    x = Conv2D(classes, (1, 1), kernel_initializer='he_normal', activation='linear', padding='valid', strides=(1, 1), kernel_regularizer='l2')(x)

    x = BilinearUpSampling2D(size=(16, 16))(x)

    model = Model(img_input, x)
    model_name = 'FCN_Vgg16_16'
    return model, model_name