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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 |