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model.py
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model.py
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import os
from keras.models import *
from keras.layers import *
from keras.regularizers import l2
def unet(input_size, classes):
inputs = Input(input_size)
conv1 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(inputs)
conv1 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv1)
pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
conv2 = Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool1)
conv2 = Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv2)
pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
conv3 = Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool2)
conv3 = Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv3)
pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)
conv4 = Conv2D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool3)
conv4 = Conv2D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv4)
drop4 = Dropout(0.5)(conv4)
pool4 = MaxPooling2D(pool_size=(2, 2))(drop4)
conv5 = Conv2D(1024, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool4)
conv5 = Conv2D(1024, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv5)
drop5 = Dropout(0.5)(conv5)
up6 = Conv2D(512, 2, activation='relu', padding='same', kernel_initializer='he_normal')(UpSampling2D(size=(2, 2))(drop5))
merge6 = concatenate([drop4, up6], axis=3)
conv6 = Conv2D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge6)
conv6 = Conv2D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv6)
up7 = Conv2D(256, 2, activation='relu', padding='same', kernel_initializer='he_normal')(UpSampling2D(size=(2, 2))(conv6))
merge7 = concatenate([conv3, up7], axis=3)
conv7 = Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge7)
conv7 = Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv7)
up8 = Conv2D(128, 2, activation='relu', padding='same', kernel_initializer='he_normal')(UpSampling2D(size=(2, 2))(conv7))
merge8 = concatenate([conv2, up8], axis=3)
conv8 = Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge8)
conv8 = Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv8)
up9 = Conv2D(64, 2, activation='relu', padding='same', kernel_initializer='he_normal')(UpSampling2D(size=(2, 2))(conv8))
merge9 = concatenate([conv1, up9], axis=3)
conv9 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge9)
conv9 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv9)
conv9 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv9)
conv10 = Conv2D(classes, 1, activation='softmax')(conv9)
model = Model(input=inputs, output=conv10)
return model
def FCN_Vgg16_32s(input_shape=None, weight_decay=0., batch_momentum=0.9, batch_shape=None, classes=13):
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(weight_decay))(img_input)
x = Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv2', kernel_regularizer=l2(weight_decay))(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(weight_decay))(x)
x = Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv2', kernel_regularizer=l2(weight_decay))(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(weight_decay))(x)
x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv2', kernel_regularizer=l2(weight_decay))(x)
x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv3', kernel_regularizer=l2(weight_decay))(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(weight_decay))(x)
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv2', kernel_regularizer=l2(weight_decay))(x)
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv3', kernel_regularizer=l2(weight_decay))(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(weight_decay))(x)
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv2', kernel_regularizer=l2(weight_decay))(x)
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv3', kernel_regularizer=l2(weight_decay))(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='block5_pool')(x)
# Convolutional layers transfered from fully-connected layers
x = Conv2D(4096, (7, 7), activation='relu', padding='same', name='fc1', kernel_regularizer=l2(weight_decay))(x)
x = Dropout(0.5)(x)
x = Conv2D(4096, (1, 1), activation='relu', padding='same', name='fc2', kernel_regularizer=l2(weight_decay))(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(weight_decay))(x)
x = BilinearUpSampling2D(size=(32, 32))(x)
model = Model(img_input, x)
weights_path = os.path.expanduser(os.path.join('~', '.keras/models/fcn_vgg16_weights_tf_dim_ordering_tf_kernels.h5'))
model.load_weights(weights_path, by_name=True)
return model