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models.py
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#U-net models- the guts of the code
#unet1, segnet are from https://github.com/mrgloom/keras-semantic-segmentation-example/blob/master/binary_segmentation/binary_crossentropy_example.py
#unet3 is from https://github.com/zhixuhao/unet
from keras.layers import Input, Conv2D, MaxPooling2D, UpSampling2D, Conv2D, Reshape, Conv2DTranspose
from keras.layers import add, concatenate
from keras.layers.normalization import BatchNormalization
from keras.layers.core import Dropout, Activation
from keras import backend as K
def get_unet(inputs, n_classes):
print("Getting unet 1 (standard)")
x = BatchNormalization()(inputs)
conv1 = Conv2D(32, (3, 3), activation='relu', padding='same')(x)
conv1 = Conv2D(32, (3, 3), activation='relu', padding='same')(conv1)
pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
conv2 = Conv2D(64, (3, 3), activation='relu', padding='same')(pool1)
conv2 = Conv2D(64, (3, 3), activation='relu', padding='same')(conv2)
pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
conv3 = Conv2D(128, (3, 3), activation='relu', padding='same')(pool2)
conv3 = Conv2D(128, (3, 3), activation='relu', padding='same')(conv3)
pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)
conv4 = Conv2D(256, (3, 3), activation='relu', padding='same')(pool3)
conv4 = Conv2D(256, (3, 3), activation='relu', padding='same')(conv4)
pool4 = MaxPooling2D(pool_size=(2, 2))(conv4)
conv5 = Conv2D(512, (3, 3), activation='relu', padding='same')(pool4)
conv5 = Conv2D(512, (3, 3), activation='relu', padding='same')(conv5)
up6 = concatenate([Conv2DTranspose(256, (2, 2), strides=(2, 2), padding='same')(conv5), conv4], axis=3)
conv6 = Conv2D(256, (3, 3), activation='relu', padding='same')(up6)
conv6 = Conv2D(256, (3, 3), activation='relu', padding='same')(conv6)
up7 = concatenate([Conv2DTranspose(128, (2, 2), strides=(2, 2), padding='same')(conv6), conv3], axis=3)
conv7 = Conv2D(128, (3, 3), activation='relu', padding='same')(up7)
conv7 = Conv2D(128, (3, 3), activation='relu', padding='same')(conv7)
up8 = concatenate([Conv2DTranspose(64, (2, 2), strides=(2, 2), padding='same')(conv7), conv2], axis=3)
conv8 = Conv2D(64, (3, 3), activation='relu', padding='same')(up8)
conv8 = Conv2D(64, (3, 3), activation='relu', padding='same')(conv8)
up9 = concatenate([Conv2DTranspose(32, (2, 2), strides=(2, 2), padding='same')(conv8), conv1], axis=3)
conv9 = Conv2D(32, (3, 3), activation='relu', padding='same')(up9)
conv9 = Conv2D(32, (3, 3), activation='relu', padding='same')(conv9)
conv10 = Conv2D(n_classes, (1, 1), activation='elu')(conv9)
return conv10
def get_unet2(inputs, n_classes):
print("Geting unet 2 (hacked together extension)")
x = BatchNormalization()(inputs)
conv1 = Conv2D(32, (3, 3), activation='relu', padding='same')(x)
conv1 = Conv2D(32, (3, 3), activation='relu', padding='same')(conv1)
pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
conv2 = Conv2D(64, (3, 3), activation='relu', padding='same')(pool1)
conv2 = Conv2D(64, (3, 3), activation='relu', padding='same')(conv2)
pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
conv3 = Conv2D(128, (3, 3), activation='relu', padding='same')(pool2)
conv3 = Conv2D(128, (3, 3), activation='relu', padding='same')(conv3)
pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)
conv4 = Conv2D(256, (3, 3), activation='relu', padding='same')(pool3)
conv4 = Conv2D(256, (3, 3), activation='relu', padding='same')(conv4)
pool4 = MaxPooling2D(pool_size=(2, 2))(conv4)
conv5 = Conv2D(512, (3, 3), activation='relu', padding='same')(pool4)
conv5 = Conv2D(512, (3, 3), activation='relu', padding='same')(conv5)
pool5 = MaxPooling2D(pool_size=(2,2))(conv5)
# new conv layer
conv6 = Conv2D(1024, (3, 3), activation='relu', padding='same')(pool5)
conv6 = Conv2D(1024, (3, 3), activation='relu', padding='same')(conv6)
up7 = concatenate([Conv2DTranspose(512, (2, 2), strides=(2, 2), padding='same')(conv6), conv5], axis=3)
conv7 = Conv2D(512, (3, 3), activation='relu', padding='same')(up7)
conv7 = Conv2D(512, (3, 3), activation='relu', padding='same')(conv7)
up8 = concatenate([Conv2DTranspose(256, (2, 2), strides=(2, 2), padding='same')(conv7), conv4], axis=3)
conv8 = Conv2D(256, (3, 3), activation='relu', padding='same')(up8)
conv8 = Conv2D(256, (3, 3), activation='relu', padding='same')(conv8)
up9 = concatenate([Conv2DTranspose(128, (2, 2), strides=(2, 2), padding='same')(conv8), conv3], axis=3)
conv9 = Conv2D(128, (3, 3), activation='relu', padding='same')(up9)
conv9 = Conv2D(128, (3, 3), activation='relu', padding='same')(conv9)
up10 = concatenate([Conv2DTranspose(64, (2, 2), strides=(2, 2), padding='same')(conv9), conv2], axis=3)
conv10 = Conv2D(64, (3, 3), activation='relu', padding='same')(up10)
conv10 = Conv2D(64, (3, 3), activation='relu', padding='same')(conv10)
up11 = concatenate([Conv2DTranspose(32, (2, 2), strides=(2, 2), padding='same')(conv10), conv1], axis=3)
conv11 = Conv2D(32, (3, 3), activation='relu', padding='same')(up11)
conv11 = Conv2D(32, (3, 3), activation='relu', padding='same')(conv11)
conv12 = Conv2D(n_classes, (1, 1), activation='elu')(conv11)
return conv12
def get_unet3(inputs, num_classes):
print("Getting unet 3 (github/zhixuhao)")
x = BatchNormalization()(inputs)
conv1 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(x)
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))(conv4)
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))(conv5))
merge6 = concatenate([conv4,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(2, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv9)
conv10 = Conv2D(num_classes, (1, 1), activation = 'elu')(conv9)
return conv10
def get_segnet_vgg16(inputs, n_classes):
x = BatchNormalization()(inputs)
# Block 1
x = Conv2D(64, (3, 3), activation='relu', padding='same')(x)
x = Conv2D(64, (3, 3), activation='relu', padding='same')(x)
x = MaxPooling2D((2, 2), strides=(2, 2))(x)
# Block 2
x = Conv2D(128, (3, 3), activation='relu', padding='same')(x)
x = Conv2D(128, (3, 3), activation='relu', padding='same')(x)
x = MaxPooling2D((2, 2), strides=(2, 2))(x)
# Block 3
x = Conv2D(256, (3, 3), activation='relu', padding='same')(x)
x = Conv2D(256, (3, 3), activation='relu', padding='same')(x)
x = Conv2D(256, (3, 3), activation='relu', padding='same')(x)
x = MaxPooling2D((2, 2), strides=(2, 2))(x)
# Block 4
x = Conv2D(512, (3, 3), activation='relu', padding='same')(x)
x = Conv2D(512, (3, 3), activation='relu', padding='same')(x)
x = Conv2D(512, (3, 3), activation='relu', padding='same')(x)
x = MaxPooling2D((2, 2), strides=(2, 2))(x)
# Block 5
x = Conv2D(512, (3, 3), activation='relu', padding='same')(x)
x = Conv2D(512, (3, 3), activation='relu', padding='same')(x)
x = Conv2D(512, (3, 3), activation='relu', padding='same')(x)
x = MaxPooling2D((2, 2), strides=(2, 2))(x)
# Up Block 1
x = UpSampling2D(size=(2, 2))(x)
x = Conv2D(512, (3, 3), activation='relu', padding='same')(x)
x = Conv2D(512, (3, 3), activation='relu', padding='same')(x)
x = Conv2D(512, (3, 3), activation='relu', padding='same')(x)
# Up Block 2
x = UpSampling2D(size=(2, 2))(x)
x = Conv2D(512, (3, 3), activation='relu', padding='same')(x)
x = Conv2D(512, (3, 3), activation='relu', padding='same')(x)
x = Conv2D(512, (3, 3), activation='relu', padding='same')(x)
# Up Block 3
x = UpSampling2D(size=(2, 2))(x)
x = Conv2D(256, (3, 3), activation='relu', padding='same')(x)
x = Conv2D(256, (3, 3), activation='relu', padding='same')(x)
x = Conv2D(256, (3, 3), activation='relu', padding='same')(x)
# Up Block 4
x = UpSampling2D(size=(2, 2))(x)
x = Conv2D(128, (3, 3), activation='relu', padding='same')(x)
x = Conv2D(128, (3, 3), activation='relu', padding='same')(x)
# Up Block 5
x = UpSampling2D(size=(2, 2))(x)
x = Conv2D(64, (3, 3), activation='relu', padding='same')(x)
x = Conv2D(64, (3, 3), activation='relu', padding='same')(x)
x = Conv2D(n_classes, (1, 1), activation='linear', padding='same')(x)
return x