def unet_mini(network_input: NetworkInput) -> KerasModel:
# Based on https://github.com/divamgupta/image-segmentation-keras/blob/master/keras_segmentation/models/unet.py#L19
inputs = Input(network_input.input_shape)
conv1 = Conv2D(32, (3, 3), activation='relu', padding='same')(inputs)
conv1 = Dropout(0.2)(conv1)
conv1 = Conv2D(32, (3, 3), activation='relu', padding='same')(conv1)
pool1 = MaxPooling2D((2, 2))(conv1)
conv2 = Conv2D(64, (3, 3), activation='relu', padding='same')(pool1)
conv2 = Dropout(0.2)(conv2)
conv2 = Conv2D(64, (3, 3), activation='relu', padding='same')(conv2)
pool2 = MaxPooling2D((2, 2))(conv2)
conv3 = Conv2D(128, (3, 3), activation='relu', padding='same')(pool2)
conv3 = Dropout(0.2)(conv3)
conv3 = Conv2D(128, (3, 3), activation='relu', padding='same')(conv3)
up1 = concatenate([UpSampling2D((2, 2))(conv3), conv2], axis=-1)
conv4 = Conv2D(64, (3, 3), activation='relu', padding='same')(up1)
conv4 = Dropout(0.2)(conv4)
conv4 = Conv2D(64, (3, 3), activation='relu', padding='same')(conv4)
up2 = concatenate([UpSampling2D((2, 2))(conv4), conv1], axis=-1)
conv5 = Conv2D(32, (3, 3), activation='relu', padding='same')(up2)
conv5 = Dropout(0.2)(conv5)
conv5 = Conv2D(32, (3, 3), activation='relu', padding='same')(conv5)
outputs = Conv2D(network_input.number_of_classes, (1, 1), padding='same')(conv5)
model = KerasModel(inputs=inputs, outputs=outputs)
model.model_name = "unet_mini"
return model
def unet(network_input: NetworkInput) -> KerasModel:
"""
:return: model -- a model that has been defined, but not yet compiled.
The model is an implementation of the Unet paper
(https://arxiv.org/pdf/1505.04597.pdf) and comes
from this repo https://github.com/zhixuhao/unet. It has
been modified to keep up with API changes in keras 2.
"""
inputs = Input(network_input.input_shape)
conv1 = Conv2D(filters=64,
kernel_size=3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(inputs)
conv1 = Conv2D(filters=64,
kernel_size=3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(conv1)
pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
conv2 = Conv2D(filters=128,
kernel_size=3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(pool1)
conv2 = Conv2D(filters=128,
kernel_size=3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(conv2)
pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
conv3 = Conv2D(filters=256,
kernel_size=3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(pool2)
conv3 = Conv2D(filters=256,
kernel_size=3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(conv3)
pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)
conv4 = Conv2D(filters=512,
kernel_size=3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(pool3)
conv4 = Conv2D(filters=512,
kernel_size=3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(conv4)
drop4 = Dropout(0.5)(conv4)
pool4 = MaxPooling2D(pool_size=(2, 2))(drop4)
conv5 = Conv2D(filters=1024,
kernel_size=3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(pool4)
conv5 = Conv2D(filters=1024,
kernel_size=3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(conv5)
drop5 = Dropout(0.5)(conv5)
up6 = UpSampling2D(size=(2, 2))(drop5)
up6 = Conv2D(filters=512,
kernel_size=2,
activation='relu',
padding='same',
kernel_initializer='he_normal')(up6)
merge6 = Concatenate(axis=3)([drop4, up6])
conv6 = Conv2D(filters=512,
kernel_size=3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(merge6)
conv6 = Conv2D(filters=512,
kernel_size=3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(conv6)
up7 = UpSampling2D(size=(2, 2))(conv6)
up7 = Conv2D(filters=256,
kernel_size=2,
activation='relu',
padding='same',
kernel_initializer='he_normal')(up7)
merge7 = Concatenate(axis=3)([conv3, up7])
conv7 = Conv2D(filters=256,
kernel_size=3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(merge7)
conv7 = Conv2D(filters=256,
kernel_size=3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(conv7)
up8 = UpSampling2D(size=(2, 2))(conv7)
up8 = Conv2D(filters=128,
kernel_size=2,
activation='relu',
padding='same',
kernel_initializer='he_normal')(up8)
merge8 = Concatenate(axis=3)([conv2, up8])
conv8 = Conv2D(filters=128,
kernel_size=3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(merge8)
conv8 = Conv2D(filters=128,
kernel_size=3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(conv8)
up9 = UpSampling2D(size=(2, 2))(conv8)
up9 = Conv2D(filters=64,
kernel_size=2,
activation='relu',
padding='same',
kernel_initializer='he_normal')(up9)
merge9 = Concatenate(axis=3)([conv1, up9])
conv9 = Conv2D(filters=64,
kernel_size=3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(merge9)
conv9 = Conv2D(filters=64,
kernel_size=3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(conv9)
conv9 = Conv2D(filters=2,
kernel_size=3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(conv9)
conv10 = Conv2D(filters=network_input.number_of_classes,
kernel_size=1,
activation='sigmoid')(conv9)
model = KerasModel(inputs=inputs, outputs=conv10)
model.model_name = "unet"
return model
