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unet_build.py
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unet_build.py
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from keras.models import Model
from keras.layers import Input, concatenate, Conv2D, MaxPooling2D, Conv2DTranspose, BatchNormalization
from keras.optimizers import Adam
import tensorflow as tf
from keras import backend as K
import os
##VERSIONS: Tensorflow 1.2.0; Keras 2.0.6
#Should work with later versions just fine, though some minor changes to imports may be useful.
#The architecture implemented here is a 256x256 U-Net, though I may add support for deeper U-Nets if this works
#SUPER IMPORTANT - DEFINE WHICH GPU TO USE, IF RELEVANT
os.environ['CUDA_VISIBLE_DEVICES'] = '1' # or '1' or whichever GPU is available on your machine
#use nvidia-smi to see which devices are currently in use by one person or another, and use the one not in use.
#Implementation heavily tweaked from https://github.com/petrosgk/Kaggle-Carvana-Image-Masking-Challenge/blob/master/model/u_net.py to use Conv2DTranspose instead of Upsampling2D+Conv2D; also simplified Conv2D calls
## Define dice coefficient metric and loss function associated with it
def dice_coef(y_true, y_pred):
smooth = 1e-5
y_true_f = K.flatten(y_true)
y_pred_f = K.flatten(y_pred)
intersection = K.sum(y_true_f * y_pred_f)
return (2. * intersection + smooth) / (K.sum(y_true_f) + K.sum(y_pred_f) + smooth)
def dice_coef_loss(y_true, y_pred):
return 1-dice_coef(y_true, y_pred)
## Define IoU metric
def mean_iou(y_true, y_pred):
prec = []
for t in np.arange(0.5, 1.0, 0.05):
y_pred_ = tf.to_int32(y_pred > t)
score, up_opt = tf.metrics.mean_iou(y_true, y_pred_, 2)
K.get_session().run(tf.local_variables_initializer())
with tf.control_dependencies([up_opt]):
score = tf.identity(score)
prec.append(score)
return K.mean(K.stack(prec), axis=0)
def get_unet_128(input_shape=(128, 128, 1),
num_classes=1, learn_rate=1e-4, loss_func='binary_crossentropy', metric_list=[dice_coef]):
inputs = Input(shape=input_shape)
# 128
down1 = Conv2D(64, (3, 3), activation='relu', padding='same')(inputs)
down1 = Conv2D(64, (3, 3), activation='relu', padding='same')(down1)
down1_pool = MaxPooling2D((2, 2))(down1)
# 64
down2 = Conv2D(128, (3, 3), activation='relu', padding='same')(down1_pool)
down2 = Conv2D(128, (3, 3), activation='relu', padding='same')(down2)
down2_pool = MaxPooling2D((2, 2))(down2)
# 32
down3 = Conv2D(256, (3, 3), activation='relu', padding='same')(down2_pool)
down3 = Conv2D(256, (3, 3), activation='relu', padding='same')(down3)
down3_pool = MaxPooling2D((2, 2))(down3)
# 16
down4 = Conv2D(512, (3, 3), activation='relu', padding='same')(down3_pool)
down4 = Conv2D(512, (3, 3), activation='relu', padding='same')(down4)
down4_pool = MaxPooling2D((2, 2))(down4)
# 8
center = Conv2D(1024, (3, 3), activation='relu', padding='same')(down4_pool)
center = Conv2D(1024, (3, 3), activation='relu', padding='same')(center)
# center
up4 = Conv2DTranspose(512, (2, 2), strides=(2, 2), padding='same')(center)
up4 = concatenate([down4, up4], axis=3)
up4 = Conv2D(512, (3, 3), activation='relu', padding='same')(up4)
up4 = Conv2D(512, (3, 3), activation='relu', padding='same')(up4)
# 16
up3 = Conv2DTranspose(256, (2, 2), strides=(2, 2), padding='same')(up4)
up3 = concatenate([down3, up3], axis=3)
up3 = Conv2D(256, (3, 3), activation='relu', padding='same')(up3)
up3 = Conv2D(256, (3, 3), activation='relu', padding='same')(up3)
# 32
up2 = Conv2DTranspose(128, (2, 2), strides=(2, 2), padding='same')(up3)
up2 = concatenate([down2, up2], axis=3)
up2 = Conv2D(128, (3, 3), activation='relu', padding='same')(up2)
up2 = Conv2D(128, (3, 3), activation='relu', padding='same')(up2)
# 64
up1 = Conv2DTranspose(64, (2, 2), strides=(2, 2), padding='same')(up2)
up1 = concatenate([down1, up1], axis=3)
up1 = Conv2D(64, (3, 3), activation='relu', padding='same')(up1)
up1 = Conv2D(64, (3, 3), activation='relu', padding='same')(up1)
# 128
classify = Conv2D(num_classes, (1, 1), activation='sigmoid')(up1)
model = Model(inputs=inputs, outputs=classify)
model.compile(optimizer=Adam(lr=learn_rate), loss=loss_func, metrics=[dice_coef])
return model
def get_unet_256(input_shape=(256, 256, 1),
num_classes=1, learn_rate=1e-4, loss_func='binary_crossentropy', metric_list=[dice_coef]):
inputs = Input(shape=input_shape)
# 256
down0 = Conv2D(32, (3, 3), activation='relu', padding='same')(inputs)
down0 = Conv2D(32, (3, 3), activation='relu', padding='same')(down0)
down0_pool = MaxPooling2D((2, 2))(down0)
# 128
down1 = Conv2D(64, (3, 3), activation='relu', padding='same')(down0_pool)
down1 = Conv2D(64, (3, 3), activation='relu', padding='same')(down1)
down1_pool = MaxPooling2D((2, 2))(down1)
# 64
down2 = Conv2D(128, (3, 3), activation='relu', padding='same')(down1_pool)
down2 = Conv2D(128, (3, 3), activation='relu', padding='same')(down2)
down2_pool = MaxPooling2D((2, 2))(down2)
# 32
down3 = Conv2D(256, (3, 3), activation='relu', padding='same')(down2_pool)
down3 = Conv2D(256, (3, 3), activation='relu', padding='same')(down3)
down3_pool = MaxPooling2D((2, 2))(down3)
# 16
down4 = Conv2D(512, (3, 3), activation='relu', padding='same')(down3_pool)
down4 = Conv2D(512, (3, 3), activation='relu', padding='same')(down4)
down4_pool = MaxPooling2D((2, 2))(down4)
# 8
center = Conv2D(1024, (3, 3), activation='relu', padding='same')(down4_pool)
center = Conv2D(1024, (3, 3), activation='relu', padding='same')(center)
# center
up4 = Conv2DTranspose(512, (2, 2), strides=(2, 2), padding='same')(center)
up4 = concatenate([down4, up4], axis=3)
up4 = Conv2D(512, (3, 3), activation='relu', padding='same')(up4)
up4 = Conv2D(512, (3, 3), activation='relu', padding='same')(up4)
# 16
up3 = Conv2DTranspose(256, (2, 2), strides=(2, 2), padding='same')(up4)
up3 = concatenate([down3, up3], axis=3)
up3 = Conv2D(256, (3, 3), activation='relu', padding='same')(up3)
up3 = Conv2D(256, (3, 3), activation='relu', padding='same')(up3)
# 32
up2 = Conv2DTranspose(128, (2, 2), strides=(2, 2), padding='same')(up3)
up2 = concatenate([down2, up2], axis=3)
up2 = Conv2D(128, (3, 3), activation='relu', padding='same')(up2)
up2 = Conv2D(128, (3, 3), activation='relu', padding='same')(up2)
# 64
up1 = Conv2DTranspose(64, (2, 2), strides=(2, 2), padding='same')(up2)
up1 = concatenate([down1, up1], axis=3)
up1 = Conv2D(64, (3, 3), activation='relu', padding='same')(up1)
up1 = Conv2D(64, (3, 3), activation='relu', padding='same')(up1)
# 128
up0 = Conv2DTranspose(32, (2, 2), strides=(2, 2), padding='same')(up1)
up0 = concatenate([down0, up0], axis=3)
up0 = Conv2D(32, (3, 3), activation='relu', padding='same')(up0)
up0 = Conv2D(32, (3, 3), activation='relu', padding='same')(up0)
# 256
classify = Conv2D(num_classes, (1, 1), activation='sigmoid')(up0)
model = Model(inputs=inputs, outputs=classify)
model.compile(optimizer=Adam(lr=learn_rate), loss=loss_func, metrics=[dice_coef])
return model
def get_unet_512(input_shape=(512, 512, 1),
num_classes=1, learn_rate=1e-4, loss_func='binary_crossentropy', metric_list=[dice_coef]):
inputs = Input(shape=input_shape)
# 512
down0a = Conv2D(16, (3, 3), activation='relu', padding='same')(inputs)
down0a = Conv2D(16, (3, 3), activation='relu', padding='same')(down0a)
down0a_pool = MaxPooling2D((2, 2))(down0a)
# 256
down0 = Conv2D(32, (3, 3), activation='relu', padding='same')(down0a_pool)
down0 = Conv2D(32, (3, 3), activation='relu', padding='same')(down0)
down0_pool = MaxPooling2D((2, 2))(down0)
# 128
down1 = Conv2D(64, (3, 3), activation='relu', padding='same')(down0_pool)
down1 = Conv2D(64, (3, 3), activation='relu', padding='same')(down1)
down1_pool = MaxPooling2D((2, 2))(down1)
# 64
down2 = Conv2D(128, (3, 3), activation='relu', padding='same')(down1_pool)
down2 = Conv2D(128, (3, 3), activation='relu', padding='same')(down2)
down2_pool = MaxPooling2D((2, 2))(down2)
# 32
down3 = Conv2D(256, (3, 3), activation='relu', padding='same')(down2_pool)
down3 = Conv2D(256, (3, 3), activation='relu', padding='same')(down3)
down3_pool = MaxPooling2D((2, 2))(down3)
# 16
down4 = Conv2D(512, (3, 3), activation='relu', padding='same')(down3_pool)
down4 = Conv2D(512, (3, 3), activation='relu', padding='same')(down4)
down4_pool = MaxPooling2D((2, 2))(down4)
# 8
center = Conv2D(1024, (3, 3), activation='relu', padding='same')(down4_pool)
center = Conv2D(1024, (3, 3), activation='relu', padding='same')(center)
# center
up4 = Conv2DTranspose(512, (2, 2), strides=(2, 2), padding='same')(center)
up4 = concatenate([down4, up4], axis=3)
up4 = Conv2D(512, (3, 3), activation='relu', padding='same')(up4)
up4 = Conv2D(512, (3, 3), activation='relu', padding='same')(up4)
# 16
up3 = Conv2DTranspose(256, (2, 2), strides=(2, 2), padding='same')(up4)
up3 = concatenate([down3, up3], axis=3)
up3 = Conv2D(256, (3, 3), activation='relu', padding='same')(up3)
up3 = Conv2D(256, (3, 3), activation='relu', padding='same')(up3)
# 32
up2 = Conv2DTranspose(128, (2, 2), strides=(2, 2), padding='same')(up3)
up2 = concatenate([down2, up2], axis=3)
up2 = Conv2D(128, (3, 3), activation='relu', padding='same')(up2)
up2 = Conv2D(128, (3, 3), activation='relu', padding='same')(up2)
# 64
up1 = Conv2DTranspose(64, (2, 2), strides=(2, 2), padding='same')(up2)
up1 = concatenate([down1, up1], axis=3)
up1 = Conv2D(64, (3, 3), activation='relu', padding='same')(up1)
up1 = Conv2D(64, (3, 3), activation='relu', padding='same')(up1)
# 128
up0 = Conv2DTranspose(32, (2, 2), strides=(2, 2), padding='same')(up1)
up0 = concatenate([down0, up0], axis=3)
up0 = Conv2D(32, (3, 3), activation='relu', padding='same')(up0)
up0 = Conv2D(32, (3, 3), activation='relu', padding='same')(up0)
# 256
up0a = Conv2DTranspose(16, (2, 2), strides=(2, 2), padding='same')(up0)
up0a = concatenate([down0a, up0a], axis=3)
up0a = Conv2D(16, (3, 3), activation='relu', padding='same')(up0a)
up0a = Conv2D(16, (3, 3), activation='relu', padding='same')(up0a)
# 512
classify = Conv2D(num_classes, (1, 1), activation='sigmoid')(up0a)
model = Model(inputs=inputs, outputs=classify)
model.compile(optimizer=Adam(lr=learn_rate), loss=loss_func, metrics=[dice_coef])
return model
def get_unet_1024(input_shape=(1024, 1024, 1),
num_classes=1, learn_rate=1e-4, loss_func='binary_crossentropy', metric_list=[dice_coef]):
inputs = Input(shape=input_shape)
# 1024
down0b = Conv2D(8, (3, 3), activation='relu', padding='same')(inputs)
down0b = Conv2D(8, (3, 3), activation='relu', padding='same')(down0b)
down0b_pool = MaxPooling2D((2, 2))(down0b)
# 512
down0a = Conv2D(16, (3, 3), activation='relu', padding='same')(down0b_pool)
down0a = Conv2D(16, (3, 3), activation='relu', padding='same')(down0a)
down0a_pool = MaxPooling2D((2, 2))(down0a)
# 256
down0 = Conv2D(32, (3, 3), activation='relu', padding='same')(down0a_pool)
down0 = Conv2D(32, (3, 3), activation='relu', padding='same')(down0)
down0_pool = MaxPooling2D((2, 2))(down0)
# 128
down1 = Conv2D(64, (3, 3), activation='relu', padding='same')(down0_pool)
down1 = Conv2D(64, (3, 3), activation='relu', padding='same')(down1)
down1_pool = MaxPooling2D((2, 2))(down1)
# 64
down2 = Conv2D(128, (3, 3), activation='relu', padding='same')(down1_pool)
down2 = Conv2D(128, (3, 3), activation='relu', padding='same')(down2)
down2_pool = MaxPooling2D((2, 2))(down2)
# 32
down3 = Conv2D(256, (3, 3), activation='relu', padding='same')(down2_pool)
down3 = Conv2D(256, (3, 3), activation='relu', padding='same')(down3)
down3_pool = MaxPooling2D((2, 2))(down3)
# 16
down4 = Conv2D(512, (3, 3), activation='relu', padding='same')(down3_pool)
down4 = Conv2D(512, (3, 3), activation='relu', padding='same')(down4)
down4_pool = MaxPooling2D((2, 2))(down4)
# 8
center = Conv2D(1024, (3, 3), activation='relu', padding='same')(down4_pool)
center = Conv2D(1024, (3, 3), activation='relu', padding='same')(center)
# center
up4 = Conv2DTranspose(512, (2, 2), strides=(2, 2), padding='same')(center)
up4 = concatenate([down4, up4], axis=3)
up4 = Conv2D(512, (3, 3), activation='relu', padding='same')(up4)
up4 = Conv2D(512, (3, 3), activation='relu', padding='same')(up4)
# 16
up3 = Conv2DTranspose(256, (2, 2), strides=(2, 2), padding='same')(up4)
up3 = concatenate([down3, up3], axis=3)
up3 = Conv2D(256, (3, 3), activation='relu', padding='same')(up3)
up3 = Conv2D(256, (3, 3), activation='relu', padding='same')(up3)
# 32
up2 = Conv2DTranspose(128, (2, 2), strides=(2, 2), padding='same')(up3)
up2 = concatenate([down2, up2], axis=3)
up2 = Conv2D(128, (3, 3), activation='relu', padding='same')(up2)
up2 = Conv2D(128, (3, 3), activation='relu', padding='same')(up2)
# 64
up1 = Conv2DTranspose(64, (2, 2), strides=(2, 2), padding='same')(up2)
up1 = concatenate([down1, up1], axis=3)
up1 = Conv2D(64, (3, 3), activation='relu', padding='same')(up1)
up1 = Conv2D(64, (3, 3), activation='relu', padding='same')(up1)
# 128
up0 = Conv2DTranspose(32, (2, 2), strides=(2, 2), padding='same')(up1)
up0 = concatenate([down0, up0], axis=3)
up0 = Conv2D(32, (3, 3), activation='relu', padding='same')(up0)
up0 = Conv2D(32, (3, 3), activation='relu', padding='same')(up0)
# 256
up0a = Conv2DTranspose(16, (2, 2), strides=(2, 2), padding='same')(up0)
up0a = concatenate([down0a, up0a], axis=3)
up0a = Conv2D(16, (3, 3), activation='relu', padding='same')(up0a)
up0a = Conv2D(16, (3, 3), activation='relu', padding='same')(up0a)
# 512
up0b = Conv2DTranspose(8, (2, 2), strides=(2, 2), padding='same')(up0a)
up0b = concatenate([down0b, up0b], axis=3)
up0b = Conv2D(8, (3, 3), activation='relu', padding='same')(up0b)
up0b = Conv2D(8, (3, 3), activation='relu', padding='same')(up0b)
# 1024
classify = Conv2D(num_classes, (1, 1), activation='sigmoid')(up0b)
model = Model(inputs=inputs, outputs=classify)
model.compile(optimizer=Adam(lr=learn_rate), loss=loss_func, metrics=[dice_coef])
return model