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3D-res-unet.py
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3D-res-unet.py
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from __future__ import print_function
import os
import keras.models as models
from skimage.transform import resize
from skimage.io import imsave
import numpy as np
np.random.seed(1337)
import tensorflow as tf
tf.set_random_seed(1337)
from keras.models import Model
from keras.layers import Input, concatenate, Conv3D, MaxPooling3D, Conv3DTranspose, AveragePooling3D, ZeroPadding3D
from keras.optimizers import RMSprop, Adam, SGD
from keras.callbacks import ModelCheckpoint, CSVLogger
from keras import backend as K
from keras.regularizers import l2
from keras.utils import plot_model
from data3D import load_train_data, load_test_data, preprocess_squeeze
K.set_image_data_format('channels_last')
project_name = '3D-Res-Unet'
img_rows = 400
img_cols = 400
img_depth = 16
smooth = 1.
def dice_coef(y_true, y_pred):
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 -dice_coef(y_true, y_pred)
def get_unet():
inputs = Input((img_depth, img_rows, img_cols, 1))
conv1 = Conv3D(32, (3, 3, 3), activation='relu', padding='same')(inputs)
conv1 = Conv3D(32, (3, 3, 3), activation='relu', padding='same')(conv1)
conc1 = concatenate([inputs, conv1], axis=4)
pool1 = MaxPooling3D(pool_size=(2, 2, 2))(conc1)
conv2 = Conv3D(64, (3, 3, 3), activation='relu', padding='same')(pool1)
conv2 = Conv3D(64, (3, 3, 3), activation='relu', padding='same')(conv2)
conc2 = concatenate([pool1, conv2], axis=4)
pool2 = MaxPooling3D(pool_size=(2, 2, 2))(conc2)
conv3 = Conv3D(128, (3, 3, 3), activation='relu', padding='same')(pool2)
conv3 = Conv3D(128, (3, 3, 3), activation='relu', padding='same')(conv3)
conc3 = concatenate([pool2, conv3], axis=4)
pool3 = MaxPooling3D(pool_size=(2, 2, 2))(conc3)
conv4 = Conv3D(256, (3, 3, 3), activation='relu', padding='same')(pool3)
conv4 = Conv3D(256, (3, 3, 3), activation='relu', padding='same')(conv4)
conc4 = concatenate([pool3, conv4], axis=4)
pool4 = MaxPooling3D(pool_size=(2, 2, 2))(conc4)
conv5 = Conv3D(512, (3, 3, 3), activation='relu', padding='same')(pool4)
conv5 = Conv3D(512, (3, 3, 3), activation='relu', padding='same')(conv5)
conc5 = concatenate([pool4, conv5], axis=4)
up6 = concatenate([Conv3DTranspose(256, (2, 2, 2), strides=(2, 2, 2), padding='same')(conc5), conv4], axis=4)
conv6 = Conv3D(256, (3, 3, 3), activation='relu', padding='same')(up6)
conv6 = Conv3D(256, (3, 3, 3), activation='relu', padding='same')(conv6)
conc6 = concatenate([up6, conv6], axis=4)
up7 = concatenate([Conv3DTranspose(128, (2, 2, 2), strides=(2, 2, 2), padding='same')(conc6), conv3], axis=4)
conv7 = Conv3D(128, (3, 3, 3), activation='relu', padding='same')(up7)
conv7 = Conv3D(128, (3, 3, 3), activation='relu', padding='same')(conv7)
conc7 = concatenate([up7, conv7], axis=4)
up8 = concatenate([Conv3DTranspose(64, (2, 2, 2), strides=(2, 2, 2), padding='same')(conc7), conv2], axis=4)
conv8 = Conv3D(64, (3, 3, 3), activation='relu', padding='same')(up8)
conv8 = Conv3D(64, (3, 3, 3), activation='relu', padding='same')(conv8)
conc8 = concatenate([up8, conv8], axis=4)
up9 = concatenate([Conv3DTranspose(32, (2, 2, 2), strides=(2, 2, 2), padding='same')(conc8), conv1], axis=4)
conv9 = Conv3D(32, (3, 3, 3), activation='relu', padding='same')(up9)
conv9 = Conv3D(32, (3, 3, 3), activation='relu', padding='same')(conv9)
conc9 = concatenate([up9, conv9], axis=4)
conv10 = Conv3D(1, (1, 1, 1), activation='sigmoid')(conc9)
model = Model(inputs=[inputs], outputs=[conv10])
model.summary()
#plot_model(model, to_file='model.png')
model.compile(optimizer=Adam(lr=1e-5, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.000000199), loss='binary_crossentropy', metrics=['accuracy'])
return model
def train():
print('-'*30)
print('Loading and preprocessing train data...')
print('-'*30)
imgs_train, imgs_mask_train = load_train_data()
imgs_mask_train = imgs_mask_train.astype('float32')
imgs_train = imgs_train.astype('float32')
imgs_mask_train /= 255. # scale masks to [0, 1]
imgs_train /= 255. # scale masks to [0, 1]
print('-'*30)
print('Creating and compiling model...')
print('-'*30)
model = get_unet()
weight_dir = 'weights'
if not os.path.exists(weight_dir):
os.mkdir(weight_dir)
model_checkpoint = ModelCheckpoint(os.path.join(weight_dir, project_name + '.h5'), monitor='val_loss', save_best_only=True)
log_dir = 'logs'
if not os.path.exists(log_dir):
os.mkdir(log_dir)
csv_logger = CSVLogger(os.path.join(log_dir, project_name + '.txt'), separator=',', append=False)
print('-'*30)
print('Fitting model...')
print('-'*30)
model.fit(imgs_train, imgs_mask_train, batch_size=1, epochs=50, verbose=1, shuffle=True, validation_split=0.10, callbacks=[model_checkpoint, csv_logger])
print('-'*30)
print('Training finished')
print('-'*30)
def predict():
print('-'*30)
print('Loading and preprocessing test data...')
print('-'*30)
imgs_test = load_test_data()
imgs_test = imgs_test.astype('float32')
imgs_test /= 255. # scale masks to [0, 1]
print('-'*30)
print('Loading saved weights...')
print('-'*30)
model = get_unet()
weight_dir = 'weights'
if not os.path.exists(weight_dir):
os.mkdir(weight_dir)
model.load_weights(os.path.join(weight_dir, project_name + '.h5'))
print('-'*30)
print('Predicting masks on test data...')
print('-'*30)
imgs_mask_test = model.predict(imgs_test, batch_size=1, verbose=1)
npy_mask_dir = 'test_mask_npy'
if not os.path.exists(npy_mask_dir):
os.mkdir(npy_mask_dir)
np.save(os.path.join(npy_mask_dir, project_name + '_mask.npy'), imgs_mask_test)
print('-' * 30)
print('Saving predicted masks to files...')
print('-' * 30)
imgs_mask_test = preprocess_squeeze(imgs_mask_test)
# imgs_mask_test /= 1.7
imgs_mask_test = np.around(imgs_mask_test, decimals=0)
imgs_mask_test = (imgs_mask_test*255.).astype(np.uint8)
count_visualize = 1
count_processed = 0
pred_dir = 'preds'
if not os.path.exists(pred_dir):
os.mkdir(pred_dir)
pred_dir = os.path.join('preds/', project_name)
if not os.path.exists(pred_dir):
os.mkdir(pred_dir)
for x in range(0, imgs_mask_test.shape[0]):
for y in range(0, imgs_mask_test.shape[1]):
if (count_visualize > 1) and (count_visualize < 16):
imsave(os.path.join(pred_dir, 'pred_' + str(f"{count_processed:03}") + '.png'), imgs_mask_test[x][y])
count_processed += 1
count_visualize += 1
if count_visualize == 17:
count_visualize = 1
if (count_processed % 100) == 0:
print('Done: {0}/{1} test images'.format(count_processed, imgs_mask_test.shape[0]*14))
print('-'*30)
print('Prediction finished')
print('-'*30)
if __name__ == '__main__':
train()
predict()