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train.py
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train.py
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import cv2
import numpy as np
from keras.models import Model
from keras.layers.advanced_activations import LeakyReLU
from keras.layers import Input, merge, SpatialDropout2D
from keras.layers import Convolution2D, AveragePooling2D, UpSampling2D
from keras.optimizers import Adam
from keras.callbacks import ModelCheckpoint, EarlyStopping
from keras.preprocessing.image import ImageDataGenerator
from keras import backend as K
from data import load_train_data, load_test_data
K.set_image_dim_ordering('th') # Theano dimension ordering in this code
img_rows = 100
img_cols = 160
stack = 10
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 create_model():
input = Input(shape=(1, img_rows, img_cols))
conv1 = Convolution2D(32, 3, 3, border_mode='same', init='he_normal')(input)
conv1 = LeakyReLU()(conv1)
conv1 = SpatialDropout2D(0.2)(conv1)
conv1 = Convolution2D(32, 3, 3, border_mode='same', init='he_normal')(conv1)
conv1 = LeakyReLU()(conv1)
conv1 = SpatialDropout2D(0.2)(conv1)
pool1 = AveragePooling2D(pool_size=(2,2))(conv1)
conv2 = Convolution2D(64, 3, 3, border_mode='same', init='he_normal')(pool1)
conv2 = LeakyReLU()(conv2)
conv2 = SpatialDropout2D(0.2)(conv2)
conv2 = Convolution2D(64, 3, 3, border_mode='same', init='he_normal')(conv2)
conv2 = LeakyReLU()(conv2)
conv2 = SpatialDropout2D(0.2)(conv2)
pool2 = AveragePooling2D(pool_size=(2,2))(conv2)
conv3 = Convolution2D(128, 3, 3, border_mode='same', init='he_normal')(pool2)
conv3 = LeakyReLU()(conv3)
conv3 = SpatialDropout2D(0.2)(conv3)
conv3 = Convolution2D(128, 3, 3, border_mode='same', init='he_normal')(conv3)
conv3 = LeakyReLU()(conv3)
conv3 = SpatialDropout2D(0.2)(conv3)
comb1 = merge([conv2, UpSampling2D(size=(2,2))(conv3)], mode='concat', concat_axis=1)
conv4 = Convolution2D(64, 3, 3, border_mode='same', init='he_normal')(comb1)
conv4 = LeakyReLU()(conv4)
conv4 = SpatialDropout2D(0.2)(conv4)
conv4 = Convolution2D(64, 3, 3, border_mode='same', init='he_normal')(conv4)
conv4 = LeakyReLU()(conv4)
conv4 = SpatialDropout2D(0.2)(conv4)
comb2 = merge([conv1, UpSampling2D(size=(2,2))(conv4)], mode='concat', concat_axis=1)
conv5 = Convolution2D(32, 3, 3, border_mode='same', init='he_normal')(comb2)
conv5 = LeakyReLU()(conv5)
conv5 = SpatialDropout2D(0.2)(conv5)
conv5 = Convolution2D(32, 3, 3, border_mode='same', init='he_normal')(conv5)
conv5 = LeakyReLU()(conv5)
conv5 = SpatialDropout2D(0.2)(conv5)
output = Convolution2D(1, 1, 1, activation='sigmoid')(conv5)
model = Model(input=input, output=output)
model.compile(optimizer=Adam(lr=3e-4), loss='binary_crossentropy')
return model
def preprocess(imgs):
imgs_p = np.ndarray((imgs.shape[0], imgs.shape[1], img_rows, img_cols), dtype=np.uint8)
for i in range(imgs.shape[0]):
imgs_p[i, 0] = cv2.resize(imgs[i, 0], (img_cols, img_rows), interpolation=cv2.INTER_CUBIC)
return imgs_p
def train_and_predict():
print('-'*30)
print('Loading and preprocessing train data...')
print('-'*30)
imgs_train, imgs_mask_train = load_train_data()
imgs_train = preprocess(imgs_train)
imgs_mask_train = preprocess(imgs_mask_train)
imgs_train = imgs_train.astype('float32')
mean = np.mean(imgs_train) # mean for data centering
std = np.std(imgs_train) # std for data normalization
imgs_train -= mean
imgs_train /= std
imgs_mask_train = imgs_mask_train.astype('float32')
imgs_mask_train /= 255. # scale masks to [0, 1]
print('-'*30)
print('Creating and compiling model...')
print('-'*30)
model = create_model()
print('-'*30)
print('Building data augmentation object...')
print('-'*30)
datagen = ImageDataGenerator(
rotation_range=15,
width_shift_range=0.15,
height_shift_range=0.15,
shear_range=0.15,
horizontal_flip=True,
vertical_flip=True)
total = imgs_train.shape[0]
img = []
count = 0
for batch in datagen.flow(imgs_train, batch_size=1, seed=1337):
img.append(batch)
count += 1
if count > total*stack:
break
imgs_train = np.array(img)[:,0]
mask = []
count = 0
for batch in datagen.flow(imgs_mask_train, batch_size=1, seed=1337):
mask.append(batch)
count += 1
if count > total*stack:
break
imgs_mask_train = np.array(mask)[:,0]
callbacks = [
EarlyStopping(monitor='loss', patience=5, verbose=0),
ModelCheckpoint('weights.hdf5', monitor='loss', save_best_only=True)
]
print('-'*30)
print('Begin training...')
print('-'*30)
model.fit(imgs_train, imgs_mask_train, batch_size=4, nb_epoch=100, verbose=1, shuffle=True,
callbacks=callbacks)
print('-'*30)
print('Loading and preprocessing test data...')
print('-'*30)
imgs_test = load_test_data()
imgs_test = preprocess(imgs_test)
imgs_test = imgs_test.astype('float32')
imgs_test -= mean
imgs_test /= std
print('-'*30)
print('Loading saved weights...')
print('-'*30)
model.load_weights('weights.hdf5')
print('-'*30)
print('Predicting masks on test data...')
print('-'*30)
imgs_mask_test = model.predict(imgs_test, verbose=1)
np.save('imgs_mask_test.npy', imgs_mask_test)
if __name__ == '__main__':
train_and_predict()