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train_unet3_conv.py
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train_unet3_conv.py
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from __future__ import print_function
import os
import numpy as np
from keras import backend as K
from keras.callbacks import ModelCheckpoint, CSVLogger
from keras.layers import MaxPooling2D, UpSampling2D, Convolution2D, Input, merge, concatenate
from keras.layers.normalization import BatchNormalization
from keras.models import Model
from skimage.io import imsave
from data import load_train_data, load_test_data
K.set_image_data_format('channels_last') # TF dimension ordering in this code
img_rows = 96
img_cols = 128
smooth = 1.
epochs = 200
def merge(inputs, mode, concat_axis=-1):
return concatenate(inputs, concat_axis)
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 precision(y_true, y_pred):
"""Precision metric.
Only computes a batch-wise average of precision.
Computes the precision, a metric for multi-label classification of
how many selected items are relevant.
"""
true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))
precision = true_positives / (predicted_positives + K.epsilon())
return precision
def recall(y_true, y_pred):
"""Recall metric.
Only computes a batch-wise average of recall.
Computes the recall, a metric for multi-label classification of
how many relevant items are selected.
"""
true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))
recall = true_positives / (possible_positives + K.epsilon())
return recall
def f1score(y_true, y_pred):
def recall(y_true, y_pred):
"""Recall metric.
Only computes a batch-wise average of recall.
Computes the recall, a metric for multi-label classification of
how many relevant items are selected.
"""
true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))
recall = true_positives / (possible_positives + K.epsilon())
return recall
def precision(y_true, y_pred):
"""Precision metric.
Only computes a batch-wise average of precision.
Computes the precision, a metric for multi-label classification of
how many selected items are relevant.
"""
true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))
precision = true_positives / (predicted_positives + K.epsilon())
return precision
precision = precision(y_true, y_pred)
recall = recall(y_true, y_pred)
return 2 * ((precision * recall) / (precision + recall))
def get_conv(f=16):
inputs = Input((img_rows, img_cols, 1))
conv1 = Convolution2D(f, 3, 3, activation='relu', border_mode='same')(inputs)
conv1 = BatchNormalization()(conv1)
conv1 = Convolution2D(f, 3, 3, activation='relu', border_mode='same')(conv1)
conv2 = MaxPooling2D(pool_size=(2, 2))(conv1)
conv2 = BatchNormalization()(conv2)
conv2 = Convolution2D(2 * f, 3, 3, activation='relu', border_mode='same')(conv2)
conv2 = BatchNormalization()(conv2)
conv2 = Convolution2D(2 * f, 3, 3, activation='relu', border_mode='same')(conv2)
conv3 = MaxPooling2D(pool_size=(2, 2))(conv2)
conv3 = BatchNormalization()(conv3)
conv3 = Convolution2D(4 * f, 3, 3, activation='relu', border_mode='same')(conv3)
conv3 = BatchNormalization()(conv3)
conv3 = Convolution2D(4 * f, 3, 3, activation='relu', border_mode='same')(conv3)
conv4 = MaxPooling2D(pool_size=(2, 2))(conv3)
conv4 = BatchNormalization()(conv4)
conv4 = Convolution2D(8 * f, 3, 3, activation='relu', border_mode='same')(conv4)
conv4 = BatchNormalization()(conv4)
conv4 = Convolution2D(8 * f, 3, 3, activation='relu', border_mode='same')(conv4)
conv5 = MaxPooling2D(pool_size=(2, 2))(conv4)
conv5 = BatchNormalization()(conv5)
conv5 = Convolution2D(16 * f, 3, 3, activation='relu', border_mode='same')(conv5)
conv5 = BatchNormalization()(conv5)
conv5 = Convolution2D(16 * f, 3, 3, activation='relu', border_mode='same')(conv5)
up1 = merge([UpSampling2D(size=(2, 2))(conv5), conv4], mode='concat', concat_axis=3)
conv6 = BatchNormalization()(up1)
conv6 = Convolution2D(8 * f, 3, 3, activation='relu', border_mode='same')(conv6)
conv6 = BatchNormalization()(conv6)
conv6 = Convolution2D(8 * f, 3, 3, activation='relu', border_mode='same')(conv6)
up2 = merge([UpSampling2D(size=(2, 2))(conv6), conv3], mode='concat', concat_axis=3)
conv7 = BatchNormalization()(up2)
conv7 = Convolution2D(4 * f, 3, 3, activation='relu', border_mode='same')(conv7)
conv7 = BatchNormalization()(conv7)
conv7 = Convolution2D(4 * f, 3, 3, activation='relu', border_mode='same')(conv7)
up3 = merge([UpSampling2D(size=(2, 2))(conv7), conv2], mode='concat', concat_axis=3)
conv8 = BatchNormalization()(up3)
conv8 = Convolution2D(2 * f, 3, 3, activation='relu', border_mode='same')(conv8)
conv8 = BatchNormalization()(conv8)
conv8 = Convolution2D(2 * f, 3, 3, activation='relu', border_mode='same')(conv8)
up4 = merge([UpSampling2D(size=(2, 2))(conv8), conv1], mode='concat', concat_axis=3)
conv9 = BatchNormalization()(up4)
conv9 = Convolution2D(f, 3, 3, activation='relu', border_mode='same')(conv9)
conv9 = BatchNormalization()(conv9)
outputs = Convolution2D(1, 1, 1, activation='hard_sigmoid', border_mode='same')(conv9)
outputs = Convolution2D(1, 11, 11, activation='hard_sigmoid', border_mode='same')(outputs)
net = Model(inputs=inputs, outputs=outputs)
net.compile(loss=dice_coef_loss, optimizer='adam', metrics=[dice_coef, 'accuracy', precision, recall, f1score])
net.summary()
return net
def train_and_predict(bit):
print('-' * 30)
print('Loading and train data (bit = ' + str(bit) + ') ...')
print('-' * 30)
imgs_bit_train, imgs_bit_mask_train, _ = load_train_data(bit)
print(imgs_bit_train.shape[0], imgs_bit_mask_train.shape[0])
imgs_bit_train = imgs_bit_train.astype('float32')
mean = np.mean(imgs_bit_train)
std = np.std(imgs_bit_train)
imgs_bit_train -= mean
imgs_bit_train /= std
imgs_bit_mask_train = imgs_bit_mask_train.astype('float32')
imgs_bit_mask_train /= 255. # scale masks to [0, 1]
print('-' * 30)
print('Creating and compiling model (bit = ' + str(bit) + ') ...')
print('-' * 30)
model = get_conv(f=16)
csv_logger = CSVLogger('log_conv_' + str(bit) + '.csv')
model_checkpoint = ModelCheckpoint('weights_conv_' + str(bit) + '.h5', monitor='val_loss', save_best_only=True)
print('-' * 30)
print('Fitting model (bit = ' + str(bit) + ') ...')
print('-' * 30)
model.fit(imgs_bit_train, imgs_bit_mask_train, batch_size=32, epochs=epochs, verbose=1, shuffle=True,
validation_split=0.2,
callbacks=[csv_logger, model_checkpoint])
print('-' * 30)
print('Loading and preprocessing test data (bit = ' + str(bit) + ') ...')
print('-' * 30)
imgs_bit_test, imgs_mask_test, imgs_bit_id_test = load_test_data(bit)
imgs_bit_test = imgs_bit_test.astype('float32')
imgs_bit_test -= mean
imgs_bit_test /= std
print('-' * 30)
print('Loading saved weights...')
print('-' * 30)
model.load_weights('weights_conv_' + str(bit) + '.h5')
print('-' * 30)
print('Predicting masks on test data (bit = ' + str(bit) + ') ...')
print('-' * 30)
imgs_mask_test = model.predict(imgs_bit_test, verbose=1)
if bit == 8:
print('-' * 30)
print('Saving predicted masks to files...')
print('-' * 30)
pred_dir = 'preds_8'
if not os.path.exists(pred_dir):
os.mkdir(pred_dir)
for image, image_id in zip(imgs_mask_test, imgs_bit_id_test):
image = (image[:, :, 0] * 255.).astype(np.uint8)
imsave(os.path.join(pred_dir, str(image_id).split('/')[-1] + '_pred_conv.png'), image)
elif bit == 16:
print('-' * 30)
print('Saving predicted masks to files...')
print('-' * 30)
pred_dir = 'preds_16'
if not os.path.exists(pred_dir):
os.mkdir(pred_dir)
for image, image_id in zip(imgs_mask_test, imgs_bit_id_test):
image = (image[:, :, 0] * 255.).astype(np.uint8)
imsave(os.path.join(pred_dir, str(image_id).split('/')[-1] + '_pred_conv.png'), image)
if __name__ == '__main__':
train_and_predict(8)
train_and_predict(16)