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train_resnet.py
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train_resnet.py
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from __future__ import print_function
import os
import numpy as np
from keras import backend as K
from keras import layers
from keras.callbacks import ModelCheckpoint, CSVLogger
from keras.layers import Activation
from keras.layers import Input, Conv2D, ZeroPadding2D, MaxPooling2D, UpSampling2D, concatenate
from keras.layers.normalization import BatchNormalization
from keras.models import Model
from keras.optimizers import Adam
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 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 identity_block(input_tensor, kernel_size, filters, stage, block):
filters1, filters2, filters3 = filters
if K.image_data_format() == 'channels_last':
bn_axis = 3
else:
bn_axis = 1
conv_name_base = 'res' + str(stage) + block + '_branch'
bn_name_base = 'bn' + str(stage) + block + '_branch'
x = Conv2D(filters1, (1, 1), name=conv_name_base + '2a')(input_tensor)
x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2a')(x)
x = Activation('relu')(x)
x = Conv2D(filters2, kernel_size, padding='same', name=conv_name_base + '2b')(x)
x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2b')(x)
x = Activation('relu')(x)
x = Conv2D(filters3, (1, 1), name=conv_name_base + '2c')(x)
x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2c')(x)
x = layers.add([x, input_tensor])
x = Activation('relu')(x)
return x
def conv_block(input_tensor, kernel_size, filters, stage, block, strides=(2, 2)):
filters1, filters2, filters3 = filters
if K.image_data_format() == 'channels_last':
bn_axis = 3
else:
bn_axis = 1
conv_name_base = 'res' + str(stage) + block + '_branch'
bn_name_base = 'bn' + str(stage) + block + '_branch'
x = Conv2D(filters1, (1, 1), strides=strides, name=conv_name_base + '2a')(input_tensor)
x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2a')(x)
x = Activation('relu')(x)
x = Conv2D(filters2, kernel_size, padding='same', name=conv_name_base + '2b')(x)
x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2b')(x)
x = Activation('relu')(x)
x = Conv2D(filters3, (1, 1), name=conv_name_base + '2c')(x)
x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2c')(x)
shortcut = Conv2D(filters3, (1, 1), strides=strides, name=conv_name_base + '1')(input_tensor)
shortcut = BatchNormalization(axis=bn_axis, name=bn_name_base + '1')(shortcut)
x = layers.add([x, shortcut])
x = Activation('relu')(x)
return x
def up_conv_block(input_tensor, kernel_size, filters, stage, block, strides=(1, 1)):
filters1, filters2, filters3 = filters
if K.image_data_format() == 'channels_last':
bn_axis = 3
else:
bn_axis = 1
up_conv_name_base = 'up' + str(stage) + block + '_branch'
conv_name_base = 'res' + str(stage) + block + '_branch'
bn_name_base = 'bn' + str(stage) + block + '_branch'
x = UpSampling2D(size=(2, 2), name=up_conv_name_base + '2a')(input_tensor)
x = Conv2D(filters1, (1, 1), strides=strides, name=conv_name_base + '2a')(x)
x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2a')(x)
x = Activation('relu')(x)
x = Conv2D(filters2, kernel_size, padding='same', name=conv_name_base + '2b')(x)
x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2b')(x)
x = Activation('relu')(x)
x = Conv2D(filters3, (1, 1), name=conv_name_base + '2c')(x)
x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2c')(x)
shortcut = UpSampling2D(size=(2, 2), name=up_conv_name_base + '1')(input_tensor)
shortcut = Conv2D(filters3, (1, 1), strides=strides, name=conv_name_base + '1')(shortcut)
shortcut = BatchNormalization(axis=bn_axis, name=bn_name_base + '1')(shortcut)
x = layers.add([x, shortcut])
x = Activation('relu')(x)
return x
def get_resnet(f=16, bn_axis=3, classes=1):
input = Input((img_rows, img_cols, 1))
x = ZeroPadding2D((4, 4))(input)
x = Conv2D(f, (7, 7), strides=(2, 2), name='conv1')(x)
x = BatchNormalization(axis=bn_axis, name='bn_conv1')(x)
x = Activation('relu')(x)
x = MaxPooling2D((3, 3), strides=(2, 2))(x)
x = conv_block(x, 3, [f, f, f * 2], stage=2, block='a', strides=(1, 1))
x = identity_block(x, 3, [f, f, f * 2], stage=2, block='b')
x2 = identity_block(x, 3, [f, f, f * 2], stage=2, block='c')
x = conv_block(x2, 3, [f * 2, f * 2, f * 4], stage=3, block='a')
x = identity_block(x, 3, [f * 2, f * 2, f * 4], stage=3, block='b')
x3 = identity_block(x, 3, [f * 2, f * 2, f * 4], stage=3, block='d')
x = conv_block(x3, 3, [f * 4, f * 4, f * 8], stage=4, block='a')
x = identity_block(x, 3, [f * 4, f * 4, f * 8], stage=4, block='b')
x4 = identity_block(x, 3, [f * 4, f * 4, f * 8], stage=4, block='f')
x = conv_block(x4, 3, [f * 8, f * 8, f * 16], stage=5, block='a')
x = identity_block(x, 3, [f * 8, f * 8, f * 16], stage=5, block='b')
x = identity_block(x, 3, [f * 8, f * 8, f * 16], stage=5, block='c')
x = up_conv_block(x, 3, [f * 16, f * 8, f * 8], stage=6, block='a')
x = identity_block(x, 3, [f * 16, f * 8, f * 8], stage=6, block='b')
x = identity_block(x, 3, [f * 16, f * 8, f * 8], stage=6, block='c')
x = concatenate([x, x4], axis=bn_axis)
x = up_conv_block(x, 3, [f * 16, f * 4, f * 4], stage=7, block='a')
x = identity_block(x, 3, [f * 16, f * 4, f * 4], stage=7, block='b')
x = identity_block(x, 3, [f * 16, f * 4, f * 4], stage=7, block='f')
x = concatenate([x, x3], axis=bn_axis)
x = up_conv_block(x, 3, [f * 8, f * 2, f * 2], stage=8, block='a')
x = identity_block(x, 3, [f * 8, f * 2, f * 2], stage=8, block='b')
x = identity_block(x, 3, [f * 8, f * 2, f * 2], stage=8, block='d')
x = concatenate([x, x2], axis=bn_axis)
x = up_conv_block(x, 3, [f * 4, f, f], stage=10, block='a', strides=(1, 1))
x = identity_block(x, 3, [f * 4, f, f], stage=10, block='b')
x = identity_block(x, 3, [f * 4, f, f], stage=10, block='c')
x = UpSampling2D(size=(2, 2))(x)
x = Conv2D(classes, (3, 3), padding='same', activation='sigmoid', name='convLast')(x)
model = Model(input, x, name='resnetUnet')
model.compile(optimizer=Adam(lr=3e-4), loss=dice_coef_loss,
metrics=[dice_coef, 'accuracy', precision, recall, f1score])
model.summary()
return model
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_resnet(f=16, bn_axis=3, classes=1)
csv_logger = CSVLogger('log_resnet_' + str(bit) + '.csv')
model_checkpoint = ModelCheckpoint('weights_resnet_' + 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_resnet_' + 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_resnet.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_resnet.png'), image)
if __name__ == '__main__':
train_and_predict(8)
train_and_predict(16)