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train_generator.py
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train_generator.py
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from __future__ import print_function
from optparse import OptionParser
import cv2, sys, os, shutil, random
import numpy as np
from keras.optimizers import Adam, SGD, RMSprop
from keras.callbacks import ModelCheckpoint, EarlyStopping
from keras.preprocessing.image import flip_axis, random_channel_shift
from keras.engine.training import slice_X
from keras_plus import LearningRateDecay
from u_model import get_unet, IMG_COLS as img_cols, IMG_ROWS as img_rows
from data import load_train_data, load_test_data, load_patient_num
from augmentation import CustomImageDataGenerator
from augmentation import random_zoom, elastic_transform, load_aug
from utils import save_pickle, load_pickle, count_enum
_dir = os.path.join(os.path.realpath(os.path.dirname(__file__)), '')
def preprocess(imgs, to_rows=None, to_cols=None):
if to_rows is None or to_cols is None:
to_rows = img_rows
to_cols = img_cols
imgs_p = np.ndarray((imgs.shape[0], imgs.shape[1], to_rows, to_cols), dtype=np.uint8)
for i in xrange(imgs.shape[0]):
imgs_p[i, 0] = cv2.resize(imgs[i, 0], (to_cols, to_rows), interpolation=cv2.INTER_CUBIC)
return imgs_p
class Learner(object):
suffix = ''
res_dir = os.path.join(_dir, 'res' + suffix)
best_weight_path = os.path.join(res_dir, 'unet.hdf5')
test_mask_res = os.path.join(res_dir, 'imgs_mask_test.npy')
test_mask_exist_res = os.path.join(res_dir, 'imgs_mask_exist_test.npy')
meanstd_path = os.path.join(res_dir, 'meanstd.dump')
valid_data_path = os.path.join(res_dir, 'valid.npy')
tensorboard_dir = os.path.join(res_dir, 'tb')
def __init__(self, model_func, validation_split):
self.model_func = model_func
self.validation_split = validation_split
self.__iter_res_dir = os.path.join(self.res_dir, 'res_iter')
self.__iter_res_file = os.path.join(self.__iter_res_dir, '{epoch:02d}-{val_loss:.4f}.unet.hdf5')
def _dir_init(self):
if not os.path.exists(self.res_dir):
os.mkdir(self.res_dir)
#iter clean
if os.path.exists(self.__iter_res_dir):
shutil.rmtree(self.__iter_res_dir)
os.mkdir(self.__iter_res_dir)
def save_meanstd(self):
data = [self.mean, self.std]
save_pickle(self.meanstd_path, data)
@classmethod
def load_meanstd(cls):
print ('Load meanstd from %s' % cls.meanstd_path)
mean, std = load_pickle(cls.meanstd_path)
return mean, std
@classmethod
def save_valid_idx(cls, idx):
save_pickle(cls.valid_data_path, idx)
@classmethod
def load_valid_idx(cls):
return load_pickle(cls.valid_data_path)
def _init_mean_std(self, data):
data = data.astype('float32')
self.mean, self.std = np.mean(data), np.std(data)
self.save_meanstd()
return data
def get_object_existance(self, mask_array):
return np.array([int(np.sum(mask_array[i, 0]) > 0) for i in xrange(len(mask_array))])
def standartize(self, array, to_float=False):
if to_float:
array = array.astype('float32')
if self.mean is None or self.std is None:
raise ValueError, 'No mean/std is initialised'
array -= self.mean
array /= self.std
return array
@classmethod
def norm_mask(cls, mask_array):
mask_array = mask_array.astype('float32')
mask_array /= 255.0
return mask_array
@classmethod
def shuffle_train(cls, data, mask):
perm = np.random.permutation(len(data))
data = data[perm]
mask = mask[perm]
return data, mask
@classmethod
def split_train_and_valid_by_patient(cls, data, mask, validation_split, shuffle=False):
print('Shuffle & split...')
patient_nums = load_patient_num()
patient_dict = count_enum(patient_nums)
pnum = len(patient_dict)
val_num = int(pnum * validation_split)
patients = patient_dict.keys()
if shuffle:
random.shuffle(patients)
val_p, train_p = patients[:val_num], patients[val_num:]
train_indexes = [i for i, c in enumerate(patient_nums) if c in set(train_p)]
val_indexes = [i for i, c in enumerate(patient_nums) if c in set(val_p)]
x_train, y_train = data[train_indexes], mask[train_indexes]
x_valid, y_valid = data[val_indexes], mask[val_indexes]
cls.save_valid_idx(val_indexes)
print ('val patients:', len(x_valid), val_p)
print ('train patients:', len(x_train), train_p)
return (x_train, y_train), (x_valid, y_valid)
@classmethod
def split_train_and_valid(cls, data, mask, validation_split, shuffle=False):
print('Shuffle & split...')
if shuffle:
data, mask = cls.shuffle_train(data, mask)
split_at = int(len(data) * (1. - validation_split))
x_train, x_valid = (slice_X(data, 0, split_at), slice_X(data, split_at))
y_train, y_valid = (slice_X(mask, 0, split_at), slice_X(mask, split_at))
cls.save_valid_idx(range(len(data))[split_at:])
return (x_train, y_train), (x_valid, y_valid)
def test(self, model, batch_size=256):
print('Loading and pre-processing test data...')
imgs_test = load_test_data()
imgs_test = preprocess(imgs_test)
imgs_test = self.standartize(imgs_test, to_float=True)
print('Loading best saved weights...')
model.load_weights(self.best_weight_path)
print('Predicting masks on test data and saving...')
imgs_mask_test = model.predict(imgs_test, batch_size=batch_size, verbose=1)
np.save(self.test_mask_res, imgs_mask_test[0])
np.save(self.test_mask_exist_res, imgs_mask_test[1])
def __pretrain_model_load(self, model, pretrained_path):
if pretrained_path is not None:
if not os.path.exists(pretrained_path):
raise ValueError, 'No such pre-trained path exists'
model.load_weights(pretrained_path)
def augmentation(self, X, Y):
print('Augmentation model...')
total = len(X)
x_train, y_train = [], []
for i in xrange(total):
if i % 100 == 0:
print ('Aug', i)
x, y = X[i], Y[i]
#standart
x_train.append(x)
y_train.append(y)
for _ in xrange(2):
_x, _y = elastic_transform(x[0], y[0], 100, 20)
x_train.append(_x.reshape((1,) + _x.shape))
y_train.append(_y.reshape((1,) + _y.shape))
#flip x
x_train.append(flip_axis(x, 2))
y_train.append(flip_axis(y, 2))
#flip y
x_train.append(flip_axis(x, 1))
y_train.append(flip_axis(y, 1))
continue
#zoom
for _ in xrange(1):
_x, _y = random_zoom(x, y, (0.9, 1.1))
x_train.append(_x)
y_train.append(_y)
#intentsity
for _ in xrange(1):
_x = random_channel_shift(x, 5.0)
x_train.append(_x)
y_train.append(y)
# for j in xrange(5):
# xs, ys = load_aug(j)
# ys = self.norm_mask(ys)
# (xn, yn), _ = self.split_train_and_valid_by_patient(xs, ys, validation_split=self.validation_split, shuffle=False)
# for i in xrange(len(xn)):
# x_train.append(xn[i])
# y_train.append(yn[i])
x_train = np.array(x_train)
y_train = np.array(y_train)
return x_train, y_train
def fit(self, x_train, y_train, x_valid, y_valid, pretrained_path):
print('Creating and compiling and fitting model...')
print('Shape:', x_train.shape)
#second output
y_train_2 = self.get_object_existance(y_train)
y_valid_2 = self.get_object_existance(y_valid)
#load model
optimizer = Adam(lr=0.0045)
#model = get_unet(optimizer)
model = self.model_func(optimizer)
#checkpoints
model_checkpoint = ModelCheckpoint(self.__iter_res_file, monitor='val_loss')
model_save_best = ModelCheckpoint(self.best_weight_path, monitor='val_loss', save_best_only=True)
early_s = EarlyStopping(monitor='val_loss', patience=10, verbose=1)
#tb = TensorBoard(self.tensorboard_dir, histogram_freq=2, write_graph=True)
#learning_rate_adapt = AdvancedLearnignRateScheduler(monitor='val_loss', patience=1, verbose=1, mode='min', decayRatio=0.5)
learning_rate_adapt = LearningRateDecay(0.95, every_n=4, verbose=1)
self.__pretrain_model_load(model, pretrained_path)
#augment
datagen = CustomImageDataGenerator(zoom_range=(0.9,1.1),
horizontal_flip=True,
vertical_flip=False,
# rotation_range=5,
channel_shift_range=5.0,
elastic=None #(100, 20)
)
# #fit
model.fit_generator(datagen.flow(x_train, [y_train, y_train_2], batch_size=64),
samples_per_epoch=len(x_train),
nb_epoch=250,
verbose=1,
callbacks=[model_save_best, model_checkpoint, early_s, learning_rate_adapt],
validation_data=(x_valid, [y_valid, y_valid_2])
)
return model
def train_and_predict(self, pretrained_path=None, split_random=True):
self._dir_init()
print('Loading and preprocessing and standarize train data...')
imgs_train, imgs_mask_train = load_train_data()
imgs_train = preprocess(imgs_train)
imgs_mask_train = preprocess(imgs_mask_train)
imgs_mask_train = self.norm_mask(imgs_mask_train)
#imgs_train = self.norm_mask(imgs_train) /255
#shuffle and split
split_func = split_random and self.split_train_and_valid or self.split_train_and_valid_by_patient
(x_train, y_train), (x_valid, y_valid) = split_func(imgs_train, imgs_mask_train,
validation_split=self.validation_split)
self._init_mean_std(x_train)
x_train = self.standartize(x_train, True)
x_valid = self.standartize(x_valid, True)
#fit
model = self.fit(x_train, y_train, x_valid, y_valid, pretrained_path)
#test
self.test(model)
def main():
parser = OptionParser()
parser.add_option("-m", "--model_name", action='store', type='str', dest='model_name', default = 'u_model')
parser.add_option("-s", "--split_random", action='store', type='int', dest='split_random', default = 1)
#
options, _ = parser.parse_args()
model_name = options.model_name
split_random = options.split_random
if model_name is None:
raise ValueError, 'model_name is not defined'
#
import imp
model_ = imp.load_source('model_', model_name + '.py')
model_func = model_.get_unet
#
lr = Learner(model_func, validation_split=0.2)
lr.train_and_predict(pretrained_path=None, split_random=split_random)
print ('Results in ', lr.res_dir)
if __name__ == '__main__':
sys.exit(main())