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train_2ddense.py
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train_2ddense.py
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"""Test ImageNet pretrained DenseNet"""
from __future__ import print_function
import sys
sys.path.insert(0,'Keras-2.0.8')
from multiprocessing.dummy import Pool as ThreadPool
import random
from medpy.io import load
import numpy as np
import argparse
from keras.optimizers import SGD
from keras.callbacks import ModelCheckpoint
import keras.backend as K
from loss import weighted_crossentropy_2ddense
import os
from keras.utils2.multi_gpu import make_parallel
from denseunet import DenseUNet
from skimage.transform import resize
K.set_image_dim_ordering('tf')
# global parameters
parser = argparse.ArgumentParser(description='Keras 2d denseunet Training')
# data folder
parser.add_argument('-data', type=str, default='data/', help='test images')
parser.add_argument('-save_path', type=str, default='Experiments/')
# other paras
parser.add_argument('-b', type=int, default=40)
parser.add_argument('-input_size', type=int, default=224)
parser.add_argument('-model_weight', type=str, default='./model/densenet161_weights_tf.h5')
parser.add_argument('-input_cols', type=int, default=3)
# data augment
parser.add_argument('-mean', type=int, default=48)
parser.add_argument('-thread_num', type=int, default=14)
args = parser.parse_args()
MEAN = args.mean
thread_num = args.thread_num
liverlist = [32,34,38,41,47,87,89,91,105,106,114,115,119]
def load_seq_crop_data_masktumor_try(Parameter_List):
img = Parameter_List[0]
tumor = Parameter_List[1]
lines = Parameter_List[2]
numid = Parameter_List[3]
minindex = Parameter_List[4]
maxindex = Parameter_List[5]
# randomly scale
scale = np.random.uniform(0.8,1.2)
deps = int(args.input_size * scale)
rows = int(args.input_size * scale)
cols = 3
sed = np.random.randint(1,numid)
cen = lines[sed-1]
cen = np.fromstring(cen, dtype=int, sep=' ')
a = min(max(minindex[0] + deps/2, cen[0]), maxindex[0]- deps/2-1)
b = min(max(minindex[1] + rows/2, cen[1]), maxindex[1]- rows/2-1)
c = min(max(minindex[2] + cols/2, cen[2]), maxindex[2]- cols/2-1)
cropp_img = img[a - deps / 2:a + deps / 2, b - rows / 2:b + rows / 2,
c - cols / 2: c + cols / 2 + 1].copy()
cropp_tumor = tumor[a - deps / 2:a + deps / 2, b - rows / 2:b + rows / 2,
c - cols / 2:c + cols / 2 + 1].copy()
cropp_img -= MEAN
# randomly flipping
flip_num = np.random.randint(0, 8)
if flip_num == 1:
cropp_img = np.flipud(cropp_img)
cropp_tumor = np.flipud(cropp_tumor)
elif flip_num == 2:
cropp_img = np.fliplr(cropp_img)
cropp_tumor = np.fliplr(cropp_tumor)
elif flip_num == 3:
cropp_img = np.rot90(cropp_img, k=1, axes=(1, 0))
cropp_tumor = np.rot90(cropp_tumor, k=1, axes=(1, 0))
elif flip_num == 4:
cropp_img = np.rot90(cropp_img, k=3, axes=(1, 0))
cropp_tumor = np.rot90(cropp_tumor, k=3, axes=(1, 0))
elif flip_num == 5:
cropp_img = np.fliplr(cropp_img)
cropp_tumor = np.fliplr(cropp_tumor)
cropp_img = np.rot90(cropp_img, k=1, axes=(1, 0))
cropp_tumor = np.rot90(cropp_tumor, k=1, axes=(1, 0))
elif flip_num == 6:
cropp_img = np.fliplr(cropp_img)
cropp_tumor = np.fliplr(cropp_tumor)
cropp_img = np.rot90(cropp_img, k=3, axes=(1, 0))
cropp_tumor = np.rot90(cropp_tumor, k=3, axes=(1, 0))
elif flip_num == 7:
cropp_img = np.flipud(cropp_img)
cropp_tumor = np.flipud(cropp_tumor)
cropp_img = np.fliplr(cropp_img)
cropp_tumor = np.fliplr(cropp_tumor)
cropp_tumor = resize(cropp_tumor, (args.input_size,args.input_size,args.input_cols), order=0, mode='edge', cval=0, clip=True, preserve_range=True)
cropp_img = resize(cropp_img, (args.input_size,args.input_size,args.input_cols), order=3, mode='constant', cval=0, clip=True, preserve_range=True)
return cropp_img, cropp_tumor[:,:,1]
def generate_arrays_from_file(batch_size, trainidx, img_list, tumor_list, tumorlines, liverlines, tumoridx, liveridx, minindex_list, maxindex_list):
while 1:
X = np.zeros((batch_size, args.input_size, args.input_size, args.input_cols), dtype='float32')
Y = np.zeros((batch_size, args.input_size, args.input_size, 1), dtype='int16')
Parameter_List = []
for idx in xrange(batch_size):
count = random.choice(trainidx)
img = img_list[count]
tumor = tumor_list[count]
minindex = minindex_list[count]
maxindex = maxindex_list[count]
num = np.random.randint(0,6)
if num < 3 or (count in liverlist):
lines = liverlines[count]
numid = liveridx[count]
else:
lines = tumorlines[count]
numid = tumoridx[count]
Parameter_List.append([img, tumor, lines, numid, minindex, maxindex])
pool = ThreadPool(thread_num)
result_list = pool.map(load_seq_crop_data_masktumor_try, Parameter_List)
pool.close()
pool.join()
for idx in xrange(len(result_list)):
X[idx, :, :, :] = result_list[idx][0]
Y[idx, :, :, 0] = result_list[idx][1]
yield (X,Y)
def load_fast_files(args):
trainidx = list(range(131))
img_list = []
tumor_list = []
minindex_list = []
maxindex_list = []
tumorlines = []
tumoridx = []
liveridx = []
liverlines = []
for idx in xrange(131):
img, img_header = load(args.data+ '/myTrainingData/volume-' + str(idx) + '.nii')
tumor, tumor_header = load(args.data + '/myTrainingData/segmentation-' + str(idx) + '.nii')
img_list.append(img)
tumor_list.append(tumor)
maxmin = np.loadtxt(args.data + '/myTrainingDataTxt/LiverBox/box_' + str(idx) + '.txt', delimiter=' ')
minindex = maxmin[0:3]
maxindex = maxmin[3:6]
minindex = np.array(minindex, dtype='int')
maxindex = np.array(maxindex, dtype='int')
minindex[0] = max(minindex[0] - 3, 0)
minindex[1] = max(minindex[1] - 3, 0)
minindex[2] = max(minindex[2] - 3, 0)
maxindex[0] = min(img.shape[0], maxindex[0] + 3)
maxindex[1] = min(img.shape[1], maxindex[1] + 3)
maxindex[2] = min(img.shape[2], maxindex[2] + 3)
minindex_list.append(minindex)
maxindex_list.append(maxindex)
f1 = open(args.data + '/myTrainingDataTxt/TumorPixels/tumor_' + str(idx) + '.txt', 'r')
tumorline = f1.readlines()
tumorlines.append(tumorline)
tumoridx.append(len(tumorline))
f1.close()
f2 = open(args.data + '/myTrainingDataTxt/LiverPixels/liver_' + str(idx) + '.txt', 'r')
liverline = f2.readlines()
liverlines.append(liverline)
liveridx.append(len(liverline))
f2.close()
return trainidx, img_list, tumor_list, tumorlines, liverlines, tumoridx, liveridx, minindex_list, maxindex_list
def train_and_predict():
print('-'*30)
print('Creating and compiling model...')
print('-'*30)
model = DenseUNet(reduction=0.5, args=args)
model.load_weights(args.model_weight, by_name=True)
model = make_parallel(model, args.b / 10, mini_batch=10)
sgd = SGD(lr=1e-3, momentum=0.9, nesterov=True)
model.compile(optimizer=sgd, loss=[weighted_crossentropy_2ddense])
trainidx, img_list, tumor_list, tumorlines, liverlines, tumoridx, liveridx, minindex_list, maxindex_list = load_fast_files(args)
print('-'*30)
print('Fitting model......')
print('-'*30)
if not os.path.exists(args.save_path):
os.mkdir(args.save_path)
if not os.path.exists(args.save_path + "/model"):
os.mkdir(args.save_path + '/model')
os.mkdir(args.save_path + '/history')
else:
if os.path.exists(args.save_path+ "/history/lossbatch.txt"):
os.remove(args.save_path + '/history/lossbatch.txt')
if os.path.exists(args.save_path + "/history/lossepoch.txt"):
os.remove(args.save_path + '/history/lossepoch.txt')
model_checkpoint = ModelCheckpoint(args.save_path + '/model/weights.{epoch:02d}-{loss:.2f}.hdf5', monitor='loss', verbose = 1,
save_best_only=False,save_weights_only=False,mode = 'min', period = 1)
steps = 27386 / args.b
model.fit_generator(generate_arrays_from_file(args.b, trainidx, img_list, tumor_list, tumorlines, liverlines, tumoridx,
liveridx, minindex_list, maxindex_list),steps_per_epoch=steps,
epochs= 6000, verbose = 1, callbacks = [model_checkpoint], max_queue_size=10,
workers=3, use_multiprocessing=True)
print ('Finised Training .......')
if __name__ == '__main__':
train_and_predict()