def train_and_predict():
print('-' * 30)
print('Creating and compiling model...')
print('-' * 30)
model = DenseUNet(reduction=0.5)
import ipdb
ipdb.set_trace()
model.load_weights(args.model_weight, by_name=True)
if args.n_gpus > 1:
print("Using %d GPUs" % (args.n_gpus))
model = make_parallel(model,
args.n_gpus,
mini_batch=max(args.batch_size / args.n_gpus, 1))
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)
model.fit_generator(
generate_arrays_from_file(args.batch_size, trainidx, img_list,
tumor_list, tumorlines, liverlines, tumoridx,
liveridx, minindex_list, maxindex_list),
steps_per_epoch=20, #lizx: lizx changed to 1
epochs=50000,
verbose=2,
callbacks=[model_checkpoint],
max_queue_size=10,
workers=1,
use_multiprocessing=False)
print('Finised Training .......')
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, int(args.b/6), mini_batch=6)
sgd = SGD(lr=1e-3, momentum=0.9, nesterov=True)
model.compile(optimizer=sgd, loss=[weighted_crossentropy_2ddense], metrics=[dice_liver, dice_lesion])
#model.compile(optimizer='adam', loss=[weighted_crossentropy_2ddense], metrics=[dice_liver, dice_lesion])
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= 600, verbose=1, callbacks=[model_checkpoint], max_queue_size=10, workers=3, use_multiprocessing=True)
print ('Finised Training .......')
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], metrics=['accuracy'])
#Load Training Data
trainidx, img_list, tumor_list, tumorlines, liverlines, tumoridx, liveridx, minindex_list, maxindex_list = load_fast_files(args)
#Load Validation Data
vtrainidx, vimg_list, vtumor_list, vtumorlines, vliverlines, vtumoridx, vliveridx, vminindex_list, vmaxindex_list = val_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')
tensor_board = TensorBoard(logdir='./logs', histogram_freq=0, write_graph=True, write_images=False)
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, tensor_board],
validation_data = generate_arrays_from_file(args.b / 2, vtrainidx, vimg_list, vtumor_list, vtumorlines, vliverlines, vtumoridx,
vliveridx, vminindex_list, vmaxindex_list), validation_steps=30, validation_freq=5,
max_queue_size=10, workers=3, use_multiprocessing=True)
print ('Finised Training .......')
def main(_):
os.environ["CUDA_VISIBLE_DEVICES"] = FLAGS.gpu_index
# Initialize model and log folders
if FLAGS.load_model is None:
curTime = datetime.now().strftime("%Y%m%d-%H%M%S")
else:
curTime = FLAGS.load_model
modelDir, logDir, sampleDir, valDir, testDir = utils.make_folders(
isTrain=FLAGS.is_train, curTime=curTime, subfolder=FLAGS.method)
# Logger
logger = logging.getLogger(__name__) # logger
logger.setLevel(logging.INFO)
utils.init_logger(logger=logger,
logDir=logDir,
isTrain=FLAGS.is_train,
name='main')
utils.print_main_parameters(logger, flags=FLAGS, isTrain=FLAGS.is_train)
# Initialize dataset
data = Dataset(name=FLAGS.dataset,
isTrain=FLAGS.is_train,
resizedFactor=FLAGS.resize_factor,
logDir=logDir)
# Initialize model
if not 'v5' in FLAGS.method:
model = UNet(decodeImgShape=data.decodeImgShape,
outputShape=data.singleImgShape,
numClasses=data.numClasses,
dataPath=data(isTrain=FLAGS.is_train),
batchSize=FLAGS.batch_size,
lr=FLAGS.learning_rate,
weightDecay=FLAGS.weight_decay,
totalIters=FLAGS.iters,
isTrain=FLAGS.is_train,
logDir=logDir,
method=FLAGS.method,
multi_test=FLAGS.multi_test,
advanced_multi_test=FLAGS.advanced_multi_test,
resize_factor=FLAGS.resize_factor,
use_dice_loss=FLAGS.use_dice_loss,
lambda_one=FLAGS.lambda_one,
name='UNet')
else:
model = DenseUNet(decodeImgShape=data.decodeImgShape,
outputShape=data.singleImgShape,
numClasses=data.numClasses,
dataPath=data(isTrain=FLAGS.is_train),
batchSize=FLAGS.batch_size,
lr=FLAGS.learning_rate,
weightDecay=FLAGS.weight_decay,
totalIters=FLAGS.iters,
isTrain=FLAGS.is_train,
logDir=logDir,
method=FLAGS.method,
multi_test=FLAGS.multi_test,
resize_factor=FLAGS.resize_factor,
use_dice_loss=FLAGS.use_dice_loss,
use_batch_norm=FLAGS.use_batch_norm,
lambda_one=FLAGS.lambda_one,
name='DenseUNet')
# Initialize solver
solver = Solver(model=model,
data=data,
is_train=FLAGS.is_train,
multi_test=FLAGS.multi_test)
# Initialize saver
saver = tf.compat.v1.train.Saver(max_to_keep=1)
if FLAGS.is_train is True:
train(solver, saver, logger, modelDir, logDir, sampleDir)
else:
test(solver, saver, modelDir, valDir, testDir, data)