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 .......')