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run_vnet3d.py
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if __name__ == '__main__':
import glob
import os
import numpy as np
import argparse
import time, re
import tensorflow as tf
from keras.optimizers import Adam, SGD
from utils import DataGenerator, dice_loss, dice_coefficient, ModelAndWeightsCheckpoint
from vnet3d import VNet
from keras.callbacks import LearningRateScheduler, Callback, TensorBoard, EarlyStopping
parser = argparse.ArgumentParser(description="Script to run UNet3D")
parser.add_argument('--core_tag', '-ct', required=True)
parser.add_argument('--nii_dir', '-I', required=True)
parser.add_argument('--batch_size', '-bs', required=True, type=int)
parser.add_argument('--image_size', '-is', required=True, type=int)
parser.add_argument('--learning_rate', '-lr', required=True, type=float)
parser.add_argument('--group_size', '-gs', required=True, type=int)
parser.add_argument('--f_root', '-fr', required=True, type=int)
parser.add_argument('--n_validation', required=True, type=int)
parser.add_argument('--n_test', required=True, type=int)
parser.add_argument('--optimizer', '-op', required=True, default='adam')
parser.add_argument('--print_summary_only', action='store_true')
parser.set_defaults(print_summary_only=False)
args = parser.parse_args()
if args.optimizer == 'adam':
args.learning_rate /= 20 # reduce lr for adam
elif args.optimizer == 'sgd':
pass
else:
raise Exception('[ERROR] optimizer = {}'.format(args.optimizer))
# Cloud settings
home_dir = os.path.expanduser("~")
hostname = os.uname()[1]
cloud_dir = '{}/gdrive/cloud/{}'.format(home_dir, hostname)
try:
os.system('mkdir -p ' + cloud_dir)
except:
pass
# Get data
# [IDs] Get sample IDs from src_dir
src_dir = args.nii_dir #'data/data_with_augmentation/'
assert os.path.exists(src_dir), "[ERROR] {} does not exist".format(src_dir)
fpaths = glob.glob(src_dir + '/*.nii.gz')
sids = sorted(set([os.path.split(x)[-1].rsplit('_', 1)[0] for x in fpaths]))
seed = 0
np.random.seed(seed)
shuffle = True
if shuffle:
np.random.shuffle(sids)
# Modules
def lr_schedule_wrapper(learning_rate):
learning_rate = learning_rate
def lr_schedule(epoch):
#learning_rate = 1e-4
if epoch > 10:
learning_rate /= 2
if epoch > 20:
learning_rate /= 2
if epoch > 50:
learning_rate /= 2
tf.summary.scalar('learning_rate', learning_rate)
#tf.compat.v1.summary.scalar('learning_rate', learning_rate)
return learning_rate
return lr_schedule
# Set params and callbacks
n_val, n_test = args.n_validation, args.n_test
n_train = len(sids) - n_val - n_test
if n_train < 0:
raise Exception("n_train({}) < n_validation({})+n_test({})".format(n_train, n_val, n_test))
elif n_train < n_val + n_test:
raise Exception("n_train({}) < n_validation({})+n_test({})".format(n_train, n_val, n_test))
train_ids = sids[:n_train]
valid_ids = sids[n_train : n_train+n_val]
test_ids = sids[n_train+n_val : n_train+n_val+n_test]
print("IDs", len(sids), len(train_ids), len(valid_ids), len(test_ids), n_train)
epochs = 100
h5_dir = os.path.join(cloud_dir, 'models')
if not os.path.exists(h5_dir):
os.system('mkdir {}'.format(h5_dir))
prefix = os.path.join(h5_dir, args.core_tag +
"_b{}".format(args.batch_size))
#"_s{}_b{}".format(args.image_size, args.batch_size))
pattern = re.compile(prefix + '_vl([\d\.-]+)')
existing_models = glob.glob(prefix + '_vl*.h5')
existing_models.sort(key = lambda x: float(pattern.search(x).groups()[0][:-1]))
model_weights = os.path.join(h5_dir, args.core_tag + '.h5')
model_architecture = os.path.join(h5_dir, args.core_tag + '.json')
checkpoint_cb = ModelAndWeightsCheckpoint(model_weights, model_architecture,
monitor='val_dice_coefficient', verbose=1, save_best_only=True, mode='max')
lr_cb = LearningRateScheduler(lr_schedule_wrapper(args.learning_rate))
earlystopping_cb = EarlyStopping(monitor='val_dice_coefficient', min_delta=0.001,
patience=15, verbose=1, mode='max', baseline=None,
restore_best_weights=True)
time_tag = time.strftime('%Y%m%d_%H%M%S', time.localtime())
tf_log_dir = '{}/{}/logs/vnet'.format(cloud_dir, hostname)
try:
os.system('mkdir -p ' + tf_log_dir)
except:
pass
if not os.path.exists(tf_log_dir):
raise Exception("{} does not exist".format(tf_log_dir))
log_dir = os.path.join(tf_log_dir, args.core_tag + '_' + time_tag)
tensorboard_cb = TensorBoard(log_dir=log_dir)
callbacks_list = [checkpoint_cb,
# lr_cb,
earlystopping_cb,
tensorboard_cb]
# Generate data
image_shape = (args.image_size,)*3
#FAIL: (144,144,144) #(160,160,144) #(192,192,144) #(208,208,144) #(240,240,144)
gen_factor = 3
train_gen = DataGenerator(train_ids, src_dir, n_samples=n_train*gen_factor,
rotation_range=0.4,
batch_size=args.batch_size, image_shape=image_shape)
valid_gen = DataGenerator(valid_ids, src_dir, n_samples=n_val*gen_factor,
rotation_range=0.4,
batch_size=args.batch_size, image_shape=image_shape)
test_gen = DataGenerator(test_ids, src_dir, n_samples=n_test*gen_factor,
rotation_range=0.4,
batch_size=args.batch_size, image_shape=image_shape)
train_steps = len(train_ids*gen_factor) // args.batch_size
valid_steps = len(valid_ids*gen_factor) // args.batch_size
# [V-Net 3D] # Fix #6: n_in=2 --> 4
model = VNet(image_shape=image_shape, n_in=4, n_out=3,
strides=1, padding='same', kernel_size=5,
groups=args.group_size, data_format='channels_first',
filters=args.f_root)
if args.optimizer == 'adam':
optimizer = Adam(lr = args.learning_rate) # FIX #2
elif args.optimizer == 'sgd':
optimizer = SGD(lr=args.learning_rate, decay=1e-6, momentum=0.99)
else:
raise Exception('[ERROR] args.optimizer = {}'.format(args.optimizer))
if len(existing_models) > 0: # if saved model exists
print(existing_models)
best_model = existing_models[0] # sorted ix 0 has lowest vl
# model.load_weights(best_model)
print(best_model)
model.compile(optimizer=optimizer, loss=dice_loss, metrics=[dice_coefficient])
if args.print_summary_only:
model.summary(line_length=150)
raise Exception("args.print_summary_only = True")
# Run model
history = model.fit_generator(train_gen,
validation_data=valid_gen,
steps_per_epoch=train_steps,
validation_steps=valid_steps,
verbose=1,
callbacks = callbacks_list,
epochs=epochs)