def main(overwrite=False):
# convert input images into an hdf5 file
if overwrite or not os.path.exists(config["data_file"]):
training_files = fetch_training_data_files()
write_data_to_file(training_files,
config["data_file"],
image_shape=config["image_shape"]
) #config["image_shape"] = (144, 144, 144)
data_file_opened = open_data_file(config["data_file"])
if not overwrite and os.path.exists(config["model_file"]):
model = load_old_model(config["model_file"])
else:
# instantiate new model
#print('start model computing')
# model = unet_model_3d(input_shape=config["input_shape"], # 4+(32, 32, 32)
# pool_size=config["pool_size"], #config["pool_size"] = (2, 2, 2), maxpooling size
# n_labels=config["n_labels"], #config["n_labels"] = len(config["labels"])
# initial_learning_rate=config["initial_learning_rate"], #config["initial_learning_rate"] = 0.00001
# deconvolution=config["deconvolution"]) #config["deconvolution"] = True # if False, will use upsampling instead of deconvolution
model = custom_unet(reu2018)
#print('model loaded')
# get training and testing generators
train_generator, validation_generator, n_train_steps, n_validation_steps = get_training_and_validation_generators(
data_file_opened,
batch_size=config["batch_size"], #config["batch_size"] = 6
data_split=config["validation_split"], #validation_split = 0.8
overwrite=overwrite,
validation_keys_file=config["validation_file"],
training_keys_file=config["training_file"],
n_labels=config["n_labels"],
labels=config["labels"],
patch_shape=config["patch_shape"],
validation_batch_size=config["validation_batch_size"],
validation_patch_overlap=config["validation_patch_overlap"],
training_patch_start_offset=config["training_patch_start_offset"],
permute=config["permute"],
augment=config["augment"],
skip_blank=config["skip_blank"],
augment_flip=config["flip"],
augment_distortion_factor=config["distort"])
# run training
print('training mdel')
train_model(
model=model,
model_file=config[
"model_file"], #config["model_file"] = os.path.abspath("tumor_segmentation_model.h5")
training_generator=train_generator,
validation_generator=validation_generator,
steps_per_epoch=n_train_steps,
validation_steps=n_validation_steps,
initial_learning_rate=config["initial_learning_rate"],
learning_rate_drop=config["learning_rate_drop"],
learning_rate_patience=config["patience"],
early_stopping_patience=config["early_stop"],
n_epochs=config["n_epochs"])
data_file_opened.close()
print("model has been trained already")
def main(overwrite=False):
# convert input images into an hdf5 file
if overwrite or not os.path.exists(config["data_file"]):
training_files = fetch_training_data_files()
print(training_files)
write_data_to_file(training_files,
config["data_file"],
image_shape=config["image_shape"])
data_file_opened = open_data_file(config["data_file"])
# get training and testing generators - generate pickel files containing IDS
train_generator, validation_generator, n_train_steps, n_validation_steps = get_training_and_validation_generators(
data_file_opened,
batch_size=config["batch_size"],
data_split=config["validation_split"],
overwrite=overwrite,
validation_keys_file=config["validation_file"],
training_keys_file=config["training_file"],
n_labels=config["n_labels"],
labels=config["labels"],
patch_shape=config["patch_shape"],
validation_batch_size=config["validation_batch_size"],
validation_patch_overlap=config["validation_patch_overlap"],
training_patch_start_offset=config["training_patch_start_offset"],
permute=config["permute"],
augment=config["augment"],
skip_blank=config["skip_blank"],
augment_flip=config["flip"],
augment_distortion_factor=config["distort"])
data_file_opened.close()
def main():
kwargs = vars(parse_args())
prediction_dir = os.path.abspath(kwargs.pop("prediction_dir"))
output_label_map = not kwargs.pop("no_label_map")
for key, value in kwargs.items():
if value:
if key == "modalities":
config["training_modalities"] = value
else:
config[key] = value
filenames, subject_ids = fetch_brats_2020_files(config["training_modalities"], group="Validation",
include_truth=False, return_subject_ids=True)
if not os.path.exists(config["data_file"]):
write_data_to_file(filenames, config["data_file"], image_shape=config["image_shape"],
subject_ids=subject_ids, save_truth=False)
pickle_dump(list(range(len(subject_ids))), config["validation_file"])
run_validation_cases(validation_keys_file=config["validation_file"],
model_file=config["model_file"],
training_modalities=config["training_modalities"],
labels=config["labels"],
hdf5_file=config["data_file"],
output_label_map=output_label_map,
output_dir=prediction_dir,
test=False,
output_basename=kwargs["output_basename"],
permute=config["permute"])
for filename_list, subject_id in zip(filenames, subject_ids):
prediction_filename = os.path.join(prediction_dir, kwargs["output_basename"].format(subject=subject_id))
print("Resampling:", prediction_filename)
ref = nib.load(filename_list[0])
pred = nib.load(prediction_filename)
pred_resampled = resample_to_img(pred, ref, interpolation="nearest")
pred_resampled.to_filename(prediction_filename)
def main(overwrite=False):
# convert input images into an hdf5 file
if overwrite or not os.path.exists(config["hdf5_file"]):
training_files = fetch_training_data_files()
write_data_to_file(training_files, config["hdf5_file"], image_shape=config["image_shape"])
hdf5_file_opened = tables.open_file(config["hdf5_file"], "r")
if not overwrite and os.path.exists(config["model_file"]):
model = load_old_model(config["model_file"])
else:
# instantiate new model
model = unet_model_3d(input_shape=config["input_shape"],
downsize_filters_factor=config["downsize_nb_filters_factor"],
pool_size=config["pool_size"], n_labels=config["n_labels"],
initial_learning_rate=config["initial_learning_rate"])
# get training and testing generators
train_generator, validation_generator, nb_train_samples, nb_test_samples = get_training_and_validation_generators(
hdf5_file_opened, batch_size=config["batch_size"], data_split=config["validation_split"], overwrite=overwrite,
validation_keys_file=config["validation_file"], training_keys_file=config["training_file"],
n_labels=config["n_labels"])
# run training
train_model(model=model, model_file=config["model_file"], training_generator=train_generator,
validation_generator=validation_generator, steps_per_epoch=nb_train_samples,
validation_steps=nb_test_samples, initial_learning_rate=config["initial_learning_rate"],
learning_rate_drop=config["learning_rate_drop"],
learning_rate_epochs=config["decay_learning_rate_every_x_epochs"], n_epochs=config["n_epochs"])
hdf5_file_opened.close()
def main():
if not os.path.exists(config["hdf5_file"]):
training_files = list()
for label_file in glob.glob("./data/training/subject-*-label.hdr"):
training_files.append((label_file.replace("label", "T1"), label_file.replace("label", "T2"), label_file))
write_data_to_file(training_files, config["hdf5_file"], image_shape=config["image_shape"])
hdf5_file_opened = tables.open_file(config["hdf5_file"], "r")
if os.path.exists(config["model_file"]):
model = load_old_model(config["model_file"])
else:
# instantiate new model
model = unet_model_3d(input_shape=config["input_shape"], n_labels=config["n_labels"])
# get training and testing generators
train_generator, validation_generator, nb_train_samples, nb_test_samples = get_training_and_validation_generators(
hdf5_file_opened, batch_size=config["batch_size"], data_split=config["validation_split"],
validation_keys_file=config["validation_file"], training_keys_file=config["training_file"],
n_labels=config["n_labels"], labels=config["labels"], augment=True)
# run training
train_model(model=model, model_file=config["model_file"], training_generator=train_generator,
validation_generator=validation_generator, steps_per_epoch=nb_train_samples,
validation_steps=nb_test_samples, initial_learning_rate=config["initial_learning_rate"],
learning_rate_drop=config["learning_rate_drop"],
learning_rate_epochs=config["decay_learning_rate_every_x_epochs"], n_epochs=config["n_epochs"])
hdf5_file_opened.close()
def main(args):
prediction_dir = os.path.abspath("./headneck/prediction_test/" +
args.organ.lower())
if not os.path.exists(prediction_dir):
os.makedirs(prediction_dir)
test_data_files, subject_ids = fetch_test_data_files(
return_subject_ids=True)
if not os.path.exists(config["data_file"]):
write_data_to_file(test_data_files,
config["data_file"],
image_shape=config["image_shape"],
subject_ids=subject_ids)
if not os.path.exists(config["test_file"]):
test_list = list(range(len(subject_ids)))
pickle_dump(test_list, config["test_file"])
run_validation_cases(validation_keys_file=config["test_file"],
model_file=config["model_file"],
training_modalities=config["training_modalities"],
labels=config["labels"],
hdf5_file=config["data_file"],
output_label_map=True,
output_dir=prediction_dir)
header = ("Background", "Organ")
masking_functions = (get_background_mask, get_organ_mask)
rows = list()
prediction_path = "./headneck/prediction_test/" + args.organ.lower() + "/"
for case_folder in glob.glob(prediction_path + "*/"):
truth_file = os.path.join(case_folder, "truth.nii.gz")
truth_image = nib.load(truth_file)
truth = truth_image.get_data()
prediction_file = os.path.join(case_folder, "prediction.nii.gz")
prediction_image = nib.load(prediction_file)
prediction = prediction_image.get_data()
rows.append([
dice_coefficient(func(truth), func(prediction))
for func in masking_functions
])
df = pd.DataFrame.from_records(rows, columns=header)
df.to_csv(prediction_path + "headneck_scores.csv")
scores = dict()
for index, score in enumerate(df.columns):
values = df.values.T[index]
scores[score] = values[np.isnan(values) == False]
plt.boxplot(list(scores.values()), labels=list(scores.keys()))
plt.ylabel("Dice Coefficient")
plt.savefig(prediction_path + "test_scores_boxplot.png")
plt.close()
def main(overwrite=False):
# convert input images into an hdf5 file
if overwrite or not os.path.exists(config["data_file"]):
training_files, subject_ids = fetch_training_data_files(
return_subject_ids=True)
write_data_to_file(training_files,
config["data_file"],
image_shape=config["image_shape"],
subject_ids=subject_ids)
data_file_opened = open_data_file(config["data_file"])
if not overwrite and os.path.exists(config["model_file"]):
print("Loading old model file from the location: ",
config["model_file"])
model = load_old_model(config["model_file"])
else:
# instantiate new model
print("Creating new model at the location: ", config["model_file"])
model = isensee2017_model(
input_shape=config["input_shape"],
n_labels=config["n_labels"],
initial_learning_rate=config["initial_learning_rate"],
n_base_filters=config["n_base_filters"])
# get training and testing generators
train_generator, validation_generator, n_train_steps, n_validation_steps = get_training_and_validation_generators(
data_file_opened,
batch_size=config["batch_size"],
data_split=config["validation_split"],
overwrite=overwrite,
validation_keys_file=config["validation_file"],
training_keys_file=config["training_file"],
n_labels=config["n_labels"],
labels=config["labels"],
patch_shape=config["patch_shape"],
validation_batch_size=config["validation_batch_size"],
validation_patch_overlap=config["validation_patch_overlap"],
training_patch_start_offset=config["training_patch_start_offset"],
permute=config["permute"],
augment=config["augment"],
skip_blank=config["skip_blank"],
augment_flip=config["flip"],
augment_distortion_factor=config["distort"])
print("Running the Training. Model file:", config["model_file"])
# run training
train_model(model=model,
model_file=config["model_file"],
training_generator=train_generator,
validation_generator=validation_generator,
steps_per_epoch=n_train_steps,
validation_steps=n_validation_steps,
initial_learning_rate=config["initial_learning_rate"],
learning_rate_drop=config["learning_rate_drop"],
learning_rate_patience=config["patience"],
early_stopping_patience=config["early_stop"],
n_epochs=config["n_epochs"])
data_file_opened.close()
def main(overwrite=False):
# convert input images into an hdf5 file
if overwrite or not os.path.exists(config["data_file"]):
training_files = fetch_training_data_files()
print("Number of Training file Found:", len(training_files))
write_data_to_file(training_files, config["data_file"], image_shape=config["image_shape"])
print("Opening data file.")
data_file_opened = open_data_file(config["data_file"])
if not overwrite and os.path.exists(config["model_file"]):
print("Loading existing model file.")
model = load_old_model(config["model_file"])
else:
# instantiate new model
print("Instantiating new model file.")
model = unet_model_3d(input_shape=config["input_shape"],
pool_size=config["pool_size"],
n_labels=config["n_labels"],
initial_learning_rate=config["initial_learning_rate"],
deconvolution=config["deconvolution"])
# get training and testing generators
print("Getting training and testing generators.")
train_generator, validation_generator, n_train_steps, n_validation_steps = get_training_and_validation_generators(
data_file_opened,
batch_size=config["batch_size"],
data_split=config["validation_split"],
overwrite=overwrite,
validation_keys_file=config["validation_file"],
training_keys_file=config["training_file"],
n_labels=config["n_labels"],
labels=config["labels"],
patch_shape=config["patch_shape"],
validation_batch_size=config["validation_batch_size"],
validation_patch_overlap=config["validation_patch_overlap"],
training_patch_start_offset=config["training_patch_start_offset"],
permute=config["permute"],
augment=config["augment"],
skip_blank=config["skip_blank"],
augment_flip=config["flip"],
augment_distortion_factor=config["distort"])
# run training
print("Running the training......")
train_model(model=model,
model_file=config["model_file"],
training_generator=train_generator,
validation_generator=validation_generator,
steps_per_epoch=n_train_steps,
validation_steps=n_validation_steps,
initial_learning_rate=config["initial_learning_rate"],
learning_rate_drop=config["learning_rate_drop"],
learning_rate_patience=config["patience"],
early_stopping_patience=config["early_stop"],
n_epochs=config["n_epochs"])
data_file_opened.close()
print("Training DONE")
def main(overwrite=False):
# convert input images into an hdf5 file
# 若有则加载旧数据集，注意，此时image_shape为之前设置的
if overwrite or not os.path.exists(config["data_file"]):
training_files, subject_ids = fetch_training_data_files(return_subject_ids=True)
write_data_to_file(training_files, config["data_file"], image_shape=config["image_shape"],
subject_ids=subject_ids)
data_file_opened = open_data_file(config["data_file"])
# 加载/创建模型文件
if not overwrite and os.path.exists(config["model_file"]):
model = load_old_model(config["model_file"])
else:
# instantiate new model
model = att_res_ds_unet.att_res_ds_unet_model(input_shape=config["input_shape"], n_labels=config["n_labels"],
initial_learning_rate=config["initial_learning_rate"],
n_base_filters=config["n_base_filters"])
from keras.utils.vis_utils import plot_model
plot_model(model, to_file='att_res_ds_uet.png', show_shapes=True)
# get training and testing generators
# ../unet3d/generator.py
# 创建生成器(generator)，用于后面训练
train_generator, validation_generator, n_train_steps, n_validation_steps = get_training_and_validation_generators(
data_file_opened,
batch_size=config["batch_size"],
data_split=config["validation_split"],
overwrite=overwrite,
validation_keys_file=config["validation_file"],
training_keys_file=config["training_file"],
n_labels=config["n_labels"],
labels=config["labels"],
patch_shape=config["patch_shape"],
validation_batch_size=config["validation_batch_size"],
validation_patch_overlap=config["validation_patch_overlap"],
training_patch_start_offset=config["training_patch_start_offset"],
permute=config["permute"],
augment=config["augment"],
skip_blank=config["skip_blank"],
augment_flip=config["flip"],
augment_distortion_factor=config["distort"])
# run training
# ../unet3d/training.py
# 训练一个keras模型
train_model(model=model,
model_file=config["model_file"],
training_generator=train_generator,
validation_generator=validation_generator,
steps_per_epoch=n_train_steps,
validation_steps=n_validation_steps,
initial_learning_rate=config["initial_learning_rate"],
learning_rate_drop=config["learning_rate_drop"],
learning_rate_patience=config["patience"],
early_stopping_patience=config["early_stop"],
n_epochs=config["n_epochs"])
data_file_opened.close()
def main(overwrite=False):
# convert input images into an hdf5 file
if overwrite or not os.path.exists(config["data_file"]):
training_files = fetch_training_data_files()
write_data_to_file(training_files,
config["data_file"],
image_shape=config["image_shape"])
data_file_opened = open_data_file(config["data_file"])
if not overwrite and os.path.exists(config["model_file"]):
model = load_old_model(config["model_file"])
else:
# instantiate new model
model = unet_model_3d(
input_shape=config["input_shape"],
pool_size=config["pool_size"],
n_labels=config["n_labels"],
initial_learning_rate=config["initial_learning_rate"],
deconvolution=config["deconvolution"])
# get training and testing generators
train_generator, validation_generator, n_train_steps, n_validation_steps = get_training_and_validation_generators(
data_file_opened,
batch_size=config["batch_size"],
data_split=config["validation_split"],
overwrite=overwrite,
validation_keys_file=config["validation_file"],
training_keys_file=config["training_file"],
n_labels=config["n_labels"],
labels=config["labels"],
patch_shape=config["patch_shape"],
validation_batch_size=config["validation_batch_size"],
validation_patch_overlap=config["validation_patch_overlap"],
training_patch_start_offset=config["training_patch_start_offset"],
permute=config["permute"],
augment=config["augment"],
skip_blank=config["skip_blank"],
augment_flip=config["flip"],
augment_distortion_factor=config["distortion_factor"],
augment_rotation_factor=config["rotation_factor"],
mirror=config["mirror"])
# run training
train_model(model=model,
model_file=config["model_file"],
training_generator=train_generator,
validation_generator=validation_generator,
steps_per_epoch=n_train_steps,
validation_steps=n_validation_steps,
initial_learning_rate=config["initial_learning_rate"],
learning_rate_drop=config["learning_rate_drop"],
learning_rate_patience=config["patience"],
early_stopping_patience=config["early_stop"],
n_epochs=config["n_epochs"],
logging_path=config["logging_path"])
data_file_opened.close()
def main(overwrite=True):
# convert input images into an hdf5 file
if overwrite or not os.path.exists(config["data_file"]):
training_files, subject_ids = fetch_training_data_files(
return_subject_ids=True)
write_data_to_file(training_files,
config["data_file"],
image_shape=config["image_shape"],
subject_ids=subject_ids)
def _save_new_h5_datafile(data_file_h5, new_image_shape):
training_files = _fetch_training_data_files('private')
# write all the data files into the hdf5 file
# if necessary crop the data to the new dimensions (if less than original)
# or add the 0 layer around it (if more than original)
write_data_to_file(training_files,
data_file_h5,
image_shape=new_image_shape)
def main():
# convert_brats_data(pre_config["data_original"], pre_config["data_preprocessed"], overwrite=False)
subject_ids, training_files = fetch_training_data_files(
pre_config["data_preprocessed"])
pickle_dump(subject_ids, pre_config["subject_ids_file"])
write_data_to_file(pre_config["npy_path"],
subject_ids,
training_files,
image_shape=pre_config["image_shape"])
def main(self, overwrite_data=True, overwrite_model=True):
# convert input images into an hdf5 file
if overwrite_data or not os.path.exists(self.config.source_data_file) or not os.path.exists(self.config.target_data_file):
'''
We write two files, one with source samples and one with target samples.
'''
source_data_files, target_data_files, subject_ids_source, subject_ids_target = self.fetch_training_data_files(return_subject_ids=True)
if not os.path.exists(self.config.source_data_file) or overwrite_data:
write_data_to_file(source_data_files, self.config.source_data_file, image_shape=self.config.image_shape,
subject_ids=subject_ids_source)
if not os.path.exists(self.config.target_data_file) or overwrite_data:
write_data_to_file(target_data_files, self.config.target_data_file, image_shape=self.config.image_shape,
subject_ids=subject_ids_target)
else:
print("Reusing previously written data file. Set overwrite_data to True to overwrite this file.")
source_data = open_data_file(self.config.source_data_file)
target_data = open_data_file(self.config.target_data_file)
# instantiate new model, compile = False because the compilation is made in JDOT.py
model, context_output_name = isensee2017_model(input_shape=self.config.input_shape, n_labels=self.config.n_labels,
initial_learning_rate=self.config.initial_learning_rate,
n_base_filters=self.config.n_base_filters,
loss_function=self.config.loss_function,
shortcut=self.config.shortcut,
depth=self.config.depth,
compile=False)
# get training and testing generators
if not self.config.depth_jdot:
context_output_name = []
jd = JDOT(model, config=self.config, source_data=source_data, target_data=target_data, context_output_name=context_output_name)
# m = jd.load_old_model(self.config.model_file)
# print(m)
if self.config.load_base_model:
print("Loading trained model")
jd.load_old_model(os.path.abspath("Data/saved_models/model_center_"+self.config.source_center)+".h5")
elif not self.config.overwrite_model:
jd.load_old_model(self.config.model_file)
else:
print("Creating new model, this will overwrite your old model")
jd.compile_model()
if self.config.train_jdot:
jd.train_model(self.config.epochs)
else:
jd.train_model_on_source(self.config.epochs)
jd.evaluate_model()
source_data.close()
target_data.close()
def main():
kwargs = vars(parse_args())
prediction_dir = os.path.abspath(kwargs.pop("prediction_dir"))
output_label_map = not kwargs.pop("no_label_map")
for key, value in kwargs.items():
if value:
if key == "modalities":
config["training_modalities"] = value
else:
config[key] = value
validate_path = kwargs["validate_path"]
subject_ids = list()
filenames = list()
blacklist = []
for root, dirs, files in os.walk(validate_path):
for f in files:
subject_id = f.split('.')[0]
if subject_id not in blacklist:
subject_ids.append(subject_id)
subject_files = list()
subject_files.append(validate_path + '/' + f)
filenames.append(tuple(subject_files))
if not os.path.exists(config["data_file"]):
write_data_to_file(filenames,
config["data_file"],
image_shape=config["image_shape"],
subject_ids=subject_ids,
save_truth=False)
pickle_dump(list(range(len(subject_ids))), config["validation_file"])
run_validation_cases(validation_keys_file=config["validation_file"],
model_file=config["model_file"],
training_modalities=config["training_modalities"],
labels=config["labels"],
hdf5_file=config["data_file"],
output_label_map=output_label_map,
output_dir=prediction_dir,
test=False,
output_basename=kwargs["output_basename"],
permute=config["permute"])
for filename_list, subject_id in zip(filenames, subject_ids):
prediction_filename = os.path.join(
prediction_dir,
kwargs["output_basename"].format(subject=subject_id))
print("Resampling:", prediction_filename)
ref = nib.load(filename_list[0])
pred = nib.load(prediction_filename)
pred_resampled = resample_to_img(pred, ref, interpolation="nearest")
pred_resampled.to_filename(prediction_filename)
def main(self, overwrite_data=True):
self.config.validation_split = 0.0
self.config.data_file = os.path.abspath("Data/generated_data/" +
self.config.data_set +
"_testing.h5")
self.config.training_file = os.path.abspath("Data/generated_data/" +
self.config.data_set +
"_testing.pkl")
self.config.validation_file = os.path.abspath(
"Data/generated_data/" + self.config.data_set +
"_testing_validation_ids.pkl")
# convert input images into an hdf5 file
if overwrite_data or not os.path.exists(self.config.data_file):
testing_files, subject_ids = self.fetch_testing_data_files(
return_subject_ids=True)
write_data_to_file(testing_files,
self.config.data_file,
image_shape=self.config.image_shape,
subject_ids=subject_ids)
data_file_opened = open_data_file(self.config.data_file)
testing_split, _ = get_validation_split(
data_file_opened,
data_split=0,
overwrite_data=self.config.overwrite_data,
training_file=self.config.training_file,
validation_file=self.config.validation_file)
# get training and testing generators
# train_generator, validation_generator, n_train_steps, n_validation_steps = get_training_and_validation_generators(
# data_file_opened,
# batch_size=self.config.batch_size,
# data_split=self.config.validation_split,
# overwrite_data=overwrite_data,
# validation_keys_file=self.config.validation_file,
# training_keys_file=self.config.training_file,
# n_labels=self.config.n_labels,
# labels=self.config.labels,
# patch_shape=self.config.patch_shape,
# validation_batch_size=self.config.validation_batch_size,
# validation_patch_overlap=self.config.validation_patch_overlap,
# training_patch_start_offset=self.config.training_patch_start_offset,
# permute=self.config.permute,
# augment=self.config.augment,
# skip_blank=self.config.skip_blank,
# augment_flip=self.config.flip,
# augment_distortion_factor=self.config.distort)
data_file_opened.close()
def fetch_training_data_files(self):
data_files = list()
for subject_dir in glob.glob(
os.path.join(os.path.dirname(__file__),
"../Data/data_" + self.config.data_set,
"training", "*")):
subject_center = subject_dir[
-9:
-7] # Retrieve for the MICCAI16 data-set the center of the patient
self.ids.append(os.path.basename(subject_dir))
subject_files = list()
for modality in self.config.training_modalities + [
"./" + self.config.GT
]: # Autre solution ? "/ManualSegmentation/ pour miccai16"
subject_files.append(
os.path.join(subject_dir, modality +
".nii.gz")) # + "/Preprocessed/ pour miccai16
data_files.append(tuple(subject_files))
write_data_to_file(data_files,
self.config.source_data_file,
image_shape=self.config.image_shape,
subject_ids=self.ids)
def prepare_data(args):
data_dir = get_h5_training_dir(BRATS_DIR, "data")
# make dir
if not os.path.exists(data_dir):
print_separator()
print("making dir", data_dir)
os.makedirs(data_dir)
print_section("convert input images into an hdf5 file")
data_filename = get_training_h5_filename(datatype="data", args=args)
print(data_filename)
data_file_path = os.path.join(data_dir, data_filename)
print("save to", data_file_path)
dataset = get_dataset(
is_test=args.is_test, is_bias_correction=args.is_bias_correction, is_denoise=args.is_denoise)
print("reading folder:", dataset)
if args.overwrite or not os.path.exists(data_file_path):
training_files = fetch_training_data_files(dataset)
write_data_to_file(training_files, data_file_path,
config=config,
image_shape=get_shape_from_string(args.image_shape),
brats_dir=BRATS_DIR,
crop=args.crop,
is_normalize=args.is_normalize,
is_hist_match=args.is_hist_match,
dataset=dataset,
is_denoise=args.is_denoise)
def main(overwrite=False):
# convert input images into an hdf5 file
print(overwrite or not os.path.exists(config["data_file"]))
print('path: ', os.path.exists(config["data_file"]))
if overwrite or not os.path.exists(config["data_file"]):
training_files = fetch_training_data_files()
# try:
write_data_to_file(
training_files,
config["data_file"],
image_shape=config["image_shape"]) #, normalize=False)
# except:
# import pdb; pdb.set_trace()
data_file_opened = open_data_file(config["data_file"])
if not overwrite and os.path.exists(config["model_file"]):
model = load_old_model(config["model_file"])
else:
# instantiate new model
model = unet_model_3d(
input_shape=config["input_shape"],
pool_size=config["pool_size"],
n_labels=config["n_labels"],
initial_learning_rate=config["initial_learning_rate"],
deconvolution=config["deconvolution"])
print(model.summary())
# get training and testing generators
train_generator, validation_generator, n_train_steps, n_validation_steps = get_training_and_validation_generators(
data_file_opened,
batch_size=config["batch_size"],
data_split=config["validation_split"],
overwrite=False, #overwrite, # set to False so that the training idcs
# are used as previously; as they are now used for the
# normalization already in write_data_to_file (above)
validation_keys_file=config["validation_file"],
training_keys_file=config["training_file"],
n_labels=config["n_labels"],
labels=config["labels"],
patch_shape=config["patch_shape"],
validation_batch_size=config["validation_batch_size"],
validation_patch_overlap=config["validation_patch_overlap"],
training_patch_start_offset=config["training_patch_start_offset"],
permute=config["permute"],
augment=config["augment"],
skip_blank=config["skip_blank"],
augment_flip=config["flip"],
augment_distortion_factor=config["distort"])
# normalize the dataset if required
# use only the training img (training_keys_file)
fetch_training_data_files()
# run training
train_model(model=model,
model_file=config["model_file"],
training_generator=train_generator,
validation_generator=validation_generator,
steps_per_epoch=n_train_steps,
validation_steps=n_validation_steps,
initial_learning_rate=config["initial_learning_rate"],
learning_rate_drop=config["learning_rate_drop"],
learning_rate_patience=config["patience"],
early_stopping_patience=config["early_stop"],
n_epochs=config["n_epochs"])
data_file_opened.close()
def main(overwrite=False):
# convert input images into an hdf5 file
if overwrite or not os.path.exists(config["data_file"]):
training_files, subject_ids = fetch_training_data_files(
return_subject_ids=True)
write_data_to_file(training_files,
config["data_file"],
image_shape=config["image_shape"],
subject_ids=subject_ids)
data_file_opened = open_data_file(config["data_file"])
if not overwrite and os.path.exists(config["model_file"]):
model = load_old_model(config["model_file"])
else:
# instantiate new model
model = isensee2017_model(
input_shape=config["input_shape"],
n_labels=config["n_labels"],
initial_learning_rate=config["initial_learning_rate"],
n_base_filters=config["n_base_filters"])
with open('isensemodel_original.txt', 'w') as fh:
# Pass the file handle in as a lambda function to make it callable
model.summary(line_length=150, print_fn=lambda x: fh.write(x + '\n'))
# Save Model
plot_model(model, to_file="isensemodel_original.png", show_shapes=True)
# get training and testing generators
train_generator, validation_generator, n_train_steps, n_validation_steps = get_training_and_validation_generators(
data_file_opened,
batch_size=config["batch_size"],
data_split=config["validation_split"],
overwrite=overwrite,
validation_keys_file=config["validation_file"],
training_keys_file=config["training_file"],
n_labels=config["n_labels"],
labels=config["labels"],
patch_shape=config["patch_shape"],
validation_batch_size=config["validation_batch_size"],
validation_patch_overlap=config["validation_patch_overlap"],
training_patch_start_offset=config["training_patch_start_offset"],
permute=config["permute"],
augment=config["augment"],
skip_blank=config["skip_blank"],
augment_flip=config["flip"],
augment_distortion_factor=config["distort"])
# run training
train_model(model=model,
model_file=config["model_file"],
training_generator=train_generator,
validation_generator=validation_generator,
steps_per_epoch=n_train_steps,
validation_steps=n_validation_steps,
initial_learning_rate=config["initial_learning_rate"],
learning_rate_drop=config["learning_rate_drop"],
learning_rate_patience=config["patience"],
early_stopping_patience=config["early_stop"],
n_epochs=config["n_epochs"])
data_file_opened.close()
def main(overwrite=False):
# convert input images into an hdf5 file
if overwrite or not os.path.exists(config["data_file"]):
training_files, subject_ids = fetch_training_data_files(return_subject_ids=True)
write_data_to_file(training_files, config["data_file"], image_shape=config["image_shape"],
subject_ids=subject_ids)
data_file_opened = open_data_file(config["data_file"])
if not overwrite and os.path.exists(config["model_file"]):
model = load_old_model(config["model_file"])
# new_model = isensee2017_model(input_shape=config["input_shape"], n_labels=config["n_labels"],
# initial_learning_rate=config["initial_learning_rate"],
# n_base_filters=config["n_base_filters"])
if config['freeze_encoder']:
last_index = list(layer.name for layer in model.layers) \
.index('up_sampling3d_1')
for layer in model.layers[:last_index]:
layer.trainable = False
from keras.optimizers import Adam
from unet3d.model.isensee2017 import weighted_dice_coefficient_loss
model.compile(optimizer=Adam(lr=config['initial_learning_rate']), loss=weighted_dice_coefficient_loss)
# for new_layer, layer in zip(new_model.layers[1:], old_model.layers[1:]):
# assert new_layer.name == layer.name
# new_layer.set_weights(layer.get_weights())
# model = new_model
else:
# instantiate new model
model = isensee2017_model(input_shape=config["input_shape"], n_labels=config["n_labels"],
initial_learning_rate=config["initial_learning_rate"],
n_base_filters=config["n_base_filters"])
model.summary()
# get training and testing generators
train_generator, validation_generator, n_train_steps, n_validation_steps = get_training_and_validation_generators(
data_file_opened,
batch_size=config["batch_size"],
data_split=config["validation_split"],
overwrite=overwrite,
validation_keys_file=config["validation_file"],
training_keys_file=config["training_file"],
n_labels=config["n_labels"],
labels=config["labels"],
patch_shape=config["patch_shape"],
validation_batch_size=config["validation_batch_size"],
validation_patch_overlap=config["validation_patch_overlap"],
training_patch_start_offset=config["training_patch_start_offset"],
augment=config["augment"],
skip_blank=config["skip_blank"])
# run training
train_model(model=model,
model_file=config["model_file"],
training_generator=train_generator,
validation_generator=validation_generator,
steps_per_epoch=n_train_steps,
validation_steps=n_validation_steps,
initial_learning_rate=config["initial_learning_rate"],
learning_rate_drop=config["learning_rate_drop"],
learning_rate_patience=config["patience"],
early_stopping_patience=config["early_stop"],
n_epochs=config["n_epochs"])
data_file_opened.close()
def main(config=None):
# convert input images into an hdf5 file
overwrite = config['overwrite']
if overwrite or not os.path.exists(config["data_file"]):
training_files, subject_ids = fetch_training_data_files(
return_subject_ids=True)
write_data_to_file(training_files,
config["data_file"],
image_shape=config["image_shape"],
subject_ids=subject_ids)
data_file_opened = open_data_file(config["data_file"])
if not overwrite and os.path.exists(config["model_file"]):
# if this happens, then the code wont care what is in "model_name" in config because it will take whatever
# the pre-trained was (either 3d_unet_residual or attention_unet) to continue training. need to be careful
# with this.
model = load_old_model(config, re_compile=False)
model.summary()
# visualize_filters_shape(model)
else:
# instantiate new model
if (config["model_name"] == "3d_unet_residual"):
"""3D Unet Residual Model"""
model = isensee2017_model(input_shape=config["input_shape"],
n_labels=config["n_labels"],
n_base_filters=config["n_base_filters"],
activation_name='softmax')
optimizer = getattr(
opts,
config["optimizer"]["name"])(**config["optimizer"].get('args'))
loss = getattr(module_metric, config["loss_fc"])
metrics = [getattr(module_metric, x) for x in config["metrics"]]
model.compile(optimizer=optimizer, loss=loss, metrics=metrics)
model.summary()
# visualize_filters_shape(model)
elif (config["model_name"] == "attention_unet"):
"""Attention Unet Model"""
model = attention_unet_model(
input_shape=config["input_shape"],
n_labels=config["n_labels"],
n_base_filters=config["n_base_filters"],
activation_name='softmax')
optimizer = getattr(
opts,
config["optimizer"]["name"])(**config["optimizer"].get('args'))
loss = getattr(module_metric, config["loss_fc"])
metrics = [getattr(module_metric, x) for x in config["metrics"]]
model.compile(optimizer=optimizer, loss=loss, metrics=metrics)
model.summary()
# visualize_filters_shape(model)
else:
"""Wrong entry for model_name"""
raise Exception(
'Look at field model_best in config.json! This field can be either 3d_unet_residual or attention_unet.'
)
# get training and testing generators
train_generator, validation_generator, n_train_steps, n_validation_steps = get_training_and_validation_generators(
data_file_opened,
batch_size=config["batch_size"],
data_split=config["validation_split"],
overwrite=overwrite,
validation_keys_file=config["validation_file"],
training_keys_file=config["training_file"],
n_labels=config["n_labels"],
labels=config["labels"],
validation_batch_size=config["validation_batch_size"],
permute=config["permute"],
augment=config["augment"],
skip_blank=config["skip_blank"],
augment_flip=config["flip"],
augment_distortion_factor=config["distort"])
# run training
train_model(model=model,
model_file=config["model_file"],
training_generator=train_generator,
validation_generator=validation_generator,
steps_per_epoch=n_train_steps,
validation_steps=n_validation_steps,
initial_learning_rate=config["optimizer"]["args"]["lr"],
learning_rate_drop=config["learning_rate_drop"],
learning_rate_patience=config["patience"],
early_stopping_patience=config["early_stop"],
n_epochs=config["n_epochs"],
model_best_path=config['model_best'])
data_file_opened.close()
def main(overwrite=False):
# convert input images into an hdf5 file
if overwrite or not os.path.exists(config["data_file"]):
training_files, subject_ids = fetch_training_data_files(
return_subject_ids=True)
write_data_to_file(training_files,
config["data_file"],
image_shape=config["image_shape"],
subject_ids=subject_ids)
data_file_opened = open_data_file(config["data_file"])
if not overwrite and os.path.exists(config["model_file"]):
base_model = load_old_model(config["model_file"])
model = get_multiGPUmodel(base_model=base_model,
n_labels=config["n_labels"],
GPU=config["GPU"])
else:
# instantiate new model
base_model, model = unet_model_3d_multiGPU(
input_shape=config["input_shape"],
pool_size=config["pool_size"],
n_labels=config["n_labels"],
initial_learning_rate=config["initial_learning_rate"],
deconvolution=config["deconvolution"],
GPU=config["GPU"])
# Save Model
plot_model(base_model,
to_file="liver_segmentation_model_581_resize_1GPU.png",
show_shapes=True)
# get training and testing generators
train_generator, validation_generator, n_train_steps, n_validation_steps = get_training_and_validation_generators(
data_file_opened,
batch_size=config["batch_size"] * config["GPU"],
data_split=config["validation_split"],
overwrite=overwrite,
validation_keys_file=config["validation_file"],
training_keys_file=config["training_file"],
n_labels=config["n_labels"],
labels=config["labels"],
patch_shape=config["patch_shape"],
validation_batch_size=config["validation_batch_size"] * config["GPU"],
validation_patch_overlap=config["validation_patch_overlap"],
training_patch_start_offset=config["training_patch_start_offset"],
permute=config["permute"],
augment=config["augment"],
skip_blank=config["skip_blank"],
augment_flip=config["flip"],
augment_distortion_factor=config["distort"])
print('INFO: Training Details', '\n Batch Size : ',
config["batch_size"] * config["GPU"], '\n Epoch Size : ',
config["n_epochs"])
# For debugging ONLY
# n_train_steps = 10
# run training
train_model(model=model,
model_file=config["model_file"],
training_generator=train_generator,
validation_generator=validation_generator,
steps_per_epoch=n_train_steps,
validation_steps=n_validation_steps,
initial_learning_rate=config["initial_learning_rate"],
learning_rate_drop=config["learning_rate_drop"],
learning_rate_patience=config["patience"],
early_stopping_patience=config["early_stop"],
n_epochs=config["n_epochs"],
base_model=base_model)
data_file_opened.close()
def main(overwrite=False):
# convert input images into an hdf5 file
if overwrite or not os.path.exists(config["data_file"]):
training_files, subject_ids = fetch_training_data_files(return_subject_ids=True)
write_data_to_file(training_files, config["data_file"], image_shape=config["image_shape"],
subject_ids=subject_ids)
data_file_opened = open_data_file(config["data_file"])
if not overwrite and os.path.exists(config["model_file"]):
base_model = load_old_model(config["model_file"])
model = get_multiGPUmodel(base_model=base_model,n_labels=config["n_labels"],GPU=config["GPU"])
else:
# instantiate new model HYbrid Dense-Unet model from HDense project
parser = argparse.ArgumentParser(description='Keras DenseUnet Training')
parser.add_argument('-b', type=int, default= 1 )#config["batch_size"])
parser.add_argument('-input_size', type=int, default= config["patch_shape"][0]) # 224 )
parser.add_argument('-input_cols', type=int, default= config["patch_shape"][2]) # 8)
args = parser.parse_args()
#print(args.b)
#model = dense_rnn_net(args)
base_model = denseunet_3d(args)
sgd = SGD(lr=1e-3, momentum=0.9, nesterov=True)
model = base_model
base_model.compile(optimizer=sgd, loss=[weighted_crossentropy])
# get training and testing generators
# Save Model
plot_model(base_model,to_file="liver_segmentation_HDenseUnet.png",show_shapes=True)
# Open the file
with open(config['model_summaryfile'],'w') as fh:
# Pass the file handle in as a lambda function to make it callable
base_model.summary(line_length=150,print_fn=lambda x: fh.write(x + '\n'))
train_generator, validation_generator, n_train_steps, n_validation_steps = get_training_and_validation_generators(
data_file_opened,
batch_size=config["batch_size"]*config["GPU"],
data_split=config["validation_split"],
overwrite=overwrite,
validation_keys_file=config["validation_file"],
training_keys_file=config["training_file"],
n_labels=config["n_labels"],
labels=config["labels"],
patch_shape=config["patch_shape"],
validation_batch_size=config["validation_batch_size"]*config["GPU"],
validation_patch_overlap=config["validation_patch_overlap"],
training_patch_start_offset=config["training_patch_start_offset"],
permute=config["permute"],
augment=config["augment"],
skip_blank=config["skip_blank"],
augment_flip=config["flip"],
augment_distortion_factor=config["distort"])
print('INFO: Training Details','\n Batch Size : ',config["batch_size"]*config["GPU"]
,'\n Epoch Size : ',config["n_epochs"])
# run training
train_model(model=model,
model_file=config["model_file"],
training_generator=train_generator,
validation_generator=validation_generator,
steps_per_epoch=n_train_steps,
validation_steps=n_validation_steps,
initial_learning_rate=config["initial_learning_rate"],
learning_rate_drop=config["learning_rate_drop"],
learning_rate_patience=config["patience"],
early_stopping_patience=config["early_stop"],
n_epochs=config["n_epochs"],base_model=base_model)
data_file_opened.close()
def main(overwrite=False):
# convert input images into an hdf5 file
pdb.set_trace()
if overwrite or not os.path.exists(config["data_file"]):
training_files, subject_ids = fetch_training_data_files(
return_subject_ids=True)
write_data_to_file(training_files,
config["data_file"],
image_shape=config["image_shape"],
modality_names=config['all_modalities'],
subject_ids=subject_ids,
mean_std_file=config['mean_std_file'])
# return
data_file_opened = open_data_file(config["data_file"])
if not overwrite and os.path.exists(config["model_file"]):
model = load_old_model(config["model_file"])
else:
# instantiate new model
model = isensee2017_model(
input_shape=config["input_shape"],
n_labels=config["n_labels"],
initial_learning_rate=config["initial_learning_rate"],
n_base_filters=config["n_base_filters"])
# get training and testing generators
# pdb.set_trace()
train_generator, validation_generator, n_train_steps, n_validation_steps = get_training_and_validation_generators(
data_file_opened,
batch_size=config["batch_size"],
data_split=config["validation_split"],
overwrite=overwrite,
validation_keys_file=config["validation_file"],
training_keys_file=config["training_file"],
n_labels=config["n_labels"],
labels=config["labels"],
patch_shape=config["patch_shape"],
validation_batch_size=config["validation_batch_size"],
validation_patch_overlap=config["validation_patch_overlap"],
training_patch_start_offset=config["training_patch_start_offset"],
permute=config["permute"],
augment=config["augment"],
skip_blank=config["skip_blank"],
augment_flip=config["flip"],
augment_distortion_factor=config["distort"],
pred_specific=config['pred_specific'],
overlap_label=config['overlap_label_generator'],
for_final_val=config['for_final_val'])
# run training
# pdb.set_trace()
time_0 = time.time()
train_model(model=model,
model_file=config["model_file"],
training_generator=train_generator,
validation_generator=validation_generator,
steps_per_epoch=n_train_steps,
validation_steps=n_validation_steps,
initial_learning_rate=config["initial_learning_rate"],
learning_rate_drop=config["learning_rate_drop"],
learning_rate_patience=config["patience"],
early_stopping_patience=config["early_stop"],
n_epochs=config["n_epochs"],
logging_file=config['logging_file'])
print('Training time:', sec2hms(time.time() - time_0))
data_file_opened.close()
def main(self, overwrite_data=True, overwrite_model=True):
# convert input images into an hdf5 file
if overwrite_data or not os.path.exists(self.config.data_file):
training_files, subject_ids = self.fetch_training_data_files(
return_subject_ids=True)
write_data_to_file(training_files,
self.config.data_file,
image_shape=self.config.image_shape,
subject_ids=subject_ids)
else:
print(
"Reusing previously written data file. Set overwrite_data to True to overwrite this file."
)
data_file_opened = open_data_file(self.config.data_file)
if not overwrite_model and os.path.exists(self.config.model_file):
model = load_old_model(self.config.model_file)
else:
# instantiate new model
model, context_output_name = isensee2017_model(
input_shape=self.config.input_shape,
n_labels=self.config.n_labels,
initial_learning_rate=self.config.initial_learning_rate,
n_base_filters=self.config.n_base_filters,
loss_function=self.config.loss_function,
shortcut=self.config.shortcut)
# get training and testing generators
train_generator, validation_generator, n_train_steps, n_validation_steps = get_training_and_validation_generators(
data_file_opened,
batch_size=self.config.batch_size,
data_split=self.config.validation_split,
overwrite_data=overwrite_data,
validation_keys_file=self.config.validation_file,
training_keys_file=self.config.training_file,
n_labels=self.config.n_labels,
labels=self.config.labels,
patch_shape=self.config.patch_shape,
validation_batch_size=self.config.validation_batch_size,
validation_patch_overlap=self.config.validation_patch_overlap,
training_patch_overlap=self.config.training_patch_overlap,
training_patch_start_offset=self.config.
training_patch_start_offset,
permute=self.config.permute,
augment=self.config.augment,
skip_blank=self.config.skip_blank,
augment_flip=self.config.flip,
augment_distortion_factor=self.config.distort)
# run training
train_model(model=model,
model_file=self.config.model_file,
training_generator=train_generator,
validation_generator=validation_generator,
steps_per_epoch=n_train_steps,
validation_steps=n_validation_steps,
initial_learning_rate=self.config.initial_learning_rate,
learning_rate_drop=self.config.learning_rate_drop,
learning_rate_patience=self.config.patience,
early_stopping_patience=self.config.early_stop,
n_epochs=self.config.epochs,
niseko=self.config.niseko)
data_file_opened.close()
def main(config=None):
# convert input images into an hdf5 file
overwrite = config['overwrite']
if overwrite or not os.path.exists(config["data_file"]):
training_files, subject_ids = fetch_training_data_files(
return_subject_ids=True)
write_data_to_file(training_files,
config["data_file"],
image_shape=config["image_shape"],
subject_ids=subject_ids,
norm_type=config['normalization_type'])
data_file_opened = open_data_file(config["data_file"])
if not overwrite and os.path.exists(config["model_file"]):
model = load_old_model(config, re_compile=False)
else:
# instantiate new model
model = isensee2017_model(input_shape=config["input_shape"],
n_labels=config["n_labels"],
n_base_filters=config["n_base_filters"],
activation_name='softmax')
optimizer = getattr(
opts,
config["optimizer"]["name"])(**config["optimizer"].get('args'))
loss = getattr(module_metric, config["loss_fc"])
metrics = [getattr(module_metric, x) for x in config["metrics"]]
model.compile(optimizer=optimizer, loss=loss, metrics=metrics)
# get training and testing generators
train_generator, validation_generator, n_train_steps, n_validation_steps = get_training_and_validation_generators(
data_file_opened,
batch_size=config["batch_size"],
data_split=config["validation_split"],
overwrite=overwrite,
validation_keys_file=config["validation_file"],
training_keys_file=config["training_file"],
n_labels=config["n_labels"],
labels=config["labels"],
validation_batch_size=config["validation_batch_size"],
permute=config["permute"],
augment=config["augment"],
skip_blank=config["skip_blank"],
augment_flip=config["flip"],
augment_distortion_factor=config["distort"])
# run training
train_model(model=model,
model_file=config["model_file"],
training_generator=train_generator,
validation_generator=validation_generator,
steps_per_epoch=n_train_steps,
validation_steps=n_validation_steps,
initial_learning_rate=config["optimizer"]["args"]["lr"],
learning_rate_drop=config["learning_rate_drop"],
learning_rate_patience=config["patience"],
early_stopping_patience=config["early_stop"],
n_epochs=config["n_epochs"],
model_best_path=config['model_best'])
data_file_opened.close()
def main(overwrite=False):
# convert input images into an hdf5 file
if overwrite or not (os.path.exists(config["data_file0"])
and os.path.exists(config["data_file1"])):
training_files, subject_ids = fetch_training_data_files(
return_subject_ids=True)
training_files0, training_files1 = training_files
subject_ids0, subject_ids1 = subject_ids
if not os.path.exists(config["data_file0"]):
write_data_to_file(training_files0,
config["data_file0"],
image_shape=config["image_shape"],
subject_ids=subject_ids0)
if not os.path.exists(config["data_file1"]):
write_data_to_file(training_files1,
config["data_file1"],
image_shape=config["image_shape"],
subject_ids=subject_ids1)
data_file_opened0 = open_data_file(config["data_file0"])
data_file_opened1 = open_data_file(config["data_file1"])
if not overwrite and os.path.exists(config["model_file"]):
model = load_old_model(config["model_file"])
else:
# instantiate new model
model = siam3dunet_model(
input_shape=config["input_shape"],
n_labels=config["n_labels"],
initial_learning_rate=config["initial_learning_rate"],
n_base_filters=config["n_base_filters"])
#model = testnet_model(input_shape=config["input_shape"], n_labels=config["n_labels"], initial_learning_rate=config["initial_learning_rate"], n_base_filters=config["n_base_filters"])
#if os.path.exists(config["model_file"]):
# model = load_weights(config["model_file"])
# get training and testing generators
train_generator, validation_generator, n_train_steps, n_validation_steps = get_training_and_validation_generators(
data_file_opened0,
data_file_opened1,
batch_size=config["batch_size"],
data_split=config["validation_split"],
overwrite=overwrite,
validation_keys_file=config["validation_file"],
training_keys_file=config["training_file"],
n_labels=config["n_labels"],
labels=config["labels"],
patch_shape=config["patch_shape"],
validation_batch_size=config["validation_batch_size"],
validation_patch_overlap=config["validation_patch_overlap"],
training_patch_start_offset=config["training_patch_start_offset"],
permute=config["permute"],
augment=config["augment"],
skip_blank=config["skip_blank"],
augment_flip=config["flip"],
augment_distortion_factor=config["distort"])
'''
train_data = []
train_label = []
for i in range(n_train_steps):
a, b = next(train_generator)
train_data.append(a)
train_label.append(b)
a0, a1 = a
for i in range(len(a0[0,0,0,0,:])):
a0_0 = a0[0,2,:,:,i]
if a0_0.min() == a0_0.max():
a0_0 = a0_0 - a0_0
else:
a0_0 = (a0_0-a0_0.min())/(a0_0.max()-a0_0.min())
#print (a0_0.shape)
#print (a0_0.max())
#print (a0_0.min())
imsave(f'vis_img/{i}.jpg', a0_0)
raise
'''
test_data, test_label = next(validation_generator)
test_g = (test_data, test_label)
train_data, train_label = next(train_generator)
train_g = (train_data, train_label)
if not overwrite and os.path.exists(config["model_file"]):
txt_file = open(f"output_log.txt", "w")
#res = model.evaluate(test_data, test_label)
#print (res)
pre = model.predict(test_data)
#print ([i for i in pre[0]])
#print ([int(i) for i in test_label[0]])
for i in range(len(pre[0])):
txt_file.write(
str(pre[0][i][0]) + ' ' + str(test_label[0][i]) + "\n")
pre_train = model.predict(train_data)
for i in range(len(pre_train[0])):
txt_file.write(
str(pre_train[0][i][0]) + ' ' + str(train_label[0][i]) + "\n")
txt_file.close()
raise
# run training
train_model(model=model,
model_file=config["model_file"],
training_generator=train_generator,
validation_generator=test_g,
steps_per_epoch=n_train_steps,
validation_steps=n_validation_steps,
initial_learning_rate=config["initial_learning_rate"],
learning_rate_drop=config["learning_rate_drop"],
learning_rate_patience=config["patience"],
early_stopping_patience=config["early_stop"],
n_epochs=config["n_epochs"])
'''
for i in range(len(train_label)):
#scores = model.evaluate(train_data[i], train_label[i], verbose=1)
scores = model.predict(train_data[i])
print (len(scores[0]))
'''
data_file_opened0.close()
data_file_opened1.close()
