def get_validation_split(data_file,
training_file,
validation_file,
data_split=0.8,
change_validation=False,
split_list=()):
"""
Splits the data into the training and validation indices list.
:param data_file: pytables hdf5 data file
:param training_file:
:param validation_file:
:param data_split:
:param overwrite:
:return:
"""
if change_validation or not os.path.exists(training_file):
nb_samples = data_file.root.data.shape[0]
sample_list = list(range(nb_samples))
training_list, validation_list = split_a_list(sample_list,
split=data_split)
print("Training list: ", training_list)
print("Validation list: ", validation_list)
training_list = split_list[0]
validation_list = split_list[1]
pickle_dump(training_list, training_file)
pickle_dump(validation_list, validation_file)
return training_list, validation_list
else:
return pickle_load(training_file), pickle_load(validation_file)
def run_validation_cases(self, validation_keys_file, training_modalities, labels, hdf5_file,
output_label_map=False, output_dir=".", threshold=0.5, overlap=16, permute=False,
save_image = False):
'''
For each patient of the testing set we run a validation.
:param validation_keys_file:
:param training_modalities:
:param labels:
:param hdf5_file:
:param output_label_map:
:param output_dir:
:param threshold:
:param overlap:
:param permute:
:param save_image:
:return:
'''
validation_indices = pickle_load(validation_keys_file)
model = self.model
data_file = tables.open_file(hdf5_file, "r")
for i, index in enumerate(validation_indices):
actual = round(i/len(validation_indices)*100, 2)
print("Running validation case: ", actual,"%")
if 'subject_ids' in data_file.root:
case_directory = os.path.join(output_dir, data_file.root.subject_ids[index].decode('utf-8'))
else:
case_directory = os.path.join(output_dir, "validation_case_{}".format(index))
run_validation_case(data_index=index, output_dir=case_directory, model=model, data_file=data_file,
training_modalities=training_modalities, output_label_map=output_label_map, labels=labels,
threshold=threshold, overlap=overlap, permute=permute, save_image=save_image)
data_file.close()
def run_validation_cases(validation_keys_file,
model_file,
training_modalities,
labels,
hdf5_file,
output_label_map=False,
output_dir=".",
threshold=0.5,
overlap=16,
permute=False):
validation_indices = pickle_load(validation_keys_file)
model = load_old_model(model_file)
data_file = tables.open_file(hdf5_file, "r")
for i, index in enumerate(validation_indices):
actual = round(i / len(validation_indices) * 100, 2)
print("Running validation case: ", actual, "%")
if 'subject_ids' in data_file.root:
case_directory = os.path.join(
output_dir, data_file.root.subject_ids[index].decode('utf-8'))
else:
case_directory = os.path.join(output_dir,
"validation_case_{}".format(index))
run_validation_case(data_index=index,
output_dir=case_directory,
model=model,
data_file=data_file,
training_modalities=training_modalities,
output_label_map=output_label_map,
labels=labels,
threshold=threshold,
overlap=overlap,
permute=permute)
data_file.close()
def load_index_patches_with_ceil(file_name):
'''
Load the array indices with lesions (if it had already been computed)
:param file_name:
:return:
'''
return pickle_load(file_name)
def is_all_cases_predicted(prediction_folder, testing_file):
data_file = pickle_load(config["testing_file"])
num_cases = len(data_file)
if not os.path.exists(prediction_folder):
return False
else:
num_predicted = len(
glob.glob(os.path.join(config["prediction_folder"], "*")))
return num_cases == num_predicted
def main():
prediction_dir = os.path.abspath("prediction")
validation_indices = pickle_load(config["validation_file"])
for i in range(len(validation_indices)):
run_validation_case(test_index=i,
out_dir=os.path.join(
prediction_dir,
"validation_case_{}".format(i)),
model_file=config["model_file"],
validation_keys_file=config["validation_file"],
training_modalities=config["training_modalities"],
output_label_map=True,
labels=config["labels"],
hdf5_file=config["data_file"])
def main(config=None):
model = load_old_model(config, re_compile=False)
data_file_opened = open_data_file(config["data_file"])
validation_idxs = pickle_load(config['validation_file'])
validation_generator = data_generator(data_file_opened, validation_idxs,
batch_size=config['validation_batch_size'],
n_labels=config['n_labels'], labels=config['labels'],
skip_blank=config['skip_blank'], shuffle_index_list=False)
steps = math.ceil(len(validation_idxs) / config['validation_batch_size'])
results = model.evaluate(validation_generator, steps=steps, verbose=1)
metrics_names = model.metrics_names
for i, x in enumerate(metrics_names):
print('{}: {}'.format(x, results[i]))
data_file_opened.close()
def main():
prediction_dir = os.path.abspath("../data/prediction_isensee2017")
if not os.path.exists(prediction_dir):
subprocess.call('mkdir' + '-p' + prediction_dir, shell=True)
validation_indices = pickle_load(config["validation_file"])
for i in range(len(validation_indices)):
run_validation_case(test_index=i,
out_dir=os.path.join(
prediction_dir,
"validation_case_{}".format(i)),
model_file=config["model_file"],
validation_keys_file=config["validation_file"],
training_modalities=config["training_modalities"],
output_label_map=True,
labels=config["labels"],
hdf5_file=config["data_file"])
def get_test_indices(testing_file):
return pickle_load(testing_file)