def test_train_classification_rad_3d():
application_data = '3d_rad_segmentation'
# read and initialize parameters for specific data dimension
parameters = parseConfig(testingDir + '/config_classification.yaml',
version_check=False)
parameters['patch_size'] = patch_size['3D']
parameters['psize'] = patch_size['3D']
parameters['model']['dimension'] = 3
parameters['model']['class_list'] = [0, 1]
# read and parse csv
training_data, headers = parseTrainingCSV(
inputDir + '/train_3d_rad_classification.csv')
# loop through selected models and train for single epoch
for model in all_models_regression:
parameters['model']['architecture'] = model
shutil.rmtree(outputDir) # overwrite previous results
Path(outputDir).mkdir(parents=True, exist_ok=True)
TrainingManager(dataframe=training_data,
headers=headers,
outputDir=outputDir,
parameters=parameters,
device='cpu',
reset_prev=True)
print('passed')
def test_train_segmentation_rad_3d():
print('Starting 3D Rad segmentation tests')
application_data = '3d_rad_segmentation'
parameters = parseConfig(inputDir + '/' + application_data + '/sample_training.yaml', version_check = False)
parameters['modality'] = 'rad'
training_data, headers = parseTrainingCSV(inputDir + '/train_' + application_data + '.csv')
for model in all_models_segmentation:
parameters['model']['architecture'] = model
currentOutputDir = os.path.join(outputDir, application_data + '_' + model)
Path(currentOutputDir).mkdir(parents=True, exist_ok=True)
TrainingManager(dataframe=training_data, headers = headers, outputDir=currentOutputDir, parameters=parameters, device='cpu')
print('passed')
def test_classification_rad_3d():
application_data = '3d_rad_segmentation'
parameters = parseConfig(inputDir + '/' + application_data +
'/sample_training_classification.yaml',
version_check=False)
training_data, headers = parseTrainingCSV(
inputDir + '/train_3d_rad_classification.csv')
for model in all_models_regression:
parameters['model']['architecture'] = model
shutil.rmtree(outputDir) # overwrite previous results
Path(outputDir).mkdir(parents=True, exist_ok=True)
TrainingManager(dataframe=training_data,
headers=headers,
outputDir=outputDir,
parameters=parameters,
device='cpu',
reset_prev=True)
print('passed')
def test_train_segmentation_rad_2d():
print('Starting 2D Rad segmentation tests')
application_data = '2d_rad_segmentation'
parameters = parseConfig(inputDir + '/' + application_data +
'/sample_training.yaml',
version_check=False)
parameters['modality'] = 'rad'
training_data, headers = parseTrainingCSV(inputDir + '/train_' +
application_data + '.csv')
for model in all_models_segmentation:
parameters['model']['architecture'] = model
shutil.rmtree(outputDir) # overwrite previous results
Path(outputDir).mkdir(parents=True, exist_ok=True)
TrainingManager(dataframe=training_data,
headers=headers,
outputDir=outputDir,
parameters=parameters,
device='cpu',
reset_prev=True)
print('passed')
def main_run(data_csv, config_file, output_dir, train_mode, device, resume, reset):
"""
Main function that runs the training and inference.
Args:
data_csv (str): The CSV file of the training data.
config_file (str): The YAML file of the training configuration.
output_dir (str): The output directory.
train_mode (bool): Whether to train or infer.
device (str): The device type.
resume (bool): Whether the previous run will be resumed or not.
reset (bool): Whether the previous run will be reset or not.
Raises:
ValueError: Parameter check from previous run.
"""
file_data_full = data_csv
model_parameters = config_file
device = device
parameters = parseConfig(model_parameters)
# in case the data being passed is already processed, check if the previous parameters exists,
# and if it does, compare the two and if they are the same, ensure no preprocess is done.
model_parameters_prev = os.path.join(os.path.dirname(output_dir), "parameters.pkl")
if train_mode:
if not (reset) or not (resume):
print(
"Trying to resume training without changing any parameters from previous run.",
flush=True,
)
if os.path.exists(model_parameters_prev):
parameters_prev = pickle.load(open(model_parameters_prev, "rb"))
if parameters != parameters_prev:
raise ValueError(
"The parameters are not the same as the ones stored in the previous run, please re-check."
)
parameters["output_dir"] = output_dir
if "-1" in device:
device = "cpu"
if train_mode: # train mode
Path(parameters["output_dir"]).mkdir(parents=True, exist_ok=True)
# parse training CSV
if "," in file_data_full:
# training and validation pre-split
data_full = None
both_csvs = file_data_full.split(",")
data_train, headers_train = parseTrainingCSV(both_csvs[0], train=train_mode)
data_validation, headers_validation = parseTrainingCSV(
both_csvs[1], train=train_mode
)
if headers_train != headers_validation:
sys.exit(
"The training and validation CSVs do not have the same header information."
)
parameters = populate_header_in_parameters(parameters, headers_train)
# if we are here, it is assumed that the user wants to do training
if train_mode:
TrainingManager_split(
dataframe_train=data_train,
dataframe_validation=data_validation,
outputDir=parameters["output_dir"],
parameters=parameters,
device=device,
resume=resume,
reset=reset,
)
else:
data_full, headers = parseTrainingCSV(file_data_full, train=train_mode)
parameters = populate_header_in_parameters(parameters, headers)
if train_mode:
TrainingManager(
dataframe=data_full,
outputDir=parameters["output_dir"],
parameters=parameters,
device=device,
resume=resume,
reset=reset,
)
else:
InferenceManager(
dataframe=data_full,
outputDir=parameters["output_dir"],
parameters=parameters,
device=device,
)