def test_object_detection(train_lst, target_lst, config):
# Load config file
context = imed_config_manager.ConfigurationManager("testing_data/model_config.json").get_config()
context.update(config)
command = "ivadomed_download_data -d findcord_tumor"
subprocess.check_output(command, shell=True)
imed.run_command(context)
def test_object_detection(train_lst, target_lst, config):
# Load config file
with open("testing_data/model_config.json", 'r') as fp:
context = json.load(fp)
context.update(config)
command = "ivadomed_download_data -d findcord_tumor"
subprocess.check_output(command, shell=True)
imed.run_command(context)
def train_worker(config, thr_incr):
current = mp.current_process()
# ID of process used to assign a GPU
ID = int(current.name[-1]) - 1
# Use GPU i from the array specified in the config file
config["gpu"] = config["gpu"][ID]
# Call ivado cmd_train
try:
# Save best validation score
best_training_dice, best_training_loss, best_validation_dice, best_validation_loss = \
ivado.run_command(config, thr_increment=thr_incr)
except:
logging.exception('Got exception on main handler')
print("Unexpected error:", sys.exc_info()[0])
raise
# Save config file in log directory
config_copy = open(config["log_directory"] + "/config_file.json", "w")
json.dump(config, config_copy, indent=4)
return config[
"log_directory"], best_training_dice, best_training_loss, best_validation_dice, best_validation_loss
def train_worker(config, thr_incr):
"""
Args:
config (dict): dictionary containing configuration details.
thr_incr (float): A threshold analysis is performed at the end of the training
using the trained model and the validation sub-dataset to find the optimal binarization
threshold. The specified value indicates the increment between 0 and 1 used during the
ROC analysis (e.g. 0.1). Flag: ``-t``, ``--thr-increment``
"""
current = mp.current_process()
# ID of process used to assign a GPU
ID = int(current.name[-1]) - 1
# Use GPU i from the array specified in the config file
config["gpu_ids"] = [config["gpu_ids"][ID]]
# Call ivado cmd_train
try:
# Save best validation score
config["command"] = "train"
best_training_dice, best_training_loss, best_validation_dice, best_validation_loss = \
ivado.run_command(config, thr_increment=thr_incr)
except Exception:
logging.exception('Got exception on main handler')
logging.info("Unexpected error:", sys.exc_info()[0])
raise
# Save config file in output path
config_copy = open(config["path_output"] + "/config_file.json", "w")
json.dump(config, config_copy, indent=4)
return config["path_output"], best_training_dice, best_training_loss, best_validation_dice, \
best_validation_loss
def get_results(context):
context["command"] = "test"
pred_mask_path = os.path.join(context["path_output"], "pred_masks")
if os.path.exists(pred_mask_path):
shutil.rmtree(pred_mask_path)
# RandomAffine will be applied during testing
if "dataset_type" in context["transformation"]["RandomAffine"]:
del context["transformation"]["RandomAffine"]["dataset_type"]
if "scale" in context["transformation"]["RandomAffine"]:
del context["transformation"]["RandomAffine"]["scale"]
return ivado.run_command(context)
def test_worker(config):
# Call ivado cmd_eval
current = mp.current_process()
# ID of process used to assign a GPU
ID = int(current.name[-1]) - 1
# Use GPU i from the array specified in the config file
config["gpu"] = config["gpu"][ID]
try:
# Save best test score
config["command"] = "test"
df_results, test_dice = ivado.run_command(config)
except:
logging.exception('Got exception on main handler')
print("Unexpected error:", sys.exc_info()[0])
raise
return config["log_directory"], test_dice, df_results
def test_worker(config):
# Call ivado cmd_eval
current = mp.current_process()
# ID of process used to assign a GPU
ID = int(current.name[-1]) - 1
# Use GPU i from the array specified in the config file
config[ConfigKW.GPU_IDS] = [config[ConfigKW.GPU_IDS][ID]]
try:
# Save best test score
config[ConfigKW.COMMAND] = "test"
df_results, test_dice = ivado.run_command(config)
except Exception:
logger.exception('Got exception on main handler')
logger.info("Unexpected error:", sys.exc_info()[0])
raise
return config[ConfigKW.PATH_OUTPUT], test_dice, df_results
