def main():
file_name = os.path.basename(__file__).split('.')[0]
name = str(file_name)
experiment_logger = utils.ExperimentLogger(name)
for i, (samples_train,
samples_val) in enumerate(utils.mask_stratified_k_fold()):
print("Running split {}".format(i))
model = Model(name, i)
stats = model.fit(samples_train, samples_val)
experiment_logger.set_split(i, stats)
# Load the best performing checkpoint
model.load()
# Do a final validation
model.validate(DataParallel(model.net), samples_val, -1)
# Predict the test data
test_predictions = utils.TestPredictions(name + '-split_{}'.format(i),
mode='test')
test_predictions.add_predictions(model.test(utils.get_test_samples()))
test_predictions.save()
experiment_logger.save()
)
parser.add_argument(
'name',
help='Use one of the experiment names here excluding the .py ending.')
parser.add_argument('test_set', help='Specify the path to the new test_set')
parser.add_argument(
'output_dir',
help='Specify the path to the output dir for the test-predictions.')
args = parser.parse_args()
name = args.name
test_set = args.test_set
output_dir = args.output_dir
experiment_logger = utils.ExperimentLogger(name, mode='val')
for i, (samples_train,
samples_val) in enumerate(utils.mask_stratified_k_fold()):
# Get the model architecture
Model = locate('experiments.' + name + '.Model')
model = Model(name, i)
# Load the best performing checkpoint
model.load()
# Predict the test data
test_predictions = utils.TestPredictions(name + '-split_{}'.format(i),
mode=output_dir)
test_predictions.add_predictions(
model.test(utils.get_test_samples(test_set)))
test_predictions.save()
