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()
'nopoolrefinenet_dpn92_dual_hypercolumn_poly_lr_aux_data_pseudo_labels',
]
ensemble_name = 'ensemble'
for i in range(5):
print('Processing fold {}'.format(i))
test_predictions_experiment = []
for name in experiments:
test_predictions_split = []
test_predictions = utils.TestPredictions('{}-split_{}'.format(name, i))
test_predictions_split.append(test_predictions.load_raw())
test_predictions_experiment.append(test_predictions_split)
test_samples = utils.get_test_samples()
predictions_mean = []
for id in tqdm(test_samples, ascii=True):
# p = n_models x h x w
p = []
for test_predictions_split in test_predictions_experiment:
test_predictions_split = np.stack([predictions[id] for predictions in test_predictions_split], axis=0)
p.append(test_predictions_split)
p = np.concatenate(p, axis=0)
prediction_ensemble = np.mean(p, axis=0)
predictions_mean.append((prediction_ensemble, id))
# Save ensembled predictions (for example for pseudo-labeling)
from nets.refinenet import RefineNetUpsampleClassifier, SCSERefineNetBlock
from nets.refinenet_hypercolumn import DualHypercolumnCatRefineNet
from nets.backbones import SCSENoPoolResNextBase
from nets.encoders.senet import se_resnext101_32x4d
from metrics import iou, mAP
from optim import NDAdam
import datasets
import utils
import meters
import losses
import tta
import settings
cpu = torch.device('cpu')
gpu = torch.device('cuda')
samples_test = utils.get_test_samples()
class Model:
def __init__(self, name, split):
self.name = name
self.split = split
self.path = os.path.join(settings.checkpoints,
name + '-split_{}'.format(split))
self.net = DualHypercolumnCatRefineNet(
SCSENoPoolResNextBase(se_resnext101_32x4d()),
num_features=128,
classifier=lambda c: RefineNetUpsampleClassifier(2 * c,
scale_factor=2),
block=SCSERefineNetBlock)
self.tta = [
)
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()
