Datasets:
jblitzar
/
github-python-corpus

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pred_test.extend(np.array(net(X).cpu().argmax(axis=1)))
toc2 = time.time()
collections.Counter(pred_test)
gt_test = gt[test_indices] - 1
overall_acc = metrics.accuracy_score(pred_test, gt_test[:-VAL_SIZE])
confusion_matrix = metrics.confusion_matrix(pred_test, gt_test[:-VAL_SIZE])
each_acc, average_acc = record.aa_and_each_accuracy(confusion_matrix)
kappa = metrics.cohen_kappa_score(pred_test, gt_test[:-VAL_SIZE])
torch.save(net.state_dict(),
"./models/" + 'HybridSN' + str(round(overall_acc, 3)) + '.pt')
KAPPA.append(kappa)
OA.append(overall_acc)
AA.append(average_acc)
TRAINING_TIME.append(toc1 - tic1)
TESTING_TIME.append(toc2 - tic2)
ELEMENT_ACC[index_iter, :] = each_acc
# # Map, Records
print("--------" + " Training Finished-----------")
record.record_output(
OA, AA, KAPPA, ELEMENT_ACC, TRAINING_TIME, TESTING_TIME,
'./report/' + 'HybridSNpatch:' + str(img_rows) + '_' + Dataset + 'split' +
str(VALIDATION_SPLIT) + 'lr' + str(lr) + PARAM_OPTIM + '.txt')
Utils.generate_png(
all_iter, net, gt_hsi, Dataset, device, total_indices,
'./classification_maps/' + 'HybridSNpatch:' + str(img_rows) + '_' + Dataset +
'split' + str(VALIDATION_SPLIT) + 'lr' + str(lr) + PARAM_OPTIM)
# <FILESEP>
"""ReBias
Copyright (c) 2020-present NAVER Corp.
MIT license
Entry point of 9-Class ImageNet experiments.
This script provides full implementations including
- Various methods (ReBias, Vanilla, Biased, LearnedMixIn, RUBi)
- Target network: ResNet-18
- Biased network: BagNet-18
- We do not provide Stylised ImageNet implementation here. See README.md for details.
- Sub-sampled 9-Class ImageNet / ImageNet-A from the full ImageNet / ImageNet-A folder.
- Please see datasets/imagenet.py for details.
- Cluster-based unbiased accuracies.
- For curious readers, `make_clusters.py` shows how to make texture clusters.
Usage:
python main_imagenet.py --train_root /path/to/your/imagenet/train
--val_root /path/to/your/imagenet/val
--imageneta_root /path/to/your/imagenet_a
"""
import fire
from datasets.imagenet import get_imagenet_dataloader
from evaluator import ImageNetEvaluator
from logger import PythonLogger
from trainer import Trainer
from models import resnet18, bagnet18, ReBiasModels
class ImageNetTrainer(Trainer):
def _set_models(self):
f_net = resnet18(**self.options.f_config)
g_nets = [bagnet18(**self.options.g_config)
for _ in range(self.options.n_g_nets)]
self.model = ReBiasModels(f_net, g_nets)
self.evaluator = ImageNetEvaluator(device=self.device)
def main(train_root,
val_root,
imageneta_root,
batch_size=128,
num_classes=9,
# optimizer config
lr=0.001,
optim='Adam',
n_epochs=120,
lr_step_size=30,
scheduler='CosineAnnealingLR',
n_f_pretrain_epochs=0,
n_g_pretrain_epochs=0,
f_lambda_outer=1,
g_lambda_inner=1,
n_g_update=1,
update_g_cls=True,
# criterion config
outer_criterion='RbfHSIC',
inner_criterion='MinusRbfHSIC',
rbf_sigma_scale_x=1,
rbf_sigma_scale_y=1,
rbf_sigma_x='median',
rbf_sigma_y='median',
update_sigma_per_epoch=True,
hsic_alg='unbiased',
feature_pos='post',
# model configs