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Update AL_center.py #6

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897286e
Update AL_center.py
im521212 Oct 2, 2024
d1aded6
Update datasets.py
im521212 Oct 7, 2024
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6 changes: 3 additions & 3 deletions AL_center.py
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Original file line number Diff line number Diff line change
Expand Up @@ -29,7 +29,7 @@

parser = argparse.ArgumentParser("Center Loss Example")
# dataset
parser.add_argument('-d', '--dataset', type=str, default='cifar10', choices=['mnist', 'cifar100', 'cifar10'])
parser.add_argument('-d', '--dataset', type=str, default='combined_wafer_data.npz', choices=['combined_wafer_data.npz'])
parser.add_argument('-j', '--workers', default=0, type=int,
help="number of data loading workers (default: 4)")
# optimization
Expand Down Expand Up @@ -281,15 +281,15 @@ def plot_features(features, labels, num_classes, epoch, prefix):
features: (num_instances, num_features).
labels: (num_instances).
"""
colors = ['C0', 'C1', 'C2', 'C3', 'C4', 'C5', 'C6', 'C7', 'C8', 'C9']
colors = ['C0', 'C1', 'C2', 'C3', 'C4', 'C5', 'C6', 'C7', 'C8']
for label_idx in range(num_classes):
plt.scatter(
features[labels==label_idx, 0],
features[labels==label_idx, 1],
c=colors[label_idx],
s=1,
)
plt.legend(['0', '1', '2', '3', '4', '5', '6', '7', '8', '9'], loc='upper right')
plt.legend(['0', '1', '2', '3', '4', '5', '6', '7', '8'], loc='upper right')
dirname = osp.join(args.save_dir, prefix)
if not osp.exists(dirname):
os.mkdir(dirname)
Expand Down
3 changes: 1 addition & 2 deletions datasets.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2,7 +2,6 @@
import torchvision
from torch.utils.data import DataLoader
from torch.utils.data import SubsetRandomSampler
from simclr.modules.transformations import TransformsSimCLR
import random
import transforms

Expand Down Expand Up @@ -293,4 +292,4 @@ def create(name, known_class_, init_percent_, batch_size, use_gpu, num_workers,
init_percent = init_percent_
if name not in __factory.keys():
raise KeyError("Unknown dataset: {}".format(name))
return __factory[name](batch_size, use_gpu, num_workers, is_filter, is_mini, unlabeled_ind_train, labeled_ind_train)
return __factory[name](batch_size, use_gpu, num_workers, is_filter, is_mini, unlabeled_ind_train, labeled_ind_train)