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iciclegcn's Issues

Q:class load_data(Dataset):

When I was running the icicleGCN.py at a new dataset, I want to know the type of __.txt and __label.txt. I can not find them at your files.
" def init(self, dataset):
self.x = np.loadtxt('data/{}.txt'.format(dataset), dtype=float)
# self.x = np.loadtxt('data/{}.txt'.format(dataset), dtype=str)
self.y = np.loadtxt('data/{}_label.txt'.format(dataset), dtype=int)"

Could you tell me the format of these two files？

Training resnet34 (from scratch) on CIFAR-10 leads to nan loss

I am training the resnet34 from scratch on CIFAR-10 dataset, also changed some params in train.py as well.

# --config.yaml--

# general
seed: 42
workers: 4
dataset_dir: "./datasets"

# train options
batch_size: 100
image_size: 224
start_epoch: 0
epochs: 1015
dataset: "CIFAR-10" #  CIFAR-10 / CIFAR-100 / STL-10 / ImageNet-10 / ImageNet-dogs / tiny-ImageNet / Flowers17

# model options
resnet: "ResNet34" # ResNet18 / ResNet34 / ResNet50
feature_dim: 128
model_path: "./save/CIFAR-10"
reload: False

# loss options
learning_rate: 0.001 #
weight_decay: 0.0001
instance_temperature: 0.5
cluster_temperature: 1.0

Training the model from scratch lead to nan just in a single epoch -

# --terminal output--
Files already downloaded and verified
Files already downloaded and verified
Step [0/600]       loss_instance: 5.28515625      loss_cluster: 2.946188449859619        re_loss: 1.126003384590149      
Step [50/600]      loss_instance: 5.009121417999268       loss_cluster: 2.4746360778808594       re_loss: 0.034027669578790665   
Step [100/600]     loss_instance: 4.926772117614746       loss_cluster: 2.2181878089904785       re_loss: 0.024739207699894905   
Step [150/600]     loss_instance: 4.885540962219238       loss_cluster: 2.2527127265930176       re_loss: 0.04029698297381401    
Step [200/600]     loss_instance: 4.774231910705566       loss_cluster: 2.339139699935913        re_loss: 0.05486036837100983    
Step [250/600]     loss_instance: 4.869399547576904       loss_cluster: 2.6640102863311768       re_loss: 0.03202003985643387    
Step [300/600]     loss_instance: 5.103700160980225       loss_cluster: 2.4775240421295166       re_loss: 0.034477513283491135   
Step [350/600]     loss_instance: 4.827446937561035       loss_cluster: 2.7474653720855713       re_loss: 0.03616001829504967    
Step [400/600]     loss_instance: 4.9964423179626465      loss_cluster: 2.49931263923645         re_loss: 0.03911386430263519    
Step [450/600]     loss_instance: 5.18294620513916        loss_cluster: 2.3889520168304443       re_loss: 0.029943885281682014   
Step [500/600]     loss_instance: nan     loss_cluster: nan      re_loss: nan    
Step [550/600]     loss_instance: nan     loss_cluster: nan      re_loss: nan

I tried default training params as well, but it also led to nan after around 60 epochs. Is there anything I am missing over here?

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