szq0214 / fkd Goto Github PK

Hi author, thank you for your great work! I am intrigued by your work and very much looking forward to your method for self-supervised learning to train a teacher model and to generate pseudo-labels, thanks a lot!

Hello,

Thank you for sharing your remarkable research with us.

I am looking to conduct further experiments based on your research, and I require probability values obtained solely through the application of softmax.

Is there a separate zip file available that contains results obtained using only softmax, without hard targets or soft targets?

Thank you.

Hi，thanks for you great work!I want to know how to generate soft label from a teacher model.In your repository,I can't find related code.Could you provide a demo to let me know how to generate soft label?

Hello, can you introduce the steps to train a custom dataset? For example, how to generate soft labels for a custom data?

Dear author, hello! The default batch size of your code is 1024. I found in experiments that the code does not converge when the batch size is set to 128 or 256.

When downloading the official Google Cloud Disk soft label, it will fail when the download reaches a certain progress. Is there any other way to download it?

Traceback (most recent call last):
File "E:\PythonFile\FKD-main\train_FKD.py", line 528, in
main()
File "E:\PythonFile\FKD-main\train_FKD.py", line 138, in main
main_worker(args.gpu, ngpus_per_node, args)
File "E:\PythonFile\FKD-main\train_FKD.py", line 328, in main_worker
train(train_loader, model, criterion_sce, optimizer, epoch, args)
File "E:\PythonFile\FKD-main\train_FKD.py", line 363, in train
for i, (images, target, soft_label) in enumerate(train_loader):
File "D:\Anconda\envs\pytorch\lib\site-packages\torch\utils\data\dataloader.py", line 521, in next
data = self._next_data()
File "D:\Anconda\envs\pytorch\lib\site-packages\torch\utils\data\dataloader.py", line 1203, in _next_data
return self._process_data(data)
File "D:\Anconda\envs\pytorch\lib\site-packages\torch\utils\data\dataloader.py", line 1229, in _process_data
data.reraise()
File "D:\Anconda\envs\pytorch\lib\site-packages\torch_utils.py", line 434, in reraise
raise exception
_pickle.UnpicklingError: Caught UnpicklingError in DataLoader worker process 0.
Original Traceback (most recent call last):
File "D:\Anconda\envs\pytorch\lib\site-packages\torch\utils\data_utils\worker.py", line 287, in _worker_loop
data = fetcher.fetch(index)
File "D:\Anconda\envs\pytorch\lib\site-packages\torch\utils\data_utils\fetch.py", line 49, in fetch
data = [self.dataset[idx] for idx in possibly_batched_index]
File "D:\Anconda\envs\pytorch\lib\site-packages\torch\utils\data_utils\fetch.py", line 49, in
data = [self.dataset[idx] for idx in possibly_batched_index]
File "E:\PythonFile\FKD-main\utils_FKD.py", line 98, in getitem
label = torch.load(label_path, map_location=torch.device('cpu'))
File "D:\Anconda\envs\pytorch\lib\site-packages\torch\serialization.py", line 608, in load
return _legacy_load(opened_file, map_location, pickle_module, **pickle_load_args)
File "D:\Anconda\envs\pytorch\lib\site-packages\torch\serialization.py", line 777, in _legacy_load
magic_number = pickle_module.load(f, **pickle_load_args)
_pickle.UnpicklingError: invalid load key, '\xff'.

Do not use the pre training file to directly train and report errors as above