hirokatsukataoka16 / fractaldb-pretrained-resnet-pytorch Goto Github PK

Good Morning,

After finishing the Fractal Category Search I proceeded to run the code fractal_renderer/make_fractaldb.py to construct FractalDB. However, in doing so I receive an error:
$ python fractal_renderer/make_fractaldb.py
Traceback (most recent call last):
File "fractal_renderer/make_fractaldb.py", line 39, in
weights = np.genfromtxt(args.weight_csv,dtype=np.str,delimiter=',')
File "/home/noahm/RSML/lib/python3.6/site-packages/numpy/lib/npyio.py", line 1772, in genfromtxt
fid = np.lib._datasource.open(fname, 'rt', encoding=encoding)
File "/home/noahm/RSML/lib/python3.6/site-packages/numpy/lib/_datasource.py", line 269, in open
return ds.open(path, mode, encoding=encoding, newline=newline)
File "/home/noahm/RSML/lib/python3.6/site-packages/numpy/lib/_datasource.py", line 623, in open
raise IOError("%s not found." % path)
OSError: ./weights/weights_0.1.csv not found.

I went to FractalDB-trained-ResNet-PyTorch/fractal_renderer/weights and found the file weights_0.1.csv was indeed there.
I am unsure of the solution to this problem.

Hi, I tried to load your resnet-50 pretrained weights but displayed error messages like the below.
It seems that the weight is corrupt. Do I need any preparations after downloading the weight? If really corrupted, would you please fix them?

Thanks in advance.

Python 3.8.10 (default, Jun 26 2021, 12:40:55)
Type 'copyright', 'credits' or 'license' for more information
IPython 8.2.0 -- An enhanced Interactive Python. Type '?' for help.

In [1]: import torch

In [2]: a = torch.load('FractalDB-1000_res50.pth')
---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
Input In [2], in <cell line: 1>()
----> 1 a = torch.load('FractalDB-1000_res50.pth')

File ~/.pyenv/versions/3.8.10/lib/python3.8/site-packages/torch/serialization.py:713, in load(f, map_location, pickle_module, **pickle_load_args)
    711             return torch.jit.load(opened_file)
    712         return _load(opened_zipfile, map_location, pickle_module, **pickle_load_args)
--> 713 return _legacy_load(opened_file, map_location, pickle_module, **pickle_load_args)

File ~/.pyenv/versions/3.8.10/lib/python3.8/site-packages/torch/serialization.py:938, in _legacy_load(f, map_location, pickle_module, **pickle_load_args)
    936 assert key in deserialized_objects
    937 typed_storage = deserialized_objects[key]
--> 938 typed_storage._storage._set_from_file(
    939     f, offset, f_should_read_directly,
    940     torch._utils._element_size(typed_storage.dtype))
    941 if offset is not None:
    942     offset = f.tell()

RuntimeError: unexpected EOF, expected 2813703 more bytes. The file might be corrupted.

In [3]: torch.__version__
Out[3]: '1.11.0'

Hello,

How large is the dataset (in GB), and how long does it take for you to generate it? I'm trying to reproduce your results, and generation is taking a super long time, even on a machine with 64 CPUs.

Also, once you generated the dataset, how did you train your classification model?

Best,

Hello @hirokatsukataoka16,

I left this question on #1 , but since that issue is closed I realized you may have missed it. My question is about finetuning -- how did you prepare the PascalVOC and Omniglot datasets? My understanding is that PascalVOC is usually in a multi-class classification setup and Omniglot is usually a few-shot learning setup. Did you make them into single-class classification tasks, and if so how exactly did you go about it? I don't believe there is any information about it in the paper.

Thank you so much again for the paper and repo! It's a really nice idea.

I am conducting a replication experiment to create a FractalDB pretrained VGG16 with TensorFlow, referencing this repository.
I have a concern regarding a hyperparameter: the default value for learning_rate in pretraining/args.py is set to 0.1, but shouldn't it be 0.01?
I checked the paper, and it mentions that the initial learning rate is 0.01. Just to be sure, I tried training for about 30 epochs with a learning rate of 0.1, but it was too high and the loss did not decrease at all.

https://github.com/hirokatsukataoka16/FractalDB-Pretrained-ResNet-PyTorch/blob/1b8fb9fe7a6f496d4a2acc4ae5e1c1ab1eab0608/pretraining/args.py#L20C5-L20C89

Thanks in advance.

In your paper, you show the model's transform result after a train in FractalDB-1k. I am so curious about the model's top-1 test accuracy of original data (FractalDB-1k). Can you show this result?

Could you also release the R50 checkpoints fine-tuned on ImageNet-1k from FractalDB1k/10k?