emadeldeen24 / ca-tcc Goto Github PK

Hi,
@emadeldeen24 Can you tell me "How to obtain train_1perc.pt"? I would be very grateful.

When i run the main.py with self_supervised mode training

the following error occurs.

ValueError: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (3,) + inhomogeneous part.

def permutation(x, max_segments=5, seg_mode="random"):
orig_steps = np.arange(x.shape[2])

num_segs = np.random.randint(1, max_segments, size=(x.shape[0]))

ret = np.zeros_like(x)
for i, pat in enumerate(x):
    if num_segs[i] > 1:
        if seg_mode == "random":
            split_points = np.random.choice(x.shape[2] - 2, num_segs[i] - 1, replace=False)
            split_points.sort()
            splits = np.split(orig_steps, split_points)
        else:
            splits = np.array_split(orig_steps, num_segs[i])
        warp = np.concatenate(np.random.permutation(splits)).ravel()     （Error comes from this line）
        ret[i] = pat[0,warp]
    else:
        ret[i] = pat
return torch.from_numpy(ret)

how to solve it? Thanks

Thank you very much for providing the code, but due to the lack of format details for data storage, I am unable to reproduce the training. Therefore, could you please provide an introduction to the storage details of the data or upload the file. Contact email: [email protected]

I had a problem running CA-TCC, and I was working with UCI datasets.I've read the（https://github.com/emadeldeen24/eval_ssl_ssc/blob/main/split_k-fold_and_few_labels.py），I still don't understand how to handle this step when using UCI data sets. Could you help me？