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Description
Hi there, I'm new in this field and I tried to use my own dataset similar to the CAR dataset structure. I'm running on Windows 10 and I couldn't install faiss , i tried using auxiliaries_nofaiss.py for all the imports, is there any solution to this? the model is able to run for 1 epoch, however, the evaluation part is taking very long time, the report is saying 119 GB, and giving me the error as below:
Any help from anyone would be appreciated. Thanks a lot.
GPU:0, dataset:rp2k, arch:resnet50, embed_dim:128, embed_init:default
loss:fastap, sampling:None, samples_per_class:0, resize256:False
bs:32, lr:1e-05, fc_lr_mul:0, decay:0.0004, gamma:0.3, tau:[20], bnft:False
Running with learning rates 1e-05...
Epoch (Train) 0: Mean Loss [0.0136]: 100%|█████████████████████████████████████████| 4605/4605 [27:19<00:00, 2.92it/s]
Computing Evaluation Metrics...: 100%|█████████████████████████████████████████████| 5585/5585 [07:55<00:00, 13.33it/s]
Traceback (most recent call last):
File "Standard_Training.py", line 365, in
main()
File "Standard_Training.py", line 344, in main
eval.evaluate(opt.dataset, LOG, save=True, **eval_params)
File "C:\Users\user\Documents\DeepMetricLearningBaselines\evaluate.py", line 57, in evaluate
ret = evaluate_one_dataset(LOG, **kwargs)
File "C:\Users\user\Documents\DeepMetricLearningBaselines\evaluate.py", line 279, in evaluate_one_dataset
F1, NMI, recall_at_ks, feature_matrix_all = aux.eval_metrics_one_dataset(model, dataloader, device=opt.device, k_vals=opt.k_vals, opt=opt)
File "C:\Users\user\Documents\DeepMetricLearningBaselines\auxiliaries_nofaiss.py", line 239, in eval_metrics_one_dataset
k_closest_points = squareform(pdist(feature_coll)).argsort(1)[:, :int(np.max(k_vals)+1)]
File "C:\Users\user\anaconda3\envs\kunhe\lib\site-packages\scipy\spatial\distance.py", line 1985, in pdist
dm = np.empty((m * (m - 1)) // 2, dtype=np.double)
MemoryError: Unable to allocate 119. GiB for an array with shape (15967113051,) and data type float64