Generating t-SNE plot

Dear authors,

Thanks for providing LIMU-BERT with us!

I followed the instructions provided and tried to generate the t-SNE plots for general IMU representations learned in the first unsupervised phase.

To replicate my issue:
I first docker run the container I provided earlier in README

In the root dir in this repo LIMU-BERT-Public, I ran the pretrain script and trained the model for 3200 epochs:

python pretrain.py v1 uci 20_120 -s limu_v1

Then I added plt.savefig('tsne.png') in the plot_tsne() method in

LIMU-BERT-Public/plot.py

Line 48 in 5d63a7a

plt.show()

uncommented the following 3 lines of code

    # label_index = 1
    # label_names, label_num = load_dataset_label_names(args.dataset_cfg, label_index)
    # data_tsne, labels_tsne = plot_embedding(output, labels, label_index=label_index, reduce=1000, label_names=label_names)

in https://github.com/dapowan/LIMU-BERT-Public/blob/master/embedding.py#L66-L68 so that it can plot the embeddings.

One extra step is to manually create the directory embed under the root path of this repo, e.g. by

mkdir embed

otherwise I got

cuda:0 (1 GPUs)
Loading the model from saved/pretrain_base_uci_20_120/limu_v1
Traceback (most recent call last):
  File "embedding.py", line 64, in <module>
    data, output, labels = generate_embedding_or_output(args=args, output_embed=True, save=save)
  File "embedding.py", line 48, in generate_embedding_or_output
    np.save(os.path.join('embed', save_name + '.npy'), output)
  File "<__array_function__ internals>", line 6, in save
  File "/usr/local/lib/python3.6/dist-packages/numpy/lib/npyio.py", line 524, in save
    file_ctx = open(file, "wb")
FileNotFoundError: [Errno 2] No such file or directory: 'embed/embed_limu_v1_uci_20_120.npy'

Then I ran:

python embedding.py v1 uci 20_120 -f limu_v1

However, what I got from the tsne.png is:

It seems that the labels here are not human activity labels.

It would be appreciated if you can help. Thanks!

Hi Bryan, as described in dataset\dataset_config.json, the activity label index of the UCI dataset is 0 instead of 1. So try label_index = 0 and re-run the experiment.

Cool, fixed now. Thanks!