Tensorboard projector for contrastive learning for embedding visualization #217
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… with UMAP,TSNE, PCA/
ziw-liu
added
enhancement
edyoshikun
commented
Jan 6, 2025
| labels = torch.cat((indices, indices)) | ||
| # Note: we assume the two augmented views are the anchor and positive samples | ||
| embeddings = torch.cat((anchor_projection, positive_projection)) | ||
| loss = self.loss_function(embeddings, labels) | ||
| self._log_step_samples(batch_idx, (anchor, pos_img), "val") | ||
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| # Store embeddings for visualization | ||
| if self.current_epoch % self.embedding_log_interval == 0 and batch_idx == 0: |
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@ziw-liu I was only logging the first batch and only from the validation.
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Would that always be the first time point(s) then?
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Note that this will not be a representative (i.e. uniformly sampled) subset of the validation set. For example, in an infection time-lapse, all logged examples will be uninfected.
edyoshikun
changed the title
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This PR adds the ability to save the embeddings and see them as a projection in tensorboard.