Visualize feature maps from the contrastive encoder #162
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* notes on standard report * Add code for generating figures --------- Co-authored-by: Alishba Imran <[email protected]>
…y and features learned by embeddings (#140) * notes on standard report * add lib of computed features * correlates PCA with computed features * compute for all timepoints * compute correlation * remove cv library usage * remove edge detection * convert to dataframe * for entire well * add std_dev feature * fix patch size --------- Co-authored-by: Soorya Pradeep <[email protected]>
* remove obsolete training and prediction scripts * lint contrastive scripts
* draft projection head per Update the projection head (normalization and size). #139 * reorganize comments in example fit config * configurable stem stride and projection dimensions * update type hint and docstring for ContrastiveEncoder * clarify embedding_dim * use the forward method directly for projected * normalize projections only when fitting the projected features saved during prediction is now *not* normalized * remove unused logger * refactor training code into translation and representation modules * extract image logging functions * use AdamW instead of Adam for contrastive learning * inline single-use argument * fix normalization * fix MLP layer order * fix output dimensions * remove L2 normalization before computing loss * compute rank of features and projections * documentation --------- Co-authored-by: Shalin Mehta <[email protected]>
* docstring * move scripts from contrastive_scripts to viscy/scripts * organize files in applications/contrastive_phenotyping * delete unused evaluation code * more cleanup * refactor evaluation metrics for translation task * refactor viscy.evaluation -> viscy.translation.evaluation_metrics and viscy.representation.evaluation * WIP: representation evaluation module * WIP: representation eval - docstrings in numpy format * WIP: more documentation * refactor: feature_extractor moved to viscy.representation.evaluation * lint * bug fix * refactored common computations and dataset * add imbalance-learn dependecy to metrics * refactor classification of embeddings * organize viscy.representation.evaluation * ruff * Soorya's plotting script * WIP: combine two versions of plot_embeddings.py * simplify representation.viscy.evaluation - move LCA to its own module * refactor of viscy.representation.evaluation * refactored and tested PCA and UMAP plots --------- Co-authored-by: Soorya Pradeep <[email protected]>
…et contrastive task (#154) * wip: sample positive and negative samples from another time point * configure time interval in triplet data module * vectorized anchor filtering * conditional augmentation for anchor anchor is augmented if the positive is another time point * example training script for the CTC dataset this is optimized to run on MPS * add example CTC prediction config for MPS
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Not urgent to merge at this moment. |
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@ziw-liu, feature visualization vs. time didn't serve the purpose in early iterations. Did you also try it after we started training time-aware models? The temporal smoothness in the output layer does not enforce the temporal smoothness in intermediate layers. This is interesting. |
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Added a script to perform PCA along the channel dimension to visualize multi-scale feature maps in the contrastive model's encoder stages.