Add code for extension

.github/workflows/ci.yaml

@@ -27,4 +27,4 @@ jobs:
         python -V
         conda info
          # stop the build if there are Python syntax errors or undefined names
-        flake8 . --count --extend-ignore=E203 --show-source --statistics --max-line-length=119
+        flake8 . --count --extend-ignore=E203,E741 --show-source --statistics --max-line-length=119

.gitignore

@@ -1,10 +1,11 @@
 joined_simple.csv
+lightning_logs/
 *.sh
 *.png
 *.pdf
 *.eps
 embed_cf/
-
+outputs2/
 outputs
 znew_scripts
 padchest_cf_images_v0

README.md

@@ -1,7 +1,8 @@
-# Counterfactual contrastive learning: robust representations via causal image synthesis
+# Robust representations for image classification via counterfactual contrastive learning
 
-This repository contains the code for the paper "Counterfactual contrastive learning: robust representations via causal image synthesis". <br>
-[Link to paper](https://arxiv.org/abs/2403.09605).
+This repository contains the code for the papers "Counterfactual contrastive learning: robust representations via causal image synthesis" and extended version "Robust representations for image classification via counterfactual contrastive learning". <br>
+[Link to Data Engineering Workshop @ MICCAI 2024 paper](https://arxiv.org/abs/2403.09605).
+[Link to extension (pre-print)]()
 
 ![alt text](figure1.png)
 
@@ -64,4 +65,4 @@ python classification/train.py experiment=base_density trainer.finetune_path=PAT
 ```
 You can choose the proportion of labelled data to use for finetuning with the flag `data.prop_train=1.0`
 
-5. Evaluate on the test set by running the notebook `evaluation/embed_density.ipynb` to run and save inference results on the test set. 
+5. Evaluate on the test set by running the inference code `evaluation/simclr_embed.py` to run and save inference results on the test set. 

causal_models/main.py

@@ -30,6 +30,8 @@ def main(args):
                 ckpt_args["data_dir"] = args.data_dir
             if args.lr < ckpt_args["lr"]:
                 ckpt_args["lr"] = args.lr
+            if args.epochs > ckpt_args["epochs"]:
+                ckpt_args["epochs"] = args.epochs
             vars(args).update(ckpt_args)
         else:
             print(f"Checkpoint not found at: {args.resume}")

causal_models/train_setup.py

@@ -47,7 +47,7 @@ def setup_dataloaders(args, cache: bool = True, shuffle_train=True):
                     "data=embed",
                     "data.batch_size=16",
                     f"data.cache={cache}",
-                    f"data.exclude_cviews={not 'cview' in args.parents_x}",
+                    f"data.exclude_cviews={'cview' not in args.parents_x}",
                 ],
             )
             print(cfg)