rm test train inference config_optimizier + modif test.yml (#133)

* rm test train config_optimizier + modif test.yml

* fix test.yml

* fix + rm inference

* fix

* comments + rm init args

* test ci

* marche pas encore

* fix

* reformat + lint

* test fix test.yml

* custom scalar

* reformat

* fix write config

* predict conf

* reformat + lint

* custom scalar

* fix comment predict

* fix

---------

Co-authored-by: leborgnelarivierec <[email protected]>

.github/workflows/tests.yml

@@ -47,15 +47,12 @@ jobs:
       run: |
         export PY4CAST_ROOTDIR=`pwd`
         coverage run -p -m pytest tests/
-        coverage run -p bin/train.py --precision 32 --model HalfUNet --model_conf config/models/halfunet32.json --dataset dummy --epochs 1 --batch_size 1 --num_pred_steps_train 1 --limit_train_batches 1 --num_workers 1
-        coverage run -p bin/train.py --precision 32 --model HalfUNet --model_conf config/models/halfunet32.json --dataset dummy --epochs 1 --batch_size 1 --num_pred_steps_train 1 --limit_train_batches 1 --num_workers 1 --strategy scaled_ar
-        coverage run -p bin/inference.py --dataset dummy --model_path /home/runner/work/py4cast/py4cast/logs/camp0/dummy/HalfUNet/runn_run_0/
-        coverage run -p bin/train.py --precision 32 --model UNETRPP --dataset dummy --epochs 1 --batch_size 1 --num_pred_steps_train 1 --limit_train_batches 1 --num_workers 1
         coverage run -p bin/main.py fit --config config/CLI/trainer.yaml --config config/CLI/dataset/dummy.yaml --config config/CLI/model/unetrpp.yaml --trainer.fast_dev_run True
-        coverage run -p bin/main.py fit --config config/CLI/trainer.yaml --config config/CLI/dataset/dummy.yaml --config config/CLI/model/unetrpp.yaml --trainer.fast_dev_run False --trainer.max_epochs 1 --data.batch_size 1 --trainer.limit_train_batches 1 --trainer.logger.init_args.version version_test
-        coverage run -p bin/main.py fit --config config/CLI/trainer.yaml --config config/CLI/dataset/dummy.yaml --config config/CLI/model/unetrpp.yaml --trainer.fast_dev_run False --trainer.max_epochs 1 --data.batch_size 1 --trainer.limit_train_batches 1 --ckpt_path /home/runner/work/py4cast/py4cast/lightning_logs/version_test/checkpoints/last.ckpt
-        coverage run -p bin/main.py test --config config/CLI/trainer.yaml --config config/CLI/dataset/dummy.yaml --config config/CLI/model/unetrpp.yaml --trainer.fast_dev_run False --trainer.max_epochs 1 --data.batch_size 1 --trainer.limit_train_batches 1 --ckpt_path /home/runner/work/py4cast/py4cast/lightning_logs/version_test/checkpoints/last.ckpt
-        
+        coverage run -p bin/main.py fit --config config/CLI/trainer.yaml --config config/CLI/dataset/dummy.yaml --config config/CLI/model/unetrpp.yaml --trainer.max_epochs 1 --data.batch_size 1 --trainer.limit_train_batches 1 --trainer.logger.version version_test
+        coverage run -p bin/main.py fit --config config/CLI/trainer.yaml --config config/CLI/dataset/dummy.yaml --config config/CLI/model/unetrpp.yaml --trainer.max_epochs 1 --data.batch_size 1 --trainer.limit_train_batches 1 --ckpt_path /home/runner/work/py4cast/py4cast/lightning_logs/version_test/checkpoints/last.ckpt
+        coverage run -p bin/main.py test --config config/CLI/trainer.yaml --config config/CLI/dataset/dummy.yaml --config config/CLI/model/unetrpp.yaml --trainer.max_epochs 1 --data.batch_size 1 --trainer.limit_train_batches 1 --ckpt_path /home/runner/work/py4cast/py4cast/lightning_logs/version_test/checkpoints/last.ckpt
+        coverage run -p bin/main.py predict --config config/CLI/trainer.yaml --config config/CLI/dataset/dummy.yaml --config config/CLI/model/unetrpp.yaml  --trainer.max_epochs 1 --data.batch_size 1 --trainer.limit_train_batches 1 --ckpt_path /home/runner/work/py4cast/py4cast/lightning_logs/version_test/checkpoints/last.ckpt
+
 
         coverage combine
         coverage report  --ignore-errors --fail-under=60

bin/main.py

@@ -61,6 +61,11 @@ def add_arguments_to_parser(self, parser):
             "model.len_train_loader",
             apply_on="instantiate",
         )
+        parser.link_arguments(
+            "data.infer_ds",
+            "model.infer_ds",
+            apply_on="instantiate",
+        )
 
 
 if __name__ == "__main__":