Refactor checkpoint and TensorBoard naming conventions; update run co…

…nfiguration

- Update configs/callbacks/hnet_checkpoint.yaml to remove sample_range_used from filename and append timestamp
- Modify configs/logging/tensorboard.yaml to include timestamp in version
- Rename sample_range_used to sample_range_trained_on in configs/run.yaml

configs/callbacks/hnet_checkpoint.yaml

@@ -3,7 +3,7 @@ checkpoint:
   save_last: True # additionally, always save model from last epoch
   verbose: False
   dirpath: "${hydra:runtime.cwd}/checkpoints/${now:%Y%m%d}/trained_on_${sample_range_trained_on}/standard_resolution/"
-  filename: "hnet_model_DOA${max_len}_${sample_range_used}_{epoch:01d}_{now:%H%M%S}"
+  filename: "hnet_model_DOA${max_len}_{epoch:01d}_${now:%H%M%S}"
   monitor: "validation_loss"
   save_top_k: 1
   mode: "min"

configs/logging/tensorboard.yaml

@@ -4,6 +4,6 @@ logger:
   _target_: lightning.pytorch.loggers.tensorboard.TensorBoardLogger
   save_dir: "${hydra:runtime.cwd}/tb_logs/"
   name: 'trained_on_${sample_range_trained_on}/standard_resolution'
-  version: "hnet_model_DOA${max_len}"
+  version: "hnet_model_DOA${max_len}_${now:%H%M%S}"
   log_graph: False
   default_hp_metric: False

configs/run.yaml

@@ -5,10 +5,12 @@ description: "${hydra:runtime.choices.lightning_datamodule}"
 max_len: 2 # Maximum DoAs to estimate
 num_workers: 4
 batch_size: 256
-nb_epochs: 30 # Mocked for testing
+nb_epochs: 30 
+# train_filename: "${hydra:runtime.cwd}/data/20241206/train/coarse_resolution_train_DOA2_3000-5000-15000"
+# test_filename: "${hydra:runtime.cwd}/data/20241206/test/coarse_resolution_test_DOA2_3000-5000-15000"
 train_filename: "${hydra:runtime.cwd}/data/reference/hung_data_train"
 test_filename: "${hydra:runtime.cwd}/data/reference/hung_data_test"
-sample_range_used: "3000-5000-15000"
+sample_range_trained_on: "3000-5000-15000"
 
 # Hydra configuration
 version_base: "1.3"