add configuration files for model

models/configs/datamodule/focus.yaml

@@ -0,0 +1,8 @@
+_target_: src.datamodules.focus_datamodule.FocusDataModule
+
+data_dir: ${data_dir}/focus # data_dir is specified in config.yaml
+csv_file: ${data_dir}/focus/metadata.csv
+batch_size: 64
+train_val_test_split_percentage: [0.7, 0.15, 0.15]
+num_workers: 0
+pin_memory: False

models/configs/model/focus.yaml

@@ -0,0 +1,9 @@
+_target_: src.models.focus_module.FocusLitModule
+
+input_size: 16875
+lin1_size: 256
+lin2_size: 256
+lin3_size: 256
+output_size: 1
+lr: 0.001
+weight_decay: 0.0005

models/configs/test.yaml

@@ -3,8 +3,8 @@
 # specify here default evaluation configuration
 defaults:
   - _self_
-  - datamodule: mnist.yaml # choose the datamodule for evaluation
-  - model: mnist.yaml
+  - datamodule: foucs.yaml # choose the datamodule for evaluation
+  - model: focus.yaml
   - callbacks: null
   - logger: null
   - trainer: default.yaml

models/configs/train.yaml

@@ -3,11 +3,11 @@
 # specify here default training configuration
 defaults:
   - _self_
-  - datamodule: mnist.yaml
-  - model: mnist.yaml
+  - datamodule: focus.yaml
+  - model: focus.yaml
   - callbacks: default.yaml
   - logger: null # set logger here or use command line (e.g. `python train.py logger=tensorboard`)
-  - trainer: default.yaml
+  - trainer: long.yaml
   - log_dir: default.yaml
 
   # experiment configs allow for version control of specific configurations
@@ -34,7 +34,7 @@ defaults:
 original_work_dir: ${hydra:runtime.cwd}
 
 # path to folder with data
-data_dir: ${original_work_dir}/data/
+data_dir: ${original_work_dir}/data
 
 # pretty print config at the start of the run using Rich library
 print_config: True

models/configs/trainer/long.yaml

@@ -0,0 +1,12 @@
+_target_: pytorch_lightning.Trainer
+
+gpus: 0
+
+min_epochs: 1
+max_epochs: 100
+
+# number of validation steps to execute at the beginning of the training
+# num_sanity_val_steps: 0
+
+# ckpt path
+resume_from_checkpoint: null