Feat: Added new rule in config auto-population and updated documentation.

configs/README.md

@@ -225,8 +225,7 @@ Here you can change everything related to actual training of the model.
 | `save_top_k`              | `-1 \| NonNegativeInt`                         | `3`           | Save top K checkpoints based on validation loss when training                                                                                    |
 | `smart_cfg_auto_populate` | `bool`                                         | `True`        | Automatically populate sensible default values for missing config fields and log warnings                                                        |
 | `n_validation_batches`    | `PositiveInt \| None`                          | `None`        | Limits the number of validation/test batches and makes the val/test loaders deterministic                                                        |
-
-**Example:**
+| `smart_cfg_auto_populate` | `bool`                                         | `True`        | Automatically populate sensible default values for missing config fields and log warnings                                                        |
 
 ```yaml
 
@@ -247,8 +246,33 @@ trainer:
   skip_last_batch: true
   log_sub_losses: true
   save_top_k: 3
+  smart_cfg_auto_populate: true
 ```
 
+### Smart Configuration Auto-population
+
+When setting `trainer.smart_cfg_auto_populate = True`, the following set of rules will be applied automatically to populate missing configuration fields with sensible defaults:
+
+#### Auto-population Rules
+
+1. **Default Optimizer and Scheduler:**
+
+   - If `training_strategy` is not defined and neither `optimizer` nor `scheduler` is set, the following defaults are applied:
+     - Optimizer: `Adam`
+     - Scheduler: `ConstantLR`
+
+1. **CosineAnnealingLR Adjustment:**
+
+   - If the `CosineAnnealingLR` scheduler is used and `T_max` is not set, it is automatically set to the number of epochs.
+
+1. **Mosaic4 Augmentation:**
+
+   - If `Mosaic4` augmentation is used without `out_width` and `out_height` parameters, they are set to match the training image size.
+
+1. **Validation/Test Views:**
+
+   - If `train_view`, `val_view`, and `test_view` are the same, and `n_validation_batches` is not explicitly set, it defaults to `10` to prevent validation/testing on the entire training set.
+
 ### Preprocessing
 
 We use [`Albumentations`](https://albumentations.ai/docs/) library for `augmentations`. [Here](https://albumentations.ai/docs/api_reference/full_reference/#pixel-level-transforms) you can see a list of all pixel level augmentations supported, and [here](https://albumentations.ai/docs/api_reference/full_reference/#spatial-level-transforms) you see all spatial level transformations. In the configuration you can specify any augmentation from these lists and their parameters.

luxonis_train/config/config.py

@@ -580,6 +580,19 @@ def smart_auto_populate(cls, instance: "Config") -> None:
                     "`Mosaic4` augmentation detected. Automatically set `out_width` and `out_height` to match `train_image_size`."
                 )
 
+        # Rule: If train, val, and test views are the same, set n_validation_batches
+        if (
+            instance.loader.train_view
+            == instance.loader.val_view
+            == instance.loader.test_view
+            and instance.trainer.n_validation_batches is None
+        ):
+            instance.trainer.n_validation_batches = 10
+            logger.warning(
+                "Train, validation, and test views are the same. Automatically set `n_validation_batches` to 10 to prevent validation/testing on the full train set. "
+                "If this behavior is not desired, set `smart_cfg_auto_populate` to `False`."
+            )
+
 
 def is_acyclic(graph: dict[str, list[str]]) -> bool:
     """Tests if graph is acyclic.