fixed some mistakes in configs

configs/README.md

@@ -148,7 +148,6 @@ Here you can change everything related to actual training of the model.
 | use_weighted_sampler    | bool                                           | False         | bool if use WeightedRandomSampler for training, only works with classification tasks                                                             |
 | epochs                  | int                                            | 100           | number of training epochs                                                                                                                        |
 | n_workers               | int                                            | 2             | number of workers for data loading                                                                                                               |
-| train_metrics_interval  | int                                            | -1            | frequency of computing metrics on train data, -1 if don't perform                                                                                |
 | validation_interval     | int                                            | 1             | frequency of computing metrics on validation data                                                                                                |
 | n_log_images            | int                                            | 4             | maximum number of images to visualize and log                                                                                                    |
 | skip_last_batch         | bool                                           | True          | whether to skip last batch while training                                                                                                        |

configs/classification_heavy_model.yaml

@@ -1,11 +1,11 @@
 # Example configuration for training a predefined heavy classification model
 
 model:
-  name: segmentation_light
+  name: classification_light
   predefined_model:
     name: ClassificationModel
     params:
-      variant: "heavy"
+      variant: heavy
 
 loader:
   params:

configs/classification_light_model.yaml

@@ -1,11 +1,11 @@
 # Example configuration for training a predefined light classification model
 
 model:
-  name: segmentation_light
+  name: classification_light
   predefined_model:
     name: ClassificationModel
     params:
-      variant: "light"
+      variant: light
 
 loader:
   params:

configs/coco_model.yaml → configs/complex_model.yaml

@@ -2,37 +2,28 @@
 
 
 model:
-  name: coco_test
+  name: complex_model
   nodes:
     - name: EfficientRep
-      params:
-        variant: "n"
 
     - name: RepPANNeck
       inputs:
         - EfficientRep
 
-    - name: ImplicitKeypointBBoxHead
+    - name: EfficientKeypointBBoxHead
       inputs:
         - RepPANNeck
-      params:
-        conf_thres: 0.25
-        iou_thres: 0.45
+
       losses:
-        name: ImplicitKeypointBBoxLoss
-        params:
-          keypoint_regression_loss_weight: 0.5
-          keypoint_visibility_loss_weight: 0.7
-          bbox_loss_weight: 0.05
-          objectness_loss_weight: 0.2
+        name: EfficientKeypointBboxLoss
+
       metrics:
         - name: ObjectKeypointSimilarity
           is_main_metric: true
         - name: MeanAveragePrecisionKeypoints
 
       visualizers:
         name: MultiVisualizer
-        attached_to: ImplicitKeypointBBoxHead
         params:
           visualizers:
             - name: KeypointVisualizer
@@ -78,7 +69,6 @@ tracker:
   save_directory: output
   is_tensorboard: True
   is_wandb: False
-  wandb_entity: luxonis
   is_mlflow: False
 
 loader:

configs/example_export.yaml

@@ -1,8 +1,8 @@
-# Example configuration for exporting a predefined segmentation model
+# Example configuration for exporting a predefined light detection model
 
 model:
-  name: coco_segmentation
-  weights: null  # specify a path to the weights here
+  name: detection_light
+  weights: null  # TODO: Specify a path to the weights here
   predefined_model:
     name: DetectionModel
     params:

configs/example_tuning.yaml

@@ -1,12 +1,12 @@
-# Example configuration for tuning a predefined segmentation model
+# Example configuration for tuning a predefined light segmentation model
+
 
 model:
-  name: coco_segmentation
+  name: segmentation_light
   predefined_model:
     name: SegmentationModel
     params:
-      backbone: MicroNet
-      task: binary
+      variant: light
 
 loader:
   params:
@@ -38,7 +38,12 @@ trainer:
       T_max: *epochs
       eta_min: 0
 
+
 tuner:
+  study_name: seg_study
+  n_trials: 10
+  storage:
+    storage_type: local
   params:
     trainer.optimizer.name_categorical: ["Adam", "SGD"]
     trainer.optimizer.params.lr_float: [0.0001, 0.001]

configs/segmentation_heavy_model.yaml

@@ -5,8 +5,7 @@ model:
   predefined_model:
     name: SegmentationModel
     params:
-      variant: "heavy"
-
+      variant: heavy
 
 loader:
   params:

configs/segmentation_light_model.yaml

@@ -5,7 +5,7 @@ model:
   predefined_model:
     name: SegmentationModel
     params:
-      variant: "light"
+      variant: light
 
 loader:
   params:

luxonis_train/config/config.py

@@ -365,7 +365,6 @@ class TrainerConfig(BaseModelExtraForbid):
         NonNegativeInt,
         Field(validation_alias=AliasChoices("n_workers", "num_workers")),
     ] = 4
-    train_metrics_interval: Literal[-1] | PositiveInt = -1
     validation_interval: Literal[-1] | PositiveInt = 5
     n_log_images: Annotated[
         NonNegativeInt,

luxonis_train/models/luxonis_lightning.py

@@ -298,10 +298,15 @@ def _initiate_nodes(
             for source_name, shape in shapes.items()
         }
 
-        for node_name, (
-            Node,
-            node_kwargs,
-        ), node_input_names, _ in traverse_graph(self.graph, nodes):
+        for (
+            node_name,
+            (
+                Node,
+                node_kwargs,
+            ),
+            node_input_names,
+            _,
+        ) in traverse_graph(self.graph, nodes):
             node_dummy_inputs: list[Packet[Tensor]] = []
             """List of dummy input packets for the node.
 
@@ -913,16 +918,6 @@ def _print_results(
                 f"{stage} main metric ({self.main_metric}): {main_metric:.4f}"
             )
 
-    def _is_train_eval_epoch(self) -> bool:
-        """Checks if train eval should be performed on current epoch
-        based on configured train_metrics_interval."""
-        train_metrics_interval = self.cfg.trainer.train_metrics_interval
-        # add +1 to current_epoch because starting epoch is at 0
-        return (
-            train_metrics_interval != -1
-            and (self.current_epoch + 1) % train_metrics_interval == 0
-        )
-
     def _average_losses(
         self, step_outputs: list[Mapping[str, Tensor | float | int]]
     ) -> dict[str, float]:

tests/configs/parking_lot_config.yaml

@@ -145,7 +145,6 @@ trainer:
   accumulate_grad_batches: 1
   epochs: 200
   n_workers: 8
-  train_metrics_interval: -1
   validation_interval: 10
   n_log_images: 8
   skip_last_batch: True