Docstring Typos

configs/classification_heavy_model.yaml

@@ -14,9 +14,9 @@ loader:
 trainer:
   preprocessing:
     train_image_size: [384, 512]
-    keep_aspect_ratio: True
+    keep_aspect_ratio: true
     normalize:
-      active: True
+      active: true
 
   batch_size: 8
   epochs: 200

configs/classification_light_model.yaml

@@ -14,9 +14,9 @@ loader:
 trainer:
   preprocessing:
     train_image_size: [384, 512]
-    keep_aspect_ratio: True
+    keep_aspect_ratio: true
     normalize:
-      active: True
+      active: true
 
   batch_size: 8
   epochs: 200

configs/complex_model.yaml

@@ -15,30 +15,30 @@ model:
         - RepPANNeck
 
       losses:
-        name: EfficientKeypointBboxLoss
+        - name: EfficientKeypointBboxLoss
 
       metrics:
         - name: ObjectKeypointSimilarity
           is_main_metric: true
         - name: MeanAveragePrecisionKeypoints
 
       visualizers:
-        name: MultiVisualizer
-        params:
-          visualizers:
-            - name: KeypointVisualizer
-              params:
-                nonvisible_color: blue
-            - name: BBoxVisualizer
-              params:
-                colors:
-                  person: "#FF5055"
+        - name: MultiVisualizer
+          params:
+            visualizers:
+              - name: KeypointVisualizer
+                params:
+                  nonvisible_color: blue
+              - name: BBoxVisualizer
+                params:
+                  colors:
+                    person: "#FF5055"
 
     - name: SegmentationHead
       inputs:
         - RepPANNeck
       losses:
-        name: BCEWithLogitsLoss
+        - name: BCEWithLogitsLoss
       metrics:
         - name: F1Score
           params:
@@ -47,9 +47,9 @@ model:
           params:
             task: binary
       visualizers:
-        name: SegmentationVisualizer
-        params:
-          colors: "#FF5055"
+        - name: SegmentationVisualizer
+          params:
+            colors: "#FF5055"
 
     - name: EfficientBBoxHead
       inputs:
@@ -58,18 +58,18 @@ model:
         conf_thres: 0.75
         iou_thres: 0.45
       losses:
-        name: AdaptiveDetectionLoss
+        - name: AdaptiveDetectionLoss
       metrics:
-        name: MeanAveragePrecision
+        - name: MeanAveragePrecision
       visualizers:
-        name: BBoxVisualizer
+        - name: BBoxVisualizer
 
 tracker:
   project_name: coco_test
   save_directory: output
-  is_tensorboard: True
-  is_wandb: False
-  is_mlflow: False
+  is_tensorboard: true
+  is_wandb: false
+  is_mlflow: false
 
 loader:
   train_view: train
@@ -86,23 +86,23 @@ trainer:
 
   n_sanity_val_steps: 1
   profiler: null
-  verbose: True
+  verbose: true
   batch_size: 8
   accumulate_grad_batches: 1
   epochs: &epochs 200
   n_workers: 8
   validation_interval: 10
   n_log_images: 8
-  skip_last_batch: True
-  log_sub_losses: True
+  skip_last_batch: true
+  log_sub_losses: true
   save_top_k: 3
 
   preprocessing:
     train_image_size: [&height 384, &width 384]
-    keep_aspect_ratio: True
-    train_rgb: True
+    keep_aspect_ratio: true
+    train_rgb: true
     normalize:
-      active: True
+      active: true
     augmentations:
       - name: Defocus
         params:
@@ -138,7 +138,7 @@ trainer:
     params:
       lr: 0.02
       momentum: 0.937
-      nesterov: True
+      nesterov: true
       weight_decay: 0.0005
 
   scheduler:

configs/detection_heavy_model.yaml

@@ -14,9 +14,9 @@ loader:
 trainer:
   preprocessing:
     train_image_size: [384, 512]
-    keep_aspect_ratio: True
+    keep_aspect_ratio: true
     normalize:
-      active: True
+      active: true
 
   batch_size: 8
   epochs: &epochs 200

configs/detection_light_model.yaml

@@ -14,9 +14,9 @@ loader:
 trainer:
   preprocessing:
     train_image_size: [384, 512]
-    keep_aspect_ratio: True
+    keep_aspect_ratio: true
     normalize:
-      active: True
+      active: true
 
   batch_size: 8
   epochs: &epochs 200

configs/example_export.yaml

@@ -15,9 +15,9 @@ loader:
 trainer:
   preprocessing:
     train_image_size: [384, 512]
-    keep_aspect_ratio: True
+    keep_aspect_ratio: true
     normalize:
-      active: True
+      active: true
 
   batch_size: 8
   epochs: &epochs 200
@@ -46,5 +46,5 @@ exporter:
   onnx:
     opset_version: 11
   blobconverter:
-    active: True
+    active: true
     shaves: 8

configs/example_tuning.yaml

@@ -15,9 +15,9 @@ loader:
 trainer:
   preprocessing:
     train_image_size: [384, 512]
-    keep_aspect_ratio: True
+    keep_aspect_ratio: true
     normalize:
-      active: True
+      active: true
     augmentations:
       - name: Defocus
         params:

configs/keypoint_bbox_heavy_model.yaml

@@ -14,9 +14,9 @@ loader:
 trainer:
   preprocessing:
     train_image_size: [384, 512]
-    keep_aspect_ratio: True
+    keep_aspect_ratio: true
     normalize:
-      active: True
+      active: true
 
   batch_size: 8
   epochs: &epochs 200

configs/keypoint_bbox_light_model.yaml

@@ -14,9 +14,9 @@ loader:
 trainer:
   preprocessing:
     train_image_size: [384, 512]
-    keep_aspect_ratio: True
+    keep_aspect_ratio: true
     normalize:
-      active: True
+      active: true
 
   batch_size: 8
   epochs: &epochs 200

configs/segmentation_heavy_model.yaml

@@ -14,9 +14,9 @@ loader:
 trainer:
   preprocessing:
     train_image_size: [384, 512]
-    keep_aspect_ratio: True
+    keep_aspect_ratio: true
     normalize:
-      active: True
+      active: true
 
   batch_size: 8
   epochs: &epochs 200

configs/segmentation_light_model.yaml

@@ -14,9 +14,9 @@ loader:
 trainer:
   preprocessing:
     train_image_size: [384, 512]
-    keep_aspect_ratio: True
+    keep_aspect_ratio: true
     normalize:
-      active: True
+      active: true
 
   batch_size: 8
   epochs: &epochs 200

luxonis_train/assigners/__init__.py

@@ -1,4 +1,4 @@
-from .atts_assigner import ATSSAssigner
+from .atss_assigner import ATSSAssigner
 from .tal_assigner import TaskAlignedAssigner
 
 __all__ = ["ATSSAssigner", "TaskAlignedAssigner"]

luxonis_train/assigners/tal_assigner.py

@@ -17,15 +17,15 @@ def __init__(
         """Task Aligned Assigner.
 
         Adapted from: U{TOOD: Task-aligned One-stage Object Detection<https://arxiv.org/pdf/2108.07755.pdf>}.
-        Cose is adapted from: U{https://github.com/Nioolek/PPYOLOE_pytorch/blob/master/ppyoloe/assigner/tal_assigner.py}.
+        Code is adapted from: U{https://github.com/Nioolek/PPYOLOE_pytorch/blob/master/ppyoloe/assigner/tal_assigner.py}.
 
         @license: U{Apache License, Version 2.0<https://github.com/Nioolek/PPYOLOE_pytorch/
             tree/master?tab=Apache-2.0-1-ov-file#readme>}
 
         @type n_classes: int
         @param n_classes: Number of classes in the dataset.
         @type topk: int
-        @param topk: Number of anchors considere in selection. Defaults to 13.
+        @param topk: Number of anchors considered in selection. Defaults to 13.
         @type alpha: float
         @param alpha: Defaults to 1.0.
         @type beta: float

luxonis_train/assigners/utils.py

@@ -64,8 +64,8 @@ def fix_collisions(
 
 
 def batch_iou(batch1: Tensor, batch2: Tensor) -> Tensor:
-    """Calculates IoU for each pair of bboxes in the batch. Bboxes must
-    be in xyxy format.
+    """Calculates IoU for each pair of bounding boxes in the batch.
+    Bounding boxes must be in the "xyxy" format.
 
     @type batch1: Tensor
     @param batch1: Tensor of shape C{[bs, N, 4]}

luxonis_train/attached_modules/base_attached_module.py

@@ -27,9 +27,9 @@ class BaseAttachedModule(
 
     Attached modules include losses, metrics and visualizers.
 
-    This class contains a default implementation of `prepare` method, which
+    This class contains a default implementation of C{prepare} method, which
     should be sufficient for most simple cases. More complex modules should
-    override the `prepare` method.
+    override the C{prepare} method.
 
     When subclassing, the following methods can be overridden:
         - L{prepare}: Prepares node outputs for the forward pass of the module.
@@ -163,7 +163,8 @@ def node_tasks(self) -> dict[TaskType, str]:
         """Getter for the tasks of the attached node.
 
         @type: dict[TaskType, str]
-        @raises RuntimeError: If the node does not have the `tasks` attribute set.
+        @raises RuntimeError: If the node does not have the C{tasks}
+            attribute set.
         """
         if self.node._tasks is None:
             raise RuntimeError(
@@ -250,7 +251,7 @@ def get_input_tensors(
         @raises IncompatibleException: If the task is not present in the inputs.
 
         @raises ValueError: If the module requires multiple labels.
-            For such cases, the `prepare` method should be overridden.
+            For such cases, the C{prepare} method should be overridden.
         """
         if task_type is not None:
             if isinstance(task_type, TaskType):