Lock Down Sample Configs (#85)

Co-authored-by: klemen1999 <[email protected]>
Co-authored-by: Martin Kozlovsky <[email protected]>

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/detection_model.yaml → configs/classification_heavy_model.yaml

@@ -1,25 +1,25 @@
-# Example configuration for training a predefined detection model
+# Example configuration for training a predefined heavy classification model
 
 model:
-  name: coco_detection
+  name: classification_light
   predefined_model:
-    name: DetectionModel
+    name: ClassificationModel
     params:
-      use_neck: True
+      variant: heavy
 
 loader:
   params:
-    dataset_name: coco_test
+    dataset_name: cifar10_test
 
 trainer:
   preprocessing:
-    train_image_size: [&height 256, &width 320]
-    keep_aspect_ratio: False
+    train_image_size: [384, 512]
+    keep_aspect_ratio: True
     normalize:
       active: True
 
-  batch_size: 4
-  epochs: &epochs 200
+  batch_size: 8
+  epochs: 200
   n_workers: 4
   validation_interval: 10
   n_log_images: 8
@@ -29,9 +29,9 @@ trainer:
     - name: TestOnTrainEnd
 
   optimizer:
-    name: SGD
+    name: Adam
     params:
-      lr: 0.02
+      lr: 0.001
 
   scheduler:
     name: ConstantLR

configs/classification_light_model.yaml

@@ -0,0 +1,37 @@
+# Example configuration for training a predefined light classification model
+
+model:
+  name: classification_light
+  predefined_model:
+    name: ClassificationModel
+    params:
+      variant: light
+
+loader:
+  params:
+    dataset_name: cifar10_test
+
+trainer:
+  preprocessing:
+    train_image_size: [384, 512]
+    keep_aspect_ratio: True
+    normalize:
+      active: True
+
+  batch_size: 8
+  epochs: 200
+  n_workers: 4
+  validation_interval: 10
+  n_log_images: 8
+
+  callbacks:
+    - name: ExportOnTrainEnd
+    - name: TestOnTrainEnd
+
+  optimizer:
+    name: Adam
+    params:
+      lr: 0.003
+
+  scheduler:
+    name: ConstantLR

configs/coco_model.yaml → configs/complex_model.yaml

@@ -2,45 +2,28 @@
 
 
 model:
-  name: coco_test
+  name: complex_model
   nodes:
     - name: EfficientRep
-      params:
-        channels_list: [64, 128, 256, 512, 1024]
-        n_repeats: [1, 6, 12, 18, 6]
-        depth_mul: 0.33
-        width_mul: 0.33
 
     - name: RepPANNeck
       inputs:
         - EfficientRep
-      params:
-        channels_list: [256, 128, 128, 256, 256, 512]
-        n_repeats: [12, 12, 12, 12]
-        depth_mul: 0.33
-        width_mul: 0.33
 
-    - 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
@@ -86,7 +69,6 @@ tracker:
   save_directory: output
   is_tensorboard: True
   is_wandb: False
-  wandb_entity: luxonis
   is_mlflow: False
 
 loader:
@@ -105,20 +87,19 @@ trainer:
   n_sanity_val_steps: 1
   profiler: null
   verbose: True
-  batch_size: 4
+  batch_size: 8
   accumulate_grad_batches: 1
   epochs: &epochs 200
   n_workers: 8
-  train_metrics_interval: -1
   validation_interval: 10
   n_log_images: 8
   skip_last_batch: True
   log_sub_losses: True
   save_top_k: 3
 
   preprocessing:
-    train_image_size: [&height 256, &width 320]
-    keep_aspect_ratio: False
+    train_image_size: [&height 384, &width 384]
+    keep_aspect_ratio: True
     train_rgb: True
     normalize:
       active: True

configs/detection_heavy_model.yaml

@@ -0,0 +1,45 @@
+# Example configuration for training a predefined heavy detection model
+
+model:
+  name: detection_heavy
+  predefined_model:
+    name: DetectionModel
+    params:
+      variant: heavy
+
+loader:
+  params:
+    dataset_name: coco_test
+
+trainer:
+  preprocessing:
+    train_image_size: [384, 512]
+    keep_aspect_ratio: True
+    normalize:
+      active: True
+
+  batch_size: 8
+  epochs: &epochs 200
+  n_workers: 4
+  validation_interval: 10
+  n_log_images: 8
+
+  callbacks:
+    - name: ExportOnTrainEnd
+    - name: TestOnTrainEnd
+
+  optimizer:
+    name: SGD
+    params:
+      lr: 0.01
+      momentum: 0.937
+      weight_decay: 0.0005
+      dampening: 0.0
+      nesterov: true
+
+  scheduler:
+    name: CosineAnnealingLR
+    params:
+      T_max: *epochs
+      eta_min: 0.0001
+      last_epoch: -1

configs/detection_light_model.yaml

@@ -0,0 +1,45 @@
+# Example configuration for training a predefined light detection model
+
+model:
+  name: detection_light
+  predefined_model:
+    name: DetectionModel
+    params:
+      variant: light
+
+loader:
+  params:
+    dataset_name: coco_test
+
+trainer:
+  preprocessing:
+    train_image_size: [384, 512]
+    keep_aspect_ratio: True
+    normalize:
+      active: True
+
+  batch_size: 8
+  epochs: &epochs 200
+  n_workers: 8
+  validation_interval: 10
+  n_log_images: 8
+
+  callbacks:
+    - name: ExportOnTrainEnd
+    - name: TestOnTrainEnd
+
+  optimizer:
+    name: SGD
+    params:
+      lr: 0.02
+      momentum: 0.937
+      weight_decay: 0.0005
+      dampening: 0.0
+      nesterov: true
+
+  scheduler:
+    name: CosineAnnealingLR
+    params:
+      T_max: *epochs
+      eta_min: 0.0002
+      last_epoch: -1