Revert TrainType typo (#1928)

* Fix Conflict

* Fix conflict

* Fix unit-tests

* Fix cli tests

* Fix cli tests

* Fix type

Author: harimkang

docs/source/guide/explanation/algorithms/classification/multi_class_classification.rst

@@ -206,7 +206,7 @@ Unlike other tasks, ``--val-data-root`` is not needed.
   $ otx train otx/algorithms/classification/configs/efficientnet_b0_cls_incr/template.yaml \
               --train-data-root=tests/assets/imagenet_dataset_class_incremental \
               params \
-              --algo_backend.train_type=SELFSUPERVISED
+              --algo_backend.train_type=Selfsupervised
 
 After self-supervised training, pretrained weights can be use for supervised (incremental) learning like the below command:
 

docs/source/guide/explanation/algorithms/segmentation/semantic_segmentation.rst

@@ -165,7 +165,7 @@ To enable self-supervised training, the command below can be executed:
   $ otx train otx/algorithms/segmentation/configs/ocr_lite_hrnet_s_mod2/template.yaml \
               --train-data-roots=tests/assets/common_semantic_segmentation_dataset/train/images \
               params \
-              --algo_backend.train_type=SELFSUPERVISED
+              --algo_backend.train_type=Selfsupervised
 
 After self-supervised training, pretrained weights can be use for supervised (incremental) learning like the below command:
 

docs/source/guide/get_started/quick_start_guide/cli_commands.rst

@@ -92,7 +92,7 @@ Building workspace folder
                             Comma-separated paths to unlabeled file list
       --task TASK           The currently supported options: ('CLASSIFICATION', 'DETECTION', 'INSTANCE_SEGMENTATION', 'SEGMENTATION', 'ACTION_CLASSIFICATION', 'ACTION_DETECTION', 'ANOMALY_CLASSIFICATION', 'ANOMALY_DETECTION', 'ANOMALY_SEGMENTATION').
       --train-type TRAIN_TYPE
-                            The currently supported options: dict_keys(['INCREMENTAL', 'SEMISUPERVISED', 'SELFSUPERVISED']).
+                            The currently supported options: dict_keys(['Incremental', 'Semisupervised', 'Selfsupervised']).
       --work-dir WORK_DIR   Location where the workspace.
       --model MODEL         Enter the name of the model you want to use. (Ex. EfficientNet-B0).
       --backbone BACKBONE   Available Backbone Type can be found using 'otx find --backbone {framework}'.
@@ -181,7 +181,7 @@ However, if you created a workspace with ``otx build``, the training process can
       --unlabeled-file-list UNLABELED_FILE_LIST
                             Comma-separated paths to unlabeled file list
       --train-type TRAIN_TYPE
-                            The currently supported options: dict_keys(['INCREMENTAL', 'SEMISUPERVISED', 'SELFSUPERVISED']).
+                            The currently supported options: dict_keys(['Incremental', 'Semisupervised', 'Selfsupervised']).
       --load-weights LOAD_WEIGHTS
                             Load model weights from previously saved checkpoint.
       --resume-from RESUME_FROM

docs/source/guide/tutorials/advanced/self_sl.rst

@@ -64,23 +64,23 @@ for **self-supervised learning** by running the following command:
 
 .. code-block::
 
-    (otx) ...$ otx build --train-data-roots data/flower_photos --model MobileNet-V3-large-1x --train-type SELFSUPERVISED --work-dir otx-workspace-CLASSIFICATION-SELFSUPERVISED
+    (otx) ...$ otx build --train-data-roots data/flower_photos --model MobileNet-V3-large-1x --train-type Selfsupervised --work-dir otx-workspace-CLASSIFICATION-Selfsupervised
 
-    [*] Workspace Path: otx-workspace-CLASSIFICATION-SELFSUPERVISED
+    [*] Workspace Path: otx-workspace-CLASSIFICATION-Selfsupervised
     [*] Load Model Template ID: Custom_Image_Classification_MobileNet-V3-large-1x
-    [*] Load Model Name: MobileNet-V3-large-1x[*]     - Updated: otx-workspace-CLASSIFICATION-SELFSUPERVISED/selfsl/model.py
-    [*]     - Updated: otx-workspace-CLASSIFICATION-SELFSUPERVISED/selfsl/data_pipeline.py
-    [*]     - Updated: otx-workspace-CLASSIFICATION-SELFSUPERVISED/deployment.py
-    [*]     - Updated: otx-workspace-CLASSIFICATION-SELFSUPERVISED/hpo_config.yaml
-    [*]     - Updated: otx-workspace-CLASSIFICATION-SELFSUPERVISED/model_hierarchical.py
-    [*]     - Updated: otx-workspace-CLASSIFICATION-SELFSUPERVISED/model_multilabel.py
-    [*] Update data configuration file to: otx-workspace-CLASSIFICATION-SELFSUPERVISED/data.yaml
+    [*] Load Model Name: MobileNet-V3-large-1x[*]     - Updated: otx-workspace-CLASSIFICATION-Selfsupervised/selfsl/model.py
+    [*]     - Updated: otx-workspace-CLASSIFICATION-Selfsupervised/selfsl/data_pipeline.py
+    [*]     - Updated: otx-workspace-CLASSIFICATION-Selfsupervised/deployment.py
+    [*]     - Updated: otx-workspace-CLASSIFICATION-Selfsupervised/hpo_config.yaml
+    [*]     - Updated: otx-workspace-CLASSIFICATION-Selfsupervised/model_hierarchical.py
+    [*]     - Updated: otx-workspace-CLASSIFICATION-Selfsupervised/model_multilabel.py
+    [*] Update data configuration file to: otx-workspace-CLASSIFICATION-Selfsupervised/data.yaml
 
 .. note::
 
     Three things must be considered to set the workspace for self-supervised learning:
 
-    1. add ``--train-type SELFSUPERVISED`` in the command to get the training components for self-supervised learning,
+    1. add ``--train-type Selfsupervised`` in the command to get the training components for self-supervised learning,
     2. update the path set as ``train-data-roots``,
     3. and add ``--work-dir`` to distinguish self-supervised learning workspace from supervised learning workspace.
 
@@ -102,7 +102,7 @@ After the workspace creation, the workspace structure is as follows:
     │   ├── train
     │   └── val
     └── template.yaml
-    otx-workspace-CLASSIFICATION-SELFSUPERVISED
+    otx-workspace-CLASSIFICATION-Selfsupervised
     ├── configuration.yaml
     ├── data.yaml
     ├── deployment.py
@@ -121,20 +121,20 @@ After the workspace creation, the workspace structure is as follows:
     For `VOC2012 dataset <http://host.robots.ox.ac.uk/pascal/VOC/voc2012>`_ used in :doc:`semantic segmentation tutorial <../base/how_to_train/semantic_segmentation>`, for example, the path ``data/VOCdevkit/VOC2012/JPEGImages`` must be set instead of ``data/VOCdevkit/VOC2012``.
 
     Please refer to :ref:`Explanation of Self-Supervised Learning for Semantic Segmentation <selfsl_semantic_segmentation>`.
-    And don't forget to add ``--train-type SELFSUPERVISED``.
+    And don't forget to add ``--train-type Selfsupervised``.
 
     .. code-block::
 
         (otx) ...$ otx build --train-data-roots data/VOCdevkit/VOC2012/JPEGImages \
                             --model Lite-HRNet-18-mod2 \
-                            --train-type SELFSUPERVISED
+                            --train-type Selfsupervised
 
 4. To start training we need to call ``otx train`` 
 command in **self-supervised learning** workspace:
 
 .. code-block::
 
-    (otx) ...$ cd otx-workspace-CLASSIFICATION-SELFSUPERVISED
+    (otx) ...$ cd otx-workspace-CLASSIFICATION-Selfsupervised
     (otx) ...$ otx train --data ../otx-workspace-CLASSIFICATION/data.yaml
     
     ...
@@ -168,7 +168,7 @@ After pre-training progress, start fine-tuning by calling the below command with
 .. code-block::
 
     (otx) ...$ cd ../otx-workspace-CLASSIFICATION
-    (otx) ...$ otx train --load-weights ../otx-workspace-CLASSIFICATION-SELFSUPERVISED/models/weights.pth
+    (otx) ...$ otx train --load-weights ../otx-workspace-CLASSIFICATION-Selfsupervised/models/weights.pth
 
     ...
 

docs/source/guide/tutorials/advanced/semi_sl.rst

@@ -73,7 +73,7 @@ Enable via ``otx build``
 1. To enable semi-supervsied learning via ``otx build``, we need to add arguments ``--unlabeled-data-roots`` and ``--train-type``. 
 OpenVINO™ Training Extensions receives the root path where unlabeled images are by ``--unlabeled-data-roots``.
 
-We should put the path where unlabeled data are contained. It also provides us ``--train-type`` to select the type of training scheme. All we have to do for that is specifying it as **SEMISUPERVISED**.
+We should put the path where unlabeled data are contained. It also provides us ``--train-type`` to select the type of training scheme. All we have to do for that is specifying it as **Semisupervised**.
 
 .. note::
 
@@ -85,7 +85,7 @@ We should put the path where unlabeled data are contained. It also provides us `
 
 .. code-block::
 
-  (otx) ...$ otx build --train-data-roots data/flower_photos --unlabeled-data-roots tests/assets/imagenet_dataset --model MobileNet-V3-large-1x --train-type SEMISUPERVISED
+  (otx) ...$ otx build --train-data-roots data/flower_photos --unlabeled-data-roots tests/assets/imagenet_dataset --model MobileNet-V3-large-1x --train-type Semisupervised
   
 
   [*] Workspace Path: otx-workspace-CLASSIFICATION
@@ -107,14 +107,14 @@ command in our workspace:
 
   (otx) ...$ otx train
 
-In the train log, you can check that the train type is set to **SEMISUPERVISED** and related configurations are properly loaded as following:
+In the train log, you can check that the train type is set to **Semisupervised** and related configurations are properly loaded as following:
 
 .. code-block::
 
   ...
   2023-02-22 06:21:54,492 | INFO : called _init_recipe()
-  2023-02-22 06:21:54,492 | INFO : train type = SEMISUPERVISED
-  2023-02-22 06:21:54,492 | INFO : train type = SEMISUPERVISED - loading training_extensions/otx/recipes/stages/classification/semisl.yaml
+  2023-02-22 06:21:54,492 | INFO : train type = Semisupervised
+  2023-02-22 06:21:54,492 | INFO : train type = Semisupervised - loading training_extensions/otx/recipes/stages/classification/semisl.yaml
   2023-02-22 06:21:54,500 | INFO : Replacing runner from EpochRunnerWithCancel to EpochRunnerWithCancel.
   2023-02-22 06:21:54,503 | INFO : initialized recipe = training_extensions/otx/recipes/stages/classification/semisl.yaml
   ...
@@ -135,16 +135,16 @@ which is one of template-specific parameters (details are provided in `quick sta
   (otx) ...$ otx train otx/algorithms/classification/configs/mobilenet_v3_large_1_cls_incr/template.yaml \
                       --train-data-roots data/flower_photos \
                       --unlabeled-data-roots tests/assets/imagenet_dataset \
-                      params --algo_backend.train_type SEMISUPERVISED
+                      params --algo_backend.train_type Semisupervised
 
-In the train log, you can check that the train type is set to **SEMISUPERVISED** and related configurations are properly loaded as following:
+In the train log, you can check that the train type is set to **Semisupervised** and related configurations are properly loaded as following:
 
 .. code-block::
 
   ...
   2023-02-22 06:21:54,492 | INFO : called _init_recipe()
-  2023-02-22 06:21:54,492 | INFO : train type = SEMISUPERVISED
-  2023-02-22 06:21:54,492 | INFO : train type = SEMISUPERVISED - loading training_extensions/otx/recipes/stages/classification/semisl.yaml
+  2023-02-22 06:21:54,492 | INFO : train type = Semisupervised
+  2023-02-22 06:21:54,492 | INFO : train type = Semisupervised - loading training_extensions/otx/recipes/stages/classification/semisl.yaml
   2023-02-22 06:21:54,500 | INFO : Replacing runner from EpochRunnerWithCancel to EpochRunnerWithCancel.
   2023-02-22 06:21:54,503 | INFO : initialized recipe = training_extensions/otx/recipes/stages/classification/semisl.yaml
   ...

otx/algorithms/action/configs/classification/configuration.yaml

@@ -245,20 +245,20 @@ algo_backend:
   header: Algo backend parameters
   train_type:
     affects_outcome_of: NONE
-    default_value: INCREMENTAL
+    default_value: Incremental
     description: Quantization preset that defines quantization scheme
     editable: false
     enum_name: TrainType
     header: Train type
     options:
-      INCREMENTAL: "INCREMENTAL"
+      Incremental: "Incremental"
     type: SELECTABLE
     ui_rules:
       action: DISABLE_EDITING
       operator: AND
       rules: []
       type: UI_RULES
-    value: INCREMENTAL
+    value: Incremental
     visible_in_ui: True
     warning: null
   mem_cache_size:

otx/algorithms/action/configs/classification/movinet/template.yaml

@@ -45,7 +45,7 @@ hyper_parameters:
         default_value: 1.0
     algo_backend:
       train_type:
-        default_value: INCREMENTAL
+        default_value: Incremental
 
 # Training resources.
 max_nodes: 1

otx/algorithms/action/configs/classification/x3d/template.yaml

@@ -45,7 +45,7 @@ hyper_parameters:
         default_value: 1.0
     algo_backend:
       train_type:
-        default_value: INCREMENTAL
+        default_value: Incremental
 
 # Training resources.
 max_nodes: 1

otx/algorithms/action/configs/detection/configuration.yaml

@@ -245,20 +245,20 @@ algo_backend:
   header: Algo backend parameters
   train_type:
     affects_outcome_of: NONE
-    default_value: INCREMENTAL
+    default_value: Incremental
     description: Quantization preset that defines quantization scheme
     editable: false
     enum_name: TrainType
     header: Train type
     options:
-      INCREMENTAL: "INCREMENTAL"
+      Incremental: "Incremental"
     type: SELECTABLE
     ui_rules:
       action: DISABLE_EDITING
       operator: AND
       rules: []
       type: UI_RULES
-    value: INCREMENTAL
+    value: Incremental
     visible_in_ui: True
     warning: null
   mem_cache_size:

otx/algorithms/action/configs/detection/x3d_fast_rcnn/template.yaml

@@ -45,7 +45,7 @@ hyper_parameters:
         default_value: 1.0
     algo_backend:
       train_type:
-        default_value: INCREMENTAL
+        default_value: Incremental
 
 # Training resources.
 max_nodes: 1