Update docs: Remove DataModuleConfig (#3841)

* Update DataModuleConfig docs

* Fix typo

docs/source/guide/explanation/additional_features/auto_configuration.rst

@@ -152,14 +152,12 @@ To use this feature, add the following parameter:
 
         .. code-block:: python
 
-            from otx.core.config.data import DataModuleConfig
             from otx.core.data.module import OTXDataModule
 
-            data_config = DataModuleConfig(..., auto_num_workers=True)
-            datamodule = OTXDataModule(..., config=data_config)
+            datamodule = OTXDataModule(..., auto_num_workers=True)
 
     .. tab-item:: CLI
 
         .. code-block:: shell
 
-            (otx) ...$ otx train ... --data.config.auto_num_workers True
+            (otx) ...$ otx train ... --data.auto_num_workers True

docs/source/guide/explanation/additional_features/class_incremental_sampler.rst

@@ -25,9 +25,9 @@ For default setting, the square root of (number of old data/number of new data)
         .. code-block:: shell
 
             (otx) ...$ otx train ... \
-                                 --data.config.train_subset.sampler.class_path otx.algo.samplers.class_incremental_sampler.ClassIncrementalSampler \
-                                 --data.config.train_subset.sampler.init_args.old_classes '[car,truck]' \
-                                 --data.config.train_subset.sampler.init_args.new_classes '[bus]'
+                                 --data.train_subset.sampler.class_path otx.algo.samplers.class_incremental_sampler.ClassIncrementalSampler \
+                                 --data.train_subset.sampler.init_args.old_classes '[car,truck]' \
+                                 --data.train_subset.sampler.init_args.new_classes '[bus]'
 
 
 Balanced Sampler
@@ -55,4 +55,4 @@ It helps ensure balanced sampling by class based on the distribution of class la
         .. code-block:: shell
 
             (otx) ...$ otx train ... \
-                                 --data.config.train_subset.sampler.class_path otx.algo.samplers.balanced_sampler.BalancedSampler
+                                 --data.train_subset.sampler.class_path otx.algo.samplers.balanced_sampler.BalancedSampler

docs/source/guide/explanation/additional_features/fast_data_loading.rst

@@ -27,14 +27,12 @@ training time in those cases.
 
       .. code-block:: python
 
-         from otx.core.config.data import DataModuleConfig
          from otx.core.data.module import OTXDataModule
 
-         data_config = DataModuleConfig(..., mem_cache_size="8GB")
-         datamodule = OTXDataModule(..., config=data_config)
+         datamodule = OTXDataModule(..., mem_cache_size="8GB")
 
    .. tab-item:: CLI
 
       .. code-block:: shell
 
-         (otx) ...$ otx train ... --data.config.mem_cache_size 8GB
+         (otx) ...$ otx train ... --data.mem_cache_size 8GB

docs/source/guide/explanation/additional_features/tiling.rst

@@ -47,25 +47,24 @@ Enable Tiling via OTX Training
 
 Currently, tiling is supported for both detection and instance segmentation models. Please refer to :doc:`../algorithms/object_detection/object_detection` and :doc:`../algorithms/segmentation/instance_segmentation` for more details.
 
-To enable tiling in OTX training, set ``data.config.tile_config.enable_tiler`` parameter to 1. Here's an example of enabling tiling:
+To enable tiling in OTX training, set ``data.tile_config.enable_tiler`` parameter to 1. Here's an example of enabling tiling:
 
 .. tab-set::
 
     .. tab-item:: API
 
         .. code-block:: python
 
-            from otx.core.config.data import DataModuleConfig, TileConfig
+            from otx.core.config.data import TileConfig
             from otx.core.data.module import OTXDataModule
 
-            data_config = DataModuleConfig(..., tile_config=TileConfig(enable_tiler=True))
-            datamodule = OTXDataModule(..., config=data_config)
+            datamodule = OTXDataModule(..., tile_config=TileConfig(enable_tiler=True))
 
     .. tab-item:: CLI
 
         .. code-block:: shell
 
-            (otx) ...$ otx train ... --data.config.tile_config.enable_tiler True
+            (otx) ...$ otx train ... --data.tile_config.enable_tiler True
 
 
 Tile Size and Tile Overlap Optimization
@@ -86,20 +85,19 @@ Here's an example of setting the object size ratio to 5%:
 
         .. code-block:: python
 
-            from otx.core.config.data import DataModuleConfig, TileConfig
+            from otx.core.config.data import TileConfig
             from otx.core.data.module import OTXDataModule
 
             tile_config = TileConfig(enable_tiler=True, enable_adaptive_tiling=True, object_tile_ratio=0.05)
-            data_config = DataModuleConfig(..., tile_config=tile_config)
-            datamodule = OTXDataModule(..., config=data_config)
+            datamodule = OTXDataModule(..., tile_config=tile_config)
 
     .. tab-item:: CLI
 
         .. code-block:: shell
 
-            (otx) ...$ otx train ... --data.config.tile_config.enable_tiler True \             # enable tiling
-                                     --data.config.tile_config.enable_adaptive_tiling True \   # enable automatic tiling parameter optimization
-                                     --data.config.tile_config.object_tile_ratio 0.05          # set the object size ratio to 5%
+            (otx) ...$ otx train ... --data.tile_config.enable_tiler True \             # enable tiling
+                                     --data.tile_config.enable_adaptive_tiling True \   # enable automatic tiling parameter optimization
+                                     --data.tile_config.object_tile_ratio 0.05          # set the object size ratio to 5%
 
 
 After determining the tile size, the tile overlap is computed by dividing the largest object size in the training dataset by the adaptive tile size. 
@@ -116,28 +114,27 @@ Since training and validation on all tiles from a high-resolution image dataset
 
 It's important to note that sampling is applied to the training and validation datasets, not the test dataset.
 
-This can be configured with ``data.config.tile_config.enable_adaptive_tiling`` parameter. Here's an example:
+This can be configured with ``data.tile_config.enable_adaptive_tiling`` parameter. Here's an example:
 
 .. tab-set::
 
     .. tab-item:: API
 
         .. code-block:: python
 
-            from otx.core.config.data import DataModuleConfig, TileConfig
+            from otx.core.config.data import TileConfig
             from otx.core.data.module import OTXDataModule
 
             tile_config = TileConfig(enable_tiler=True, enable_adaptive_tiling=True, sampling_ratio=0.5)
-            data_config = DataModuleConfig(..., tile_config=tile_config)
-            datamodule = OTXDataModule(..., config=data_config)
+            datamodule = OTXDataModule(..., tile_config=tile_config)
 
     .. tab-item:: CLI
 
         .. code-block:: shell
 
-            (otx) ...$ otx train ... --data.config.tile_config.enable_tiler True
-                                     --data.config.tile_config.enable_adaptive_tiling True
-                                     --data.config.tile_config.sampling_ratio 0.5
+            (otx) ...$ otx train ... --data.tile_config.enable_tiler True
+                                     --data.tile_config.enable_adaptive_tiling True
+                                     --data.tile_config.sampling_ratio 0.5
 
 
 Manual Tiling Parameter Configuration
@@ -151,21 +148,20 @@ Users can disable adaptive tiling and customize the tiling process by setting th
 
         .. code-block:: python
 
-            from otx.core.config.data import DataModuleConfig, TileConfig
+            from otx.core.config.data import TileConfig
             from otx.core.data.module import OTXDataModule
 
             tile_config = TileConfig(enable_tiler=True, enable_adaptive_tiling=False, tile_size=(512,512), tile_overlap=0.2)
-            data_config = DataModuleConfig(..., tile_config=tile_config)
-            datamodule = OTXDataModule(..., config=data_config)
+            datamodule = OTXDataModule(..., tile_config=tile_config)
 
     .. tab-item:: CLI
 
         .. code-block:: shell
 
-            (otx) ...$ otx train ... --data.config.tile_config.enable_tiler True
-                                     --data.config.tile_config.enable_adaptive_tiling False
-                                     --data.config.tile_config.tile_size '[512,512]'
-                                     --data.config.tile_config.tile_overlap 0.2
+            (otx) ...$ otx train ... --data.tile_config.enable_tiler True
+                                     --data.tile_config.enable_adaptive_tiling False
+                                     --data.tile_config.tile_size '[512,512]'
+                                     --data.tile_config.tile_overlap 0.2
 
 By specifying these parameters, automatic tiling parameter optimization is disabled, and the tile size is configured to 512x512 pixels with a 10% overlap between tiles.
 

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

@@ -159,7 +159,7 @@ You can select the model configuration and run Semi-SL training with the command
     (otx) ...$ otx train \
                 --config {semi_sl_config_path} \
                 --data_root {path_to_labeled_data} \
-                --data.config.unlabeled_subset.data_root {path_to_unlabeled_data}
+                --data.unlabeled_subset.data_root {path_to_unlabeled_data}
 
 Below are the results of comparing supervised learning and semi-supervised learning for each label per class on three datasets, three models.
 

docs/source/guide/explanation/algorithms/visual_prompting/fine_tuning.rst

@@ -93,5 +93,5 @@ According to datasets, ``learning rate`` and ``batch size`` can be adjusted like
     (otx) ...$ otx train \
         --config <model_config_path> \
         --data_root <path_to_data_root> \
-        --data.config.train_subset.batch_size <batch_size_to_be_updated> \
+        --data.train_subset.batch_size <batch_size_to_be_updated> \
         --optimizer.lr <learning_rate_to_be_updated>

docs/source/guide/get_started/cli_commands.rst

@@ -349,12 +349,12 @@ The results will be saved in ``./otx-workspace/`` folder by default. The output
     You also can visualize the training using ``Tensorboard`` as these logs are located in ``<work_dir>/tensorboard``.
 
 .. note::
-    ``--data.config.mem_cache_size`` provides in-memory caching for decoded images in main memory.
+    ``--data.mem_cache_size`` provides in-memory caching for decoded images in main memory.
     If the batch size is large, such as for classification tasks, or if your dataset contains high-resolution images,
     image decoding can account for a non-negligible overhead in data pre-processing.
     This option can be useful for maximizing GPU utilization and reducing model training time in those cases.
     If your machine has enough main memory, we recommend increasing this value as much as possible.
-    For example, you can cache approximately 10,000 of ``500x375~500x439`` sized images with ``--data.config.mem_cache_size 8GB``.
+    For example, you can cache approximately 10,000 of ``500x375~500x439`` sized images with ``--data.mem_cache_size 8GB``.
 
 It is also possible to start training by omitting the recipe and just passing the paths to dataset roots, then the :doc:`auto-configuration <../explanation/additional_features/auto_configuration>` will be enabled. Based on the dataset, OpenVINO™ Training Extensions will choose the task type and recipe with the best accuracy/speed trade-off.
 
@@ -363,7 +363,7 @@ For example, that is how you can change the max epochs and the batch size for th
 
 .. code-block:: shell
 
-    (otx) ...$ otx train ... --data.config.train_subset.batch_size <batch-size> --max_epochs <max-epochs>
+    (otx) ...$ otx train ... --data.train_subset.batch_size <batch-size> --max_epochs <max-epochs>
 
 .. note::
 

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

@@ -127,7 +127,7 @@ Training
             (otx) ...$ otx train \
                         --config src/otx/recipe/classification/multi_class_cls/mobilenet_v3_large_semisl.yaml \
                         --data_root data/flower_photos/labeled \
-                        --data.config.unlabeled_subset.data_root data/flower_photos/unlabeled
+                        --data.unlabeled_subset.data_root data/flower_photos/unlabeled
 
     .. tab-item:: API (from_config)
 
@@ -137,7 +137,7 @@ Training
 
             data_root = "data/flower_photos"
             recipe = "src/otx/recipe/classification/multi_class_cls/mobilenet_v3_large_semisl.yaml"
-            overrides = {"data.config.unlabeled_subset.data_root": "data/flower_photos/unlabeled"}
+            overrides = {"data.unlabeled_subset.data_root": "data/flower_photos/unlabeled"}
 
             engine = Engine.from_config(
                       config_path=recipe,
@@ -152,12 +152,11 @@ Training
 
         .. code-block:: python
 
-            from otx.core.config.data import DataModuleConfig, UnlabeledDataConfig
+            from otx.core.config.data import UnlabeledDataConfig
             from otx.core.data.module import OTXDataModule
             from otx.engine import Engine
 
-            data_config = DataModuleConfig(..., unlabeled_subset=UnlabeledDataConfig(data_root="data/flower_photos/unlabeled", ...))
-            datamodule = OTXDataModule(..., config=data_config)
+            datamodule = OTXDataModule(..., unlabeled_subset=UnlabeledDataConfig(data_root="data/flower_photos/unlabeled", ...))
 
             engine = Engine(..., datamodule=datamodule)
 

docs/source/guide/tutorials/base/how_to_train/action_classification.rst

@@ -160,19 +160,18 @@ For example, to decrease the batch size to 4, fix the number of epochs to 100, e
 
         .. code-block:: shell
 
-            (otx) ...$ otx train ... --data.config.train_subset.batch_size 4 \
+            (otx) ...$ otx train ... --data.train_subset.batch_size 4 \
                                      --max_epochs 100
 
     .. tab-item:: API
 
         .. code-block:: python
 
-            from otx.core.config.data import DataModuleConfig, SubsetConfig
+            from otx.core.config.data import SubsetConfig
             from otx.core.data.module import OTXDataModule
             from otx.engine import Engine
 
-            data_config = DataModuleConfig(..., train_subset=SubsetConfig(..., batch_size=4))
-            datamodule = OTXDataModule(..., config=data_config)
+            datamodule = OTXDataModule(..., train_subset=SubsetConfig(..., batch_size=4))
 
             engine = Engine(..., datamodule=datamodule)
 

docs/source/guide/tutorials/base/how_to_train/action_detection.rst

@@ -153,19 +153,18 @@ For example, to decrease the batch size to 4, fix the number of epochs to 100, e
 
         .. code-block:: shell
 
-            (otx) ...$ otx train ... --data.config.train_subset.batch_size 4 \
+            (otx) ...$ otx train ... --data.train_subset.batch_size 4 \
                                      --max_epochs 100
 
     .. tab-item:: API
 
         .. code-block:: python
 
-            from otx.core.config.data import DataModuleConfig, SubsetConfig
+            from otx.core.config.data import SubsetConfig
             from otx.core.data.module import OTXDataModule
             from otx.engine import Engine
 
-            data_config = DataModuleConfig(..., train_subset=SubsetConfig(..., batch_size=4))
-            datamodule = OTXDataModule(..., config=data_config)
+            datamodule = OTXDataModule(..., train_subset=SubsetConfig(..., batch_size=4))
 
             engine = Engine(..., datamodule=datamodule)
 

docs/source/guide/tutorials/base/how_to_train/anomaly_detection.rst

@@ -183,19 +183,18 @@ For example, to decrease the batch size to 4, fix the number of epochs to 100, e
 
         .. code-block:: shell
 
-            (otx) ...$ otx train ... --data.config.train_subset.batch_size 4 \
+            (otx) ...$ otx train ... --data.train_subset.batch_size 4 \
                                      --max_epochs 100
 
     .. tab-item:: API
 
         .. code-block:: python
 
-            from otx.core.config.data import DataModuleConfig, SubsetConfig
+            from otx.core.config.data import SubsetConfig
             from otx.core.data.module import OTXDataModule
             from otx.engine import Engine
 
-            data_config = DataModuleConfig(..., train_subset=SubsetConfig(..., batch_size=4))
-            datamodule = OTXDataModule(..., config=data_config)
+            datamodule = OTXDataModule(..., train_subset=SubsetConfig(..., batch_size=4))
 
             engine = Engine(..., datamodule=datamodule)
 

docs/source/guide/tutorials/base/how_to_train/classification.rst

@@ -251,19 +251,18 @@ For example, to decrease the batch size to 4, fix the number of epochs to 100, e
 
         .. code-block:: shell
 
-            (otx) ...$ otx train ... --data.config.train_subset.batch_size 4 \
+            (otx) ...$ otx train ... --data.train_subset.batch_size 4 \
                                      --max_epochs 100
 
     .. tab-item:: API
 
         .. code-block:: python
 
-            from otx.core.config.data import DataModuleConfig, SubsetConfig
+            from otx.core.config.data import SubsetConfig
             from otx.core.data.module import OTXDataModule
             from otx.engine import Engine
 
-            data_config = DataModuleConfig(..., train_subset=SubsetConfig(..., batch_size=4))
-            datamodule = OTXDataModule(..., config=data_config)
+            datamodule = OTXDataModule(..., train_subset=SubsetConfig(..., batch_size=4))
 
             engine = Engine(..., datamodule=datamodule)
 

docs/source/guide/tutorials/base/how_to_train/detection.rst

@@ -263,19 +263,18 @@ For example, to decrease the batch size to 4, fix the number of epochs to 100, e
 
         .. code-block:: shell
 
-            (otx) ...$ otx train ... --data.config.train_subset.batch_size 4 \
+            (otx) ...$ otx train ... --data.train_subset.batch_size 4 \
                                      --max_epochs 100
 
     .. tab-item:: API
 
         .. code-block:: python
 
-            from otx.core.config.data import DataModuleConfig, SubsetConfig
+            from otx.core.config.data import SubsetConfig
             from otx.core.data.module import OTXDataModule
             from otx.engine import Engine
 
-            data_config = DataModuleConfig(..., train_subset=SubsetConfig(..., batch_size=4))
-            datamodule = OTXDataModule(..., config=data_config)
+            datamodule = OTXDataModule(..., train_subset=SubsetConfig(..., batch_size=4))
 
             engine = Engine(..., datamodule=datamodule)
 

docs/source/guide/tutorials/base/how_to_train/instance_segmentation.rst

@@ -277,19 +277,18 @@ For example, to decrease the batch size to 4, fix the number of epochs to 100 an
 
         .. code-block:: shell
 
-            (otx) ...$ otx train ... --data.config.train_subset.batch_size 4 \
+            (otx) ...$ otx train ... --data.train_subset.batch_size 4 \
                                      --max_epochs 100
 
     .. tab-item:: API
 
         .. code-block:: python
 
-            from otx.core.config.data import DataModuleConfig, SubsetConfig
+            from otx.core.config.data import SubsetConfig
             from otx.core.data.module import OTXDataModule
             from otx.engine import Engine
 
-            data_config = DataModuleConfig(..., train_subset=SubsetConfig(..., batch_size=4))
-            datamodule = OTXDataModule(..., config=data_config)
+            datamodule = OTXDataModule(..., train_subset=SubsetConfig(..., batch_size=4))
 
             engine = Engine(..., datamodule=datamodule)