Add documentation for framework conversion

CHANGELOG.md

@@ -18,6 +18,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
   (<https://github.com/openvinotoolkit/datumaro/pull/1635>)
 - Optimize path assignment to handle point cloud in JSON without images
   (<https://github.com/openvinotoolkit/datumaro/pull/1643>)
+- Add documentation for framework conversion
+  (<https://github.com/openvinotoolkit/datumaro/pull/1659>)
 
 ### Bug fixes
 - Fix assertion to compare hashkeys against expected value

docs/source/docs/jupyter_notebook_examples/e2e_example.rst

@@ -11,6 +11,7 @@ Here we provide E2E examples from Datumaro to model trainers.
    notebooks/10_noisy_label_detection_cls
    notebooks/13_noisy_label_detection_det
    notebooks/16_missing_annotation_detection
+   notebooks/22_framework_converter
 
 .. grid:: 1 2 2 2
    :gutter: 2
@@ -42,3 +43,10 @@ Here we provide E2E examples from Datumaro to model trainers.
          :color: primary
          :outline:
          :expand:
+
+   .. grid-item-card::
+
+      .. button-ref:: notebooks/22_framework_converter
+         :color: primary
+         :outline:
+         :expand:

docs/source/docs/level-up/advanced_skills/12_project_versioning.rst → docs/source/docs/level-up/advanced_skills/13_project_versioning.rst

@@ -1,5 +1,5 @@
 ============================
-Level 12: Project Versioning
+Level 13: Project Versioning
 ============================
 
 Project versioning is a concept unique to Datumaro. Datumaro project includes a data source and revision tree,

docs/source/docs/level-up/advanced_skills/13_pseudo_label_generation.rst → docs/source/docs/level-up/advanced_skills/14_pseudo_label_generation.rst

@@ -1,5 +1,5 @@
 =================================
-Level 13: Pseudo Label Generation
+Level 14: Pseudo Label Generation
 =================================
 
 TBD

docs/source/docs/level-up/advanced_skills/14_data_pruning.rst → docs/source/docs/level-up/advanced_skills/15_data_pruning.rst

@@ -1,5 +1,5 @@
 =====================================================
-Level 14: Dataset Pruning
+Level 15: Dataset Pruning
 =====================================================
 
 

docs/source/docs/level-up/advanced_skills/index.rst

@@ -5,16 +5,16 @@ Advanced Skills
    :maxdepth: 1
    :hidden:
 
-   12_project_versioning
-   13_pseudo_label_generation
-   14_data_pruning
+   13_project_versioning
+   14_pseudo_label_generation
+   15_data_pruning
 
 .. grid:: 1 2 2 2
    :gutter: 2
 
    .. grid-item-card::
 
-      .. button-ref:: 12_project_versioning
+      .. button-ref:: 13_project_versioning
          :color: primary
          :outline:
          :expand:
@@ -25,7 +25,7 @@ Advanced Skills
 
    .. grid-item-card::
 
-      .. button-ref:: 13_pseudo_label_generation
+      .. button-ref:: 14_pseudo_label_generation
          :color: primary
          :outline:
          :expand:
@@ -36,7 +36,7 @@ Advanced Skills
 
    .. grid-item-card::
 
-      .. button-ref:: 14_data_pruning
+      .. button-ref:: 15_data_pruning
          :color: primary
          :outline:
          :expand:

docs/source/docs/level-up/intermediate_skills/12_framework_conversion.rst

@@ -0,0 +1,56 @@
+============================
+Level 12: Framework Conversion
+============================
+
+Datumaro allows seamless conversion of datasets to popular deep learning frameworks, such as PyTorch and TensorFlow.
+This is particularly useful when you are working with a dataset that needs to be used across different frameworks
+without manual reformatting.
+
+Datumaro provides the FrameworkConverter class, which can be used to convert a dataset for various tasks
+like classification, detection, and segmentation.
+
+Supported Tasks
+  - Classification
+  - Multilabel Classification
+  - Detection
+  - Instance Segmentation
+  - Semantic Segmentation
+  - Tabular Data
+
+.. tab-set::
+
+  .. tab-item:: Python
+
+    With the PyTorch framework, you can convert a Datumaro dataset like this:
+
+    .. code-block:: python
+
+      from datumaro.plugins.framework_converter import FrameworkConverter
+      from torchvision import transforms
+
+      transform = transforms.Compose([transforms.ToTensor()])
+      dm_dataset = ...  # Load your dataset here
+
+    First, we have to specify the dataset, subset, and task
+
+    .. code-block:: python
+
+      multi_framework_dataset = FrameworkConverter(dm_dataset, subset="train", task="classification")
+      train_dataset = multi_framework_dataset.to_framework(framework="torch", transform=transform)
+
+    Through this, we convert the dataset to PyTorch format
+
+    .. code-block:: python
+
+      from torch.utils.data import DataLoader
+      train_loader = DataLoader(train_dataset, batch_size=32)
+
+    Now we can use the train_dataset with PyTorch DataLoader
+
+In this example:
+
+- `subset="train"` indicates that we are working with the training portion of the dataset.
+
+- `task="classification"` specifies that this is a classification task.
+
+- The dataset is converted to PyTorch-compatible format using the `to_framework` method.

docs/source/docs/level-up/intermediate_skills/index.rst

@@ -13,6 +13,7 @@ Intermediate Skills
    09_data_filtering
    10_data_exploration
    11_data_generation
+   12_framework_conversion
 
 .. grid:: 1 2 2 2
    :gutter: 2
@@ -102,3 +103,14 @@ Intermediate Skills
 
       :bdg-info:`CLI`
       :bdg-warning:`Python`
+
+   .. grid-item-card::
+
+      .. button-ref:: 12_framework_conversion
+         :color: primary
+         :outline:
+         :expand:
+
+         Level 12: Framework Conversion
+
+      :bdg-warning:`Python`