Turn on/off classification augmentations

CHANGELOG.md

@@ -4,6 +4,11 @@ All notable changes to this project will be documented in this file.
 
 ## \[Unreleased\]
 
+### New features
+
+- Turn on/off classification augmentations
+  (https://github.com/openvinotoolkit/training_extensions/pull/4039)
+
 ### Enhancements
 
 - Update visual prompting pipeline for multi-label zero-shot learning support

src/otx/core/data/transform_libs/torchvision.py

@@ -1377,7 +1377,7 @@ def forward(self, *_inputs: T_OTXDataEntity) -> T_OTXDataEntity | None:
         inputs.image = img
         inputs.img_info = _resize_image_info(inputs.img_info, img.shape[:2])
 
-        bboxes = inputs.bboxes
+        bboxes = getattr(inputs, "bboxes", [])
         num_bboxes = len(bboxes)
         if num_bboxes:
             bboxes = project_bboxes(bboxes, warp_matrix)
@@ -3471,6 +3471,8 @@ def generate(cls, config: SubsetConfig) -> Compose:
         transforms = []
         for cfg_transform in config.transforms:
             if isinstance(cfg_transform, (dict, DictConfig)):
+                if not cfg_transform.get("enable", True):  # Optional "enable: false" flag would remove the transform
+                    continue
                 cls._configure_input_size(cfg_transform, input_size)
             transform = cls._dispatch_transform(cfg_transform)
             transforms.append(transform)

src/otx/recipe/_base_/data/classification.yaml

@@ -18,10 +18,20 @@ train_subset:
     - class_path: otx.core.data.transform_libs.torchvision.RandomResizedCrop
       init_args:
         scale: $(input_size)
+    - class_path: otx.core.data.transform_libs.torchvision.PhotoMetricDistortion
+      enable: false
+    - class_path: otx.core.data.transform_libs.torchvision.RandomAffine
+      enable: false
     - class_path: otx.core.data.transform_libs.torchvision.RandomFlip
       init_args:
         prob: 0.5
         is_numpy_to_tvtensor: true
+    - class_path: torchvision.transforms.v2.RandomVerticalFlip
+      enable: false
+    - class_path: torchvision.transforms.v2.GaussianBlur
+      enable: false
+      init_args:
+        kernel_size: 5
     - class_path: torchvision.transforms.v2.ToDtype
       init_args:
         dtype: ${as_torch_dtype:torch.float32}
@@ -30,6 +40,8 @@ train_subset:
       init_args:
         mean: [123.675, 116.28, 103.53]
         std: [58.395, 57.12, 57.375]
+    - class_path: torchvision.transforms.v2.GaussianNoise
+      enable: false
   sampler:
     class_path: otx.algo.samplers.balanced_sampler.BalancedSampler
 

src/otx/recipe/classification/h_label_cls/efficientnet_b0.yaml

@@ -43,10 +43,20 @@ overrides:
         - class_path: otx.core.data.transform_libs.torchvision.EfficientNetRandomCrop
           init_args:
             scale: $(input_size)
+        - class_path: otx.core.data.transform_libs.torchvision.PhotoMetricDistortion
+          enable: false
+        - class_path: otx.core.data.transform_libs.torchvision.RandomAffine
+          enable: false
         - class_path: otx.core.data.transform_libs.torchvision.RandomFlip
           init_args:
             prob: 0.5
             is_numpy_to_tvtensor: true
+        - class_path: torchvision.transforms.v2.RandomVerticalFlip
+          enable: false
+        - class_path: torchvision.transforms.v2.GaussianBlur
+          enable: false
+          init_args:
+            kernel_size: 5
         - class_path: torchvision.transforms.v2.ToDtype
           init_args:
             dtype: ${as_torch_dtype:torch.float32}
@@ -55,3 +65,5 @@ overrides:
           init_args:
             mean: [123.675, 116.28, 103.53]
             std: [58.395, 57.12, 57.375]
+        - class_path: torchvision.transforms.v2.GaussianNoise
+          enable: false

src/otx/recipe/classification/h_label_cls/efficientnet_v2.yaml

@@ -35,10 +35,20 @@ overrides:
         - class_path: otx.core.data.transform_libs.torchvision.EfficientNetRandomCrop
           init_args:
             scale: $(input_size)
+        - class_path: otx.core.data.transform_libs.torchvision.PhotoMetricDistortion
+          enable: false
+        - class_path: otx.core.data.transform_libs.torchvision.RandomAffine
+          enable: false
         - class_path: otx.core.data.transform_libs.torchvision.RandomFlip
           init_args:
             prob: 0.5
             is_numpy_to_tvtensor: true
+        - class_path: torchvision.transforms.v2.RandomVerticalFlip
+          enable: false
+        - class_path: torchvision.transforms.v2.GaussianBlur
+          enable: false
+          init_args:
+            kernel_size: 5
         - class_path: torchvision.transforms.v2.ToDtype
           init_args:
             dtype: ${as_torch_dtype:torch.float32}
@@ -47,3 +57,5 @@ overrides:
           init_args:
             mean: [123.675, 116.28, 103.53]
             std: [58.395, 57.12, 57.375]
+        - class_path: torchvision.transforms.v2.GaussianNoise
+          enable: false

src/otx/recipe/classification/multi_class_cls/efficientnet_b0.yaml

@@ -42,10 +42,20 @@ overrides:
         - class_path: otx.core.data.transform_libs.torchvision.EfficientNetRandomCrop
           init_args:
             scale: $(input_size)
+        - class_path: otx.core.data.transform_libs.torchvision.PhotoMetricDistortion
+          enable: false
+        - class_path: otx.core.data.transform_libs.torchvision.RandomAffine
+          enable: false
         - class_path: otx.core.data.transform_libs.torchvision.RandomFlip
           init_args:
             prob: 0.5
             is_numpy_to_tvtensor: true
+        - class_path: torchvision.transforms.v2.RandomVerticalFlip
+          enable: false
+        - class_path: torchvision.transforms.v2.GaussianBlur
+          enable: false
+          init_args:
+            kernel_size: 5
         - class_path: torchvision.transforms.v2.ToDtype
           init_args:
             dtype: ${as_torch_dtype:torch.float32}
@@ -54,3 +64,5 @@ overrides:
           init_args:
             mean: [123.675, 116.28, 103.53]
             std: [58.395, 57.12, 57.375]
+        - class_path: torchvision.transforms.v2.GaussianNoise
+          enable: false

src/otx/recipe/classification/multi_class_cls/efficientnet_v2.yaml

@@ -42,10 +42,20 @@ overrides:
         - class_path: otx.core.data.transform_libs.torchvision.EfficientNetRandomCrop
           init_args:
             scale: $(input_size)
+        - class_path: otx.core.data.transform_libs.torchvision.PhotoMetricDistortion
+          enable: false
+        - class_path: otx.core.data.transform_libs.torchvision.RandomAffine
+          enable: false
         - class_path: otx.core.data.transform_libs.torchvision.RandomFlip
           init_args:
             prob: 0.5
             is_numpy_to_tvtensor: true
+        - class_path: torchvision.transforms.v2.RandomVerticalFlip
+          enable: false
+        - class_path: torchvision.transforms.v2.GaussianBlur
+          enable: false
+          init_args:
+            kernel_size: 5
         - class_path: torchvision.transforms.v2.ToDtype
           init_args:
             dtype: ${as_torch_dtype:torch.float32}
@@ -54,3 +64,5 @@ overrides:
           init_args:
             mean: [123.675, 116.28, 103.53]
             std: [58.395, 57.12, 57.375]
+        - class_path: torchvision.transforms.v2.GaussianNoise
+          enable: false

src/otx/recipe/classification/multi_label_cls/efficientnet_b0.yaml

@@ -44,10 +44,20 @@ overrides:
         - class_path: otx.core.data.transform_libs.torchvision.EfficientNetRandomCrop
           init_args:
             scale: $(input_size)
+        - class_path: otx.core.data.transform_libs.torchvision.PhotoMetricDistortion
+          enable: false
+        - class_path: otx.core.data.transform_libs.torchvision.RandomAffine
+          enable: false
         - class_path: otx.core.data.transform_libs.torchvision.RandomFlip
           init_args:
             prob: 0.5
             is_numpy_to_tvtensor: true
+        - class_path: torchvision.transforms.v2.RandomVerticalFlip
+          enable: false
+        - class_path: torchvision.transforms.v2.GaussianBlur
+          enable: false
+          init_args:
+            kernel_size: 5
         - class_path: torchvision.transforms.v2.ToDtype
           init_args:
             dtype: ${as_torch_dtype:torch.float32}
@@ -56,3 +66,5 @@ overrides:
           init_args:
             mean: [123.675, 116.28, 103.53]
             std: [58.395, 57.12, 57.375]
+        - class_path: torchvision.transforms.v2.GaussianNoise
+          enable: false

src/otx/recipe/classification/multi_label_cls/efficientnet_v2.yaml

@@ -48,10 +48,20 @@ overrides:
         - class_path: otx.core.data.transform_libs.torchvision.EfficientNetRandomCrop
           init_args:
             scale: $(input_size)
+        - class_path: otx.core.data.transform_libs.torchvision.PhotoMetricDistortion
+          enable: false
+        - class_path: otx.core.data.transform_libs.torchvision.RandomAffine
+          enable: false
         - class_path: otx.core.data.transform_libs.torchvision.RandomFlip
           init_args:
             prob: 0.5
             is_numpy_to_tvtensor: true
+        - class_path: torchvision.transforms.v2.RandomVerticalFlip
+          enable: false
+        - class_path: torchvision.transforms.v2.GaussianBlur
+          enable: false
+          init_args:
+            kernel_size: 5
         - class_path: torchvision.transforms.v2.ToDtype
           init_args:
             dtype: ${as_torch_dtype:torch.float32}
@@ -60,3 +70,5 @@ overrides:
           init_args:
             mean: [123.675, 116.28, 103.53]
             std: [58.395, 57.12, 57.375]
+        - class_path: torchvision.transforms.v2.GaussianNoise
+          enable: false

tests/integration/api/test_augmentation.py

@@ -0,0 +1,93 @@
+# Copyright (C) 2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+from __future__ import annotations
+
+import itertools
+
+import pytest
+from datumaro import Dataset as DmDataset
+from otx.core.config.data import SamplerConfig, SubsetConfig
+from otx.core.data.factory import OTXDatasetFactory
+from otx.core.data.mem_cache import MemCacheHandlerSingleton
+from otx.core.types.task import OTXTaskType
+from otx.engine.utils.auto_configurator import AutoConfigurator
+
+
+def _test_augmentation(
+    recipe: str,
+    target_dataset_per_task: dict,
+    configurable_augs: list[str],
+) -> None:
+    # Load recipe
+    recipe_tokens = recipe.split("/")
+    model_name = recipe_tokens[-1].split(".")[0]
+    task_name = recipe_tokens[-2]
+    task = OTXTaskType(task_name.upper())
+    config = AutoConfigurator(
+        data_root=target_dataset_per_task[task_name],
+        task=task,
+        model_name=model_name,
+    ).config
+    train_config = config["data"]["train_subset"]
+    train_config["input_size"] = (32, 32)
+
+    # Load dataset
+    dm_dataset = DmDataset.import_from(
+        target_dataset_per_task[task_name],
+        format=config["data"]["data_format"],
+    )
+    mem_cache_handler = MemCacheHandlerSingleton.create(
+        mode="sinlgeprocessing",
+        mem_size=0,
+    )
+
+    # Evaluate all on/off aug combinations
+    img_shape = None
+    for switches in itertools.product([True, False], repeat=len(configurable_augs)):
+        # Configure on/off
+        for aug_name, switch in zip(configurable_augs, switches):
+            aug_found = False
+            for aug_config in train_config["transforms"]:
+                if aug_name in aug_config["class_path"]:
+                    aug_config["enable"] = switch
+                    aug_found = True
+                    break
+            assert aug_found, f"{aug_name} not found in {recipe}"
+        # Create dataset
+        dataset = OTXDatasetFactory.create(
+            task=task,
+            dm_subset=dm_dataset,
+            cfg_subset=SubsetConfig(sampler=SamplerConfig(**train_config.pop("sampler", {})), **train_config),
+            mem_cache_handler=mem_cache_handler,
+        )
+
+        # Check if all aug combinations are size-compatible
+        data = dataset[0]
+        if not img_shape:
+            img_shape = data.img_info.img_shape
+        else:
+            assert img_shape == data.img_info.img_shape
+
+
+CLS_RECIPES = [
+    recipe for recipe in pytest.RECIPE_LIST if "_cls" in recipe and "semi" not in recipe and "tv_" not in recipe
+]
+
+
+@pytest.mark.parametrize(
+    "recipe",
+    CLS_RECIPES,
+)
+def test_augmentation_cls(
+    recipe: str,
+    fxt_target_dataset_per_task: dict,
+):
+    configurable_augs = [
+        "PhotoMetricDistortion",
+        "RandomAffine",
+        "RandomVerticalFlip",
+        "GaussianBlur",
+        "GaussianNoise",
+    ]
+    _test_augmentation(recipe, fxt_target_dataset_per_task, configurable_augs)

tests/unit/core/data/test_transform_libs.py

@@ -3,6 +3,7 @@
 #
 from __future__ import annotations
 
+from copy import deepcopy
 from typing import Any
 
 import pytest
@@ -119,7 +120,7 @@ def test_transform(self) -> None:
 
 
 class TestTorchVisionTransformLib:
-    @pytest.fixture(params=["from_dict", "from_list", "from_compose"])
+    @pytest.fixture(params=["from_dict", "from_obj", "from_compose"])
     def fxt_config(self, request) -> list[dict[str, Any]]:
         if request.param == "from_compose":
             return v2.Compose(
@@ -186,6 +187,46 @@ def test_transform(
         item = dataset[0]
         assert isinstance(item, data_entity_cls)
 
+    def test_transform_enable_flag(self) -> None:
+        prefix = "torchvision.transforms.v2"
+        cfg_str = f"""
+        transforms:
+          - class_path: {prefix}.RandomResizedCrop
+            init_args:
+                size: [224, 224]
+                antialias: True
+          - class_path: {prefix}.RandomHorizontalFlip
+            init_args:
+                p: 0.5
+          - class_path: {prefix}.ToDtype
+            init_args:
+                dtype: ${{as_torch_dtype:torch.float32}}
+                scale: True
+          - class_path: {prefix}.Normalize
+            init_args:
+                mean: [0.485, 0.456, 0.406]
+                std: [0.229, 0.224, 0.225]
+        """
+        cfg_org = OmegaConf.create(cfg_str)
+
+        cfg = deepcopy(cfg_org)
+        cfg.transforms[0].enable = False  # Remove 1st
+        transform = TorchVisionTransformLib.generate(cfg)
+        assert len(transform.transforms) == 3
+        assert "RandomResizedCrop" not in repr(transform)
+
+        cfg = deepcopy(cfg_org)
+        cfg.transforms[1].enable = False  # Remove 2nd
+        transform = TorchVisionTransformLib.generate(cfg)
+        assert len(transform.transforms) == 3
+        assert "RandomHorizontalFlip" not in repr(transform)
+
+        cfg = deepcopy(cfg_org)
+        cfg.transforms[2].enable = True  # No effect
+        transform = TorchVisionTransformLib.generate(cfg)
+        assert len(transform.transforms) == 4
+        assert "ToDtype" in repr(transform)
+
     @pytest.fixture()
     def fxt_config_w_input_size(self) -> list[dict[str, Any]]:
         cfg = """