fix unittest

src/otx/core/model/instance_segmentation.py

@@ -12,12 +12,13 @@
 
 import numpy as np
 import torch
-from mmengine.structures.instance_data import InstanceData
 from model_api.models import Model
 from model_api.tilers import InstanceSegmentationTiler
 from torchvision import tv_tensors
 
 from otx.algo.explain.explain_algo import InstSegExplainAlgo, feature_vector_fn
+from otx.algo.instance_segmentation.mmdet.models.detectors.two_stage import TwoStageDetector
+from otx.algo.utils.mmengine_utils import InstanceData
 from otx.core.config.data import TileConfig
 from otx.core.data.entity.base import OTXBatchLossEntity
 from otx.core.data.entity.instance_segmentation import InstanceSegBatchDataEntity, InstanceSegBatchPredEntity
@@ -35,8 +36,6 @@
 if TYPE_CHECKING:
     from lightning.pytorch.cli import LRSchedulerCallable, OptimizerCallable
     from mmdet.models.data_preprocessors import DetDataPreprocessor
-    from mmdet.models.detectors import TwoStageDetector
-    from mmdet.structures import OptSampleList
     from model_api.models.utils import InstanceSegmentationResult
     from omegaconf import DictConfig
     from torch import nn
@@ -257,8 +256,7 @@ def forward_explain(self, inputs: InstanceSegBatchDataEntity) -> InstanceSegBatc
     @staticmethod
     def _forward_explain_inst_seg(
         self: TwoStageDetector,
-        inputs: torch.Tensor,
-        data_samples: OptSampleList = None,
+        entity: InstanceSegBatchDataEntity,
         mode: str = "tensor",  # noqa: ARG004
     ) -> dict[str, torch.Tensor]:
         """Forward func of the BaseDetector instance, which located in is in ExplainableOTXInstanceSegModel().model."""
@@ -267,20 +265,21 @@ def _forward_explain_inst_seg(
         for param in self.parameters():
             param.requires_grad = False
 
-        x = self.extract_feat(inputs)
+        x = self.extract_feat(entity.images)
 
         feature_vector = self.feature_vector_fn(x)
-        predictions = self.get_results_from_head(x, data_samples)
+        predictions = self.get_results_from_head(x, entity)
 
         if isinstance(predictions, tuple) and isinstance(predictions[0], torch.Tensor):
             # Export case, consists of tensors
             # For OV task saliency map are generated on MAPI side
             saliency_map = torch.empty(1, dtype=torch.uint8)
-
         elif isinstance(predictions, list) and isinstance(predictions[0], InstanceData):
             # Predict case, consists of InstanceData
             saliency_map = self.explain_fn(predictions)
-            predictions = self.add_pred_to_datasample(data_samples, predictions)
+        else:
+            msg = f"Unexpected predictions type: {type(predictions)}"
+            raise TypeError(msg)
 
         return {
             "predictions": predictions,
@@ -291,7 +290,7 @@ def _forward_explain_inst_seg(
     def get_results_from_head(
         self,
         x: tuple[torch.Tensor],
-        data_samples: OptSampleList | None,
+        entity: InstanceSegBatchDataEntity,
     ) -> tuple[torch.Tensor] | list[InstanceData]:
         """Get the results from the head of the instance segmentation model.
 
@@ -306,9 +305,9 @@ def get_results_from_head(
         from otx.algo.instance_segmentation.rtmdet_inst import RTMDetInstTiny
 
         if isinstance(self, RTMDetInstTiny):
-            return self.model.bbox_head.predict(x, data_samples, rescale=False)
-        rpn_results_list = self.model.rpn_head.predict(x, data_samples, rescale=False)
-        return self.model.roi_head.predict(x, rpn_results_list, data_samples, rescale=True)
+            return self.model.bbox_head.predict(x, entity, rescale=False)
+        rpn_results_list = self.model.rpn_head.predict(x, entity, rescale=False)
+        return self.model.roi_head.predict(x, rpn_results_list, entity, rescale=True)
 
     def get_explain_fn(self) -> Callable:
         """Returns explain function."""

src/otx/recipe/rotated_detection/maskrcnn_efficientnetb2b.yaml

@@ -28,66 +28,89 @@ engine:
 
 callback_monitor: val/map_50
 
-data: ../_base_/data/mmdet_base.yaml
+data: ../_base_/data/torchvision_base.yaml
 overrides:
   max_epochs: 100
   data:
     task: ROTATED_DETECTION
     config:
+      stack_images: true
+      data_format: coco_instances
       include_polygons: true
       train_subset:
         batch_size: 4
         transforms:
-          - type: LoadImageFromFile
-            backend_args: null
-          - type: LoadAnnotations
-            with_bbox: true
-            with_mask: true
-          - type: Resize
-            keep_ratio: true
-            scale:
-              - 1024
-              - 1024
-          - type: RandomFlip
-            prob: 0.5
-          - type: PackDetInputs
+          - class_path: otx.core.data.transform_libs.torchvision.Resize
+            init_args:
+              keep_ratio: true
+              transform_bbox: true
+              transform_mask: true
+              scale:
+                - 1024
+                - 1024
+          - class_path: otx.core.data.transform_libs.torchvision.Pad
+            init_args:
+              size_divisor: 32
+              transform_mask: true
+          - class_path: otx.core.data.transform_libs.torchvision.RandomFlip
+            init_args:
+              prob: 0.5
+              is_numpy_to_tvtensor: true
+          - class_path: torchvision.transforms.v2.ToDtype
+            init_args:
+              dtype: ${as_torch_dtype:torch.float32}
+              scale: False
+          - class_path: torchvision.transforms.v2.Normalize
+            init_args:
+              mean: [123.675, 116.28, 103.53]
+              std: [1.0, 1.0, 1.0]
+        sampler:
+          class_path: otx.algo.samplers.balanced_sampler.BalancedSampler
       val_subset:
         batch_size: 1
         transforms:
-          - type: LoadImageFromFile
-            backend_args: null
-          - type: Resize
-            keep_ratio: true
-            scale:
-              - 1024
-              - 1024
-          - type: LoadAnnotations
-            with_bbox: true
-            with_mask: true
-          - type: PackDetInputs
-            meta_keys:
-              - img_id
-              - img_path
-              - ori_shape
-              - img_shape
-              - scale_factor
+          - class_path: otx.core.data.transform_libs.torchvision.Resize
+            init_args:
+              keep_ratio: true
+              transform_bbox: false
+              transform_mask: false
+              scale:
+                - 1024
+                - 1024
+          - class_path: otx.core.data.transform_libs.torchvision.Pad
+            init_args:
+              size_divisor: 32
+              transform_mask: false
+              is_numpy_to_tvtensor: true
+          - class_path: torchvision.transforms.v2.ToDtype
+            init_args:
+              dtype: ${as_torch_dtype:torch.float32}
+              scale: False
+          - class_path: torchvision.transforms.v2.Normalize
+            init_args:
+              mean: [123.675, 116.28, 103.53]
+              std: [1.0, 1.0, 1.0]
       test_subset:
         batch_size: 1
         transforms:
-          - type: LoadImageFromFile
-            backend_args: null
-          - type: Resize
-            keep_ratio: true
-            scale:
-              - 1024
-              - 1024
-          - type: LoadAnnotations
-            with_bbox: true
-            with_mask: true
-          - type: PackDetInputs
-            meta_keys:
-              - img_id
-              - img_path
-              - ori_shape
-              - img_shape
-              - scale_factor
+          - class_path: otx.core.data.transform_libs.torchvision.Resize
+            init_args:
+              keep_ratio: true
+              transform_bbox: false
+              transform_mask: false
+              scale:
+                - 1024
+                - 1024
+          - class_path: otx.core.data.transform_libs.torchvision.Pad
+            init_args:
+              size_divisor: 32
+              transform_mask: false
+              is_numpy_to_tvtensor: true
+          - class_path: torchvision.transforms.v2.ToDtype
+            init_args:
+              dtype: ${as_torch_dtype:torch.float32}
+              scale: False
+          - class_path: torchvision.transforms.v2.Normalize
+            init_args:
+              mean: [123.675, 116.28, 103.53]
+              std: [1.0, 1.0, 1.0]

src/otx/recipe/rotated_detection/maskrcnn_r50.yaml

@@ -28,66 +28,87 @@ engine:
 
 callback_monitor: val/map_50
 
-data: ../_base_/data/mmdet_base.yaml
+data: ../_base_/data/torchvision_base.yaml
 overrides:
   max_epochs: 100
   data:
     task: ROTATED_DETECTION
     config:
+      stack_images: true
+      data_format: coco_instances
       include_polygons: true
       train_subset:
         batch_size: 4
         transforms:
-          - type: LoadImageFromFile
-            backend_args: null
-          - type: LoadAnnotations
-            with_bbox: true
-            with_mask: true
-          - type: Resize
-            keep_ratio: true
-            scale:
-              - 1024
-              - 1024
-          - type: RandomFlip
-            prob: 0.5
-          - type: PackDetInputs
+          - class_path: otx.core.data.transform_libs.torchvision.Resize
+            init_args:
+              keep_ratio: true
+              transform_bbox: true
+              transform_mask: true
+              scale:
+                - 1024
+                - 1024
+          - class_path: otx.core.data.transform_libs.torchvision.Pad
+            init_args:
+              size_divisor: 32
+              transform_mask: true
+          - class_path: otx.core.data.transform_libs.torchvision.RandomFlip
+            init_args:
+              prob: 0.5
+              is_numpy_to_tvtensor: true
+          - class_path: torchvision.transforms.v2.ToDtype
+            init_args:
+              dtype: ${as_torch_dtype:torch.float32}
+              scale: False
+          - class_path: torchvision.transforms.v2.Normalize
+            init_args:
+              mean: [123.675, 116.28, 103.53]
+              std: [58.395, 57.12, 57.375]
       val_subset:
         batch_size: 1
         transforms:
-          - type: LoadImageFromFile
-            backend_args: null
-          - type: Resize
-            keep_ratio: true
-            scale:
-              - 1024
-              - 1024
-          - type: LoadAnnotations
-            with_bbox: true
-            with_mask: true
-          - type: PackDetInputs
-            meta_keys:
-              - img_id
-              - img_path
-              - ori_shape
-              - img_shape
-              - scale_factor
+          - class_path: otx.core.data.transform_libs.torchvision.Resize
+            init_args:
+              keep_ratio: true
+              transform_bbox: false
+              transform_mask: false
+              scale:
+                - 1024
+                - 1024
+          - class_path: otx.core.data.transform_libs.torchvision.Pad
+            init_args:
+              size_divisor: 32
+              transform_mask: false
+              is_numpy_to_tvtensor: true
+          - class_path: torchvision.transforms.v2.ToDtype
+            init_args:
+              dtype: ${as_torch_dtype:torch.float32}
+              scale: False
+          - class_path: torchvision.transforms.v2.Normalize
+            init_args:
+              mean: [123.675, 116.28, 103.53]
+              std: [58.395, 57.12, 57.375]
       test_subset:
         batch_size: 1
         transforms:
-          - type: LoadImageFromFile
-            backend_args: null
-          - type: Resize
-            keep_ratio: true
-            scale:
-              - 1024
-              - 1024
-          - type: LoadAnnotations
-            with_bbox: true
-            with_mask: true
-          - type: PackDetInputs
-            meta_keys:
-              - img_id
-              - img_path
-              - ori_shape
-              - img_shape
-              - scale_factor
+          - class_path: otx.core.data.transform_libs.torchvision.Resize
+            init_args:
+              keep_ratio: true
+              transform_bbox: false
+              transform_mask: false
+              scale:
+                - 1024
+                - 1024
+          - class_path: otx.core.data.transform_libs.torchvision.Pad
+            init_args:
+              size_divisor: 32
+              transform_mask: false
+              is_numpy_to_tvtensor: true
+          - class_path: torchvision.transforms.v2.ToDtype
+            init_args:
+              dtype: ${as_torch_dtype:torch.float32}
+              scale: False
+          - class_path: torchvision.transforms.v2.Normalize
+            init_args:
+              mean: [123.675, 116.28, 103.53]
+              std: [58.395, 57.12, 57.375]

tests/unit/core/model/test_inst_segmentation.py

@@ -26,25 +26,25 @@ def test_get_explain_fn(self, otx_model):
         explain_fn = otx_model.get_explain_fn()
         assert callable(explain_fn)
 
-    def test_forward_explain_inst_seg(self, otx_model, fxt_data_sample):
-        inputs = torch.randn(1, 3, 224, 224)
+    def test_forward_explain_inst_seg(self, otx_model, fxt_inst_seg_data_entity):
+        inputs = fxt_inst_seg_data_entity[2]
+        inputs.images = torch.randn(1, 3, 224, 224)
         otx_model.model.feature_vector_fn = feature_vector_fn
         otx_model.model.explain_fn = otx_model.get_explain_fn()
-        result = otx_model._forward_explain_inst_seg(otx_model.model, inputs, fxt_data_sample, mode="predict")
+        result = otx_model._forward_explain_inst_seg(otx_model.model, inputs, mode="predict")
 
         assert "predictions" in result
         assert "feature_vector" in result
         assert "saliency_map" in result
 
     def test_customize_inputs(self, otx_model, fxt_inst_seg_data_entity) -> None:
         output_data = otx_model._customize_inputs(fxt_inst_seg_data_entity[2])
-        assert output_data is not None
-        assert "gt_instances" in output_data["data_samples"][-1]
-        assert "masks" in output_data["data_samples"][-1].gt_instances
-        assert output_data["data_samples"][-1].metainfo["pad_shape"] == output_data["inputs"].shape[-2:]
+        assert output_data["mode"] == "loss"
+        assert output_data["entity"] == fxt_inst_seg_data_entity[2]
 
     def test_forward_explain(self, otx_model, fxt_inst_seg_data_entity):
         inputs = fxt_inst_seg_data_entity[2]
+        inputs.images = [image.float() for image in inputs.images]
         otx_model.training = False
         otx_model.explain_mode = True
         outputs = otx_model.forward_explain(inputs)

tests/unit/engine/utils/test_api.py

@@ -33,7 +33,6 @@ def test_list_models_pattern() -> None:
         "efficientnet_v2",
         "maskrcnn_efficientnetb2b",
         "maskrcnn_efficientnetb2b_tile",
-        "maskrcnn_efficientnetb2b_tv",
         "tv_efficientnet_b3",
         "tv_efficientnet_v2_l",
     ]