Add missing tile recipes and various tile recipe changes

src/otx/algo/detection/base_models/detection_transformer.py

@@ -98,19 +98,24 @@ def export(
         if explain_mode:
             msg = "Explain mode is not supported for DETR models yet."
             raise NotImplementedError(msg)
-        return self.postprocess(self._forward_features(batch_inputs), deploy_mode=True)
+
+        return self.postprocess(
+            self._forward_features(batch_inputs),
+            [meta["img_shape"] for meta in batch_img_metas],
+            deploy_mode=True,
+        )
 
     def postprocess(
         self,
         outputs: dict[str, Tensor],
-        original_size: tuple[int, int] | None = None,
+        original_sizes: list[tuple[int, int]],
         deploy_mode: bool = False,
     ) -> dict[str, Tensor] | tuple[list[Tensor], list[Tensor], list[Tensor]]:
         """Post-processes the model outputs.
 
         Args:
             outputs (dict[str, Tensor]): The model outputs.
-            original_size (tuple[int, int], optional): The original size of the input images. Defaults to None.
+            original_sizes (list[tuple[int, int]]): The original image sizes.
             deploy_mode (bool, optional): Whether to run in deploy mode. Defaults to False.
 
         Returns:
@@ -120,9 +125,9 @@ def postprocess(
 
         # convert bbox to xyxy and rescale back to original size (resize in OTX)
         bbox_pred = box_convert(boxes, in_fmt="cxcywh", out_fmt="xyxy")
-        if not deploy_mode and original_size is not None:
-            original_size_tensor = torch.tensor(original_size).to(bbox_pred.device)
-            bbox_pred *= original_size_tensor.repeat(1, 2).unsqueeze(1)
+        if not deploy_mode:
+            original_size_tensor = torch.tensor(original_sizes).to(bbox_pred.device)
+            bbox_pred *= original_size_tensor.flip(1).repeat(1, 2).unsqueeze(1)
 
         # perform scores computation and gather topk results
         scores = nn.functional.sigmoid(logits)
@@ -136,7 +141,7 @@ def postprocess(
 
         scores_list, boxes_list, labels_list = [], [], []
 
-        for sc, bb, ll in zip(scores, boxes, labels):
+        for sc, bb, ll, original_size in zip(scores, boxes, labels, original_sizes):
             scores_list.append(sc)
             boxes_list.append(
                 BoundingBoxes(bb, format="xyxy", canvas_size=original_size),

src/otx/algo/detection/rtdetr.py

@@ -75,13 +75,14 @@ def _customize_inputs(
         # prepare bboxes for the model
         for bb, ll in zip(entity.bboxes, entity.labels):
             # convert to cxcywh if needed
-            converted_bboxes = (
-                box_convert(bb, in_fmt="xyxy", out_fmt="cxcywh") if bb.format == BoundingBoxFormat.XYXY else bb
-            )
-            # normalize the bboxes
-            scaled_bboxes = converted_bboxes / torch.tensor(bb.canvas_size[::-1]).tile(2)[None].to(
-                converted_bboxes.device,
-            )
+            if len(scaled_bboxes := bb):
+                converted_bboxes = (
+                    box_convert(bb, in_fmt="xyxy", out_fmt="cxcywh") if bb.format == BoundingBoxFormat.XYXY else bb
+                )
+                # normalize the bboxes
+                scaled_bboxes = converted_bboxes / torch.tensor(bb.canvas_size[::-1]).tile(2)[None].to(
+                    converted_bboxes.device,
+                )
             targets.append({"boxes": scaled_bboxes, "labels": ll})
 
         return {
@@ -109,7 +110,8 @@ def _customize_outputs(
                     raise TypeError(msg)
             return losses
 
-        scores, bboxes, labels = self.model.postprocess(outputs, [img_info.img_shape for img_info in inputs.imgs_info])
+        original_sizes = [img_info.ori_shape for img_info in inputs.imgs_info]
+        scores, bboxes, labels = self.model.postprocess(outputs, original_sizes)
 
         return DetBatchPredEntity(
             batch_size=len(outputs),

src/otx/algo/detection/yolox.py

@@ -5,7 +5,7 @@
 
 from __future__ import annotations
 
-from typing import TYPE_CHECKING, Any
+from typing import TYPE_CHECKING, Any, Literal
 
 from otx.algo.common.losses import CrossEntropyLoss, L1Loss
 from otx.algo.detection.backbones import CSPDarknet
@@ -76,13 +76,16 @@ def _exporter(self) -> OTXModelExporter:
             raise ValueError(msg)
 
         swap_rgb = not isinstance(self, YOLOXTINY)  # only YOLOX-TINY uses RGB
+        resize_mode: Literal["standard", "fit_to_window_letterbox"] = "fit_to_window_letterbox"
+        if self.tile_config.enable_tiler:
+            resize_mode = "standard"
 
         return OTXNativeModelExporter(
             task_level_export_parameters=self._export_parameters,
             input_size=(1, 3, *self.input_size),
             mean=self.mean,
             std=self.std,
-            resize_mode="fit_to_window_letterbox",
+            resize_mode=resize_mode,
             pad_value=114,
             swap_rgb=swap_rgb,
             via_onnx=True,

src/otx/core/data/dataset/tile.py

@@ -218,6 +218,9 @@ def __init__(self, dataset: OTXDataset, tile_config: TileConfig) -> None:
             dataset.mem_cache_handler,
             dataset.mem_cache_img_max_size,
             dataset.max_refetch,
+            dataset.image_color_channel,
+            dataset.stack_images,
+            dataset.to_tv_image,
         )
         self.tile_config = tile_config
         self._dataset = dataset

src/otx/core/data/entity/tile.py

@@ -125,7 +125,7 @@ def unbind(self) -> list[tuple[TileAttrDictList, DetBatchDataEntity]]:
                     labels=[[] for _ in range(self.batch_size)],
                 ),
             )
-        return list(zip(batch_tile_attr_list, batch_data_entities))
+        return list(zip(batch_tile_attr_list, batch_data_entities, strict=True))
 
     @classmethod
     def collate_fn(cls, batch_entities: list[TileDetDataEntity]) -> TileBatchDetDataEntity:
@@ -218,7 +218,7 @@ def unbind(self) -> list[tuple[TileAttrDictList, InstanceSegBatchDataEntity]]:
             )
             for i in range(0, len(tiles), self.batch_size)
         ]
-        return list(zip(batch_tile_attr_list, batch_data_entities))
+        return list(zip(batch_tile_attr_list, batch_data_entities, strict=True))
 
     @classmethod
     def collate_fn(cls, batch_entities: list[TileInstSegDataEntity]) -> TileBatchInstSegDataEntity:

src/otx/core/utils/tile_merge.py

@@ -29,7 +29,7 @@ class TileMerge(Generic[T_OTXDataEntity, T_OTXBatchPredEntity]):
         img_infos (list[ImageInfo]): Original image information before tiling.
         num_classes (int): Number of classes.
         tile_config (TileConfig): Tile configuration.
-        explain_mode (bool): Whether or not tiles have explain features. Default: False.
+        explain_mode (bool, optional): Whether or not tiles have explain features. Default: False.
     """
 
     def __init__(
@@ -119,8 +119,8 @@ def merge(
         img_ids = []
         explain_mode = self.explain_mode
 
-        for tile_preds, tile_attrs in zip(batch_tile_preds, batch_tile_attrs):
-            batch_size = tile_preds.batch_size
+        for tile_preds, tile_attrs in zip(batch_tile_preds, batch_tile_attrs, strict=True):
+            batch_size = len(tile_attrs)
             saliency_maps = tile_preds.saliency_map if explain_mode else [[] for _ in range(batch_size)]
             feature_vectors = tile_preds.feature_vector if explain_mode else [[] for _ in range(batch_size)]
             for tile_attr, tile_img_info, tile_bboxes, tile_labels, tile_scores, tile_s_map, tile_f_vect in zip(
@@ -131,6 +131,7 @@ def merge(
                 tile_preds.scores,
                 saliency_maps,
                 feature_vectors,
+                strict=True,
             ):
                 offset_x, offset_y, _, _ = tile_attr["roi"]
                 tile_bboxes[:, 0::2] += offset_x
@@ -156,7 +157,7 @@ def merge(
 
         return [
             self._merge_entities(image_info, entities_to_merge[img_id], explain_mode)
-            for img_id, image_info in zip(img_ids, self.img_infos)
+            for img_id, image_info in zip(img_ids, self.img_infos, strict=True)
         ]
 
     def _merge_entities(
@@ -319,8 +320,8 @@ def merge(
         img_ids = []
         explain_mode = self.explain_mode
 
-        for tile_preds, tile_attrs in zip(batch_tile_preds, batch_tile_attrs):
-            feature_vectors = tile_preds.feature_vector if explain_mode else [[] for _ in range(tile_preds.batch_size)]
+        for tile_preds, tile_attrs in zip(batch_tile_preds, batch_tile_attrs, strict=True):
+            feature_vectors = tile_preds.feature_vector if explain_mode else [[] for _ in range(len(tile_attrs))]
             for tile_attr, tile_img_info, tile_bboxes, tile_labels, tile_scores, tile_masks, tile_f_vect in zip(
                 tile_attrs,
                 tile_preds.imgs_info,
@@ -329,6 +330,7 @@ def merge(
                 tile_preds.scores,
                 tile_preds.masks,
                 feature_vectors,
+                strict=True,
             ):
                 keep_indices = tile_masks.to_sparse().sum((1, 2)).to_dense() > 0
                 keep_indices = keep_indices.nonzero(as_tuple=True)[0]
@@ -363,7 +365,7 @@ def merge(
 
         return [
             self._merge_entities(image_info, entities_to_merge[img_id], explain_mode)
-            for img_id, image_info in zip(img_ids, self.img_infos)
+            for img_id, image_info in zip(img_ids, self.img_infos, strict=True)
         ]
 
     def _merge_entities(

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

@@ -0,0 +1,82 @@
+task: DETECTION
+input_size:
+  - 800
+  - 800
+mem_cache_size: 1GB
+mem_cache_img_max_size: null
+image_color_channel: RGB
+stack_images: true
+data_format: coco_instances
+unannotated_items_ratio: 0.0
+tile_config:
+  enable_tiler: true
+  enable_adaptive_tiling: true
+train_subset:
+  subset_name: train
+  transform_lib_type: TORCHVISION
+  batch_size: 1
+  num_workers: 2
+  to_tv_image: false
+  transforms:
+    - class_path: otx.core.data.transform_libs.torchvision.Resize
+      init_args:
+        scale: $(input_size)
+        keep_ratio: false
+        transform_bbox: 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}
+    - class_path: torchvision.transforms.v2.Normalize
+      init_args:
+        mean: [0.0, 0.0, 0.0]
+        std: [255.0, 255.0, 255.0]
+  sampler:
+    class_path: torch.utils.data.RandomSampler
+
+val_subset:
+  subset_name: val
+  transform_lib_type: TORCHVISION
+  batch_size: 1
+  num_workers: 2
+  to_tv_image: false
+  transforms:
+    - class_path: otx.core.data.transform_libs.torchvision.Resize
+      init_args:
+        scale: $(input_size)
+        keep_ratio: false
+        is_numpy_to_tvtensor: true
+    - class_path: torchvision.transforms.v2.ToDtype
+      init_args:
+        dtype: ${as_torch_dtype:torch.float32}
+    - class_path: torchvision.transforms.v2.Normalize
+      init_args:
+        mean: [0.0, 0.0, 0.0]
+        std: [255.0, 255.0, 255.0]
+  sampler:
+    class_path: torch.utils.data.RandomSampler
+
+test_subset:
+  subset_name: test
+  transform_lib_type: TORCHVISION
+  batch_size: 1
+  num_workers: 2
+  to_tv_image: false
+  transforms:
+    - class_path: otx.core.data.transform_libs.torchvision.Resize
+      init_args:
+        scale: $(input_size)
+        keep_ratio: false
+        is_numpy_to_tvtensor: true
+    - class_path: torchvision.transforms.v2.ToDtype
+      init_args:
+        dtype: ${as_torch_dtype:torch.float32}
+    - class_path: torchvision.transforms.v2.Normalize
+      init_args:
+        mean: [0.0, 0.0, 0.0]
+        std: [255.0, 255.0, 255.0]
+  sampler:
+    class_path: torch.utils.data.RandomSampler

src/otx/recipe/detection/atss_mobilenetv2_tile.yaml

@@ -28,14 +28,10 @@ engine:
 
 callback_monitor: val/map_50
 
-data: ../_base_/data/detection.yaml
+data: ../_base_/data/detection_tile.yaml
 overrides:
   gradient_clip_val: 35.0
   data:
-    tile_config:
-      enable_tiler: true
-      enable_adaptive_tiling: true
-
     train_subset:
       batch_size: 8
       sampler:

src/otx/recipe/detection/atss_resnext101_tile.yaml

@@ -0,0 +1,51 @@
+model:
+  class_path: otx.algo.detection.atss.ResNeXt101ATSS
+  init_args:
+    label_info: 80
+
+    optimizer:
+      class_path: torch.optim.SGD
+      init_args:
+        lr: 0.004
+        momentum: 0.9
+        weight_decay: 0.0001
+
+    scheduler:
+      class_path: otx.core.schedulers.LinearWarmupSchedulerCallable
+      init_args:
+        num_warmup_steps: 3
+        main_scheduler_callable:
+          class_path: lightning.pytorch.cli.ReduceLROnPlateau
+          init_args:
+            mode: max
+            factor: 0.1
+            patience: 4
+            monitor: val/map_50
+
+engine:
+  task: DETECTION
+  device: auto
+
+callback_monitor: val/map_50
+
+data: ../_base_/data/detection_tile.yaml
+overrides:
+  gradient_clip_val: 35.0
+  callbacks:
+    - class_path: otx.algo.callbacks.adaptive_train_scheduling.AdaptiveTrainScheduling
+      init_args:
+        max_interval: 5
+        decay: -0.025
+        min_lrschedule_patience: 3
+
+  data:
+    train_subset:
+      batch_size: 4
+      sampler:
+        class_path: otx.algo.samplers.balanced_sampler.BalancedSampler
+
+    val_subset:
+      batch_size: 4
+
+    test_subset:
+      batch_size: 4

src/otx/recipe/detection/rtdetr_101.yaml

@@ -88,7 +88,6 @@ overrides:
           init_args:
             scale: $(input_size)
             keep_ratio: false
-            transform_bbox: true
             is_numpy_to_tvtensor: true
         - class_path: torchvision.transforms.v2.ToDtype
           init_args:
@@ -103,7 +102,6 @@ overrides:
           init_args:
             scale: $(input_size)
             keep_ratio: false
-            transform_bbox: true
             is_numpy_to_tvtensor: true
         - class_path: torchvision.transforms.v2.ToDtype
           init_args: