[Enhancement] Support Slide Vertex TRT

configs/mmrotate/rotated-detection_static.py

@@ -6,4 +6,5 @@
         score_threshold=0.05,
         iou_threshold=0.1,
         pre_top_k=3000,
-        keep_top_k=2000))
+        keep_top_k=2000,
+        max_output_boxes_per_class=2000))

csrc/mmdeploy/backend_ops/tensorrt/common_impl/nms/allClassRotatedNMS.cu

@@ -295,7 +295,7 @@ __host__ __device__ __forceinline__ T single_box_iou_rotated(T const *const box1
   const T area1 = box1.w * box1.h;
   const T area2 = box2.w * box2.h;
   if (area1 < 1e-14 || area2 < 1e-14) {
-    return 0.f;
+    return 1.0f;
   }
 
   const T intersection = rotated_boxes_intersection<T>(box1, box2);

docs/en/03-benchmark/benchmark.md

@@ -1641,6 +1641,18 @@ Users can directly test the performance through [how_to_evaluate_a_model.md](../
     <td align="center">-</td>
     <td align="center">-</td>
   </tr>
+  <tr>
+    <td align="center"><a href="https://github.com/open-mmlab/mmrotate/blob/main/configs/gliding_vertex/gliding_vertex_r50_fpn_1x_dota_le90.py">GlidingVertex</a></td>
+    <td align="center">Rotated Detection</td>
+    <td align="center">DOTA-v1.0</td>
+    <td align="center">mAP</td>
+    <td align="center">0.732</td>
+    <td align="center">-</td>
+    <td align="center">0.733</td>
+    <td align="center">0.731</td>
+    <td align="center">-</td>
+    <td align="center">-</td>
+  </tr>
 </tbody>
 </table>
 </div>

docs/en/03-benchmark/supported_models.md

@@ -73,6 +73,8 @@ The table below lists the models that are guaranteed to be exportable to other b
 | PointPillars                | MMDetection3d    |      ?      |      Y      |    Y     |  N   |   N   |    Y     |     [config](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/pointpillars)      |
 | CenterPoint (pillar)        | MMDetection3d    |      ?      |      Y      |    Y     |  N   |   N   |    Y     |      [config](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/centerpoint)      |
 | RotatedRetinaNet            | RotatedDetection |      N      |      Y      |    Y     |  N   |   N   |    N     | [config](https://github.com/open-mmlab/mmrotate/blob/main/configs/rotated_retinanet/README.md)  |
+| Oriented RCNN               | RotatedDetection |      N      |      Y      |    Y     |  N   |   N   |    N     |   [config](https://github.com/open-mmlab/mmrotate/blob/main/configs/oriented_rcnn/README.md)    |
+| Gliding Vertex              | RotatedDetection |      N      |      N      |    Y     |  N   |   N   |    N     |   [config](https://github.com/open-mmlab/mmrotate/blob/main/configs/gliding_vertex/README.md)   |
 
 ### Note
 

docs/en/04-supported-codebases/mmrotate.md

@@ -12,6 +12,7 @@ Please refer to [official installation guide](https://mmrotate.readthedocs.io/en
 | :--------------- | :--------------- | :----------: | :------: | :--: | :---: | :------: | :--------------------------------------------------------------------------------------------: |
 | RotatedRetinaNet | RotatedDetection |      Y       |    Y     |  N   |   N   |    N     | [config](https://github.com/open-mmlab/mmrotate/blob/main/configs/rotated_retinanet/README.md) |
 | Oriented RCNN    | RotatedDetection |      Y       |    Y     |  N   |   N   |    N     |   [config](https://github.com/open-mmlab/mmrotate/blob/main/configs/oriented_rcnn/README.md)   |
+| Gliding Vertex   | RotatedDetection |      N       |    Y     |  N   |   N   |    N     |  [config](https://github.com/open-mmlab/mmrotate/blob/main/configs/gliding_vertex/README.md)   |
 
 ### Example
 

docs/zh_cn/03-benchmark/benchmark.md

@@ -1638,6 +1638,18 @@ GPU: ncnn, TensorRT, PPLNN
     <td align="center">-</td>
     <td align="center">-</td>
   </tr>
+  <tr>
+    <td align="center"><a href="https://github.com/open-mmlab/mmrotate/blob/main/configs/gliding_vertex/gliding_vertex_r50_fpn_1x_dota_le90.py">GlidingVertex</a></td>
+    <td align="center">Rotated Detection</td>
+    <td align="center">DOTA-v1.0</td>
+    <td align="center">mAP</td>
+    <td align="center">0.732</td>
+    <td align="center">-</td>
+    <td align="center">0.733</td>
+    <td align="center">0.731</td>
+    <td align="center">-</td>
+    <td align="center">-</td>
+  </tr>
 </tbody>
 </table>
 </div>

docs/zh_cn/03-benchmark/supported_models.md

@@ -72,6 +72,7 @@
 | CenterPoint (pillar)        | MMDetection3d    |      ?      |      Y      |    Y     |  N   |   N   |    Y     |      [config](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/centerpoint)      |
 | RotatedRetinaNet            | RotatedDetection |      N      |      Y      |    Y     |  N   |   N   |    N     | [config](https://github.com/open-mmlab/mmrotate/blob/main/configs/rotated_retinanet/README.md)  |
 | Oriented RCNN               | RotatedDetection |      N      |      Y      |    Y     |  N   |   N   |    N     |   [config](https://github.com/open-mmlab/mmrotate/blob/main/configs/oriented_rcnn/README.md)    |
+| Gliding Vertex              | RotatedDetection |      N      |      N      |    Y     |  N   |   N   |    N     |   [config](https://github.com/open-mmlab/mmrotate/blob/main/configs/gliding_vertex/README.md)   |
 
 ## Note
 

mmdeploy/codebase/mmrotate/core/bbox/__init__.py

@@ -1,4 +1,5 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 from .delta_midpointoffset_rbbox_coder import *  # noqa: F401,F403
 from .delta_xywha_rbbox_coder import *  # noqa: F401,F403
+from .gliding_vertex_coder import *  # noqa: F401,F403
 from .transforms import *  # noqa: F401,F403

mmdeploy/codebase/mmrotate/core/bbox/gliding_vertex_coder.py

@@ -0,0 +1,31 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch
+
+from mmdeploy.core import FUNCTION_REWRITER
+
+
+@FUNCTION_REWRITER.register_rewriter(
+    'mmrotate.core.bbox.coder.gliding_vertex_coder'
+    '.GVFixCoder.decode')
+def gvfixcoder__decode(ctx, self, hbboxes, fix_deltas):
+    """Rewriter for GVFixCoder decode, support more dimension input."""
+
+    from mmrotate.core.bbox.transforms import poly2obb
+    x1 = hbboxes[..., 0::4]
+    y1 = hbboxes[..., 1::4]
+    x2 = hbboxes[..., 2::4]
+    y2 = hbboxes[..., 3::4]
+    w = hbboxes[..., 2::4] - hbboxes[..., 0::4]
+    h = hbboxes[..., 3::4] - hbboxes[..., 1::4]
+
+    pred_t_x = x1 + w * fix_deltas[..., 0::4]
+    pred_r_y = y1 + h * fix_deltas[..., 1::4]
+    pred_d_x = x2 - w * fix_deltas[..., 2::4]
+    pred_l_y = y2 - h * fix_deltas[..., 3::4]
+
+    polys = torch.stack(
+        [pred_t_x, y1, x2, pred_r_y, pred_d_x, y2, x1, pred_l_y], dim=-1)
+    polys = polys.flatten(2)
+    rbboxes = poly2obb(polys, self.version)
+
+    return rbboxes

mmdeploy/codebase/mmrotate/core/post_processing/bbox_nms.py

@@ -77,6 +77,7 @@ def select_rnms_index(scores: torch.Tensor,
 
 def _multiclass_nms_rotated(boxes: Tensor,
                             scores: Tensor,
+                            max_output_boxes_per_class: int = 1000,
                             iou_threshold: float = 0.1,
                             score_threshold: float = 0.05,
                             pre_top_k: int = -1,

mmdeploy/codebase/mmrotate/deploy/rotated_detection_model.py

@@ -75,6 +75,33 @@ def _init_wrapper(self, backend: Backend, backend_files: Sequence[str],
             output_names=output_names,
             deploy_cfg=self.deploy_cfg)
 
+    @staticmethod
+    def __clear_outputs(
+        test_outputs: List[Union[torch.Tensor, np.ndarray]]
+    ) -> List[Union[List[torch.Tensor], List[np.ndarray]]]:
+        """Removes additional outputs and detections with zero and negative
+        score.
+
+        Args:
+            test_outputs (List[Union[torch.Tensor, np.ndarray]]):
+                outputs of forward_test.
+
+        Returns:
+            List[Union[List[torch.Tensor], List[np.ndarray]]]:
+                outputs with without zero score object.
+        """
+        batch_size = len(test_outputs[0])
+
+        num_outputs = len(test_outputs)
+        outputs = [[None for _ in range(batch_size)]
+                   for _ in range(num_outputs)]
+
+        for i in range(batch_size):
+            inds = test_outputs[0][i, :, -1] > 0.0
+            for output_id in range(num_outputs):
+                outputs[output_id][i] = test_outputs[output_id][i, inds, ...]
+        return outputs
+
     def forward(self, img: Sequence[torch.Tensor],
                 img_metas: Sequence[Sequence[dict]], *args, **kwargs) -> list:
         """Run forward inference.
@@ -91,6 +118,7 @@ def forward(self, img: Sequence[torch.Tensor],
         input_img = img[0].contiguous()
         img_metas = img_metas[0]
         outputs = self.forward_test(input_img, img_metas, *args, **kwargs)
+        outputs = End2EndModel.__clear_outputs(outputs)
         batch_dets, batch_labels = outputs[:2]
         batch_size = input_img.shape[0]
         rescale = kwargs.get('rescale', False)

mmdeploy/codebase/mmrotate/models/__init__.py

@@ -1,19 +1,4 @@
 # Copyright (c) OpenMMLab. All rights reserved.
-from .oriented_standard_roi_head import (
-    oriented_standard_roi_head__simple_test,
-    oriented_standard_roi_head__simple_test_bboxes)
-from .roi_extractors import rotated_single_roi_extractor__forward__tensorrt
-from .rotated_anchor_head import rotated_anchor_head__get_bbox
-from .rotated_bbox_head import rotated_bbox_head__get_bboxes
-from .rotated_rpn_head import rotated_rpn_head__get_bboxes
-from .single_stage_rotated_detector import \
-    single_stage_rotated_detector__simple_test
-
-__all__ = [
-    'single_stage_rotated_detector__simple_test',
-    'rotated_anchor_head__get_bbox', 'rotated_rpn_head__get_bboxes',
-    'oriented_standard_roi_head__simple_test',
-    'oriented_standard_roi_head__simple_test_bboxes',
-    'rotated_bbox_head__get_bboxes',
-    'rotated_single_roi_extractor__forward__tensorrt'
-]
+from .dense_heads import *  # noqa: F401,F403
+from .roi_heads import *  # noqa: F401,F403
+from .single_stage_rotated_detector import *  # noqa: F401,F403

mmdeploy/codebase/mmrotate/models/dense_heads/__init__.py

@@ -0,0 +1,9 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from .oriented_rpn_head import oriented_rpn_head__get_bboxes
+from .rotated_anchor_head import rotated_anchor_head__get_bbox
+from .rotated_rpn_head import rotated_rpn_head__get_bboxes
+
+__all__ = [
+    'oriented_rpn_head__get_bboxes', 'rotated_anchor_head__get_bbox',
+    'rotated_rpn_head__get_bboxes'
+]

mmdeploy/codebase/mmrotate/models/dense_heads/oriented_rpn_head.py

@@ -0,0 +1,141 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch
+
+from mmdeploy.codebase.mmdet import (get_post_processing_params,
+                                     pad_with_value_if_necessary)
+from mmdeploy.codebase.mmrotate.core.post_processing import \
+    fake_multiclass_nms_rotated
+from mmdeploy.core import FUNCTION_REWRITER
+from mmdeploy.utils import is_dynamic_shape
+
+
+@FUNCTION_REWRITER.register_rewriter(
+    'mmrotate.models.dense_heads.OrientedRPNHead.get_bboxes')
+def oriented_rpn_head__get_bboxes(ctx,
+                                  self,
+                                  cls_scores,
+                                  bbox_preds,
+                                  score_factors=None,
+                                  img_metas=None,
+                                  cfg=None,
+                                  rescale=False,
+                                  with_nms=True,
+                                  **kwargs):
+    """Rewrite `get_bboxes` of `RPNHead` for default backend.
+
+    Rewrite this function to deploy model, transform network output for a
+    batch into bbox predictions.
+
+    Args:
+        ctx (ContextCaller): The context with additional information.
+        self (FoveaHead): The instance of the class FoveaHead.
+        cls_scores (list[Tensor]): Box scores for each scale level
+            with shape (N, num_anchors * num_classes, H, W).
+        bbox_preds (list[Tensor]): Box energies / deltas for each scale
+            level with shape (N, num_anchors * 4, H, W).
+        score_factors (list[Tensor], Optional): Score factor for
+            all scale level, each is a 4D-tensor, has shape
+            (batch_size, num_priors * 1, H, W). Default None.
+        img_metas (list[dict]):  Meta information of the image, e.g.,
+            image size, scaling factor, etc.
+        cfg (mmcv.Config | None): Test / postprocessing configuration,
+            if None, test_cfg would be used. Default: None.
+        rescale (bool): If True, return boxes in original image space.
+            Default False.
+        with_nms (bool): If True, do nms before return boxes.
+            Default: True.
+    Returns:
+        If with_nms == True:
+            tuple[Tensor, Tensor]: tuple[Tensor, Tensor]: (dets, labels),
+            `dets` of shape [N, num_det, 5] and `labels` of shape
+            [N, num_det].
+        Else:
+            tuple[Tensor, Tensor, Tensor]: batch_mlvl_bboxes, batch_mlvl_scores
+    """
+    assert len(cls_scores) == len(bbox_preds)
+    deploy_cfg = ctx.cfg
+    is_dynamic_flag = is_dynamic_shape(deploy_cfg)
+    num_levels = len(cls_scores)
+
+    device = cls_scores[0].device
+    featmap_sizes = [cls_scores[i].shape[-2:] for i in range(num_levels)]
+    mlvl_anchors = self.anchor_generator.grid_anchors(
+        featmap_sizes, device=device)
+
+    mlvl_cls_scores = [cls_scores[i].detach() for i in range(num_levels)]
+    mlvl_bbox_preds = [bbox_preds[i].detach() for i in range(num_levels)]
+    assert len(mlvl_cls_scores) == len(mlvl_bbox_preds) == len(mlvl_anchors)
+
+    cfg = self.test_cfg if cfg is None else cfg
+    batch_size = mlvl_cls_scores[0].shape[0]
+    pre_topk = cfg.get('nms_pre', -1)
+
+    # loop over features, decode boxes
+    mlvl_valid_bboxes = []
+    mlvl_scores = []
+    mlvl_valid_anchors = []
+    for level_id, cls_score, bbox_pred, anchors in zip(
+            range(num_levels), mlvl_cls_scores, mlvl_bbox_preds, mlvl_anchors):
+        assert cls_score.size()[-2:] == bbox_pred.size()[-2:]
+        cls_score = cls_score.permute(0, 2, 3, 1)
+        if self.use_sigmoid_cls:
+            cls_score = cls_score.reshape(batch_size, -1)
+            scores = cls_score.sigmoid()
+        else:
+            cls_score = cls_score.reshape(batch_size, -1, 2)
+            # We set FG labels to [0, num_class-1] and BG label to
+            # num_class in RPN head since mmdet v2.5, which is unified to
+            # be consistent with other head since mmdet v2.0. In mmdet v2.0
+            # to v2.4 we keep BG label as 0 and FG label as 1 in rpn head.
+            scores = cls_score.softmax(-1)[..., 0]
+        scores = scores.reshape(batch_size, -1, 1)
+        bbox_pred = bbox_pred.permute(0, 2, 3, 1).reshape(batch_size, -1, 6)
+
+        # use static anchor if input shape is static
+        if not is_dynamic_flag:
+            anchors = anchors.data
+
+        anchors = anchors.unsqueeze(0)
+
+        # topk in tensorrt does not support shape<k
+        # concate zero to enable topk,
+        scores = pad_with_value_if_necessary(scores, 1, pre_topk, 0.)
+        bbox_pred = pad_with_value_if_necessary(bbox_pred, 1, pre_topk)
+        anchors = pad_with_value_if_necessary(anchors, 1, pre_topk)
+
+        if pre_topk > 0:
+            _, topk_inds = scores.squeeze(2).topk(pre_topk)
+            batch_inds = torch.arange(batch_size, device=device).unsqueeze(-1)
+            prior_inds = topk_inds.new_zeros((1, 1))
+            anchors = anchors[prior_inds, topk_inds, :]
+            bbox_pred = bbox_pred[batch_inds, topk_inds, :]
+            scores = scores[batch_inds, topk_inds, :]
+        mlvl_valid_bboxes.append(bbox_pred)
+        mlvl_scores.append(scores)
+        mlvl_valid_anchors.append(anchors)
+
+    batch_mlvl_bboxes = torch.cat(mlvl_valid_bboxes, dim=1)
+    batch_mlvl_scores = torch.cat(mlvl_scores, dim=1)
+    batch_mlvl_anchors = torch.cat(mlvl_valid_anchors, dim=1)
+    batch_mlvl_bboxes = self.bbox_coder.decode(
+        batch_mlvl_anchors,
+        batch_mlvl_bboxes,
+        max_shape=img_metas[0]['img_shape'])
+    # ignore background class
+    if not self.use_sigmoid_cls:
+        batch_mlvl_scores = batch_mlvl_scores[..., :self.num_classes]
+    if not with_nms:
+        return batch_mlvl_bboxes, batch_mlvl_scores
+
+    post_params = get_post_processing_params(deploy_cfg)
+    iou_threshold = cfg.nms.get('iou_threshold', post_params.iou_threshold)
+    keep_top_k = cfg.get('max_per_img', post_params.keep_top_k)
+    # only one class in rpn
+    max_output_boxes_per_class = keep_top_k
+    return fake_multiclass_nms_rotated(
+        batch_mlvl_bboxes,
+        batch_mlvl_scores,
+        max_output_boxes_per_class,
+        iou_threshold=iou_threshold,
+        keep_top_k=keep_top_k,
+        version=self.version)

mmdeploy/codebase/mmrotate/models/rotated_rpn_head.py → mmdeploy/codebase/mmrotate/models/dense_heads/rotated_rpn_head.py

@@ -3,8 +3,9 @@
 
 from mmdeploy.codebase.mmdet import (get_post_processing_params,
                                      pad_with_value_if_necessary)
-from mmdeploy.codebase.mmrotate.core.post_processing import \
-    fake_multiclass_nms_rotated
+# from mmdeploy.codebase.mmrotate.core.post_processing import \
+#     multiclass_nms_rotated
+from mmdeploy.codebase.mmdet.core.post_processing import multiclass_nms
 from mmdeploy.core import FUNCTION_REWRITER
 from mmdeploy.utils import is_dynamic_shape
 
@@ -89,7 +90,7 @@ def rotated_rpn_head__get_bboxes(ctx,
             # to v2.4 we keep BG label as 0 and FG label as 1 in rpn head.
             scores = cls_score.softmax(-1)[..., 0]
         scores = scores.reshape(batch_size, -1, 1)
-        bbox_pred = bbox_pred.permute(0, 2, 3, 1).reshape(batch_size, -1, 6)
+        bbox_pred = bbox_pred.permute(0, 2, 3, 1).reshape(batch_size, -1, 4)
 
         # use static anchor if input shape is static
         if not is_dynamic_flag:
@@ -129,13 +130,16 @@ def rotated_rpn_head__get_bboxes(ctx,
 
     post_params = get_post_processing_params(deploy_cfg)
     iou_threshold = cfg.nms.get('iou_threshold', post_params.iou_threshold)
+    score_threshold = cfg.get('score_thr', post_params.score_threshold)
+    pre_top_k = post_params.pre_top_k
     keep_top_k = cfg.get('max_per_img', post_params.keep_top_k)
     # only one class in rpn
     max_output_boxes_per_class = keep_top_k
-    return fake_multiclass_nms_rotated(
+    return multiclass_nms(
         batch_mlvl_bboxes,
         batch_mlvl_scores,
         max_output_boxes_per_class,
         iou_threshold=iou_threshold,
-        keep_top_k=keep_top_k,
-        version=self.version)
+        score_threshold=score_threshold,
+        pre_top_k=pre_top_k,
+        keep_top_k=keep_top_k)

mmdeploy/codebase/mmrotate/models/roi_heads/__init__.py

@@ -0,0 +1,14 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from .gv_bbox_head import gv_bbox_head__get_bboxes
+from .gv_ratio_roi_head import gv_ratio_roi_head__simple_test_bboxes
+from .oriented_standard_roi_head import \
+    oriented_standard_roi_head__simple_test_bboxes
+from .roi_extractors import rotated_single_roi_extractor__forward__tensorrt
+from .rotated_bbox_head import rotated_bbox_head__get_bboxes
+
+__all__ = [
+    'gv_bbox_head__get_bboxes', 'gv_ratio_roi_head__simple_test_bboxes',
+    'oriented_standard_roi_head__simple_test_bboxes',
+    'rotated_single_roi_extractor__forward__tensorrt',
+    'rotated_bbox_head__get_bboxes'
+]