support htc

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

@@ -214,6 +214,7 @@ Besides python API, mmdeploy SDK also provides other FFI (Foreign Function Inter
 |                 [CenterNet](https://github.com/open-mmlab/mmdetection/tree/main/configs/centernet)                  |   Object Detection    |      Y      |    Y     |  N   |   ?   |    Y     |
 |                    [RTMDet](https://github.com/open-mmlab/mmdetection/tree/main/configs/rtmdet)                     |   Object Detection    |      Y      |    Y     |  N   |   ?   |    Y     |
 |           [Cascade Mask R-CNN](https://github.com/open-mmlab/mmdetection/tree/main/configs/cascade_rcnn)            | Instance Segmentation |      Y      |    Y     |  N   |   N   |    Y     |
+|                       [HTC](https://github.com/open-mmlab/mmdetection/tree/main/configs/htc)                        | Instance Segmentation |      Y      |    Y     |  N   |   ?   |    Y     |
 |                 [Mask R-CNN](https://github.com/open-mmlab/mmdetection/tree/main/configs/mask_rcnn)                 | Instance Segmentation |      Y      |    Y     |  N   |   N   |    Y     |
 |                [Swin Transformer](https://github.com/open-mmlab/mmdetection/tree/main/configs/swin)                 | Instance Segmentation |      Y      |    Y     |  N   |   N   |    Y     |
 |                      [SOLO](https://github.com/open-mmlab/mmdetection/tree/main/configs/solo)                       | Instance Segmentation |      Y      |    N     |  N   |   N   |    Y     |

docs/zh_cn/04-supported-codebases/mmdet.md

@@ -217,6 +217,7 @@ cv2.imwrite('output_detection.png', img)
 |                 [CenterNet](https://github.com/open-mmlab/mmdetection/tree/main/configs/centernet)                  |   Object Detection    |      Y      |    Y     |  N   |   ?   |    Y     |
 |                    [RTMDet](https://github.com/open-mmlab/mmdetection/tree/main/configs/rtmdet)                     |   Object Detection    |      Y      |    Y     |  N   |   ?   |    Y     |
 |           [Cascade Mask R-CNN](https://github.com/open-mmlab/mmdetection/tree/main/configs/cascade_rcnn)            | Instance Segmentation |      Y      |    Y     |  N   |   N   |    Y     |
+|                       [HTC](https://github.com/open-mmlab/mmdetection/tree/main/configs/htc)                        | Instance Segmentation |      Y      |    Y     |  N   |   ?   |    Y     |
 |                 [Mask R-CNN](https://github.com/open-mmlab/mmdetection/tree/main/configs/mask_rcnn)                 | Instance Segmentation |      Y      |    Y     |  N   |   N   |    Y     |
 |                [Swin Transformer](https://github.com/open-mmlab/mmdetection/tree/main/configs/swin)                 | Instance Segmentation |      Y      |    Y     |  N   |   N   |    Y     |
 |                      [SOLO](https://github.com/open-mmlab/mmdetection/tree/main/configs/solo)                       | Instance Segmentation |      Y      |    N     |  N   |   N   |    Y     |

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

@@ -2,5 +2,6 @@
 from . import bbox_head  # noqa: F401,F403
 from . import cascade_roi_head  # noqa: F401,F403
 from . import fcn_mask_head  # noqa: F401,F403
+from . import htc_roi_head  # noqa: F401,F403
 from . import single_level_roi_extractor  # noqa: F401,F403
 from . import standard_roi_head  # noqa: F401,F403

mmdeploy/codebase/mmdet/models/roi_heads/cascade_roi_head.py

@@ -16,7 +16,8 @@ def cascade_roi_head__predict_bbox(self,
                                    batch_img_metas: List[dict],
                                    rpn_results_list: List[Tensor],
                                    rcnn_test_cfg: ConfigType,
-                                   rescale: bool = False) -> List[Tensor]:
+                                   rescale: bool = False,
+                                   **kwargs) -> List[Tensor]:
     """Rewrite `predict_bbox` of `CascadeRoIHead` for default backend.
 
     Args:
@@ -52,8 +53,7 @@ def cascade_roi_head__predict_bbox(self,
     ms_scores = []
     max_shape = batch_img_metas[0]['img_shape']
     for i in range(self.num_stages):
-        bbox_results = self._bbox_forward(i, x, rois)
-
+        bbox_results = self._bbox_forward(i, x, rois, **kwargs)
         cls_score = bbox_results['cls_score']
         bbox_pred = bbox_results['bbox_pred']
         # Recover the batch dimension

mmdeploy/codebase/mmdet/models/roi_heads/htc_roi_head.py

@@ -0,0 +1,48 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from typing import List, Tuple
+
+import torch
+from torch import Tensor
+
+from mmdeploy.core import FUNCTION_REWRITER
+
+
+@FUNCTION_REWRITER.register_rewriter(
+    'mmdet.models.roi_heads.htc_roi_head.HybridTaskCascadeRoIHead.predict_mask'
+)
+def htc_roi_head__predict_mask(self,
+                               x: Tuple[Tensor],
+                               semantic_heat: Tensor,
+                               batch_img_metas: List[dict],
+                               results_list: List[Tensor],
+                               rescale: bool = False) -> List[Tensor]:
+    dets, det_labels = results_list
+
+    batch_size = dets.size(0)
+    det_bboxes = dets[..., :4]
+    batch_index = torch.arange(
+        det_bboxes.size(0),
+        device=det_bboxes.device).float().view(-1, 1, 1).expand(
+            det_bboxes.size(0), det_bboxes.size(1), 1)
+    mask_rois = torch.cat([batch_index, det_bboxes], dim=-1)
+    mask_rois = mask_rois.view(-1, 5)
+
+    mask_results = self._mask_forward(
+        stage=-1,
+        x=x,
+        rois=mask_rois,
+        semantic_feat=semantic_heat,
+        training=False)
+
+    mask_preds = mask_results['mask_preds'][0]
+    num_det = det_bboxes.shape[1]
+    segm_results = self.mask_head[-1].predict_by_feat(
+        mask_preds,
+        results_list,
+        batch_img_metas,
+        self.test_cfg,
+        rescale=rescale)
+    segm_results = segm_results.reshape(batch_size, num_det,
+                                        segm_results.shape[-2],
+                                        segm_results.shape[-1])
+    return dets, det_labels, segm_results

tests/regression/mmdet.yml

@@ -456,3 +456,11 @@ models:
         backend_test: *default_backend_test
       - deploy_config: configs/mmdet/instance-seg/instance-seg_tensorrt_dynamic-320x320-1344x1344.py
         backend_test: *default_backend_test
+
+  - name: HTC
+    metafile: configs/htc/metafile.yml
+    model_configs:
+      - configs/htc/htc_r50_fpn_1x_coco.py
+    pipelines:
+      - *pipeline_seg_ort_dynamic_fp32
+      - *pipeline_seg_trt_dynamic_fp32