CodeCamp #102: support SOLO deployment with OpenVINO (#1454)

* feat: support solo mmdet3.x openvino 2.0 api * feat: support solo mmdet3.x openvino * fix: lint * fix: add solo head test * docs: add supported_modesl * docs: add supported_models * fix: fix unreasonable code * fix: fix ci failed * feat: add linspace func rewrite * fix: fix unreasonable rewrite linspace__onnx * fix: change func name from __onnx to __default * feat: add solo test regression
2022-12-22 00:02:25 +08:00 · 2022-12-22 00:02:25 +08:00 · 6e91614171
parent aaa880e594
commit 6e91614171
20 changed files with 533 additions and 29 deletions
--- a/docs/en/03-benchmark/benchmark.md
+++ b/docs/en/03-benchmark/benchmark.md
@ -638,6 +638,7 @@ Users can directly test the performance through [how_to_evaluate_a_model.md](../
    <th align="center" colspan="3">TensorRT</th>
    <th align="center">PPLNN</th>
    <th align="center">Ascend</th>
+    <th algin="center">OpenVINO</th>
  </tr>
 </thead>
 <tbody>
@ -654,6 +655,7 @@ Users can directly test the performance through [how_to_evaluate_a_model.md](../
    <td align="center">int8</td>
    <td align="center">fp16</td>
    <td align="center">fp32</td>
+    <td align="center">fp32</td>
  </tr>
  <tr>
    <td align="center"><a href="https://github.com/open-mmlab/mmdetection/tree/master/configs/yolo/yolov3_d53_320_273e_coco.py">YOLOV3</a></td>
@ -668,6 +670,7 @@ Users can directly test the performance through [how_to_evaluate_a_model.md](../
    <td align="center">33.5</td>
    <td align="center">-</td>
    <td align="center">-</td>
+    <td align="center">-</td>
  </tr>
  <tr>
    <td align="center"><a href="https://github.com/open-mmlab/mmdetection/tree/master/configs/ssd/ssd300_coco.py">SSD</a></td>
@ -682,6 +685,7 @@ Users can directly test the performance through [how_to_evaluate_a_model.md](../
    <td align="center">-</td>
    <td align="center">-</td>
    <td align="center">-</td>
+    <td align="center">-</td>
  </tr>
  <tr>
    <td align="center"><a href="https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_r50_fpn_1x_coco.py">RetinaNet</a></td>
@ -696,6 +700,7 @@ Users can directly test the performance through [how_to_evaluate_a_model.md](../
    <td align="center">36.3</td>
    <td align="center">36.5</td>
    <td align="center">36.4</td>
+    <td align="center">-</td>
  </tr>
  <tr>
    <td align="center"><a href="https://github.com/open-mmlab/mmdetection/tree/master/configs/fcos/fcos_r50_caffe_fpn_gn-head_1x_coco.py">FCOS</a></td>
@ -710,6 +715,7 @@ Users can directly test the performance through [how_to_evaluate_a_model.md](../
    <td align="center">-</td>
    <td align="center">-</td>
    <td align="center">-</td>
+    <td align="center">-</td>
  </tr>
  <tr>
    <td align="center"><a href="https://github.com/open-mmlab/mmdetection/tree/master/configs/fsaf/fsaf_r50_fpn_1x_coco.py">FSAF</a></td>
@ -724,6 +730,7 @@ Users can directly test the performance through [how_to_evaluate_a_model.md](../
    <td align="center">37.2</td>
    <td align="center">37.4</td>
    <td align="center">-</td>
+    <td align="center">-</td>
  </tr>
  <tr>
    <td align="center"><a href="https://github.com/open-mmlab/mmdetection/tree/3.x/configs/centernet/centernet_r18_8xb16-crop512-140e_coco.py">CenterNet</a></td>
@ -738,6 +745,7 @@ Users can directly test the performance through [how_to_evaluate_a_model.md](../
    <td align="center">-</td>
    <td align="center">-</td>
    <td align="center">-</td>
+    <td align="center">-</td>
  </tr>
  <tr>
    <td align="center"><a href="https://github.com/open-mmlab/mmdetection/tree/master/configs/yolox/yolox_s_8x8_300e_coco.py">YOLOX</a></td>
@ -752,6 +760,7 @@ Users can directly test the performance through [how_to_evaluate_a_model.md](../
    <td align="center">29.3</td>
    <td align="center">-</td>
    <td align="center">-</td>
+    <td align="center">-</td>
  </tr>
  <tr>
    <td align="center"><a href="https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py">Faster R-CNN</a></td>
@ -766,6 +775,7 @@ Users can directly test the performance through [how_to_evaluate_a_model.md](../
    <td align="center">37.1</td>
    <td align="center">37.3</td>
    <td align="center">37.2</td>
+    <td align="center">-</td>
  </tr>
  <tr>
    <td align="center"><a href="https://github.com/open-mmlab/mmdetection/tree/master/configs/atss/atss_r50_fpn_1x_coco.py">ATSS</a></td>
@ -780,6 +790,7 @@ Users can directly test the performance through [how_to_evaluate_a_model.md](../
    <td align="center">-</td>
    <td align="center">-</td>
    <td align="center">-</td>
+    <td align="center">-</td>
  </tr>
  <tr>
    <td align="center"><a href="https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_rcnn_r50_caffe_fpn_1x_coco.py">Cascade R-CNN</a></td>
@ -794,6 +805,7 @@ Users can directly test the performance through [how_to_evaluate_a_model.md](../
    <td align="center">-</td>
    <td align="center">40.4</td>
    <td align="center">-</td>
+    <td align="center">-</td>
  </tr>
  <tr>
    <td align="center"><a href="https://github.com/open-mmlab/mmdetection/tree/master/configs/gfl/gfl_r50_fpn_1x_coco.py">GFL</a></td>
@ -808,6 +820,7 @@ Users can directly test the performance through [how_to_evaluate_a_model.md](../
    <td align="center">-</td>
    <td align="center">-</td>
    <td align="center">-</td>
+    <td align="center">-</td>
  </tr>
  <tr>
    <td align="center"><a href="https://github.com/open-mmlab/mmdetection/tree/master/configs/reppoints/reppoints_moment_r50_fpn_1x_coco.py">RepPoints</a></td>
@ -822,6 +835,7 @@ Users can directly test the performance through [how_to_evaluate_a_model.md](../
    <td align="center">-</td>
    <td align="center">-</td>
    <td align="center">-</td>
+    <td align="center">-</td>
  </tr>
  <tr>
    <td align="center"><a href="https://github.com/open-mmlab/mmdetection/tree/master/configs/detr/detr_r50_8x2_150e_coco.py">DETR</a></td>
@ -835,6 +849,8 @@ Users can directly test the performance through [how_to_evaluate_a_model.md](../
    <td align="center">40.1</td>
    <td align="center">-</td>
    <td align="center">-</td>
+    <td align="center">-</td>
+    <td align="center">-</td>
  </tr>
  <tr>
    <td align="center" rowspan="2"><a href="https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py">Mask R-CNN</a></td>
@ -849,6 +865,7 @@ Users can directly test the performance through [how_to_evaluate_a_model.md](../
    <td align="center">-</td>
    <td align="center">38.0</td>
    <td align="center">-</td>
+    <td align="center">-</td>
  </tr>
  <tr>
    <td align="center">mask AP</td>
@ -860,6 +877,7 @@ Users can directly test the performance through [how_to_evaluate_a_model.md](../
    <td align="center">-</td>
    <td align="center">-</td>
    <td align="center">-</td>
+    <td align="center">-</td>
  </tr>
  <tr>
    <td align="center" rowspan="2"><a href="https://github.com/open-mmlab/mmdetection/blob/master/configs/swin/mask_rcnn_swin-t-p4-w7_fpn_1x_coco.py">Swin-Transformer</a></td>
@ -874,6 +892,7 @@ Users can directly test the performance through [how_to_evaluate_a_model.md](../
    <td align="center">-</td>
    <td align="center">-</td>
    <td align="center">-</td>
+    <td align="center">-</td>
  </tr>
  <tr>
    <td align="center">mask AP</td>
@ -885,6 +904,22 @@ Users can directly test the performance through [how_to_evaluate_a_model.md](../
    <td align="center">-</td>
    <td align="center">-</td>
    <td align="center">-</td>
+    <td align="center">-</td>
+  </tr>
+  <tr>
+    <td align="center" rowspan="2"><a href="https://github.com/open-mmlab/mmdetection/tree/3.x/configs/solo">SOLO</a></td>
+    <td align="center" rowspan="2">Instance Segmentation</td>
+    <td align="center" rowspan="2">COCO2017</td>
+    <td align="center">mask AP</td>
+    <td align="center">33.1</td>
+    <td align="center">-</td>
+    <td align="center">-</td>
+    <td align="center">-</td>
+    <td align="center">-</td>
+    <td align="center">-</td>
+    <td align="center">-</td>
+    <td align="center">-</td>
+    <td align="center">32.7</td>
  </tr>
 </tbody>
 </table>
--- a/docs/en/03-benchmark/supported_models.md
+++ b/docs/en/03-benchmark/supported_models.md
@ -22,6 +22,7 @@ The table below lists the models that are guaranteed to be exportable to other b
 | [RepPoints](https://github.com/open-mmlab/mmdetection/tree/3.x/configs/reppoints)                       | MMDetection      |      N      |      N      |    Y     |  N   |   ?   |    Y     |   N    |  N   |
 | [DETR](https://github.com/open-mmlab/mmdetection/tree/3.x/configs/detr)                                 | MMDetection      |      N      |      Y      |    Y     |  N   |   ?   |    N     |   N    |  N   |
 | [CenterNet](https://github.com/open-mmlab/mmdetection/tree/3.x/configs/centernet)                       | MMDetection      |      N      |      Y      |    Y     |  N   |   ?   |    N     |   N    |  N   |
+| [SOLO](https://github.com/open-mmlab/mmdetection/tree/3.x/configs/solo)                                 | MMDetection      |      N      |      N      |    N     |  N   |   N   |    Y     |   N    |  N   |
 | [ResNet](https://github.com/open-mmlab/mmclassification/tree/1.x/configs/resnet)                        | MMClassification |      Y      |      Y      |    Y     |  Y   |   Y   |    Y     |   Y    |  Y   |
 | [ResNeXt](https://github.com/open-mmlab/mmclassification/tree/1.x/configs/resnext)                      | MMClassification |      Y      |      Y      |    Y     |  Y   |   Y   |    Y     |   Y    |  Y   |
 | [SE-ResNet](https://github.com/open-mmlab/mmclassification/tree/1.x/configs/seresnet)                   | MMClassification |      Y      |      Y      |    Y     |  Y   |   Y   |    Y     |   Y    |  Y   |
--- a/docs/en/04-supported-codebases/mmdet.md
+++ b/docs/en/04-supported-codebases/mmdet.md
@ -210,3 +210,4 @@ Besides python API, mmdeploy SDK also provides other FFI (Foreign Function Inter
 | [Cascade Mask R-CNN](https://github.com/open-mmlab/mmdetection/tree/3.x/configs/cascade_rcnn) | Instance Segmentation |      Y      |    N     |  N   |   N   |    Y     |
 |      [Mask R-CNN](https://github.com/open-mmlab/mmdetection/tree/3.x/configs/mask_rcnn)       | Instance Segmentation |      Y      |    Y     |  N   |   N   |    Y     |
 |      [Swin Transformer](https://github.com/open-mmlab/mmdetection/tree/3.x/configs/swin)      | Instance Segmentation |      Y      |    Y     |  N   |   N   |    N     |
+|            [SOLO](https://github.com/open-mmlab/mmdetection/tree/3.x/configs/solo)            | Instance Segmentation |      N      |    N     |  N   |   N   |    Y     |
--- a/docs/zh_cn/03-benchmark/benchmark.md
+++ b/docs/zh_cn/03-benchmark/benchmark.md
@ -633,6 +633,7 @@ GPU: ncnn, TensorRT, PPLNN
    <th align="center" colspan="3">TensorRT</th>
    <th align="center">PPLNN</th>
    <th align="center">Ascend</th>
+    <th algin="center">OpenVINO</th>
  </tr>
 </thead>
 <tbody>
@ -649,6 +650,7 @@ GPU: ncnn, TensorRT, PPLNN
    <td align="center">int8</td>
    <td align="center">fp16</td>
    <td align="center">fp32</td>
+    <td align="center">fp32</td>
  </tr>
  <tr>
    <td align="center"><a href="https://github.com/open-mmlab/mmdetection/tree/master/configs/yolo/yolov3_d53_320_273e_coco.py">YOLOV3</a></td>
@ -663,6 +665,7 @@ GPU: ncnn, TensorRT, PPLNN
    <td align="center">33.5</td>
    <td align="center">-</td>
    <td align="center">-</td>
+    <td align="center">-</td>
  </tr>
  <tr>
    <td align="center"><a href="https://github.com/open-mmlab/mmdetection/tree/master/configs/ssd/ssd300_coco.py">SSD</a></td>
@ -677,6 +680,7 @@ GPU: ncnn, TensorRT, PPLNN
    <td align="center">-</td>
    <td align="center">-</td>
    <td align="center">-</td>
+    <td align="center">-</td>
  </tr>
  <tr>
    <td align="center"><a href="https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_r50_fpn_1x_coco.py">RetinaNet</a></td>
@ -691,6 +695,7 @@ GPU: ncnn, TensorRT, PPLNN
    <td align="center">36.3</td>
    <td align="center">36.5</td>
    <td align="center">36.4</td>
+    <td align="center">-</td>
  </tr>
  <tr>
    <td align="center"><a href="https://github.com/open-mmlab/mmdetection/tree/master/configs/fcos/fcos_r50_caffe_fpn_gn-head_1x_coco.py">FCOS</a></td>
@ -705,6 +710,7 @@ GPU: ncnn, TensorRT, PPLNN
    <td align="center">-</td>
    <td align="center">-</td>
    <td align="center">-</td>
+    <td align="center">-</td>
  </tr>
  <tr>
    <td align="center"><a href="https://github.com/open-mmlab/mmdetection/tree/master/configs/fsaf/fsaf_r50_fpn_1x_coco.py">FSAF</a></td>
@ -719,6 +725,7 @@ GPU: ncnn, TensorRT, PPLNN
    <td align="center">37.2</td>
    <td align="center">37.4</td>
    <td align="center">-</td>
+    <td align="center">-</td>
  </tr>
  <tr>
    <td align="center"><a href="https://github.com/open-mmlab/mmdetection/tree/3.x/configs/centernet/centernet_r18_8xb16-crop512-140e_coco.py">CenterNet</a></td>
@ -733,6 +740,7 @@ GPU: ncnn, TensorRT, PPLNN
    <td align="center">-</td>
    <td align="center">-</td>
    <td align="center">-</td>
+    <td align="center">-</td>
  </tr>
  <tr>
    <td align="center"><a href="https://github.com/open-mmlab/mmdetection/tree/master/configs/yolox/yolox_s_8x8_300e_coco.py">YOLOX</a></td>
@ -747,6 +755,7 @@ GPU: ncnn, TensorRT, PPLNN
    <td align="center">29.3</td>
    <td align="center">-</td>
    <td align="center">-</td>
+    <td align="center">-</td>
  </tr>
  <tr>
    <td align="center"><a href="https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py">Faster R-CNN</a></td>
@ -761,6 +770,7 @@ GPU: ncnn, TensorRT, PPLNN
    <td align="center">37.1</td>
    <td align="center">37.3</td>
    <td align="center">37.2</td>
+    <td align="center">-</td>
  </tr>
  <tr>
    <td align="center"><a href="https://github.com/open-mmlab/mmdetection/tree/master/configs/atss/atss_r50_fpn_1x_coco.py">ATSS</a></td>
@ -775,6 +785,7 @@ GPU: ncnn, TensorRT, PPLNN
    <td align="center">-</td>
    <td align="center">-</td>
    <td align="center">-</td>
+    <td align="center">-</td>
  </tr>
  <tr>
    <td align="center"><a href="https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_rcnn_r50_caffe_fpn_1x_coco.py">Cascade R-CNN</a></td>
@ -789,6 +800,7 @@ GPU: ncnn, TensorRT, PPLNN
    <td align="center">-</td>
    <td align="center">40.4</td>
    <td align="center">-</td>
+    <td align="center">-</td>
  </tr>
  <tr>
    <td align="center"><a href="https://github.com/open-mmlab/mmdetection/tree/master/configs/gfl/gfl_r50_fpn_1x_coco.py">GFL</a></td>
@ -803,6 +815,7 @@ GPU: ncnn, TensorRT, PPLNN
    <td align="center">-</td>
    <td align="center">-</td>
    <td align="center">-</td>
+    <td align="center">-</td>
  </tr>
  <tr>
    <td align="center"><a href="https://github.com/open-mmlab/mmdetection/tree/master/configs/reppoints/reppoints_moment_r50_fpn_1x_coco.py">RepPoints</a></td>
@ -817,6 +830,7 @@ GPU: ncnn, TensorRT, PPLNN
    <td align="center">-</td>
    <td align="center">-</td>
    <td align="center">-</td>
+    <td align="center">-</td>
  </tr>
  <tr>
    <td align="center"><a href="https://github.com/open-mmlab/mmdetection/tree/master/configs/detr/detr_r50_8x2_150e_coco.py">DETR</a></td>
@ -830,6 +844,8 @@ GPU: ncnn, TensorRT, PPLNN
    <td align="center">40.1</td>
    <td align="center">-</td>
    <td align="center">-</td>
+    <td align="center">-</td>
+    <td align="center">-</td>
  </tr>
  <tr>
    <td align="center" rowspan="2"><a href="https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py">Mask R-CNN</a></td>
@ -844,6 +860,7 @@ GPU: ncnn, TensorRT, PPLNN
    <td align="center">-</td>
    <td align="center">38.0</td>
    <td align="center">-</td>
+    <td align="center">-</td>
  </tr>
  <tr>
    <td align="center">mask AP</td>
@ -855,6 +872,7 @@ GPU: ncnn, TensorRT, PPLNN
    <td align="center">-</td>
    <td align="center">-</td>
    <td align="center">-</td>
+    <td align="center">-</td>
  </tr>
  <tr>
    <td align="center" rowspan="2"><a href="https://github.com/open-mmlab/mmdetection/blob/master/configs/swin/mask_rcnn_swin-t-p4-w7_fpn_1x_coco.py">Swin-Transformer</a></td>
@ -869,6 +887,7 @@ GPU: ncnn, TensorRT, PPLNN
    <td align="center">-</td>
    <td align="center">-</td>
    <td align="center">-</td>
+    <td align="center">-</td>
  </tr>
  <tr>
    <td align="center">mask AP</td>
@ -880,6 +899,22 @@ GPU: ncnn, TensorRT, PPLNN
    <td align="center">-</td>
    <td align="center">-</td>
    <td align="center">-</td>
+    <td align="center">-</td>
+  </tr>
+  <tr>
+    <td align="center" rowspan="2"><a href="https://github.com/open-mmlab/mmdetection/tree/3.x/configs/solo">SOLO</a></td>
+    <td align="center" rowspan="2">Instance Segmentation</td>
+    <td align="center" rowspan="2">COCO2017</td>
+    <td align="center">mask AP</td>
+    <td align="center">33.1</td>
+    <td align="center">-</td>
+    <td align="center">-</td>
+    <td align="center">-</td>
+    <td align="center">-</td>
+    <td align="center">-</td>
+    <td align="center">-</td>
+    <td align="center">-</td>
+    <td align="center">32.7</td>
  </tr>
 </tbody>
 </table>
--- a/docs/zh_cn/03-benchmark/supported_models.md
+++ b/docs/zh_cn/03-benchmark/supported_models.md
@ -22,6 +22,7 @@
 | [RepPoints](https://github.com/open-mmlab/mmdetection/tree/3.x/configs/reppoints)                       | MMDetection      |      N      |      N      |    Y     |  N   |   ?   |    Y     |   N    |  N   |
 | [DETR](https://github.com/open-mmlab/mmdetection/tree/3.x/configs/detr)                                 | MMDetection      |      N      |      Y      |    Y     |  N   |   ?   |    N     |   N    |  N   |
 | [CenterNet](https://github.com/open-mmlab/mmdetection/tree/3.x/configs/centernet)                       | MMDetection      |      N      |      Y      |    Y     |  N   |   ?   |    N     |   N    |  N   |
+| [SOLO](https://github.com/open-mmlab/mmdetection/tree/3.x/configs/solo)                                 | MMDetection      |      N      |      N      |    N     |  N   |   N   |    Y     |   N    |  N   |
 | [ResNet](https://github.com/open-mmlab/mmclassification/tree/1.x/configs/resnet)                        | MMClassification |      Y      |      Y      |    Y     |  Y   |   Y   |    Y     |   Y    |  Y   |
 | [ResNeXt](https://github.com/open-mmlab/mmclassification/tree/1.x/configs/resnext)                      | MMClassification |      Y      |      Y      |    Y     |  Y   |   Y   |    Y     |   Y    |  Y   |
 | [SE-ResNet](https://github.com/open-mmlab/mmclassification/tree/1.x/configs/seresnet)                   | MMClassification |      Y      |      Y      |    Y     |  Y   |   Y   |    Y     |   Y    |  Y   |
--- a/docs/zh_cn/04-supported-codebases/mmdet.md
+++ b/docs/zh_cn/04-supported-codebases/mmdet.md
@ -192,24 +192,25 @@ cv2.imwrite('output_detection.png', img)

 ## 模型支持列表

-|                                             Model                                             |         Task         | OnnxRuntime | TensorRT | ncnn | PPLNN | OpenVINO |
-| :-------------------------------------------------------------------------------------------: | :------------------: | :---------: | :------: | :--: | :---: | :------: |
-|            [ATSS](https://github.com/open-mmlab/mmdetection/tree/3.x/configs/atss)            |   ObjectDetection    |      Y      |    Y     |  N   |   N   |    Y     |
-|            [FCOS](https://github.com/open-mmlab/mmdetection/tree/3.x/configs/fcos)            |   ObjectDetection    |      Y      |    Y     |  Y   |   N   |    Y     |
-|        [FoveaBox](https://github.com/open-mmlab/mmdetection/tree/3.x/configs/foveabox)        |   ObjectDetection    |      Y      |    N     |  N   |   N   |    Y     |
-|            [FSAF](https://github.com/open-mmlab/mmdetection/tree/3.x/configs/fsaf)            |   ObjectDetection    |      Y      |    Y     |  Y   |   Y   |    Y     |
-|       [RetinaNet](https://github.com/open-mmlab/mmdetection/tree/3.x/configs/retinanet)       |   ObjectDetection    |      Y      |    Y     |  Y   |   Y   |    Y     |
-|             [SSD](https://github.com/open-mmlab/mmdetection/tree/3.x/configs/ssd)             |   ObjectDetection    |      Y      |    Y     |  Y   |   N   |    Y     |
-|           [VFNet](https://github.com/open-mmlab/mmdetection/tree/3.x/configs/vfnet)           |   ObjectDetection    |      N      |    N     |  N   |   N   |    Y     |
-|           [YOLOv3](https://github.com/open-mmlab/mmdetection/tree/3.x/configs/yolo)           |   ObjectDetection    |      Y      |    Y     |  Y   |   N   |    Y     |
-|           [YOLOX](https://github.com/open-mmlab/mmdetection/tree/3.x/configs/yolox)           |   ObjectDetection    |      Y      |    Y     |  Y   |   N   |    Y     |
-|   [Cascade R-CNN](https://github.com/open-mmlab/mmdetection/tree/3.x/configs/cascade_rcnn)    |   ObjectDetection    |      Y      |    Y     |  N   |   Y   |    Y     |
-|    [Faster R-CNN](https://github.com/open-mmlab/mmdetection/tree/3.x/configs/faster_rcnn)     |   ObjectDetection    |      Y      |    Y     |  Y   |   Y   |    Y     |
-| [Faster R-CNN + DCN](https://github.com/open-mmlab/mmdetection/tree/3.x/configs/faster_rcnn)  |   ObjectDetection    |      Y      |    Y     |  Y   |   Y   |    Y     |
-|             [GFL](https://github.com/open-mmlab/mmdetection/tree/3.x/configs/gfl)             |   ObjectDetection    |      Y      |    Y     |  N   |   ?   |    Y     |
-|       [RepPoints](https://github.com/open-mmlab/mmdetection/tree/3.x/configs/reppoints)       |   ObjectDetection    |      N      |    Y     |  N   |   ?   |    Y     |
-|            [DETR](https://github.com/open-mmlab/mmdetection/tree/3.x/configs/detr)            |   ObjectDetection    |      Y      |    Y     |  N   |   ?   |    Y     |
-|       [CenterNet](https://github.com/open-mmlab/mmdetection/tree/3.x/configs/centernet)       |   Object Detection   |      Y      |    Y     |  N   |   ?   |    ?     |
-| [Cascade Mask R-CNN](https://github.com/open-mmlab/mmdetection/tree/3.x/configs/cascade_rcnn) | InstanceSegmentation |      Y      |    N     |  N   |   N   |    Y     |
-|      [Mask R-CNN](https://github.com/open-mmlab/mmdetection/tree/3.x/configs/mask_rcnn)       | InstanceSegmentation |      Y      |    Y     |  N   |   N   |    Y     |
-|      [Swin Transformer](https://github.com/open-mmlab/mmdetection/tree/3.x/configs/swin)      | InstanceSegmentation |      Y      |    Y     |  N   |   N   |    N     |
+|                                             Model                                             |         Task          | OnnxRuntime | TensorRT | ncnn | PPLNN | OpenVINO |
+| :-------------------------------------------------------------------------------------------: | :-------------------: | :---------: | :------: | :--: | :---: | :------: |
+|            [ATSS](https://github.com/open-mmlab/mmdetection/tree/3.x/configs/atss)            |    ObjectDetection    |      Y      |    Y     |  N   |   N   |    Y     |
+|            [FCOS](https://github.com/open-mmlab/mmdetection/tree/3.x/configs/fcos)            |    ObjectDetection    |      Y      |    Y     |  Y   |   N   |    Y     |
+|        [FoveaBox](https://github.com/open-mmlab/mmdetection/tree/3.x/configs/foveabox)        |    ObjectDetection    |      Y      |    N     |  N   |   N   |    Y     |
+|            [FSAF](https://github.com/open-mmlab/mmdetection/tree/3.x/configs/fsaf)            |    ObjectDetection    |      Y      |    Y     |  Y   |   Y   |    Y     |
+|       [RetinaNet](https://github.com/open-mmlab/mmdetection/tree/3.x/configs/retinanet)       |    ObjectDetection    |      Y      |    Y     |  Y   |   Y   |    Y     |
+|             [SSD](https://github.com/open-mmlab/mmdetection/tree/3.x/configs/ssd)             |    ObjectDetection    |      Y      |    Y     |  Y   |   N   |    Y     |
+|           [VFNet](https://github.com/open-mmlab/mmdetection/tree/3.x/configs/vfnet)           |    ObjectDetection    |      N      |    N     |  N   |   N   |    Y     |
+|           [YOLOv3](https://github.com/open-mmlab/mmdetection/tree/3.x/configs/yolo)           |    ObjectDetection    |      Y      |    Y     |  Y   |   N   |    Y     |
+|           [YOLOX](https://github.com/open-mmlab/mmdetection/tree/3.x/configs/yolox)           |    ObjectDetection    |      Y      |    Y     |  Y   |   N   |    Y     |
+|   [Cascade R-CNN](https://github.com/open-mmlab/mmdetection/tree/3.x/configs/cascade_rcnn)    |    ObjectDetection    |      Y      |    Y     |  N   |   Y   |    Y     |
+|    [Faster R-CNN](https://github.com/open-mmlab/mmdetection/tree/3.x/configs/faster_rcnn)     |    ObjectDetection    |      Y      |    Y     |  Y   |   Y   |    Y     |
+| [Faster R-CNN + DCN](https://github.com/open-mmlab/mmdetection/tree/3.x/configs/faster_rcnn)  |    ObjectDetection    |      Y      |    Y     |  Y   |   Y   |    Y     |
+|             [GFL](https://github.com/open-mmlab/mmdetection/tree/3.x/configs/gfl)             |    ObjectDetection    |      Y      |    Y     |  N   |   ?   |    Y     |
+|       [RepPoints](https://github.com/open-mmlab/mmdetection/tree/3.x/configs/reppoints)       |    ObjectDetection    |      N      |    Y     |  N   |   ?   |    Y     |
+|            [DETR](https://github.com/open-mmlab/mmdetection/tree/3.x/configs/detr)            |    ObjectDetection    |      Y      |    Y     |  N   |   ?   |    Y     |
+|       [CenterNet](https://github.com/open-mmlab/mmdetection/tree/3.x/configs/centernet)       |   Object Detection    |      Y      |    Y     |  N   |   ?   |    ?     |
+| [Cascade Mask R-CNN](https://github.com/open-mmlab/mmdetection/tree/3.x/configs/cascade_rcnn) | InstanceSegmentation  |      Y      |    N     |  N   |   N   |    Y     |
+|      [Mask R-CNN](https://github.com/open-mmlab/mmdetection/tree/3.x/configs/mask_rcnn)       | InstanceSegmentation  |      Y      |    Y     |  N   |   N   |    Y     |
+|      [Swin Transformer](https://github.com/open-mmlab/mmdetection/tree/3.x/configs/swin)      | InstanceSegmentation  |      Y      |    Y     |  N   |   N   |    N     |
+|            [SOLO](https://github.com/open-mmlab/mmdetection/tree/3.x/configs/solo)            | Instance Segmentation |      N      |    N     |  N   |   N   |    Y     |
--- a/mmdeploy/codebase/mmdet/models/init.py
+++ b/mmdeploy/codebase/mmdet/models/init.py
@ -2,6 +2,7 @@
 from . import backbones  # noqa: F401, F403
 from . import dense_heads  # noqa: F401,F403
 from . import detectors  # noqa: F401,F403
+from . import layers  # noqa: F401,F403
 from . import necks  # noqa: F401,F403
 from . import roi_heads  # noqa: F401,F403
 from . import task_modules  # noqa: F401,F403
--- a/mmdeploy/codebase/mmdet/models/dense_heads/init.py
+++ b/mmdeploy/codebase/mmdet/models/dense_heads/init.py
@ -7,5 +7,6 @@ from . import gfl_head  # noqa: F401,F403
 from . import reppoints_head  # noqa: F401,F403
 from . import rpn_head  # noqa: F401,F403
 from . import rtmdet_head  # noqa: F401,F403
+from . import solo_head  # noqa: F401,F403
 from . import yolo_head  # noqa: F401,F403
 from . import yolox_head  # noqa: F401,F403
--- a/mmdeploy/codebase/mmdet/models/dense_heads/solo_head.py
+++ b/mmdeploy/codebase/mmdet/models/dense_heads/solo_head.py
@ -0,0 +1,102 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from typing import Dict, List
+
+import torch
+from mmdet.models.layers import mask_matrix_nms
+from mmdet.utils import OptConfigType
+from torch import Tensor
+from torch.nn import functional as F
+
+from mmdeploy.codebase.mmdet.deploy import get_post_processing_params
+from mmdeploy.core import FUNCTION_REWRITER
+
+
+@FUNCTION_REWRITER.register_rewriter(
+    'mmdet.models.dense_heads.SOLOHead.predict_by_feat', backend='openvino')
+def solohead__predict_by_feat__openvino(self,
+                                        mlvl_mask_preds: List[Tensor],
+                                        mlvl_cls_scores: List[Tensor],
+                                        batch_img_metas: List[Dict],
+                                        cfg: OptConfigType = None,
+                                        **kwargs):
+    """Rewrite `predict_by_feat` of `SOLOHead` for openvino backend."""
+
+    ctx = FUNCTION_REWRITER.get_context()
+    batch_size = mlvl_cls_scores[0].size(0)
+    cfg = self.test_cfg
+    mlvl_cls_scores = [
+        item.permute(0, 2, 3, 1).view(item.size(0), -1, self.cls_out_channels)
+        for item in mlvl_cls_scores
+    ]
+
+    # avoid setting items
+    lvl_strides = [
+        torch.ones_like(mlvl_cls_scores[lvl][0, :, 0]) * self.strides[lvl]
+        for lvl in range(len(mlvl_cls_scores))
+    ]
+    strides = torch.cat(lvl_strides, 0)
+    assert len(mlvl_mask_preds) == len(mlvl_cls_scores)
+    batch_mlvl_cls_scores = torch.cat(mlvl_cls_scores, dim=1)
+    batch_mlvl_mask_preds = torch.cat(mlvl_mask_preds, dim=1)
+    featmap_size = batch_mlvl_mask_preds.size()[-2:]
+    batch_mlvl_cls_scores, cls_labels = torch.max(batch_mlvl_cls_scores, -1)
+
+    score_mask = (batch_mlvl_cls_scores > cfg.score_thr)
+    # pad zero to filter items
+    batch_mlvl_cls_scores = batch_mlvl_cls_scores.where(
+        score_mask, batch_mlvl_cls_scores.new_zeros(1)).view(-1)
+
+    cls_labels = cls_labels.view(-1)
+
+    mask_preds = batch_mlvl_mask_preds.view(-1, featmap_size[0],
+                                            featmap_size[1])
+
+    masks = (mask_preds > cfg.mask_thr)
+    sum_masks = masks.sum((1, 2))
+    keep = sum_masks > strides
+    # pad zero to filter items
+    cls_scores = batch_mlvl_cls_scores.where(
+        keep, batch_mlvl_cls_scores.new_zeros(1))
+    sum_masks = sum_masks.where(keep, sum_masks.new_ones(1))
+
+    # maskness
+    mask_scores = (mask_preds * masks).sum((1, 2)) / sum_masks
+    cls_scores *= mask_scores
+    sum_masks = sum_masks.where(keep, sum_masks.new_zeros(1))
+
+    scores, labels, _, keep_inds = mask_matrix_nms(
+        masks,
+        cls_labels,
+        cls_scores,
+        mask_area=sum_masks,
+        nms_pre=cfg.nms_pre,
+        max_num=cfg.max_per_img,
+        kernel=cfg.kernel,
+        sigma=cfg.sigma,
+        filter_thr=cfg.filter_thr)
+
+    h, w = batch_img_metas[0]['img_shape'][:2]
+    mask_preds = mask_preds[keep_inds].unsqueeze(0)
+
+    mmdet_params = get_post_processing_params(ctx.cfg)
+    export_postprocess_mask = mmdet_params.get('export_postprocess_mask', True)
+    if export_postprocess_mask:
+        upsampled_size = (featmap_size[0] * 4, featmap_size[1] * 4)
+        mask_preds = F.interpolate(
+            mask_preds, size=upsampled_size, mode='bilinear')
+        bboxes = scores.new_zeros(batch_size, scores.shape[-1], 4)
+    else:
+
+        bboxes = scores.new_zeros(batch_size, scores.shape[-1], 2)
+        # full screen box so we can postprocess mask outside the model
+        bboxes = torch.cat([
+            bboxes,
+            bboxes.new_full((*bboxes.shape[:2], 1), w),
+            bboxes.new_full((*bboxes.shape[:2], 1), h)
+        ],
+                           dim=-1)
+
+    labels = labels.reshape(batch_size, -1)
+    dets = torch.cat([bboxes, scores.reshape(batch_size, -1, 1)], dim=-1)
+
+    return dets, labels, mask_preds
--- a/mmdeploy/codebase/mmdet/models/detectors/init.py
+++ b/mmdeploy/codebase/mmdet/models/detectors/init.py
@ -1,4 +1,4 @@
 # Copyright (c) OpenMMLab. All rights reserved.
-from . import single_stage, two_stage
+from . import single_stage, single_stage_instance_seg, two_stage

-__all__ = ['single_stage', 'two_stage']
+__all__ = ['single_stage', 'single_stage_instance_seg', 'two_stage']
--- a/mmdeploy/codebase/mmdet/models/detectors/single_stage_instance_seg.py
+++ b/mmdeploy/codebase/mmdet/models/detectors/single_stage_instance_seg.py
@ -0,0 +1,67 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch
+from mmdet.models.detectors.base import ForwardResults
+from mmdet.structures.det_data_sample import OptSampleList
+
+from mmdeploy.core import FUNCTION_REWRITER, mark
+from mmdeploy.utils import is_dynamic_shape
+from .single_stage import _set_metainfo
+
+
+@mark(
+    'instance_segmentor_forward',
+    inputs=['input'],
+    outputs=['dets', 'labels', 'masks'])
+def __forward_impl_instance_seg(self, batch_inputs, data_samples, **kwargs):
+    """Rewrite and adding mark for `forward`.
+
+    Encapsulate this function for rewriting `forward` of BaseDetector.
+    1. Add mark for BaseDetector.
+    2. Support both dynamic and static export to onnx.
+    """
+    x = self.extract_feat(batch_inputs)
+    mask_outs = self.mask_head.predict(x, data_samples, rescale=False)
+    return mask_outs
+
+
+@FUNCTION_REWRITER.register_rewriter(
+    'mmdet.models.detectors.single_stage_instance_seg.'
+    'SingleStageInstanceSegmentor.forward')
+def single_stage_instance_segmentor__forward(
+        self,
+        batch_inputs: torch.Tensor,
+        data_samples: OptSampleList = None,
+        mode: str = 'tensor',
+        **kwargs) -> ForwardResults:
+    """Rewrite `forward` for default backend.
+    Support configured dynamic/static shape for model input and return
+    detection result as Tensor instead of numpy array.
+    Args:
+        batch_inputs (Tensor): Inputs with shape (N, C, H, W).
+        data_samples (List[:obj:`DetDataSample`]): The Data
+            Samples. It usually includes information such as
+            `gt_instance`, `gt_panoptic_seg` and `gt_sem_seg`.
+        rescale (bool): Whether to rescale the results.
+            Defaults to True.
+    Returns:
+        tuple[Tensor]: Detection results of the
+        input images.
+            - dets (Tensor): Classification bboxes and scores.
+                Has a shape (num_instances, 5)
+            - labels (Tensor): Labels of bboxes, has a shape
+                (num_instances, ).
+    """
+    ctx = FUNCTION_REWRITER.get_context()
+    deploy_cfg = ctx.cfg
+
+    # get origin input shape as tensor to support onnx dynamic shape
+    is_dynamic_flag = is_dynamic_shape(deploy_cfg)
+    img_shape = torch._shape_as_tensor(batch_inputs)[2:]
+    if not is_dynamic_flag:
+        img_shape = [int(val) for val in img_shape]
+
+    # set the metainfo
+    data_samples = _set_metainfo(data_samples, img_shape)
+
+    return __forward_impl_instance_seg(
+        self, batch_inputs, data_samples=data_samples, **kwargs)
--- a/mmdeploy/codebase/mmdet/models/layers/init.py
+++ b/mmdeploy/codebase/mmdet/models/layers/init.py
@ -1,3 +1,6 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 # recovery for mmyolo
 from mmdeploy.mmcv.ops import multiclass_nms  # noqa: F401, F403
+from . import matrix_nms  # noqa: F401, F403
+
+__all__ = ['multiclass_nms']
--- a/mmdeploy/codebase/mmdet/models/layers/matrix_nms.py
+++ b/mmdeploy/codebase/mmdet/models/layers/matrix_nms.py
@ -0,0 +1,103 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch
+
+from mmdeploy.core import FUNCTION_REWRITER
+
+
+@FUNCTION_REWRITER.register_rewriter(
+    func_name='mmdet.models.layers.matrix_nms.mask_matrix_nms')
+def mask_matrix_nms__default(masks,
+                             labels,
+                             scores,
+                             filter_thr=-1,
+                             nms_pre=-1,
+                             max_num=-1,
+                             kernel='gaussian',
+                             sigma=2.0,
+                             mask_area=None):
+    """Matrix NMS for multi-class masks.
+
+    Args:
+        masks (Tensor): Has shape (num_instances, h, w)
+        labels (Tensor): Labels of corresponding masks,
+            has shape (num_instances,).
+        scores (Tensor): Mask scores of corresponding masks,
+            has shape (num_instances).
+        filter_thr (float): Score threshold to filter the masks
+            after matrix nms. Default: -1, which means do not
+            use filter_thr.
+        nms_pre (int): The max number of instances to do the matrix nms.
+            Default: -1, which means do not use nms_pre.
+        max_num (int, optional): If there are more than max_num masks after
+            matrix, only top max_num will be kept. Default: -1, which means
+            do not use max_num.
+        kernel (str): 'linear' or 'gaussian'.
+        sigma (float): std in gaussian method.
+        mask_area (Tensor): The sum of seg_masks.
+
+    Returns:
+        tuple(Tensor): Processed mask results.
+
+            - scores (Tensor): Updated scores, has shape (n,).
+            - labels (Tensor): Remained labels, has shape (n,).
+            - masks (Tensor): Remained masks, has shape (n, w, h).
+            - keep_inds (Tensor): The indices number of
+                the remaining mask in the input mask, has shape (n,).
+    """
+    assert len(labels) == len(masks) == len(scores)
+    assert len(masks) == len(mask_area)
+    # sort and keep top nms_pre
+    nms_pre = max(0, nms_pre)
+    if nms_pre <= 0:
+        nms_pre = scores.shape[0]
+    scores, sort_inds = torch.topk(scores, nms_pre)
+
+    keep_inds = sort_inds
+    masks = masks[sort_inds]
+    mask_area = mask_area[sort_inds]
+    labels = labels[sort_inds]
+    num_masks = labels.size(0)
+    flatten_masks = masks.reshape(num_masks, -1).float()
+    # inter.
+    inter_matrix = torch.mm(flatten_masks, flatten_masks.transpose(1, 0))
+    expanded_mask_area = mask_area.unsqueeze(1)
+    total_area = expanded_mask_area + expanded_mask_area.transpose(
+        1, 0) - inter_matrix
+    total_mask = total_area > 0
+    total_area = total_area.where(total_mask, total_area.new_ones(1))
+    iou_matrix = (inter_matrix / total_area).triu(diagonal=1)
+    expanded_labels = labels.unsqueeze(1)
+    label_matrix = expanded_labels == expanded_labels.transpose(1, 0)
+
+    # iou decay
+    decay_iou = iou_matrix.where(label_matrix, iou_matrix.new_zeros(1))
+
+    # iou compensation
+    compensate_iou, _ = decay_iou.max(0)
+    compensate_iou = compensate_iou.expand(num_masks,
+                                           num_masks).transpose(1, 0)
+
+    # calculate the decay_coefficient
+    if kernel == 'gaussian':
+        decay_matrix = torch.exp(-1 * sigma * (decay_iou**2))
+        compensate_matrix = torch.exp(-1 * sigma * (compensate_iou**2))
+        decay_coefficient, _ = (decay_matrix / compensate_matrix).min(0)
+    elif kernel == 'linear':
+        decay_matrix = (1 - decay_iou) / (1 - compensate_iou)
+        decay_coefficient, _ = decay_matrix.min(0)
+    else:
+        raise NotImplementedError(
+            f'{kernel} kernel is not supported in matrix nms!')
+    # update the score.
+    scores = scores * decay_coefficient
+
+    keep = scores >= filter_thr
+    scores = scores.where(keep, scores.new_zeros(1))
+
+    # sort and keep top max_num
+    scores, sort_inds = torch.topk(scores, max(max_num, 0))
+    keep_inds = keep_inds[sort_inds]
+    masks = masks[sort_inds]
+    labels = labels[sort_inds]
+
+    return scores, labels, masks, keep_inds
--- a/mmdeploy/pytorch/functions/init.py
+++ b/mmdeploy/pytorch/functions/init.py
@ -9,6 +9,7 @@ from . import getattribute  # noqa: F401,F403
 from . import group_norm  # noqa: F401,F403
 from . import interpolate  # noqa: F401,F403
 from . import linear  # noqa: F401,F403
+from . import linspace  # noqa: F401,F403
 from . import masked_fill  # noqa: F401,F403
 from . import mod  # noqa: F401,F403
 from . import multi_head_attention_forward  # noqa: F401,F403
--- a/mmdeploy/pytorch/functions/linspace.py
+++ b/mmdeploy/pytorch/functions/linspace.py
@ -0,0 +1,20 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch
+from torch.types import Number
+
+from mmdeploy.core import FUNCTION_REWRITER
+
+
+@FUNCTION_REWRITER.register_rewriter(func_name='torch.linspace')
+def linspace__default(start: Number, end: Number, steps: int = None, **kwargs):
+    """Rewrite `linspace` for onnxruntime."""
+    steps = 100 if steps is None else steps
+    dtype = kwargs.pop('dtype', torch.float32)
+    dtype = dtype if dtype else torch.float32
+    if steps == 1:
+        output = torch.arange(start, end + 1, dtype=dtype, **kwargs)[:steps]
+    else:
+        output = torch.arange(
+            start, end + 1, (end - start) / (steps - 1), dtype=dtype,
+            **kwargs)[:steps]
+    return output
--- a/mmdeploy/pytorch/functions/triu.py
+++ b/mmdeploy/pytorch/functions/triu.py
@ -5,6 +5,7 @@ from mmdeploy.core import FUNCTION_REWRITER


@FUNCTION_REWRITER.register_rewriter(func_name='torch.triu')
+@FUNCTION_REWRITER.register_rewriter(func_name='torch.Tensor.triu')
 def triu__default(input: torch.Tensor,
                  diagonal: int = 0,
                  *args,
--- a/tests/regression/mmdet.yml
+++ b/tests/regression/mmdet.yml
@ -339,3 +339,10 @@ models:
        convert_image: *convert_image
        backend_test: *default_backend_test
        sdk_config: *sdk_dynamic
+
+  - name: SOLO
+    metafile: configs/solo/metafile.yml
+    model_configs:
+      - configs/solo/solo_r50_fpn_1x_coco.py
+    pipelines:
+      - *pipeline_seg_openvino_dynamic_fp32
--- a/tests/test_codebase/test_mmcls/test_mmcls_models.py
+++ b/tests/test_codebase/test_mmcls/test_mmcls_models.py
@ -71,16 +71,12 @@ def test_baseclassifier_forward():

        def __init__(self, backbone):
            super().__init__(backbone=backbone)
+            self.head = lambda x: x
+            self.predict = lambda x, data_samples: x

        def extract_feat(self, batch_inputs: torch.Tensor):
            return batch_inputs

-        def head(self, x):
-            return x
-
-        def predict(self, x, data_samples):
-            return x
-
    backbone_cfg = dict(
        type='ResNet',
        depth=18,
--- a/tests/test_codebase/test_mmdet/test_mmdet_models.py
+++ b/tests/test_codebase/test_mmdet/test_mmdet_models.py
@ -2155,3 +2155,96 @@ def test_detrhead__predict_by_feat(backend_type: Backend, ir_type: str):
        run_with_backend=False)

    assert rewrite_outputs is not None
+
+
+def get_solo_head_model():
+    test_cfg = Config(
+        dict(
+            nms_pre=500,
+            score_thr=0.1,
+            mask_thr=0.5,
+            filter_thr=0.05,
+            kernel='gaussian',  # gaussian/linear
+            sigma=2.0,
+            max_per_img=100))
+    from mmdet.models.dense_heads import SOLOHead
+    model = SOLOHead(4, 32, feat_channels=32, test_cfg=test_cfg)
+
+    model.requires_grad_(False)
+    return model
+
+
+@pytest.mark.parametrize('backend_type', [Backend.OPENVINO])
+def test_solo_head_predict_by_feat(backend_type: Backend):
+    """Test predict_by_feat rewrite of solo head."""
+    check_backend(backend_type)
+    solo_head = get_solo_head_model()
+    s = 128
+    solo_head.cpu().eval()
+    batch_img_metas = [{'img_shape': (s, s, 3), 'ori_shape': (s, s, 3)}]
+
+    output_names = ['dets', 'labels', 'masks']
+    deploy_cfg = Config(
+        dict(
+            backend_config=dict(type=backend_type.value),
+            onnx_config=dict(output_names=output_names, input_shape=None),
+            codebase_config=dict(
+                type='mmdet',
+                task='ObjectDetection',
+                post_processing=dict(
+                    score_threshold=0.05,
+                    iou_threshold=0.5,
+                    max_output_boxes_per_class=20,
+                    pre_top_k=-1,
+                    keep_top_k=10,
+                    background_label_id=-1,
+                    export_postprocess_mask=True))))
+    seed_everything(1234)
+    num_grids = [24, 20, 16, 12, 8]
+    mask_preds = [
+        torch.rand(1, num_grid**2, s // 4, s // 4) for num_grid in num_grids
+    ]
+    seed_everything(5678)
+    cls_scores = [
+        torch.rand(1, solo_head.num_classes, num_grid, num_grid)
+        for num_grid in num_grids
+    ]
+
+    # to get outputs of pytorch model
+    model_inputs = {
+        'mlvl_mask_preds': mask_preds,
+        'mlvl_cls_scores': cls_scores,
+        'batch_img_metas': batch_img_metas,
+    }
+    model_outputs = get_model_outputs(solo_head, 'predict_by_feat',
+                                      model_inputs)
+
+    wrapped_model = WrapModel(
+        solo_head, 'predict_by_feat', batch_img_metas=batch_img_metas)
+    rewrite_inputs = {
+        'mlvl_mask_preds': mask_preds,
+        'mlvl_cls_scores': cls_scores,
+    }
+    rewrite_outputs, is_backend_output = get_rewrite_outputs(
+        wrapped_model=wrapped_model,
+        model_inputs=rewrite_inputs,
+        deploy_cfg=deploy_cfg)
+
+    if is_backend_output:
+        # hard code to make two tensors with the same shape
+        # rewrite and original codes applied different nms strategy
+        min_shape = min(model_outputs[0].bboxes.shape[0],
+                        rewrite_outputs[0].shape[1], 5)
+        for i in range(len(model_outputs)):
+            assert np.allclose(
+                model_outputs[i].scores[:min_shape],
+                rewrite_outputs[0][i, :min_shape, 4],
+                rtol=1e-03,
+                atol=1e-05)
+            assert np.allclose(
+                model_outputs[i].labels[:min_shape],
+                rewrite_outputs[1][i, :min_shape],
+                rtol=1e-03,
+                atol=1e-05)
+    else:
+        assert rewrite_outputs is not None
--- a/tests/test_pytorch/test_pytorch_functions.py
+++ b/tests/test_pytorch/test_pytorch_functions.py
@ -555,3 +555,38 @@ def test_prepare_onnx_paddings__tensorrt():
        run_with_backend=True)
    assert torch.allclose(
        pytorch_output, rewrite_output[0], rtol=1e-3, atol=1e-5)
+
+
+@backend_checker(Backend.ONNXRUNTIME)
+def test_linspace__default():
+    import random
+
+    deploy_cfg_ort = Config(
+        dict(
+            onnx_config=dict(input_shape=None),
+            backend_config=dict(type='onnxruntime')))
+
+    def linspace_caller(*arg, **kwargs):
+        return torch.linspace(*arg, **kwargs)
+
+    steps_list = [1, random.randint(1, 1000)]
+    for steps in steps_list:
+        start = random.random() * 100
+        end = random.random() * 100 + start
+
+        model_output = linspace_caller(start=start, end=end, steps=steps)
+
+        wrapped_func = WrapFunction(
+            linspace_caller, start=start, end=end, steps=steps)
+
+        rewrite_outputs, is_backend_output = get_rewrite_outputs(
+            wrapped_func,
+            model_inputs={},
+            deploy_cfg=deploy_cfg_ort,
+            run_with_backend=True)
+
+        if is_backend_output:
+            rewrite_outputs = rewrite_outputs[0]
+
+        assert np.allclose(
+            model_output, rewrite_outputs, rtol=1e-03, atol=1e-05)