CodeCamp #101: Support MMDetection 3.x RTMDet model deployment on RV1…

…126 (#1551)

* * partition rtmdet

* * add rtmdet deploy config

* * add rtmdet deploy config

* * modify rtmdet pipline anchor_generator's info dump
* support rtmdet infer in sdk

* fix a bug

* * fix a bug in csrc/mmdeploy/preprocess/transform/normalize.cpp

* * fix a bug

* * update docs

* * fix lint

* * update several urls in docs

configs/mmdet/detection/detection_rknn-int8_static-640x640.py

@@ -0,0 +1,19 @@
+_base_ = ['../_base_/base_static.py', '../../_base_/backends/rknn.py']
+
+onnx_config = dict(input_shape=[640, 640])
+
+codebase_config = dict(model_type='rknn')
+
+backend_config = dict(input_size_list=[[3, 640, 640]])
+
+# rtmdet for rknn-toolkit and rknn-toolkit2
+# partition_config = dict(
+#     type='rknn',  # the partition policy name
+#     apply_marks=True,  # should always be set to True
+#     partition_cfg=[
+#         dict(
+#             save_file='model.onnx',  # name to save the partitioned onnx
+#             start=['detector_forward:input'],  # [mark_name:input, ...]
+#             end=['rtmdet_head:output'],  # [mark_name:output, ...]
+#             output_names=[f'pred_maps.{i}' for i in range(6)]) # output names
+#     ])

csrc/mmdeploy/codebase/mmdet/rtmdet_head.cpp

@@ -0,0 +1,194 @@
+// Copyright (c) OpenMMLab. All rights reserved.
+#include "rtmdet_head.h"
+
+#include <math.h>
+
+#include <algorithm>
+#include <numeric>
+
+#include "mmdeploy/core/model.h"
+#include "mmdeploy/core/utils/device_utils.h"
+#include "mmdeploy/core/utils/formatter.h"
+#include "utils.h"
+
+namespace mmdeploy::mmdet {
+
+RTMDetSepBNHead::RTMDetSepBNHead(const Value& cfg) : MMDetection(cfg) {
+  auto init = [&]() -> Result<void> {
+    auto model = cfg["context"]["model"].get<Model>();
+    if (cfg.contains("params")) {
+      nms_pre_ = cfg["params"].value("nms_pre", -1);
+      score_thr_ = cfg["params"].value("score_thr", 0.02f);
+      min_bbox_size_ = cfg["params"].value("min_bbox_size", 0);
+      max_per_img_ = cfg["params"].value("max_per_img", 100);
+      iou_threshold_ = cfg["params"].contains("nms")
+                           ? cfg["params"]["nms"].value("iou_threshold", 0.45f)
+                           : 0.45f;
+      if (cfg["params"].contains("anchor_generator")) {
+        offset_ = cfg["params"]["anchor_generator"].value("offset", 0);
+        from_value(cfg["params"]["anchor_generator"]["strides"], strides_);
+      }
+    }
+    return success();
+  };
+  init().value();
+}
+
+Result<Value> RTMDetSepBNHead::operator()(const Value& prep_res, const Value& infer_res) {
+  MMDEPLOY_DEBUG("prep_res: {}\ninfer_res: {}", prep_res, infer_res);
+  try {
+    std::vector<Tensor> cls_scores;
+    std::vector<Tensor> bbox_preds;
+    const Device kHost{0, 0};
+    int i = 0;
+    int divisor = infer_res.size() / 2;
+    for (auto iter = infer_res.begin(); iter != infer_res.end(); iter++) {
+      auto pred_map = iter->get<Tensor>();
+      OUTCOME_TRY(auto _pred_map, MakeAvailableOnDevice(pred_map, kHost, stream()));
+      if (i < divisor)
+        cls_scores.push_back(_pred_map);
+      else
+        bbox_preds.push_back(_pred_map);
+      i++;
+    }
+    OUTCOME_TRY(stream().Wait());
+    OUTCOME_TRY(auto result, GetBBoxes(prep_res["img_metas"], bbox_preds, cls_scores));
+    return to_value(result);
+  } catch (...) {
+    return Status(eFail);
+  }
+}
+
+static float sigmoid(float x) { return 1.0 / (1.0 + expf(-x)); }
+
+Result<Detections> RTMDetSepBNHead::GetBBoxes(const Value& prep_res,
+                                              const std::vector<Tensor>& bbox_preds,
+                                              const std::vector<Tensor>& cls_scores) const {
+  MMDEPLOY_DEBUG("bbox_pred: {}, {}", bbox_preds[0].shape(), dets[0].data_type());
+  MMDEPLOY_DEBUG("cls_score: {}, {}", scores[0].shape(), scores[0].data_type());
+
+  std::vector<float> filter_boxes;
+  std::vector<float> obj_probs;
+  std::vector<int> class_ids;
+
+  for (int i = 0; i < bbox_preds.size(); i++) {
+    RTMDetFeatDeocde(bbox_preds[i], cls_scores[i], strides_[i], offset_, filter_boxes, obj_probs,
+                     class_ids);
+  }
+
+  std::vector<int> indexArray;
+  for (int i = 0; i < obj_probs.size(); ++i) {
+    indexArray.push_back(i);
+  }
+  Sort(obj_probs, class_ids, indexArray);
+
+  Tensor dets(TensorDesc{Device{0, 0}, DataType::kFLOAT,
+                         TensorShape{int(filter_boxes.size() / 4), 4}, "dets"});
+  std::copy(filter_boxes.begin(), filter_boxes.end(), dets.data<float>());
+  NMS(dets, iou_threshold_, indexArray);
+
+  Detections objs;
+  std::vector<float> scale_factor;
+  if (prep_res.contains("scale_factor")) {
+    from_value(prep_res["scale_factor"], scale_factor);
+  } else {
+    scale_factor = {1.f, 1.f, 1.f, 1.f};
+  }
+  int ori_width = prep_res["ori_shape"][2].get<int>();
+  int ori_height = prep_res["ori_shape"][1].get<int>();
+  auto det_ptr = dets.data<float>();
+  for (int i = 0; i < indexArray.size(); ++i) {
+    if (indexArray[i] == -1) {
+      continue;
+    }
+    int j = indexArray[i];
+    auto x1 = det_ptr[j * 4 + 0];
+    auto y1 = det_ptr[j * 4 + 1];
+    auto x2 = det_ptr[j * 4 + 2];
+    auto y2 = det_ptr[j * 4 + 3];
+    int label_id = class_ids[i];
+    float score = obj_probs[i];
+
+    MMDEPLOY_DEBUG("{}-th box: ({}, {}, {}, {}), {}, {}", i, x1, y1, x2, y2, label_id, score);
+
+    auto rect =
+        MapToOriginImage(x1, y1, x2, y2, scale_factor.data(), 0, 0, ori_width, ori_height, 0, 0);
+    if (rect[2] - rect[0] < min_bbox_size_ || rect[3] - rect[1] < min_bbox_size_) {
+      MMDEPLOY_DEBUG("ignore small bbox with width '{}' and height '{}", rect[2] - rect[0],
+                     rect[3] - rect[1]);
+      continue;
+    }
+    Detection det{};
+    det.index = i;
+    det.label_id = label_id;
+    det.score = score;
+    det.bbox = rect;
+    objs.push_back(std::move(det));
+  }
+
+  return objs;
+}
+
+int RTMDetSepBNHead::RTMDetFeatDeocde(const Tensor& bbox_pred, const Tensor& cls_score,
+                                      const float stride, const float offset,
+                                      std::vector<float>& filter_boxes,
+                                      std::vector<float>& obj_probs,
+                                      std::vector<int>& class_ids) const {
+  int cls_param_num = cls_score.shape(1);
+  int feat_h = bbox_pred.shape(2);
+  int feat_w = bbox_pred.shape(3);
+  int feat_size = feat_h * feat_w;
+  auto bbox_ptr = bbox_pred.data<float>();
+  auto score_ptr = cls_score.data<float>();  // (b, c, h, w)
+  int valid_count = 0;
+  for (int i = 0; i < feat_h; i++) {
+    for (int j = 0; j < feat_w; j++) {
+      float max_score = score_ptr[i * feat_w + j];
+      int class_id = 0;
+      for (int k = 0; k < cls_param_num; k++) {
+        float score = score_ptr[k * feat_size + i * feat_w + j];
+        if (score > max_score) {
+          max_score = score;
+          class_id = k;
+        }
+      }
+      max_score = sigmoid(max_score);
+      if (max_score < score_thr_) continue;
+
+      obj_probs.push_back(max_score);
+      class_ids.push_back(class_id);
+
+      float tl_x = bbox_ptr[0 * feat_size + i * feat_w + j];
+      float tl_y = bbox_ptr[1 * feat_size + i * feat_w + j];
+      float br_x = bbox_ptr[2 * feat_size + i * feat_w + j];
+      float br_y = bbox_ptr[3 * feat_size + i * feat_w + j];
+
+      auto box = RTMDetdecode(tl_x, tl_y, br_x, br_y, stride, offset, j, i);
+
+      tl_x = box[0];
+      tl_y = box[1];
+      br_x = box[2];
+      br_y = box[3];
+
+      filter_boxes.push_back(tl_x);
+      filter_boxes.push_back(tl_y);
+      filter_boxes.push_back(br_x);
+      filter_boxes.push_back(br_y);
+      valid_count++;
+    }
+  }
+  return valid_count;
+}
+
+std::array<float, 4> RTMDetSepBNHead::RTMDetdecode(float tl_x, float tl_y, float br_x, float br_y,
+                                                   float stride, float offset, int j, int i) const {
+  tl_x = (offset + j) * stride - tl_x;
+  tl_y = (offset + i) * stride - tl_y;
+  br_x = (offset + j) * stride + br_x;
+  br_y = (offset + i) * stride + br_y;
+  return std::array<float, 4>{tl_x, tl_y, br_x, br_y};
+}
+
+MMDEPLOY_REGISTER_CODEBASE_COMPONENT(MMDetection, RTMDetSepBNHead);
+
+}  // namespace mmdeploy::mmdet

csrc/mmdeploy/codebase/mmdet/rtmdet_head.h

@@ -0,0 +1,34 @@
+// Copyright (c) OpenMMLab. All rights reserved.
+#ifndef MMDEPLOY_CODEBASE_MMDET_RTMDET_HEAD_H_
+#define MMDEPLOY_CODEBASE_MMDET_RTMDET_HEAD_H_
+
+#include "mmdeploy/codebase/mmdet/mmdet.h"
+#include "mmdeploy/core/tensor.h"
+
+namespace mmdeploy::mmdet {
+
+class RTMDetSepBNHead : public MMDetection {
+ public:
+  explicit RTMDetSepBNHead(const Value& cfg);
+  Result<Value> operator()(const Value& prep_res, const Value& infer_res);
+  Result<Detections> GetBBoxes(const Value& prep_res, const std::vector<Tensor>& bbox_preds,
+                               const std::vector<Tensor>& cls_scores) const;
+  int RTMDetFeatDeocde(const Tensor& bbox_pred, const Tensor& cls_score, const float stride,
+                       const float offset, std::vector<float>& filter_boxes,
+                       std::vector<float>& obj_probs, std::vector<int>& class_ids) const;
+  std::array<float, 4> RTMDetdecode(float tl_x, float tl_y, float br_x, float br_y, float stride,
+                                    float offset, int j, int i) const;
+
+ private:
+  float score_thr_{0.4f};
+  int nms_pre_{1000};
+  float iou_threshold_{0.45f};
+  int min_bbox_size_{0};
+  int max_per_img_{100};
+  float offset_{0.0f};
+  std::vector<float> strides_;
+};
+
+}  // namespace mmdeploy::mmdet
+
+#endif  // MMDEPLOY_CODEBASE_MMDET_RTMDET_HEAD_H_

csrc/mmdeploy/preprocess/transform/normalize.cpp

@@ -99,13 +99,14 @@ class Normalize : public Transform {
       Tensor dst;
       if (to_float_) {
         OUTCOME_TRY(normalize_.Apply(tensor, dst));
+        data[key] = std::move(dst);
       } else if (to_rgb_) {
         auto src_mat = to_mat(tensor, PixelFormat::kBGR);
         Mat dst_mat;
         OUTCOME_TRY(cvt_color_.Apply(src_mat, dst_mat, PixelFormat::kBGR));
         dst = to_tensor(src_mat);
+        data[key] = std::move(dst);
       }
-      data[key] = std::move(dst);
 
       for (auto& v : mean_) {
         data["img_norm_cfg"]["mean"].push_back(v);

docs/en/01-how-to-build/rockchip.md

@@ -156,6 +156,22 @@ label: 65, score: 0.95
       ])
   ```
 
+  RTMDet: you may paste the following partition configuration into [detection_rknn-int8_static-640x640.py](https://github.com/open-mmlab/mmdeploy/blob/dev-1.x/configs/mmdet/detection/detection_rknn-int8_static-640x640.py):
+
+  ```python
+  # rtmdet for rknn-toolkit and rknn-toolkit2
+  partition_config = dict(
+      type='rknn',  # the partition policy name
+      apply_marks=True,  # should always be set to True
+      partition_cfg=[
+          dict(
+              save_file='model.onnx',  # name to save the partitioned onnx
+              start=['detector_forward:input'],  # [mark_name:input, ...]
+              end=['rtmdet_head:output'],  # [mark_name:output, ...]
+              output_names=[f'pred_maps.{i}' for i in range(6)]) # output names
+      ])
+  ```
+
   RetinaNet & SSD & FSAF with rknn-toolkit2, you may paste the following partition configuration into [detection_rknn_static-320x320.py](https://github.com/open-mmlab/mmdeploy/tree/1.x/configs/mmdet/detection/detection_rknn_static-320x320.py). Users with rknn-toolkit can directly use default config.
 
   ```python

docs/en/02-how-to-run/convert_model.md

@@ -11,7 +11,7 @@ Notes:
 ### Prerequisite
 
 1. Install and build your target backend. You could refer to [ONNXRuntime-install](../05-supported-backends/onnxruntime.md), [TensorRT-install](../05-supported-backends/tensorrt.md), [ncnn-install](../05-supported-backends/ncnn.md), [PPLNN-install](../05-supported-backends/pplnn.md), [OpenVINO-install](../05-supported-backends/openvino.md) for more information.
-2. Install and build your target codebase. You could refer to [MMClassification-install](https://github.com/open-mmlab/mmclassification/blob/1.x/docs/en/get_started.md#installation), [MMDetection-install](https://github.com/open-mmlab/mmdetection/blob/3.x/docs/en/get_started.md), [MMSegmentation-install](https://github.com/open-mmlab/mmsegmentation/blob/1.x/docs/en/get_started.md#installation), [MMOCR-install](https://github.com/open-mmlab/mmocr/blob/1.x/docs/en/get_started/install.md), [MMEditing-install](https://github.com/open-mmlab/mmediting/blob/1.x/docs/en/2_get_started.md#installation).
+2. Install and build your target codebase. You could refer to [MMClassification-install](https://github.com/open-mmlab/mmclassification/blob/1.x/docs/en/get_started.md#installation), [MMDetection-install](https://github.com/open-mmlab/mmdetection/blob/3.x/docs/en/get_started.md), [MMSegmentation-install](https://github.com/open-mmlab/mmsegmentation/blob/1.x/docs/en/get_started.md#installation), [MMOCR-install](https://github.com/open-mmlab/mmocr/blob/1.x/docs/en/get_started/install.md), [MMEditing-install](https://github.com/open-mmlab/mmediting/blob/1.x/docs/en/get_started/install.md).
 
 ### Usage
 

docs/en/02-how-to-run/write_config.md

@@ -177,4 +177,4 @@ detection_tensorrt-int8_dynamic-320x320-1344x1344.py
 
 ## 6. How to write model config
 
-According to model's codebase, write the model config file. Model's config file is used to initialize the model, referring to [MMClassification](https://github.com/open-mmlab/mmclassification/blob/1.x/docs/en/user_guides/config.md), [MMDetection](https://github.com/open-mmlab/mmdetection/blob/3.x/docs/en/user_guides/config.md), [MMSegmentation](https://github.com/open-mmlab/mmsegmentation/blob/1.x/docs/en/user_guides/1_config.md), [MMOCR](https://github.com/open-mmlab/mmocr/blob/1.x/docs/en/user_guides/config.md), [MMEditing](https://github.com/open-mmlab/mmediting/blob/1.x/docs/en/user_guides/1_config.md).
+According to model's codebase, write the model config file. Model's config file is used to initialize the model, referring to [MMClassification](https://github.com/open-mmlab/mmclassification/blob/1.x/docs/en/user_guides/config.md), [MMDetection](https://github.com/open-mmlab/mmdetection/blob/3.x/docs/en/user_guides/config.md), [MMSegmentation](https://github.com/open-mmlab/mmsegmentation/blob/1.x/docs/en/user_guides/1_config.md), [MMOCR](https://github.com/open-mmlab/mmocr/blob/1.x/docs/en/user_guides/config.md), [MMEditing](https://github.com/open-mmlab/mmediting/blob/1.x/docs/en/user_guides/config.md).

docs/zh_cn/01-how-to-build/rockchip.md

@@ -134,6 +134,24 @@ python tools/deploy.py \
 
 ```
 
+- RTMDet
+
+将下面的模型拆分配置写入到 [detection_rknn-int8_static-640x640.py](https://github.com/open-mmlab/mmdeploy/blob/dev-1.x/configs/mmdet/detection/detection_rknn-int8_static-640x640.py)
+
+```python
+# rtmdet for rknn-toolkit and rknn-toolkit2
+partition_config = dict(
+    type='rknn',  # the partition policy name
+    apply_marks=True,  # should always be set to True
+    partition_cfg=[
+        dict(
+            save_file='model.onnx',  # name to save the partitioned onnx
+            start=['detector_forward:input'],  # [mark_name:input, ...]
+            end=['rtmdet_head:output'],  # [mark_name:output, ...]
+            output_names=[f'pred_maps.{i}' for i in range(6)]) # output names
+    ])
+```
+
 - RetinaNet & SSD & FSAF with rknn-toolkit2
 
 将下面的模型拆分配置写入到 [detection_rknn_static.py](https://github.com/open-mmlab/mmdeploy/blob/1.x/configs/mmdet/detection/detection_rknn_static-320x320.py)。使用 rknn-toolkit 的用户则不用。

docs/zh_cn/02-how-to-run/convert_model.md

@@ -26,7 +26,7 @@
 ### 准备工作
 
 1. 安装您的目标后端。 您可以参考 [ONNXRuntime-install](../05-supported-backends/onnxruntime.md) ，[TensorRT-install](../05-supported-backends/tensorrt.md) ，[ncnn-install](../05-supported-backends/ncnn.md) ，[PPLNN-install](../05-supported-backends/pplnn.md), [OpenVINO-install](../05-supported-backends/openvino.md)。
-2. 安装您的目标代码库。 您可以参考 [MMClassification-install](https://github.com/open-mmlab/mmclassification/blob/1.x/docs/zh_CN/get_started.md#%E5%AE%89%E8%A3%85)， [MMDetection-install](https://github.com/open-mmlab/mmdetection/blob/3.x/docs/zh_cn/get_started.md)， [MMSegmentation-install](https://github.com/open-mmlab/mmsegmentation/blob/1.x/docs/zh_cn/get_started.md#installation)， [MMOCR-install](https://github.com/open-mmlab/mmocr/blob/1.x/docs/zh_cn/get_started/install.md)， [MMEditing-install](https://github.com/open-mmlab/mmediting/blob/1.x/docs/en/2_get_started.md#installation)。
+2. 安装您的目标代码库。 您可以参考 [MMClassification-install](https://github.com/open-mmlab/mmclassification/blob/1.x/docs/zh_CN/get_started.md#%E5%AE%89%E8%A3%85)， [MMDetection-install](https://github.com/open-mmlab/mmdetection/blob/3.x/docs/zh_cn/get_started.md)， [MMSegmentation-install](https://github.com/open-mmlab/mmsegmentation/blob/1.x/docs/zh_cn/get_started.md#installation)， [MMOCR-install](https://github.com/open-mmlab/mmocr/blob/1.x/docs/zh_cn/get_started/install.md)， [MMEditing-install](https://github.com/open-mmlab/mmediting/blob/1.x/docs/en/get_started/install.md)。
 
 ### 使用方法
 

docs/zh_cn/02-how-to-run/write_config.md

@@ -187,4 +187,4 @@ detection_tensorrt-int8_dynamic-320x320-1344x1344.py
 
 ## 6. 如何编写模型配置文件
 
-请根据模型具体任务的代码库，编写模型配置文件。 模型配置文件用于初始化模型，详情请参考[MMClassification](https://github.com/open-mmlab/mmclassification/blob/1.x/docs/zh_CN/user_guides/config.md)，[MMDetection](https://github.com/open-mmlab/mmdetection/blob/3.x/docs/zh_cn/user_guides/config.md)， [MMSegmentation](https://github.com/open-mmlab/mmsegmentation/blob/1.x/docs/zh_cn/user_guides/1_config.md)， [MMOCR](https://github.com/open-mmlab/mmocr/blob/1.x/docs/en/user_guides/config.md)，[MMEditing](https://github.com/open-mmlab/mmediting/blob/1.x/docs/en/user_guides/1_config.md)。
+请根据模型具体任务的代码库，编写模型配置文件。 模型配置文件用于初始化模型，详情请参考[MMClassification](https://github.com/open-mmlab/mmclassification/blob/1.x/docs/zh_CN/user_guides/config.md)，[MMDetection](https://github.com/open-mmlab/mmdetection/blob/3.x/docs/zh_cn/user_guides/config.md)， [MMSegmentation](https://github.com/open-mmlab/mmsegmentation/blob/1.x/docs/zh_cn/user_guides/1_config.md)， [MMOCR](https://github.com/open-mmlab/mmocr/blob/1.x/docs/en/user_guides/config.md)，[MMEditing](https://github.com/open-mmlab/mmediting/blob/1.x/docs/en/user_guides/config.md)。

mmdeploy/codebase/mmdet/deploy/object_detection.py

@@ -313,7 +313,8 @@ def get_postprocess(self, *args, **kwargs) -> Dict:
                 params['mask_thr_binary'] = params['rcnn']['mask_thr_binary']
                 type = 'ResizeInstanceMask'  # for instance-seg
         if get_backend(self.deploy_cfg) == Backend.RKNN:
-            if 'YOLO' in self.model_cfg.model.type:
+            if 'YOLO' in self.model_cfg.model.type or \
+               'RTMDet' in self.model_cfg.model.type:
                 bbox_head = self.model_cfg.model.bbox_head
                 type = bbox_head.type
                 params['anchor_generator'] = bbox_head.get(

mmdeploy/codebase/mmdet/deploy/object_detection_model.py

@@ -755,6 +755,14 @@ def _get_bboxes(self, outputs: List[Tensor], metainfos: Any):
                 batch_img_metas=metainfos,
                 cfg=self.model_cfg._cfg_dict.model.test_cfg,
                 rescale=True)
+        elif head_cfg.type == 'RTMDetSepBNHead':
+            divisor = round(len(outputs) / 2)
+            ret = head.predict_by_feat(
+                outputs[:divisor],
+                outputs[divisor:],
+                batch_img_metas=metainfos,
+                cfg=self.model_cfg._cfg_dict.model.test_cfg,
+                rescale=True)
         elif head_cfg.type in ('RetinaHead', 'SSDHead', 'FSAFHead'):
             partition_cfgs = get_partition_config(self.deploy_cfg)
             if partition_cfgs is None:  # bbox decoding done in rknn model

mmdeploy/codebase/mmdet/models/dense_heads/rtmdet_head.py

@@ -7,7 +7,7 @@
 from torch import Tensor
 
 from mmdeploy.codebase.mmdet import get_post_processing_params
-from mmdeploy.core import FUNCTION_REWRITER
+from mmdeploy.core import FUNCTION_REWRITER, mark
 from mmdeploy.mmcv.ops import multiclass_nms
 
 
@@ -51,6 +51,12 @@ def rtmdet_head__predict_by_feat(self,
             tensor in the tuple is (N, num_box), and each element
             represents the class label of the corresponding box.
     """
+
+    @mark('rtmdet_head', inputs=['cls_scores', 'bbox_preds'])
+    def __mark_pred_maps(cls_scores, bbox_preds):
+        return cls_scores, bbox_preds
+
+    cls_scores, bbox_preds = __mark_pred_maps(cls_scores, bbox_preds)
     ctx = FUNCTION_REWRITER.get_context()
     assert len(cls_scores) == len(bbox_preds)
     device = cls_scores[0].device