CodeCamp2023-671

csrc/mmdeploy/backend_ops/onnxruntime/modulated_deform_conv/modulated_deform_conv.cpp

@@ -14,7 +14,7 @@ void parallel_unroll_gemm(const float *A, const float *B, const float *V, const
                           const int32_t M, const int32_t N, const int32_t K, const float alpha,
                           const float beta, float *Y, const int32_t start_row,
                           const int32_t end_row) {
-  float tmp[N];  // tmp
+  std::vector<float> tmp(N);
   for (int32_t m = start_row; m < end_row; ++m) {
     for (int32_t n = 0; n < N; n++) {
       tmp[n] = 0;

csrc/mmdeploy/backend_ops/onnxruntime/nms_match/nms_match.cpp

@@ -0,0 +1,129 @@
+// Copyright (c) OpenMMLab. All rights reserved
+#include "nms_match.h"
+
+#include <assert.h>
+
+#include <algorithm>
+#include <cassert>
+#include <cmath>
+#include <iostream>
+#include <iterator>
+#include <numeric>
+#include <vector>
+
+#include "ort_utils.h"
+
+namespace mmdeploy {
+struct Box {
+  float x1, y1, x2, y2;
+};
+
+float nms_match_iou(Box box1, Box box2) {
+  auto inter_x1 = std::max(box1.x1, box2.x1);
+  auto inter_y1 = std::max(box1.y1, box2.y1);
+  auto inter_x2 = std::min(box1.x2, box2.x2);
+  auto inter_y2 = std::min(box1.y2, box2.y2);
+
+  auto eps = 1e-10;
+
+  auto w = std::max(static_cast<float>(0), inter_x2 - inter_x1);
+  auto h = std::max(static_cast<float>(0), inter_y2 - inter_y1);
+
+  auto area1 = (box1.x2 - box1.x1) * (box1.y2 - box1.y1);
+  auto area2 = (box2.x2 - box2.x1) * (box2.y2 - box2.y1);
+  auto inter = w * h;
+  auto ovr = inter / (area1 + area2 - inter + eps);
+  return ovr;
+}
+NMSMatchKernel::NMSMatchKernel(const OrtApi& api, const OrtKernelInfo* info)
+    : ort_(api), info_(info) {
+  // create allocator
+  allocator_ = Ort::AllocatorWithDefaultOptions();
+}
+
+void NMSMatchKernel::Compute(OrtKernelContext* context) {
+  const OrtValue* boxes = ort_.KernelContext_GetInput(context, 0);
+  const float* boxes_data = reinterpret_cast<const float*>(ort_.GetTensorData<float>(boxes));
+  const OrtValue* scores = ort_.KernelContext_GetInput(context, 1);
+  const float* scores_data = reinterpret_cast<const float*>(ort_.GetTensorData<float>(scores));
+  const OrtValue* iou_threshold_ = ort_.KernelContext_GetInput(context, 2);
+  const float iou_threshold_data = ort_.GetTensorData<float>(iou_threshold_)[0];
+  const OrtValue* score_threshold_ = ort_.KernelContext_GetInput(context, 3);
+  const float score_threshold_data = ort_.GetTensorData<float>(score_threshold_)[0];
+
+  OrtTensorDimensions boxes_dim(ort_, boxes);
+  OrtTensorDimensions scores_dim(ort_, scores);
+  // loop over batch
+  int64_t nbatch = boxes_dim[0];
+  int64_t nboxes = boxes_dim[1];
+  int64_t nclass = scores_dim[1];
+  assert(boxes_dim[2] == 4);  //(x1, x2, y1, y2)
+  // alloc some temp memory
+  bool* select = (bool*)allocator_.Alloc(sizeof(bool) * nbatch * nboxes);
+
+  std::vector<int64_t> res_order;
+  for (int64_t k = 0; k < nbatch; k++) {
+    for (int64_t g = 0; g < nclass; g++) {
+      for (int64_t i = 0; i < nboxes; i++) {
+        select[i] = true;
+      }
+      // scores
+      // k * nboxes * nclass means per batch
+      // g * nboxes means per class
+      // batch = 2 boxes = 3 classes = 4
+      std::vector<float> tmp_sc;
+      // get the class scores
+      for (int i = 0; i < nboxes; i++) {
+        tmp_sc.push_back(scores_data[k * nboxes * nclass + g * nboxes + i]);
+      }
+
+      std::vector<int64_t> order(tmp_sc.size());
+      std::iota(order.begin(), order.end(), 0);
+      std::sort(order.begin(), order.end(),
+                [&tmp_sc](int64_t id1, int64_t id2) { return tmp_sc[id1] > tmp_sc[id2]; });
+      for (int64_t _i = 0; _i < nboxes; _i++) {
+        auto i = order[_i];
+        if (select[i] == false) continue;
+        std::vector<int64_t> v_i;
+        for (int64_t _j = _i + 1; _j < nboxes; _j++) {
+          auto j = order[_j];
+          if (select[j] == false) continue;
+          Box vbox1, vbox2;
+          vbox1.x1 = boxes_data[k * nboxes * 4 + i * 4];
+          vbox1.y1 = boxes_data[k * nboxes * 4 + i * 4 + 1];
+          vbox1.x2 = boxes_data[k * nboxes * 4 + i * 4 + 2];
+          vbox1.y2 = boxes_data[k * nboxes * 4 + i * 4 + 3];
+
+          vbox2.x1 = boxes_data[k * nboxes * 4 + j * 4];
+          vbox2.y1 = boxes_data[k * nboxes * 4 + j * 4 + 1];
+          vbox2.x2 = boxes_data[k * nboxes * 4 + j * 4 + 2];
+          vbox2.y2 = boxes_data[k * nboxes * 4 + j * 4 + 3];
+
+          auto ovr = nms_match_iou(vbox1, vbox2);
+          if (ovr >= iou_threshold_data) {
+            select[j] = false;
+            v_i.push_back(j);
+          }
+        }
+        if (tmp_sc[i] > score_threshold_data && v_i.size() != 0) {
+          for (int v_i_idx = 0; v_i_idx < v_i.size(); v_i_idx++) {
+            res_order.push_back(k);
+            res_order.push_back(g);
+            res_order.push_back(i);
+            res_order.push_back(v_i[v_i_idx]);
+          }
+        }
+      }
+    }
+  }
+  std::vector<int64_t> inds_dims({(int64_t)res_order.size() / 4, 4});
+
+  OrtValue* res = ort_.KernelContext_GetOutput(context, 0, inds_dims.data(), inds_dims.size());
+  int64_t* res_data = ort_.GetTensorMutableData<int64_t>(res);
+
+  memcpy(res_data, res_order.data(), sizeof(int64_t) * res_order.size());
+
+  allocator_.Free(select);
+}
+REGISTER_ONNXRUNTIME_OPS(mmdeploy, NMSMatchOp);
+}  // namespace mmdeploy

csrc/mmdeploy/backend_ops/onnxruntime/nms_match/nms_match.h

@@ -0,0 +1,46 @@
+// Copyright (c) OpenMMLab. All rights reserved.
+#ifndef ONNXRUNTIME_NMS_MATCH_H
+#define ONNXRUNTIME_NMS_MATCH_H
+
+#include <assert.h>
+#include <onnxruntime_cxx_api.h>
+
+#include <cmath>
+#include <mutex>
+#include <string>
+#include <vector>
+
+namespace mmdeploy {
+struct NMSMatchKernel {
+  NMSMatchKernel(const OrtApi& api, const OrtKernelInfo* info);
+
+  void Compute(OrtKernelContext* context);
+
+ private:
+  Ort::CustomOpApi ort_;
+  const OrtKernelInfo* info_;
+  Ort::AllocatorWithDefaultOptions allocator_;
+};
+
+struct NMSMatchOp : Ort::CustomOpBase<NMSMatchOp, NMSMatchKernel> {
+  void* CreateKernel(const OrtApi& api, const OrtKernelInfo* info) const {
+    return new NMSMatchKernel(api, info);
+  }
+  const char* GetName() const { return "NMSMatch"; }
+
+  size_t GetInputTypeCount() const { return 4; }
+  ONNXTensorElementDataType GetInputType(size_t) const {
+    return ONNX_TENSOR_ELEMENT_DATA_TYPE_FLOAT;
+  }
+
+  size_t GetOutputTypeCount() const { return 1; }
+  ONNXTensorElementDataType GetOutputType(size_t) const {
+    return ONNX_TENSOR_ELEMENT_DATA_TYPE_INT64;
+  }
+
+  // force cpu
+  const char* GetExecutionProviderType() const { return "CPUExecutionProvider"; }
+};
+}  // namespace mmdeploy
+
+#endif  // ONNXRUNTIME_NMS_MATCH_H

mmdeploy/mmcv/ops/__init__.py

@@ -6,11 +6,13 @@
 from . import roi_align  # noqa: F401,F403
 from . import roi_align_rotated  # noqa: F401,F403
 from . import transformer  # noqa: F401,F403
-from .nms import ONNXNMSop, TRTBatchedNMSop, multiclass_nms
-from .nms_rotated import (ONNXNMSRotatedOp, TRTBatchedRotatedNMSop,
-                          multiclass_nms_rotated)
+from .nms import ONNXNMSop, TRTBatchedNMSop, multiclass_nms  # noqa: F401,F403
+from .nms_match import ONNXNMSMatchOp, multiclass_nms_match
+from .nms_rotated import multiclass_nms_rotated  # noqa: F401,F403
+from .nms_rotated import ONNXNMSRotatedOp, TRTBatchedRotatedNMSop
 
 __all__ = [
     'ONNXNMSop', 'TRTBatchedNMSop', 'TRTBatchedRotatedNMSop',
-    'ONNXNMSRotatedOp', 'multiclass_nms', 'multiclass_nms_rotated'
+    'ONNXNMSRotatedOp', 'multiclass_nms_rotated'
+    'multiclass_nms', 'ONNXNMSMatchOp', 'multiclass_nms_match'
 ]

mmdeploy/mmcv/ops/nms.py

@@ -7,6 +7,7 @@
 from mmdeploy.core import FUNCTION_REWRITER, mark
 from mmdeploy.utils import IR, is_dynamic_batch
 from mmdeploy.utils.constants import Backend
+from .nms_match import multiclass_nms_match
 from .nms_rotated import multiclass_nms_rotated
 
 
@@ -529,6 +530,15 @@ def multiclass_nms(boxes: Tensor,
             score_threshold=score_threshold,
             pre_top_k=pre_top_k,
             keep_top_k=keep_top_k)
+    elif nms_type == 'nms_match':
+        return multiclass_nms_match(
+            boxes,
+            scores,
+            max_output_boxes_per_class=max_output_boxes_per_class,
+            iou_threshold=iou_threshold,
+            score_threshold=score_threshold,
+            pre_top_k=pre_top_k,
+            keep_top_k=keep_top_k)
     else:
         raise NotImplementedError(f'Unsupported nms type: {nms_type}.')