Add roi_align_rotated onnxruntime op

csrc/backend_ops/onnxruntime/roi_align_rotated/roi_align_rotated.cpp

@@ -0,0 +1,237 @@
+// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+// Modified from
+// https://github.com/facebookresearch/detectron2/tree/master/detectron2/layers/csrc/ROIAlignRotated
+#include "roi_align_rotated.h"
+
+#include "ort_utils.h"
+
+namespace mmdeploy {
+// implementation taken from Caffe2
+struct PreCalc {
+  int pos1;
+  int pos2;
+  int pos3;
+  int pos4;
+  float w1;
+  float w2;
+  float w3;
+  float w4;
+};
+
+void pre_calc_for_bilinear_interpolate(const int height, const int width, const int pooled_height,
+                                       const int pooled_width, const int iy_upper,
+                                       const int ix_upper, float roi_start_h, float roi_start_w,
+                                       float bin_size_h, float bin_size_w, int roi_bin_grid_h,
+                                       int roi_bin_grid_w, float roi_center_h, float roi_center_w,
+                                       float cos_theta, float sin_theta,
+                                       std::vector<PreCalc> &pre_calc) {
+  int pre_calc_index = 0;
+  for (int ph = 0; ph < pooled_height; ph++) {
+    for (int pw = 0; pw < pooled_width; pw++) {
+      for (int iy = 0; iy < iy_upper; iy++) {
+        const float yy = roi_start_h + ph * bin_size_h +
+                         static_cast<float>(iy + .5f) * bin_size_h /
+                             static_cast<float>(roi_bin_grid_h);  // e.g., 0.5, 1.5
+        for (int ix = 0; ix < ix_upper; ix++) {
+          const float xx =
+              roi_start_w + pw * bin_size_w +
+              static_cast<float>(ix + .5f) * bin_size_w / static_cast<float>(roi_bin_grid_w);
+
+          // Rotate by theta around the center and translate
+          // In image space, (y, x) is the order for Right Handed System,
+          // and this is essentially multiplying the point by a rotation matrix
+          // to rotate it counterclockwise through angle theta.
+          float y = yy * cos_theta - xx * sin_theta + roi_center_h;
+          float x = yy * sin_theta + xx * cos_theta + roi_center_w;
+          // deal with: inverse elements are out of feature map boundary
+          if (y < -1.0 || y > height || x < -1.0 || x > width) {
+            // empty
+            PreCalc pc;
+            pc.pos1 = 0;
+            pc.pos2 = 0;
+            pc.pos3 = 0;
+            pc.pos4 = 0;
+            pc.w1 = 0;
+            pc.w2 = 0;
+            pc.w3 = 0;
+            pc.w4 = 0;
+            pre_calc[pre_calc_index] = pc;
+            pre_calc_index += 1;
+            continue;
+          }
+
+          if (y < 0) {
+            y = 0;
+          }
+          if (x < 0) {
+            x = 0;
+          }
+
+          int y_low = (int)y;
+          int x_low = (int)x;
+          int y_high;
+          int x_high;
+
+          if (y_low >= height - 1) {
+            y_high = y_low = height - 1;
+            y = (float)y_low;
+          } else {
+            y_high = y_low + 1;
+          }
+
+          if (x_low >= width - 1) {
+            x_high = x_low = width - 1;
+            x = (float)x_low;
+          } else {
+            x_high = x_low + 1;
+          }
+
+          float ly = y - y_low;
+          float lx = x - x_low;
+          float hy = 1. - ly, hx = 1. - lx;
+          float w1 = hy * hx, w2 = hy * lx, w3 = ly * hx, w4 = ly * lx;
+
+          // save weights and indices
+          PreCalc pc;
+          pc.pos1 = y_low * width + x_low;
+          pc.pos2 = y_low * width + x_high;
+          pc.pos3 = y_high * width + x_low;
+          pc.pos4 = y_high * width + x_high;
+          pc.w1 = w1;
+          pc.w2 = w2;
+          pc.w3 = w3;
+          pc.w4 = w4;
+          pre_calc[pre_calc_index] = pc;
+
+          pre_calc_index += 1;
+        }
+      }
+    }
+  }
+}
+
+void ROIAlignRotatedForwardCPU(const int nthreads, const float *input, const float *rois,
+                               float *output, const float &spatial_scale, const int aligned,
+                               const int clockwise, const int channels, const int height,
+                               const int width, const int pooled_height, const int pooled_width,
+                               const int sampling_ratio) {
+  int n_rois = nthreads / channels / pooled_width / pooled_height;
+  // (n, c, ph, pw) is an element in the pooled output
+  // can be parallelized using omp
+  // #pragma omp parallel for num_threads(32)
+  for (int n = 0; n < n_rois; n++) {
+    int index_n = n * channels * pooled_width * pooled_height;
+
+    const float *current_roi = rois + n * 6;
+    int roi_batch_ind = current_roi[0];
+
+    // Do not use rounding; this implementation detail is critical
+    float offset = aligned ? (float)0.5 : (float)0.0;
+    float roi_center_w = current_roi[1] * spatial_scale - offset;
+    float roi_center_h = current_roi[2] * spatial_scale - offset;
+    float roi_width = current_roi[3] * spatial_scale;
+    float roi_height = current_roi[4] * spatial_scale;
+    // float theta = current_roi[5] * M_PI / 180.0;
+    float theta = current_roi[5];  // Radian angle by default
+    if (clockwise) {
+      theta = -theta;
+    }
+    float cos_theta = cos(theta);
+    float sin_theta = sin(theta);
+    if (!aligned) {  // for backward-compatibility only
+      roi_width = std::max(roi_width, (float)1.);
+      roi_height = std::max(roi_height, (float)1.);
+    }
+
+    float bin_size_h = static_cast<float>(roi_height) / static_cast<float>(pooled_height);
+    float bin_size_w = static_cast<float>(roi_width) / static_cast<float>(pooled_width);
+
+    // We use roi_bin_grid to sample the grid and mimic integral
+    int roi_bin_grid_h =
+        (sampling_ratio > 0) ? sampling_ratio : ceil(roi_height / pooled_height);  // e.g., = 2
+    int roi_bin_grid_w = (sampling_ratio > 0) ? sampling_ratio : ceil(roi_width / pooled_width);
+
+    // We do average (integral) pooling inside a bin
+    const float count = std::max(roi_bin_grid_h * roi_bin_grid_w, 1);  // e.g. = 4
+
+    // we want to precalculate indices and weights shared by all channels,
+    // this is the key point of optimization
+    std::vector<PreCalc> pre_calc(roi_bin_grid_h * roi_bin_grid_w * pooled_width * pooled_height);
+
+    // roi_start_h and roi_start_w are computed wrt the center of RoI (x, y).
+    // Appropriate translation needs to be applied after.
+    float roi_start_h = -roi_height / 2.0;
+    float roi_start_w = -roi_width / 2.0;
+
+    pre_calc_for_bilinear_interpolate(height, width, pooled_height, pooled_width, roi_bin_grid_h,
+                                      roi_bin_grid_w, roi_start_h, roi_start_w, bin_size_h,
+                                      bin_size_w, roi_bin_grid_h, roi_bin_grid_w, roi_center_h,
+                                      roi_center_w, cos_theta, sin_theta, pre_calc);
+
+    for (int c = 0; c < channels; c++) {
+      int index_n_c = index_n + c * pooled_width * pooled_height;
+      const float *offset_input = input + (roi_batch_ind * channels + c) * height * width;
+      int pre_calc_index = 0;
+
+      for (int ph = 0; ph < pooled_height; ph++) {
+        for (int pw = 0; pw < pooled_width; pw++) {
+          int index = index_n_c + ph * pooled_width + pw;
+
+          float output_val = 0.;
+          for (int iy = 0; iy < roi_bin_grid_h; iy++) {
+            for (int ix = 0; ix < roi_bin_grid_w; ix++) {
+              PreCalc pc = pre_calc[pre_calc_index];
+              output_val += pc.w1 * offset_input[pc.pos1] + pc.w2 * offset_input[pc.pos2] +
+                            pc.w3 * offset_input[pc.pos3] + pc.w4 * offset_input[pc.pos4];
+
+              pre_calc_index += 1;
+            }
+          }
+          output_val /= count;
+
+          output[index] = output_val;
+        }  // for pw
+      }    // for ph
+    }      // for c
+  }        // for n
+}
+
+void MMCVRoIAlignRotatedKernel::Compute(OrtKernelContext *context) {
+  // Setup inputs
+  const OrtValue *input_X = ort_.KernelContext_GetInput(context, 0);
+  const float *X_data = reinterpret_cast<const float *>(ort_.GetTensorData<float>(input_X));
+  const OrtValue *input_rois = ort_.KernelContext_GetInput(context, 1);
+  const float *rois =
+      reinterpret_cast<const float *>(ort_.GetTensorData<const float *>(input_rois));
+
+  // Setup output
+  OrtTensorDimensions out_dimensions(ort_, input_X);
+  OrtTensorDimensions roi_dimensions(ort_, input_rois);
+
+  int batch_size = out_dimensions.data()[0];
+  int input_channels = out_dimensions.data()[1];
+  int input_height = out_dimensions.data()[2];
+  int input_width = out_dimensions.data()[3];
+
+  out_dimensions.data()[0] = roi_dimensions.data()[0];
+  out_dimensions.data()[2] = aligned_height_;
+  out_dimensions.data()[3] = aligned_width_;
+
+  OrtValue *output =
+      ort_.KernelContext_GetOutput(context, 0, out_dimensions.data(), out_dimensions.size());
+  float *out = ort_.GetTensorMutableData<float>(output);
+  OrtTensorTypeAndShapeInfo *output_info = ort_.GetTensorTypeAndShape(output);
+  ort_.ReleaseTensorTypeAndShapeInfo(output_info);
+
+  // TODO: forward here
+  int output_size = out_dimensions.data()[0];
+  for (auto i = 1; i < out_dimensions.size(); ++i) {
+    output_size *= out_dimensions.data()[i];
+  }
+  ROIAlignRotatedForwardCPU(output_size, X_data, rois, out, spatial_scale_, aligned_, clockwise_,
+                            input_channels, input_height, input_width, aligned_height_,
+                            aligned_width_, sampling_ratio_);
+}
+
+REGISTER_ONNXRUNTIME_OPS(mmdeploy, MMCVRoIAlignRotatedCustomOp);
+}  // namespace mmdeploy

csrc/backend_ops/onnxruntime/roi_align_rotated/roi_align_rotated.h

@@ -0,0 +1,59 @@
+// Copyright (c) OpenMMLab. All rights reserved
+#ifndef ONNXRUNTIME_ROI_ALIGN_ROTATED_H
+#define ONNXRUNTIME_ROI_ALIGN_ROTATED_H
+
+#include <assert.h>
+#include <onnxruntime_cxx_api.h>
+
+#include <cmath>
+#include <mutex>
+#include <string>
+#include <vector>
+
+namespace mmdeploy {
+struct MMCVRoIAlignRotatedKernel {
+ public:
+  MMCVRoIAlignRotatedKernel(Ort::CustomOpApi ort, const OrtKernelInfo* info) : ort_(ort) {
+    aligned_height_ = ort_.KernelInfoGetAttribute<int64_t>(info, "output_height");
+    aligned_width_ = ort_.KernelInfoGetAttribute<int64_t>(info, "output_width");
+    sampling_ratio_ = ort_.KernelInfoGetAttribute<int64_t>(info, "sampling_ratio");
+    spatial_scale_ = ort_.KernelInfoGetAttribute<float>(info, "spatial_scale");
+    aligned_ = ort_.KernelInfoGetAttribute<int64_t>(info, "aligned");
+    clockwise_ = ort_.KernelInfoGetAttribute<int64_t>(info, "clockwise");
+  }
+
+  void Compute(OrtKernelContext* context);
+
+ private:
+  Ort::CustomOpApi ort_;
+  int aligned_height_;
+  int aligned_width_;
+  float spatial_scale_;
+  int sampling_ratio_;
+  int aligned_;
+  int clockwise_;
+};
+
+struct MMCVRoIAlignRotatedCustomOp
+    : Ort::CustomOpBase<MMCVRoIAlignRotatedCustomOp, MMCVRoIAlignRotatedKernel> {
+  void* CreateKernel(Ort::CustomOpApi api, const OrtKernelInfo* info) const {
+    return new MMCVRoIAlignRotatedKernel(api, info);
+  }
+  const char* GetName() const { return "MMCVRoIAlignRotated"; }
+
+  size_t GetInputTypeCount() const { return 2; }
+  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_FLOAT;
+  }
+
+  // force cpu
+  const char* GetExecutionProviderType() const { return "CPUExecutionProvider"; }
+};
+}  // namespace mmdeploy
+
+#endif  // ONNXRUNTIME_ROI_ALIGN_ROTATED_H

docs/en/backends/onnxruntime.md

@@ -60,6 +60,7 @@ make -j$(nproc)
 | [grid_sampler](../ops/onnxruntime.md#grid_sampler)                           |   Y   |   N   | master            |
 | [MMCVModulatedDeformConv2d](../ops/onnxruntime.md#mmcvmodulateddeformconv2d) |   Y   |   N   | master            |
 | [NMSRotated](../ops/onnxruntime.md#nmsrotated)                               |   Y   |   N   | master            |
+| [RoIAlignRotated](../ops/onnxruntime.md#roialignrotated)                     |   Y   |   N   | master            |
 
 ### How to add a new custom op
 

docs/en/ops/onnxruntime.md

@@ -21,6 +21,12 @@
     - [Inputs](#inputs-2)
     - [Outputs](#outputs-2)
     - [Type Constraints](#type-constraints-2)
+  - [RoIAlignRotated](#roialignrotated)
+    - [Description](#description-3)
+    - [Parameters](#parameters-3)
+    - [Inputs](#inputs-3)
+    - [Outputs](#outputs-3)
+    - [Type Constraints](#type-constraints-3)
 
 <!-- TOC -->
 
@@ -132,3 +138,41 @@ Non Max Suppression for rotated bboxes.
 #### Type Constraints
 
 - T:tensor(float32, Linear)
+
+
+### RoIAlignRotated
+
+#### Description
+
+Perform RoIAlignRotated on output feature, used in bbox_head of most two-stage rotated object detectors.
+
+#### Parameters
+
+| Type    | Parameter        | Description                                                                                                   |
+| ------- | ---------------- | ------------------------------------------------------------------------------------------------------------- |
+| `int`   | `output_height`  | height of output roi                                                                                          |
+| `int`   | `output_width`   | width of output roi                                                                                           |
+| `float` | `spatial_scale`  | used to scale the input boxes                                                                                 |
+| `int`   | `sampling_ratio` | number of input samples to take for each output sample. `0` means to take samples densely for current models. |
+| `int`   | `aligned`        | If `aligned=0`, use the legacy implementation in MMDetection. Else, align the results more perfectly.         |
+| `int`   | `clockwise`           | If True, the angle in each proposal follows a clockwise fashion in image space, otherwise, the angle is counterclockwise. Default: False. |
+
+#### Inputs
+
+<dl>
+<dt><tt>input</tt>: T</dt>
+<dd>Input feature map; 4D tensor of shape (N, C, H, W), where N is the batch size, C is the numbers of channels, H and W are the height and width of the data.</dd>
+<dt><tt>rois</tt>: T</dt>
+<dd>RoIs (Regions of Interest) to pool over; 2-D tensor of shape (num_rois, 6) given as [[batch_index, cx, cy, w, h, theta], ...]. The RoIs' coordinates are the coordinate system of input.</dd>
+</dl>
+
+#### Outputs
+
+<dl>
+<dt><tt>feat</tt>: T</dt>
+<dd>RoI pooled output, 4-D tensor of shape (num_rois, C, output_height, output_width). The r-th batch element feat[r-1] is a pooled feature map corresponding to the r-th RoI RoIs[r-1].<dd>
+</dl>
+
+#### Type Constraints
+
+- T:tensor(float32)

mmdeploy/mmcv/ops/__init__.py

@@ -4,8 +4,9 @@
 from .nms import *  # noqa: F401,F403
 from .nms_rotated import *  # noqa: F401,F403
 from .roi_align import roi_align_default
+from .roi_align_rotated import roi_align_rotated_default
 
 __all__ = [
     'roi_align_default', 'modulated_deform_conv_default',
-    'deform_conv_openvino'
+    'deform_conv_openvino', 'roi_align_rotated_default'
 ]