mmdeploy/csrc/backend_ops/ncnn/ops/gather/gather.cpp

// Copyright (c) OpenMMLab. All rights reserved.
#include "gather.h"

#include "../ncnn_ops_definer.h"
#include "assert.h"

namespace mmdeploy {
using namespace ncnn;
DEFINE_LAYER_CREATOR(Gather)
DEFINE_NCNN_OPS(Gather, Gather)
Gather::Gather() {
  one_blob_only = false;
  support_inplace = false;
}

int Gather::load_param(const ParamDict &pd) {
  axis = pd.get(0, 0);

  return 0;
}

// Gather only support 1-dim of indices, because the data and indices all has
// implicit batch in ncnn, this will lead to wrong shape to match onnx result.
// When indices dim equals to 1, after eliminating implicit batch, the indices
// dim still be 1. So there is only 1 implicit batch in data, this will make
// the shape match onnx result.
int Gather::forward(const std::vector<Mat> &bottom_blobs, std::vector<Mat> &top_blobs,
                    const Option &opt) const {
  const Mat &bottom_blob = bottom_blobs[0];
  const Mat &indices = bottom_blobs[1];
  int dims = bottom_blob.dims;
  int indices_dims = indices.dims;
  size_t elemsize = bottom_blob.elemsize;
  int positive_axis = axis < 0 ? dims + axis : axis;
  Mat &top_blob = top_blobs[0];
  assert(indices.dims == 1);
  const float *indices_ptr = indices;

  if (dims == 1 && indices_dims == 1)  // positive_axis == 0
  {
    int w = indices.w;
    top_blob.create(w, elemsize, opt.blob_allocator);
    if (top_blob.empty()) {
      return -100;
    }
    const float *ptr = bottom_blob;
    float *outptr = top_blob;
    for (int i = 0; i < w; i++) {
      float indice = indices_ptr[i];
      outptr[i] = ptr[(int)(indice + 0.5)];
    }

    return 0;
  }

  if (dims == 2 && positive_axis == 0 && indices_dims == 1) {
    int w = bottom_blob.w;
    int h = bottom_blob.h;
    top_blob.create(w, indices.w, elemsize, opt.blob_allocator);
    // w -> w
    // h -> indices.w
    // h * w -> indices.w * w
    if (top_blob.empty()) {
      return -100;
    }
    const float *ptr = bottom_blob;
    float *outptr = top_blob;
    for (int i = 0; i < indices.w; i++) {
      const int selected = (int)(indices_ptr[i] + 0.5);
      memcpy(top_blob.row(i), bottom_blob.row(selected), w * elemsize);
    }

    return 0;
  }

  if (dims == 2 && positive_axis == 1 && indices_dims == 1) {
    int w = bottom_blob.w;
    int h = bottom_blob.h;
    top_blob.create(indices.w, h, elemsize, opt.blob_allocator);
    // w -> h
    // h -> indices.w
    // h * w -> indices.w * h
    if (top_blob.empty()) {
      return -100;
    }
    const float *ptr = bottom_blob;
    float *outptr = top_blob;
    for (int j = 0; j < h; j++) {
      for (int i = 0; i < indices.w; i++) {
        int selected = (int)(indices_ptr[i] + 0.5);
        outptr[j * indices.w + i] = ptr[j * w + selected];
      }
    }
    return 0;
  }

  if (dims == 3 && positive_axis == 0 && indices_dims == 1) {
    int w = bottom_blob.w;
    int h = bottom_blob.h;
    int channels = bottom_blob.c;
    top_blob.create(w, h, indices.w, elemsize, opt.blob_allocator);

    if (top_blob.empty()) {
      return -100;
    }
    for (int i = 0; i < indices.w; i++) {
      int selected = (int)(indices_ptr[i] + 0.5);
      const unsigned char *ptr = bottom_blob.channel(selected);
      unsigned char *outptr = top_blob.channel(i);

      memcpy(outptr, ptr, w * h * elemsize);
    }
    return 0;
  }

  if (dims == 3 && positive_axis == 1 && indices_dims == 1) {
    int w = bottom_blob.w;
    int h = bottom_blob.h;
    int channels = bottom_blob.c;
    top_blob.create(w, indices.w, channels, elemsize, opt.blob_allocator);
#pragma omp parallel for num_threads(opt.num_threads)
    // use parallel programming
    for (int i = 0; i < channels; i++) {
      float *outptr = top_blob.channel(i);
      const float *ptr = bottom_blob.channel(i);
      for (int j = 0; j < indices.w; j++) {
        int selected = (int)(indices_ptr[j] + 0.5);
        for (int k = 0; k < w; k++) {
          outptr[j * w + k] = ptr[selected * w + k];
        }
      }
    }

    return 0;
  }

  if (dims == 3 && positive_axis == 2 && indices_dims == 1) {
    int w = bottom_blob.w;
    int h = bottom_blob.h;
    int channels = bottom_blob.c;
    top_blob.create(indices.w, h, channels, elemsize, opt.blob_allocator);
#pragma omp parallel for num_threads(opt.num_threads)
    // use parallel programming
    for (int i = 0; i < channels; i++) {
      float *outptr = top_blob.channel(i);
      const float *ptr = bottom_blob.channel(i);
      for (int j = 0; j < h; j++) {
        for (int k = 0; k < indices.w; k++) {
          int selected = (int)(indices_ptr[k] + 0.5);
          outptr[j * indices.w + k] = ptr[j * w + selected];
        }
      }
    }
    return 0;
  }

  return 0;
}

}  //  namespace mmdeploy