psroi_pool_kernel.cpp

/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */

#ifdef PSROI_POOL_OP

#include <cmath>
#include <vector>
#include "operators/kernel/detection_kernel.h"

#include "fpga/V1/api.h"
#include "fpga/V1/image.h"
namespace paddle_mobile {
namespace operators {

template <>
bool PSRoiPoolKernel<FPGA, float>::Init(PSRoiPoolParam<FPGA>* param) {
  auto dims = param->input_x_->dims();
  PADDLE_MOBILE_ENFORCE(dims[1] * dims[3] % IMAGE_ALIGNMENT == 0,
                        "data not aligned");

  param->float_input = std::make_shared<Tensor>();
  param->float_input->mutable_data<float>(param->input_x_->dims());
  // param->float_output = std::make_shared<Tensor>();

  auto input = param->input_x_;
  fpga::BypassArgs args = {fpga::DATA_TYPE_FP16};
  args.input_layout_type = fpga::LAYOUT_HWC;
  args.output_layout_type = fpga::LAYOUT_HWC;
  args.input_data_type = fpga::DATA_TYPE_FP16;
  args.output_data_type = fpga::DATA_TYPE_FP32;
  args.image.address = input->data<half>();
  args.image.height = (uint32_t)input->dims()[2];
  args.image.width = (uint32_t)input->dims()[3];
  args.image.channels = (uint32_t)input->dims()[1];
  args.output.address = param->float_input->mutable_data<float>();
  args.output.scale_address = param->float_input->scale;
  param->input_arg = args;

  auto* rois = param->input_rois_;
  int rois_num = rois->dims()[0];
  framework::DDim dims_out_new = framework::make_ddim(
      {rois_num, param->output_->dims()[1], param->output_->dims()[2],
       param->output_->dims()[3]});
  param->output_->Resize(dims_out_new);
  // fpga::format_fp16_ofm(param->output_);

  param->output_->mutable_data<float>(dims_out_new);
  //  auto output = param->float_output.get();
  // param->output_ = output;
  /* args.input_data_type = fpga::DATA_TYPE_FP32;
   args.output_data_type = fpga::DATA_TYPE_FP16;
   args.image.address = output->data<float>();
   args.image.height = (uint32_t)output->dims()[2];
   args.image.width = (uint32_t)output->dims()[3];
   args.image.channels = (uint32_t)output->dims()[1]  ;
   args.output.address = param->output_->mutable_data<half>();
   args.output.scale_address = param->output_->scale;
   param->output_arg = args;*/

  return true;
}

template <typename Dtype>
void PSROIPooling(
const Dtype* bottom_data, const int channels,
const int height, const int width,
const int pooled_height, const int pooled_width,
const Dtype* bottom_rois, const int output_dim,
const int group_size, Dtype* top_data,
int index, int nid,
const Dtype Bin_size_h,
const Dtype Bin_size_w,
const Dtype roi_start_h,
const Dtype roi_start_w, 
const int ctop, const int ph, const int roi_batch_ind) 
{
	int pw = index;
	int hstart = floor(static_cast<Dtype>(ph) * Bin_size_h + roi_start_h);
	int wstart = floor(static_cast<Dtype>(pw)* Bin_size_w + roi_start_w);
	int hend = ceil(static_cast<Dtype>(ph + 1) * Bin_size_h + roi_start_h);
	int wend = ceil(static_cast<Dtype>(pw + 1) * Bin_size_w + roi_start_w);

	// Add roi offsets and clip to input boundaries
	hstart = std::min(std::max(hstart, 0), height);
	hend = std::min(std::max(hend, 0), height);
	wstart = std::min(std::max(wstart, 0), width);
	wend = std::min(std::max(wend, 0), width);
	bool is_empty = (hend <= hstart) || (wend <= wstart);

	int c = (ctop*group_size + ph)*group_size + pw;

	Dtype bin_area = (hend - hstart)*(wend - wstart);
	bottom_data += (roi_batch_ind * channels + c) * height * width;
	Dtype out_sum = 0;
	for (int h = hstart; h < hend; ++h) {
		for (int w = wstart; w < wend; ++w) {
			int bottom_index = h * width + w;
			out_sum += bottom_data[bottom_index];
		}
	}
	
	top_data[nid + index] = is_empty? 0. : out_sum/bin_area;

}

void convert_to_chw(float **data_in, int channel, int height, int width,
                    int num) {
   float* data_in_tmp = *data_in;
  float *data_tmp =
      (float *)fpga::fpga_malloc(channel * height * width * sizeof(float));  // NOLINT
  int64_t amount_per_side = width * height;
  for (int n = 0; n < num; n++) {
    for (int h = 0; h < height; h++) {
      for (int w = 0; w < width; w++) {
        for (int c = 0; c < channel; c++) {
          *(data_tmp + n * height * width * channel + c * amount_per_side +
            width * h + w) = *((*data_in)++);
        }
      }
    }
  }
  *data_in = data_tmp;
  fpga::fpga_free(data_in_tmp);
}

void convert_to_hwc(float **data_in, int channel, int height, int width,
                    int num) {
  float* data_in_tmp = *data_in;
  float *data_tmp = reinterpret_cast<float *>(
      fpga::fpga_malloc(num * channel * height * width * sizeof(float)));
  int64_t amount_per_row = width * channel;
  for (int n = 0; n < num; n++) {
    for (int c = 0; c < channel; c++) {
      for (int h = 0; h < height; h++) {
        int64_t offset_height = h * amount_per_row;
        for (int w = 0; w < width; w++) {
          *(data_tmp + n * channel * height * width + offset_height +
            w * channel + c) = *((*data_in)++);
        }
      }
    }
  }
  *data_in = data_tmp;
  fpga::fpga_free(data_in_tmp);
}


template <>
void PSRoiPoolKernel<FPGA, float>::Compute(const PSRoiPoolParam<FPGA>& param) {
  auto input_tensor = param.float_input.get();
  fpga::PerformBypass(param.input_arg);
  fpga::fpga_invalidate(input_tensor->data<float>(),
                        input_tensor->numel() * sizeof(float));

  auto* in = input_tensor;
  auto* rois = param.input_rois_;
  auto* out = param.output_;  // param.float_output.get();

  auto pooled_height = param.pooled_height_;
  auto pooled_width = param.pooled_width_;
  auto spatial_scale = param.spatial_scale_;
  auto output_channels = param.output_channels_;

  auto in_dims = in->dims();
  int batch_size = in_dims[0];
  int input_channels = in_dims[1];
  int height = in_dims[2];
  int width = in_dims[3];
  int rois_num = rois->dims()[0];

  auto data_nhwc = in->mutable_data<float>();
  convert_to_chw(&data_nhwc, input_channels, height, width, 1);
  framework::DDim dims_out_new = framework::make_ddim(
      {rois_num, (param.output_)->dims()[1], (((param.output_)->dims()[2])),
       (param.output_)->dims()[3]});
  (param.output_)->Resize(dims_out_new);

  const float* input_data = data_nhwc;  // in->data<float>();
  framework::Tensor rois_batch_id_list;
  rois_batch_id_list.Resize({rois_num});
  auto rois_batch_id_data = rois_batch_id_list.mutable_data<int>();

  PADDLE_MOBILE_ENFORCE(rois->NumLevels() > 0, "ROIS should not be empty");

  auto rois_lod = rois->lod().back();
  int rois_batch_size = rois_lod.size() - 1;
  PADDLE_MOBILE_ENFORCE(
      rois_batch_size == batch_size,
      "the rois_batch_size and input(X) batch_size should be the same.");
  int rois_num_with_lod = rois_lod[rois_batch_size];
  PADDLE_MOBILE_ENFORCE(rois_num_with_lod == rois_num,
                        "the rois_num from input and lod must be the same");

  PADDLE_MOBILE_ENFORCE(
      input_channels == output_channels * pooled_height * pooled_width,
      "the channels of input X should equal the product of "
      "output_channels x pooled_height x pooled_width");

  // calculate batch id index for each roi according to LoD
  //for (int n = 0; n < rois_batch_size; ++n) {
    //for (size_t i = rois_lod[n]; i < rois_lod[n + 1]; ++i) {
      //rois_batch_id_data[i] = n;
   // }
  //}
  auto output_data = out->mutable_data<float>();
  auto input_rois = rois->data<float>();

  // calculate psroipooling, parallel processing can be implemented per ROI
	  for (int n = 0; n < rois_num; ++n) {
		  // [start, end) interval for spatial sampling
		  auto offset_input_rois = input_rois + n * 4;
		  auto roi_start_w = static_cast<float>(round(offset_input_rois[0])) * spatial_scale;
		  auto roi_start_h = static_cast<float>(round(offset_input_rois[1])) * spatial_scale;
		  auto roi_end_w = static_cast<float>(round(offset_input_rois[2]) + 1.) * spatial_scale;
		  auto roi_end_h = static_cast<float>(round(offset_input_rois[3]) + 1.) * spatial_scale;
  
		  // Force too small rois to be 1 x 1
		  auto roi_height = std::max(roi_end_h - roi_start_h, 0.1f);  // avoid 0
		  auto roi_width = std::max(roi_end_w - roi_start_w, 0.1f);
  
		  // Compute bin size w and h at input feature map
		  auto bin_size_h = roi_height / static_cast<float>(pooled_height);
		  auto bin_size_w = roi_width / static_cast<float>(pooled_width); 
  
		  int roi_batch_ind = 0;//rois_batch_id_data[n];
		  //std::cout << "roi_batch_ind: " << roi_batch_ind << std::endl;
		  for(int c = 0; c < output_channels; ++c){
			  
			  for(int ph = 0; ph < pooled_height; ph++){
				  int index = pooled_width;
				  int nid = n * output_channels * pooled_height * pooled_width + c * pooled_width * pooled_height + ph * pooled_width;
				  for(int idx = 0; idx < index; idx++){
					PSROIPooling<float>(input_data,input_channels,height,width,pooled_height,pooled_width,
					  input_rois,output_channels,pooled_height,output_data, idx, nid, bin_size_h, bin_size_w, roi_start_h, roi_start_w, c, ph, roi_batch_ind);
				  }
			  }
		  }
	  }

  convert_to_hwc(&output_data, output_channels, pooled_height,
                              pooled_width, rois_num);
  out->reset_data_ptr(output_data);
}

}  // namespace operators
}  // namespace paddle_mobile

#endif  // PSROI_POOL_OP