mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-10-31 11:56:44 +08:00
d14828cb18
* add adaptivepool2d for tensorrt plugin * update code * update code * update code to fix bug
51 lines
2.0 KiB
Plaintext
Executable File
51 lines
2.0 KiB
Plaintext
Executable File
#include "adaptive_pool2d_kernel.h"
|
|
|
|
namespace fastdeploy {
|
|
|
|
__global__ void CudaCastKernel(const float* in, float* out, int edge, int out_bc_offset, int in_bc_offset, int ih, int iw, int oh, int ow, bool is_avg) {
|
|
int position = blockDim.x * blockIdx.x + threadIdx.x;
|
|
if (position >= edge) {
|
|
return;
|
|
}
|
|
int offset = floorf(float(position) / out_bc_offset);
|
|
int h = floorf(float(position % out_bc_offset) / ow);
|
|
int w = (position % out_bc_offset) % ow;
|
|
int hstart = floorf(static_cast<float>(h * ih) / oh);
|
|
int hend = ceilf(static_cast<float>((h + 1) * ih) / oh);
|
|
int wstart = floorf(static_cast<float>(w * iw) / ow);
|
|
int wend = ceilf(static_cast<float>((w + 1) * iw) / ow);
|
|
if(is_avg) {
|
|
out[position] = 0.0;
|
|
} else {
|
|
out[position] = in[offset * in_bc_offset + hstart * iw + wstart];
|
|
}
|
|
for (int h = hstart; h < hend; ++h) {
|
|
for (int w = wstart; w < wend; ++w) {
|
|
int input_idx = h * iw + w;
|
|
if(is_avg) {
|
|
out[position] = out[position] + in[offset * in_bc_offset + input_idx];
|
|
} else {
|
|
out[position] = max(out[position], in[offset * in_bc_offset + input_idx]);
|
|
}
|
|
}
|
|
}
|
|
out[position] = out[position] / ((hend - hstart) * (wend - wstart));
|
|
}
|
|
|
|
void CudaAdaptivePool(const std::vector<int64_t>& input_dims, const std::vector<int64_t>& output_dims, float* output, const float* input, void* compute_stream, const std::string& pooling_type){
|
|
auto casted_compute_stream = reinterpret_cast<cudaStream_t>(compute_stream);
|
|
int out_bc_offset = output_dims[2] * output_dims[3];
|
|
int in_bc_offset = input_dims[2] * input_dims[3];
|
|
int jobs = 1;
|
|
for(int i : output_dims) {
|
|
jobs *= i;
|
|
}
|
|
bool is_avg = pooling_type == "avg";
|
|
int threads = 256;
|
|
int blocks = ceil(jobs / static_cast<float>(threads));
|
|
CudaCastKernel<<<blocks, threads, 0, casted_compute_stream>>>(
|
|
input,
|
|
output,
|
|
jobs, out_bc_offset, in_bc_offset, int(input_dims[2]), int(input_dims[3]), int(output_dims[2]), int(output_dims[3]), is_avg);
|
|
}
|
|
} // namespace fastdeploy |