mirror of https://github.com/open-mmlab/mmcv.git
266 lines
9.5 KiB
C++
266 lines
9.5 KiB
C++
// Copyright (c) OpenMMLab. All rights reserved
|
|
#include "roi_align.h"
|
|
|
|
#include "../ort_mmcv_utils.h"
|
|
|
|
// 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, 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);
|
|
|
|
float x = xx;
|
|
float y = yy;
|
|
// 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 ROIAlignForwardCPU(const int nthreads, const float *input,
|
|
const float *rois, float *output, float *argmax_y,
|
|
float *argmax_x, const int pooled_height,
|
|
const int pooled_width, const float spatial_scale,
|
|
const int sampling_ratio,
|
|
const int pool_mode, // 0 - max pool, 1 - avg pool
|
|
const bool aligned, const int channels,
|
|
const int height, const int width) {
|
|
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 *offset_rois = rois + n * 5;
|
|
int roi_batch_ind = offset_rois[0];
|
|
|
|
// Do not use rounding; this implementation detail is critical
|
|
float offset = aligned ? (float)0.5 : (float)0.0;
|
|
float roi_start_w = offset_rois[1] * spatial_scale - offset;
|
|
float roi_start_h = offset_rois[2] * spatial_scale - offset;
|
|
float roi_end_w = offset_rois[3] * spatial_scale - offset;
|
|
float roi_end_h = offset_rois[4] * spatial_scale - offset;
|
|
|
|
float roi_width = roi_end_w - roi_start_w;
|
|
float roi_height = roi_end_h - roi_start_h;
|
|
if (aligned) {
|
|
/*AT_ASSERTM(roi_width >= 0 && roi_height >= 0,
|
|
"ROIs in ROIAlign cannot have non-negative size!");*/
|
|
assert(roi_width >= 0 && roi_height >= 0);
|
|
} else { // 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);
|
|
|
|
// When the grid is empty, output zeros == 0/1, instead of NaN.
|
|
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);
|
|
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, 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.;
|
|
float maxval = -10000;
|
|
float maxidx_y = -1.f, maxidx_x = -1.f;
|
|
for (int iy = 0; iy < roi_bin_grid_h; iy++) {
|
|
const float y = roi_start_h + ph * bin_size_h +
|
|
static_cast<float>(iy + .5f) * bin_size_h /
|
|
static_cast<float>(roi_bin_grid_h);
|
|
for (int ix = 0; ix < roi_bin_grid_w; ix++) {
|
|
const float x = roi_start_w + pw * bin_size_w +
|
|
static_cast<float>(ix + .5f) * bin_size_w /
|
|
static_cast<float>(roi_bin_grid_w);
|
|
PreCalc pc = pre_calc[pre_calc_index];
|
|
float 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];
|
|
if (val > maxval) {
|
|
maxval = val;
|
|
maxidx_y = y;
|
|
maxidx_x = x;
|
|
}
|
|
output_val += val;
|
|
pre_calc_index += 1;
|
|
}
|
|
}
|
|
if (pool_mode == 0) {
|
|
// We do max pooling inside a bin
|
|
output[index] = maxval;
|
|
argmax_y[index] = maxidx_y;
|
|
argmax_x[index] = maxidx_x;
|
|
} else if (pool_mode == 1) {
|
|
// We do average (integral) pooling inside a bin
|
|
output[index] = output_val / count;
|
|
} // if
|
|
} // for pw
|
|
} // for ph
|
|
} // for c
|
|
} // for n
|
|
}
|
|
|
|
void MMCVRoiAlignKernel::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];
|
|
}
|
|
|
|
int poolMod = 1;
|
|
if (pool_mode_ == "max") poolMod = 0;
|
|
|
|
float *argmax_x = nullptr, *argmax_y = nullptr;
|
|
if (poolMod == 0) {
|
|
argmax_y = new float[output_size];
|
|
argmax_x = new float[output_size];
|
|
}
|
|
|
|
ROIAlignForwardCPU(output_size, X_data, rois, out, argmax_y, argmax_x,
|
|
aligned_height_, aligned_width_, spatial_scale_,
|
|
sampling_ratio_, poolMod, aligned_, input_channels,
|
|
input_height, input_width);
|
|
|
|
if (argmax_x) delete argmax_x;
|
|
if (argmax_y) delete argmax_y;
|
|
}
|