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PaddleOCR/ppocr/ext_op/roi_align_rotated/roi_align_rotated.cc

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// This code is refer from:
// https://github.com/open-mmlab/mmcv/blob/master/mmcv/ops/csrc/pytorch/cpu/roi_align_rotated.cpp
#include <cassert>
#include <cmath>
#include <vector>
#include "paddle/extension.h"
#define PADDLE_WITH_CUDA
#define CHECK_INPUT_SAME(x1, x2) \
PD_CHECK(x1.place() == x2.place(), "input must be same place.")
#define CHECK_INPUT_CPU(x) PD_CHECK(x.is_cpu(), #x " must be a CPU Tensor.")
template <typename T> struct PreCalc {
int pos1;
int pos2;
int pos3;
int pos4;
T w1;
T w2;
T w3;
T w4;
};
template <typename T>
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,
T roi_start_h, T roi_start_w, T bin_size_h, T bin_size_w,
int roi_bin_grid_h, int roi_bin_grid_w, T roi_center_h, T roi_center_w,
T cos_theta, T sin_theta, std::vector<PreCalc<T>> &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 T yy = roi_start_h + ph * bin_size_h +
static_cast<T>(iy + .5f) * bin_size_h /
static_cast<T>(roi_bin_grid_h); // e.g., 0.5, 1.5
for (int ix = 0; ix < ix_upper; ix++) {
const T xx = roi_start_w + pw * bin_size_w +
static_cast<T>(ix + .5f) * bin_size_w /
static_cast<T>(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.
T y = yy * cos_theta - xx * sin_theta + roi_center_h;
T 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<T> 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 = (T)y_low;
} else {
y_high = y_low + 1;
}
if (x_low >= width - 1) {
x_high = x_low = width - 1;
x = (T)x_low;
} else {
x_high = x_low + 1;
}
T ly = y - y_low;
T lx = x - x_low;
T hy = 1. - ly, hx = 1. - lx;
T w1 = hy * hx, w2 = hy * lx, w3 = ly * hx, w4 = ly * lx;
// save weights and indices
PreCalc<T> 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;
}
}
}
}
}
template <typename T>
void roi_align_rotated_cpu_forward(const int nthreads, const T *input,
const T &spatial_scale, const bool aligned,
const bool clockwise, const int channels,
const int height, const int width,
const int pooled_height,
const int pooled_width,
const int sampling_ratio, const T *rois,
T *output) {
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 T *current_roi = rois + n * 6;
int roi_batch_ind = current_roi[0];
// Do not use rounding; this implementation detail is critical
T offset = aligned ? (T)0.5 : (T)0.0;
T roi_center_w = current_roi[1] * spatial_scale - offset;
T roi_center_h = current_roi[2] * spatial_scale - offset;
T roi_width = current_roi[3] * spatial_scale;
T roi_height = current_roi[4] * spatial_scale;
T theta = current_roi[5];
if (clockwise) {
theta = -theta; // If clockwise, the angle needs to be reversed.
}
T cos_theta = cos(theta);
T sin_theta = sin(theta);
if (aligned) {
assert(roi_width >= 0 && roi_height >= 0);
} else { // for backward-compatibility only
roi_width = std::max(roi_width, (T)1.);
roi_height = std::max(roi_height, (T)1.);
}
T bin_size_h = static_cast<T>(roi_height) / static_cast<T>(pooled_height);
T bin_size_w = static_cast<T>(roi_width) / static_cast<T>(pooled_width);
// We use roi_bin_grid to sample the grid and mimic integral
int roi_bin_grid_h = (sampling_ratio > 0)
? sampling_ratio
: ceilf(roi_height / pooled_height); // e.g., = 2
int roi_bin_grid_w =
(sampling_ratio > 0) ? sampling_ratio : ceilf(roi_width / pooled_width);
// We do average (integral) pooling inside a bin
const T 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<T>> 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.
T roi_start_h = -roi_height / 2.0;
T 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 T *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;
T output_val = 0.;
for (int iy = 0; iy < roi_bin_grid_h; iy++) {
for (int ix = 0; ix < roi_bin_grid_w; ix++) {
PreCalc<T> 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
}
template <typename T>
void bilinear_interpolate_gradient(const int height, const int width, T y, T x,
T &w1, T &w2, T &w3, T &w4, int &x_low,
int &x_high, int &y_low, int &y_high) {
// deal with cases that inverse elements are out of feature map boundary
if (y < -1.0 || y > height || x < -1.0 || x > width) {
// empty
w1 = w2 = w3 = w4 = 0.;
x_low = x_high = y_low = y_high = -1;
return;
}
if (y < 0) {
y = 0;
}
if (x < 0) {
x = 0;
}
y_low = (int)y;
x_low = (int)x;
if (y_low >= height - 1) {
y_high = y_low = height - 1;
y = (T)y_low;
} else {
y_high = y_low + 1;
}
if (x_low >= width - 1) {
x_high = x_low = width - 1;
x = (T)x_low;
} else {
x_high = x_low + 1;
}
T ly = y - y_low;
T lx = x - x_low;
T hy = 1. - ly, hx = 1. - lx;
// reference in forward
// T v1 = input[y_low * width + x_low];
// T v2 = input[y_low * width + x_high];
// T v3 = input[y_high * width + x_low];
// T v4 = input[y_high * width + x_high];
// T val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4);
w1 = hy * hx, w2 = hy * lx, w3 = ly * hx, w4 = ly * lx;
return;
}
template <class T> inline void add(T *address, const T &val) {
*address += val;
}
template <typename T>
void roi_align_rotated_cpu_backward(
const int nthreads,
// may not be contiguous. should index using n_stride, etc
const T *grad_output, const T &spatial_scale, const bool aligned,
const bool clockwise, const int channels, const int height, const int width,
const int pooled_height, const int pooled_width, const int sampling_ratio,
T *grad_input, const T *rois, const int n_stride, const int c_stride,
const int h_stride, const int w_stride) {
for (int index = 0; index < nthreads; index++) {
// (n, c, ph, pw) is an element in the pooled output
int pw = index % pooled_width;
int ph = (index / pooled_width) % pooled_height;
int c = (index / pooled_width / pooled_height) % channels;
int n = index / pooled_width / pooled_height / channels;
const T *current_roi = rois + n * 6;
int roi_batch_ind = current_roi[0];
// Do not use rounding; this implementation detail is critical
T offset = aligned ? (T)0.5 : (T)0.0;
T roi_center_w = current_roi[1] * spatial_scale - offset;
T roi_center_h = current_roi[2] * spatial_scale - offset;
T roi_width = current_roi[3] * spatial_scale;
T roi_height = current_roi[4] * spatial_scale;
T theta = current_roi[5];
if (clockwise) {
theta = -theta; // If clockwise, the angle needs to be reversed.
}
T cos_theta = cos(theta);
T sin_theta = sin(theta);
if (aligned) {
assert(roi_width >= 0 && roi_height >= 0);
} else { // for backward-compatibility only
roi_width = std::max(roi_width, (T)1.);
roi_height = std::max(roi_height, (T)1.);
}
T bin_size_h = static_cast<T>(roi_height) / static_cast<T>(pooled_height);
T bin_size_w = static_cast<T>(roi_width) / static_cast<T>(pooled_width);
T *offset_grad_input =
grad_input + ((roi_batch_ind * channels + c) * height * width);
int output_offset = n * n_stride + c * c_stride;
const T *offset_grad_output = grad_output + output_offset;
const T grad_output_this_bin =
offset_grad_output[ph * h_stride + pw * w_stride];
// We use roi_bin_grid to sample the grid and mimic integral
int roi_bin_grid_h = (sampling_ratio > 0)
? sampling_ratio
: ceilf(roi_height / pooled_height); // e.g., = 2
int roi_bin_grid_w =
(sampling_ratio > 0) ? sampling_ratio : ceilf(roi_width / pooled_width);
// roi_start_h and roi_start_w are computed wrt the center of RoI (x, y).
// Appropriate translation needs to be applied after.
T roi_start_h = -roi_height / 2.0;
T roi_start_w = -roi_width / 2.0;
// We do average (integral) pooling inside a bin
const T count = roi_bin_grid_h * roi_bin_grid_w; // e.g. = 4
for (int iy = 0; iy < roi_bin_grid_h; iy++) {
const T yy = roi_start_h + ph * bin_size_h +
static_cast<T>(iy + .5f) * bin_size_h /
static_cast<T>(roi_bin_grid_h); // e.g., 0.5, 1.5
for (int ix = 0; ix < roi_bin_grid_w; ix++) {
const T xx = roi_start_w + pw * bin_size_w +
static_cast<T>(ix + .5f) * bin_size_w /
static_cast<T>(roi_bin_grid_w);
// Rotate by theta around the center and translate
T y = yy * cos_theta - xx * sin_theta + roi_center_h;
T x = yy * sin_theta + xx * cos_theta + roi_center_w;
T w1, w2, w3, w4;
int x_low, x_high, y_low, y_high;
bilinear_interpolate_gradient(height, width, y, x, w1, w2, w3, w4,
x_low, x_high, y_low, y_high);
T g1 = grad_output_this_bin * w1 / count;
T g2 = grad_output_this_bin * w2 / count;
T g3 = grad_output_this_bin * w3 / count;
T g4 = grad_output_this_bin * w4 / count;
if (x_low >= 0 && x_high >= 0 && y_low >= 0 && y_high >= 0) {
// atomic add is not needed for now since it is single threaded
add(offset_grad_input + y_low * width + x_low, static_cast<T>(g1));
add(offset_grad_input + y_low * width + x_high, static_cast<T>(g2));
add(offset_grad_input + y_high * width + x_low, static_cast<T>(g3));
add(offset_grad_input + y_high * width + x_high, static_cast<T>(g4));
} // if
} // ix
} // iy
} // for
} // ROIAlignRotatedBackward
std::vector<paddle::Tensor>
RoIAlignRotatedCPUForward(const paddle::Tensor &input,
const paddle::Tensor &rois, int aligned_height,
int aligned_width, float spatial_scale,
int sampling_ratio, bool aligned, bool clockwise) {
CHECK_INPUT_CPU(input);
CHECK_INPUT_CPU(rois);
auto num_rois = rois.shape()[0];
auto channels = input.shape()[1];
auto height = input.shape()[2];
auto width = input.shape()[3];
auto output =
paddle::empty({num_rois, channels, aligned_height, aligned_width},
input.type(), paddle::CPUPlace());
auto output_size = output.numel();
PD_DISPATCH_FLOATING_TYPES(
input.type(), "roi_align_rotated_cpu_forward", ([&] {
roi_align_rotated_cpu_forward<data_t>(
output_size, input.data<data_t>(),
static_cast<data_t>(spatial_scale), aligned, clockwise, channels,
height, width, aligned_height, aligned_width, sampling_ratio,
rois.data<data_t>(), output.data<data_t>());
}));
return {output};
}
std::vector<paddle::Tensor> RoIAlignRotatedCPUBackward(
const paddle::Tensor &input, const paddle::Tensor &rois,
const paddle::Tensor &grad_output, int aligned_height, int aligned_width,
float spatial_scale, int sampling_ratio, bool aligned, bool clockwise) {
auto batch_size = input.shape()[0];
auto channels = input.shape()[1];
auto height = input.shape()[2];
auto width = input.shape()[3];
auto grad_input = paddle::full({batch_size, channels, height, width}, 0.0,
input.type(), paddle::CPUPlace());
// get stride values to ensure indexing into gradients is correct.
int n_stride = grad_output.shape()[0];
int c_stride = grad_output.shape()[1];
int h_stride = grad_output.shape()[2];
int w_stride = grad_output.shape()[3];
PD_DISPATCH_FLOATING_TYPES(
grad_output.type(), "roi_align_rotated_cpu_backward", [&] {
roi_align_rotated_cpu_backward<data_t>(
grad_output.numel(), grad_output.data<data_t>(),
static_cast<data_t>(spatial_scale), aligned, clockwise, channels,
height, width, aligned_height, aligned_width, sampling_ratio,
grad_input.data<data_t>(), rois.data<data_t>(), n_stride, c_stride,
h_stride, w_stride);
});
return {grad_input};
}
#ifdef PADDLE_WITH_CUDA
std::vector<paddle::Tensor>
RoIAlignRotatedCUDAForward(const paddle::Tensor &input,
const paddle::Tensor &rois, int aligned_height,
int aligned_width, float spatial_scale,
int sampling_ratio, bool aligned, bool clockwise);
#endif
#ifdef PADDLE_WITH_CUDA
std::vector<paddle::Tensor> RoIAlignRotatedCUDABackward(
const paddle::Tensor &input, const paddle::Tensor &rois,
const paddle::Tensor &grad_output, int aligned_height, int aligned_width,
float spatial_scale, int sampling_ratio, bool aligned, bool clockwise);
#endif
std::vector<paddle::Tensor>
RoIAlignRotatedForward(const paddle::Tensor &input, const paddle::Tensor &rois,
int aligned_height, int aligned_width,
float spatial_scale, int sampling_ratio, bool aligned,
bool clockwise) {
CHECK_INPUT_SAME(input, rois);
if (input.is_cpu()) {
return RoIAlignRotatedCPUForward(input, rois, aligned_height, aligned_width,
spatial_scale, sampling_ratio, aligned,
clockwise);
#ifdef PADDLE_WITH_CUDA
} else if (input.is_gpu()) {
return RoIAlignRotatedCUDAForward(input, rois, aligned_height,
aligned_width, spatial_scale,
sampling_ratio, aligned, clockwise);
#endif
} else {
PD_THROW("Unsupported device type for forward function of roi align "
"rotated operator.");
}
}
std::vector<paddle::Tensor>
RoIAlignRotatedBackward(const paddle::Tensor &input, const paddle::Tensor &rois,
const paddle::Tensor &grad_output, int aligned_height,
int aligned_width, float spatial_scale,
int sampling_ratio, bool aligned, bool clockwise) {
CHECK_INPUT_SAME(input, rois);
if (input.is_cpu()) {
return RoIAlignRotatedCPUBackward(input, rois, grad_output, aligned_height,
aligned_width, spatial_scale,
sampling_ratio, aligned, clockwise);
#ifdef PADDLE_WITH_CUDA
} else if (input.is_gpu()) {
return RoIAlignRotatedCUDABackward(input, rois, grad_output, aligned_height,
aligned_width, spatial_scale,
sampling_ratio, aligned, clockwise);
#endif
} else {
PD_THROW("Unsupported device type for forward function of roi align "
"rotated operator.");
}
}
std::vector<std::vector<int64_t>> InferShape(std::vector<int64_t> input_shape,
std::vector<int64_t> rois_shape) {
return {{rois_shape[0], input_shape[1], input_shape[2], input_shape[3]}};
}
std::vector<std::vector<int64_t>>
InferBackShape(std::vector<int64_t> input_shape,
std::vector<int64_t> rois_shape) {
return {input_shape};
}
std::vector<paddle::DataType> InferDtype(paddle::DataType input_dtype,
paddle::DataType rois_dtype) {
return {input_dtype};
}
PD_BUILD_OP(roi_align_rotated)
.Inputs({"Input", "Rois"})
.Outputs({"Output"})
.Attrs({"aligned_height: int", "aligned_width: int", "spatial_scale: float",
"sampling_ratio: int", "aligned: bool", "clockwise: bool"})
.SetKernelFn(PD_KERNEL(RoIAlignRotatedForward))
.SetInferShapeFn(PD_INFER_SHAPE(InferShape))
.SetInferDtypeFn(PD_INFER_DTYPE(InferDtype));
PD_BUILD_GRAD_OP(roi_align_rotated)
.Inputs({"Input", "Rois", paddle::Grad("Output")})
.Attrs({"aligned_height: int", "aligned_width: int", "spatial_scale: float",
"sampling_ratio: int", "aligned: bool", "clockwise: bool"})
.Outputs({paddle::Grad("Input")})
.SetKernelFn(PD_KERNEL(RoIAlignRotatedBackward))
.SetInferShapeFn(PD_INFER_SHAPE(InferBackShape));
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