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ppocr/ext_op/__init__.py
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ppocr/ext_op/__init__.py
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from .roi_align_rotated.roi_align_rotated import RoIAlignRotated
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528
ppocr/ext_op/roi_align_rotated/roi_align_rotated.cc
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528
ppocr/ext_op/roi_align_rotated/roi_align_rotated.cc
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// This code is refer from:
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// https://github.com/open-mmlab/mmcv/blob/master/mmcv/ops/csrc/pytorch/cpu/roi_align_rotated.cpp
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#include <cassert>
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#include <cmath>
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#include <vector>
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#include "paddle/extension.h"
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#define PADDLE_WITH_CUDA
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#define CHECK_INPUT_SAME(x1, x2) \
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PD_CHECK(x1.place() == x2.place(), "input must be same place.")
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#define CHECK_INPUT_CPU(x) PD_CHECK(x.is_cpu(), #x " must be a CPU Tensor.")
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template <typename T> struct PreCalc {
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int pos1;
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int pos2;
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int pos3;
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int pos4;
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T w1;
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T w2;
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T w3;
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T w4;
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};
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template <typename T>
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void pre_calc_for_bilinear_interpolate(
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const int height, const int width, const int pooled_height,
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const int pooled_width, const int iy_upper, const int ix_upper,
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T roi_start_h, T roi_start_w, T bin_size_h, T bin_size_w,
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int roi_bin_grid_h, int roi_bin_grid_w, T roi_center_h, T roi_center_w,
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T cos_theta, T sin_theta, std::vector<PreCalc<T>> &pre_calc) {
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int pre_calc_index = 0;
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for (int ph = 0; ph < pooled_height; ph++) {
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for (int pw = 0; pw < pooled_width; pw++) {
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for (int iy = 0; iy < iy_upper; iy++) {
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const T yy = roi_start_h + ph * bin_size_h +
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static_cast<T>(iy + .5f) * bin_size_h /
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static_cast<T>(roi_bin_grid_h); // e.g., 0.5, 1.5
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for (int ix = 0; ix < ix_upper; ix++) {
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const T xx = roi_start_w + pw * bin_size_w +
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static_cast<T>(ix + .5f) * bin_size_w /
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static_cast<T>(roi_bin_grid_w);
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// Rotate by theta around the center and translate
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// In image space, (y, x) is the order for Right Handed System,
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// and this is essentially multiplying the point by a rotation matrix
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// to rotate it counterclockwise through angle theta.
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T y = yy * cos_theta - xx * sin_theta + roi_center_h;
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T x = yy * sin_theta + xx * cos_theta + roi_center_w;
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// deal with: inverse elements are out of feature map boundary
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if (y < -1.0 || y > height || x < -1.0 || x > width) {
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// empty
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PreCalc<T> pc;
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pc.pos1 = 0;
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pc.pos2 = 0;
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pc.pos3 = 0;
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pc.pos4 = 0;
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pc.w1 = 0;
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pc.w2 = 0;
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pc.w3 = 0;
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pc.w4 = 0;
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pre_calc[pre_calc_index] = pc;
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pre_calc_index += 1;
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continue;
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}
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if (y < 0) {
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y = 0;
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}
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if (x < 0) {
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x = 0;
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}
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int y_low = (int)y;
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int x_low = (int)x;
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int y_high;
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int x_high;
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if (y_low >= height - 1) {
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y_high = y_low = height - 1;
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y = (T)y_low;
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} else {
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y_high = y_low + 1;
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}
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if (x_low >= width - 1) {
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x_high = x_low = width - 1;
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x = (T)x_low;
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} else {
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x_high = x_low + 1;
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}
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T ly = y - y_low;
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T lx = x - x_low;
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T hy = 1. - ly, hx = 1. - lx;
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T w1 = hy * hx, w2 = hy * lx, w3 = ly * hx, w4 = ly * lx;
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// save weights and indices
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PreCalc<T> pc;
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pc.pos1 = y_low * width + x_low;
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pc.pos2 = y_low * width + x_high;
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pc.pos3 = y_high * width + x_low;
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pc.pos4 = y_high * width + x_high;
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pc.w1 = w1;
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pc.w2 = w2;
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pc.w3 = w3;
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pc.w4 = w4;
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pre_calc[pre_calc_index] = pc;
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pre_calc_index += 1;
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}
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}
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}
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}
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}
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template <typename T>
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void roi_align_rotated_cpu_forward(const int nthreads, const T *input,
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const T &spatial_scale, const bool aligned,
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const bool clockwise, const int channels,
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const int height, const int width,
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const int pooled_height,
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const int pooled_width,
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const int sampling_ratio, const T *rois,
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T *output) {
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int n_rois = nthreads / channels / pooled_width / pooled_height;
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// (n, c, ph, pw) is an element in the pooled output
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// can be parallelized using omp
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// #pragma omp parallel for num_threads(32)
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for (int n = 0; n < n_rois; n++) {
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int index_n = n * channels * pooled_width * pooled_height;
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const T *current_roi = rois + n * 6;
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int roi_batch_ind = current_roi[0];
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// Do not use rounding; this implementation detail is critical
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T offset = aligned ? (T)0.5 : (T)0.0;
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T roi_center_w = current_roi[1] * spatial_scale - offset;
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T roi_center_h = current_roi[2] * spatial_scale - offset;
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T roi_width = current_roi[3] * spatial_scale;
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T roi_height = current_roi[4] * spatial_scale;
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T theta = current_roi[5];
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if (clockwise) {
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theta = -theta; // If clockwise, the angle needs to be reversed.
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}
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T cos_theta = cos(theta);
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T sin_theta = sin(theta);
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if (aligned) {
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assert(roi_width >= 0 && roi_height >= 0);
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} else { // for backward-compatibility only
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roi_width = std::max(roi_width, (T)1.);
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roi_height = std::max(roi_height, (T)1.);
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}
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T bin_size_h = static_cast<T>(roi_height) / static_cast<T>(pooled_height);
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T bin_size_w = static_cast<T>(roi_width) / static_cast<T>(pooled_width);
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// We use roi_bin_grid to sample the grid and mimic integral
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int roi_bin_grid_h = (sampling_ratio > 0)
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? sampling_ratio
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: ceilf(roi_height / pooled_height); // e.g., = 2
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int roi_bin_grid_w =
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(sampling_ratio > 0) ? sampling_ratio : ceilf(roi_width / pooled_width);
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// We do average (integral) pooling inside a bin
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const T count = std::max(roi_bin_grid_h * roi_bin_grid_w, 1); // e.g. = 4
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// we want to precalculate indices and weights shared by all channels,
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// this is the key point of optimization
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std::vector<PreCalc<T>> pre_calc(roi_bin_grid_h * roi_bin_grid_w *
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pooled_width * pooled_height);
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// roi_start_h and roi_start_w are computed wrt the center of RoI (x, y).
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// Appropriate translation needs to be applied after.
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T roi_start_h = -roi_height / 2.0;
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T roi_start_w = -roi_width / 2.0;
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pre_calc_for_bilinear_interpolate(
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height, width, pooled_height, pooled_width, roi_bin_grid_h,
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roi_bin_grid_w, roi_start_h, roi_start_w, bin_size_h, bin_size_w,
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roi_bin_grid_h, roi_bin_grid_w, roi_center_h, roi_center_w, cos_theta,
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sin_theta, pre_calc);
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for (int c = 0; c < channels; c++) {
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int index_n_c = index_n + c * pooled_width * pooled_height;
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const T *offset_input =
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input + (roi_batch_ind * channels + c) * height * width;
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int pre_calc_index = 0;
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for (int ph = 0; ph < pooled_height; ph++) {
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for (int pw = 0; pw < pooled_width; pw++) {
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int index = index_n_c + ph * pooled_width + pw;
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T output_val = 0.;
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for (int iy = 0; iy < roi_bin_grid_h; iy++) {
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for (int ix = 0; ix < roi_bin_grid_w; ix++) {
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PreCalc<T> pc = pre_calc[pre_calc_index];
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output_val += pc.w1 * offset_input[pc.pos1] +
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pc.w2 * offset_input[pc.pos2] +
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pc.w3 * offset_input[pc.pos3] +
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pc.w4 * offset_input[pc.pos4];
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pre_calc_index += 1;
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}
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}
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output_val /= count;
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output[index] = output_val;
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} // for pw
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} // for ph
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} // for c
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} // for n
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}
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template <typename T>
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void bilinear_interpolate_gradient(const int height, const int width, T y, T x,
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T &w1, T &w2, T &w3, T &w4, int &x_low,
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int &x_high, int &y_low, int &y_high) {
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// deal with cases that inverse elements are out of feature map boundary
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if (y < -1.0 || y > height || x < -1.0 || x > width) {
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// empty
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w1 = w2 = w3 = w4 = 0.;
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x_low = x_high = y_low = y_high = -1;
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return;
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}
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if (y < 0) {
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y = 0;
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}
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if (x < 0) {
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x = 0;
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}
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y_low = (int)y;
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x_low = (int)x;
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if (y_low >= height - 1) {
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y_high = y_low = height - 1;
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y = (T)y_low;
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} else {
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y_high = y_low + 1;
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}
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if (x_low >= width - 1) {
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x_high = x_low = width - 1;
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x = (T)x_low;
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} else {
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x_high = x_low + 1;
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}
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T ly = y - y_low;
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T lx = x - x_low;
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T hy = 1. - ly, hx = 1. - lx;
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// reference in forward
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// T v1 = input[y_low * width + x_low];
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// T v2 = input[y_low * width + x_high];
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// T v3 = input[y_high * width + x_low];
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// T v4 = input[y_high * width + x_high];
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// T val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4);
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w1 = hy * hx, w2 = hy * lx, w3 = ly * hx, w4 = ly * lx;
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return;
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}
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template <class T> inline void add(T *address, const T &val) {
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*address += val;
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}
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template <typename T>
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void roi_align_rotated_cpu_backward(
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const int nthreads,
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// may not be contiguous. should index using n_stride, etc
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const T *grad_output, const T &spatial_scale, const bool aligned,
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const bool clockwise, const int channels, const int height, const int width,
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const int pooled_height, const int pooled_width, const int sampling_ratio,
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T *grad_input, const T *rois, const int n_stride, const int c_stride,
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const int h_stride, const int w_stride) {
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for (int index = 0; index < nthreads; index++) {
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// (n, c, ph, pw) is an element in the pooled output
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int pw = index % pooled_width;
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int ph = (index / pooled_width) % pooled_height;
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int c = (index / pooled_width / pooled_height) % channels;
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int n = index / pooled_width / pooled_height / channels;
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const T *current_roi = rois + n * 6;
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int roi_batch_ind = current_roi[0];
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// Do not use rounding; this implementation detail is critical
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T offset = aligned ? (T)0.5 : (T)0.0;
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T roi_center_w = current_roi[1] * spatial_scale - offset;
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T roi_center_h = current_roi[2] * spatial_scale - offset;
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T roi_width = current_roi[3] * spatial_scale;
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T roi_height = current_roi[4] * spatial_scale;
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T theta = current_roi[5];
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if (clockwise) {
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theta = -theta; // If clockwise, the angle needs to be reversed.
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}
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T cos_theta = cos(theta);
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T sin_theta = sin(theta);
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if (aligned) {
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assert(roi_width >= 0 && roi_height >= 0);
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} else { // for backward-compatibility only
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roi_width = std::max(roi_width, (T)1.);
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roi_height = std::max(roi_height, (T)1.);
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}
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T bin_size_h = static_cast<T>(roi_height) / static_cast<T>(pooled_height);
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T bin_size_w = static_cast<T>(roi_width) / static_cast<T>(pooled_width);
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T *offset_grad_input =
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grad_input + ((roi_batch_ind * channels + c) * height * width);
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int output_offset = n * n_stride + c * c_stride;
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const T *offset_grad_output = grad_output + output_offset;
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const T grad_output_this_bin =
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offset_grad_output[ph * h_stride + pw * w_stride];
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// We use roi_bin_grid to sample the grid and mimic integral
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int roi_bin_grid_h = (sampling_ratio > 0)
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? sampling_ratio
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: ceilf(roi_height / pooled_height); // e.g., = 2
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int roi_bin_grid_w =
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(sampling_ratio > 0) ? sampling_ratio : ceilf(roi_width / pooled_width);
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// roi_start_h and roi_start_w are computed wrt the center of RoI (x, y).
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// Appropriate translation needs to be applied after.
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T roi_start_h = -roi_height / 2.0;
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T roi_start_w = -roi_width / 2.0;
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// We do average (integral) pooling inside a bin
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const T count = roi_bin_grid_h * roi_bin_grid_w; // e.g. = 4
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for (int iy = 0; iy < roi_bin_grid_h; iy++) {
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const T yy = roi_start_h + ph * bin_size_h +
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static_cast<T>(iy + .5f) * bin_size_h /
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static_cast<T>(roi_bin_grid_h); // e.g., 0.5, 1.5
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for (int ix = 0; ix < roi_bin_grid_w; ix++) {
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const T xx = roi_start_w + pw * bin_size_w +
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static_cast<T>(ix + .5f) * bin_size_w /
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static_cast<T>(roi_bin_grid_w);
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// Rotate by theta around the center and translate
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T y = yy * cos_theta - xx * sin_theta + roi_center_h;
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T x = yy * sin_theta + xx * cos_theta + roi_center_w;
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T w1, w2, w3, w4;
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int x_low, x_high, y_low, y_high;
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bilinear_interpolate_gradient(height, width, y, x, w1, w2, w3, w4,
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x_low, x_high, y_low, y_high);
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T g1 = grad_output_this_bin * w1 / count;
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T g2 = grad_output_this_bin * w2 / count;
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T g3 = grad_output_this_bin * w3 / count;
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T g4 = grad_output_this_bin * w4 / count;
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if (x_low >= 0 && x_high >= 0 && y_low >= 0 && y_high >= 0) {
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// atomic add is not needed for now since it is single threaded
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add(offset_grad_input + y_low * width + x_low, static_cast<T>(g1));
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add(offset_grad_input + y_low * width + x_high, static_cast<T>(g2));
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add(offset_grad_input + y_high * width + x_low, static_cast<T>(g3));
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add(offset_grad_input + y_high * width + x_high, static_cast<T>(g4));
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} // if
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} // ix
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} // iy
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} // for
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} // ROIAlignRotatedBackward
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std::vector<paddle::Tensor>
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RoIAlignRotatedCPUForward(const paddle::Tensor &input,
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const paddle::Tensor &rois, int aligned_height,
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int aligned_width, float spatial_scale,
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int sampling_ratio, bool aligned, bool clockwise) {
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CHECK_INPUT_CPU(input);
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CHECK_INPUT_CPU(rois);
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auto num_rois = rois.shape()[0];
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auto channels = input.shape()[1];
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auto height = input.shape()[2];
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auto width = input.shape()[3];
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auto output =
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paddle::empty({num_rois, channels, aligned_height, aligned_width},
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input.type(), paddle::CPUPlace());
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||||
auto output_size = output.numel();
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PD_DISPATCH_FLOATING_TYPES(
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input.type(), "roi_align_rotated_cpu_forward", ([&] {
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||||
roi_align_rotated_cpu_forward<data_t>(
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||||
output_size, input.data<data_t>(),
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||||
static_cast<data_t>(spatial_scale), aligned, clockwise, channels,
|
||||
height, width, aligned_height, aligned_width, sampling_ratio,
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||||
rois.data<data_t>(), output.data<data_t>());
|
||||
}));
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||||
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||||
return {output};
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||||
}
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||||
|
||||
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));
|
||||
381
ppocr/ext_op/roi_align_rotated/roi_align_rotated.cu
Normal file
381
ppocr/ext_op/roi_align_rotated/roi_align_rotated.cu
Normal file
@@ -0,0 +1,381 @@
|
||||
|
||||
// This code is refer from:
|
||||
// https://github.com/open-mmlab/mmcv/blob/master/mmcv/ops/csrc/common/cuda/roi_align_rotated_cuda_kernel.cuh
|
||||
|
||||
#include <cassert>
|
||||
#include <cmath>
|
||||
#include <vector>
|
||||
|
||||
#include "paddle/extension.h"
|
||||
#include <cuda.h>
|
||||
|
||||
#define CUDA_1D_KERNEL_LOOP(i, n) \
|
||||
for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < (n); \
|
||||
i += blockDim.x * gridDim.x)
|
||||
|
||||
#define THREADS_PER_BLOCK 512
|
||||
|
||||
inline int GET_BLOCKS(const int N) {
|
||||
int optimal_block_num = (N + THREADS_PER_BLOCK - 1) / THREADS_PER_BLOCK;
|
||||
int max_block_num = 4096;
|
||||
return min(optimal_block_num, max_block_num);
|
||||
}
|
||||
|
||||
#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ < 600
|
||||
|
||||
static __inline__ __device__ double atomicAdd(double *address, double val) {
|
||||
unsigned long long int *address_as_ull = (unsigned long long int *)address;
|
||||
unsigned long long int old = *address_as_ull, assumed;
|
||||
if (val == 0.0)
|
||||
return __longlong_as_double(old);
|
||||
do {
|
||||
assumed = old;
|
||||
old = atomicCAS(address_as_ull, assumed,
|
||||
__double_as_longlong(val + __longlong_as_double(assumed)));
|
||||
} while (assumed != old);
|
||||
return __longlong_as_double(old);
|
||||
}
|
||||
|
||||
#endif
|
||||
|
||||
template <typename T>
|
||||
__device__ T bilinear_interpolate(const T *input, const int height,
|
||||
const int width, T y, T x,
|
||||
const int index /* index for debug only*/) {
|
||||
// deal with cases that inverse elements are out of feature map boundary
|
||||
if (y < -1.0 || y > height || x < -1.0 || x > width)
|
||||
return 0;
|
||||
|
||||
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;
|
||||
// do bilinear interpolation
|
||||
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 w1 = hy * hx, w2 = hy * lx, w3 = ly * hx, w4 = ly * lx;
|
||||
|
||||
T val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4);
|
||||
|
||||
return val;
|
||||
}
|
||||
|
||||
template <typename T>
|
||||
__device__ 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,
|
||||
const int index /* index for debug only*/) {
|
||||
// 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;
|
||||
}
|
||||
|
||||
/*** Forward ***/
|
||||
template <typename scalar_t>
|
||||
__global__ void roi_align_rotated_cuda_forward_kernel(
|
||||
const int nthreads, const scalar_t *bottom_data,
|
||||
const scalar_t *bottom_rois, const scalar_t spatial_scale,
|
||||
const int sample_num, const bool aligned, const bool clockwise,
|
||||
const int channels, const int height, const int width,
|
||||
const int pooled_height, const int pooled_width, scalar_t *top_data) {
|
||||
CUDA_1D_KERNEL_LOOP(index, nthreads) {
|
||||
// (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 scalar_t *offset_bottom_rois = bottom_rois + n * 6;
|
||||
int roi_batch_ind = offset_bottom_rois[0];
|
||||
|
||||
// Do not using rounding; this implementation detail is critical
|
||||
scalar_t offset = aligned ? (scalar_t)0.5 : (scalar_t)0.0;
|
||||
scalar_t roi_center_w = offset_bottom_rois[1] * spatial_scale - offset;
|
||||
scalar_t roi_center_h = offset_bottom_rois[2] * spatial_scale - offset;
|
||||
scalar_t roi_width = offset_bottom_rois[3] * spatial_scale;
|
||||
scalar_t roi_height = offset_bottom_rois[4] * spatial_scale;
|
||||
// scalar_t theta = offset_bottom_rois[5] * M_PI / 180.0;
|
||||
scalar_t theta = offset_bottom_rois[5];
|
||||
if (clockwise) {
|
||||
theta = -theta; // If clockwise, the angle needs to be reversed.
|
||||
}
|
||||
if (!aligned) { // for backward-compatibility only
|
||||
// Force malformed ROIs to be 1x1
|
||||
roi_width = max(roi_width, (scalar_t)1.);
|
||||
roi_height = max(roi_height, (scalar_t)1.);
|
||||
}
|
||||
scalar_t bin_size_h = static_cast<scalar_t>(roi_height) /
|
||||
static_cast<scalar_t>(pooled_height);
|
||||
scalar_t bin_size_w =
|
||||
static_cast<scalar_t>(roi_width) / static_cast<scalar_t>(pooled_width);
|
||||
|
||||
const scalar_t *offset_bottom_data =
|
||||
bottom_data + (roi_batch_ind * channels + c) * height * width;
|
||||
|
||||
// We use roi_bin_grid to sample the grid and mimic integral
|
||||
int roi_bin_grid_h = (sample_num > 0)
|
||||
? sample_num
|
||||
: ceilf(roi_height / pooled_height); // e.g., = 2
|
||||
int roi_bin_grid_w =
|
||||
(sample_num > 0) ? sample_num : 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.
|
||||
scalar_t roi_start_h = -roi_height / 2.0;
|
||||
scalar_t roi_start_w = -roi_width / 2.0;
|
||||
scalar_t cosscalar_theta = cos(theta);
|
||||
scalar_t sinscalar_theta = sin(theta);
|
||||
|
||||
// We do average (integral) pooling inside a bin
|
||||
const scalar_t count = max(roi_bin_grid_h * roi_bin_grid_w, 1); // e.g. = 4
|
||||
|
||||
scalar_t output_val = 0.;
|
||||
for (int iy = 0; iy < roi_bin_grid_h; iy++) { // e.g., iy = 0, 1
|
||||
const scalar_t yy =
|
||||
roi_start_h + ph * bin_size_h +
|
||||
static_cast<scalar_t>(iy + .5f) * bin_size_h /
|
||||
static_cast<scalar_t>(roi_bin_grid_h); // e.g., 0.5, 1.5
|
||||
for (int ix = 0; ix < roi_bin_grid_w; ix++) {
|
||||
const scalar_t xx = roi_start_w + pw * bin_size_w +
|
||||
static_cast<scalar_t>(ix + .5f) * bin_size_w /
|
||||
static_cast<scalar_t>(roi_bin_grid_w);
|
||||
|
||||
// Rotate by theta (counterclockwise) around the center and translate
|
||||
scalar_t y = yy * cosscalar_theta - xx * sinscalar_theta + roi_center_h;
|
||||
scalar_t x = yy * sinscalar_theta + xx * cosscalar_theta + roi_center_w;
|
||||
|
||||
scalar_t val = bilinear_interpolate<scalar_t>(
|
||||
offset_bottom_data, height, width, y, x, index);
|
||||
output_val += val;
|
||||
}
|
||||
}
|
||||
output_val /= count;
|
||||
|
||||
top_data[index] = output_val;
|
||||
}
|
||||
}
|
||||
|
||||
/*** Backward ***/
|
||||
template <typename scalar_t>
|
||||
__global__ void roi_align_rotated_backward_cuda_kernel(
|
||||
const int nthreads, const scalar_t *top_diff, const scalar_t *bottom_rois,
|
||||
const scalar_t spatial_scale, const int sample_num, const bool aligned,
|
||||
const bool clockwise, const int channels, const int height, const int width,
|
||||
const int pooled_height, const int pooled_width, scalar_t *bottom_diff) {
|
||||
CUDA_1D_KERNEL_LOOP(index, nthreads) {
|
||||
// (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 scalar_t *offset_bottom_rois = bottom_rois + n * 6;
|
||||
int roi_batch_ind = offset_bottom_rois[0];
|
||||
|
||||
// Do not round
|
||||
scalar_t offset = aligned ? (scalar_t)0.5 : (scalar_t)0.0;
|
||||
scalar_t roi_center_w = offset_bottom_rois[1] * spatial_scale - offset;
|
||||
scalar_t roi_center_h = offset_bottom_rois[2] * spatial_scale - offset;
|
||||
scalar_t roi_width = offset_bottom_rois[3] * spatial_scale;
|
||||
scalar_t roi_height = offset_bottom_rois[4] * spatial_scale;
|
||||
// scalar_t theta = offset_bottom_rois[5] * M_PI / 180.0;
|
||||
scalar_t theta = offset_bottom_rois[5];
|
||||
if (clockwise) {
|
||||
theta = -theta; // If clockwise, the angle needs to be reversed.
|
||||
}
|
||||
if (!aligned) { // for backward-compatibility only
|
||||
// Force malformed ROIs to be 1x1
|
||||
roi_width = max(roi_width, (scalar_t)1.);
|
||||
roi_height = max(roi_height, (scalar_t)1.);
|
||||
}
|
||||
scalar_t bin_size_h = static_cast<scalar_t>(roi_height) /
|
||||
static_cast<scalar_t>(pooled_height);
|
||||
scalar_t bin_size_w =
|
||||
static_cast<scalar_t>(roi_width) / static_cast<scalar_t>(pooled_width);
|
||||
|
||||
scalar_t *offset_bottom_diff =
|
||||
bottom_diff + (roi_batch_ind * channels + c) * height * width;
|
||||
|
||||
int top_offset = (n * channels + c) * pooled_height * pooled_width;
|
||||
const scalar_t *offset_top_diff = top_diff + top_offset;
|
||||
const scalar_t top_diff_this_bin = offset_top_diff[ph * pooled_width + pw];
|
||||
|
||||
// We use roi_bin_grid to sample the grid and mimic integral
|
||||
int roi_bin_grid_h = (sample_num > 0)
|
||||
? sample_num
|
||||
: ceilf(roi_height / pooled_height); // e.g., = 2
|
||||
int roi_bin_grid_w =
|
||||
(sample_num > 0) ? sample_num : 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.
|
||||
scalar_t roi_start_h = -roi_height / 2.0;
|
||||
scalar_t roi_start_w = -roi_width / 2.0;
|
||||
scalar_t cosTheta = cos(theta);
|
||||
scalar_t sinTheta = sin(theta);
|
||||
|
||||
// We do average (integral) pooling inside a bin
|
||||
const scalar_t count = roi_bin_grid_h * roi_bin_grid_w; // e.g. = 4
|
||||
|
||||
for (int iy = 0; iy < roi_bin_grid_h; iy++) { // e.g., iy = 0, 1
|
||||
const scalar_t yy =
|
||||
roi_start_h + ph * bin_size_h +
|
||||
static_cast<scalar_t>(iy + .5f) * bin_size_h /
|
||||
static_cast<scalar_t>(roi_bin_grid_h); // e.g., 0.5, 1.5
|
||||
for (int ix = 0; ix < roi_bin_grid_w; ix++) {
|
||||
const scalar_t xx = roi_start_w + pw * bin_size_w +
|
||||
static_cast<scalar_t>(ix + .5f) * bin_size_w /
|
||||
static_cast<scalar_t>(roi_bin_grid_w);
|
||||
|
||||
// Rotate by theta around the center and translate
|
||||
scalar_t y = yy * cosTheta - xx * sinTheta + roi_center_h;
|
||||
scalar_t x = yy * sinTheta + xx * cosTheta + roi_center_w;
|
||||
|
||||
scalar_t w1, w2, w3, w4;
|
||||
int x_low, x_high, y_low, y_high;
|
||||
|
||||
bilinear_interpolate_gradient<scalar_t>(height, width, y, x, w1, w2, w3,
|
||||
w4, x_low, x_high, y_low,
|
||||
y_high, index);
|
||||
|
||||
scalar_t g1 = top_diff_this_bin * w1 / count;
|
||||
scalar_t g2 = top_diff_this_bin * w2 / count;
|
||||
scalar_t g3 = top_diff_this_bin * w3 / count;
|
||||
scalar_t g4 = top_diff_this_bin * w4 / count;
|
||||
|
||||
if (x_low >= 0 && x_high >= 0 && y_low >= 0 && y_high >= 0) {
|
||||
atomicAdd(offset_bottom_diff + y_low * width + x_low, g1);
|
||||
atomicAdd(offset_bottom_diff + y_low * width + x_high, g2);
|
||||
atomicAdd(offset_bottom_diff + y_high * width + x_low, g3);
|
||||
atomicAdd(offset_bottom_diff + y_high * width + x_high, g4);
|
||||
} // if
|
||||
} // ix
|
||||
} // iy
|
||||
} // CUDA_1D_KERNEL_LOOP
|
||||
} // RoIAlignBackward
|
||||
|
||||
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) {
|
||||
|
||||
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::GPUPlace());
|
||||
auto output_size = output.numel();
|
||||
|
||||
PD_DISPATCH_FLOATING_TYPES(
|
||||
input.type(), "roi_align_rotated_cuda_forward_kernel", ([&] {
|
||||
roi_align_rotated_cuda_forward_kernel<data_t>
|
||||
<<<GET_BLOCKS(output_size), THREADS_PER_BLOCK>>>(
|
||||
output_size, input.data<data_t>(), rois.data<data_t>(),
|
||||
static_cast<data_t>(spatial_scale), sampling_ratio, aligned,
|
||||
clockwise, channels, height, width, aligned_height,
|
||||
aligned_width, output.data<data_t>());
|
||||
}));
|
||||
|
||||
return {output};
|
||||
}
|
||||
|
||||
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) {
|
||||
|
||||
auto num_rois = rois.shape()[0];
|
||||
|
||||
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::GPUPlace());
|
||||
|
||||
const int output_size = num_rois * aligned_height * aligned_width * channels;
|
||||
|
||||
PD_DISPATCH_FLOATING_TYPES(
|
||||
grad_output.type(), "roi_align_rotated_backward_cuda_kernel", ([&] {
|
||||
roi_align_rotated_backward_cuda_kernel<data_t>
|
||||
<<<GET_BLOCKS(output_size), THREADS_PER_BLOCK>>>(
|
||||
output_size, grad_output.data<data_t>(), rois.data<data_t>(),
|
||||
spatial_scale, sampling_ratio, aligned, clockwise, channels,
|
||||
height, width, aligned_height, aligned_width,
|
||||
grad_input.data<data_t>());
|
||||
}));
|
||||
return {grad_input};
|
||||
}
|
||||
69
ppocr/ext_op/roi_align_rotated/roi_align_rotated.py
Normal file
69
ppocr/ext_op/roi_align_rotated/roi_align_rotated.py
Normal file
@@ -0,0 +1,69 @@
|
||||
# copyright (c) 2022 PaddlePaddle Authors. All Rights Reserve.
|
||||
#
|
||||
# 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.
|
||||
"""
|
||||
This code is refer from:
|
||||
https://github.com/open-mmlab/mmcv/blob/master/mmcv/ops/roi_align_rotated.py
|
||||
"""
|
||||
|
||||
import paddle
|
||||
import paddle.nn as nn
|
||||
from paddle.utils.cpp_extension import load
|
||||
|
||||
custom_ops = load(
|
||||
name="custom_jit_ops",
|
||||
sources=[
|
||||
"ppocr/ext_op/roi_align_rotated/roi_align_rotated.cc",
|
||||
"ppocr/ext_op/roi_align_rotated/roi_align_rotated.cu",
|
||||
],
|
||||
)
|
||||
|
||||
roi_align_rotated = custom_ops.roi_align_rotated
|
||||
|
||||
|
||||
class RoIAlignRotated(nn.Layer):
|
||||
"""RoI align pooling layer for rotated proposals."""
|
||||
|
||||
def __init__(
|
||||
self, out_size, spatial_scale, sample_num=0, aligned=True, clockwise=False
|
||||
):
|
||||
super(RoIAlignRotated, self).__init__()
|
||||
|
||||
if isinstance(out_size, int):
|
||||
self.out_h = out_size
|
||||
self.out_w = out_size
|
||||
elif isinstance(out_size, tuple):
|
||||
assert len(out_size) == 2
|
||||
assert isinstance(out_size[0], int)
|
||||
assert isinstance(out_size[1], int)
|
||||
self.out_h, self.out_w = out_size
|
||||
else:
|
||||
raise TypeError('"out_size" must be an integer or tuple of integers')
|
||||
|
||||
self.spatial_scale = float(spatial_scale)
|
||||
self.sample_num = int(sample_num)
|
||||
self.aligned = aligned
|
||||
self.clockwise = clockwise
|
||||
|
||||
def forward(self, feats, rois):
|
||||
output = roi_align_rotated(
|
||||
feats,
|
||||
rois,
|
||||
self.out_h,
|
||||
self.out_w,
|
||||
self.spatial_scale,
|
||||
self.sample_num,
|
||||
self.aligned,
|
||||
self.clockwise,
|
||||
)
|
||||
return output
|
||||
Reference in New Issue
Block a user