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542 lines
22 KiB
542 lines
22 KiB
/* Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License. */
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#pragma once
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#include <algorithm>
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#include "paddle/fluid/framework/op_registry.h"
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#include "paddle/fluid/operators/math/math_function.h"
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namespace paddle {
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namespace operators {
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template <typename T>
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inline HOSTDEVICE T PrRoIPoolingGetData(const T* data, const int h, const int w,
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const int height, const int width) {
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bool overflow = (h < 0) || (w < 0) || (h >= height) || (w >= width);
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T retVal = overflow ? 0.0f : data[h * width + w];
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return retVal;
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}
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template <typename T>
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inline HOSTDEVICE T PrRoIPoolingMatCalculation(const T* this_data,
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const int s_h, const int s_w,
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const int e_h, const int e_w,
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const T y0, const T x0,
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const T y1, const T x1,
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const int h0, const int w0) {
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T alpha, beta, lim_alpha, lim_beta, tmp;
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T sum_out = 0;
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alpha = x0 - static_cast<T>(s_w);
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beta = y0 - static_cast<T>(s_h);
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lim_alpha = x1 - static_cast<T>(s_w);
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lim_beta = y1 - static_cast<T>(s_h);
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tmp = (lim_alpha - 0.5f * lim_alpha * lim_alpha - alpha +
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0.5f * alpha * alpha) *
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(lim_beta - 0.5f * lim_beta * lim_beta - beta + 0.5f * beta * beta);
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sum_out += PrRoIPoolingGetData(this_data, s_h, s_w, h0, w0) * tmp;
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alpha = static_cast<T>(e_w) - x1;
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lim_alpha = static_cast<T>(e_w) - x0;
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tmp = (lim_alpha - 0.5f * lim_alpha * lim_alpha - alpha +
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0.5f * alpha * alpha) *
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(lim_beta - 0.5f * lim_beta * lim_beta - beta + 0.5f * beta * beta);
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sum_out += PrRoIPoolingGetData(this_data, s_h, e_w, h0, w0) * tmp;
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alpha = x0 - static_cast<T>(s_w);
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beta = static_cast<T>(e_h) - y1;
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lim_alpha = x1 - static_cast<T>(s_w);
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lim_beta = static_cast<T>(e_h) - y0;
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tmp = (lim_alpha - 0.5f * lim_alpha * lim_alpha - alpha +
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0.5f * alpha * alpha) *
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(lim_beta - 0.5f * lim_beta * lim_beta - beta + 0.5f * beta * beta);
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sum_out += PrRoIPoolingGetData(this_data, e_h, s_w, h0, w0) * tmp;
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alpha = static_cast<T>(e_w) - x1;
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lim_alpha = static_cast<T>(e_w) - x0;
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tmp = (lim_alpha - 0.5f * lim_alpha * lim_alpha - alpha +
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0.5f * alpha * alpha) *
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(lim_beta - 0.5f * lim_beta * lim_beta - beta + 0.5f * beta * beta);
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sum_out += PrRoIPoolingGetData(this_data, e_h, e_w, h0, w0) * tmp;
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return sum_out;
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}
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template <typename T>
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inline HOSTDEVICE void PrRoIPoolingDistributeDiff(T* diff, const T top_diff,
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const int h, const int w,
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const int height,
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const int width,
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const T coeff) {
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bool overflow = (h < 0) || (w < 0) || (h >= height) || (w >= width);
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if (!overflow) {
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*(diff + h * width + w) += top_diff * coeff;
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}
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}
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template <typename T, typename Functor>
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HOSTDEVICE void PrRoIPoolingMatDistributeDiff(
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T* diff, const T top_diff, const int s_h, const int s_w, const int e_h,
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const int e_w, const T y0, const T x0, const T y1, const T x1, const int h0,
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const int w0, Functor functor) {
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T alpha, beta, lim_alpha, lim_beta, tmp;
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alpha = x0 - static_cast<T>(s_w);
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beta = y0 - static_cast<T>(s_h);
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lim_alpha = x1 - static_cast<T>(s_w);
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lim_beta = y1 - static_cast<T>(s_h);
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tmp = (lim_alpha - 0.5f * lim_alpha * lim_alpha - alpha +
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0.5f * alpha * alpha) *
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(lim_beta - 0.5f * lim_beta * lim_beta - beta + 0.5f * beta * beta);
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functor(diff, top_diff, s_h, s_w, h0, w0, tmp);
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alpha = static_cast<T>(e_w) - x1;
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lim_alpha = static_cast<T>(e_w) - x0;
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tmp = (lim_alpha - 0.5f * lim_alpha * lim_alpha - alpha +
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0.5f * alpha * alpha) *
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(lim_beta - 0.5f * lim_beta * lim_beta - beta + 0.5f * beta * beta);
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functor(diff, top_diff, s_h, e_w, h0, w0, tmp);
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alpha = x0 - static_cast<T>(s_w);
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beta = static_cast<T>(e_h) - y1;
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lim_alpha = x1 - static_cast<T>(s_w);
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lim_beta = static_cast<T>(e_h) - y0;
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tmp = (lim_alpha - 0.5f * lim_alpha * lim_alpha - alpha +
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0.5f * alpha * alpha) *
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(lim_beta - 0.5f * lim_beta * lim_beta - beta + 0.5f * beta * beta);
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functor(diff, top_diff, e_h, s_w, h0, w0, tmp);
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alpha = static_cast<T>(e_w) - x1;
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lim_alpha = static_cast<T>(e_w) - x0;
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tmp = (lim_alpha - 0.5f * lim_alpha * lim_alpha - alpha +
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0.5f * alpha * alpha) *
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(lim_beta - 0.5f * lim_beta * lim_beta - beta + 0.5f * beta * beta);
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functor(diff, top_diff, e_h, e_w, h0, w0, tmp);
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}
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template <typename T>
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inline HOSTDEVICE void CPUAccumulateRois(T* offset, T data) {
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*offset += data;
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}
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template <typename T>
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inline HOSTDEVICE static T PrRoIPoolingGetCoeff(T dh, T dw) {
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dw = dw > 0 ? dw : -dw;
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dh = dh > 0 ? dh : -dh;
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return (1.0f - dh) * (1.0f - dw);
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}
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template <typename T, typename H, typename W>
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inline HOSTDEVICE static T PrRoIPoolingInterpolation(const T* data, const H h,
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const W w,
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const int height,
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const int width) {
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T retVal = 0.0f;
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int h1 = floorf(h);
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int w1 = floorf(w);
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retVal +=
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PrRoIPoolingGetData(data, h1, w1, height, width) *
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PrRoIPoolingGetCoeff(h - static_cast<T>(h1), w - static_cast<T>(w1));
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h1 = floorf(h) + 1;
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w1 = floorf(w);
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retVal +=
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PrRoIPoolingGetData(data, h1, w1, height, width) *
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PrRoIPoolingGetCoeff(h - static_cast<T>(h1), w - static_cast<T>(w1));
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h1 = floorf(h);
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w1 = floorf(w) + 1;
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retVal +=
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PrRoIPoolingGetData(data, h1, w1, height, width) *
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PrRoIPoolingGetCoeff(h - static_cast<T>(h1), w - static_cast<T>(w1));
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h1 = floorf(h) + 1;
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w1 = floorf(w) + 1;
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retVal +=
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PrRoIPoolingGetData(data, h1, w1, height, width) *
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PrRoIPoolingGetCoeff(h - static_cast<T>(h1), w - static_cast<T>(w1));
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return retVal;
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}
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template <typename T>
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inline HOSTDEVICE T PrRoIPoolingSingleCoorIntegral(T s, T t, T c1, T c2) {
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return 0.5f * (t * t - s * s) * c2 +
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(t - 0.5f * t * t - s + 0.5f * s * s) * c1;
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}
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template <typename T, typename Functor, typename MaxFunctor,
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typename MinFunctor>
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inline HOSTDEVICE void PrRoIPoolingCoorBackward(
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int s_w, int e_w, int s_h, int e_h, int width, int height, T win_start_w,
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T win_start_h, T win_end_w, T win_end_h, int pw, int ph,
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const int pooled_width, const int pooled_height, T win_size,
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const float spatial_scale, const T* this_bottom_data,
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const T* this_top_data, T* this_data_grad, const T* this_out_grad,
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Functor functor, MaxFunctor maxFunctor, MinFunctor minFunctor) {
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T g_x1_y = 0.f;
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T g_x2_y = 0.f;
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T g_x_y1 = 0.f;
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T g_x_y2 = 0.f;
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for (int h_iter = s_h; h_iter < e_h; ++h_iter) {
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g_x1_y += PrRoIPoolingSingleCoorIntegral(
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maxFunctor(win_start_h, static_cast<T>(h_iter)) - h_iter,
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minFunctor(win_end_h, static_cast<T>(h_iter + 1)) - h_iter,
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PrRoIPoolingInterpolation(this_bottom_data, h_iter, win_start_w, height,
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width),
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PrRoIPoolingInterpolation(this_bottom_data, h_iter + 1, win_start_w,
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height, width));
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g_x2_y += PrRoIPoolingSingleCoorIntegral(
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maxFunctor(win_start_h, static_cast<T>(h_iter)) - h_iter,
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minFunctor(win_end_h, static_cast<T>(h_iter + 1)) - h_iter,
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PrRoIPoolingInterpolation(this_bottom_data, h_iter, win_end_w, height,
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width),
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PrRoIPoolingInterpolation(this_bottom_data, h_iter + 1, win_end_w,
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height, width));
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}
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for (int w_iter = s_w; w_iter < e_w; ++w_iter) {
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g_x_y1 += PrRoIPoolingSingleCoorIntegral(
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maxFunctor(win_start_w, static_cast<T>(w_iter)) - w_iter,
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minFunctor(win_end_w, static_cast<T>(w_iter + 1)) - w_iter,
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PrRoIPoolingInterpolation(this_bottom_data, win_start_h, w_iter, height,
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width),
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PrRoIPoolingInterpolation(this_bottom_data, win_start_h, w_iter + 1,
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height, width));
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g_x_y2 += PrRoIPoolingSingleCoorIntegral(
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maxFunctor(win_start_w, static_cast<T>(w_iter)) - w_iter,
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minFunctor(win_end_w, static_cast<T>(w_iter + 1)) - w_iter,
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PrRoIPoolingInterpolation(this_bottom_data, win_end_h, w_iter, height,
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width),
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PrRoIPoolingInterpolation(this_bottom_data, win_end_h, w_iter + 1,
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height, width));
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}
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float partial_x1 = -g_x1_y + (win_end_h - win_start_h) * (*this_top_data);
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float partial_y1 = -g_x_y1 + (win_end_w - win_start_w) * (*this_top_data);
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float partial_x2 = g_x2_y - (win_end_h - win_start_h) * (*this_top_data);
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float partial_y2 = g_x_y2 - (win_end_w - win_start_w) * (*this_top_data);
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partial_x1 = partial_x1 / win_size * spatial_scale;
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partial_x2 = partial_x2 / win_size * spatial_scale;
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partial_y1 = partial_y1 / win_size * spatial_scale;
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partial_y2 = partial_y2 / win_size * spatial_scale;
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functor(this_data_grad + 0,
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(partial_x1 * (1.0 - static_cast<T>(pw) / pooled_width) +
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partial_x2 * (1.0 - static_cast<T>(pw + 1) / pooled_width)) *
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(*this_out_grad));
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functor(this_data_grad + 1,
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(partial_y1 * (1.0 - static_cast<T>(ph) / pooled_height) +
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partial_y2 * (1.0 - static_cast<T>(ph + 1) / pooled_height)) *
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(*this_out_grad));
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functor(this_data_grad + 2,
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(partial_x2 * static_cast<T>(pw + 1) / pooled_width +
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partial_x1 * static_cast<T>(pw) / pooled_width) *
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(*this_out_grad));
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functor(this_data_grad + 3,
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(partial_y2 * static_cast<T>(ph + 1) / pooled_height +
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partial_y1 * static_cast<T>(ph) / pooled_height) *
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(*this_out_grad));
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}
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template <typename DeviceContext, typename T>
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class CPUPRROIPoolOpKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& ctx) const override {
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auto* in = ctx.Input<framework::Tensor>("X");
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auto* rois = ctx.Input<framework::LoDTensor>("ROIs");
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auto* out = ctx.Output<framework::Tensor>("Out");
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auto pooled_height = ctx.Attr<int>("pooled_height");
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auto pooled_width = ctx.Attr<int>("pooled_width");
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auto spatial_scale = ctx.Attr<float>("spatial_scale");
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auto in_dims = in->dims();
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int batch_size = in_dims[0];
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int input_channels = in_dims[1];
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auto output_channels = input_channels;
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int height = in_dims[2];
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int width = in_dims[3];
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int rois_num = rois->dims()[0];
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if (rois_num == 0) return;
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auto in_stride = framework::stride(in_dims);
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auto out_stride = framework::stride(out->dims());
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const T* input_data = in->data<T>();
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framework::Tensor rois_batch_id_list;
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rois_batch_id_list.Resize({rois_num});
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int* rois_batch_id_data =
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rois_batch_id_list.mutable_data<int>(ctx.GetPlace());
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if (ctx.HasInput("BatchRoINums") || rois->lod().empty()) {
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auto* batchroinum = ctx.Input<framework::Tensor>("BatchRoINums");
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auto* batch_index = batchroinum->data<int64_t>();
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int rois_batch_size = batchroinum->dims()[0];
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size_t c = 0;
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for (int n = 0; n < rois_batch_size; ++n) {
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for (int64_t k = 0; k < batch_index[n]; ++k) {
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rois_batch_id_data[c] = n;
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c = c + 1;
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}
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}
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} else {
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PADDLE_ENFORCE_EQ(rois->lod().empty(), false,
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platform::errors::InvalidArgument(
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"the lod of Input ROIs should not be empty when "
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"BatchRoINums is None!"));
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auto rois_lod = rois->lod().back();
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int rois_batch_size = rois_lod.size() - 1;
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PADDLE_ENFORCE_EQ(
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rois_batch_size, batch_size,
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platform::errors::InvalidArgument("the rois_batch_size and input(X) "
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"batch_size should be the same."));
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int rois_num_with_lod = rois_lod[rois_batch_size];
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PADDLE_ENFORCE_EQ(
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rois_num_with_lod, rois_num,
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platform::errors::InvalidArgument(
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"the rois_num from input and lod must be the same"));
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// calculate batch id index for each roi according to LoD
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for (int n = 0; n < rois_batch_size; ++n) {
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for (size_t i = rois_lod[n]; i < rois_lod[n + 1]; ++i) {
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rois_batch_id_data[i] = n;
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}
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}
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}
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T* output_data = out->mutable_data<T>(ctx.GetPlace());
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const T* input_rois = rois->data<T>();
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// calculate prroipooling, parallel processing can be implemented per ROI
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for (int n = 0; n < rois_num; ++n) {
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// set roi batch id
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int roi_batch_id = rois_batch_id_data[n];
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// [start, end) interval for spatial sampling
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const T* offset_input_rois = input_rois + n * 4;
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T roi_start_w = static_cast<T>(offset_input_rois[0]) * spatial_scale;
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T roi_start_h = static_cast<T>(offset_input_rois[1]) * spatial_scale;
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T roi_end_w = static_cast<T>(offset_input_rois[2]) * spatial_scale;
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T roi_end_h = static_cast<T>(offset_input_rois[3]) * spatial_scale;
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T roi_width = std::max(roi_end_w - roi_start_w, static_cast<T>(0.0));
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T roi_height = std::max(roi_end_h - roi_start_h, static_cast<T>(0.0));
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// Compute w and h at input feature map
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T bin_size_h = roi_height / static_cast<T>(pooled_height);
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T bin_size_w = roi_width / static_cast<T>(pooled_width);
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T win_size = std::max(static_cast<T>(0.0), bin_size_w * bin_size_h);
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// calculate each pixel of the output feature map.
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int out_roi_offset = n * out_stride[0];
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for (int c = 0; c < output_channels; ++c) {
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// per category
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int out_plane_offset = out_roi_offset + c * out_stride[1];
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for (int ph = 0; ph < pooled_height; ++ph) {
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int out_row_offset = out_plane_offset + ph * out_stride[2];
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for (int pw = 0; pw < pooled_width; ++pw) {
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// calculate w and h at input feature map
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T win_start_h = static_cast<T>(ph) * bin_size_h + roi_start_h;
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T win_start_w = static_cast<T>(pw) * bin_size_w + roi_start_w;
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T win_end_h = win_start_h + bin_size_h;
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T win_end_w = win_start_w + bin_size_w;
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// Add roi offsets and clip to input boundaries
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int s_w = std::floor(win_start_w);
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int e_w = std::ceil(win_end_w);
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int s_h = std::floor(win_start_h);
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int e_h = std::ceil(win_end_h);
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int output_index = out_row_offset + pw;
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int input_channel = c;
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int input_plane_offset =
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roi_batch_id * in_stride[0] + input_channel * in_stride[1];
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const T* offset_input_data = input_data + input_plane_offset;
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T sum_out = 0.;
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if (win_size > static_cast<T>(0.0)) {
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for (int w_iter = s_w; w_iter < e_w; ++w_iter) {
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for (int h_iter = s_h; h_iter < e_h; ++h_iter) {
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sum_out += PrRoIPoolingMatCalculation(
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offset_input_data, h_iter, w_iter, h_iter + 1, w_iter + 1,
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std::max(win_start_h, static_cast<T>(h_iter)),
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std::max(win_start_w, static_cast<T>(w_iter)),
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std::min(win_end_h,
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static_cast<T>(h_iter) + static_cast<T>(1.0)),
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std::min(win_end_w,
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static_cast<T>(w_iter) + static_cast<T>(1.0)),
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height, width);
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}
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}
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|
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output_data[output_index] = sum_out / win_size;
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} else {
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output_data[output_index] = 0.;
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}
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|
}
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|
}
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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 DeviceContext, typename T>
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class CPUPRROIPoolGradOpKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& ctx) const override {
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auto* in = ctx.Input<framework::Tensor>("X");
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auto* out = ctx.Input<framework::Tensor>("Out");
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auto* rois = ctx.Input<framework::LoDTensor>("ROIs");
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auto* output_grad =
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|
ctx.Input<framework::Tensor>(framework::GradVarName("Out"));
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|
auto* input_grad =
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|
ctx.Output<framework::Tensor>(framework::GradVarName("X"));
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|
auto* input_roi_grad =
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|
ctx.Output<framework::Tensor>(framework::GradVarName("ROIs"));
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|
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auto pooled_height = ctx.Attr<int>("pooled_height");
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auto pooled_width = ctx.Attr<int>("pooled_width");
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auto spatial_scale = ctx.Attr<float>("spatial_scale");
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|
|
|
if (input_grad || input_roi_grad) {
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|
auto in_dims = in->dims();
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auto* in_data = in->data<T>();
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auto* out_data = out->data<T>();
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|
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|
int input_channels = in_dims[1];
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|
auto output_channels = input_channels;
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int height = in_dims[2];
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int width = in_dims[3];
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|
int rois_num = rois->dims()[0];
|
|
|
|
// set roi batch id
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|
framework::Tensor rois_batch_id_list;
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|
rois_batch_id_list.Resize({rois_num});
|
|
int* rois_batch_id_data =
|
|
rois_batch_id_list.mutable_data<int>(ctx.GetPlace());
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|
if (ctx.HasInput("BatchRoINums") || rois->lod().empty()) {
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|
auto* batchroinum = ctx.Input<framework::Tensor>("BatchRoINums");
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|
auto* batch_index = batchroinum->data<int64_t>();
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|
int rois_batch_size = batchroinum->dims()[0];
|
|
size_t c = 0;
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|
for (int n = 0; n < rois_batch_size; ++n) {
|
|
for (int64_t k = 0; k < batch_index[n]; ++k) {
|
|
rois_batch_id_data[c] = n;
|
|
c = c + 1;
|
|
}
|
|
}
|
|
} else {
|
|
auto rois_lod = rois->lod().back();
|
|
int rois_batch_size = rois_lod.size() - 1;
|
|
// 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;
|
|
}
|
|
}
|
|
}
|
|
|
|
const T* input_rois = rois->data<T>();
|
|
const T* output_grad_data = output_grad->data<T>();
|
|
|
|
input_grad->mutable_data<T>(ctx.GetPlace());
|
|
input_roi_grad->mutable_data<T>(ctx.GetPlace());
|
|
// set gradient of X to be 0. before backpropagate.
|
|
math::SetConstant<DeviceContext, T> set_zero;
|
|
set_zero(ctx.template device_context<DeviceContext>(), input_grad,
|
|
static_cast<T>(0));
|
|
set_zero(ctx.template device_context<DeviceContext>(), input_roi_grad,
|
|
static_cast<T>(0));
|
|
|
|
T* input_grad_data = input_grad->mutable_data<T>(ctx.GetPlace());
|
|
T* input_roi_grad_data = input_roi_grad->mutable_data<T>(ctx.GetPlace());
|
|
|
|
// backpropagate gradient per output pixel
|
|
int output_grad_size = output_grad->numel();
|
|
for (int i = 0; i < output_grad_size; ++i) {
|
|
// The output is in order (n, c, ph, pw)
|
|
int pw = i % pooled_width;
|
|
int ph = (i / pooled_width) % pooled_height;
|
|
int c = (i / pooled_width / pooled_height) % output_channels;
|
|
int n = i / pooled_width / pooled_height / output_channels;
|
|
|
|
// set roi_batch_id
|
|
int roi_batch_id = rois_batch_id_data[n];
|
|
int input_channel = c;
|
|
int input_offset =
|
|
(roi_batch_id * input_channels + input_channel) * height * width;
|
|
T* offset_input_grad_data = input_grad_data + input_offset;
|
|
const T* offset_output_grad_data = output_grad_data + i;
|
|
const T* offset_out_data = out_data + i;
|
|
|
|
// [start, end) interval for spatial sampling
|
|
const T* offset_input_rois = input_rois + n * 4;
|
|
T roi_start_w = static_cast<T>(offset_input_rois[0]) * spatial_scale;
|
|
T roi_start_h = static_cast<T>(offset_input_rois[1]) * spatial_scale;
|
|
T roi_end_w = static_cast<T>(offset_input_rois[2]) * spatial_scale;
|
|
T roi_end_h = static_cast<T>(offset_input_rois[3]) * spatial_scale;
|
|
T* offset_input_roi_grad_data = input_roi_grad_data + n * 4;
|
|
|
|
T roi_width = std::max(roi_end_w - roi_start_w, static_cast<T>(0.0));
|
|
T roi_height = std::max(roi_end_h - roi_start_h, static_cast<T>(0.0));
|
|
|
|
// Compute w and h at input feature map
|
|
T bin_size_h = roi_height / static_cast<T>(pooled_height);
|
|
T bin_size_w = roi_width / static_cast<T>(pooled_width);
|
|
|
|
T win_start_w = roi_start_w + bin_size_w * pw;
|
|
T win_start_h = roi_start_h + bin_size_h * ph;
|
|
T win_end_w = win_start_w + bin_size_w;
|
|
T win_end_h = win_start_h + bin_size_h;
|
|
|
|
T win_size = std::max(static_cast<T>(0.0), bin_size_w * bin_size_h);
|
|
|
|
T sum_out = win_size == static_cast<T>(0.)
|
|
? static_cast<T>(0.)
|
|
: *offset_output_grad_data / win_size;
|
|
|
|
int s_w = std::floor(win_start_w);
|
|
int e_w = std::ceil(win_end_w);
|
|
int s_h = std::floor(win_start_h);
|
|
int e_h = std::ceil(win_end_h);
|
|
|
|
for (int w_iter = s_w; w_iter < e_w; ++w_iter) {
|
|
for (int h_iter = s_h; h_iter < e_h; ++h_iter) {
|
|
PrRoIPoolingMatDistributeDiff(
|
|
offset_input_grad_data, sum_out, h_iter, w_iter, h_iter + 1,
|
|
w_iter + 1, std::max(win_start_h, static_cast<T>(h_iter)),
|
|
std::max(win_start_w, static_cast<T>(w_iter)),
|
|
std::min(win_end_h,
|
|
static_cast<T>(h_iter) + static_cast<T>(1.0)),
|
|
std::min(win_end_w,
|
|
static_cast<T>(w_iter) + static_cast<T>(1.0)),
|
|
height, width, PrRoIPoolingDistributeDiff<T>);
|
|
}
|
|
}
|
|
|
|
const T* offset_in_data = in_data + input_offset;
|
|
PrRoIPoolingCoorBackward(
|
|
s_w, e_w, s_h, e_h, width, height, win_start_w, win_start_h,
|
|
win_end_w, win_end_h, pw, ph, pooled_width, pooled_height, win_size,
|
|
spatial_scale, offset_in_data, offset_out_data,
|
|
offset_input_roi_grad_data, offset_output_grad_data,
|
|
CPUAccumulateRois<T>,
|
|
[](const T x, const T y) { return std::max(x, y); },
|
|
[](const T x, const T y) { return std::min(x, y); });
|
|
}
|
|
}
|
|
}
|
|
};
|
|
|
|
} // namespace operators
|
|
} // namespace paddle
|