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395 lines
15 KiB
395 lines
15 KiB
/* Copyright (c) 2018 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 <limits>
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#include <vector>
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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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using Tensor = framework::Tensor;
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using LoDTensor = framework::LoDTensor;
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static constexpr int kROISize = 4;
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template <class T>
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void PreCalcForBilinearInterpolate(
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const platform::DeviceContext& ctx, const int height, const int width,
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const int pooled_height, const int pooled_width, const int iy_upper,
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const int ix_upper, T roi_ymin, T roi_xmin, T bin_size_h, T bin_size_w,
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int roi_bin_grid_h, int roi_bin_grid_w, Tensor* pre_pos, Tensor* pre_w) {
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int pre_calc_index = 0;
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int* pre_pos_data = pre_pos->mutable_data<int>(ctx.GetPlace());
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T* pre_w_data = pre_w->mutable_data<T>(ctx.GetPlace());
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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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// calculate y of sample points
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T y = roi_ymin + 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);
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// calculate x of samle points
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for (int ix = 0; ix < ix_upper; ix++) {
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T x = roi_xmin + 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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// deal with elements out of map
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if (y < -1.0 || y > height || x < -1.0 || x > width) {
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for (int i = 0; i < kROISize; ++i) {
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pre_pos_data[i + pre_calc_index * kROISize] = 0;
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pre_w_data[i + pre_calc_index * kROISize] = 0;
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}
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pre_calc_index += 1;
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continue;
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}
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y = y <= 0 ? 0 : y;
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x = x <= 0 ? 0 : x;
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int y_low = static_cast<int>(y);
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int x_low = static_cast<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 = static_cast<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 = static_cast<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, lx = x - x_low;
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T hy = 1. - ly, hx = 1. - lx;
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pre_pos_data[pre_calc_index * kROISize] = y_low * width + x_low;
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pre_pos_data[pre_calc_index * kROISize + 1] = y_low * width + x_high;
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pre_pos_data[pre_calc_index * kROISize + 2] = y_high * width + x_low;
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pre_pos_data[pre_calc_index * kROISize + 3] = y_high * width + x_high;
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pre_w_data[pre_calc_index * kROISize] = hy * hx;
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pre_w_data[pre_calc_index * kROISize + 1] = hy * lx;
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pre_w_data[pre_calc_index * kROISize + 2] = ly * hx;
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pre_w_data[pre_calc_index * kROISize + 3] = ly * lx;
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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 <class T>
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void bilinear_interpolate_gradient(const int height, const int width, T y, T x,
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const T out_grad_this_bin, const T count,
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T* batch_grad_data) {
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int x_low, y_low, x_high, y_high;
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T w1, w2, w3, w4;
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if (y < -1.0 || y > height || x < -1.0 || x > width) {
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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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y = y <= 0 ? 0 : y;
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x = x <= 0 ? 0 : x;
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y_low = static_cast<int>(y);
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x_low = static_cast<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 = static_cast<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 = static_cast<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, lx = x - x_low;
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T hy = 1. - ly, hx = 1. - lx;
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w1 = hy * hx, w2 = hy * lx, w3 = ly * hx, w4 = ly * lx;
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T diff1 = out_grad_this_bin * w1 / count;
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T diff2 = out_grad_this_bin * w2 / count;
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T diff3 = out_grad_this_bin * w3 / count;
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T diff4 = out_grad_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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*(batch_grad_data + y_low * width + x_low) += diff1;
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*(batch_grad_data + y_low * width + x_high) += diff2;
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*(batch_grad_data + y_high * width + x_low) += diff3;
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*(batch_grad_data + y_high * width + x_high) += diff4;
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}
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}
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template <typename DeviceContext, typename T>
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class CPUROIAlignOpKernel : 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 sampling_ratio = ctx.Attr<int>("sampling_ratio");
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auto& dev_ctx = ctx.template device_context<DeviceContext>();
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auto in_dims = in->dims();
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int batch_size = in_dims[0];
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int channels = in_dims[1];
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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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auto in_stride = framework::stride(in_dims);
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auto roi_stride = framework::stride(rois->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 roi_batch_id_list;
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roi_batch_id_list.Resize({rois_num});
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int* roi_batch_id_data =
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roi_batch_id_list.mutable_data<int>(ctx.GetPlace());
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int rois_batch_size;
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if (ctx.HasInput("RoisNum")) {
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auto* rois_num_t = ctx.Input<framework::Tensor>("RoisNum");
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rois_batch_size = rois_num_t->numel();
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PADDLE_ENFORCE_EQ(
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rois_batch_size, batch_size,
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platform::errors::InvalidArgument(
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"The batch size of rois and the batch size of images "
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" must be the same. But received the batch size of rois is %d, "
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"and the batch size of images is %d",
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rois_batch_size, batch_size));
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auto* rois_num_data = rois_num_t->data<int>();
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int start = 0;
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for (int n = 0; n < rois_batch_size; ++n) {
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for (int i = start; i < start + rois_num_data[n]; ++i) {
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roi_batch_id_data[i] = n;
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}
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start += rois_num_data[n];
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}
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} else {
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auto lod = rois->lod();
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PADDLE_ENFORCE_EQ(lod.empty(), false,
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platform::errors::InvalidArgument(
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"Input(ROIs) Tensor of ROIAlignOp "
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"does not contain LoD information."));
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auto rois_lod = 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(
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"The rois_batch_size and imgs "
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"batch_size must be the same. But received rois_batch_size = %d, "
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"batch_size = %d",
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rois_batch_size, batch_size));
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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, rois_num_with_lod,
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platform::errors::InvalidArgument(
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"The actual number of rois and the number of rois "
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"provided from Input(RoIsLoD) in RoIAlign must be the same."
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" But received actual number of rois is %d, and the number "
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"of rois from RoIsLoD is %d",
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rois_num, rois_num_with_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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roi_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* rois_data = rois->data<T>();
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for (int n = 0; n < rois_num; ++n) {
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int roi_batch_id = roi_batch_id_data[n];
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T roi_xmin = rois_data[0] * spatial_scale;
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T roi_ymin = rois_data[1] * spatial_scale;
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T roi_xmax = rois_data[2] * spatial_scale;
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T roi_ymax = rois_data[3] * spatial_scale;
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T roi_width = std::max(roi_xmax - roi_xmin, static_cast<T>(1.));
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T roi_height = std::max(roi_ymax - roi_ymin, static_cast<T>(1.));
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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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const T* batch_data = input_data + roi_batch_id * in_stride[0];
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int roi_bin_grid_h = (sampling_ratio > 0)
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? sampling_ratio
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: ceil(roi_height / pooled_height);
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int roi_bin_grid_w = (sampling_ratio > 0)
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? sampling_ratio
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: ceil(roi_width / pooled_width);
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const T count = roi_bin_grid_h * roi_bin_grid_w;
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Tensor pre_pos;
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Tensor pre_w;
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int pre_size = count * out_stride[1];
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pre_pos.Resize({pre_size, kROISize});
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pre_w.Resize({pre_size, kROISize});
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PreCalcForBilinearInterpolate(
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dev_ctx, height, width, pooled_height, pooled_width, roi_bin_grid_h,
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roi_bin_grid_w, roi_ymin, roi_xmin, bin_size_h, bin_size_w,
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roi_bin_grid_h, roi_bin_grid_w, &pre_pos, &pre_w);
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const int* pre_pos_data = pre_pos.data<int>();
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const T* pre_w_data = pre_w.data<T>();
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for (int c = 0; c < channels; c++) {
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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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const int pool_index = 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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for (int i = 0; i < kROISize; i++) {
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int pos = pre_pos_data[pre_calc_index * kROISize + i];
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T w = pre_w_data[pre_calc_index * kROISize + i];
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output_val += w * batch_data[pos];
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}
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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_data[pool_index] = output_val;
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}
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}
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batch_data += in_stride[1];
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output_data += out_stride[1];
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}
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rois_data += roi_stride[0];
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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 CPUROIAlignGradOpKernel : 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_grad =
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ctx.Input<framework::Tensor>(framework::GradVarName("Out"));
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auto* in_grad = ctx.Output<framework::Tensor>(framework::GradVarName("X"));
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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 sampling_ratio = ctx.Attr<int>("sampling_ratio");
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auto in_dims = in->dims();
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int channels = in_dims[1];
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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 (!in_grad) {
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return;
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}
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Tensor roi_batch_id_list;
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roi_batch_id_list.Resize({rois_num});
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int* roi_batch_id_data =
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roi_batch_id_list.mutable_data<int>(ctx.GetPlace());
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int rois_batch_size;
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if (ctx.HasInput("RoisNum")) {
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auto* rois_num_t = ctx.Input<framework::Tensor>("RoisNum");
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rois_batch_size = rois_num_t->numel();
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auto* rois_num_data = rois_num_t->data<int>();
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int start = 0;
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for (int n = 0; n < rois_batch_size; ++n) {
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for (int i = start; i < start + rois_num_data[n]; ++i) {
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roi_batch_id_data[i] = n;
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}
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start += rois_num_data[n];
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}
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} else {
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auto rois_lod = rois->lod().back();
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rois_batch_size = rois_lod.size() - 1;
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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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roi_batch_id_data[i] = n;
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}
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}
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}
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in_grad->mutable_data<T>(ctx.GetPlace());
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auto& dev_ctx = ctx.template device_context<DeviceContext>();
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math::SetConstant<DeviceContext, T> set_zero;
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set_zero(dev_ctx, in_grad, static_cast<T>(0));
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int output_grad_size = out_grad->numel();
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if ((!out_grad->IsInitialized()) || (output_grad_size <= 0)) {
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return;
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}
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const T* rois_data = rois->data<T>();
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const T* out_grad_data = out_grad->data<T>();
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T* in_grad_data = in_grad->mutable_data<T>(ctx.GetPlace());
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auto in_stride = framework::stride(in->dims());
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auto roi_stride = framework::stride(rois->dims());
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auto out_stride = framework::stride(out_grad->dims());
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for (int n = 0; n < rois_num; ++n) {
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int roi_batch_idx = roi_batch_id_data[n];
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T roi_xmin = rois_data[0] * spatial_scale;
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T roi_ymin = rois_data[1] * spatial_scale;
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T roi_xmax = rois_data[2] * spatial_scale;
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T roi_ymax = rois_data[3] * spatial_scale;
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T roi_width = std::max(roi_xmax - roi_xmin, static_cast<T>(1.));
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T roi_height = std::max(roi_ymax - roi_ymin, static_cast<T>(1.));
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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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for (int c = 0; c < channels; ++c) {
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T* batch_grad_data =
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in_grad_data + roi_batch_idx * in_stride[0] + c * in_stride[1];
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const T* batch_out_grad_data =
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out_grad_data + n * out_stride[0] + c * out_stride[1];
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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 pool_index = ph * pooled_width + pw;
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T out_grad_this_bin = batch_out_grad_data[pool_index];
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int roi_bin_grid_h = (sampling_ratio > 0)
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? sampling_ratio
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: ceil(roi_height / pooled_height);
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int roi_bin_grid_w = (sampling_ratio > 0)
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? sampling_ratio
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: ceil(roi_width / pooled_width);
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T count = roi_bin_grid_h * roi_bin_grid_w;
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for (int iy = 0; iy < roi_bin_grid_h; iy++) {
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const T y = roi_ymin + 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);
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for (int ix = 0; ix < roi_bin_grid_w; ix++) {
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const T x = roi_xmin + 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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bilinear_interpolate_gradient(height, width, y, x,
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out_grad_this_bin, count,
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batch_grad_data);
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}
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}
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}
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}
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}
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rois_data += roi_stride[0];
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}
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}
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};
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} // namespace operators
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} // namespace paddle
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