You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
333 lines
13 KiB
333 lines
13 KiB
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
|
|
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. */
|
|
|
|
#pragma once
|
|
#include <algorithm>
|
|
#include <limits>
|
|
#include "paddle/fluid/framework/op_registry.h"
|
|
#include "paddle/fluid/operators/math/math_function.h"
|
|
|
|
namespace paddle {
|
|
namespace operators {
|
|
|
|
using Tensor = framework::Tensor;
|
|
using LoDTensor = framework::LoDTensor;
|
|
|
|
static constexpr int kROISize = 4;
|
|
|
|
template <class T>
|
|
void PreCalcForBilinearInterpolate(
|
|
const platform::DeviceContext& ctx, const int height, const int width,
|
|
const int pooled_height, const int pooled_width, const int iy_upper,
|
|
const int ix_upper, T roi_ymin, T roi_xmin, T bin_size_h, T bin_size_w,
|
|
int roi_bin_grid_h, int roi_bin_grid_w, Tensor* pre_pos, Tensor* pre_w) {
|
|
int pre_calc_index = 0;
|
|
int* pre_pos_data = pre_pos->mutable_data<int>(ctx.GetPlace());
|
|
T* pre_w_data = pre_w->mutable_data<T>(ctx.GetPlace());
|
|
for (int ph = 0; ph < pooled_height; ph++) {
|
|
for (int pw = 0; pw < pooled_width; pw++) {
|
|
for (int iy = 0; iy < iy_upper; iy++) {
|
|
// calculate y of sample points
|
|
T y = roi_ymin + ph * bin_size_h +
|
|
static_cast<T>(iy + .5f) * bin_size_h /
|
|
static_cast<T>(roi_bin_grid_h);
|
|
// calculate x of samle points
|
|
for (int ix = 0; ix < ix_upper; ix++) {
|
|
T x = roi_xmin + pw * bin_size_w +
|
|
static_cast<T>(ix + .5f) * bin_size_w /
|
|
static_cast<T>(roi_bin_grid_w);
|
|
// deal with elements out of map
|
|
if (y < -1.0 || y > height || x < -1.0 || x > width) {
|
|
for (int i = 0; i < kROISize; ++i) {
|
|
pre_pos_data[i + pre_calc_index * kROISize] = 0;
|
|
pre_w_data[i + pre_calc_index * kROISize] = 0;
|
|
}
|
|
pre_calc_index += 1;
|
|
continue;
|
|
}
|
|
y = y <= 0 ? 0 : y;
|
|
x = x <= 0 ? 0 : x;
|
|
|
|
int y_low = static_cast<int>(y);
|
|
int x_low = static_cast<int>(x);
|
|
int y_high;
|
|
int x_high;
|
|
if (y_low >= height - 1) {
|
|
y_high = y_low = height - 1;
|
|
y = static_cast<T>(y_low);
|
|
} else {
|
|
y_high = y_low + 1;
|
|
}
|
|
if (x_low >= width - 1) {
|
|
x_high = x_low = width - 1;
|
|
x = static_cast<T>(x_low);
|
|
} else {
|
|
x_high = x_low + 1;
|
|
}
|
|
T ly = y - y_low, lx = x - x_low;
|
|
T hy = 1. - ly, hx = 1. - lx;
|
|
pre_pos_data[pre_calc_index * kROISize] = y_low * width + x_low;
|
|
pre_pos_data[pre_calc_index * kROISize + 1] = y_low * width + x_high;
|
|
pre_pos_data[pre_calc_index * kROISize + 2] = y_high * width + x_low;
|
|
pre_pos_data[pre_calc_index * kROISize + 3] = y_high * width + x_high;
|
|
pre_w_data[pre_calc_index * kROISize] = hy * hx;
|
|
pre_w_data[pre_calc_index * kROISize + 1] = hy * lx;
|
|
pre_w_data[pre_calc_index * kROISize + 2] = ly * hx;
|
|
pre_w_data[pre_calc_index * kROISize + 3] = ly * lx;
|
|
pre_calc_index += 1;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
template <class T>
|
|
void bilinear_interpolate_gradient(const int height, const int width, T y, T x,
|
|
const T out_grad_this_bin, const T count,
|
|
T* batch_grad_data) {
|
|
int x_low, y_low, x_high, y_high;
|
|
T w1, w2, w3, w4;
|
|
if (y < -1.0 || y > height || x < -1.0 || x > width) {
|
|
w1 = w2 = w3 = w4 = 0;
|
|
x_low = x_high = y_low = y_high = -1;
|
|
return;
|
|
}
|
|
y = y <= 0 ? 0 : y;
|
|
x = x <= 0 ? 0 : x;
|
|
y_low = static_cast<int>(y);
|
|
x_low = static_cast<int>(x);
|
|
if (y_low >= height - 1) {
|
|
y_high = y_low = height - 1;
|
|
y = static_cast<T>(y_low);
|
|
} else {
|
|
y_high = y_low + 1;
|
|
}
|
|
|
|
if (x_low >= width - 1) {
|
|
x_high = x_low = width - 1;
|
|
x = static_cast<T>(x_low);
|
|
} else {
|
|
x_high = x_low + 1;
|
|
}
|
|
|
|
T ly = y - y_low, lx = x - x_low;
|
|
T hy = 1. - ly, hx = 1. - lx;
|
|
w1 = hy * hx, w2 = hy * lx, w3 = ly * hx, w4 = ly * lx;
|
|
T diff1 = out_grad_this_bin * w1 / count;
|
|
T diff2 = out_grad_this_bin * w2 / count;
|
|
T diff3 = out_grad_this_bin * w3 / count;
|
|
T diff4 = out_grad_this_bin * w4 / count;
|
|
if (x_low >= 0 && x_high >= 0 && y_low >= 0 && y_high >= 0) {
|
|
*(batch_grad_data + y_low * width + x_low) += diff1;
|
|
*(batch_grad_data + y_low * width + x_high) += diff2;
|
|
*(batch_grad_data + y_high * width + x_low) += diff3;
|
|
*(batch_grad_data + y_high * width + x_high) += diff4;
|
|
}
|
|
}
|
|
|
|
template <typename DeviceContext, typename T>
|
|
class CPUROIAlignOpKernel : public framework::OpKernel<T> {
|
|
public:
|
|
void Compute(const framework::ExecutionContext& ctx) const override {
|
|
auto* in = ctx.Input<framework::Tensor>("X");
|
|
auto* rois = ctx.Input<framework::LoDTensor>("ROIs");
|
|
auto* out = ctx.Output<framework::Tensor>("Out");
|
|
auto pooled_height = ctx.Attr<int>("pooled_height");
|
|
auto pooled_width = ctx.Attr<int>("pooled_width");
|
|
auto spatial_scale = ctx.Attr<float>("spatial_scale");
|
|
auto sampling_ratio = ctx.Attr<int>("sampling_ratio");
|
|
|
|
auto& dev_ctx = ctx.template device_context<DeviceContext>();
|
|
|
|
auto in_dims = in->dims();
|
|
int batch_size = in_dims[0];
|
|
int channels = in_dims[1];
|
|
int height = in_dims[2];
|
|
int width = in_dims[3];
|
|
int rois_num = rois->dims()[0];
|
|
|
|
auto in_stride = framework::stride(in_dims);
|
|
auto roi_stride = framework::stride(rois->dims());
|
|
auto out_stride = framework::stride(out->dims());
|
|
|
|
const T* input_data = in->data<T>();
|
|
framework::Tensor roi_batch_id_list;
|
|
roi_batch_id_list.Resize({rois_num});
|
|
int* roi_batch_id_data =
|
|
roi_batch_id_list.mutable_data<int>(ctx.GetPlace());
|
|
|
|
auto rois_lod = rois->lod().back();
|
|
int rois_batch_size = rois_lod.size() - 1;
|
|
PADDLE_ENFORCE_EQ(
|
|
rois_batch_size, batch_size,
|
|
"The rois_batch_size and imgs batch_size must be the same.");
|
|
int rois_num_with_lod = rois_lod[rois_batch_size];
|
|
PADDLE_ENFORCE_EQ(rois_num, rois_num_with_lod,
|
|
"The rois_num from input and lod must be the same.");
|
|
for (int n = 0; n < rois_batch_size; ++n) {
|
|
for (size_t i = rois_lod[n]; i < rois_lod[n + 1]; ++i) {
|
|
roi_batch_id_data[i] = n;
|
|
}
|
|
}
|
|
T* output_data = out->mutable_data<T>(ctx.GetPlace());
|
|
const T* rois_data = rois->data<T>();
|
|
for (int n = 0; n < rois_num; ++n) {
|
|
int roi_batch_id = roi_batch_id_data[n];
|
|
T roi_xmin = rois_data[0] * spatial_scale;
|
|
T roi_ymin = rois_data[1] * spatial_scale;
|
|
T roi_xmax = rois_data[2] * spatial_scale;
|
|
T roi_ymax = rois_data[3] * spatial_scale;
|
|
|
|
T roi_width = std::max(roi_xmax - roi_xmin, static_cast<T>(1.));
|
|
T roi_height = std::max(roi_ymax - roi_ymin, static_cast<T>(1.));
|
|
T bin_size_h = static_cast<T>(roi_height) / static_cast<T>(pooled_height);
|
|
T bin_size_w = static_cast<T>(roi_width) / static_cast<T>(pooled_width);
|
|
const T* batch_data = input_data + roi_batch_id * in_stride[0];
|
|
|
|
int roi_bin_grid_h = (sampling_ratio > 0)
|
|
? sampling_ratio
|
|
: ceil(roi_height / pooled_height);
|
|
int roi_bin_grid_w = (sampling_ratio > 0)
|
|
? sampling_ratio
|
|
: ceil(roi_width / pooled_width);
|
|
const T count = roi_bin_grid_h * roi_bin_grid_w;
|
|
Tensor pre_pos;
|
|
Tensor pre_w;
|
|
int pre_size = count * out_stride[1];
|
|
pre_pos.Resize({pre_size, kROISize});
|
|
pre_w.Resize({pre_size, kROISize});
|
|
|
|
PreCalcForBilinearInterpolate(
|
|
dev_ctx, height, width, pooled_height, pooled_width, roi_bin_grid_h,
|
|
roi_bin_grid_w, roi_ymin, roi_xmin, bin_size_h, bin_size_w,
|
|
roi_bin_grid_h, roi_bin_grid_w, &pre_pos, &pre_w);
|
|
const int* pre_pos_data = pre_pos.data<int>();
|
|
const T* pre_w_data = pre_w.data<T>();
|
|
for (int c = 0; c < channels; c++) {
|
|
int pre_calc_index = 0;
|
|
for (int ph = 0; ph < pooled_height; ph++) {
|
|
for (int pw = 0; pw < pooled_width; pw++) {
|
|
const int pool_index = ph * pooled_width + pw;
|
|
T output_val = 0;
|
|
for (int iy = 0; iy < roi_bin_grid_h; iy++) {
|
|
for (int ix = 0; ix < roi_bin_grid_w; ix++) {
|
|
for (int i = 0; i < kROISize; i++) {
|
|
int pos = pre_pos_data[pre_calc_index * kROISize + i];
|
|
T w = pre_w_data[pre_calc_index * kROISize + i];
|
|
output_val += w * batch_data[pos];
|
|
}
|
|
pre_calc_index += 1;
|
|
}
|
|
}
|
|
output_val /= count;
|
|
output_data[pool_index] = output_val;
|
|
}
|
|
}
|
|
batch_data += in_stride[1];
|
|
output_data += out_stride[1];
|
|
}
|
|
rois_data += roi_stride[0];
|
|
}
|
|
}
|
|
};
|
|
|
|
template <typename DeviceContext, typename T>
|
|
class CPUROIAlignGradOpKernel : public framework::OpKernel<T> {
|
|
public:
|
|
void Compute(const framework::ExecutionContext& ctx) const override {
|
|
auto* in = ctx.Input<framework::Tensor>("X");
|
|
auto* rois = ctx.Input<framework::LoDTensor>("ROIs");
|
|
auto* out_grad =
|
|
ctx.Input<framework::Tensor>(framework::GradVarName("Out"));
|
|
auto* in_grad = ctx.Output<framework::Tensor>(framework::GradVarName("X"));
|
|
|
|
auto pooled_height = ctx.Attr<int>("pooled_height");
|
|
auto pooled_width = ctx.Attr<int>("pooled_width");
|
|
auto spatial_scale = ctx.Attr<float>("spatial_scale");
|
|
auto sampling_ratio = ctx.Attr<int>("sampling_ratio");
|
|
auto in_dims = in->dims();
|
|
if (!in_grad) {
|
|
return;
|
|
}
|
|
int channels = in_dims[1];
|
|
int height = in_dims[2];
|
|
int width = in_dims[3];
|
|
int rois_num = rois->dims()[0];
|
|
Tensor roi_batch_id_list;
|
|
roi_batch_id_list.Resize({rois_num});
|
|
int* roi_batch_id_data =
|
|
roi_batch_id_list.mutable_data<int>(ctx.GetPlace());
|
|
|
|
auto rois_lod = rois->lod().back();
|
|
int rois_batch_size = rois_lod.size() - 1;
|
|
for (int n = 0; n < rois_batch_size; ++n) {
|
|
for (size_t i = rois_lod[n]; i < rois_lod[n + 1]; ++i) {
|
|
roi_batch_id_data[i] = n;
|
|
}
|
|
}
|
|
|
|
const T* rois_data = rois->data<T>();
|
|
const T* out_grad_data = out_grad->data<T>();
|
|
T* in_grad_data = in_grad->mutable_data<T>(ctx.GetPlace());
|
|
|
|
auto in_stride = framework::stride(in->dims());
|
|
auto roi_stride = framework::stride(rois->dims());
|
|
auto out_stride = framework::stride(out_grad->dims());
|
|
|
|
for (int n = 0; n < rois_num; ++n) {
|
|
int roi_batch_idx = roi_batch_id_data[n];
|
|
T roi_xmin = rois_data[0] * spatial_scale;
|
|
T roi_ymin = rois_data[1] * spatial_scale;
|
|
T roi_xmax = rois_data[2] * spatial_scale;
|
|
T roi_ymax = rois_data[3] * spatial_scale;
|
|
T roi_width = std::max(roi_xmax - roi_xmin, static_cast<T>(1.));
|
|
T roi_height = std::max(roi_ymax - roi_ymin, static_cast<T>(1.));
|
|
T bin_size_h = static_cast<T>(roi_height) / static_cast<T>(pooled_height);
|
|
T bin_size_w = static_cast<T>(roi_width) / static_cast<T>(pooled_width);
|
|
for (int c = 0; c < channels; ++c) {
|
|
T* batch_grad_data =
|
|
in_grad_data + roi_batch_idx * in_stride[0] + c * in_stride[1];
|
|
const T* batch_out_grad_data =
|
|
out_grad_data + n * out_stride[0] + c * out_stride[1];
|
|
for (int ph = 0; ph < pooled_height; ++ph) {
|
|
for (int pw = 0; pw < pooled_width; ++pw) {
|
|
int pool_index = ph * pooled_width + pw;
|
|
T out_grad_this_bin = batch_out_grad_data[pool_index];
|
|
int roi_bin_grid_h = (sampling_ratio > 0)
|
|
? sampling_ratio
|
|
: ceil(roi_height / pooled_height);
|
|
int roi_bin_grid_w = (sampling_ratio > 0)
|
|
? sampling_ratio
|
|
: ceil(roi_width / pooled_width);
|
|
T count = roi_bin_grid_h * roi_bin_grid_w;
|
|
for (int iy = 0; iy < roi_bin_grid_h; iy++) {
|
|
const T y = roi_ymin + ph * bin_size_h +
|
|
static_cast<T>(iy + .5f) * bin_size_h /
|
|
static_cast<T>(roi_bin_grid_h);
|
|
for (int ix = 0; ix < roi_bin_grid_w; ix++) {
|
|
const T x = roi_xmin + pw * bin_size_w +
|
|
static_cast<T>(ix + .5f) * bin_size_w /
|
|
static_cast<T>(roi_bin_grid_w);
|
|
bilinear_interpolate_gradient(height, width, y, x,
|
|
out_grad_this_bin, count,
|
|
batch_grad_data);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
rois_data += roi_stride[0];
|
|
}
|
|
}
|
|
};
|
|
} // namespace operators
|
|
} // namespace paddle
|