wehub-resource-sync
392 lines
15 KiB
C++
392 lines
15 KiB
C++
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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//
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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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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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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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#include "paddle/phi/kernels/grid_sample_kernel.h"
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#include "paddle/phi/core/kernel_registry.h"
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#include "paddle/phi/kernels/cpu/grid_sample_utils.h"
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#include "paddle/phi/kernels/funcs/eigen/common.h"
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#include "paddle/phi/kernels/funcs/math_function.h"
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namespace phi {
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using Array4 = Eigen::DSizes<int64_t, 4>;
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using Array5 = Eigen::DSizes<int64_t, 5>;
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template <typename T>
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static inline void Clip(const CPUContext& dev_ctx,
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DenseTensor* grid_slice,
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const int max_val, // height-1 or width-1
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bool align_corners,
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std::string padding_mode) {
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auto& place = *dev_ctx.eigen_device();
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auto grid_slice_t = EigenTensor<T, 3>::From(*grid_slice);
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if (padding_mode == "border") {
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grid_slice_t.device(place) = grid_slice_t.cwiseMax(static_cast<T>(0))
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.cwiseMin(static_cast<T>(max_val));
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} else if (padding_mode == "reflection") {
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if (align_corners) {
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auto double_range = static_cast<T>(max_val * 2);
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auto grid_abs = grid_slice_t.abs();
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auto extra = grid_abs - (grid_abs / double_range).floor() * double_range;
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grid_slice_t.device(place) = extra.cwiseMin(double_range - extra);
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if (max_val == 0) {
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grid_slice_t.device(place) = grid_slice_t.constant(static_cast<T>(0));
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}
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} else {
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auto double_range = static_cast<T>((max_val + 1) * 2);
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auto grid_abs = (grid_slice_t + static_cast<T>(0.5)).abs();
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auto extra = grid_abs - (grid_abs / double_range).floor() * double_range;
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grid_slice_t.device(place) =
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extra.cwiseMin(double_range - extra) - static_cast<T>(0.5);
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grid_slice_t.device(place) = grid_slice_t.cwiseMax(static_cast<T>(0))
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.cwiseMin(static_cast<T>(max_val));
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}
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}
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}
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template <typename T>
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static inline void Clip3D(const CPUContext& dev_ctx,
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DenseTensor* grid_slice,
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const int max_val, // height-1 or width-1
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bool align_corners,
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std::string padding_mode) {
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auto& place = *dev_ctx.eigen_device();
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auto grid_slice_t = EigenTensor<T, 4>::From(*grid_slice);
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if (padding_mode == "border") {
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grid_slice_t.device(place) = grid_slice_t.cwiseMax(static_cast<T>(0))
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.cwiseMin(static_cast<T>(max_val));
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} else if (padding_mode == "reflection") {
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if (align_corners) {
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auto double_range = static_cast<T>(max_val * 2);
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auto grid_abs = grid_slice_t.abs();
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auto extra = grid_abs - (grid_abs / double_range).floor() * double_range;
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grid_slice_t.device(place) = extra.cwiseMin(double_range - extra);
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if (max_val == 0) {
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grid_slice_t.device(place) = grid_slice_t.constant(static_cast<T>(0));
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}
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} else {
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auto double_range = static_cast<T>((max_val + 1) * 2);
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auto grid_abs = (grid_slice_t + static_cast<T>(0.5)).abs();
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auto extra = grid_abs - (grid_abs / double_range).floor() * double_range;
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grid_slice_t.device(place) =
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extra.cwiseMin(double_range - extra) - static_cast<T>(0.5);
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grid_slice_t.device(place) = grid_slice_t.cwiseMax(static_cast<T>(0))
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.cwiseMin(static_cast<T>(max_val));
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}
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}
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}
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template <typename T>
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static void CalcGridLocations(const CPUContext& dev_ctx,
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const DenseTensor& grid,
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const int in_h,
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const int in_w,
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bool align_corners,
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std::string padding_mode,
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DenseTensor* grid_x,
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DenseTensor* grid_y) {
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const int n = static_cast<int>(grid.dims()[0]);
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const int out_h = static_cast<int>(grid.dims()[1]);
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const int out_w = static_cast<int>(grid.dims()[2]);
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// split grid with shape (n, h, w, 2) into (x, y) by the 3rd Dim
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grid_x->Resize({n, out_h, out_w});
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grid_y->Resize({n, out_h, out_w});
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T* grid_x_data = dev_ctx.Alloc<T>(grid_x);
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T* grid_y_data = dev_ctx.Alloc<T>(grid_y);
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const T* grid_data = grid.data<T>();
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for (int64_t i = 0; i < static_cast<int64_t>(n) * out_h * out_w; i++) {
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grid_x_data[i] = grid_data[2 * i];
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grid_y_data[i] = grid_data[(2 * i) + 1];
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}
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Unnormalize<T>(dev_ctx, grid_x, in_w - 1, align_corners);
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Unnormalize<T>(dev_ctx, grid_y, in_h - 1, align_corners);
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Clip<T>(dev_ctx, grid_x, in_w - 1, align_corners, padding_mode);
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Clip<T>(dev_ctx, grid_y, in_h - 1, align_corners, padding_mode);
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}
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template <typename T>
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static void Calc3DGridLocations(const CPUContext& dev_ctx,
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const DenseTensor& grid,
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const int in_d,
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const int in_h,
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const int in_w,
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bool align_corners,
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std::string padding_mode,
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DenseTensor* grid_x,
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DenseTensor* grid_y,
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DenseTensor* grid_z) {
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const int n = static_cast<int>(grid.dims()[0]);
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const int out_d = static_cast<int>(grid.dims()[1]);
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const int out_h = static_cast<int>(grid.dims()[2]);
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const int out_w = static_cast<int>(grid.dims()[3]);
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// split grid with shape (n, d, h, w, 3) into (x, y, z) by the 3rd Dim
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grid_x->Resize({n, out_d, out_h, out_w});
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grid_y->Resize({n, out_d, out_h, out_w});
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grid_z->Resize({n, out_d, out_h, out_w});
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T* grid_x_data = dev_ctx.Alloc<T>(grid_x);
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T* grid_y_data = dev_ctx.Alloc<T>(grid_y);
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T* grid_z_data = dev_ctx.Alloc<T>(grid_z);
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const T* grid_data = grid.data<T>();
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for (int64_t i = 0; i < static_cast<int64_t>(n) * out_d * out_h * out_w;
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i++) {
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grid_x_data[i] = grid_data[3 * i];
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grid_y_data[i] = grid_data[(3 * i) + 1];
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grid_z_data[i] = grid_data[(3 * i) + 2];
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}
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Unnormalize3D<T>(dev_ctx, grid_x, in_w - 1, align_corners);
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Unnormalize3D<T>(dev_ctx, grid_y, in_h - 1, align_corners);
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Unnormalize3D<T>(dev_ctx, grid_z, in_d - 1, align_corners);
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Clip3D<T>(dev_ctx, grid_x, in_w - 1, align_corners, padding_mode);
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Clip3D<T>(dev_ctx, grid_y, in_h - 1, align_corners, padding_mode);
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Clip3D<T>(dev_ctx, grid_z, in_d - 1, align_corners, padding_mode);
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}
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template <typename T>
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static void BilinearInter(const CPUContext& dev_ctx,
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const DenseTensor& input,
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DenseTensor* grid_x,
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DenseTensor* grid_y,
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DenseTensor* out) {
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auto& place = *dev_ctx.eigen_device();
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const int n = static_cast<int>(grid_x->dims()[0]);
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const int out_h = static_cast<int>(grid_x->dims()[1]);
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const int out_w = static_cast<int>(grid_x->dims()[2]);
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const int c = static_cast<int>(input.dims()[1]);
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DenseTensor x_w, x_e, y_n, y_s;
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DenseTensor d_w, d_e, d_n, d_s;
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DenseTensor v_wn, v_en, v_ws, v_es;
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AllNeighbors<T>(dev_ctx,
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input,
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grid_x,
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grid_y,
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&x_w,
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&x_e,
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&y_n,
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&y_s,
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&d_w,
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&d_e,
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&d_n,
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&d_s,
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&v_wn,
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&v_en,
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&v_ws,
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&v_es);
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auto d_w_t = EigenTensor<T, 3>::From(d_w);
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auto d_e_t = EigenTensor<T, 3>::From(d_e);
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auto d_n_t = EigenTensor<T, 3>::From(d_n);
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auto d_s_t = EigenTensor<T, 3>::From(d_s);
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auto d_w_scaled_t =
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d_w_t.reshape(Array4(n, 1, out_h, out_w)).broadcast(Array4(1, c, 1, 1));
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auto d_e_scaled_t =
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d_e_t.reshape(Array4(n, 1, out_h, out_w)).broadcast(Array4(1, c, 1, 1));
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auto d_n_scaled_t =
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d_n_t.reshape(Array4(n, 1, out_h, out_w)).broadcast(Array4(1, c, 1, 1));
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auto d_s_scaled_t =
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d_s_t.reshape(Array4(n, 1, out_h, out_w)).broadcast(Array4(1, c, 1, 1));
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auto v_wn_t = EigenTensor<T, 4>::From(v_wn);
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auto v_en_t = EigenTensor<T, 4>::From(v_en);
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auto v_ws_t = EigenTensor<T, 4>::From(v_ws);
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auto v_es_t = EigenTensor<T, 4>::From(v_es);
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auto output_t = EigenTensor<T, 4>::From(*out);
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// bilinear interpolation by 4 corner points
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output_t.device(place) = v_wn_t * d_e_scaled_t * d_s_scaled_t +
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v_en_t * d_w_scaled_t * d_s_scaled_t +
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v_ws_t * d_e_scaled_t * d_n_scaled_t +
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v_es_t * d_w_scaled_t * d_n_scaled_t;
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}
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template <typename T>
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static void Bilinear3DInter(const CPUContext& dev_ctx,
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const DenseTensor& input,
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DenseTensor* grid_x,
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DenseTensor* grid_y,
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DenseTensor* grid_z,
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DenseTensor* out) {
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auto& place = *dev_ctx.eigen_device();
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const int n = static_cast<int>(grid_x->dims()[0]);
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const int out_d = static_cast<int>(grid_x->dims()[1]);
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const int out_h = static_cast<int>(grid_x->dims()[2]);
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const int out_w = static_cast<int>(grid_x->dims()[3]);
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const int c = static_cast<int>(input.dims()[1]);
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// get corner pixel values from (x, y, z)
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// for 4d, we used north-east-south-west
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// for 5d, we add top-bottom
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DenseTensor x_w, x_e, y_n, y_s, z_t, z_b;
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DenseTensor d_w, d_e, d_n, d_s, d_t, d_b;
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DenseTensor v_twn, v_ten, v_tws, v_tes, v_bwn, v_ben, v_bws, v_bes;
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All3DNeighbors<T>(dev_ctx,
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input,
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grid_x,
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grid_y,
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grid_z,
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&x_w,
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&x_e,
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&y_n,
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&y_s,
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&z_t,
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&z_b,
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&d_w,
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&d_e,
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&d_n,
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&d_s,
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&d_t,
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&d_b,
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&v_twn,
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&v_ten,
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&v_tws,
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&v_tes,
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&v_bwn,
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&v_ben,
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&v_bws,
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&v_bes);
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auto d_w_t = EigenTensor<T, 4>::From(d_w);
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auto d_e_t = EigenTensor<T, 4>::From(d_e);
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auto d_n_t = EigenTensor<T, 4>::From(d_n);
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auto d_s_t = EigenTensor<T, 4>::From(d_s);
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auto d_t_t = EigenTensor<T, 4>::From(d_t);
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auto d_b_t = EigenTensor<T, 4>::From(d_b);
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auto d_w_scaled_t = d_w_t.reshape(Array5(n, 1, out_d, out_h, out_w))
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.broadcast(Array5(1, c, 1, 1, 1));
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auto d_e_scaled_t = d_e_t.reshape(Array5(n, 1, out_d, out_h, out_w))
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.broadcast(Array5(1, c, 1, 1, 1));
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auto d_n_scaled_t = d_n_t.reshape(Array5(n, 1, out_d, out_h, out_w))
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.broadcast(Array5(1, c, 1, 1, 1));
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auto d_s_scaled_t = d_s_t.reshape(Array5(n, 1, out_d, out_h, out_w))
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.broadcast(Array5(1, c, 1, 1, 1));
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auto d_t_scaled_t = d_t_t.reshape(Array5(n, 1, out_d, out_h, out_w))
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.broadcast(Array5(1, c, 1, 1, 1));
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auto d_b_scaled_t = d_b_t.reshape(Array5(n, 1, out_d, out_h, out_w))
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.broadcast(Array5(1, c, 1, 1, 1));
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auto v_twn_t = EigenTensor<T, 5>::From(v_twn);
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auto v_ten_t = EigenTensor<T, 5>::From(v_ten);
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auto v_tws_t = EigenTensor<T, 5>::From(v_tws);
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auto v_tes_t = EigenTensor<T, 5>::From(v_tes);
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auto v_bwn_t = EigenTensor<T, 5>::From(v_bwn);
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auto v_ben_t = EigenTensor<T, 5>::From(v_ben);
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auto v_bws_t = EigenTensor<T, 5>::From(v_bws);
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auto v_bes_t = EigenTensor<T, 5>::From(v_bes);
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auto output_t = EigenTensor<T, 5>::From(*out);
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// bilinear interpolation by 4 corner points
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output_t.device(place) =
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v_twn_t * d_e_scaled_t * d_s_scaled_t * d_b_scaled_t +
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v_ten_t * d_w_scaled_t * d_s_scaled_t * d_b_scaled_t +
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v_tws_t * d_e_scaled_t * d_n_scaled_t * d_b_scaled_t +
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v_tes_t * d_w_scaled_t * d_n_scaled_t * d_b_scaled_t +
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v_bwn_t * d_e_scaled_t * d_s_scaled_t * d_t_scaled_t +
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v_ben_t * d_w_scaled_t * d_s_scaled_t * d_t_scaled_t +
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v_bws_t * d_e_scaled_t * d_n_scaled_t * d_t_scaled_t +
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v_bes_t * d_w_scaled_t * d_n_scaled_t * d_t_scaled_t;
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}
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template <typename T, typename Context>
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void GridSampleKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const DenseTensor& grid,
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const std::string& mode,
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const std::string& padding_mode,
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bool align_corners,
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DenseTensor* out) {
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if (out && out->numel() == 0) {
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dev_ctx.template Alloc<T>(out);
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return;
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}
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std::string enum_mode;
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if (mode == "nearest") {
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enum_mode = "nearest";
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} else {
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enum_mode = "bilinear";
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}
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if (x.dims().size() == 4) {
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const int n = static_cast<int>(grid.dims()[0]);
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const int out_h = static_cast<int>(grid.dims()[1]);
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const int out_w = static_cast<int>(grid.dims()[2]);
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const int c = static_cast<int>(x.dims()[1]);
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const int in_h = static_cast<int>(x.dims()[2]);
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const int in_w = static_cast<int>(x.dims()[3]);
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out->Resize({n, c, out_h, out_w});
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dev_ctx.template Alloc<T>(out);
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funcs::SetConstant<Context, T>()(dev_ctx, out, static_cast<T>(0));
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DenseTensor grid_x, grid_y;
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CalcGridLocations<T>(dev_ctx,
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grid,
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in_h,
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in_w,
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align_corners,
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padding_mode,
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&grid_x,
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&grid_y);
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if (enum_mode == "bilinear") {
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BilinearInter<T>(dev_ctx, x, &grid_x, &grid_y, out);
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} else if (enum_mode == "nearest") {
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GetGridPointValue_nearest<T>(x, out, grid_x, grid_y);
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}
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} else {
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const int n = static_cast<int>(grid.dims()[0]);
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const int out_d = static_cast<int>(grid.dims()[1]);
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const int out_h = static_cast<int>(grid.dims()[2]);
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const int out_w = static_cast<int>(grid.dims()[3]);
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const int c = static_cast<int>(x.dims()[1]);
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const int in_d = static_cast<int>(x.dims()[2]);
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const int in_h = static_cast<int>(x.dims()[3]);
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const int in_w = static_cast<int>(x.dims()[4]);
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out->Resize({n, c, out_d, out_h, out_w});
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dev_ctx.template Alloc<T>(out);
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funcs::SetConstant<Context, T>()(dev_ctx, out, static_cast<T>(0));
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DenseTensor grid_x, grid_y, grid_z;
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Calc3DGridLocations<T>(dev_ctx,
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grid,
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in_d,
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in_h,
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in_w,
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align_corners,
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padding_mode,
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&grid_x,
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&grid_y,
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&grid_z);
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if (enum_mode == "bilinear") {
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Bilinear3DInter<T>(dev_ctx, x, &grid_x, &grid_y, &grid_z, out);
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} else if (enum_mode == "nearest") {
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Get3DGridPointValue_nearest<T>(x, out, grid_x, grid_y, grid_z);
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}
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}
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}
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} // namespace phi
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PD_REGISTER_KERNEL(
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grid_sample, CPU, ALL_LAYOUT, phi::GridSampleKernel, float, double) {}
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