wehub-resource-sync
592 lines
22 KiB
C++
592 lines
22 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/pad3d_kernel.h"
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#include "paddle/phi/backends/cpu/cpu_context.h"
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#include "paddle/phi/core/kernel_registry.h"
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#include "paddle/phi/kernels/full_kernel.h"
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namespace phi {
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template <typename T>
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void ConstPad3DFuncNCDHW(const T* in_data,
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T* out_data,
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const int in_depth,
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const int in_height,
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const int in_width,
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const int out_depth UNUSED,
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const int out_height,
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const int out_width,
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const int pad_front,
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const int pad_top,
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const int pad_left,
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const int out_d,
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const int out_h,
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const int out_w,
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const T value) {
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int in_d = out_d - pad_front;
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int in_h = out_h - pad_top;
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int in_w = out_w - pad_left;
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out_data[out_d * out_height * out_width + out_h * out_width + out_w] =
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(in_d < 0 || in_h < 0 || in_w < 0 || in_d >= in_depth ||
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in_h >= in_height || in_w >= in_width)
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? value
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: in_data[in_d * in_height * in_width + in_h * in_width + in_w];
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}
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template <typename T>
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void ConstPad3DFuncNDHWC(const T* in_data,
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T* out_data,
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const int channels,
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const int in_depth,
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const int in_height,
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const int in_width,
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const int out_depth UNUSED,
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const int out_height,
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const int out_width,
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const int pad_front,
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const int pad_top,
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const int pad_left,
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const int out_d,
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const int out_h,
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const int out_w,
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const T value) {
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int in_d = out_d - pad_front;
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int in_h = out_h - pad_top;
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int in_w = out_w - pad_left;
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const int out_index =
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(out_d * out_height * out_width + out_h * out_width + out_w) * channels;
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if (in_d < 0 || in_h < 0 || in_w < 0 || in_d >= in_depth ||
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in_h >= in_height || in_w >= in_width) {
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for (int c = 0; c < channels; ++c) {
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out_data[out_index + c] = value;
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}
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} else {
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const int in_index =
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(in_d * in_height * in_width + in_h * in_width + in_w) * channels;
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for (int c = 0; c < channels; ++c) {
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out_data[out_index + c] = in_data[in_index + c];
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}
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}
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}
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template <typename T>
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void ReflectPad3DFuncNCDHW(const T* in_data,
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T* out_data,
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const int in_depth,
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const int in_height,
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const int in_width,
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const int out_depth UNUSED,
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const int out_height,
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const int out_width,
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const int pad_front,
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const int pad_top,
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const int pad_left,
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const int out_d,
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const int out_h,
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const int out_w,
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const T value UNUSED) {
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int in_d = out_d - pad_front;
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int in_h = out_h - pad_top;
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int in_w = out_w - pad_left;
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in_d = std::max(in_d, -in_d); // reflect by 0
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in_d = std::min(in_d, 2 * in_depth - in_d - 2); // reflect by in_depth
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in_h = std::max(in_h, -in_h); // reflect by 0
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in_h = std::min(in_h, 2 * in_height - in_h - 2); // reflect by in_height
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in_w = std::max(in_w, -in_w); // reflect by 0
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in_w = std::min(in_w, 2 * in_width - in_w - 2); // reflect by in_width
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out_data[out_d * out_height * out_width + out_h * out_width + out_w] =
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in_data[in_d * in_height * in_width + in_h * in_width + in_w];
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}
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template <typename T>
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void ReflectPad3DFuncNDHWC(const T* in_data,
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T* out_data,
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const int channels,
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const int in_depth,
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const int in_height,
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const int in_width,
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const int out_depth UNUSED,
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const int out_height,
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const int out_width,
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const int pad_front,
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const int pad_top,
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const int pad_left,
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const int out_d,
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const int out_h,
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const int out_w,
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const T value UNUSED) {
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int in_d = out_d - pad_front;
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int in_h = out_h - pad_top;
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int in_w = out_w - pad_left;
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in_d = std::max(in_d, -in_d);
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in_d = std::min(in_d, 2 * in_depth - in_d - 2);
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in_h = std::max(in_h, -in_h);
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in_h = std::min(in_h, 2 * in_height - in_h - 2);
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in_w = std::max(in_w, -in_w);
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in_w = std::min(in_w, 2 * in_width - in_w - 2);
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const int out_index =
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(out_d * out_height * out_width + out_h * out_width + out_w) * channels;
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const int in_index =
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(in_d * in_height * in_width + in_h * in_width + in_w) * channels;
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for (int c = 0; c < channels; ++c) {
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out_data[out_index + c] = in_data[in_index + c];
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}
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}
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template <typename T>
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void ReplicatePad3DFuncNCDHW(const T* in_data,
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T* out_data,
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const int in_depth,
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const int in_height,
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const int in_width,
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const int out_depth UNUSED,
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const int out_height,
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const int out_width,
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const int pad_front,
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const int pad_top,
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const int pad_left,
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const int out_d,
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const int out_h,
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const int out_w,
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const T value UNUSED) {
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int in_d = std::min(in_depth - 1, std::max(out_d - pad_front, 0));
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int in_h = std::min(in_height - 1, std::max(out_h - pad_top, 0));
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int in_w = std::min(in_width - 1, std::max(out_w - pad_left, 0));
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out_data[out_d * out_height * out_width + out_h * out_width + out_w] =
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in_data[in_d * in_height * in_width + in_h * in_width + in_w];
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}
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template <typename T>
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void ReplicatePad3DFuncNDHWC(const T* in_data,
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T* out_data,
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const int channels,
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const int in_depth,
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const int in_height,
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const int in_width,
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const int out_depth UNUSED,
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const int out_height,
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const int out_width,
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const int pad_front,
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const int pad_top,
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const int pad_left,
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const int out_d,
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const int out_h,
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const int out_w,
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const T value UNUSED) {
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int in_d = std::min(in_depth - 1, std::max(out_d - pad_front, 0));
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int in_h = std::min(in_height - 1, std::max(out_h - pad_top, 0));
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int in_w = std::min(in_width - 1, std::max(out_w - pad_left, 0));
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const int out_index =
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(out_d * out_height * out_width + out_h * out_width + out_w) * channels;
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const int in_index =
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(in_d * in_height * in_width + in_h * in_width + in_w) * channels;
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for (int c = 0; c < channels; ++c) {
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out_data[out_index + c] = in_data[in_index + c];
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}
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}
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template <typename T>
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void CircularPad3DFuncNCDHW(const T* in_data,
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T* out_data,
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const int in_depth,
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const int in_height,
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const int in_width,
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const int out_depth UNUSED,
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const int out_height,
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const int out_width,
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const int pad_front,
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const int pad_top,
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const int pad_left,
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const int out_d,
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const int out_h,
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const int out_w,
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const T value UNUSED) {
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int in_d = ((out_d - pad_front) % in_depth + in_depth) % in_depth;
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int in_h = ((out_h - pad_top) % in_height + in_height) % in_height;
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int in_w = ((out_w - pad_left) % in_width + in_width) % in_width;
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out_data[out_d * out_height * out_width + out_h * out_width + out_w] =
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in_data[in_d * in_height * in_width + in_h * in_width + in_w];
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}
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template <typename T>
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void CircularPad3DFuncNDHWC(const T* in_data,
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T* out_data,
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const int channels,
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const int in_depth,
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const int in_height,
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const int in_width,
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const int out_depth UNUSED,
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const int out_height,
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const int out_width,
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const int pad_front,
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const int pad_top,
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const int pad_left,
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const int out_d,
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const int out_h,
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const int out_w,
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const T value UNUSED) {
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int in_d = ((out_d - pad_front) % in_depth + in_depth) % in_depth;
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int in_h = ((out_h - pad_top) % in_height + in_height) % in_height;
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int in_w = ((out_w - pad_left) % in_width + in_width) % in_width;
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const int out_index =
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(out_d * out_height * out_width + out_h * out_width + out_w) * channels;
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const int in_index =
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(in_d * in_height * in_width + in_h * in_width + in_w) * channels;
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for (int c = 0; c < channels; ++c) {
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out_data[out_index + c] = in_data[in_index + c];
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}
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}
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template <typename T>
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void Pad3DNCDHW(const T* in_data,
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const int num,
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const int channels,
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const int in_depth,
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const int in_height,
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const int in_width,
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const int out_depth,
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const int out_height,
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const int out_width,
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const int pad_front,
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const int pad_top,
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const int pad_left,
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T value,
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T* out_data,
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void (*pad_func)(const T*,
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T*,
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const int,
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const int,
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const int,
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const int,
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const int,
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const int,
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const int,
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const int,
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const int,
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const int,
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const int,
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const int,
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const T)) {
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for (int n = 0; n < num; ++n) {
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for (int c = 0; c < channels; ++c) {
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for (int out_d = 0; out_d < out_depth; ++out_d) {
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for (int out_h = 0; out_h < out_height; ++out_h) {
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for (int out_w = 0; out_w < out_width; ++out_w) {
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pad_func(in_data,
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out_data,
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in_depth,
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in_height,
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in_width,
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out_depth,
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out_height,
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out_width,
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pad_front,
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pad_top,
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pad_left,
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out_d,
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out_h,
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out_w,
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value);
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}
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}
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}
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in_data += in_depth * in_height * in_width;
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out_data += out_depth * out_height * out_width;
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}
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}
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}
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template <typename T>
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void Pad3DNDHWC(const T* in_data,
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const int num,
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const int channels,
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const int in_depth,
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const int in_height,
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const int in_width,
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const int out_depth,
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const int out_height,
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const int out_width,
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const int pad_front,
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const int pad_top,
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const int pad_left,
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T value,
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T* out_data,
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void (*pad_func)(const T*,
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T*,
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const int,
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const int,
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const int,
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const int,
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const int,
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const int,
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const int,
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const int,
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const int,
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const int,
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const int,
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const int,
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const int,
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const T)) {
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for (int n = 0; n < num; ++n) {
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for (int out_d = 0; out_d < out_depth; ++out_d) {
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for (int out_h = 0; out_h < out_height; ++out_h) {
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for (int out_w = 0; out_w < out_width; ++out_w) {
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pad_func(in_data,
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out_data,
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channels,
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in_depth,
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in_height,
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in_width,
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out_depth,
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out_height,
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out_width,
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pad_front,
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pad_top,
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pad_left,
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out_d,
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out_h,
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out_w,
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value);
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}
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}
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}
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in_data += in_depth * in_height * in_width * channels;
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out_data += out_depth * out_height * out_width * channels;
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}
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}
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template <typename T, typename Context>
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void Pad3dKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const IntArray& paddings,
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const std::string& mode,
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double pad_value,
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const std::string& data_format,
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DenseTensor* out) {
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T value = static_cast<T>(pad_value);
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std::vector<int64_t> pads = paddings.GetData();
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auto in_dims = x.dims();
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const T* in_data = x.data<T>();
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if (data_format == "NCDHW") {
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out->Resize({in_dims[0],
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in_dims[1],
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in_dims[2] + pads[4] + pads[5],
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in_dims[3] + pads[2] + pads[3],
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in_dims[4] + pads[0] + pads[1]});
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} else {
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out->Resize({in_dims[0],
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in_dims[1] + pads[4] + pads[5],
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in_dims[2] + pads[2] + pads[3],
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in_dims[3] + pads[0] + pads[1],
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in_dims[4]});
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}
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auto out_dims = out->dims();
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T* out_data = dev_ctx.template Alloc<T>(out);
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if (x.numel() == 0) {
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Full<T, Context>(dev_ctx, out->dims(), pad_value, out);
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return;
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}
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int channels = static_cast<int>(in_dims[1]);
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int in_depth = static_cast<int>(in_dims[2]);
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int in_height = static_cast<int>(in_dims[3]);
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int in_width = static_cast<int>(in_dims[4]);
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int out_depth = static_cast<int>(out_dims[2]);
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int out_height = static_cast<int>(out_dims[3]);
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int out_width = static_cast<int>(out_dims[4]);
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if (data_format == "NDHWC") {
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channels = static_cast<int>(in_dims[4]);
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in_depth = static_cast<int>(in_dims[1]);
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in_height = static_cast<int>(in_dims[2]);
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in_width = static_cast<int>(in_dims[3]);
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out_depth = static_cast<int>(out_dims[1]);
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out_height = static_cast<int>(out_dims[2]);
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out_width = static_cast<int>(out_dims[3]);
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}
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if (mode == "reflect") {
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PADDLE_ENFORCE_GT(
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in_depth,
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pads[4],
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errors::InvalidArgument("The depth of Input(X)'s dimension should be "
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"greater than pad_front"
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" in reflect mode"
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", but received depth(%d) and pad_front(%d).",
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in_depth,
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pads[4]));
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PADDLE_ENFORCE_GT(
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in_depth,
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pads[5],
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errors::InvalidArgument("The depth of Input(X)'s dimension should be "
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"greater than pad_back"
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" in reflect mode"
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", but received depth(%d) and pad_back(%d).",
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in_depth,
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pads[5]));
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PADDLE_ENFORCE_GT(
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in_height,
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pads[2],
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errors::InvalidArgument("The height of Input(X)'s dimension should be "
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"greater than pad_top"
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" in reflect mode"
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", but received depth(%d) and pad_top(%d).",
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in_height,
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pads[2]));
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PADDLE_ENFORCE_GT(
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in_height,
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pads[3],
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errors::InvalidArgument("The height of Input(X)'s dimension should be "
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"greater than pad_bottom"
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" in reflect mode"
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", but received depth(%d) and pad_bottom(%d).",
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in_height,
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pads[3]));
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PADDLE_ENFORCE_GT(
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in_width,
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pads[0],
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errors::InvalidArgument("The width of Input(X)'s dimension should be "
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"greater than pad_left"
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" in reflect mode"
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", but received depth(%d) and pad_left(%d).",
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in_width,
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pads[0]));
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PADDLE_ENFORCE_GT(
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in_width,
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pads[1],
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errors::InvalidArgument("The width of Input(X)'s dimension should be "
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"greater than pad_right"
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" in reflect mode"
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", but received depth(%d) and pad_right(%d).",
|
|
in_width,
|
|
pads[1]));
|
|
} else if (mode == "circular" || mode == "replicate") {
|
|
PADDLE_ENFORCE_NE(in_depth * in_height * in_width,
|
|
0,
|
|
errors::InvalidArgument(
|
|
"The input tensor size can not be 0 for circular "
|
|
"or replicate padding mode."));
|
|
}
|
|
|
|
const int pad_left = static_cast<int>(pads[0]);
|
|
const int pad_top = static_cast<int>(pads[2]);
|
|
const int pad_front = static_cast<int>(pads[4]);
|
|
const int num = static_cast<int>(in_dims[0]);
|
|
if (data_format == "NCDHW") {
|
|
std::map<std::string,
|
|
void (*)(const T*,
|
|
T*,
|
|
const int,
|
|
const int,
|
|
const int,
|
|
const int,
|
|
const int,
|
|
const int,
|
|
const int,
|
|
const int,
|
|
const int,
|
|
const int,
|
|
const int,
|
|
const int,
|
|
const T)>
|
|
func_map;
|
|
|
|
func_map["reflect"] = ReflectPad3DFuncNCDHW;
|
|
func_map["replicate"] = ReplicatePad3DFuncNCDHW;
|
|
func_map["circular"] = CircularPad3DFuncNCDHW;
|
|
func_map["constant"] = ConstPad3DFuncNCDHW;
|
|
Pad3DNCDHW(in_data,
|
|
num,
|
|
channels,
|
|
in_depth,
|
|
in_height,
|
|
in_width,
|
|
out_depth,
|
|
out_height,
|
|
out_width,
|
|
pad_front,
|
|
pad_top,
|
|
pad_left,
|
|
value,
|
|
out_data,
|
|
func_map[mode]);
|
|
} else {
|
|
std::map<std::string,
|
|
void (*)(const T*,
|
|
T*,
|
|
const int,
|
|
const int,
|
|
const int,
|
|
const int,
|
|
const int,
|
|
const int,
|
|
const int,
|
|
const int,
|
|
const int,
|
|
const int,
|
|
const int,
|
|
const int,
|
|
const int,
|
|
const T)>
|
|
func_map;
|
|
|
|
func_map["reflect"] = ReflectPad3DFuncNDHWC;
|
|
func_map["replicate"] = ReplicatePad3DFuncNDHWC;
|
|
func_map["circular"] = CircularPad3DFuncNDHWC;
|
|
func_map["constant"] = ConstPad3DFuncNDHWC;
|
|
Pad3DNDHWC(in_data,
|
|
num,
|
|
channels,
|
|
in_depth,
|
|
in_height,
|
|
in_width,
|
|
out_depth,
|
|
out_height,
|
|
out_width,
|
|
pad_front,
|
|
pad_top,
|
|
pad_left,
|
|
value,
|
|
out_data,
|
|
func_map[mode]);
|
|
}
|
|
}
|
|
|
|
} // namespace phi
|
|
|
|
PD_REGISTER_KERNEL(pad3d,
|
|
CPU,
|
|
ALL_LAYOUT,
|
|
phi::Pad3dKernel,
|
|
float,
|
|
double,
|
|
int,
|
|
int64_t,
|
|
phi::complex64,
|
|
phi::complex128) {}
|