chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
@@ -0,0 +1,487 @@
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// 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_grad_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/funcs/math_function.h"
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namespace phi {
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template <typename T>
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void ConstPad3DGradNCDHW(T* d_in_data,
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const T* d_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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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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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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d_in_data[in_d * in_height * in_width + in_h * in_width + in_w] =
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d_out_data[out_d * out_height * out_width + out_h * out_width + out_w];
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}
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}
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template <typename T>
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void ConstPad3DGradNDHWC(T* d_in_data,
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const T* d_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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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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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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d_in_data[in_index + c] = d_out_data[out_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 ReflectPad3DGradNCDHW(T* d_in_data,
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const T* d_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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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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d_in_data[in_d * in_height * in_width + in_h * in_width + in_w] +=
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d_out_data[out_d * out_height * out_width + out_h * out_width + out_w];
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}
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template <typename T>
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void ReflectPad3DGradNDHWC(T* d_in_data,
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const T* d_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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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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d_in_data[in_index + c] += d_out_data[out_index + c];
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}
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}
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template <typename T>
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void ReplicatePad3DGradNCDHW(T* d_in_data,
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const T* d_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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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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d_in_data[in_d * in_height * in_width + in_h * in_width + in_w] +=
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d_out_data[out_d * out_height * out_width + out_h * out_width + out_w];
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}
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template <typename T>
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void ReplicatePad3DGradNDHWC(T* d_in_data,
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const T* d_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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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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d_in_data[in_index + c] += d_out_data[out_index + c];
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}
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}
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template <typename T>
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void CircularPad3DGradNCDHW(T* d_in_data,
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const T* d_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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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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d_in_data[in_d * in_height * in_width + in_h * in_width + in_w] +=
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d_out_data[out_d * out_height * out_width + out_h * out_width + out_w];
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}
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template <typename T>
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void CircularPad3DGradNDHWC(T* d_in_data,
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const T* d_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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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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d_in_data[in_index + c] += d_out_data[out_index + c];
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}
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}
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template <typename T>
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void Pad3DGradNCDHW(T* d_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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const T* d_out_data,
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void (*pad_func)(T*,
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const 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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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(d_in_data,
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d_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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}
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}
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}
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d_in_data += in_depth * in_height * in_width;
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d_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 Pad3DGradNDHWC(T* d_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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const T* d_out_data,
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void (*pad_func)(T*,
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const 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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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(d_in_data,
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d_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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}
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}
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}
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d_in_data += in_depth * in_height * in_width * channels;
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d_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 Pad3dGradKernel(const Context& dev_ctx,
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const DenseTensor& x UNUSED,
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const DenseTensor& out_grad,
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const IntArray& paddings,
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const std::string& mode,
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double pad_value UNUSED,
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const std::string& data_format,
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DenseTensor* x_grad) {
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std::vector<int64_t> pads = paddings.GetData();
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auto* d_out = &out_grad;
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auto* d_in = x_grad;
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auto d_in_dims = d_in->dims();
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auto d_out_dims = d_out->dims();
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const T* d_out_data = d_out->data<T>();
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T* d_in_data = dev_ctx.template Alloc<T>(d_in);
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if (x.numel() == 0) return;
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funcs::SetConstant<Context, T>()(dev_ctx, d_in, static_cast<T>(0));
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const int pad_left = static_cast<int>(pads[0]);
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const int pad_top = static_cast<int>(pads[2]);
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const int pad_front = static_cast<int>(pads[4]);
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const int num = static_cast<int>(d_in_dims[0]);
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if (data_format == "NCDHW") {
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const int channels = static_cast<int>(d_in_dims[1]);
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const int in_depth = static_cast<int>(d_in_dims[2]);
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const int in_height = static_cast<int>(d_in_dims[3]);
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const int in_width = static_cast<int>(d_in_dims[4]);
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const int out_depth = static_cast<int>(d_out_dims[2]);
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const int out_height = static_cast<int>(d_out_dims[3]);
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const int out_width = static_cast<int>(d_out_dims[4]);
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std::map<std::string,
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void (*)(T*,
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const T*,
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const int,
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||||
const int,
|
||||
const int,
|
||||
const int,
|
||||
const int,
|
||||
const int,
|
||||
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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func_map;
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func_map["reflect"] = ReflectPad3DGradNCDHW;
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func_map["replicate"] = ReplicatePad3DGradNCDHW;
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func_map["circular"] = CircularPad3DGradNCDHW;
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func_map["constant"] = ConstPad3DGradNCDHW;
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Pad3DGradNCDHW(d_in_data,
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num,
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channels,
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in_depth,
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in_height,
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||||
in_width,
|
||||
out_depth,
|
||||
out_height,
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||||
out_width,
|
||||
pad_front,
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pad_top,
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||||
pad_left,
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d_out_data,
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func_map[mode]);
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} else {
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const int channels = static_cast<int>(d_in_dims[4]);
|
||||
const int in_depth = static_cast<int>(d_in_dims[1]);
|
||||
const int in_height = static_cast<int>(d_in_dims[2]);
|
||||
const int in_width = static_cast<int>(d_in_dims[3]);
|
||||
const int out_depth = static_cast<int>(d_out_dims[1]);
|
||||
const int out_height = static_cast<int>(d_out_dims[2]);
|
||||
const int out_width = static_cast<int>(d_out_dims[3]);
|
||||
|
||||
std::map<std::string,
|
||||
void (*)(T*,
|
||||
const 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)>
|
||||
func_map;
|
||||
|
||||
func_map["reflect"] = ReflectPad3DGradNDHWC;
|
||||
func_map["replicate"] = ReplicatePad3DGradNDHWC;
|
||||
func_map["circular"] = CircularPad3DGradNDHWC;
|
||||
func_map["constant"] = ConstPad3DGradNDHWC;
|
||||
|
||||
Pad3DGradNDHWC(d_in_data,
|
||||
num,
|
||||
channels,
|
||||
in_depth,
|
||||
in_height,
|
||||
in_width,
|
||||
out_depth,
|
||||
out_height,
|
||||
out_width,
|
||||
pad_front,
|
||||
pad_top,
|
||||
pad_left,
|
||||
d_out_data,
|
||||
func_map[mode]);
|
||||
}
|
||||
}
|
||||
} // namespace phi
|
||||
|
||||
PD_REGISTER_KERNEL(pad3d_grad,
|
||||
CPU,
|
||||
ALL_LAYOUT,
|
||||
phi::Pad3dGradKernel,
|
||||
float,
|
||||
double,
|
||||
phi::complex64,
|
||||
phi::complex128) {}
|
||||
Reference in New Issue
Block a user