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Paddle
“742d758747f2f0a1f6879e30c27ac268597a0913”上不存在“...inference/tests/git@gitcode.net:RobotFutures/Paddle.git”
未验证 提交 382e460b 编写于 作者: C chentianyu03 提交者： GitHub
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[Phi]add pad3d kernel into phi (#40701) 

* add pad3d kernel into phi

* add pad3d infermeta

* fix build error

* remove raw pad3d infershape function
上级 0e1191f4
隐藏空白更改
内联 并排
Showing with 2347 addition and 1523 deletion
paddle/fluid/operators/pad3d_op.cc
浏览文件 @ 382e460b
...@@ -16,7 +16,9 @@ limitations under the License. */ ...@@ -16,7 +16,9 @@ limitations under the License. */
#include <memory> #include <memory>
#include <string> #include <string>
#include <vector> #include <vector>
#include "paddle/fluid/framework/infershape_utils.h"
#include "paddle/fluid/framework/op_registry.h" #include "paddle/fluid/framework/op_registry.h"
#include "paddle/phi/infermeta/unary.h"
#include "paddle/phi/kernels/funcs/math_function.h" #include "paddle/phi/kernels/funcs/math_function.h"
namespace paddle { namespace paddle {
...@@ -24,734 +26,10 @@ namespace operators { ...@@ -24,734 +26,10 @@ namespace operators {
using framework::Tensor; using framework::Tensor;
template <typename T>
void ConstPad3DFuncNCDHW(const T* in_data, T* out_data, const int in_depth,
const int in_height, const int in_width,
const int out_depth, const int out_height,
const int out_width, const int pad_front,
const int pad_top, const int pad_left, const int out_d,
const int out_h, const int out_w, const T value) {
int in_d = out_d - pad_front;
int in_h = out_h - pad_top;
int in_w = out_w - pad_left;
out_data[out_d * out_height * out_width + out_h * out_width + out_w] =
(in_d < 0 || in_h < 0 || in_w < 0 || in_d >= in_depth ||
in_h >= in_height || in_w >= in_width)
? value
: in_data[in_d * in_height * in_width + in_h * in_width + in_w];
}
template <typename T>
void ConstPad3DFuncNDHWC(const T* in_data, T* out_data, const int channels,
const int in_depth, const int in_height,
const int in_width, const int out_depth,
const int out_height, const int out_width,
const int pad_front, const int pad_top,
const int pad_left, const int out_d, const int out_h,
const int out_w, const T value) {
int in_d = out_d - pad_front;
int in_h = out_h - pad_top;
int in_w = out_w - pad_left;
const int out_index =
(out_d * out_height * out_width + out_h * out_width + out_w) * channels;
if (in_d < 0 || in_h < 0 || in_w < 0 || in_d >= in_depth ||
in_h >= in_height || in_w >= in_width) {
for (int c = 0; c < channels; ++c) {
out_data[out_index + c] = value;
}
} else {
const int in_index =
(in_d * in_height * in_width + in_h * in_width + in_w) * channels;
for (int c = 0; c < channels; ++c) {
out_data[out_index + c] = in_data[in_index + c];
}
}
}
template <typename T>
void ReflectPad3DFuncNCDHW(const T* in_data, T* out_data, const int in_depth,
const int in_height, const int in_width,
const int out_depth, const int out_height,
const int out_width, const int pad_front,
const int pad_top, const int pad_left,
const int out_d, const int out_h, const int out_w,
const T value) {
int in_d = out_d - pad_front;
int in_h = out_h - pad_top;
int in_w = out_w - pad_left;
in_d = std::max(in_d, -in_d); // reflect by 0
in_d = std::min(in_d, 2 * in_depth - in_d - 2); // reflect by in_depth
in_h = std::max(in_h, -in_h); // reflect by 0
in_h = std::min(in_h, 2 * in_height - in_h - 2); // reflect by in_height
in_w = std::max(in_w, -in_w); // reflect by 0
in_w = std::min(in_w, 2 * in_width - in_w - 2); // reflect by in_width
out_data[out_d * out_height * out_width + out_h * out_width + out_w] =
in_data[in_d * in_height * in_width + in_h * in_width + in_w];
}
template <typename T>
void ReflectPad3DFuncNDHWC(const T* in_data, T* out_data, const int channels,
const int in_depth, const int in_height,
const int in_width, const int out_depth,
const int out_height, const int out_width,
const int pad_front, const int pad_top,
const int pad_left, const int out_d, const int out_h,
const int out_w, const T value) {
int in_d = out_d - pad_front;
int in_h = out_h - pad_top;
int in_w = out_w - pad_left;
in_d = std::max(in_d, -in_d);
in_d = std::min(in_d, 2 * in_depth - in_d - 2);
in_h = std::max(in_h, -in_h);
in_h = std::min(in_h, 2 * in_height - in_h - 2);
in_w = std::max(in_w, -in_w);
in_w = std::min(in_w, 2 * in_width - in_w - 2);
const int out_index =
(out_d * out_height * out_width + out_h * out_width + out_w) * channels;
const int in_index =
(in_d * in_height * in_width + in_h * in_width + in_w) * channels;
for (int c = 0; c < channels; ++c) {
out_data[out_index + c] = in_data[in_index + c];
}
}
template <typename T>
void ReplicatePad3DFuncNCDHW(const T* in_data, T* out_data, const int in_depth,
const int in_height, const int in_width,
const int out_depth, const int out_height,
const int out_width, const int pad_front,
const int pad_top, const int pad_left,
const int out_d, const int out_h, const int out_w,
const T value) {
int in_d = std::min(in_depth - 1, std::max(out_d - pad_front, 0));
int in_h = std::min(in_height - 1, std::max(out_h - pad_top, 0));
int in_w = std::min(in_width - 1, std::max(out_w - pad_left, 0));
out_data[out_d * out_height * out_width + out_h * out_width + out_w] =
in_data[in_d * in_height * in_width + in_h * in_width + in_w];
}
template <typename T>
void ReplicatePad3DFuncNDHWC(const T* in_data, T* out_data, const int channels,
const int in_depth, const int in_height,
const int in_width, const int out_depth,
const int out_height, const int out_width,
const int pad_front, const int pad_top,
const int pad_left, const int out_d,
const int out_h, const int out_w, const T value) {
int in_d = std::min(in_depth - 1, std::max(out_d - pad_front, 0));
int in_h = std::min(in_height - 1, std::max(out_h - pad_top, 0));
int in_w = std::min(in_width - 1, std::max(out_w - pad_left, 0));
const int out_index =
(out_d * out_height * out_width + out_h * out_width + out_w) * channels;
const int in_index =
(in_d * in_height * in_width + in_h * in_width + in_w) * channels;
for (int c = 0; c < channels; ++c) {
out_data[out_index + c] = in_data[in_index + c];
}
}
template <typename T>
void CircularPad3DFuncNCDHW(const T* in_data, T* out_data, const int in_depth,
const int in_height, const int in_width,
const int out_depth, const int out_height,
const int out_width, const int pad_front,
const int pad_top, const int pad_left,
const int out_d, const int out_h, const int out_w,
const T value) {
int in_d = ((out_d - pad_front) % in_depth + in_depth) % in_depth;
int in_h = ((out_h - pad_top) % in_height + in_height) % in_height;
int in_w = ((out_w - pad_left) % in_width + in_width) % in_width;
out_data[out_d * out_height * out_width + out_h * out_width + out_w] =
in_data[in_d * in_height * in_width + in_h * in_width + in_w];
}
template <typename T>
void CircularPad3DFuncNDHWC(const T* in_data, T* out_data, const int channels,
const int in_depth, const int in_height,
const int in_width, const int out_depth,
const int out_height, const int out_width,
const int pad_front, const int pad_top,
const int pad_left, const int out_d,
const int out_h, const int out_w, const T value) {
int in_d = ((out_d - pad_front) % in_depth + in_depth) % in_depth;
int in_h = ((out_h - pad_top) % in_height + in_height) % in_height;
int in_w = ((out_w - pad_left) % in_width + in_width) % in_width;
const int out_index =
(out_d * out_height * out_width + out_h * out_width + out_w) * channels;
const int in_index =
(in_d * in_height * in_width + in_h * in_width + in_w) * channels;
for (int c = 0; c < channels; ++c) {
out_data[out_index + c] = in_data[in_index + c];
}
}
template <typename T>
void Pad3DNCDHW(const T* in_data, const int num, const int channels,
const int in_depth, const int in_height, const int in_width,
const int out_depth, const int out_height, const int out_width,
const int pad_front, const int pad_top, const int pad_left,
T value, T* out_data,
void (*pad_func)(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)) {
for (int n = 0; n < num; ++n) {
for (int c = 0; c < channels; ++c) {
for (int out_d = 0; out_d < out_depth; ++out_d) {
for (int out_h = 0; out_h < out_height; ++out_h) {
for (int out_w = 0; out_w < out_width; ++out_w) {
pad_func(in_data, out_data, in_depth, in_height, in_width,
out_depth, out_height, out_width, pad_front, pad_top,
pad_left, out_d, out_h, out_w, value);
}
}
}
in_data += in_depth * in_height * in_width;
out_data += out_depth * out_height * out_width;
}
}
}
template <typename T>
void Pad3DNDHWC(const T* in_data, const int num, const int channels,
const int in_depth, const int in_height, const int in_width,
const int out_depth, const int out_height, const int out_width,
const int pad_front, const int pad_top, const int pad_left,
T value, T* out_data,
void (*pad_func)(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)) {
for (int n = 0; n < num; ++n) {
for (int out_d = 0; out_d < out_depth; ++out_d) {
for (int out_h = 0; out_h < out_height; ++out_h) {
for (int out_w = 0; out_w < out_width; ++out_w) {
pad_func(in_data, out_data, channels, in_depth, in_height, in_width,
out_depth, out_height, out_width, pad_front, pad_top,
pad_left, out_d, out_h, out_w, value);
}
}
}
in_data += in_depth * in_height * in_width * channels;
out_data += out_depth * out_height * out_width * channels;
}
}
template <typename T>
void ConstPad3DGradNCDHW(T* d_in_data, const T* d_out_data, const int in_depth,
const int in_height, const int in_width,
const int out_depth, const int out_height,
const int out_width, const int pad_front,
const int pad_top, const int pad_left, const int out_d,
const int out_h, const int out_w) {
int in_d = out_d - pad_front;
int in_h = out_h - pad_top;
int in_w = out_w - pad_left;
if (!(in_d < 0 || in_h < 0 || in_w < 0 || in_d >= in_depth ||
in_h >= in_height || in_w >= in_width)) {
d_in_data[in_d * in_height * in_width + in_h * in_width + in_w] =
d_out_data[out_d * out_height * out_width + out_h * out_width + out_w];
}
}
template <typename T>
void ConstPad3DGradNDHWC(T* d_in_data, const T* d_out_data, const int channels,
const int in_depth, const int in_height,
const int in_width, const int out_depth,
const int out_height, const int out_width,
const int pad_front, const int pad_top,
const int pad_left, const int out_d, const int out_h,
const int out_w) {
int in_d = out_d - pad_front;
int in_h = out_h - pad_top;
int in_w = out_w - pad_left;
const int out_index =
(out_d * out_height * out_width + out_h * out_width + out_w) * channels;
if (!(in_d < 0 || in_h < 0 || in_w < 0 || in_d >= in_depth ||
in_h >= in_height || in_w >= in_width)) {
const int in_index =
(in_d * in_height * in_width + in_h * in_width + in_w) * channels;
for (int c = 0; c < channels; ++c) {
d_in_data[in_index + c] = d_out_data[out_index + c];
}
}
}
template <typename T>
void ReflectPad3DGradNCDHW(T* d_in_data, const T* d_out_data,
const int in_depth, const int in_height,
const int in_width, const int out_depth,
const int out_height, const int out_width,
const int pad_front, const int pad_top,
const int pad_left, const int out_d, const int out_h,
const int out_w) {
int in_d = out_d - pad_front;
int in_h = out_h - pad_top;
int in_w = out_w - pad_left;
in_d = std::max(in_d, -in_d); // reflect by 0
in_d = std::min(in_d, 2 * in_depth - in_d - 2); // reflect by in_depth
in_h = std::max(in_h, -in_h); // reflect by 0
in_h = std::min(in_h, 2 * in_height - in_h - 2); // reflect by in_height
in_w = std::max(in_w, -in_w); // reflect by 0
in_w = std::min(in_w, 2 * in_width - in_w - 2); // reflect by in_width
d_in_data[in_d * in_height * in_width + in_h * in_width + in_w] +=
d_out_data[out_d * out_height * out_width + out_h * out_width + out_w];
}
template <typename T>
void ReflectPad3DGradNDHWC(T* d_in_data, const T* d_out_data,
const int channels, const int in_depth,
const int in_height, const int in_width,
const int out_depth, const int out_height,
const int out_width, const int pad_front,
const int pad_top, const int pad_left,
const int out_d, const int out_h, const int out_w) {
int in_d = out_d - pad_front;
int in_h = out_h - pad_top;
int in_w = out_w - pad_left;
in_d = std::max(in_d, -in_d);
in_d = std::min(in_d, 2 * in_depth - in_d - 2);
in_h = std::max(in_h, -in_h);
in_h = std::min(in_h, 2 * in_height - in_h - 2);
in_w = std::max(in_w, -in_w);
in_w = std::min(in_w, 2 * in_width - in_w - 2);
const int out_index =
(out_d * out_height * out_width + out_h * out_width + out_w) * channels;
const int in_index =
(in_d * in_height * in_width + in_h * in_width + in_w) * channels;
for (int c = 0; c < channels; ++c) {
d_in_data[in_index + c] += d_out_data[out_index + c];
}
}
template <typename T>
void ReplicatePad3DGradNCDHW(T* d_in_data, const T* d_out_data,
const int in_depth, const int in_height,
const int in_width, const int out_depth,
const int out_height, const int out_width,
const int pad_front, const int pad_top,
const int pad_left, const int out_d,
const int out_h, const int out_w) {
int in_d = std::min(in_depth - 1, std::max(out_d - pad_front, 0));
int in_h = std::min(in_height - 1, std::max(out_h - pad_top, 0));
int in_w = std::min(in_width - 1, std::max(out_w - pad_left, 0));
d_in_data[in_d * in_height * in_width + in_h * in_width + in_w] +=
d_out_data[out_d * out_height * out_width + out_h * out_width + out_w];
}
template <typename T>
void ReplicatePad3DGradNDHWC(T* d_in_data, const T* d_out_data,
const int channels, const int in_depth,
const int in_height, const int in_width,
const int out_depth, const int out_height,
const int out_width, const int pad_front,
const int pad_top, const int pad_left,
const int out_d, const int out_h,
const int out_w) {
int in_d = std::min(in_depth - 1, std::max(out_d - pad_front, 0));
int in_h = std::min(in_height - 1, std::max(out_h - pad_top, 0));
int in_w = std::min(in_width - 1, std::max(out_w - pad_left, 0));
const int out_index =
(out_d * out_height * out_width + out_h * out_width + out_w) * channels;
const int in_index =
(in_d * in_height * in_width + in_h * in_width + in_w) * channels;
for (int c = 0; c < channels; ++c) {
d_in_data[in_index + c] += d_out_data[out_index + c];
}
}
template <typename T>
void CircularPad3DGradNCDHW(T* d_in_data, const T* d_out_data,
const int in_depth, const int in_height,
const int in_width, const int out_depth,
const int out_height, const int out_width,
const int pad_front, const int pad_top,
const int pad_left, const int out_d,
const int out_h, const int out_w) {
int in_d = ((out_d - pad_front) % in_depth + in_depth) % in_depth;
int in_h = ((out_h - pad_top) % in_height + in_height) % in_height;
int in_w = ((out_w - pad_left) % in_width + in_width) % in_width;
d_in_data[in_d * in_height * in_width + in_h * in_width + in_w] +=
d_out_data[out_d * out_height * out_width + out_h * out_width + out_w];
}
template <typename T>
void CircularPad3DGradNDHWC(T* d_in_data, const T* d_out_data,
const int channels, const int in_depth,
const int in_height, const int in_width,
const int out_depth, const int out_height,
const int out_width, const int pad_front,
const int pad_top, const int pad_left,
const int out_d, const int out_h, const int out_w) {
int in_d = ((out_d - pad_front) % in_depth + in_depth) % in_depth;
int in_h = ((out_h - pad_top) % in_height + in_height) % in_height;
int in_w = ((out_w - pad_left) % in_width + in_width) % in_width;
const int out_index =
(out_d * out_height * out_width + out_h * out_width + out_w) * channels;
const int in_index =
(in_d * in_height * in_width + in_h * in_width + in_w) * channels;
for (int c = 0; c < channels; ++c) {
d_in_data[in_index + c] += d_out_data[out_index + c];
}
}
template <typename T>
void Pad3DGradNCDHW(T* d_in_data, const int num, const int channels,
const int in_depth, const int in_height, const int in_width,
const int out_depth, const int out_height,
const int out_width, const int pad_front, const int pad_top,
const int pad_left, const T* d_out_data,
void (*pad_func)(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)) {
for (int n = 0; n < num; ++n) {
for (int c = 0; c < channels; ++c) {
for (int out_d = 0; out_d < out_depth; ++out_d) {
for (int out_h = 0; out_h < out_height; ++out_h) {
for (int out_w = 0; out_w < out_width; ++out_w) {
pad_func(d_in_data, d_out_data, in_depth, in_height, in_width,
out_depth, out_height, out_width, pad_front, pad_top,
pad_left, out_d, out_h, out_w);
}
}
}
d_in_data += in_depth * in_height * in_width;
d_out_data += out_depth * out_height * out_width;
}
}
}
template <typename T>
void Pad3DGradNDHWC(T* d_in_data, const int num, const int channels,
const int in_depth, const int in_height, const int in_width,
const int out_depth, const int out_height,
const int out_width, const int pad_front, const int pad_top,
const int pad_left, const T* d_out_data,
void (*pad_func)(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)) {
for (int n = 0; n < num; ++n) {
for (int out_d = 0; out_d < out_depth; ++out_d) {
for (int out_h = 0; out_h < out_height; ++out_h) {
for (int out_w = 0; out_w < out_width; ++out_w) {
pad_func(d_in_data, d_out_data, channels, in_depth, in_height,
in_width, out_depth, out_height, out_width, pad_front,
pad_top, pad_left, out_d, out_h, out_w);
}
}
}
d_in_data += in_depth * in_height * in_width * channels;
d_out_data += out_depth * out_height * out_width * channels;
}
}
static inline std::vector<int> GetPaddings(
const framework::ExecutionContext& context) {
std::vector<int> paddings(6);
auto* paddings_t = context.Input<Tensor>("Paddings");
if (paddings_t) {
auto paddings_data = paddings_t->data<int>();
std::memcpy(paddings.data(), paddings_data, paddings.size() * sizeof(int));
} else {
auto pads = context.Attr<std::vector<int>>("paddings");
std::copy(pads.begin(), pads.end(), paddings.data());
}
return paddings;
}
template <typename T>
class Pad3dCPUKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
std::vector<int> pads = GetPaddings(context);
auto mode = context.Attr<std::string>("mode");
auto data_format = context.Attr<std::string>("data_format");
T value = static_cast<T>(context.Attr<float>("value"));
auto* x = context.Input<Tensor>("X");
auto in_dims = x->dims();
const T* in_data = x->data<T>();
auto* out = context.Output<Tensor>("Out");
if (data_format == "NCDHW") {
out->Resize({in_dims[0], in_dims[1], in_dims[2] + pads[4] + pads[5],
in_dims[3] + pads[2] + pads[3],
in_dims[4] + pads[0] + pads[1]});
} else {
out->Resize({in_dims[0], in_dims[1] + pads[4] + pads[5],
in_dims[2] + pads[2] + pads[3],
in_dims[3] + pads[0] + pads[1], in_dims[4]});
}
auto out_dims = out->dims();
T* out_data = out->mutable_data<T>(context.GetPlace());
int channels = in_dims[1];
int in_depth = in_dims[2];
int in_height = in_dims[3];
int in_width = in_dims[4];
int out_depth = out_dims[2];
int out_height = out_dims[3];
int out_width = out_dims[4];
if (data_format == "NDHWC") {
channels = in_dims[4];
in_depth = in_dims[1];
in_height = in_dims[2];
in_width = in_dims[3];
out_depth = out_dims[1];
out_height = out_dims[2];
out_width = out_dims[3];
}
if (mode == "reflect") {
PADDLE_ENFORCE_GT(in_depth, pads[4],
platform::errors::InvalidArgument(
"The depth of Input(X)'s dimension should be "
"greater than pad_front"
" in reflect mode"
", but received depth(%d) and pad_front(%d).",
in_depth, pads[4]));
PADDLE_ENFORCE_GT(in_depth, pads[5],
platform::errors::InvalidArgument(
"The depth of Input(X)'s dimension should be "
"greater than pad_back"
" in reflect mode"
", but received depth(%d) and pad_back(%d).",
in_depth, pads[5]));
PADDLE_ENFORCE_GT(in_height, pads[2],
platform::errors::InvalidArgument(
"The height of Input(X)'s dimension should be "
"greater than pad_top"
" in reflect mode"
", but received depth(%d) and pad_top(%d).",
in_height, pads[2]));
PADDLE_ENFORCE_GT(in_height, pads[3],
platform::errors::InvalidArgument(
"The height of Input(X)'s dimension should be "
"greater than pad_bottom"
" in reflect mode"
", but received depth(%d) and pad_bottom(%d).",
in_height, pads[3]));
PADDLE_ENFORCE_GT(in_width, pads[0],
platform::errors::InvalidArgument(
"The width of Input(X)'s dimension should be "
"greater than pad_left"
" in reflect mode"
", but received depth(%d) and pad_left(%d).",
in_width, pads[0]));
PADDLE_ENFORCE_GT(in_width, pads[1],
platform::errors::InvalidArgument(
"The width of Input(X)'s dimension should be "
"greater than pad_right"
" in reflect mode"
", 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,
platform::errors::InvalidArgument(
"The input tensor size can not be 0 for circular "
"or replicate padding mode."));
}
const int pad_left = pads[0];
const int pad_top = pads[2];
const int pad_front = pads[4];
const int num = 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]);
}
}
};
template <typename T>
class Pad3dGradCPUKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
std::vector<int> pads = GetPaddings(context);
auto mode = context.Attr<std::string>("mode");
auto data_format = context.Attr<std::string>("data_format");
auto* d_out = context.Input<Tensor>(framework::GradVarName("Out"));
auto* d_in = context.Output<Tensor>(framework::GradVarName("X"));
auto d_in_dims = d_in->dims();
auto d_out_dims = d_out->dims();
const T* d_out_data = d_out->data<T>();
T* d_in_data = d_in->mutable_data<T>(context.GetPlace());
phi::funcs::SetConstant<platform::CPUDeviceContext, T> set_zero;
set_zero(context.template device_context<platform::CPUDeviceContext>(),
d_in, static_cast<T>(0));
const int pad_left = pads[0];
const int pad_top = pads[2];
const int pad_front = pads[4];
const int num = d_in_dims[0];
if (data_format == "NCDHW") {
const int channels = d_in_dims[1];
const int in_depth = d_in_dims[2];
const int in_height = d_in_dims[3];
const int in_width = d_in_dims[4];
const int out_depth = d_out_dims[2];
const int out_height = d_out_dims[3];
const int out_width = d_out_dims[4];
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)>
func_map;
func_map["reflect"] = ReflectPad3DGradNCDHW;
func_map["replicate"] = ReplicatePad3DGradNCDHW;
func_map["circular"] = CircularPad3DGradNCDHW;
func_map["constant"] = ConstPad3DGradNCDHW;
Pad3DGradNCDHW(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]);
} else {
const int channels = d_in_dims[4];
const int in_depth = d_in_dims[1];
const int in_height = d_in_dims[2];
const int in_width = d_in_dims[3];
const int out_depth = d_out_dims[1];
const int out_height = d_out_dims[2];
const int out_width = 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]);
}
}
};
class Pad3dOp : public framework::OperatorWithKernel { class Pad3dOp : public framework::OperatorWithKernel {
public: public:
using framework::OperatorWithKernel::OperatorWithKernel; using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "Pad3d");
OP_INOUT_CHECK(ctx->HasOutput("Out"), "Output", "Out", "Pad3d");
auto x_dim = ctx->GetInputDim("X");
PADDLE_ENFORCE_EQ(x_dim.size(), 5,
platform::errors::InvalidArgument(
"The size of Input(X)'s dimension should be equal to "
"5, but received %d. ",
x_dim.size()));
std::vector<int64_t> out_dims(x_dim.size());
auto data_format = ctx->Attrs().Get<std::string>("data_format");
out_dims[0] = x_dim[0];
if (ctx->HasInput("Paddings")) {
auto paddings_dim = ctx->GetInputDim("Paddings");
PADDLE_ENFORCE_EQ(paddings_dim.size(), 1,
platform::errors::InvalidArgument(
"Size of Input(Paddings)'s dimension should be "
"equal to 1, but received %d.",
paddings_dim.size()));
if (ctx->IsRuntime()) {
PADDLE_ENFORCE_EQ(paddings_dim[0], 6,
platform::errors::InvalidArgument(
"Shape of Input(Paddings) should be equal to "
"[6], but received [%d].",
paddings_dim[0]));
}
out_dims[1] = x_dim[1];
out_dims[2] = x_dim[2];
out_dims[3] = x_dim[3];
} else {
auto paddings = ctx->Attrs().Get<std::vector<int>>("paddings");
PADDLE_ENFORCE_EQ(
paddings.size(), 6,
platform::errors::InvalidArgument(
"Size of paddings should be equal to 4, but received %d.",
static_cast<int>(paddings.size())));
if (data_format == "NCDHW") {
out_dims[1] = x_dim[1]; // channel
out_dims[2] = ((!ctx->IsRuntime()) && (x_dim[2] < 0))
? x_dim[2]
: (x_dim[2] + paddings[4] + paddings[5]); // depth
out_dims[3] = ((!ctx->IsRuntime()) && (x_dim[3] < 0))
? x_dim[3]
: (x_dim[3] + paddings[2] + paddings[3]); // height
out_dims[4] = ((!ctx->IsRuntime()) && (x_dim[4] < 0))
? x_dim[4]
: (x_dim[4] + paddings[0] + paddings[1]); // width
} else { // NDHWC
out_dims[4] = x_dim[4]; // channel
out_dims[1] = ((!ctx->IsRuntime()) && (x_dim[1] < 0))
? x_dim[1]
: (x_dim[1] + paddings[4] + paddings[5]); // depth
out_dims[2] = ((!ctx->IsRuntime()) && (x_dim[2] < 0))
? x_dim[2]
: (x_dim[2] + paddings[2] + paddings[3]); // height
out_dims[3] = ((!ctx->IsRuntime()) && (x_dim[3] < 0))
? x_dim[3]
: (x_dim[3] + paddings[0] + paddings[1]); // width
}
}
ctx->SetOutputDim("Out", phi::make_ddim(out_dims));
ctx->ShareLoD("X", /*->*/ "Out");
}
protected: protected:
framework::OpKernelType GetExpectedKernelType( framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override { const framework::ExecutionContext& ctx) const override {
...@@ -921,15 +199,14 @@ DECLARE_NO_NEED_BUFFER_VARS_INFERER(Pad3dOpGradNoNeedBufferVarsInferer, "X"); ...@@ -921,15 +199,14 @@ DECLARE_NO_NEED_BUFFER_VARS_INFERER(Pad3dOpGradNoNeedBufferVarsInferer, "X");
namespace ops = paddle::operators; namespace ops = paddle::operators;
DECLARE_INFER_SHAPE_FUNCTOR(pad3d, Pad3dInferShapeFunctor,
PD_INFER_META(phi::Pad3dInferMeta));
REGISTER_OPERATOR(pad3d, ops::Pad3dOp, ops::Pad3dOpMaker, REGISTER_OPERATOR(pad3d, ops::Pad3dOp, ops::Pad3dOpMaker,
ops::Pad3dOpGradMaker<paddle::framework::OpDesc>, ops::Pad3dOpGradMaker<paddle::framework::OpDesc>,
ops::Pad3dOpGradMaker<paddle::imperative::OpBase>); ops::Pad3dOpGradMaker<paddle::imperative::OpBase>,
Pad3dInferShapeFunctor);
REGISTER_OPERATOR(pad3d_grad, ops::Pad3dOpGrad, REGISTER_OPERATOR(pad3d_grad, ops::Pad3dOpGrad,
ops::Pad3dOpDoubleGradMaker<paddle::framework::OpDesc>, ops::Pad3dOpDoubleGradMaker<paddle::framework::OpDesc>,
ops::Pad3dOpDoubleGradMaker<paddle::imperative::OpBase>, ops::Pad3dOpDoubleGradMaker<paddle::imperative::OpBase>,
ops::Pad3dOpGradNoNeedBufferVarsInferer); ops::Pad3dOpGradNoNeedBufferVarsInferer);
REGISTER_OP_CPU_KERNEL(pad3d, ops::Pad3dCPUKernel<float>,
ops::Pad3dCPUKernel<double>, ops::Pad3dCPUKernel<int>,
ops::Pad3dCPUKernel<int64_t>);
REGISTER_OP_CPU_KERNEL(pad3d_grad, ops::Pad3dGradCPUKernel<float>,
ops::Pad3dGradCPUKernel<double>);
paddle/fluid/operators/pad3d_op.cu 已删除 100644 → 0
浏览文件 @ 0e1191f4
/* Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include <algorithm>
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/platform/device/gpu/gpu_info.h"
#include "paddle/fluid/platform/device/gpu/gpu_primitives.h"
#include "paddle/phi/kernels/funcs/math_function.h"
namespace paddle {
namespace operators {
using platform::PADDLE_CUDA_NUM_THREADS;
using framework::Tensor;
template <typename T>
__global__ void Pad3DConstNCDHW(const int nthreads, const T* in_data,
const int num, const int channels,
const int in_depth, const int in_height,
const int in_width, const int out_depth,
const int out_height, const int out_width,
const int pad_front, const int pad_top,
const int pad_left, T value, T* out_data) {
CUDA_KERNEL_LOOP(index, nthreads) {
int nc = index / out_width;
const int out_w = index % out_width;
const int out_h = nc % out_height;
nc /= out_height;
const int out_d = nc % out_depth;
nc /= out_depth;
int in_d = out_d - pad_front;
int in_h = out_h - pad_top;
int in_w = out_w - pad_left;
out_data[index] =
(in_d < 0 || in_h < 0 || in_w < 0 || in_d >= in_depth ||
in_h >= in_height || in_w >= in_width)
? value
: in_data[nc * in_depth * in_height * in_width +
in_d * in_height * in_width + in_h * in_width + in_w];
}
}
template <typename T>
__global__ void Pad3DConstNDHWC(const int nthreads, const T* in_data,
const int num, const int channels,
const int in_depth, const int in_height,
const int in_width, const int out_depth,
const int out_height, const int out_width,
const int pad_front, const int pad_top,
const int pad_left, T value, T* out_data) {
CUDA_KERNEL_LOOP(index, nthreads) {
int n = index / channels;
const int c = index % channels;
const int out_w = n % out_width;
n /= out_width;
const int out_h = n % out_height;
n /= out_height;
const int out_d = n % out_depth;
n /= out_depth;
const int in_d = out_d - pad_front;
const int in_h = out_h - pad_top;
const int in_w = out_w - pad_left;
out_data[index] =
(in_d < 0 || in_h < 0 || in_w < 0 || in_d >= in_depth ||
in_h >= in_height || in_w >= in_width)
? value
: in_data[n * in_depth * in_height * in_width * channels +
in_d * in_height * in_width * channels +
in_h * in_width * channels + in_w * channels + c];
}
}
template <typename T>
__global__ void Pad3DReflectNCDHW(const int nthreads, const T* in_data,
const int num, const int channels,
const int in_depth, const int in_height,
const int in_width, const int out_depth,
const int out_height, const int out_width,
const int pad_front, const int pad_top,
const int pad_left, T* out_data) {
CUDA_KERNEL_LOOP(index, nthreads) {
int nc = index / out_width;
const int out_w = index % out_width;
const int out_h = nc % out_height;
nc /= out_height;
const int out_d = nc % out_depth;
nc /= out_depth;
int in_d = out_d - pad_front;
int in_h = out_h - pad_top;
int in_w = out_w - pad_left;
in_d = max(in_d, -in_d); // reflect by 0
in_d = min(in_d, 2 * in_depth - in_d - 2); // reflect by in_depth
in_h = max(in_h, -in_h); // reflect by 0
in_h = min(in_h, 2 * in_height - in_h - 2); // reflect by in_height
in_w = max(in_w, -in_w); // reflect by 0
in_w = min(in_w, 2 * in_width - in_w - 2); // reflect by in_width
out_data[index] =
in_data[(nc * in_depth * in_height + in_d * in_height + in_h) *
in_width +
in_w];
}
}
template <typename T>
__global__ void Pad3DReflectNDHWC(const int nthreads, const T* in_data,
const int num, const int channels,
const int in_depth, const int in_height,
const int in_width, const int out_depth,
const int out_height, const int out_width,
const int pad_front, const int pad_top,
const int pad_left, T* out_data) {
CUDA_KERNEL_LOOP(index, nthreads) {
int n = index / channels;
const int c = index % channels;
const int out_w = n % out_width;
n /= out_width;
const int out_h = n % out_height;
n /= out_height;
const int out_d = n % out_depth;
n /= out_depth;
int in_d = out_d - pad_front;
int in_h = out_h - pad_top;
int in_w = out_w - pad_left;
in_d = max(in_d, -in_d);
in_d = min(in_d, 2 * in_depth - in_d - 2);
in_h = max(in_h, -in_h);
in_h = min(in_h, 2 * in_height - in_h - 2);
in_w = max(in_w, -in_w);
in_w = min(in_w, 2 * in_width - in_w - 2);
out_data[index] = in_data[n * in_depth * in_height * in_width * channels +
in_d * in_height * in_width * channels +
in_h * in_width * channels + in_w * channels + c];
}
}
template <typename T>
__global__ void Pad3DReplicateNCDHW(const int nthreads, const T* in_data,
const int num, const int channels,
const int in_depth, const int in_height,
const int in_width, const int out_depth,
const int out_height, const int out_width,
const int pad_front, const int pad_top,
const int pad_left, T* out_data) {
CUDA_KERNEL_LOOP(index, nthreads) {
int nc = index / out_width;
const int out_w = index % out_width;
const int out_h = nc % out_height;
nc /= out_height;
const int out_d = nc % out_depth;
nc /= out_depth;
int in_d = min(in_depth - 1, max(out_d - pad_front, 0));
int in_h = min(in_height - 1, max(out_h - pad_top, 0));
int in_w = min(in_width - 1, max(out_w - pad_left, 0));
out_data[index] =
in_data[(nc * in_depth * in_height + in_d * in_height + in_h) *
in_width +
in_w];
}
}
template <typename T>
__global__ void Pad3DReplicateNDHWC(const int nthreads, const T* in_data,
const int num, const int channels,
const int in_depth, const int in_height,
const int in_width, const int out_depth,
const int out_height, const int out_width,
const int pad_front, const int pad_top,
const int pad_left, T* out_data) {
CUDA_KERNEL_LOOP(index, nthreads) {
int n = index / channels;
const int c = index % channels;
const int out_w = n % out_width;
n /= out_width;
const int out_h = n % out_height;
n /= out_height;
const int out_d = n % out_depth;
n /= out_depth;
int in_d = min(in_depth - 1, max(out_d - pad_front, 0));
int in_h = min(in_height - 1, max(out_h - pad_top, 0));
int in_w = min(in_width - 1, max(out_w - pad_left, 0));
out_data[index] = in_data[n * in_depth * in_height * in_width * channels +
in_d * in_height * in_width * channels +
in_h * in_width * channels + in_w * channels + c];
}
}
template <typename T>
__global__ void Pad3DCircularNCDHW(const int nthreads, const T* in_data,
const int num, const int channels,
const int in_depth, const int in_height,
const int in_width, const int out_depth,
const int out_height, const int out_width,
const int pad_front, const int pad_top,
const int pad_left, T* out_data) {
CUDA_KERNEL_LOOP(index, nthreads) {
int nc = index / out_width;
const int out_w = index % out_width;
const int out_h = nc % out_height;
nc /= out_height;
const int out_d = nc % out_depth;
nc /= out_depth;
int in_d = ((out_d - pad_front) % in_depth + in_depth) % in_depth;
int in_h = ((out_h - pad_top) % in_height + in_height) % in_height;
int in_w = ((out_w - pad_left) % in_width + in_width) % in_width;
out_data[index] =
in_data[(nc * in_depth * in_height + in_d * in_height + in_h) *
in_width +
in_w];
}
}
template <typename T>
__global__ void Pad3DCircularNDHWC(const int nthreads, const T* in_data,
const int num, const int channels,
const int in_depth, const int in_height,
const int in_width, const int out_depth,
const int out_height, const int out_width,
const int pad_front, const int pad_top,
const int pad_left, T* out_data) {
CUDA_KERNEL_LOOP(index, nthreads) {
int n = index / channels;
const int c = index % channels;
const int out_w = n % out_width;
n /= out_width;
const int out_h = n % out_height;
n /= out_height;
const int out_d = n % out_depth;
n /= out_depth;
int in_d = ((out_d - pad_front) % in_depth + in_depth) % in_depth;
int in_h = ((out_h - pad_top) % in_height + in_height) % in_height;
int in_w = ((out_w - pad_left) % in_width + in_width) % in_width;
out_data[index] = in_data[n * in_depth * in_height * in_width * channels +
in_d * in_height * in_width * channels +
in_h * in_width * channels + in_w * channels + c];
}
}
template <typename T>
__global__ void Pad3DGradConstNCDHW(const int in_size, T* d_in_data,
const int num, const int channels,
const int in_depth, const int in_height,
const int in_width, const int out_depth,
const int out_height, const int out_width,
const int pad_front, const int pad_top,
const int pad_left, const T* d_out_data) {
CUDA_KERNEL_LOOP(in_index, in_size) {
const int in_w = in_index % in_width;
int nc = in_index / in_width;
const int in_h = nc % in_height;
nc /= in_height;
const int in_d = nc % in_depth;
nc /= in_depth;
const int out_d = in_d + pad_front;
const int out_h = in_h + pad_top;
const int out_w = in_w + pad_left;
d_in_data[in_index] =
d_out_data[nc * out_depth * out_height * out_width +
out_d * out_height * out_width + out_h * out_width + out_w];
}
}
template <typename T>
__global__ void Pad3DGradConstNDHWC(const int in_size, T* d_in_data,
const int num, const int channels,
const int in_depth, const int in_height,
const int in_width, const int out_depth,
const int out_height, const int out_width,
const int pad_front, const int pad_top,
const int pad_left, const T* d_out_data) {
CUDA_KERNEL_LOOP(in_index, in_size) {
const int c = in_index % channels;
int n = in_index / channels;
const int in_w = n % in_width;
n /= in_width;
const int in_h = n % in_height;
n /= in_height;
const int in_d = n % in_depth;
n /= in_depth;
const int out_d = in_d + pad_front;
const int out_h = in_h + pad_top;
const int out_w = in_w + pad_left;
d_in_data[in_index] =
d_out_data[n * out_depth * out_height * out_width * channels +
out_d * out_height * out_width * channels +
out_h * out_width * channels + out_w * channels + c];
}
}
template <typename T>
__global__ void Pad3DGradReflectNCDHW(const int out_size, T* d_in_data,
const int num, const int channels,
const int in_depth, const int in_height,
const int in_width, const int out_depth,
const int out_height, const int out_width,
const int pad_front, const int pad_top,
const int pad_left, const T* d_out_data) {
CUDA_KERNEL_LOOP(out_index, out_size) {
int nc = out_index / out_width;
const int out_w = out_index % out_width;
const int out_h = nc % out_height;
nc /= out_height;
const int out_d = nc % out_depth;
nc /= out_depth;
int in_d = out_d - pad_front;
int in_h = out_h - pad_top;
int in_w = out_w - pad_left;
in_d = max(in_d, -in_d);
in_h = max(in_h, -in_h);
in_w = max(in_w, -in_w);
in_d = min(in_d, 2 * in_depth - in_d - 2);
in_h = min(in_h, 2 * in_height - in_h - 2);
in_w = min(in_w, 2 * in_width - in_w - 2);
platform::CudaAtomicAdd(
&d_in_data[nc * in_depth * in_height * in_width +
in_d * in_height * in_width + in_h * in_width + in_w],
d_out_data[out_index]);
}
}
template <typename T>
__global__ void Pad3DGradReflectNDHWC(const int out_size, T* d_in_data,
const int num, const int channels,
const int in_depth, const int in_height,
const int in_width, const int out_depth,
const int out_height, const int out_width,
const int pad_front, const int pad_top,
const int pad_left, const T* d_out_data) {
CUDA_KERNEL_LOOP(out_index, out_size) {
const int c = out_index % channels;
int n = out_index / channels;
const int out_w = n % out_width;
n /= out_width;
const int out_h = n % out_height;
n /= out_height;
const int out_d = n % out_depth;
n /= out_depth;
int in_d = out_d - pad_front;
int in_h = out_h - pad_top;
int in_w = out_w - pad_left;
in_d = max(in_d, -in_d);
in_h = max(in_h, -in_h);
in_w = max(in_w, -in_w);
in_d = min(in_d, in_depth * 2 - in_d - 2);
in_h = min(in_h, in_height * 2 - in_h - 2);
in_w = min(in_w, in_width * 2 - in_w - 2);
platform::CudaAtomicAdd(
&d_in_data[n * in_depth * in_height * in_width * channels +
in_d * in_height * in_width * channels +
in_h * in_width * channels + in_w * channels + c],
d_out_data[out_index]);
}
}
template <typename T>
__global__ void Pad3DGradReplicateNCDHW(
const int out_size, T* d_in_data, const int num, const int channels,
const int in_depth, const int in_height, const int in_width,
const int out_depth, const int out_height, const int out_width,
const int pad_front, const int pad_top, const int pad_left,
const T* d_out_data) {
CUDA_KERNEL_LOOP(out_index, out_size) {
int nc = out_index / out_width;
const int out_w = out_index % out_width;
const int out_h = nc % out_height;
nc /= out_height;
const int out_d = nc % out_depth;
nc /= out_depth;
const int in_d = min(in_depth - 1, max(out_d - pad_front, 0));
const int in_h = min(in_height - 1, max(out_h - pad_top, 0));
const int in_w = min(in_width - 1, max(out_w - pad_left, 0));
platform::CudaAtomicAdd(
&d_in_data[nc * in_depth * in_height * in_width +
in_d * in_height * in_width + in_h * in_width + in_w],
d_out_data[out_index]);
}
}
template <typename T>
__global__ void Pad3DGradReplicateNDHWC(
const int out_size, T* d_in_data, const int num, const int channels,
const int in_depth, const int in_height, const int in_width,
const int out_depth, const int out_height, const int out_width,
const int pad_front, const int pad_top, const int pad_left,
const T* d_out_data) {
CUDA_KERNEL_LOOP(out_index, out_size) {
const int c = out_index % channels;
int n = out_index / channels;
const int out_w = n % out_width;
n /= out_width;
const int out_h = n % out_height;
n /= out_height;
const int out_d = n % out_depth;
n /= out_depth;
const int in_d = min(in_depth - 1, max(out_d - pad_front, 0));
const int in_h = min(in_height - 1, max(out_h - pad_top, 0));
const int in_w = min(in_width - 1, max(out_w - pad_left, 0));
platform::CudaAtomicAdd(
&d_in_data[n * in_depth * in_height * in_width * channels +
in_d * in_height * in_width * channels +
in_h * in_width * channels + in_w * channels + c],
d_out_data[out_index]);
}
}
template <typename T>
__global__ void Pad3DGradCircularNCDHW(const int out_size, T* d_in_data,
const int num, const int channels,
const int in_depth, const int in_height,
const int in_width, const int out_depth,
const int out_height,
const int out_width, const int pad_front,
const int pad_top, const int pad_left,
const T* d_out_data) {
CUDA_KERNEL_LOOP(out_index, out_size) {
int nc = out_index / out_width;
const int out_w = out_index % out_width;
const int out_h = nc % out_height;
nc /= out_height;
const int out_d = nc % out_depth;
nc /= out_depth;
int in_d = ((out_d - pad_front) % in_depth + in_depth) % in_depth;
int in_h = ((out_h - pad_top) % in_height + in_height) % in_height;
int in_w = ((out_w - pad_left) % in_width + in_width) % in_width;
platform::CudaAtomicAdd(
&d_in_data[nc * in_depth * in_height * in_width +
in_d * in_height * in_width + in_h * in_width + in_w],
d_out_data[out_index]);
}
}
template <typename T>
__global__ void Pad3DGradCircularNDHWC(const int out_size, T* d_in_data,
const int num, const int channels,
const int in_depth, const int in_height,
const int in_width, const int out_depth,
const int out_height,
const int out_width, const int pad_front,
const int pad_top, const int pad_left,
const T* d_out_data) {
CUDA_KERNEL_LOOP(out_index, out_size) {
const int c = out_index % channels;
int n = out_index / channels;
const int out_w = n % out_width;
n /= out_width;
const int out_h = n % out_height;
n /= out_height;
const int out_d = n % out_depth;
n /= out_depth;
int in_d = ((out_d - pad_front) % in_depth + in_depth) % in_depth;
int in_h = ((out_h - pad_top) % in_height + in_height) % in_height;
int in_w = ((out_w - pad_left) % in_width + in_width) % in_width;
platform::CudaAtomicAdd(
&d_in_data[n * in_depth * in_height * in_width * channels +
in_d * in_height * in_width * channels +
in_h * in_width * channels + in_w * channels + c],
d_out_data[out_index]);
}
}
static inline std::vector<int> GetPaddings(
const framework::ExecutionContext& context) {
std::vector<int> paddings(6);
auto* paddings_data = context.Input<Tensor>("Paddings");
if (paddings_data) {
Tensor pads;
framework::TensorCopySync(*paddings_data, platform::CPUPlace(), &pads);
auto pads_data = pads.data<int>();
std::memcpy(paddings.data(), pads_data, paddings.size() * sizeof(int));
} else {
auto pads = context.Attr<std::vector<int>>("paddings");
std::copy(pads.begin(), pads.end(), paddings.data());
}
return paddings;
}
template <typename T>
class Pad3dCUDAKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
std::vector<int> pads = GetPaddings(context);
auto mode = context.Attr<std::string>("mode");
auto data_format = context.Attr<std::string>("data_format");
T value = static_cast<T>(context.Attr<float>("value"));
auto* x = context.Input<Tensor>("X");
auto in_dims = x->dims();
const T* in_data = x->data<T>();
auto* out = context.Output<Tensor>("Out");
auto out_dims = out->dims();
if (data_format == "NCDHW") {
out_dims[0] = in_dims[0];
out_dims[1] = in_dims[1];
out_dims[2] = in_dims[2] + pads[4] + pads[5];
out_dims[3] = in_dims[3] + pads[2] + pads[3];
out_dims[4] = in_dims[4] + pads[0] + pads[1];
} else {
out_dims[0] = in_dims[0];
out_dims[1] = in_dims[1] + pads[4] + pads[5];
out_dims[2] = in_dims[2] + pads[2] + pads[3];
out_dims[3] = in_dims[3] + pads[0] + pads[1];
out_dims[4] = in_dims[4];
}
T* out_data = out->mutable_data<T>(out_dims, context.GetPlace());
int channels = in_dims[1];
int in_depth = in_dims[2];
int in_height = in_dims[3];
int in_width = in_dims[4];
int out_depth = out_dims[2];
int out_height = out_dims[3];
int out_width = out_dims[4];
if (data_format == "NDHWC") {
channels = in_dims[4];
in_depth = in_dims[1];
in_height = in_dims[2];
in_width = in_dims[3];
out_depth = out_dims[1];
out_height = out_dims[2];
out_width = out_dims[3];
}
if (mode == "reflect") {
PADDLE_ENFORCE_GT(in_depth, pads[4],
platform::errors::InvalidArgument(
"The depth of Input(X)'s dimension should be "
"greater than pad_front"
" in reflect mode"
", but received depth(%d) and pad_front(%d).",
in_depth, pads[4]));
PADDLE_ENFORCE_GT(in_depth, pads[5],
platform::errors::InvalidArgument(
"The depth of Input(X)'s dimension should be "
"greater than pad_back"
" in reflect mode"
", but received depth(%d) and pad_back(%d).",
in_depth, pads[5]));
PADDLE_ENFORCE_GT(in_height, pads[2],
platform::errors::InvalidArgument(
"The height of Input(X)'s dimension should be "
"greater than pad_top"
" in reflect mode"
", but received depth(%d) and pad_top(%d).",
in_height, pads[2]));
PADDLE_ENFORCE_GT(in_height, pads[3],
platform::errors::InvalidArgument(
"The height of Input(X)'s dimension should be "
"greater than pad_bottom"
" in reflect mode"
", but received depth(%d) and pad_bottom(%d).",
in_height, pads[3]));
PADDLE_ENFORCE_GT(in_width, pads[0],
platform::errors::InvalidArgument(
"The width of Input(X)'s dimension should be "
"greater than pad_left"
" in reflect mode"
", but received depth(%d) and pad_left(%d).",
in_width, pads[0]));
PADDLE_ENFORCE_GT(in_width, pads[1],
platform::errors::InvalidArgument(
"The width of Input(X)'s dimension should be "
"greater than pad_right"
" in reflect mode"
", 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,
platform::errors::InvalidArgument(
"The input tensor size can not be 0 for circular "
"or replicate padding mode."));
}
const int pad_left = pads[0];
const int pad_top = pads[2];
const int pad_front = pads[4];
const int num = in_dims[0];
auto stream = context.cuda_device_context().stream();
int block = PADDLE_CUDA_NUM_THREADS;
const int out_size = out->numel();
int grid = (out_size + block - 1) / block;
if (data_format == "NCDHW") {
if (mode == "reflect") {
Pad3DReflectNCDHW<T><<<grid, block, 0, stream>>>(
out_size, in_data, num, channels, in_depth, in_height, in_width,
out_depth, out_height, out_width, pad_front, pad_top, pad_left,
out_data);
} else if (mode == "replicate") {
Pad3DReplicateNCDHW<T><<<grid, block, 0, stream>>>(
out_size, in_data, num, channels, in_depth, in_height, in_width,
out_depth, out_height, out_width, pad_front, pad_top, pad_left,
out_data);
} else if (mode == "circular") {
Pad3DCircularNCDHW<T><<<grid, block, 0, stream>>>(
out_size, in_data, num, channels, in_depth, in_height, in_width,
out_depth, out_height, out_width, pad_front, pad_top, pad_left,
out_data);
} else {
Pad3DConstNCDHW<T><<<grid, block, 0, stream>>>(
out_size, 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);
}
} else {
if (mode == "reflect") {
Pad3DReflectNDHWC<T><<<grid, block, 0, stream>>>(
out_size, in_data, num, channels, in_depth, in_height, in_width,
out_depth, out_height, out_width, pad_front, pad_top, pad_left,
out_data);
} else if (mode == "replicate") {
Pad3DReplicateNDHWC<T><<<grid, block, 0, stream>>>(
out_size, in_data, num, channels, in_depth, in_height, in_width,
out_depth, out_height, out_width, pad_front, pad_top, pad_left,
out_data);
} else if (mode == "circular") {
Pad3DCircularNDHWC<T><<<grid, block, 0, stream>>>(
out_size, in_data, num, channels, in_depth, in_height, in_width,
out_depth, out_height, out_width, pad_front, pad_top, pad_left,
out_data);
} else {
Pad3DConstNDHWC<T><<<grid, block, 0, stream>>>(
out_size, 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);
}
}
}
};
template <typename T>
class Pad3dGradCUDAKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
std::vector<int> pads = GetPaddings(context);
auto mode = context.Attr<std::string>("mode");
auto data_format = context.Attr<std::string>("data_format");
auto* d_out = context.Input<Tensor>(framework::GradVarName("Out"));
auto* d_in = context.Output<Tensor>(framework::GradVarName("X"));
auto d_in_dims = d_in->dims();
auto d_out_dims = d_out->dims();
const T* d_out_data = d_out->data<T>();
T* d_in_data = d_in->mutable_data<T>(context.GetPlace());
phi::funcs::SetConstant<platform::CUDADeviceContext, T> set_zero;
set_zero(context.template device_context<platform::CUDADeviceContext>(),
d_in, static_cast<T>(0));
const int pad_left = pads[0];
const int pad_top = pads[2];
const int pad_front = pads[4];
const int num = d_in_dims[0];
auto stream = context.cuda_device_context().stream();
int block = PADDLE_CUDA_NUM_THREADS;
const int out_size = d_out->numel();
const int in_size = d_in->numel();
int grid = (out_size + block - 1) / block;
if (data_format == "NCDHW") {
const int channels = d_in_dims[1];
const int in_depth = d_in_dims[2];
const int in_height = d_in_dims[3];
const int in_width = d_in_dims[4];
const int out_depth = d_out_dims[2];
const int out_height = d_out_dims[3];
const int out_width = d_out_dims[4];
if (mode == "reflect") {
Pad3DGradReflectNCDHW<T><<<grid, block, 0, stream>>>(
out_size, 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);
} else if (mode == "replicate") {
Pad3DGradReplicateNCDHW<T><<<grid, block, 0, stream>>>(
out_size, 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);
} else if (mode == "circular") {
Pad3DGradCircularNCDHW<T><<<grid, block, 0, stream>>>(
out_size, 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);
} else {
grid = (in_size + block - 1) / block;
Pad3DGradConstNCDHW<T><<<grid, block, 0, stream>>>(
in_size, 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);
}
} else {
const int channels = d_in_dims[4];
const int in_depth = d_in_dims[1];
const int in_height = d_in_dims[2];
const int in_width = d_in_dims[3];
const int out_depth = d_out_dims[1];
const int out_height = d_out_dims[2];
const int out_width = d_out_dims[3];
if (mode == "reflect") {
Pad3DGradReflectNDHWC<T><<<grid, block, 0, stream>>>(
out_size, 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);
} else if (mode == "replicate") {
Pad3DGradReplicateNDHWC<T><<<grid, block, 0, stream>>>(
out_size, 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);
} else if (mode == "circular") {
Pad3DGradCircularNDHWC<T><<<grid, block, 0, stream>>>(
out_size, 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);
} else {
grid = (in_size + block - 1) / block;
Pad3DGradConstNDHWC<T><<<grid, block, 0, stream>>>(
in_size, 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);
}
}
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
namespace plat = paddle::platform;
REGISTER_OP_CUDA_KERNEL(pad3d, ops::Pad3dCUDAKernel<plat::float16>,
ops::Pad3dCUDAKernel<float>,
ops::Pad3dCUDAKernel<double>, ops::Pad3dCUDAKernel<int>,
ops::Pad3dCUDAKernel<int64_t>);
REGISTER_OP_CUDA_KERNEL(pad3d_grad, ops::Pad3dGradCUDAKernel<plat::float16>,
ops::Pad3dGradCUDAKernel<float>,
ops::Pad3dGradCUDAKernel<double>);
paddle/phi/infermeta/unary.cc
浏览文件 @ 382e460b
...@@ -877,6 +877,77 @@ void PadInferMeta(const MetaTensor& input, ...@@ -877,6 +877,77 @@ void PadInferMeta(const MetaTensor& input,
out->set_dtype(input.dtype()); out->set_dtype(input.dtype());
} }
void Pad3dInferMeta(const MetaTensor& x,
const ScalarArray& paddings_scalar_array,
const std::string& mode,
float value,
const std::string& data_format,
MetaTensor* out,
MetaConfig config) {
auto x_dim = x.dims();
PADDLE_ENFORCE_EQ(x_dim.size(),
5,
errors::InvalidArgument(
"The size of Input(X)'s dimension should be equal to "
"5, but received %d. ",
x_dim.size()));
std::vector<int64_t> out_dims(x_dim.size());
out_dims[0] = x_dim[0];
if (paddings_scalar_array.FromTensor()) {
if (config.is_runtime) {
PADDLE_ENFORCE_EQ(
paddings_scalar_array.GetData().size(),
6,
errors::InvalidArgument("Shape of Input(Paddings) should be equal to "
"[6], but received [%d].",
paddings_scalar_array.GetData().size()));
}
out_dims[1] = x_dim[1];
out_dims[2] = x_dim[2];
out_dims[3] = x_dim[3];
} else {
auto paddings = paddings_scalar_array.GetData();
PADDLE_ENFORCE_EQ(
paddings.size(),
6,
errors::InvalidArgument(
"Size of paddings should be equal to 6, but received %d.",
static_cast<int>(paddings.size())));
if (data_format == "NCDHW") {
out_dims[1] = x_dim[1]; // channel
out_dims[2] = ((!config.is_runtime) && (x_dim[2] < 0))
? x_dim[2]
: (x_dim[2] + paddings[4] + paddings[5]); // depth
out_dims[3] = ((!config.is_runtime) && (x_dim[3] < 0))
? x_dim[3]
: (x_dim[3] + paddings[2] + paddings[3]); // height
out_dims[4] = ((!config.is_runtime) && (x_dim[4] < 0))
? x_dim[4]
: (x_dim[4] + paddings[0] + paddings[1]); // width
} else { // NDHWC
out_dims[4] = x_dim[4]; // channel
out_dims[1] = ((!config.is_runtime) && (x_dim[1] < 0))
? x_dim[1]
: (x_dim[1] + paddings[4] + paddings[5]); // depth
out_dims[2] = ((!config.is_runtime) && (x_dim[2] < 0))
? x_dim[2]
: (x_dim[2] + paddings[2] + paddings[3]); // height
out_dims[3] = ((!config.is_runtime) && (x_dim[3] < 0))
? x_dim[3]
: (x_dim[3] + paddings[0] + paddings[1]); // width
}
}
out->set_dims(phi::make_ddim(out_dims));
out->set_dtype(x.dtype());
out->share_lod(x);
}
void PixelShuffleInferMeta(const MetaTensor& x, void PixelShuffleInferMeta(const MetaTensor& x,
int upscale_factor, int upscale_factor,
const std::string& data_format, const std::string& data_format,
......
paddle/phi/infermeta/unary.h
浏览文件 @ 382e460b
...@@ -147,6 +147,14 @@ void PadInferMeta(const MetaTensor& input, ...@@ -147,6 +147,14 @@ void PadInferMeta(const MetaTensor& input,
MetaTensor* out, MetaTensor* out,
MetaConfig config = MetaConfig()); MetaConfig config = MetaConfig());
void Pad3dInferMeta(const MetaTensor& x,
const ScalarArray& paddings,
const std::string& mode,
float value,
const std::string& data_format,
MetaTensor* out,
MetaConfig config = MetaConfig());
void PixelShuffleInferMeta(const MetaTensor& x, void PixelShuffleInferMeta(const MetaTensor& x,
int upscale_factor, int upscale_factor,
const std::string& data_format, const std::string& data_format,
......
paddle/phi/kernels/cpu/pad3d_grad_kernel.cc 0 → 100644
浏览文件 @ 382e460b
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/phi/kernels/pad3d_grad_kernel.h"
#include "paddle/phi/backends/cpu/cpu_context.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/kernels/funcs/math_function.h"
namespace phi {
template <typename T>
void ConstPad3DGradNCDHW(T* d_in_data,
const T* d_out_data,
const int in_depth,
const int in_height,
const int in_width,
const int out_depth,
const int out_height,
const int out_width,
const int pad_front,
const int pad_top,
const int pad_left,
const int out_d,
const int out_h,
const int out_w) {
int in_d = out_d - pad_front;
int in_h = out_h - pad_top;
int in_w = out_w - pad_left;
if (!(in_d < 0 || in_h < 0 || in_w < 0 || in_d >= in_depth ||
in_h >= in_height || in_w >= in_width)) {
d_in_data[in_d * in_height * in_width + in_h * in_width + in_w] =
d_out_data[out_d * out_height * out_width + out_h * out_width + out_w];
}
}
template <typename T>
void ConstPad3DGradNDHWC(T* d_in_data,
const T* d_out_data,
const int channels,
const int in_depth,
const int in_height,
const int in_width,
const int out_depth,
const int out_height,
const int out_width,
const int pad_front,
const int pad_top,
const int pad_left,
const int out_d,
const int out_h,
const int out_w) {
int in_d = out_d - pad_front;
int in_h = out_h - pad_top;
int in_w = out_w - pad_left;
const int out_index =
(out_d * out_height * out_width + out_h * out_width + out_w) * channels;
if (!(in_d < 0 || in_h < 0 || in_w < 0 || in_d >= in_depth ||
in_h >= in_height || in_w >= in_width)) {
const int in_index =
(in_d * in_height * in_width + in_h * in_width + in_w) * channels;
for (int c = 0; c < channels; ++c) {
d_in_data[in_index + c] = d_out_data[out_index + c];
}
}
}
template <typename T>
void ReflectPad3DGradNCDHW(T* d_in_data,
const T* d_out_data,
const int in_depth,
const int in_height,
const int in_width,
const int out_depth,
const int out_height,
const int out_width,
const int pad_front,
const int pad_top,
const int pad_left,
const int out_d,
const int out_h,
const int out_w) {
int in_d = out_d - pad_front;
int in_h = out_h - pad_top;
int in_w = out_w - pad_left;
in_d = std::max(in_d, -in_d); // reflect by 0
in_d = std::min(in_d, 2 * in_depth - in_d - 2); // reflect by in_depth
in_h = std::max(in_h, -in_h); // reflect by 0
in_h = std::min(in_h, 2 * in_height - in_h - 2); // reflect by in_height
in_w = std::max(in_w, -in_w); // reflect by 0
in_w = std::min(in_w, 2 * in_width - in_w - 2); // reflect by in_width
d_in_data[in_d * in_height * in_width + in_h * in_width + in_w] +=
d_out_data[out_d * out_height * out_width + out_h * out_width + out_w];
}
template <typename T>
void ReflectPad3DGradNDHWC(T* d_in_data,
const T* d_out_data,
const int channels,
const int in_depth,
const int in_height,
const int in_width,
const int out_depth,
const int out_height,
const int out_width,
const int pad_front,
const int pad_top,
const int pad_left,
const int out_d,
const int out_h,
const int out_w) {
int in_d = out_d - pad_front;
int in_h = out_h - pad_top;
int in_w = out_w - pad_left;
in_d = std::max(in_d, -in_d);
in_d = std::min(in_d, 2 * in_depth - in_d - 2);
in_h = std::max(in_h, -in_h);
in_h = std::min(in_h, 2 * in_height - in_h - 2);
in_w = std::max(in_w, -in_w);
in_w = std::min(in_w, 2 * in_width - in_w - 2);
const int out_index =
(out_d * out_height * out_width + out_h * out_width + out_w) * channels;
const int in_index =
(in_d * in_height * in_width + in_h * in_width + in_w) * channels;
for (int c = 0; c < channels; ++c) {
d_in_data[in_index + c] += d_out_data[out_index + c];
}
}
template <typename T>
void ReplicatePad3DGradNCDHW(T* d_in_data,
const T* d_out_data,
const int in_depth,
const int in_height,
const int in_width,
const int out_depth,
const int out_height,
const int out_width,
const int pad_front,
const int pad_top,
const int pad_left,
const int out_d,
const int out_h,
const int out_w) {
int in_d = std::min(in_depth - 1, std::max(out_d - pad_front, 0));
int in_h = std::min(in_height - 1, std::max(out_h - pad_top, 0));
int in_w = std::min(in_width - 1, std::max(out_w - pad_left, 0));
d_in_data[in_d * in_height * in_width + in_h * in_width + in_w] +=
d_out_data[out_d * out_height * out_width + out_h * out_width + out_w];
}
template <typename T>
void ReplicatePad3DGradNDHWC(T* d_in_data,
const T* d_out_data,
const int channels,
const int in_depth,
const int in_height,
const int in_width,
const int out_depth,
const int out_height,
const int out_width,
const int pad_front,
const int pad_top,
const int pad_left,
const int out_d,
const int out_h,
const int out_w) {
int in_d = std::min(in_depth - 1, std::max(out_d - pad_front, 0));
int in_h = std::min(in_height - 1, std::max(out_h - pad_top, 0));
int in_w = std::min(in_width - 1, std::max(out_w - pad_left, 0));
const int out_index =
(out_d * out_height * out_width + out_h * out_width + out_w) * channels;
const int in_index =
(in_d * in_height * in_width + in_h * in_width + in_w) * channels;
for (int c = 0; c < channels; ++c) {
d_in_data[in_index + c] += d_out_data[out_index + c];
}
}
template <typename T>
void CircularPad3DGradNCDHW(T* d_in_data,
const T* d_out_data,
const int in_depth,
const int in_height,
const int in_width,
const int out_depth,
const int out_height,
const int out_width,
const int pad_front,
const int pad_top,
const int pad_left,
const int out_d,
const int out_h,
const int out_w) {
int in_d = ((out_d - pad_front) % in_depth + in_depth) % in_depth;
int in_h = ((out_h - pad_top) % in_height + in_height) % in_height;
int in_w = ((out_w - pad_left) % in_width + in_width) % in_width;
d_in_data[in_d * in_height * in_width + in_h * in_width + in_w] +=
d_out_data[out_d * out_height * out_width + out_h * out_width + out_w];
}
template <typename T>
void CircularPad3DGradNDHWC(T* d_in_data,
const T* d_out_data,
const int channels,
const int in_depth,
const int in_height,
const int in_width,
const int out_depth,
const int out_height,
const int out_width,
const int pad_front,
const int pad_top,
const int pad_left,
const int out_d,
const int out_h,
const int out_w) {
int in_d = ((out_d - pad_front) % in_depth + in_depth) % in_depth;
int in_h = ((out_h - pad_top) % in_height + in_height) % in_height;
int in_w = ((out_w - pad_left) % in_width + in_width) % in_width;
const int out_index =
(out_d * out_height * out_width + out_h * out_width + out_w) * channels;
const int in_index =
(in_d * in_height * in_width + in_h * in_width + in_w) * channels;
for (int c = 0; c < channels; ++c) {
d_in_data[in_index + c] += d_out_data[out_index + c];
}
}
template <typename T>
void Pad3DGradNCDHW(T* d_in_data,
const int num,
const int channels,
const int in_depth,
const int in_height,
const int in_width,
const int out_depth,
const int out_height,
const int out_width,
const int pad_front,
const int pad_top,
const int pad_left,
const T* d_out_data,
void (*pad_func)(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)) {
for (int n = 0; n < num; ++n) {
for (int c = 0; c < channels; ++c) {
for (int out_d = 0; out_d < out_depth; ++out_d) {
for (int out_h = 0; out_h < out_height; ++out_h) {
for (int out_w = 0; out_w < out_width; ++out_w) {
pad_func(d_in_data,
d_out_data,
in_depth,
in_height,
in_width,
out_depth,
out_height,
out_width,
pad_front,
pad_top,
pad_left,
out_d,
out_h,
out_w);
}
}
}
d_in_data += in_depth * in_height * in_width;
d_out_data += out_depth * out_height * out_width;
}
}
}
template <typename T>
void Pad3DGradNDHWC(T* d_in_data,
const int num,
const int channels,
const int in_depth,
const int in_height,
const int in_width,
const int out_depth,
const int out_height,
const int out_width,
const int pad_front,
const int pad_top,
const int pad_left,
const T* d_out_data,
void (*pad_func)(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)) {
for (int n = 0; n < num; ++n) {
for (int out_d = 0; out_d < out_depth; ++out_d) {
for (int out_h = 0; out_h < out_height; ++out_h) {
for (int out_w = 0; out_w < out_width; ++out_w) {
pad_func(d_in_data,
d_out_data,
channels,
in_depth,
in_height,
in_width,
out_depth,
out_height,
out_width,
pad_front,
pad_top,
pad_left,
out_d,
out_h,
out_w);
}
}
}
d_in_data += in_depth * in_height * in_width * channels;
d_out_data += out_depth * out_height * out_width * channels;
}
}
template <typename T, typename Context>
void Pad3dGradKernel(const Context& dev_ctx,
const DenseTensor& x,
const DenseTensor& out_grad,
const ScalarArray& paddings,
const std::string& mode,
float pad_value,
const std::string& data_format,
DenseTensor* x_grad) {
std::vector<int64_t> pads = paddings.GetData();
auto* d_out = &out_grad;
auto* d_in = x_grad;
auto d_in_dims = d_in->dims();
auto d_out_dims = d_out->dims();
const T* d_out_data = d_out->data<T>();
T* d_in_data = dev_ctx.template Alloc<T>(d_in);
phi::funcs::SetConstant<Context, T>()(dev_ctx, d_in, static_cast<T>(0));
const int pad_left = pads[0];
const int pad_top = pads[2];
const int pad_front = pads[4];
const int num = d_in_dims[0];
if (data_format == "NCDHW") {
const int channels = d_in_dims[1];
const int in_depth = d_in_dims[2];
const int in_height = d_in_dims[3];
const int in_width = d_in_dims[4];
const int out_depth = d_out_dims[2];
const int out_height = d_out_dims[3];
const int out_width = d_out_dims[4];
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)>
func_map;
func_map["reflect"] = ReflectPad3DGradNCDHW;
func_map["replicate"] = ReplicatePad3DGradNCDHW;
func_map["circular"] = CircularPad3DGradNCDHW;
func_map["constant"] = ConstPad3DGradNCDHW;
Pad3DGradNCDHW(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]);
} else {
const int channels = d_in_dims[4];
const int in_depth = d_in_dims[1];
const int in_height = d_in_dims[2];
const int in_width = d_in_dims[3];
const int out_depth = d_out_dims[1];
const int out_height = d_out_dims[2];
const int out_width = 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) {}
paddle/phi/kernels/cpu/pad3d_kernel.cc 0 → 100644
浏览文件 @ 382e460b
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/phi/kernels/pad3d_kernel.h"
#include "paddle/phi/backends/cpu/cpu_context.h"
#include "paddle/phi/core/kernel_registry.h"
namespace phi {
template <typename T>
void ConstPad3DFuncNCDHW(const T* in_data,
T* out_data,
const int in_depth,
const int in_height,
const int in_width,
const int out_depth,
const int out_height,
const int out_width,
const int pad_front,
const int pad_top,
const int pad_left,
const int out_d,
const int out_h,
const int out_w,
const T value) {
int in_d = out_d - pad_front;
int in_h = out_h - pad_top;
int in_w = out_w - pad_left;
out_data[out_d * out_height * out_width + out_h * out_width + out_w] =
(in_d < 0 || in_h < 0 || in_w < 0 || in_d >= in_depth ||
in_h >= in_height || in_w >= in_width)
? value
: in_data[in_d * in_height * in_width + in_h * in_width + in_w];
}
template <typename T>
void ConstPad3DFuncNDHWC(const T* in_data,
T* out_data,
const int channels,
const int in_depth,
const int in_height,
const int in_width,
const int out_depth,
const int out_height,
const int out_width,
const int pad_front,
const int pad_top,
const int pad_left,
const int out_d,
const int out_h,
const int out_w,
const T value) {
int in_d = out_d - pad_front;
int in_h = out_h - pad_top;
int in_w = out_w - pad_left;
const int out_index =
(out_d * out_height * out_width + out_h * out_width + out_w) * channels;
if (in_d < 0 || in_h < 0 || in_w < 0 || in_d >= in_depth ||
in_h >= in_height || in_w >= in_width) {
for (int c = 0; c < channels; ++c) {
out_data[out_index + c] = value;
}
} else {
const int in_index =
(in_d * in_height * in_width + in_h * in_width + in_w) * channels;
for (int c = 0; c < channels; ++c) {
out_data[out_index + c] = in_data[in_index + c];
}
}
}
template <typename T>
void ReflectPad3DFuncNCDHW(const T* in_data,
T* out_data,
const int in_depth,
const int in_height,
const int in_width,
const int out_depth,
const int out_height,
const int out_width,
const int pad_front,
const int pad_top,
const int pad_left,
const int out_d,
const int out_h,
const int out_w,
const T value) {
int in_d = out_d - pad_front;
int in_h = out_h - pad_top;
int in_w = out_w - pad_left;
in_d = std::max(in_d, -in_d); // reflect by 0
in_d = std::min(in_d, 2 * in_depth - in_d - 2); // reflect by in_depth
in_h = std::max(in_h, -in_h); // reflect by 0
in_h = std::min(in_h, 2 * in_height - in_h - 2); // reflect by in_height
in_w = std::max(in_w, -in_w); // reflect by 0
in_w = std::min(in_w, 2 * in_width - in_w - 2); // reflect by in_width
out_data[out_d * out_height * out_width + out_h * out_width + out_w] =
in_data[in_d * in_height * in_width + in_h * in_width + in_w];
}
template <typename T>
void ReflectPad3DFuncNDHWC(const T* in_data,
T* out_data,
const int channels,
const int in_depth,
const int in_height,
const int in_width,
const int out_depth,
const int out_height,
const int out_width,
const int pad_front,
const int pad_top,
const int pad_left,
const int out_d,
const int out_h,
const int out_w,
const T value) {
int in_d = out_d - pad_front;
int in_h = out_h - pad_top;
int in_w = out_w - pad_left;
in_d = std::max(in_d, -in_d);
in_d = std::min(in_d, 2 * in_depth - in_d - 2);
in_h = std::max(in_h, -in_h);
in_h = std::min(in_h, 2 * in_height - in_h - 2);
in_w = std::max(in_w, -in_w);
in_w = std::min(in_w, 2 * in_width - in_w - 2);
const int out_index =
(out_d * out_height * out_width + out_h * out_width + out_w) * channels;
const int in_index =
(in_d * in_height * in_width + in_h * in_width + in_w) * channels;
for (int c = 0; c < channels; ++c) {
out_data[out_index + c] = in_data[in_index + c];
}
}
template <typename T>
void ReplicatePad3DFuncNCDHW(const T* in_data,
T* out_data,
const int in_depth,
const int in_height,
const int in_width,
const int out_depth,
const int out_height,
const int out_width,
const int pad_front,
const int pad_top,
const int pad_left,
const int out_d,
const int out_h,
const int out_w,
const T value) {
int in_d = std::min(in_depth - 1, std::max(out_d - pad_front, 0));
int in_h = std::min(in_height - 1, std::max(out_h - pad_top, 0));
int in_w = std::min(in_width - 1, std::max(out_w - pad_left, 0));
out_data[out_d * out_height * out_width + out_h * out_width + out_w] =
in_data[in_d * in_height * in_width + in_h * in_width + in_w];
}
template <typename T>
void ReplicatePad3DFuncNDHWC(const T* in_data,
T* out_data,
const int channels,
const int in_depth,
const int in_height,
const int in_width,
const int out_depth,
const int out_height,
const int out_width,
const int pad_front,
const int pad_top,
const int pad_left,
const int out_d,
const int out_h,
const int out_w,
const T value) {
int in_d = std::min(in_depth - 1, std::max(out_d - pad_front, 0));
int in_h = std::min(in_height - 1, std::max(out_h - pad_top, 0));
int in_w = std::min(in_width - 1, std::max(out_w - pad_left, 0));
const int out_index =
(out_d * out_height * out_width + out_h * out_width + out_w) * channels;
const int in_index =
(in_d * in_height * in_width + in_h * in_width + in_w) * channels;
for (int c = 0; c < channels; ++c) {
out_data[out_index + c] = in_data[in_index + c];
}
}
template <typename T>
void CircularPad3DFuncNCDHW(const T* in_data,
T* out_data,
const int in_depth,
const int in_height,
const int in_width,
const int out_depth,
const int out_height,
const int out_width,
const int pad_front,
const int pad_top,
const int pad_left,
const int out_d,
const int out_h,
const int out_w,
const T value) {
int in_d = ((out_d - pad_front) % in_depth + in_depth) % in_depth;
int in_h = ((out_h - pad_top) % in_height + in_height) % in_height;
int in_w = ((out_w - pad_left) % in_width + in_width) % in_width;
out_data[out_d * out_height * out_width + out_h * out_width + out_w] =
in_data[in_d * in_height * in_width + in_h * in_width + in_w];
}
template <typename T>
void CircularPad3DFuncNDHWC(const T* in_data,
T* out_data,
const int channels,
const int in_depth,
const int in_height,
const int in_width,
const int out_depth,
const int out_height,
const int out_width,
const int pad_front,
const int pad_top,
const int pad_left,
const int out_d,
const int out_h,
const int out_w,
const T value) {
int in_d = ((out_d - pad_front) % in_depth + in_depth) % in_depth;
int in_h = ((out_h - pad_top) % in_height + in_height) % in_height;
int in_w = ((out_w - pad_left) % in_width + in_width) % in_width;
const int out_index =
(out_d * out_height * out_width + out_h * out_width + out_w) * channels;
const int in_index =
(in_d * in_height * in_width + in_h * in_width + in_w) * channels;
for (int c = 0; c < channels; ++c) {
out_data[out_index + c] = in_data[in_index + c];
}
}
template <typename T>
void Pad3DNCDHW(const T* in_data,
const int num,
const int channels,
const int in_depth,
const int in_height,
const int in_width,
const int out_depth,
const int out_height,
const int out_width,
const int pad_front,
const int pad_top,
const int pad_left,
T value,
T* out_data,
void (*pad_func)(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)) {
for (int n = 0; n < num; ++n) {
for (int c = 0; c < channels; ++c) {
for (int out_d = 0; out_d < out_depth; ++out_d) {
for (int out_h = 0; out_h < out_height; ++out_h) {
for (int out_w = 0; out_w < out_width; ++out_w) {
pad_func(in_data,
out_data,
in_depth,
in_height,
in_width,
out_depth,
out_height,
out_width,
pad_front,
pad_top,
pad_left,
out_d,
out_h,
out_w,
value);
}
}
}
in_data += in_depth * in_height * in_width;
out_data += out_depth * out_height * out_width;
}
}
}
template <typename T>
void Pad3DNDHWC(const T* in_data,
const int num,
const int channels,
const int in_depth,
const int in_height,
const int in_width,
const int out_depth,
const int out_height,
const int out_width,
const int pad_front,
const int pad_top,
const int pad_left,
T value,
T* out_data,
void (*pad_func)(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)) {
for (int n = 0; n < num; ++n) {
for (int out_d = 0; out_d < out_depth; ++out_d) {
for (int out_h = 0; out_h < out_height; ++out_h) {
for (int out_w = 0; out_w < out_width; ++out_w) {
pad_func(in_data,
out_data,
channels,
in_depth,
in_height,
in_width,
out_depth,
out_height,
out_width,
pad_front,
pad_top,
pad_left,
out_d,
out_h,
out_w,
value);
}
}
}
in_data += in_depth * in_height * in_width * channels;
out_data += out_depth * out_height * out_width * channels;
}
}
template <typename T, typename Context>
void Pad3dKernel(const Context& dev_ctx,
const DenseTensor& x,
const ScalarArray& paddings,
const std::string& mode,
float pad_value,
const std::string& data_format,
DenseTensor* out) {
T value = static_cast<T>(pad_value);
std::vector<int64_t> pads = paddings.GetData();
auto in_dims = x.dims();
const T* in_data = x.data<T>();
if (data_format == "NCDHW") {
out->Resize({in_dims[0],
in_dims[1],
in_dims[2] + pads[4] + pads[5],
in_dims[3] + pads[2] + pads[3],
in_dims[4] + pads[0] + pads[1]});
} else {
out->Resize({in_dims[0],
in_dims[1] + pads[4] + pads[5],
in_dims[2] + pads[2] + pads[3],
in_dims[3] + pads[0] + pads[1],
in_dims[4]});
}
auto out_dims = out->dims();
T* out_data = dev_ctx.template Alloc<T>(out);
int channels = in_dims[1];
int in_depth = in_dims[2];
int in_height = in_dims[3];
int in_width = in_dims[4];
int out_depth = out_dims[2];
int out_height = out_dims[3];
int out_width = out_dims[4];
if (data_format == "NDHWC") {
channels = in_dims[4];
in_depth = in_dims[1];
in_height = in_dims[2];
in_width = in_dims[3];
out_depth = out_dims[1];
out_height = out_dims[2];
out_width = out_dims[3];
}
if (mode == "reflect") {
PADDLE_ENFORCE_GT(
in_depth,
pads[4],
errors::InvalidArgument("The depth of Input(X)'s dimension should be "
"greater than pad_front"
" in reflect mode"
", but received depth(%d) and pad_front(%d).",
in_depth,
pads[4]));
PADDLE_ENFORCE_GT(
in_depth,
pads[5],
errors::InvalidArgument("The depth of Input(X)'s dimension should be "
"greater than pad_back"
" in reflect mode"
", but received depth(%d) and pad_back(%d).",
in_depth,
pads[5]));
PADDLE_ENFORCE_GT(
in_height,
pads[2],
errors::InvalidArgument("The height of Input(X)'s dimension should be "
"greater than pad_top"
" in reflect mode"
", but received depth(%d) and pad_top(%d).",
in_height,
pads[2]));
PADDLE_ENFORCE_GT(
in_height,
pads[3],
errors::InvalidArgument("The height of Input(X)'s dimension should be "
"greater than pad_bottom"
" in reflect mode"
", but received depth(%d) and pad_bottom(%d).",
in_height,
pads[3]));
PADDLE_ENFORCE_GT(
in_width,
pads[0],
errors::InvalidArgument("The width of Input(X)'s dimension should be "
"greater than pad_left"
" in reflect mode"
", but received depth(%d) and pad_left(%d).",
in_width,
pads[0]));
PADDLE_ENFORCE_GT(
in_width,
pads[1],
errors::InvalidArgument("The width of Input(X)'s dimension should be "
"greater than pad_right"
" in reflect mode"
", 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 = pads[0];
const int pad_top = pads[2];
const int pad_front = pads[4];
const int num = 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) {}
paddle/phi/kernels/gpu/pad3d_grad_kernel.cu 0 → 100644
浏览文件 @ 382e460b
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/phi/kernels/pad3d_grad_kernel.h"
#include "paddle/fluid/platform/device/gpu/gpu_info.h"
#include "paddle/fluid/platform/device/gpu/gpu_primitives.h"
#include "paddle/phi/backends/gpu/gpu_context.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/kernels/funcs/math_function.h"
namespace phi {
using paddle::platform::PADDLE_CUDA_NUM_THREADS;
template <typename T>
__global__ void Pad3DGradConstNCDHW(const int in_size,
T* d_in_data,
const int num,
const int channels,
const int in_depth,
const int in_height,
const int in_width,
const int out_depth,
const int out_height,
const int out_width,
const int pad_front,
const int pad_top,
const int pad_left,
const T* d_out_data) {
CUDA_KERNEL_LOOP(in_index, in_size) {
const int in_w = in_index % in_width;
int nc = in_index / in_width;
const int in_h = nc % in_height;
nc /= in_height;
const int in_d = nc % in_depth;
nc /= in_depth;
const int out_d = in_d + pad_front;
const int out_h = in_h + pad_top;
const int out_w = in_w + pad_left;
d_in_data[in_index] =
d_out_data[nc * out_depth * out_height * out_width +
out_d * out_height * out_width + out_h * out_width + out_w];
}
}
template <typename T>
__global__ void Pad3DGradConstNDHWC(const int in_size,
T* d_in_data,
const int num,
const int channels,
const int in_depth,
const int in_height,
const int in_width,
const int out_depth,
const int out_height,
const int out_width,
const int pad_front,
const int pad_top,
const int pad_left,
const T* d_out_data) {
CUDA_KERNEL_LOOP(in_index, in_size) {
const int c = in_index % channels;
int n = in_index / channels;
const int in_w = n % in_width;
n /= in_width;
const int in_h = n % in_height;
n /= in_height;
const int in_d = n % in_depth;
n /= in_depth;
const int out_d = in_d + pad_front;
const int out_h = in_h + pad_top;
const int out_w = in_w + pad_left;
d_in_data[in_index] =
d_out_data[n * out_depth * out_height * out_width * channels +
out_d * out_height * out_width * channels +
out_h * out_width * channels + out_w * channels + c];
}
}
template <typename T>
__global__ void Pad3DGradReflectNCDHW(const int out_size,
T* d_in_data,
const int num,
const int channels,
const int in_depth,
const int in_height,
const int in_width,
const int out_depth,
const int out_height,
const int out_width,
const int pad_front,
const int pad_top,
const int pad_left,
const T* d_out_data) {
CUDA_KERNEL_LOOP(out_index, out_size) {
int nc = out_index / out_width;
const int out_w = out_index % out_width;
const int out_h = nc % out_height;
nc /= out_height;
const int out_d = nc % out_depth;
nc /= out_depth;
int in_d = out_d - pad_front;
int in_h = out_h - pad_top;
int in_w = out_w - pad_left;
in_d = max(in_d, -in_d);
in_h = max(in_h, -in_h);
in_w = max(in_w, -in_w);
in_d = min(in_d, 2 * in_depth - in_d - 2);
in_h = min(in_h, 2 * in_height - in_h - 2);
in_w = min(in_w, 2 * in_width - in_w - 2);
paddle::platform::CudaAtomicAdd(
&d_in_data[nc * in_depth * in_height * in_width +
in_d * in_height * in_width + in_h * in_width + in_w],
d_out_data[out_index]);
}
}
template <typename T>
__global__ void Pad3DGradReflectNDHWC(const int out_size,
T* d_in_data,
const int num,
const int channels,
const int in_depth,
const int in_height,
const int in_width,
const int out_depth,
const int out_height,
const int out_width,
const int pad_front,
const int pad_top,
const int pad_left,
const T* d_out_data) {
CUDA_KERNEL_LOOP(out_index, out_size) {
const int c = out_index % channels;
int n = out_index / channels;
const int out_w = n % out_width;
n /= out_width;
const int out_h = n % out_height;
n /= out_height;
const int out_d = n % out_depth;
n /= out_depth;
int in_d = out_d - pad_front;
int in_h = out_h - pad_top;
int in_w = out_w - pad_left;
in_d = max(in_d, -in_d);
in_h = max(in_h, -in_h);
in_w = max(in_w, -in_w);
in_d = min(in_d, in_depth * 2 - in_d - 2);
in_h = min(in_h, in_height * 2 - in_h - 2);
in_w = min(in_w, in_width * 2 - in_w - 2);
paddle::platform::CudaAtomicAdd(
&d_in_data[n * in_depth * in_height * in_width * channels +
in_d * in_height * in_width * channels +
in_h * in_width * channels + in_w * channels + c],
d_out_data[out_index]);
}
}
template <typename T>
__global__ void Pad3DGradReplicateNCDHW(const int out_size,
T* d_in_data,
const int num,
const int channels,
const int in_depth,
const int in_height,
const int in_width,
const int out_depth,
const int out_height,
const int out_width,
const int pad_front,
const int pad_top,
const int pad_left,
const T* d_out_data) {
CUDA_KERNEL_LOOP(out_index, out_size) {
int nc = out_index / out_width;
const int out_w = out_index % out_width;
const int out_h = nc % out_height;
nc /= out_height;
const int out_d = nc % out_depth;
nc /= out_depth;
const int in_d = min(in_depth - 1, max(out_d - pad_front, 0));
const int in_h = min(in_height - 1, max(out_h - pad_top, 0));
const int in_w = min(in_width - 1, max(out_w - pad_left, 0));
paddle::platform::CudaAtomicAdd(
&d_in_data[nc * in_depth * in_height * in_width +
in_d * in_height * in_width + in_h * in_width + in_w],
d_out_data[out_index]);
}
}
template <typename T>
__global__ void Pad3DGradReplicateNDHWC(const int out_size,
T* d_in_data,
const int num,
const int channels,
const int in_depth,
const int in_height,
const int in_width,
const int out_depth,
const int out_height,
const int out_width,
const int pad_front,
const int pad_top,
const int pad_left,
const T* d_out_data) {
CUDA_KERNEL_LOOP(out_index, out_size) {
const int c = out_index % channels;
int n = out_index / channels;
const int out_w = n % out_width;
n /= out_width;
const int out_h = n % out_height;
n /= out_height;
const int out_d = n % out_depth;
n /= out_depth;
const int in_d = min(in_depth - 1, max(out_d - pad_front, 0));
const int in_h = min(in_height - 1, max(out_h - pad_top, 0));
const int in_w = min(in_width - 1, max(out_w - pad_left, 0));
paddle::platform::CudaAtomicAdd(
&d_in_data[n * in_depth * in_height * in_width * channels +
in_d * in_height * in_width * channels +
in_h * in_width * channels + in_w * channels + c],
d_out_data[out_index]);
}
}
template <typename T>
__global__ void Pad3DGradCircularNCDHW(const int out_size,
T* d_in_data,
const int num,
const int channels,
const int in_depth,
const int in_height,
const int in_width,
const int out_depth,
const int out_height,
const int out_width,
const int pad_front,
const int pad_top,
const int pad_left,
const T* d_out_data) {
CUDA_KERNEL_LOOP(out_index, out_size) {
int nc = out_index / out_width;
const int out_w = out_index % out_width;
const int out_h = nc % out_height;
nc /= out_height;
const int out_d = nc % out_depth;
nc /= out_depth;
int in_d = ((out_d - pad_front) % in_depth + in_depth) % in_depth;
int in_h = ((out_h - pad_top) % in_height + in_height) % in_height;
int in_w = ((out_w - pad_left) % in_width + in_width) % in_width;
paddle::platform::CudaAtomicAdd(
&d_in_data[nc * in_depth * in_height * in_width +
in_d * in_height * in_width + in_h * in_width + in_w],
d_out_data[out_index]);
}
}
template <typename T>
__global__ void Pad3DGradCircularNDHWC(const int out_size,
T* d_in_data,
const int num,
const int channels,
const int in_depth,
const int in_height,
const int in_width,
const int out_depth,
const int out_height,
const int out_width,
const int pad_front,
const int pad_top,
const int pad_left,
const T* d_out_data) {
CUDA_KERNEL_LOOP(out_index, out_size) {
const int c = out_index % channels;
int n = out_index / channels;
const int out_w = n % out_width;
n /= out_width;
const int out_h = n % out_height;
n /= out_height;
const int out_d = n % out_depth;
n /= out_depth;
int in_d = ((out_d - pad_front) % in_depth + in_depth) % in_depth;
int in_h = ((out_h - pad_top) % in_height + in_height) % in_height;
int in_w = ((out_w - pad_left) % in_width + in_width) % in_width;
paddle::platform::CudaAtomicAdd(
&d_in_data[n * in_depth * in_height * in_width * channels +
in_d * in_height * in_width * channels +
in_h * in_width * channels + in_w * channels + c],
d_out_data[out_index]);
}
}
template <typename T, typename Context>
void Pad3dGradKernel(const Context& dev_ctx,
const DenseTensor& x,
const DenseTensor& out_grad,
const ScalarArray& paddings,
const std::string& mode,
float pad_value,
const std::string& data_format,
DenseTensor* x_grad) {
std::vector<int64_t> pads = paddings.GetData();
auto* d_out = &out_grad;
auto* d_in = x_grad;
auto d_in_dims = d_in->dims();
auto d_out_dims = d_out->dims();
const T* d_out_data = d_out->data<T>();
T* d_in_data = dev_ctx.template Alloc<T>(d_in);
phi::funcs::SetConstant<Context, T>()(dev_ctx, d_in, static_cast<T>(0));
const int pad_left = pads[0];
const int pad_top = pads[2];
const int pad_front = pads[4];
const int num = d_in_dims[0];
auto stream = dev_ctx.stream();
int block = PADDLE_CUDA_NUM_THREADS;
const int out_size = d_out->numel();
const int in_size = d_in->numel();
int grid = (out_size + block - 1) / block;
if (data_format == "NCDHW") {
const int channels = d_in_dims[1];
const int in_depth = d_in_dims[2];
const int in_height = d_in_dims[3];
const int in_width = d_in_dims[4];
const int out_depth = d_out_dims[2];
const int out_height = d_out_dims[3];
const int out_width = d_out_dims[4];
if (mode == "reflect") {
Pad3DGradReflectNCDHW<T><<<grid, block, 0, stream>>>(out_size,
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);
} else if (mode == "replicate") {
Pad3DGradReplicateNCDHW<T><<<grid, block, 0, stream>>>(out_size,
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);
} else if (mode == "circular") {
Pad3DGradCircularNCDHW<T><<<grid, block, 0, stream>>>(out_size,
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);
} else {
grid = (in_size + block - 1) / block;
Pad3DGradConstNCDHW<T><<<grid, block, 0, stream>>>(in_size,
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);
}
} else {
const int channels = d_in_dims[4];
const int in_depth = d_in_dims[1];
const int in_height = d_in_dims[2];
const int in_width = d_in_dims[3];
const int out_depth = d_out_dims[1];
const int out_height = d_out_dims[2];
const int out_width = d_out_dims[3];
if (mode == "reflect") {
Pad3DGradReflectNDHWC<T><<<grid, block, 0, stream>>>(out_size,
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);
} else if (mode == "replicate") {
Pad3DGradReplicateNDHWC<T><<<grid, block, 0, stream>>>(out_size,
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);
} else if (mode == "circular") {
Pad3DGradCircularNDHWC<T><<<grid, block, 0, stream>>>(out_size,
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);
} else {
grid = (in_size + block - 1) / block;
Pad3DGradConstNDHWC<T><<<grid, block, 0, stream>>>(in_size,
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);
}
}
}
} // namespace phi
PD_REGISTER_KERNEL(
pad3d_grad, GPU, ALL_LAYOUT, phi::Pad3dGradKernel, float, double) {}
paddle/phi/kernels/gpu/pad3d_kernel.cu 0 → 100644
浏览文件 @ 382e460b
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/phi/kernels/pad3d_kernel.h"
#include <algorithm>
#include "paddle/fluid/platform/device/gpu/gpu_info.h"
#include "paddle/fluid/platform/device/gpu/gpu_primitives.h"
#include "paddle/phi/backends/gpu/gpu_context.h"
#include "paddle/phi/core/kernel_registry.h"
namespace phi {
using paddle::platform::PADDLE_CUDA_NUM_THREADS;
template <typename T>
__global__ void Pad3DConstNCDHW(const int nthreads,
const T* in_data,
const int num,
const int channels,
const int in_depth,
const int in_height,
const int in_width,
const int out_depth,
const int out_height,
const int out_width,
const int pad_front,
const int pad_top,
const int pad_left,
T value,
T* out_data) {
CUDA_KERNEL_LOOP(index, nthreads) {
int nc = index / out_width;
const int out_w = index % out_width;
const int out_h = nc % out_height;
nc /= out_height;
const int out_d = nc % out_depth;
nc /= out_depth;
int in_d = out_d - pad_front;
int in_h = out_h - pad_top;
int in_w = out_w - pad_left;
out_data[index] =
(in_d < 0 || in_h < 0 || in_w < 0 || in_d >= in_depth ||
in_h >= in_height || in_w >= in_width)
? value
: in_data[nc * in_depth * in_height * in_width +
in_d * in_height * in_width + in_h * in_width + in_w];
}
}
template <typename T>
__global__ void Pad3DConstNDHWC(const int nthreads,
const T* in_data,
const int num,
const int channels,
const int in_depth,
const int in_height,
const int in_width,
const int out_depth,
const int out_height,
const int out_width,
const int pad_front,
const int pad_top,
const int pad_left,
T value,
T* out_data) {
CUDA_KERNEL_LOOP(index, nthreads) {
int n = index / channels;
const int c = index % channels;
const int out_w = n % out_width;
n /= out_width;
const int out_h = n % out_height;
n /= out_height;
const int out_d = n % out_depth;
n /= out_depth;
const int in_d = out_d - pad_front;
const int in_h = out_h - pad_top;
const int in_w = out_w - pad_left;
out_data[index] =
(in_d < 0 || in_h < 0 || in_w < 0 || in_d >= in_depth ||
in_h >= in_height || in_w >= in_width)
? value
: in_data[n * in_depth * in_height * in_width * channels +
in_d * in_height * in_width * channels +
in_h * in_width * channels + in_w * channels + c];
}
}
template <typename T>
__global__ void Pad3DReflectNCDHW(const int nthreads,
const T* in_data,
const int num,
const int channels,
const int in_depth,
const int in_height,
const int in_width,
const int out_depth,
const int out_height,
const int out_width,
const int pad_front,
const int pad_top,
const int pad_left,
T* out_data) {
CUDA_KERNEL_LOOP(index, nthreads) {
int nc = index / out_width;
const int out_w = index % out_width;
const int out_h = nc % out_height;
nc /= out_height;
const int out_d = nc % out_depth;
nc /= out_depth;
int in_d = out_d - pad_front;
int in_h = out_h - pad_top;
int in_w = out_w - pad_left;
in_d = max(in_d, -in_d); // reflect by 0
in_d = min(in_d, 2 * in_depth - in_d - 2); // reflect by in_depth
in_h = max(in_h, -in_h); // reflect by 0
in_h = min(in_h, 2 * in_height - in_h - 2); // reflect by in_height
in_w = max(in_w, -in_w); // reflect by 0
in_w = min(in_w, 2 * in_width - in_w - 2); // reflect by in_width
out_data[index] =
in_data[(nc * in_depth * in_height + in_d * in_height + in_h) *
in_width +
in_w];
}
}
template <typename T>
__global__ void Pad3DReflectNDHWC(const int nthreads,
const T* in_data,
const int num,
const int channels,
const int in_depth,
const int in_height,
const int in_width,
const int out_depth,
const int out_height,
const int out_width,
const int pad_front,
const int pad_top,
const int pad_left,
T* out_data) {
CUDA_KERNEL_LOOP(index, nthreads) {
int n = index / channels;
const int c = index % channels;
const int out_w = n % out_width;
n /= out_width;
const int out_h = n % out_height;
n /= out_height;
const int out_d = n % out_depth;
n /= out_depth;
int in_d = out_d - pad_front;
int in_h = out_h - pad_top;
int in_w = out_w - pad_left;
in_d = max(in_d, -in_d);
in_d = min(in_d, 2 * in_depth - in_d - 2);
in_h = max(in_h, -in_h);
in_h = min(in_h, 2 * in_height - in_h - 2);
in_w = max(in_w, -in_w);
in_w = min(in_w, 2 * in_width - in_w - 2);
out_data[index] = in_data[n * in_depth * in_height * in_width * channels +
in_d * in_height * in_width * channels +
in_h * in_width * channels + in_w * channels + c];
}
}
template <typename T>
__global__ void Pad3DReplicateNCDHW(const int nthreads,
const T* in_data,
const int num,
const int channels,
const int in_depth,
const int in_height,
const int in_width,
const int out_depth,
const int out_height,
const int out_width,
const int pad_front,
const int pad_top,
const int pad_left,
T* out_data) {
CUDA_KERNEL_LOOP(index, nthreads) {
int nc = index / out_width;
const int out_w = index % out_width;
const int out_h = nc % out_height;
nc /= out_height;
const int out_d = nc % out_depth;
nc /= out_depth;
int in_d = min(in_depth - 1, max(out_d - pad_front, 0));
int in_h = min(in_height - 1, max(out_h - pad_top, 0));
int in_w = min(in_width - 1, max(out_w - pad_left, 0));
out_data[index] =
in_data[(nc * in_depth * in_height + in_d * in_height + in_h) *
in_width +
in_w];
}
}
template <typename T>
__global__ void Pad3DReplicateNDHWC(const int nthreads,
const T* in_data,
const int num,
const int channels,
const int in_depth,
const int in_height,
const int in_width,
const int out_depth,
const int out_height,
const int out_width,
const int pad_front,
const int pad_top,
const int pad_left,
T* out_data) {
CUDA_KERNEL_LOOP(index, nthreads) {
int n = index / channels;
const int c = index % channels;
const int out_w = n % out_width;
n /= out_width;
const int out_h = n % out_height;
n /= out_height;
const int out_d = n % out_depth;
n /= out_depth;
int in_d = min(in_depth - 1, max(out_d - pad_front, 0));
int in_h = min(in_height - 1, max(out_h - pad_top, 0));
int in_w = min(in_width - 1, max(out_w - pad_left, 0));
out_data[index] = in_data[n * in_depth * in_height * in_width * channels +
in_d * in_height * in_width * channels +
in_h * in_width * channels + in_w * channels + c];
}
}
template <typename T>
__global__ void Pad3DCircularNCDHW(const int nthreads,
const T* in_data,
const int num,
const int channels,
const int in_depth,
const int in_height,
const int in_width,
const int out_depth,
const int out_height,
const int out_width,
const int pad_front,
const int pad_top,
const int pad_left,
T* out_data) {
CUDA_KERNEL_LOOP(index, nthreads) {
int nc = index / out_width;
const int out_w = index % out_width;
const int out_h = nc % out_height;
nc /= out_height;
const int out_d = nc % out_depth;
nc /= out_depth;
int in_d = ((out_d - pad_front) % in_depth + in_depth) % in_depth;
int in_h = ((out_h - pad_top) % in_height + in_height) % in_height;
int in_w = ((out_w - pad_left) % in_width + in_width) % in_width;
out_data[index] =
in_data[(nc * in_depth * in_height + in_d * in_height + in_h) *
in_width +
in_w];
}
}
template <typename T>
__global__ void Pad3DCircularNDHWC(const int nthreads,
const T* in_data,
const int num,
const int channels,
const int in_depth,
const int in_height,
const int in_width,
const int out_depth,
const int out_height,
const int out_width,
const int pad_front,
const int pad_top,
const int pad_left,
T* out_data) {
CUDA_KERNEL_LOOP(index, nthreads) {
int n = index / channels;
const int c = index % channels;
const int out_w = n % out_width;
n /= out_width;
const int out_h = n % out_height;
n /= out_height;
const int out_d = n % out_depth;
n /= out_depth;
int in_d = ((out_d - pad_front) % in_depth + in_depth) % in_depth;
int in_h = ((out_h - pad_top) % in_height + in_height) % in_height;
int in_w = ((out_w - pad_left) % in_width + in_width) % in_width;
out_data[index] = in_data[n * in_depth * in_height * in_width * channels +
in_d * in_height * in_width * channels +
in_h * in_width * channels + in_w * channels + c];
}
}
template <typename T, typename Context>
void Pad3dKernel(const Context& dev_ctx,
const DenseTensor& x,
const ScalarArray& paddings,
const std::string& mode,
float pad_value,
const std::string& data_format,
DenseTensor* out) {
std::vector<int64_t> pads = paddings.GetData();
auto in_dims = x.dims();
const T* in_data = x.data<T>();
auto out_dims = out->dims();
T value = static_cast<T>(pad_value);
if (data_format == "NCDHW") {
out_dims[0] = in_dims[0];
out_dims[1] = in_dims[1];
out_dims[2] = in_dims[2] + pads[4] + pads[5];
out_dims[3] = in_dims[3] + pads[2] + pads[3];
out_dims[4] = in_dims[4] + pads[0] + pads[1];
} else {
out_dims[0] = in_dims[0];
out_dims[1] = in_dims[1] + pads[4] + pads[5];
out_dims[2] = in_dims[2] + pads[2] + pads[3];
out_dims[3] = in_dims[3] + pads[0] + pads[1];
out_dims[4] = in_dims[4];
}
out->Resize(out_dims);
T* out_data = dev_ctx.template Alloc<T>(out);
int channels = in_dims[1];
int in_depth = in_dims[2];
int in_height = in_dims[3];
int in_width = in_dims[4];
int out_depth = out_dims[2];
int out_height = out_dims[3];
int out_width = out_dims[4];
if (data_format == "NDHWC") {
channels = in_dims[4];
in_depth = in_dims[1];
in_height = in_dims[2];
in_width = in_dims[3];
out_depth = out_dims[1];
out_height = out_dims[2];
out_width = out_dims[3];
}
if (mode == "reflect") {
PADDLE_ENFORCE_GT(
in_depth,
pads[4],
errors::InvalidArgument("The depth of Input(X)'s dimension should be "
"greater than pad_front"
" in reflect mode"
", but received depth(%d) and pad_front(%d).",
in_depth,
pads[4]));
PADDLE_ENFORCE_GT(
in_depth,
pads[5],
errors::InvalidArgument("The depth of Input(X)'s dimension should be "
"greater than pad_back"
" in reflect mode"
", but received depth(%d) and pad_back(%d).",
in_depth,
pads[5]));
PADDLE_ENFORCE_GT(
in_height,
pads[2],
errors::InvalidArgument("The height of Input(X)'s dimension should be "
"greater than pad_top"
" in reflect mode"
", but received depth(%d) and pad_top(%d).",
in_height,
pads[2]));
PADDLE_ENFORCE_GT(
in_height,
pads[3],
errors::InvalidArgument("The height of Input(X)'s dimension should be "
"greater than pad_bottom"
" in reflect mode"
", but received depth(%d) and pad_bottom(%d).",
in_height,
pads[3]));
PADDLE_ENFORCE_GT(
in_width,
pads[0],
errors::InvalidArgument("The width of Input(X)'s dimension should be "
"greater than pad_left"
" in reflect mode"
", but received depth(%d) and pad_left(%d).",
in_width,
pads[0]));
PADDLE_ENFORCE_GT(
in_width,
pads[1],
errors::InvalidArgument("The width of Input(X)'s dimension should be "
"greater than pad_right"
" in reflect mode"
", 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 = pads[0];
const int pad_top = pads[2];
const int pad_front = pads[4];
const int num = in_dims[0];
auto stream = dev_ctx.stream();
int block = PADDLE_CUDA_NUM_THREADS;
const int out_size = out->numel();
int grid = (out_size + block - 1) / block;
if (data_format == "NCDHW") {
if (mode == "reflect") {
Pad3DReflectNCDHW<T><<<grid, block, 0, stream>>>(out_size,
in_data,
num,
channels,
in_depth,
in_height,
in_width,
out_depth,
out_height,
out_width,
pad_front,
pad_top,
pad_left,
out_data);
} else if (mode == "replicate") {
Pad3DReplicateNCDHW<T><<<grid, block, 0, stream>>>(out_size,
in_data,
num,
channels,
in_depth,
in_height,
in_width,
out_depth,
out_height,
out_width,
pad_front,
pad_top,
pad_left,
out_data);
} else if (mode == "circular") {
Pad3DCircularNCDHW<T><<<grid, block, 0, stream>>>(out_size,
in_data,
num,
channels,
in_depth,
in_height,
in_width,
out_depth,
out_height,
out_width,
pad_front,
pad_top,
pad_left,
out_data);
} else {
Pad3DConstNCDHW<T><<<grid, block, 0, stream>>>(out_size,
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);
}
} else {
if (mode == "reflect") {
Pad3DReflectNDHWC<T><<<grid, block, 0, stream>>>(out_size,
in_data,
num,
channels,
in_depth,
in_height,
in_width,
out_depth,
out_height,
out_width,
pad_front,
pad_top,
pad_left,
out_data);
} else if (mode == "replicate") {
Pad3DReplicateNDHWC<T><<<grid, block, 0, stream>>>(out_size,
in_data,
num,
channels,
in_depth,
in_height,
in_width,
out_depth,
out_height,
out_width,
pad_front,
pad_top,
pad_left,
out_data);
} else if (mode == "circular") {
Pad3DCircularNDHWC<T><<<grid, block, 0, stream>>>(out_size,
in_data,
num,
channels,
in_depth,
in_height,
in_width,
out_depth,
out_height,
out_width,
pad_front,
pad_top,
pad_left,
out_data);
} else {
Pad3DConstNDHWC<T><<<grid, block, 0, stream>>>(out_size,
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);
}
}
}
} // namespace phi
PD_REGISTER_KERNEL(pad3d,
GPU,
ALL_LAYOUT,
phi::Pad3dKernel,
phi::dtype::float16,
float,
double,
int,
int64_t) {}
paddle/phi/kernels/pad3d_grad_kernel.h 0 → 100644
浏览文件 @ 382e460b
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#pragma once
#include "paddle/phi/common/scalar_array.h"
#include "paddle/phi/core/dense_tensor.h"
namespace phi {
template <typename T, typename Context>
void Pad3dGradKernel(const Context& dev_ctx,
const DenseTensor& x,
const DenseTensor& out_grad,
const ScalarArray& paddings,
const std::string& mode,
float pad_value,
const std::string& data_format,
DenseTensor* x_grad);
} // namespace phi
paddle/phi/kernels/pad3d_kernel.h 0 → 100644
浏览文件 @ 382e460b
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#pragma once
#include "paddle/phi/common/scalar_array.h"
#include "paddle/phi/core/dense_tensor.h"
namespace phi {
template <typename T, typename Context>
void Pad3dKernel(const Context& dev_ctx,
const DenseTensor& x,
const ScalarArray& paddings,
const std::string& mode,
float pad_value,
const std::string& data_format,
DenseTensor* out);
} // namespace phi
paddle/phi/ops/compat/pad3d_sig.cc 0 → 100644
浏览文件 @ 382e460b
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/phi/core/compat/op_utils.h"
namespace phi {
KernelSignature Pad3dOpArgumentMapping(const ArgumentMappingContext& ctx) {
if (ctx.HasInput("Paddings")) {
return KernelSignature(
"pad3d", {"X"}, {"Paddings", "mode", "value", "data_format"}, {"Out"});
}
return KernelSignature(
"pad3d", {"X"}, {"paddings", "mode", "value", "data_format"}, {"Out"});
}
KernelSignature Pad3dGradOpArgumentMapping(const ArgumentMappingContext& ctx) {
if (ctx.HasInput("Paddings")) {
return KernelSignature("pad3d_grad",
{"X", GradVarName("Out")},
{"Paddings", "mode", "value", "data_format"},
{GradVarName("X")});
}
return KernelSignature("pad3d_grad",
{"X", GradVarName("Out")},
{"paddings", "mode", "value", "data_format"},
{GradVarName("X")});
}
} // namespace phi
PD_REGISTER_ARG_MAPPING_FN(pad3d, phi::Pad3dOpArgumentMapping);
PD_REGISTER_ARG_MAPPING_FN(pad3d_grad, phi::Pad3dGradOpArgumentMapping);
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