chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
@@ -0,0 +1,740 @@
|
||||
// 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/phi/backends/gpu/gpu_context.h"
|
||||
#include "paddle/phi/backends/gpu/gpu_primitives.h"
|
||||
#include "paddle/phi/core/kernel_registry.h"
|
||||
#include "paddle/phi/kernels/full_kernel.h"
|
||||
namespace phi {
|
||||
|
||||
template <typename T, typename IndexType>
|
||||
__global__ void Pad3DConstNCDHW(const IndexType nthreads,
|
||||
const T* in_data,
|
||||
const IndexType num,
|
||||
const IndexType channels,
|
||||
const IndexType in_depth,
|
||||
const IndexType in_height,
|
||||
const IndexType in_width,
|
||||
const IndexType out_depth,
|
||||
const IndexType out_height,
|
||||
const IndexType out_width,
|
||||
const IndexType pad_front,
|
||||
const IndexType pad_top,
|
||||
const IndexType pad_left,
|
||||
T value,
|
||||
T* out_data) {
|
||||
CUDA_KERNEL_LOOP_TYPE(index, nthreads, IndexType) {
|
||||
IndexType nc = index / out_width;
|
||||
|
||||
const IndexType out_w = index % out_width;
|
||||
const IndexType out_h = nc % out_height;
|
||||
nc /= out_height;
|
||||
const IndexType out_d = nc % out_depth;
|
||||
nc /= out_depth;
|
||||
|
||||
IndexType in_d = out_d - pad_front;
|
||||
IndexType in_h = out_h - pad_top;
|
||||
IndexType 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, typename IndexType>
|
||||
__global__ void Pad3DConstNDHWC(const IndexType nthreads,
|
||||
const T* in_data,
|
||||
const IndexType num,
|
||||
const IndexType channels,
|
||||
const IndexType in_depth,
|
||||
const IndexType in_height,
|
||||
const IndexType in_width,
|
||||
const IndexType out_depth,
|
||||
const IndexType out_height,
|
||||
const IndexType out_width,
|
||||
const IndexType pad_front,
|
||||
const IndexType pad_top,
|
||||
const IndexType pad_left,
|
||||
T value,
|
||||
T* out_data) {
|
||||
CUDA_KERNEL_LOOP_TYPE(index, nthreads, IndexType) {
|
||||
IndexType n = index / channels;
|
||||
const IndexType c = index % channels;
|
||||
const IndexType out_w = n % out_width;
|
||||
n /= out_width;
|
||||
const IndexType out_h = n % out_height;
|
||||
n /= out_height;
|
||||
const IndexType out_d = n % out_depth;
|
||||
n /= out_depth;
|
||||
const IndexType in_d = out_d - pad_front;
|
||||
const IndexType in_h = out_h - pad_top;
|
||||
const IndexType 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, typename IndexType>
|
||||
__global__ void Pad3DReflectNCDHW(const IndexType nthreads,
|
||||
const T* in_data,
|
||||
const IndexType num,
|
||||
const IndexType channels,
|
||||
const IndexType in_depth,
|
||||
const IndexType in_height,
|
||||
const IndexType in_width,
|
||||
const IndexType out_depth,
|
||||
const IndexType out_height,
|
||||
const IndexType out_width,
|
||||
const IndexType pad_front,
|
||||
const IndexType pad_top,
|
||||
const IndexType pad_left,
|
||||
T* out_data) {
|
||||
CUDA_KERNEL_LOOP_TYPE(index, nthreads, IndexType) {
|
||||
IndexType nc = index / out_width;
|
||||
|
||||
const IndexType out_w = index % out_width;
|
||||
const IndexType out_h = nc % out_height;
|
||||
nc /= out_height;
|
||||
const IndexType out_d = nc % out_depth;
|
||||
nc /= out_depth;
|
||||
|
||||
IndexType in_d = out_d - pad_front;
|
||||
IndexType in_h = out_h - pad_top;
|
||||
IndexType 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, typename IndexType>
|
||||
__global__ void Pad3DReflectNDHWC(const IndexType nthreads,
|
||||
const T* in_data,
|
||||
const IndexType num,
|
||||
const IndexType channels,
|
||||
const IndexType in_depth,
|
||||
const IndexType in_height,
|
||||
const IndexType in_width,
|
||||
const IndexType out_depth,
|
||||
const IndexType out_height,
|
||||
const IndexType out_width,
|
||||
const IndexType pad_front,
|
||||
const IndexType pad_top,
|
||||
const IndexType pad_left,
|
||||
T* out_data) {
|
||||
CUDA_KERNEL_LOOP_TYPE(index, nthreads, IndexType) {
|
||||
IndexType n = index / channels;
|
||||
const IndexType c = index % channels;
|
||||
const IndexType out_w = n % out_width;
|
||||
n /= out_width;
|
||||
const IndexType out_h = n % out_height;
|
||||
n /= out_height;
|
||||
const IndexType out_d = n % out_depth;
|
||||
n /= out_depth;
|
||||
IndexType in_d = out_d - pad_front;
|
||||
IndexType in_h = out_h - pad_top;
|
||||
IndexType 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, typename IndexType>
|
||||
__global__ void Pad3DReplicateNCDHW(const IndexType nthreads,
|
||||
const T* in_data,
|
||||
const IndexType num,
|
||||
const IndexType channels,
|
||||
const IndexType in_depth,
|
||||
const IndexType in_height,
|
||||
const IndexType in_width,
|
||||
const IndexType out_depth,
|
||||
const IndexType out_height,
|
||||
const IndexType out_width,
|
||||
const IndexType pad_front,
|
||||
const IndexType pad_top,
|
||||
const IndexType pad_left,
|
||||
T* out_data) {
|
||||
CUDA_KERNEL_LOOP_TYPE(index, nthreads, IndexType) {
|
||||
IndexType nc = index / out_width;
|
||||
|
||||
const IndexType out_w = index % out_width;
|
||||
const IndexType out_h = nc % out_height;
|
||||
nc /= out_height;
|
||||
const IndexType out_d = nc % out_depth;
|
||||
nc /= out_depth;
|
||||
|
||||
IndexType in_d =
|
||||
min(in_depth - 1, max(out_d - pad_front, static_cast<IndexType>(0)));
|
||||
IndexType in_h =
|
||||
min(in_height - 1, max(out_h - pad_top, static_cast<IndexType>(0)));
|
||||
IndexType in_w =
|
||||
min(in_width - 1, max(out_w - pad_left, static_cast<IndexType>(0)));
|
||||
|
||||
out_data[index] =
|
||||
in_data[(nc * in_depth * in_height + in_d * in_height + in_h) *
|
||||
in_width +
|
||||
in_w];
|
||||
}
|
||||
}
|
||||
|
||||
template <typename T, typename IndexType>
|
||||
__global__ void Pad3DReplicateNDHWC(const IndexType nthreads,
|
||||
const T* in_data,
|
||||
const IndexType num,
|
||||
const IndexType channels,
|
||||
const IndexType in_depth,
|
||||
const IndexType in_height,
|
||||
const IndexType in_width,
|
||||
const IndexType out_depth,
|
||||
const IndexType out_height,
|
||||
const IndexType out_width,
|
||||
const IndexType pad_front,
|
||||
const IndexType pad_top,
|
||||
const IndexType pad_left,
|
||||
T* out_data) {
|
||||
CUDA_KERNEL_LOOP_TYPE(index, nthreads, IndexType) {
|
||||
IndexType n = index / channels;
|
||||
const IndexType c = index % channels;
|
||||
const IndexType out_w = n % out_width;
|
||||
n /= out_width;
|
||||
const IndexType out_h = n % out_height;
|
||||
n /= out_height;
|
||||
const IndexType out_d = n % out_depth;
|
||||
n /= out_depth;
|
||||
|
||||
IndexType in_d =
|
||||
min(in_depth - 1, max(out_d - pad_front, static_cast<IndexType>(0)));
|
||||
IndexType in_h =
|
||||
min(in_height - 1, max(out_h - pad_top, static_cast<IndexType>(0)));
|
||||
IndexType in_w =
|
||||
min(in_width - 1, max(out_w - pad_left, static_cast<IndexType>(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, typename IndexType>
|
||||
__global__ void Pad3DCircularNCDHW(const IndexType nthreads,
|
||||
const T* in_data,
|
||||
const IndexType num,
|
||||
const IndexType channels,
|
||||
const IndexType in_depth,
|
||||
const IndexType in_height,
|
||||
const IndexType in_width,
|
||||
const IndexType out_depth,
|
||||
const IndexType out_height,
|
||||
const IndexType out_width,
|
||||
const IndexType pad_front,
|
||||
const IndexType pad_top,
|
||||
const IndexType pad_left,
|
||||
T* out_data) {
|
||||
CUDA_KERNEL_LOOP_TYPE(index, nthreads, IndexType) {
|
||||
IndexType nc = index / out_width;
|
||||
|
||||
const IndexType out_w = index % out_width;
|
||||
const IndexType out_h = nc % out_height;
|
||||
nc /= out_height;
|
||||
const IndexType out_d = nc % out_depth;
|
||||
nc /= out_depth;
|
||||
|
||||
IndexType in_d = ((out_d - pad_front) % in_depth + in_depth) % in_depth;
|
||||
IndexType in_h = ((out_h - pad_top) % in_height + in_height) % in_height;
|
||||
IndexType 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, typename IndexType>
|
||||
__global__ void Pad3DCircularNDHWC(const IndexType nthreads,
|
||||
const T* in_data,
|
||||
const IndexType num,
|
||||
const IndexType channels,
|
||||
const IndexType in_depth,
|
||||
const IndexType in_height,
|
||||
const IndexType in_width,
|
||||
const IndexType out_depth,
|
||||
const IndexType out_height,
|
||||
const IndexType out_width,
|
||||
const IndexType pad_front,
|
||||
const IndexType pad_top,
|
||||
const IndexType pad_left,
|
||||
T* out_data) {
|
||||
CUDA_KERNEL_LOOP_TYPE(index, nthreads, IndexType) {
|
||||
IndexType n = index / channels;
|
||||
const IndexType c = index % channels;
|
||||
const IndexType out_w = n % out_width;
|
||||
n /= out_width;
|
||||
const IndexType out_h = n % out_height;
|
||||
n /= out_height;
|
||||
const IndexType out_d = n % out_depth;
|
||||
n /= out_depth;
|
||||
|
||||
IndexType in_d = ((out_d - pad_front) % in_depth + in_depth) % in_depth;
|
||||
IndexType in_h = ((out_h - pad_top) % in_height + in_height) % in_height;
|
||||
IndexType 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 IntArray& paddings,
|
||||
const std::string& mode,
|
||||
double 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);
|
||||
if (x.numel() == 0) {
|
||||
Full<T, Context>(dev_ctx, out->dims(), pad_value, out);
|
||||
return;
|
||||
}
|
||||
|
||||
int64_t channels = in_dims[1];
|
||||
int64_t in_depth = in_dims[2];
|
||||
int64_t in_height = in_dims[3];
|
||||
int64_t in_width = in_dims[4];
|
||||
int64_t out_depth = out_dims[2];
|
||||
int64_t out_height = out_dims[3];
|
||||
int64_t 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 int64_t pad_left = pads[0];
|
||||
const int64_t pad_top = pads[2];
|
||||
const int64_t pad_front = pads[4];
|
||||
const int64_t num = in_dims[0];
|
||||
|
||||
auto stream = dev_ctx.stream();
|
||||
int block = PADDLE_CUDA_NUM_THREADS;
|
||||
const size_t out_size = out->numel();
|
||||
uint32_t grid = (out_size + block - 1) / block;
|
||||
|
||||
bool use_int32_index = true;
|
||||
if (out_size > std::numeric_limits<int32_t>::max()) {
|
||||
use_int32_index = false;
|
||||
} else {
|
||||
for (int i = 0; i < out_dims.size(); ++i) {
|
||||
if (out_dims[i] > std::numeric_limits<int32_t>::max()) {
|
||||
use_int32_index = false;
|
||||
break;
|
||||
}
|
||||
}
|
||||
}
|
||||
if (use_int32_index) {
|
||||
if (data_format == "NCDHW") {
|
||||
if (mode == "reflect") {
|
||||
Pad3DReflectNCDHW<T, int32_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, int32_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, int32_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, int32_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, int32_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, int32_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, int32_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, int32_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 (data_format == "NCDHW") {
|
||||
if (mode == "reflect") {
|
||||
Pad3DReflectNCDHW<T, int64_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, int64_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, int64_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, int64_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, int64_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, int64_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, int64_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, int64_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::float16,
|
||||
phi::bfloat16,
|
||||
float,
|
||||
double,
|
||||
int,
|
||||
int64_t,
|
||||
phi::complex64,
|
||||
phi::complex128) {}
|
||||
Reference in New Issue
Block a user