163 lines
6.0 KiB
C++
163 lines
6.0 KiB
C++
// 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/roi_align_kernel.h"
|
|
|
|
#include "paddle/phi/backends/xpu/enforce_xpu.h"
|
|
#include "paddle/phi/backends/xpu/xpu_context.h"
|
|
#include "paddle/phi/common/memory_utils.h"
|
|
#include "paddle/phi/core/kernel_registry.h"
|
|
#include "paddle/phi/kernels/full_kernel.h"
|
|
namespace phi {
|
|
|
|
template <typename T, typename Context>
|
|
void RoiAlignKernel(const Context& dev_ctx,
|
|
const DenseTensor& x,
|
|
const DenseTensor& boxes,
|
|
const optional<DenseTensor>& boxes_num,
|
|
int pooled_height,
|
|
int pooled_width,
|
|
float spatial_scale,
|
|
int sampling_ratio,
|
|
bool aligned,
|
|
DenseTensor* out) {
|
|
const auto& in_dims = x.dims();
|
|
int batch_size = in_dims[0];
|
|
int channels = in_dims[1];
|
|
int height = in_dims[2];
|
|
int width = in_dims[3];
|
|
|
|
int rois_num = boxes.dims()[0];
|
|
|
|
if (x.numel() == 0 || boxes.numel() == 0) {
|
|
Full<T, Context>(dev_ctx, out->dims(), 0, out);
|
|
return;
|
|
}
|
|
|
|
DenseTensor roi_batch_id_list;
|
|
roi_batch_id_list.Resize({rois_num});
|
|
auto cplace = CPUPlace();
|
|
int* roi_batch_id_data = dev_ctx.template HostAlloc<int>(&roi_batch_id_list);
|
|
auto xplace = dev_ctx.GetPlace();
|
|
int rois_batch_size = 0;
|
|
int* cpu_lod = nullptr;
|
|
if (boxes_num) {
|
|
rois_batch_size = boxes_num->numel();
|
|
PADDLE_ENFORCE_EQ(
|
|
rois_batch_size,
|
|
batch_size,
|
|
errors::InvalidArgument(
|
|
"The rois_batch_size and imgs "
|
|
"batch_size must be the same. But received rois_batch_size = %d, "
|
|
"batch_size = %d",
|
|
rois_batch_size,
|
|
batch_size));
|
|
|
|
if (boxes_num->dtype() == DataType::INT64) {
|
|
std::vector<int64_t> rois_num_list(rois_batch_size);
|
|
memory_utils::Copy(cplace,
|
|
rois_num_list.data(),
|
|
xplace,
|
|
boxes_num->data<int64_t>(),
|
|
sizeof(int64_t) * rois_batch_size);
|
|
cpu_lod = new int[rois_batch_size + 1];
|
|
cpu_lod[0] = 0;
|
|
for (int64_t i = 0; i < rois_batch_size; i++) {
|
|
cpu_lod[i + 1] = cpu_lod[i] + rois_num_list[i];
|
|
}
|
|
} else if (boxes_num->dtype() == DataType::INT32) {
|
|
std::vector<int> rois_num_list(rois_batch_size);
|
|
memory_utils::Copy(cplace,
|
|
rois_num_list.data(),
|
|
xplace,
|
|
boxes_num->data<int>(),
|
|
sizeof(int) * rois_batch_size);
|
|
cpu_lod = new int[rois_batch_size + 1];
|
|
cpu_lod[0] = 0;
|
|
for (int i = 0; i < rois_batch_size; i++) {
|
|
cpu_lod[i + 1] = cpu_lod[i] + rois_num_list[i];
|
|
}
|
|
}
|
|
} else {
|
|
auto lod = boxes.lod();
|
|
PADDLE_ENFORCE_EQ(lod.empty(),
|
|
false,
|
|
errors::InvalidArgument("Input(ROIs) in ROIAlignOp does "
|
|
"not contain LoD information."));
|
|
auto rois_lod = lod.back();
|
|
rois_batch_size = rois_lod.size() - 1;
|
|
PADDLE_ENFORCE_EQ(
|
|
rois_batch_size,
|
|
batch_size,
|
|
errors::InvalidArgument(
|
|
"The batch size of rois and batch size "
|
|
"of images must be the same. But received rois batch size = %d, "
|
|
"and images batch size = %d",
|
|
rois_batch_size,
|
|
batch_size));
|
|
int rois_num_with_lod = rois_lod[rois_batch_size];
|
|
PADDLE_ENFORCE_EQ(
|
|
rois_num,
|
|
rois_num_with_lod,
|
|
errors::InvalidArgument(
|
|
"The actual number of rois and the number of rois "
|
|
"provided from Input(RoIsLoD) in RoIAlign must be the same."
|
|
" But received actual number of rois is %d, and the number "
|
|
"of rois from RoIsLoD is %d",
|
|
rois_num,
|
|
rois_num_with_lod));
|
|
for (int n = 0; n < rois_batch_size; ++n) {
|
|
for (size_t i = rois_lod[n]; i < rois_lod[n + 1]; ++i) {
|
|
roi_batch_id_data[i] = n;
|
|
}
|
|
}
|
|
cpu_lod = new int[rois_batch_size + 1];
|
|
for (int i = 0; i < rois_batch_size + 1; i++) {
|
|
cpu_lod[i] = rois_lod[i];
|
|
}
|
|
}
|
|
|
|
xpu::ctx_guard RAII_GUARD(dev_ctx.x_context());
|
|
int* roi_id_data = RAII_GUARD.alloc_l3_or_gm<int>(rois_batch_size + 1);
|
|
PADDLE_ENFORCE_NOT_NULL(
|
|
roi_id_data, errors::ResourceExhausted("XPU has no enough memory"));
|
|
memory_utils::Copy(xplace,
|
|
roi_id_data,
|
|
cplace,
|
|
cpu_lod,
|
|
(rois_batch_size + 1) * sizeof(int));
|
|
delete[] cpu_lod;
|
|
int r = xpu::roi_align<T, int>(dev_ctx.x_context(),
|
|
x.data<T>(),
|
|
dev_ctx.template Alloc<T>(out),
|
|
boxes.data<T>(),
|
|
roi_id_data,
|
|
batch_size,
|
|
channels,
|
|
height,
|
|
width,
|
|
out->dims()[0],
|
|
pooled_height,
|
|
pooled_width,
|
|
spatial_scale,
|
|
sampling_ratio,
|
|
true,
|
|
aligned);
|
|
PADDLE_ENFORCE_XDNN_SUCCESS(r, "roi_align");
|
|
}
|
|
|
|
} // namespace phi
|
|
|
|
PD_REGISTER_KERNEL(roi_align, XPU, ALL_LAYOUT, phi::RoiAlignKernel, float) {}
|