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未验证 提交 0a878be8 编写于 作者: F FDInSky 提交者： GitHub
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modify some op for dyg rcnn (#23648) 

* test=develop modify some op for dyg rcnn
上级 ab05cdc4
隐藏空白更改
内联 并排
Showing with 380 addition and 116 deletion
paddle/fluid/operators/detection/generate_proposals_op.cc
浏览文件 @ 0a878be8
......@@ -341,6 +341,7 @@ class GenerateProposalsKernel : public framework::OpKernel<T> {
lod0.push_back(0);
anchors.Resize({anchors.numel() / 4, 4});
variances.Resize({variances.numel() / 4, 4});
std::vector<int64_t> tmp_lod;
int64_t num_proposals = 0;
for (int64_t i = 0; i < num; ++i) {
......@@ -362,6 +363,16 @@ class GenerateProposalsKernel : public framework::OpKernel<T> {
AppendProposals(rpn_roi_probs, num_proposals, scores);
num_proposals += proposals.dims()[0];
lod0.push_back(num_proposals);
tmp_lod.push_back(num_proposals);
}
if (context.HasOutput("RpnRoisLod")) {
auto *rpn_rois_lod = context.Output<Tensor>("RpnRoisLod");
rpn_rois_lod->mutable_data<int64_t>({num}, context.GetPlace());
int64_t *lod_data = rpn_rois_lod->data<int64_t>();
for (int i = 0; i < num; i++) {
lod_data[i] = tmp_lod[i];
}
rpn_rois_lod->Resize({num, 1});
}
rpn_rois->set_lod(lod);
rpn_roi_probs->set_lod(lod);
......@@ -464,6 +475,7 @@ class GenerateProposalsOpMaker : public framework::OpProtoAndCheckerMaker {
"(LoDTensor), Output proposals with shape (rois_num, 4).");
AddOutput("RpnRoiProbs",
"(LoDTensor) Scores of proposals with shape (rois_num, 1).");
AddOutput("RpnRoisLod", "(Tensor), rpn rois's lod info").AsDispensable();
AddAttr<int>("pre_nms_topN",
"Number of top scoring RPN proposals to keep before "
"applying NMS.");
......
paddle/fluid/operators/detection/generate_proposals_op.cu
浏览文件 @ 0a878be8
......@@ -416,9 +416,12 @@ class CUDAGenerateProposalsKernel : public framework::OpKernel<T> {
T *rpn_roi_probs_data = rpn_roi_probs->data<T>();
auto place = boost::get<platform::CUDAPlace>(dev_ctx.GetPlace());
auto cpu_place = platform::CPUPlace();
int64_t num_proposals = 0;
std::vector<size_t> offset(1, 0);
std::vector<int64_t> tmp_lod;
for (int64_t i = 0; i < num; ++i) {
Tensor im_info_slice = im_info->Slice(i, i + 1);
Tensor bbox_deltas_slice = bbox_deltas_swap.Slice(i, i + 1);
......@@ -444,6 +447,15 @@ class CUDAGenerateProposalsKernel : public framework::OpKernel<T> {
dev_ctx.Wait();
num_proposals += proposals.dims()[0];
offset.emplace_back(num_proposals);
tmp_lod.push_back(num_proposals);
}
if (context.HasOutput("RpnRoisLod")) {
auto *rpn_rois_lod = context.Output<Tensor>("RpnRoisLod");
rpn_rois_lod->mutable_data<int64_t>({num}, context.GetPlace());
int64_t *lod_data = rpn_rois_lod->data<int64_t>();
memory::Copy(place, lod_data, cpu_place, &tmp_lod[0],
sizeof(int64_t) * num, dev_ctx.stream());
rpn_rois_lod->Resize({num});
}
framework::LoD lod;
lod.emplace_back(offset);
......
paddle/fluid/operators/roi_align_op.cc
浏览文件 @ 0a878be8
......@@ -35,6 +35,14 @@ class ROIAlignOp : public framework::OperatorWithKernel {
auto input_dims = ctx->GetInputDim("X");
auto rois_dims = ctx->GetInputDim("ROIs");
if (ctx->HasInput("RoisLod")) {
auto rois_lod_dims = ctx->GetInputDim("RoisLod");
PADDLE_ENFORCE_EQ(
rois_lod_dims.size(), 1,
platform::errors::InvalidArgument("The RoisLod dimension should be 1"
", but got dim = %d",
rois_lod_dims.size()));
}
PADDLE_ENFORCE_EQ(
input_dims.size(), 4,
platform::errors::InvalidArgument(
......@@ -136,6 +144,10 @@ class ROIAlignOpMaker : public framework::OpProtoAndCheckerMaker {
"given as [[x1, y1, x2, y2], ...]. "
"(x1, y1) is the top left coordinates, and "
"(x2, y2) is the bottom right coordinates.");
AddInput("RoisLod",
"(Tensor), "
"The lod info of rois.")
.AsDispensable();
AddOutput("Out",
"(Tensor), "
"The output of ROIAlignOp is a 4-D tensor with shape "
......@@ -190,6 +202,7 @@ class ROIAlignGradMaker : public framework::SingleGradOpMaker<T> {
op->SetType("roi_align_grad");
op->SetInput("X", this->Input("X"));
op->SetInput("ROIs", this->Input("ROIs"));
op->SetInput("RoisLod", this->Input("RoisLod"));
op->SetInput(framework::GradVarName("Out"), this->OutputGrad("Out"));
op->SetOutput(framework::GradVarName("X"), this->InputGrad("X"));
op->SetAttrMap(this->Attrs());
......@@ -210,8 +223,10 @@ REGISTER_OPERATOR(roi_align_grad, ops::ROIAlignGradOp,
REGISTER_OP_CPU_KERNEL(
roi_align,
ops::CPUROIAlignOpKernel<paddle::platform::CPUDeviceContext, float>,
ops::CPUROIAlignOpKernel<paddle::platform::CPUDeviceContext, double>);
ops::CPUROIAlignOpKernel<paddle::platform::CPUDeviceContext, double>,
ops::CPUROIAlignOpKernel<paddle::platform::CPUDeviceContext, int>);
REGISTER_OP_CPU_KERNEL(
roi_align_grad,
ops::CPUROIAlignGradOpKernel<paddle::platform::CPUDeviceContext, float>,
ops::CPUROIAlignGradOpKernel<paddle::platform::CPUDeviceContext, double>);
ops::CPUROIAlignGradOpKernel<paddle::platform::CPUDeviceContext, double>,
ops::CPUROIAlignGradOpKernel<paddle::platform::CPUDeviceContext, int>);
paddle/fluid/operators/roi_align_op.cu
浏览文件 @ 0a878be8
......@@ -12,6 +12,7 @@ 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 <vector>
#include "paddle/fluid/memory/memory.h"
#include "paddle/fluid/operators/roi_align_op.h"
#include "paddle/fluid/platform/cuda_primitives.h"
......@@ -258,32 +259,53 @@ class GPUROIAlignOpKernel : public framework::OpKernel<T> {
roi_batch_id_list.Resize({rois_num});
auto cplace = platform::CPUPlace();
int* roi_batch_id_data = roi_batch_id_list.mutable_data<int>(cplace);
auto lod = rois->lod();
PADDLE_ENFORCE_EQ(
lod.empty(), false,
"Input(ROIs) Tensor of ROIAlignOp does not contain LoD information.");
auto rois_lod = lod.back();
int rois_batch_size = rois_lod.size() - 1;
PADDLE_ENFORCE_EQ(
rois_batch_size, batch_size,
platform::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));
int rois_num_with_lod = rois_lod[rois_batch_size];
PADDLE_ENFORCE_EQ(rois_num, rois_num_with_lod,
"The rois_num from input and lod must be the same.");
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;
auto& dev_ctx = ctx.cuda_device_context();
auto gplace = boost::get<platform::CUDAPlace>(ctx.GetPlace());
if (ctx.HasInput("RoisLod")) {
auto* rois_lod = ctx.Input<Tensor>("RoisLod");
int rois_batch_size = rois_lod->numel();
PADDLE_ENFORCE_EQ(
rois_batch_size - 1, batch_size,
platform::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));
std::vector<int64_t> rois_lod_(rois_batch_size);
memory::Copy(cplace, rois_lod_.data(), gplace, rois_lod->data<int64_t>(),
sizeof(int64_t) * rois_batch_size, 0);
for (int n = 0; n < rois_batch_size - 1; ++n) {
for (size_t i = rois_lod_[n]; i < rois_lod_[n + 1]; ++i) {
roi_batch_id_data[i] = n;
}
}
} else {
auto lod = rois->lod();
PADDLE_ENFORCE_EQ(
lod.empty(), false,
"Input(ROIs) Tensor of ROIAlignOp does not contain LoD information.");
auto rois_lod = lod.back();
int rois_batch_size = rois_lod.size() - 1;
PADDLE_ENFORCE_EQ(
rois_batch_size, batch_size,
platform::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));
int rois_num_with_lod = rois_lod[rois_batch_size];
PADDLE_ENFORCE_EQ(rois_num, rois_num_with_lod,
"The rois_num from input and lod must be the same.");
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;
}
}
}
auto& dev_ctx = ctx.cuda_device_context();
int bytes = roi_batch_id_list.numel() * sizeof(int);
auto roi_ptr = memory::Alloc(dev_ctx, bytes);
int* roi_id_data = reinterpret_cast<int*>(roi_ptr->ptr());
const auto gplace = boost::get<platform::CUDAPlace>(ctx.GetPlace());
memory::Copy(gplace, roi_id_data, cplace, roi_batch_id_data, bytes,
dev_ctx.stream());
GPUROIAlignForward<T><<<blocks, threads, 0, dev_ctx.stream()>>>(
......@@ -320,19 +342,33 @@ class GPUROIAlignGradOpKernel : public framework::OpKernel<T> {
roi_batch_id_list.Resize({rois_num});
auto cplace = platform::CPUPlace();
int* roi_batch_id_data = roi_batch_id_list.mutable_data<int>(cplace);
auto rois_lod = rois->lod().back();
int rois_batch_size = rois_lod.size() - 1;
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;
auto& dev_ctx = ctx.cuda_device_context();
auto gplace = boost::get<platform::CUDAPlace>(ctx.GetPlace());
if (ctx.HasInput("RoisLod")) {
auto* rois_lod = ctx.Input<Tensor>("RoisLod");
int rois_batch_size = rois_lod->numel();
std::vector<int64_t> rois_lod_(rois_batch_size);
memory::Copy(cplace, rois_lod_.data(), gplace, rois_lod->data<int64_t>(),
sizeof(int64_t) * rois_batch_size, 0);
for (int n = 0; n < rois_batch_size - 1; ++n) {
for (size_t i = rois_lod_[n]; i < rois_lod_[n + 1]; ++i) {
roi_batch_id_data[i] = n;
}
}
} else {
auto rois_lod = rois->lod().back();
int rois_batch_size = rois_lod.size() - 1;
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;
}
}
}
auto& dev_ctx = ctx.cuda_device_context();
auto roi_ptr =
memory::Alloc(dev_ctx, roi_batch_id_list.numel() * sizeof(int));
int* roi_id_data = reinterpret_cast<int*>(roi_ptr->ptr());
int bytes = roi_batch_id_list.numel() * sizeof(int);
const auto gplace = boost::get<platform::CUDAPlace>(ctx.GetPlace());
memory::Copy(gplace, roi_id_data, cplace, roi_batch_id_data, bytes,
dev_ctx.stream());
in_grad->mutable_data<T>(ctx.GetPlace());
......
paddle/fluid/operators/roi_align_op.h
浏览文件 @ 0a878be8
......@@ -12,6 +12,7 @@ limitations under the License. */
#pragma once
#include <algorithm>
#include <limits>
#include <vector>
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/math/math_function.h"
......@@ -163,26 +164,44 @@ class CPUROIAlignOpKernel : public framework::OpKernel<T> {
roi_batch_id_list.Resize({rois_num});
int* roi_batch_id_data =
roi_batch_id_list.mutable_data<int>(ctx.GetPlace());
auto lod = rois->lod();
PADDLE_ENFORCE_EQ(
lod.empty(), false,
"Input(ROIs) Tensor of ROIAlignOp does not contain LoD information.");
auto rois_lod = lod.back();
int rois_batch_size = rois_lod.size() - 1;
PADDLE_ENFORCE_EQ(
rois_batch_size, batch_size,
platform::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));
int rois_num_with_lod = rois_lod[rois_batch_size];
PADDLE_ENFORCE_EQ(rois_num, rois_num_with_lod,
"The rois_num from input and lod must be the same.");
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;
int rois_batch_size;
if (ctx.HasInput("RoisLod")) {
auto* rois_lod_t = ctx.Input<framework::Tensor>("RoisLod");
rois_batch_size = rois_lod_t->numel();
PADDLE_ENFORCE_EQ(
rois_batch_size - 1, batch_size,
platform::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));
auto* rois_lod = rois_lod_t->data<int64_t>();
for (int n = 0; n < rois_batch_size - 1; ++n) {
for (int i = rois_lod[n]; i < rois_lod[n + 1]; ++i) {
roi_batch_id_data[i] = n;
}
}
} else {
auto lod = rois->lod();
PADDLE_ENFORCE_EQ(
lod.empty(), false,
"Input(ROIs) Tensor of ROIAlignOp does not contain LoD information.");
auto rois_lod = lod.back();
int rois_batch_size = rois_lod.size() - 1;
PADDLE_ENFORCE_EQ(
rois_batch_size, batch_size,
platform::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));
int rois_num_with_lod = rois_lod[rois_batch_size];
PADDLE_ENFORCE_EQ(rois_num, rois_num_with_lod,
"The rois_num from input and lod must be the same.");
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;
}
}
}
T* output_data = out->mutable_data<T>(ctx.GetPlace());
......@@ -276,11 +295,23 @@ class CPUROIAlignGradOpKernel : public framework::OpKernel<T> {
int* roi_batch_id_data =
roi_batch_id_list.mutable_data<int>(ctx.GetPlace());
auto rois_lod = rois->lod().back();
int rois_batch_size = rois_lod.size() - 1;
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;
int rois_batch_size;
if (ctx.HasInput("RoisLod")) {
auto* rois_lod_t = ctx.Input<framework::Tensor>("RoisLod");
rois_batch_size = rois_lod_t->numel();
auto* rois_lod = rois_lod_t->data<int64_t>();
for (int n = 0; n < rois_batch_size - 1; ++n) {
for (int i = rois_lod[n]; i < rois_lod[n + 1]; ++i) {
roi_batch_id_data[i] = n;
}
}
} else {
auto rois_lod = rois->lod().back();
rois_batch_size = rois_lod.size() - 1;
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;
}
}
}
in_grad->mutable_data<T>(ctx.GetPlace());
......
paddle/fluid/operators/roi_pool_op.cc
浏览文件 @ 0a878be8
......@@ -36,7 +36,10 @@ class ROIPoolOp : public framework::OperatorWithKernel {
"Output(Argmax) of ROIPoolOp should not be null.");
auto input_dims = ctx->GetInputDim("X");
auto rois_dims = ctx->GetInputDim("ROIs");
if (ctx->HasInput("RoisLod")) {
auto rois_lod_dims = ctx->GetInputDim("RoisLod");
PADDLE_ENFORCE(rois_lod_dims.size() == 1, "");
}
PADDLE_ENFORCE(input_dims.size() == 4,
"The format of input tensor is NCHW.");
PADDLE_ENFORCE(rois_dims.size() == 2,
......@@ -115,6 +118,7 @@ class ROIPoolOpMaker : public framework::OpProtoAndCheckerMaker {
"Where batch_id is the id of the data, "
"(x1, y1) is the top left coordinates, and "
"(x2, y2) is the bottom right coordinates.");
AddInput("RoisLod", "(Tensor), The lod info of rois.").AsDispensable();
AddOutput("Out",
"(Tensor), "
"The output of ROIPoolOp is a 4-D tensor with shape "
......@@ -171,6 +175,7 @@ class ROIPoolGradMaker : public framework::SingleGradOpMaker<T> {
op->SetType("roi_pool_grad");
op->SetInput("X", this->Input("X"));
op->SetInput("ROIs", this->Input("ROIs"));
op->SetInput("RoisLod", this->Input("RoisLod"));
op->SetInput("Argmax", this->Output("Argmax"));
op->SetInput(framework::GradVarName("Out"), this->OutputGrad("Out"));
op->SetOutput(framework::GradVarName("X"), this->InputGrad("X"));
......@@ -189,8 +194,10 @@ REGISTER_OPERATOR(roi_pool_grad, ops::ROIPoolGradOp);
REGISTER_OP_CPU_KERNEL(
roi_pool,
ops::CPUROIPoolOpKernel<paddle::platform::CPUDeviceContext, float>,
ops::CPUROIPoolOpKernel<paddle::platform::CPUDeviceContext, double>);
ops::CPUROIPoolOpKernel<paddle::platform::CPUDeviceContext, double>,
ops::CPUROIPoolOpKernel<paddle::platform::CPUDeviceContext, int>);
REGISTER_OP_CPU_KERNEL(
roi_pool_grad,
ops::CPUROIPoolGradOpKernel<paddle::platform::CPUDeviceContext, float>,
ops::CPUROIPoolGradOpKernel<paddle::platform::CPUDeviceContext, double>);
ops::CPUROIPoolGradOpKernel<paddle::platform::CPUDeviceContext, double>,
ops::CPUROIPoolGradOpKernel<paddle::platform::CPUDeviceContext, int>);
paddle/fluid/operators/roi_pool_op.cu
浏览文件 @ 0a878be8
......@@ -11,7 +11,7 @@ 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 <vector>
#include "paddle/fluid/memory/memory.h"
#include "paddle/fluid/operators/roi_pool_op.h"
#include "paddle/fluid/platform/cuda_primitives.h"
......@@ -155,25 +155,40 @@ class GPUROIPoolOpKernel : public framework::OpKernel<T> {
roi_batch_id_list.Resize({rois_num});
auto cplace = platform::CPUPlace();
int* roi_batch_id_data = roi_batch_id_list.mutable_data<int>(cplace);
auto rois_lod = rois->lod().back();
int rois_batch_size = rois_lod.size() - 1;
PADDLE_ENFORCE_EQ(
rois_batch_size, batch_size,
"The rois_batch_size and imgs batch_size must be the same.");
int rois_num_with_lod = rois_lod[rois_batch_size];
PADDLE_ENFORCE_EQ(rois_num, rois_num_with_lod,
"The rois_num from input and lod must be the same.");
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;
auto& dev_ctx = ctx.cuda_device_context();
auto gplace = boost::get<platform::CUDAPlace>(ctx.GetPlace());
if (ctx.HasInput("RoisLod")) {
auto* rois_lod = ctx.Input<Tensor>("RoisLod");
int rois_batch_size = rois_lod->numel();
PADDLE_ENFORCE_EQ(
rois_batch_size - 1, batch_size,
"The rois_batch_size and imgs batch_size must be the same.");
std::vector<int64_t> rois_lod_(rois_batch_size);
memory::Copy(cplace, rois_lod_.data(), gplace, rois_lod->data<int64_t>(),
sizeof(int64_t) * rois_batch_size, 0);
for (int n = 0; n < rois_batch_size - 1; ++n) {
for (size_t i = rois_lod_[n]; i < rois_lod_[n + 1]; ++i) {
roi_batch_id_data[i] = n;
}
}
} else {
auto rois_lod = rois->lod().back();
int rois_batch_size = rois_lod.size() - 1;
PADDLE_ENFORCE_EQ(
rois_batch_size, batch_size,
"The rois_batch_size and imgs batch_size must be the same.");
int rois_num_with_lod = rois_lod[rois_batch_size];
PADDLE_ENFORCE_EQ(rois_num, rois_num_with_lod,
"The rois_num from input and lod must be the same.");
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;
}
}
}
auto& dev_ctx = ctx.cuda_device_context();
int bytes = roi_batch_id_list.numel() * sizeof(int);
auto roi_ptr = memory::Alloc(dev_ctx, bytes);
int* roi_id_data = reinterpret_cast<int*>(roi_ptr->ptr());
const auto gplace = boost::get<platform::CUDAPlace>(ctx.GetPlace());
memory::Copy(gplace, roi_id_data, cplace, roi_batch_id_data, bytes,
dev_ctx.stream());
......@@ -191,6 +206,7 @@ class GPUROIPoolGradOpKernel : public framework::OpKernel<T> {
void Compute(const framework::ExecutionContext& ctx) const override {
auto* in = ctx.Input<Tensor>("X");
auto* rois = ctx.Input<LoDTensor>("ROIs");
auto* rois_lod = ctx.Input<Tensor>("RoisLod");
auto* argmax = ctx.Input<Tensor>("Argmax");
auto* out_grad = ctx.Input<Tensor>(framework::GradVarName("Out"));
......@@ -210,19 +226,33 @@ class GPUROIPoolGradOpKernel : public framework::OpKernel<T> {
roi_batch_id_list.Resize({rois_num});
auto cplace = platform::CPUPlace();
int* roi_batch_id_data = roi_batch_id_list.mutable_data<int>(cplace);
auto rois_lod = rois->lod().back();
int rois_batch_size = rois_lod.size() - 1;
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;
}
}
auto& dev_ctx = ctx.cuda_device_context();
auto gplace = boost::get<platform::CUDAPlace>(ctx.GetPlace());
if (ctx.HasInput("RoisLod")) {
auto* rois_lod = ctx.Input<Tensor>("RoisLod");
int rois_batch_size = rois_lod->numel();
std::vector<int64_t> rois_lod_(rois_batch_size);
memory::Copy(cplace, rois_lod_.data(), gplace,
rois_lod->data<int64_t>(),
sizeof(int64_t) * rois_batch_size, 0);
for (int n = 0; n < rois_batch_size - 1; ++n) {
for (size_t i = rois_lod_[n]; i < rois_lod_[n + 1]; ++i) {
roi_batch_id_data[i] = n;
}
}
} else {
auto rois_lod = rois->lod().back();
int rois_batch_size = rois_lod.size() - 1;
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;
}
}
}
int bytes = roi_batch_id_list.numel() * sizeof(int);
auto roi_ptr = memory::Alloc(dev_ctx, bytes);
int* roi_id_data = reinterpret_cast<int*>(roi_ptr->ptr());
const auto gplace = boost::get<platform::CUDAPlace>(ctx.GetPlace());
memory::Copy(gplace, roi_id_data, cplace, roi_batch_id_data, bytes,
dev_ctx.stream());
......
paddle/fluid/operators/roi_pool_op.h
浏览文件 @ 0a878be8
......@@ -15,7 +15,9 @@ limitations under the License. */
#pragma once
#include <algorithm>
#include <limits>
#include <vector>
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/memory/memcpy.h"
#include "paddle/fluid/operators/math/math_function.h"
namespace paddle {
......@@ -55,17 +57,32 @@ class CPUROIPoolOpKernel : public framework::OpKernel<T> {
int* roi_batch_id_data =
roi_batch_id_list.mutable_data<int>(ctx.GetPlace());
auto rois_lod = rois->lod().back();
int rois_batch_size = rois_lod.size() - 1;
PADDLE_ENFORCE_EQ(
rois_batch_size, batch_size,
"The rois_batch_size and imgs batch_size must be the same.");
int rois_num_with_lod = rois_lod[rois_batch_size];
PADDLE_ENFORCE_EQ(rois_num, rois_num_with_lod,
"The rois_num from input and lod must be the same.");
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;
int rois_batch_size;
if (ctx.HasInput("RoisLod")) {
auto* rois_lod_t = ctx.Input<framework::Tensor>("RoisLod");
rois_batch_size = rois_lod_t->numel();
PADDLE_ENFORCE_EQ(
rois_batch_size - 1, batch_size,
"The rois_batch_size and imgs batch_size must be the same.");
auto* rois_lod = rois_lod_t->data<int64_t>();
for (int n = 0; n < rois_batch_size - 1; ++n) {
for (int i = rois_lod[n]; i < rois_lod[n + 1]; ++i) {
roi_batch_id_data[i] = n;
}
}
} else {
auto rois_lod = rois->lod().back();
rois_batch_size = rois_lod.size() - 1;
PADDLE_ENFORCE_EQ(
rois_batch_size, batch_size,
"The rois_batch_size and imgs batch_size must be the same.");
int rois_num_with_lod = rois_lod[rois_batch_size];
PADDLE_ENFORCE_EQ(rois_num, rois_num_with_lod,
"The rois_num from input and lod must be the same.");
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;
}
}
}
......@@ -163,11 +180,23 @@ class CPUROIPoolGradOpKernel : public framework::OpKernel<T> {
int* roi_batch_id_data =
roi_batch_id_list.mutable_data<int>(ctx.GetPlace());
auto rois_lod = rois->lod().back();
int rois_batch_size = rois_lod.size() - 1;
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;
int rois_batch_size;
if (ctx.HasInput("RoisLod")) {
auto* rois_lod_t = ctx.Input<framework::Tensor>("RoisLod");
rois_batch_size = rois_lod_t->numel();
auto* rois_lod = rois_lod_t->data<int64_t>();
for (int n = 0; n < rois_batch_size - 1; ++n) {
for (int i = rois_lod[n]; i < rois_lod[n + 1]; ++i) {
roi_batch_id_data[i] = n;
}
}
} else {
auto rois_lod = rois->lod().back();
rois_batch_size = rois_lod.size() - 1;
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;
}
}
}
......
python/paddle/fluid/layers/detection.py
浏览文件 @ 0a878be8
......@@ -2779,6 +2779,8 @@ def generate_proposals(scores,
dtype=bbox_deltas.dtype)
rpn_roi_probs = helper.create_variable_for_type_inference(
dtype=scores.dtype)
rpn_rois_lod = helper.create_variable_for_type_inference(dtype='int32')
helper.append_op(
type="generate_proposals",
inputs={
......@@ -2795,12 +2797,16 @@ def generate_proposals(scores,
'min_size': min_size,
'eta': eta
},
outputs={'RpnRois': rpn_rois,
'RpnRoiProbs': rpn_roi_probs})
outputs={
'RpnRois': rpn_rois,
'RpnRoiProbs': rpn_roi_probs,
'RpnRoisLod': rpn_rois_lod
})
rpn_rois.stop_gradient = True
rpn_roi_probs.stop_gradient = True
rpn_rois_lod.stop_gradient = True
return rpn_rois, rpn_roi_probs
return rpn_rois, rpn_roi_probs, rpn_rois_lod
def box_clip(input, im_info, name=None):
......
python/paddle/fluid/layers/nn.py
浏览文件 @ 0a878be8
......@@ -6606,7 +6606,12 @@ def label_smooth(label,
@templatedoc()
def roi_pool(input, rois, pooled_height=1, pooled_width=1, spatial_scale=1.0):
def roi_pool(input,
rois,
pooled_height=1,
pooled_width=1,
spatial_scale=1.0,
rois_lod=None):
"""
This operator implements the roi_pooling layer.
Region of interest pooling (also known as RoI pooling) is to perform max pooling on inputs of nonuniform sizes to obtain fixed-size feature maps (e.g. 7*7).
......@@ -6622,6 +6627,7 @@ def roi_pool(input, rois, pooled_height=1, pooled_width=1, spatial_scale=1.0):
Args:
input (Variable): Input feature, 4D-Tensor with the shape of [N,C,H,W], where N is the batch size, C is the input channel, H is Height, W is weight. The data type is float32 or float64.
rois (Variable): ROIs (Regions of Interest) to pool over. 2D-LoDTensor with the shape of [num_rois,4], the lod level is 1. Given as [[x1, y1, x2, y2], ...], (x1, y1) is the top left coordinates, and (x2, y2) is the bottom right coordinates.
rois_lod (Variable): The lod info of rois. Default: None
pooled_height (int, optional): The pooled output height, data type is int32. Default: 1
pooled_width (int, optional): The pooled output height, data type is int32. Default: 1
spatial_scale (float, optional): Multiplicative spatial scale factor to translate ROI coords from their input scale to the scale used when pooling. Default: 1.0
......@@ -6644,19 +6650,22 @@ def roi_pool(input, rois, pooled_height=1, pooled_width=1, spatial_scale=1.0):
input_data = np.array([i for i in range(1,17)]).reshape(1,1,4,4).astype(DATATYPE)
roi_data =fluid.create_lod_tensor(np.array([[1., 1., 2., 2.], [1.5, 1.5, 3., 3.]]).astype(DATATYPE),[[2]], place)
rois_lod_data = np.array([0, 2])
x = fluid.data(name='input', shape=[None,1,4,4], dtype=DATATYPE)
rois = fluid.data(name='roi', shape=[None,4], dtype=DATATYPE)
rois_lod = fluid.data(name='rois_lod', shape=[None], dtype='int64')
pool_out = fluid.layers.roi_pool(
input=x,
rois=rois,
pooled_height=1,
pooled_width=1,
spatial_scale=1.0)
spatial_scale=1.0,
rois_lod=rois_lod)
exe = fluid.Executor(place)
out, = exe.run(feed={'input':input_data ,'roi':roi_data}, fetch_list=[pool_out.name])
out, = exe.run(feed={'input':input_data ,'roi':roi_data, 'rois_lod': rois_lod_data}, fetch_list=[pool_out.name])
print(out) #array([[[[11.]]], [[[16.]]]], dtype=float32)
print(np.array(out).shape) # (2, 1, 1, 1)
"""
......@@ -6667,7 +6676,8 @@ def roi_pool(input, rois, pooled_height=1, pooled_width=1, spatial_scale=1.0):
helper.append_op(
type="roi_pool",
inputs={"X": input,
"ROIs": rois},
"ROIs": rois,
"RoisLod": rois_lod},
outputs={"Out": pool_out,
"Argmax": argmaxes},
attrs={
......@@ -6685,7 +6695,8 @@ def roi_align(input,
pooled_width=1,
spatial_scale=1.0,
sampling_ratio=-1,
name=None):
name=None,
rois_lod=None):
"""
${comment}
......@@ -6695,7 +6706,8 @@ def roi_align(input,
a 2-D LoDTensor of shape (num_rois, 4), the lod level is 1. The
data type is float32 or float64. Given as [[x1, y1, x2, y2], ...],
(x1, y1) is the top left coordinates, and (x2, y2) is the bottom
right coordinates.
right coordinates.
rois_lod (Variable): The lod info of rois. Default: None
pooled_height (int32, optional): ${pooled_height_comment} Default: 1
pooled_width (int32, optional): ${pooled_width_comment} Default: 1
spatial_scale (float32, optional): ${spatial_scale_comment} Default: 1.0
......@@ -6718,12 +6730,14 @@ def roi_align(input,
name='data', shape=[None, 256, 32, 32], dtype='float32')
rois = fluid.data(
name='rois', shape=[None, 4], dtype='float32')
rois_lod = fluid.data(name='rois_lod', shape=[None], dtype='int64')
align_out = fluid.layers.roi_align(input=x,
rois=rois,
pooled_height=7,
pooled_width=7,
spatial_scale=0.5,
sampling_ratio=-1)
sampling_ratio=-1,
rois_lod=rois_lod)
"""
check_variable_and_dtype(input, 'input', ['float32', 'float64'],
'roi_align')
......@@ -6734,7 +6748,8 @@ def roi_align(input,
helper.append_op(
type="roi_align",
inputs={"X": input,
"ROIs": rois},
"ROIs": rois,
"RoisLod": rois_lod},
outputs={"Out": align_out},
attrs={
"pooled_height": pooled_height,
......
python/paddle/fluid/tests/test_detection.py
浏览文件 @ 0a878be8
......@@ -480,7 +480,7 @@ class TestGenerateProposals(unittest.TestCase):
name='bbox_deltas',
shape=[num_anchors * 4, 8, 8],
dtype='float32')
rpn_rois, rpn_roi_probs = fluid.layers.generate_proposals(
rpn_rois, rpn_roi_probs, _ = fluid.layers.generate_proposals(
name='generate_proposals',
scores=scores,
bbox_deltas=bbox_deltas,
......
python/paddle/fluid/tests/unittests/test_generate_proposals_op.py
浏览文件 @ 0a878be8
......@@ -281,7 +281,9 @@ class TestGenerateProposalsOp(OpTest):
self.outputs = {
'RpnRois': (self.rpn_rois[0], [self.lod]),
'RpnRoiProbs': (self.rpn_roi_probs[0], [self.lod])
'RpnRoiProbs': (self.rpn_roi_probs[0], [self.lod]),
'RpnRoisLod': (np.asarray(
self.lod, dtype=np.int32))
}
def test_check_output(self):
......
python/paddle/fluid/tests/unittests/test_layers.py
浏览文件 @ 0a878be8
......@@ -3217,7 +3217,9 @@ class TestBook(LayerTest):
x = layers.data(name="x", shape=[256, 30, 30], dtype="float32")
rois = layers.data(
name="rois", shape=[4], dtype="float32", lod_level=1)
output = layers.roi_pool(x, rois, 7, 7, 0.6)
rois_lod = layers.data(
name="rois_lod", shape=[None, ], dtype="int", lod_level=1)
output = layers.roi_pool(x, rois, 7, 7, 0.6, rois_lod)
return (output)
def test_sequence_enumerate(self):
......@@ -3232,7 +3234,10 @@ class TestBook(LayerTest):
x = layers.data(name="x", shape=[256, 30, 30], dtype="float32")
rois = layers.data(
name="rois", shape=[4], dtype="float32", lod_level=1)
output = layers.roi_align(x, rois, 14, 14, 0.5, 2)
rois_lod = layers.data(
name="rois_lod", shape=[None, ], dtype="int", lod_level=1)
output = layers.roi_align(x, rois, 14, 14, 0.5, 2, 'roi_align',
rois_lod)
return (output)
def test_roi_perspective_transform(self):
......
python/paddle/fluid/tests/unittests/test_roi_align_op.py
浏览文件 @ 0a878be8
......@@ -26,7 +26,11 @@ class TestROIAlignOp(OpTest):
self.init_test_case()
self.make_rois()
self.calc_roi_align()
self.inputs = {'X': self.x, 'ROIs': (self.rois[:, 1:5], self.rois_lod)}
self.inputs = {
'X': self.x,
'ROIs': (self.rois[:, 1:5], self.rois_lod),
}
self.attrs = {
'spatial_scale': self.spatial_scale,
'pooled_height': self.pooled_height,
......@@ -170,5 +174,34 @@ class TestROIAlignOp(OpTest):
self.check_grad(['X'], 'Out')
class TestROIAlignInLodOp(TestROIAlignOp):
def set_data(self):
self.init_test_case()
self.make_rois()
self.calc_roi_align()
seq_len = self.rois_lod[0]
cur_len = 0
lod = [cur_len]
for l in seq_len:
cur_len += l
lod.append(cur_len)
self.inputs = {
'X': self.x,
'ROIs': (self.rois[:, 1:5], self.rois_lod),
'RoisLod': np.asarray(lod).astype('int64')
}
self.attrs = {
'spatial_scale': self.spatial_scale,
'pooled_height': self.pooled_height,
'pooled_width': self.pooled_width,
'sampling_ratio': self.sampling_ratio
}
self.outputs = {'Out': self.out_data}
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/tests/unittests/test_roi_pool_op.py
浏览文件 @ 0a878be8
......@@ -28,7 +28,10 @@ class TestROIPoolOp(OpTest):
self.make_rois()
self.calc_roi_pool()
self.inputs = {'X': self.x, 'ROIs': (self.rois[:, 1:5], self.rois_lod)}
self.inputs = {
'X': self.x,
'ROIs': (self.rois[:, 1:5], self.rois_lod),
}
self.attrs = {
'spatial_scale': self.spatial_scale,
......@@ -138,5 +141,33 @@ class TestROIPoolOp(OpTest):
self.check_grad(['X'], 'Out')
class TestROIPoolInLodOp(TestROIPoolOp):
def set_data(self):
self.init_test_case()
self.make_rois()
self.calc_roi_pool()
seq_len = self.rois_lod[0]
cur_len = 0
lod = [cur_len]
for l in seq_len:
cur_len += l
lod.append(cur_len)
self.inputs = {
'X': self.x,
'ROIs': (self.rois[:, 1:5], self.rois_lod),
'RoisLod': np.asarray(lod).astype('int64')
}
self.attrs = {
'spatial_scale': self.spatial_scale,
'pooled_height': self.pooled_height,
'pooled_width': self.pooled_width
}
self.outputs = {'Out': self.outs, 'Argmax': self.argmaxes}
if __name__ == '__main__':
unittest.main()
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