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Paddle/paddle/fluid/operators/detection/generate_proposal_labels_op.cc

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/* Copyright (c) 2018 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 <math.h>
#include <algorithm>
#include <string>
#include <vector>
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/detection/bbox_util.h"
#include "paddle/fluid/operators/gather.h"
#include "paddle/fluid/operators/math/concat_and_split.h"
#include "paddle/fluid/operators/math/math_function.h"
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
using LoDTensor = framework::LoDTensor;
const int kBoxDim = 4;
template <typename T>
void AppendRois(LoDTensor* out, int64_t offset, Tensor* to_add) {
auto* out_data = out->data<T>();
auto* to_add_data = to_add->data<T>();
memcpy(out_data + offset, to_add_data, to_add->numel() * sizeof(T));
}
class GenerateProposalLabelsOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("RpnRois"),
"Input(RpnRois) shouldn't be null.");
PADDLE_ENFORCE(ctx->HasInput("GtClasses"),
"Input(GtClasses) shouldn't be null.");
PADDLE_ENFORCE(ctx->HasInput("IsCrowd"),
"Input(IsCrowd) shouldn't be null.");
PADDLE_ENFORCE(ctx->HasInput("GtBoxes"),
"Input(GtBoxes) shouldn't be null.");
PADDLE_ENFORCE(ctx->HasInput("ImInfo"), "Input(ImInfo) shouldn't be null.");
PADDLE_ENFORCE(ctx->HasOutput("Rois"),
"Output(Rois) of RpnTargetAssignOp should not be null");
PADDLE_ENFORCE(
ctx->HasOutput("LabelsInt32"),
"Output(LabelsInt32) of RpnTargetAssignOp should not be null");
PADDLE_ENFORCE(
ctx->HasOutput("BboxTargets"),
"Output(BboxTargets) of RpnTargetAssignOp should not be null");
PADDLE_ENFORCE(
ctx->HasOutput("BboxInsideWeights"),
"Output(BboxInsideWeights) of RpnTargetAssignOp should not be null");
PADDLE_ENFORCE(
ctx->HasOutput("BboxOutsideWeights"),
"Output(BboxOutsideWeights) of RpnTargetAssignOp should not be null");
auto rpn_rois_dims = ctx->GetInputDim("RpnRois");
auto gt_classes_dims = ctx->GetInputDim("GtClasses");
auto is_crowd_dims = ctx->GetInputDim("IsCrowd");
auto gt_boxes_dims = ctx->GetInputDim("GtBoxes");
auto im_info_dims = ctx->GetInputDim("ImInfo");
PADDLE_ENFORCE_EQ(rpn_rois_dims.size(), 2,
"The rank of Input(RpnRois) must be 2.");
PADDLE_ENFORCE_EQ(gt_boxes_dims.size(), 2,
"The rank of Input(GtBoxes) must be 2.");
PADDLE_ENFORCE_EQ(im_info_dims.size(), 2,
"The rank of Input(ImInfo) must be 2.");
int class_nums = ctx->Attrs().Get<int>("class_nums");
ctx->SetOutputDim("Rois", {-1, 4});
ctx->SetOutputDim("LabelsInt32", {-1, 1});
ctx->SetOutputDim("BboxTargets", {-1, 4 * class_nums});
ctx->SetOutputDim("BboxInsideWeights", {-1, 4 * class_nums});
ctx->SetOutputDim("BboxOutsideWeights", {-1, 4 * class_nums});
}
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override {
auto data_type = framework::GetDataTypeOfVar(ctx.InputVar("RpnRois"));
return framework::OpKernelType(data_type, platform::CPUPlace());
}
};
template <typename T>
void Concat(const platform::CPUDeviceContext& context,
const Tensor& in_tensor_a, const Tensor& in_tensor_b,
Tensor* out_tensor) {
int axis = 0;
std::vector<Tensor> inputs;
inputs.emplace_back(in_tensor_a);
inputs.emplace_back(in_tensor_b);
math::ConcatFunctor<platform::CPUDeviceContext, T> concat_functor;
concat_functor(context, inputs, axis, out_tensor);
}
template <typename T>
std::vector<std::vector<int>> SampleFgBgGt(
const platform::CPUDeviceContext& context, Tensor* iou,
const Tensor& is_crowd, const int batch_size_per_im,
const float fg_fraction, const float fg_thresh, const float bg_thresh_hi,
const float bg_thresh_lo, std::minstd_rand engine, const bool use_random) {
std::vector<int> fg_inds;
std::vector<int> bg_inds;
std::vector<int> gt_inds;
int64_t gt_num = is_crowd.numel();
const int* crowd_data = is_crowd.data<int>();
T* proposal_to_gt_overlaps = iou->data<T>();
int64_t row = iou->dims()[0];
int64_t col = iou->dims()[1];
float epsilon = 0.00001;
// Follow the Faster RCNN's implementation
for (int64_t i = 0; i < row; ++i) {
const T* v = proposal_to_gt_overlaps + i * col;
T max_overlap = *std::max_element(v, v + col);
if ((i < gt_num) && (crowd_data[i])) {
max_overlap = -1.0;
}
if (max_overlap > fg_thresh) {
for (int64_t j = 0; j < col; ++j) {
T val = proposal_to_gt_overlaps[i * col + j];
auto diff = std::abs(max_overlap - val);
if (diff < epsilon) {
fg_inds.emplace_back(i);
gt_inds.emplace_back(j);
break;
}
}
} else {
if ((max_overlap >= bg_thresh_lo) && (max_overlap < bg_thresh_hi)) {
bg_inds.emplace_back(i);
}
}
}
// Reservoir Sampling
std::uniform_real_distribution<float> uniform(0, 1);
int fg_rois_per_im = std::floor(batch_size_per_im * fg_fraction);
int fg_rois_this_image = fg_inds.size();
int fg_rois_per_this_image = std::min(fg_rois_per_im, fg_rois_this_image);
if (use_random) {
const int64_t fg_size = static_cast<int64_t>(fg_inds.size());
if (fg_size > fg_rois_per_this_image) {
for (int64_t i = fg_rois_per_this_image; i < fg_size; ++i) {
int rng_ind = std::floor(uniform(engine) * i);
if (rng_ind < fg_rois_per_this_image) {
std::iter_swap(fg_inds.begin() + rng_ind, fg_inds.begin() + i);
std::iter_swap(gt_inds.begin() + rng_ind, gt_inds.begin() + i);
}
}
}
}
std::vector<int> new_fg_inds(fg_inds.begin(),
fg_inds.begin() + fg_rois_per_this_image);
std::vector<int> new_gt_inds(gt_inds.begin(),
gt_inds.begin() + fg_rois_per_this_image);
int bg_rois_per_image = batch_size_per_im - fg_rois_per_this_image;
int bg_rois_this_image = bg_inds.size();
int bg_rois_per_this_image = std::min(bg_rois_per_image, bg_rois_this_image);
if (use_random) {
const int64_t bg_size = static_cast<int64_t>(bg_inds.size());
if (bg_size > bg_rois_per_this_image) {
for (int64_t i = bg_rois_per_this_image; i < bg_size; ++i) {
int rng_ind = std::floor(uniform(engine) * i);
if (rng_ind < fg_rois_per_this_image)
std::iter_swap(bg_inds.begin() + rng_ind, bg_inds.begin() + i);
}
}
}
std::vector<int> new_bg_inds(bg_inds.begin(),
bg_inds.begin() + bg_rois_per_this_image);
std::vector<std::vector<int>> res;
res.emplace_back(new_fg_inds);
res.emplace_back(new_bg_inds);
res.emplace_back(new_gt_inds);
return res;
}
template <typename T>
void GatherBoxesLabels(const platform::CPUDeviceContext& context,
const Tensor& boxes, const Tensor& gt_boxes,
const Tensor& gt_classes,
const std::vector<int>& fg_inds,
const std::vector<int>& bg_inds,
const std::vector<int>& gt_inds, Tensor* sampled_boxes,
Tensor* sampled_labels, Tensor* sampled_gts) {
int fg_num = fg_inds.size();
int bg_num = bg_inds.size();
Tensor fg_inds_t, bg_inds_t, gt_box_inds_t, gt_label_inds_t;
int* fg_inds_data = fg_inds_t.mutable_data<int>({fg_num}, context.GetPlace());
int* bg_inds_data = bg_inds_t.mutable_data<int>({bg_num}, context.GetPlace());
int* gt_box_inds_data =
gt_box_inds_t.mutable_data<int>({fg_num}, context.GetPlace());
int* gt_label_inds_data =
gt_label_inds_t.mutable_data<int>({fg_num}, context.GetPlace());
std::copy(fg_inds.begin(), fg_inds.end(), fg_inds_data);
std::copy(bg_inds.begin(), bg_inds.end(), bg_inds_data);
std::copy(gt_inds.begin(), gt_inds.end(), gt_box_inds_data);
std::copy(gt_inds.begin(), gt_inds.end(), gt_label_inds_data);
Tensor fg_boxes, bg_boxes, fg_labels, bg_labels;
fg_boxes.mutable_data<T>({fg_num, kBoxDim}, context.GetPlace());
CPUGather<T>(context, boxes, fg_inds_t, &fg_boxes);
bg_boxes.mutable_data<T>({bg_num, kBoxDim}, context.GetPlace());
CPUGather<T>(context, boxes, bg_inds_t, &bg_boxes);
Concat<T>(context, fg_boxes, bg_boxes, sampled_boxes);
CPUGather<T>(context, gt_boxes, gt_box_inds_t, sampled_gts);
fg_labels.mutable_data<int>({fg_num}, context.GetPlace());
CPUGather<int>(context, gt_classes, gt_label_inds_t, &fg_labels);
bg_labels.mutable_data<int>({bg_num}, context.GetPlace());
math::set_constant(context, &bg_labels, 0);
Concat<int>(context, fg_labels, bg_labels, sampled_labels);
}
template <typename T>
std::vector<Tensor> SampleRoisForOneImage(
const platform::CPUDeviceContext& context, Tensor* rpn_rois,
Tensor* gt_classes, Tensor* is_crowd, Tensor* gt_boxes, Tensor* im_info,
const int batch_size_per_im, const float fg_fraction, const float fg_thresh,
const float bg_thresh_hi, const float bg_thresh_lo,
const std::vector<float>& bbox_reg_weights, const int class_nums,
std::minstd_rand engine, bool use_random) {
auto rpn_rois_et = framework::EigenTensor<T, 2>::From(*rpn_rois);
auto im_scale = im_info->data<T>()[2];
rpn_rois_et = rpn_rois_et / im_scale;
Tensor boxes;
int proposals_num = gt_boxes->dims()[0] + rpn_rois->dims()[0];
boxes.mutable_data<T>({proposals_num, kBoxDim}, context.GetPlace());
Concat<T>(context, *gt_boxes, *rpn_rois, &boxes);
// Overlaps
Tensor proposal_to_gt_overlaps;
proposal_to_gt_overlaps.mutable_data<T>({proposals_num, gt_boxes->dims()[0]},
context.GetPlace());
BboxOverlaps<T>(boxes, *gt_boxes, &proposal_to_gt_overlaps);
// Generate proposal index
std::vector<std::vector<int>> fg_bg_gt = SampleFgBgGt<T>(
context, &proposal_to_gt_overlaps, *is_crowd, batch_size_per_im,
fg_fraction, fg_thresh, bg_thresh_hi, bg_thresh_lo, engine, use_random);
std::vector<int> fg_inds = fg_bg_gt[0];
std::vector<int> bg_inds = fg_bg_gt[1];
std::vector<int> gt_inds = fg_bg_gt[2];
// Gather boxes and labels
Tensor sampled_boxes, sampled_labels, sampled_gts;
int fg_num = fg_inds.size();
int bg_num = bg_inds.size();
int boxes_num = fg_num + bg_num;
framework::DDim bbox_dim({boxes_num, kBoxDim});
sampled_boxes.mutable_data<T>(bbox_dim, context.GetPlace());
sampled_labels.mutable_data<int>({boxes_num}, context.GetPlace());
sampled_gts.mutable_data<T>({fg_num, kBoxDim}, context.GetPlace());
GatherBoxesLabels<T>(context, boxes, *gt_boxes, *gt_classes, fg_inds, bg_inds,
gt_inds, &sampled_boxes, &sampled_labels, &sampled_gts);
// Compute targets
Tensor bbox_targets_single;
bbox_targets_single.mutable_data<T>(bbox_dim, context.GetPlace());
BoxToDelta<T>(fg_num, sampled_boxes, sampled_gts, bbox_reg_weights.data(),
false, &bbox_targets_single);
// Scale rois
Tensor sampled_rois;
sampled_rois.mutable_data<T>(sampled_boxes.dims(), context.GetPlace());
auto sampled_rois_et = framework::EigenTensor<T, 2>::From(sampled_rois);
auto sampled_boxes_et = framework::EigenTensor<T, 2>::From(sampled_boxes);
sampled_rois_et = sampled_boxes_et * im_scale;
// Expand box targets
Tensor bbox_targets, bbox_inside_weights, bbox_outside_weights;
framework::DDim bbox_expand_dim({boxes_num, kBoxDim * class_nums});
bbox_targets.mutable_data<T>(bbox_expand_dim, context.GetPlace());
bbox_inside_weights.mutable_data<T>(bbox_expand_dim, context.GetPlace());
bbox_outside_weights.mutable_data<T>(bbox_expand_dim, context.GetPlace());
math::set_constant(context, &bbox_targets, 0.0);
math::set_constant(context, &bbox_inside_weights, 0.0);
math::set_constant(context, &bbox_outside_weights, 0.0);
auto* bbox_targets_single_data = bbox_targets_single.data<T>();
auto* sampled_labels_data = sampled_labels.data<int>();
auto* bbox_targets_data = bbox_targets.data<T>();
auto* bbox_inside_weights_data = bbox_inside_weights.data<T>();
auto* bbox_outside_weights_data = bbox_outside_weights.data<T>();
int width = kBoxDim * class_nums;
for (int64_t i = 0; i < boxes_num; ++i) {
int label = sampled_labels_data[i];
if (label > 0) {
int dst_idx = i * width + kBoxDim * label;
int src_idx = kBoxDim * i;
bbox_targets_data[dst_idx] = bbox_targets_single_data[src_idx];
bbox_targets_data[dst_idx + 1] = bbox_targets_single_data[src_idx + 1];
bbox_targets_data[dst_idx + 2] = bbox_targets_single_data[src_idx + 2];
bbox_targets_data[dst_idx + 3] = bbox_targets_single_data[src_idx + 3];
bbox_inside_weights_data[dst_idx] = 1;
bbox_inside_weights_data[dst_idx + 1] = 1;
bbox_inside_weights_data[dst_idx + 2] = 1;
bbox_inside_weights_data[dst_idx + 3] = 1;
bbox_outside_weights_data[dst_idx] = 1;
bbox_outside_weights_data[dst_idx + 1] = 1;
bbox_outside_weights_data[dst_idx + 2] = 1;
bbox_outside_weights_data[dst_idx + 3] = 1;
}
}
std::vector<Tensor> res;
res.emplace_back(sampled_rois);
res.emplace_back(sampled_labels);
res.emplace_back(bbox_targets);
res.emplace_back(bbox_inside_weights);
res.emplace_back(bbox_outside_weights);
return res;
}
template <typename T>
class GenerateProposalLabelsKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto* rpn_rois = context.Input<LoDTensor>("RpnRois");
auto* gt_classes = context.Input<LoDTensor>("GtClasses");
auto* is_crowd = context.Input<LoDTensor>("IsCrowd");
auto* gt_boxes = context.Input<LoDTensor>("GtBoxes");
auto* im_info = context.Input<LoDTensor>("ImInfo");
auto* rois = context.Output<LoDTensor>("Rois");
auto* labels_int32 = context.Output<LoDTensor>("LabelsInt32");
auto* bbox_targets = context.Output<LoDTensor>("BboxTargets");
auto* bbox_inside_weights = context.Output<LoDTensor>("BboxInsideWeights");
auto* bbox_outside_weights =
context.Output<LoDTensor>("BboxOutsideWeights");
int batch_size_per_im = context.Attr<int>("batch_size_per_im");
float fg_fraction = context.Attr<float>("fg_fraction");
float fg_thresh = context.Attr<float>("fg_thresh");
float bg_thresh_hi = context.Attr<float>("bg_thresh_hi");
float bg_thresh_lo = context.Attr<float>("bg_thresh_lo");
std::vector<float> bbox_reg_weights =
context.Attr<std::vector<float>>("bbox_reg_weights");
int class_nums = context.Attr<int>("class_nums");
bool use_random = context.Attr<bool>("use_random");
PADDLE_ENFORCE_EQ(rpn_rois->lod().size(), 1UL,
"GenerateProposalLabelsOp rpn_rois needs 1 level of LoD");
PADDLE_ENFORCE_EQ(
gt_classes->lod().size(), 1UL,
"GenerateProposalLabelsOp gt_classes needs 1 level of LoD");
PADDLE_ENFORCE_EQ(is_crowd->lod().size(), 1UL,
"GenerateProposalLabelsOp is_crowd needs 1 level of LoD");
PADDLE_ENFORCE_EQ(gt_boxes->lod().size(), 1UL,
"GenerateProposalLabelsOp gt_boxes needs 1 level of LoD");
int64_t n = static_cast<int64_t>(rpn_rois->lod().back().size() - 1);
rois->mutable_data<T>({n * batch_size_per_im, kBoxDim}, context.GetPlace());
labels_int32->mutable_data<int>({n * batch_size_per_im, 1},
context.GetPlace());
bbox_targets->mutable_data<T>({n * batch_size_per_im, kBoxDim * class_nums},
context.GetPlace());
bbox_inside_weights->mutable_data<T>(
{n * batch_size_per_im, kBoxDim * class_nums}, context.GetPlace());
bbox_outside_weights->mutable_data<T>(
{n * batch_size_per_im, kBoxDim * class_nums}, context.GetPlace());
std::random_device rnd;
std::minstd_rand engine;
int seed = rnd();
engine.seed(seed);
framework::LoD lod;
std::vector<size_t> lod0(1, 0);
int64_t num_rois = 0;
auto& dev_ctx = context.device_context<platform::CPUDeviceContext>();
auto rpn_rois_lod = rpn_rois->lod().back();
auto gt_classes_lod = gt_classes->lod().back();
auto is_crowd_lod = is_crowd->lod().back();
auto gt_boxes_lod = gt_boxes->lod().back();
for (int i = 0; i < n; ++i) {
Tensor rpn_rois_slice =
rpn_rois->Slice(rpn_rois_lod[i], rpn_rois_lod[i + 1]);
Tensor gt_classes_slice =
gt_classes->Slice(gt_classes_lod[i], gt_classes_lod[i + 1]);
Tensor is_crowd_slice =
is_crowd->Slice(is_crowd_lod[i], is_crowd_lod[i + 1]);
Tensor gt_boxes_slice =
gt_boxes->Slice(gt_boxes_lod[i], gt_boxes_lod[i + 1]);
Tensor im_info_slice = im_info->Slice(i, i + 1);
std::vector<Tensor> tensor_output = SampleRoisForOneImage<T>(
dev_ctx, &rpn_rois_slice, &gt_classes_slice, &is_crowd_slice,
&gt_boxes_slice, &im_info_slice, batch_size_per_im, fg_fraction,
fg_thresh, bg_thresh_hi, bg_thresh_lo, bbox_reg_weights, class_nums,
engine, use_random);
Tensor sampled_rois = tensor_output[0];
Tensor sampled_labels_int32 = tensor_output[1];
Tensor sampled_bbox_targets = tensor_output[2];
Tensor sampled_bbox_inside_weights = tensor_output[3];
Tensor sampled_bbox_outside_weights = tensor_output[4];
AppendRois<T>(rois, kBoxDim * num_rois, &sampled_rois);
AppendRois<int>(labels_int32, num_rois, &sampled_labels_int32);
AppendRois<T>(bbox_targets, kBoxDim * num_rois * class_nums,
&sampled_bbox_targets);
AppendRois<T>(bbox_inside_weights, kBoxDim * num_rois * class_nums,
&sampled_bbox_inside_weights);
AppendRois<T>(bbox_outside_weights, kBoxDim * num_rois * class_nums,
&sampled_bbox_outside_weights);
num_rois += sampled_rois.dims()[0];
lod0.emplace_back(num_rois);
}
lod.emplace_back(lod0);
rois->set_lod(lod);
labels_int32->set_lod(lod);
bbox_targets->set_lod(lod);
bbox_inside_weights->set_lod(lod);
bbox_outside_weights->set_lod(lod);
rois->Resize({num_rois, kBoxDim});
labels_int32->Resize({num_rois, 1});
bbox_targets->Resize({num_rois, kBoxDim * class_nums});
bbox_inside_weights->Resize({num_rois, kBoxDim * class_nums});
bbox_outside_weights->Resize({num_rois, kBoxDim * class_nums});
}
};
class GenerateProposalLabelsOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput(
"RpnRois",
"(LoDTensor), This input is a 2D LoDTensor with shape [N, 4]. "
"N is the number of the GenerateProposalOp's output, "
"each element is a bounding box with [xmin, ymin, xmax, ymax] format.");
AddInput("GtClasses",
"(LoDTensor), This input is a 2D LoDTensor with shape [M, 1]. "
"M is the number of groundtruth, "
"each element is a class label of groundtruth.");
AddInput(
"IsCrowd",
"(LoDTensor), This input is a 2D LoDTensor with shape [M, 1]. "
"M is the number of groundtruth, "
"each element is a flag indicates whether a groundtruth is crowd.");
AddInput(
"GtBoxes",
"(LoDTensor), This input is a 2D LoDTensor with shape [M, 4]. "
"M is the number of groundtruth, "
"each element is a bounding box with [xmin, ymin, xmax, ymax] format.");
AddInput("ImInfo",
"(Tensor), This input is a 2D Tensor with shape [B, 3]. "
"B is the number of input images, "
"each element consists of im_height, im_width, im_scale.");
AddOutput(
"Rois",
"(LoDTensor), This output is a 2D LoDTensor with shape [P, 4]. "
"P usuall equal to batch_size_per_im * batch_size, "
"each element is a bounding box with [xmin, ymin, xmax, ymax] format.");
AddOutput("LabelsInt32",
"(LoDTensor), This output is a 2D LoDTensor with shape [P], "
"each element repersents a class label of a roi");
AddOutput("BboxTargets",
"(LoDTensor), This output is a 2D LoDTensor with shape [P, 4 * "
"class_nums], "
"each element repersents a box label of a roi");
AddOutput(
"BboxInsideWeights",
"(LoDTensor), This output is a 2D LoDTensor with shape [P, 4 * "
"class_nums], "
"each element indicates whether a box should contribute to loss.");
AddOutput(
"BboxOutsideWeights",
"(LoDTensor), This output is a 2D LoDTensor with shape [P, 4 * "
"class_nums], "
"each element indicates whether a box should contribute to loss.");
AddAttr<int>("batch_size_per_im", "Batch size of rois per images.");
AddAttr<float>("fg_fraction",
"Foreground fraction in total batch_size_per_im.");
AddAttr<float>(
"fg_thresh",
"Overlap threshold which is used to chose foreground sample.");
AddAttr<float>("bg_thresh_hi",
"Overlap threshold upper bound which is used to chose "
"background sample.");
AddAttr<float>("bg_thresh_lo",
"Overlap threshold lower bound which is used to chose "
"background sample.");
AddAttr<std::vector<float>>("bbox_reg_weights", "Box regression weights.");
AddAttr<int>("class_nums", "Class number.");
AddAttr<bool>(
"use_random",
"Use random sampling to choose foreground and background boxes.")
.SetDefault(true);
AddComment(R"DOC(
This operator can be, for given the GenerateProposalOp output bounding boxes and groundtruth,
to sample foreground boxes and background boxes, and compute loss target.
RpnRois is the output boxes of RPN and was processed by generate_proposal_op, these boxes
were combined with groundtruth boxes and sampled according to batch_size_per_im and fg_fraction,
If an instance with a groundtruth overlap greater than fg_thresh, then it was considered as a foreground sample.
If an instance with a groundtruth overlap greater than bg_thresh_lo and lower than bg_thresh_hi,
then it was considered as a background sample.
After all foreground and background boxes are chosen (so called Rois),
then we apply random sampling to make sure
the number of foreground boxes is no more than batch_size_per_im * fg_fraction.
For each box in Rois, we assign the classification (class label) and regression targets (box label) to it.
Finally BboxInsideWeights and BboxOutsideWeights are used to specify whether it would contribute to training loss.
)DOC");
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(generate_proposal_labels, ops::GenerateProposalLabelsOp,
ops::GenerateProposalLabelsOpMaker,
paddle::framework::EmptyGradOpMaker);
REGISTER_OP_CPU_KERNEL(generate_proposal_labels,
ops::GenerateProposalLabelsKernel<float>,
ops::GenerateProposalLabelsKernel<double>);
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