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未验证 提交 02414aac 编写于 作者: Z zhiboniu 提交者： GitHub
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Phi matrixnums (#44437) 

* phi_matrix_nms

* remove old kernels and add optest check_eager

* reoder args

* reoder args in infermate

* update

* get back legacy dygraph
上级 c770053c
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Showing with 564 addition and 348 deletion
paddle/fluid/operators/detection/matrix_nms_op.cc
浏览文件 @ 02414aac
......@@ -11,9 +11,11 @@ distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
limitations under the License. */
#include "paddle/fluid/framework/infershape_utils.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/op_version_registry.h"
#include "paddle/fluid/operators/detection/nms_util.h"
#include "paddle/phi/infermeta/binary.h"
namespace paddle {
namespace operators {
......@@ -25,55 +27,6 @@ class MatrixNMSOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
OP_INOUT_CHECK(ctx->HasInput("BBoxes"), "Input", "BBoxes", "MatrixNMS");
OP_INOUT_CHECK(ctx->HasInput("Scores"), "Input", "Scores", "MatrixNMS");
OP_INOUT_CHECK(ctx->HasOutput("Out"), "Output", "Out", "MatrixNMS");
auto box_dims = ctx->GetInputDim("BBoxes");
auto score_dims = ctx->GetInputDim("Scores");
auto score_size = score_dims.size();
if (ctx->IsRuntime()) {
PADDLE_ENFORCE_EQ(score_size == 3,
true,
platform::errors::InvalidArgument(
"The rank of Input(Scores) must be 3. "
"But received rank = %d.",
score_size));
PADDLE_ENFORCE_EQ(box_dims.size(),
3,
platform::errors::InvalidArgument(
"The rank of Input(BBoxes) must be 3."
"But received rank = %d.",
box_dims.size()));
PADDLE_ENFORCE_EQ(box_dims[2] == 4,
true,
platform::errors::InvalidArgument(
"The last dimension of Input (BBoxes) must be 4, "
"represents the layout of coordinate "
"[xmin, ymin, xmax, ymax]."));
PADDLE_ENFORCE_EQ(
box_dims[1],
score_dims[2],
platform::errors::InvalidArgument(
"The 2nd dimension of Input(BBoxes) must be equal to "
"last dimension of Input(Scores), which represents the "
"predicted bboxes."
"But received box_dims[1](%s) != socre_dims[2](%s)",
box_dims[1],
score_dims[2]));
}
ctx->SetOutputDim("Out", {box_dims[1], box_dims[2] + 2});
ctx->SetOutputDim("Index", {box_dims[1], 1});
if (ctx->HasOutput("RoisNum")) {
ctx->SetOutputDim("RoisNum", {-1});
}
if (!ctx->IsRuntime()) {
ctx->SetLoDLevel("Out", std::max(ctx->GetLoDLevel("BBoxes"), 1));
ctx->SetLoDLevel("Index", std::max(ctx->GetLoDLevel("BBoxes"), 1));
}
}
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override {
......@@ -83,266 +36,6 @@ class MatrixNMSOp : public framework::OperatorWithKernel {
}
};
template <typename T, bool gaussian>
struct decay_score;
template <typename T>
struct decay_score<T, true> {
T operator()(T iou, T max_iou, T sigma) {
return std::exp((max_iou * max_iou - iou * iou) * sigma);
}
};
template <typename T>
struct decay_score<T, false> {
T operator()(T iou, T max_iou, T sigma) {
return (1. - iou) / (1. - max_iou);
}
};
template <typename T, bool gaussian>
void NMSMatrix(const Tensor& bbox,
const Tensor& scores,
const T score_threshold,
const T post_threshold,
const float sigma,
const int64_t top_k,
const bool normalized,
std::vector<int>* selected_indices,
std::vector<T>* decayed_scores) {
int64_t num_boxes = bbox.dims()[0];
int64_t box_size = bbox.dims()[1];
auto score_ptr = scores.data<T>();
auto bbox_ptr = bbox.data<T>();
std::vector<int32_t> perm(num_boxes);
std::iota(perm.begin(), perm.end(), 0);
auto end = std::remove_if(
perm.begin(), perm.end(), [&score_ptr, score_threshold](int32_t idx) {
return score_ptr[idx] <= score_threshold;
});
auto sort_fn = [&score_ptr](int32_t lhs, int32_t rhs) {
return score_ptr[lhs] > score_ptr[rhs];
};
int64_t num_pre = std::distance(perm.begin(), end);
if (num_pre <= 0) {
return;
}
if (top_k > -1 && num_pre > top_k) {
num_pre = top_k;
}
std::partial_sort(perm.begin(), perm.begin() + num_pre, end, sort_fn);
std::vector<T> iou_matrix((num_pre * (num_pre - 1)) >> 1);
std::vector<T> iou_max(num_pre);
iou_max[0] = 0.;
for (int64_t i = 1; i < num_pre; i++) {
T max_iou = 0.;
auto idx_a = perm[i];
for (int64_t j = 0; j < i; j++) {
auto idx_b = perm[j];
auto iou = JaccardOverlap<T>(
bbox_ptr + idx_a * box_size, bbox_ptr + idx_b * box_size, normalized);
max_iou = std::max(max_iou, iou);
iou_matrix[i * (i - 1) / 2 + j] = iou;
}
iou_max[i] = max_iou;
}
if (score_ptr[perm[0]] > post_threshold) {
selected_indices->push_back(perm[0]);
decayed_scores->push_back(score_ptr[perm[0]]);
}
decay_score<T, gaussian> decay_fn;
for (int64_t i = 1; i < num_pre; i++) {
T min_decay = 1.;
for (int64_t j = 0; j < i; j++) {
auto max_iou = iou_max[j];
auto iou = iou_matrix[i * (i - 1) / 2 + j];
auto decay = decay_fn(iou, max_iou, sigma);
min_decay = std::min(min_decay, decay);
}
auto ds = min_decay * score_ptr[perm[i]];
if (ds <= post_threshold) continue;
selected_indices->push_back(perm[i]);
decayed_scores->push_back(ds);
}
}
template <typename T>
class MatrixNMSKernel : public framework::OpKernel<T> {
public:
size_t MultiClassMatrixNMS(const Tensor& scores,
const Tensor& bboxes,
std::vector<T>* out,
std::vector<int>* indices,
int start,
int64_t background_label,
int64_t nms_top_k,
int64_t keep_top_k,
bool normalized,
T score_threshold,
T post_threshold,
bool use_gaussian,
float gaussian_sigma) const {
std::vector<int> all_indices;
std::vector<T> all_scores;
std::vector<T> all_classes;
all_indices.reserve(scores.numel());
all_scores.reserve(scores.numel());
all_classes.reserve(scores.numel());
size_t num_det = 0;
auto class_num = scores.dims()[0];
Tensor score_slice;
for (int64_t c = 0; c < class_num; ++c) {
if (c == background_label) continue;
score_slice = scores.Slice(c, c + 1);
if (use_gaussian) {
NMSMatrix<T, true>(bboxes,
score_slice,
score_threshold,
post_threshold,
gaussian_sigma,
nms_top_k,
normalized,
&all_indices,
&all_scores);
} else {
NMSMatrix<T, false>(bboxes,
score_slice,
score_threshold,
post_threshold,
gaussian_sigma,
nms_top_k,
normalized,
&all_indices,
&all_scores);
}
for (size_t i = 0; i < all_indices.size() - num_det; i++) {
all_classes.push_back(static_cast<T>(c));
}
num_det = all_indices.size();
}
if (num_det <= 0) {
return num_det;
}
if (keep_top_k > -1) {
auto k = static_cast<size_t>(keep_top_k);
if (num_det > k) num_det = k;
}
std::vector<int32_t> perm(all_indices.size());
std::iota(perm.begin(), perm.end(), 0);
std::partial_sort(perm.begin(),
perm.begin() + num_det,
perm.end(),
[&all_scores](int lhs, int rhs) {
return all_scores[lhs] > all_scores[rhs];
});
for (size_t i = 0; i < num_det; i++) {
auto p = perm[i];
auto idx = all_indices[p];
auto cls = all_classes[p];
auto score = all_scores[p];
auto bbox = bboxes.data<T>() + idx * bboxes.dims()[1];
(*indices).push_back(start + idx);
(*out).push_back(cls);
(*out).push_back(score);
for (int j = 0; j < bboxes.dims()[1]; j++) {
(*out).push_back(bbox[j]);
}
}
return num_det;
}
void Compute(const framework::ExecutionContext& ctx) const override {
auto* boxes = ctx.Input<LoDTensor>("BBoxes");
auto* scores = ctx.Input<LoDTensor>("Scores");
auto* outs = ctx.Output<LoDTensor>("Out");
auto* index = ctx.Output<LoDTensor>("Index");
auto background_label = ctx.Attr<int>("background_label");
auto nms_top_k = ctx.Attr<int>("nms_top_k");
auto keep_top_k = ctx.Attr<int>("keep_top_k");
auto normalized = ctx.Attr<bool>("normalized");
auto score_threshold = ctx.Attr<float>("score_threshold");
auto post_threshold = ctx.Attr<float>("post_threshold");
auto use_gaussian = ctx.Attr<bool>("use_gaussian");
auto gaussian_sigma = ctx.Attr<float>("gaussian_sigma");
auto score_dims = scores->dims();
auto batch_size = score_dims[0];
auto num_boxes = score_dims[2];
auto box_dim = boxes->dims()[2];
auto out_dim = box_dim + 2;
Tensor boxes_slice, scores_slice;
size_t num_out = 0;
std::vector<size_t> offsets = {0};
std::vector<T> detections;
std::vector<int> indices;
std::vector<int> num_per_batch;
detections.reserve(out_dim * num_boxes * batch_size);
indices.reserve(num_boxes * batch_size);
num_per_batch.reserve(batch_size);
for (int i = 0; i < batch_size; ++i) {
scores_slice = scores->Slice(i, i + 1);
scores_slice.Resize({score_dims[1], score_dims[2]});
boxes_slice = boxes->Slice(i, i + 1);
boxes_slice.Resize({score_dims[2], box_dim});
int start = i * score_dims[2];
num_out = MultiClassMatrixNMS(scores_slice,
boxes_slice,
&detections,
&indices,
start,
background_label,
nms_top_k,
keep_top_k,
normalized,
score_threshold,
post_threshold,
use_gaussian,
gaussian_sigma);
offsets.push_back(offsets.back() + num_out);
num_per_batch.emplace_back(num_out);
}
int64_t num_kept = offsets.back();
if (num_kept == 0) {
outs->mutable_data<T>({0, out_dim}, ctx.GetPlace());
index->mutable_data<int>({0, 1}, ctx.GetPlace());
} else {
outs->mutable_data<T>({num_kept, out_dim}, ctx.GetPlace());
index->mutable_data<int>({num_kept, 1}, ctx.GetPlace());
std::copy(detections.begin(), detections.end(), outs->data<T>());
std::copy(indices.begin(), indices.end(), index->data<int>());
}
if (ctx.HasOutput("RoisNum")) {
auto* rois_num = ctx.Output<Tensor>("RoisNum");
rois_num->mutable_data<int>({batch_size}, ctx.GetPlace());
std::copy(
num_per_batch.begin(), num_per_batch.end(), rois_num->data<int>());
}
framework::LoD lod;
lod.emplace_back(offsets);
outs->set_lod(lod);
index->set_lod(lod);
}
};
class MatrixNMSOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
......@@ -435,16 +128,19 @@ https://arxiv.org/abs/2003.10152
} // namespace operators
} // namespace paddle
DECLARE_INFER_SHAPE_FUNCTOR(matrix_nms,
MatrixNMSInferShapeFunctor,
PD_INFER_META(phi::MatrixNMSInferMeta));
namespace ops = paddle::operators;
REGISTER_OPERATOR(
matrix_nms,
ops::MatrixNMSOp,
ops::MatrixNMSOpMaker,
paddle::framework::EmptyGradOpMaker<paddle::framework::OpDesc>,
paddle::framework::EmptyGradOpMaker<paddle::imperative::OpBase>);
REGISTER_OP_CPU_KERNEL(matrix_nms,
ops::MatrixNMSKernel<float>,
ops::MatrixNMSKernel<double>);
paddle::framework::EmptyGradOpMaker<paddle::imperative::OpBase>,
MatrixNMSInferShapeFunctor);
REGISTER_OP_VERSION(matrix_nms)
.AddCheckpoint(R"ROC(Upgrade matrix_nms: add a new output [RoisNum].)ROC",
paddle::framework::compatible::OpVersionDesc().NewOutput(
......
paddle/phi/api/yaml/legacy_api.yaml
浏览文件 @ 02414aac
......@@ -1501,6 +1501,14 @@
func : matmul
backward : matmul_grad
- api : matrix_nms
args : (Tensor bboxes, Tensor scores, float score_threshold, int nms_top_k, int keep_top_k, float post_threshold=0., bool use_gaussian = false, float gaussian_sigma = 2.0, int background_label = 0, bool normalized = true)
output : Tensor(out), Tensor(index), Tensor(roisnum)
infer_meta :
func : MatrixNMSInferMeta
kernel :
func : matrix_nms
# matrix_power
- api : matrix_power
args : (Tensor x, int n)
......
paddle/phi/infermeta/binary.cc
浏览文件 @ 02414aac
......@@ -1687,6 +1687,64 @@ void MatmulWithFlattenInferMeta(const MetaTensor& x,
out->share_lod(x);
}
void MatrixNMSInferMeta(const MetaTensor& bboxes,
const MetaTensor& scores,
float score_threshold,
int nms_top_k,
int keep_top_k,
float post_threshold,
bool use_gaussian,
float gaussian_sigma,
int background_label,
bool normalized,
MetaTensor* out,
MetaTensor* index,
MetaTensor* roisnum,
MetaConfig config) {
auto box_dims = bboxes.dims();
auto score_dims = scores.dims();
auto score_size = score_dims.size();
if (config.is_runtime) {
PADDLE_ENFORCE_EQ(
score_size == 3,
true,
errors::InvalidArgument("The rank of Input(Scores) must be 3. "
"But received rank = %d.",
score_size));
PADDLE_ENFORCE_EQ(
box_dims.size(),
3,
errors::InvalidArgument("The rank of Input(BBoxes) must be 3."
"But received rank = %d.",
box_dims.size()));
PADDLE_ENFORCE_EQ(box_dims[2] == 4,
true,
errors::InvalidArgument(
"The last dimension of Input (BBoxes) must be 4, "
"represents the layout of coordinate "
"[xmin, ymin, xmax, ymax]."));
PADDLE_ENFORCE_EQ(
box_dims[1],
score_dims[2],
errors::InvalidArgument(
"The 2nd dimension of Input(BBoxes) must be equal to "
"last dimension of Input(Scores), which represents the "
"predicted bboxes."
"But received box_dims[1](%s) != socre_dims[2](%s)",
box_dims[1],
score_dims[2]));
}
out->set_dims({box_dims[1], box_dims[2] + 2});
out->set_dtype(bboxes.dtype());
index->set_dims({box_dims[1], 1});
index->set_dtype(phi::DataType::INT32);
if (roisnum != nullptr) {
roisnum->set_dims({-1});
roisnum->set_dtype(phi::DataType::INT32);
}
}
void MatrixRankTolInferMeta(const MetaTensor& x,
const MetaTensor& atol_tensor,
bool use_default_tol,
......
paddle/phi/infermeta/binary.h
浏览文件 @ 02414aac
......@@ -249,6 +249,21 @@ void MatmulWithFlattenInferMeta(const MetaTensor& x,
int y_num_col_dims,
MetaTensor* out);
void MatrixNMSInferMeta(const MetaTensor& bboxes,
const MetaTensor& scores,
float score_threshold,
int nms_top_k,
int keep_top_k,
float post_threshold,
bool use_gaussian,
float gaussian_sigma,
int background_label,
bool normalized,
MetaTensor* out,
MetaTensor* index,
MetaTensor* roisnum,
MetaConfig config = MetaConfig());
void MatrixRankTolInferMeta(const MetaTensor& x,
const MetaTensor& atol_tensor,
bool use_default_tol,
......
paddle/phi/kernels/cpu/matrix_nms_kernel.cc 0 → 100644
浏览文件 @ 02414aac
// 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/matrix_nms_kernel.h"
#include "paddle/phi/backends/cpu/cpu_context.h"
#include "paddle/phi/core/kernel_registry.h"
namespace phi {
template <class T>
static inline T BBoxArea(const T* box, const bool normalized) {
if (box[2] < box[0] || box[3] < box[1]) {
// If coordinate values are is invalid
// (e.g. xmax < xmin or ymax < ymin), return 0.
return static_cast<T>(0.);
} else {
const T w = box[2] - box[0];
const T h = box[3] - box[1];
if (normalized) {
return w * h;
} else {
// If coordinate values are not within range [0, 1].
return (w + 1) * (h + 1);
}
}
}
template <class T>
static inline T JaccardOverlap(const T* box1,
const T* box2,
const bool normalized) {
if (box2[0] > box1[2] || box2[2] < box1[0] || box2[1] > box1[3] ||
box2[3] < box1[1]) {
return static_cast<T>(0.);
} else {
const T inter_xmin = std::max(box1[0], box2[0]);
const T inter_ymin = std::max(box1[1], box2[1]);
const T inter_xmax = std::min(box1[2], box2[2]);
const T inter_ymax = std::min(box1[3], box2[3]);
T norm = normalized ? static_cast<T>(0.) : static_cast<T>(1.);
T inter_w = inter_xmax - inter_xmin + norm;
T inter_h = inter_ymax - inter_ymin + norm;
const T inter_area = inter_w * inter_h;
const T bbox1_area = BBoxArea<T>(box1, normalized);
const T bbox2_area = BBoxArea<T>(box2, normalized);
return inter_area / (bbox1_area + bbox2_area - inter_area);
}
}
template <typename T, bool gaussian>
struct decay_score;
template <typename T>
struct decay_score<T, true> {
T operator()(T iou, T max_iou, T sigma) {
return std::exp((max_iou * max_iou - iou * iou) * sigma);
}
};
template <typename T>
struct decay_score<T, false> {
T operator()(T iou, T max_iou, T sigma) {
return (1. - iou) / (1. - max_iou);
}
};
template <typename T, bool gaussian>
void NMSMatrix(const DenseTensor& bbox,
const DenseTensor& scores,
const T score_threshold,
const T post_threshold,
const float sigma,
const int64_t top_k,
const bool normalized,
std::vector<int>* selected_indices,
std::vector<T>* decayed_scores) {
int64_t num_boxes = bbox.dims()[0];
int64_t box_size = bbox.dims()[1];
auto score_ptr = scores.data<T>();
auto bbox_ptr = bbox.data<T>();
std::vector<int32_t> perm(num_boxes);
std::iota(perm.begin(), perm.end(), 0);
auto end = std::remove_if(
perm.begin(), perm.end(), [&score_ptr, score_threshold](int32_t idx) {
return score_ptr[idx] <= score_threshold;
});
auto sort_fn = [&score_ptr](int32_t lhs, int32_t rhs) {
return score_ptr[lhs] > score_ptr[rhs];
};
int64_t num_pre = std::distance(perm.begin(), end);
if (num_pre <= 0) {
return;
}
if (top_k > -1 && num_pre > top_k) {
num_pre = top_k;
}
std::partial_sort(perm.begin(), perm.begin() + num_pre, end, sort_fn);
std::vector<T> iou_matrix((num_pre * (num_pre - 1)) >> 1);
std::vector<T> iou_max(num_pre);
iou_max[0] = 0.;
for (int64_t i = 1; i < num_pre; i++) {
T max_iou = 0.;
auto idx_a = perm[i];
for (int64_t j = 0; j < i; j++) {
auto idx_b = perm[j];
auto iou = JaccardOverlap<T>(
bbox_ptr + idx_a * box_size, bbox_ptr + idx_b * box_size, normalized);
max_iou = std::max(max_iou, iou);
iou_matrix[i * (i - 1) / 2 + j] = iou;
}
iou_max[i] = max_iou;
}
if (score_ptr[perm[0]] > post_threshold) {
selected_indices->push_back(perm[0]);
decayed_scores->push_back(score_ptr[perm[0]]);
}
decay_score<T, gaussian> decay_fn;
for (int64_t i = 1; i < num_pre; i++) {
T min_decay = 1.;
for (int64_t j = 0; j < i; j++) {
auto max_iou = iou_max[j];
auto iou = iou_matrix[i * (i - 1) / 2 + j];
auto decay = decay_fn(iou, max_iou, sigma);
min_decay = std::min(min_decay, decay);
}
auto ds = min_decay * score_ptr[perm[i]];
if (ds <= post_threshold) continue;
selected_indices->push_back(perm[i]);
decayed_scores->push_back(ds);
}
}
template <typename T>
size_t MultiClassMatrixNMS(const DenseTensor& scores,
const DenseTensor& bboxes,
std::vector<T>* out,
std::vector<int>* indices,
int start,
int64_t background_label,
int64_t nms_top_k,
int64_t keep_top_k,
bool normalized,
T score_threshold,
T post_threshold,
bool use_gaussian,
float gaussian_sigma) {
std::vector<int> all_indices;
std::vector<T> all_scores;
std::vector<T> all_classes;
all_indices.reserve(scores.numel());
all_scores.reserve(scores.numel());
all_classes.reserve(scores.numel());
size_t num_det = 0;
auto class_num = scores.dims()[0];
DenseTensor score_slice;
for (int64_t c = 0; c < class_num; ++c) {
if (c == background_label) continue;
score_slice = scores.Slice(c, c + 1);
if (use_gaussian) {
NMSMatrix<T, true>(bboxes,
score_slice,
score_threshold,
post_threshold,
gaussian_sigma,
nms_top_k,
normalized,
&all_indices,
&all_scores);
} else {
NMSMatrix<T, false>(bboxes,
score_slice,
score_threshold,
post_threshold,
gaussian_sigma,
nms_top_k,
normalized,
&all_indices,
&all_scores);
}
for (size_t i = 0; i < all_indices.size() - num_det; i++) {
all_classes.push_back(static_cast<T>(c));
}
num_det = all_indices.size();
}
if (num_det <= 0) {
return num_det;
}
if (keep_top_k > -1) {
auto k = static_cast<size_t>(keep_top_k);
if (num_det > k) num_det = k;
}
std::vector<int32_t> perm(all_indices.size());
std::iota(perm.begin(), perm.end(), 0);
std::partial_sort(perm.begin(),
perm.begin() + num_det,
perm.end(),
[&all_scores](int lhs, int rhs) {
return all_scores[lhs] > all_scores[rhs];
});
for (size_t i = 0; i < num_det; i++) {
auto p = perm[i];
auto idx = all_indices[p];
auto cls = all_classes[p];
auto score = all_scores[p];
auto bbox = bboxes.data<T>() + idx * bboxes.dims()[1];
(*indices).push_back(start + idx);
(*out).push_back(cls);
(*out).push_back(score);
for (int j = 0; j < bboxes.dims()[1]; j++) {
(*out).push_back(bbox[j]);
}
}
return num_det;
}
template <typename T, typename Context>
void MatrixNMSKernel(const Context& ctx,
const DenseTensor& bboxes,
const DenseTensor& scores,
float score_threshold,
int nms_top_k,
int keep_top_k,
float post_threshold,
bool use_gaussian,
float gaussian_sigma,
int background_label,
bool normalized,
DenseTensor* out,
DenseTensor* index,
DenseTensor* roisnum) {
auto score_dims = scores.dims();
auto batch_size = score_dims[0];
auto num_boxes = score_dims[2];
auto box_dim = bboxes.dims()[2];
auto out_dim = box_dim + 2;
DenseTensor boxes_slice, scores_slice;
size_t num_out = 0;
std::vector<size_t> offsets = {0};
std::vector<T> detections;
std::vector<int> indices;
std::vector<int> num_per_batch;
detections.reserve(out_dim * num_boxes * batch_size);
indices.reserve(num_boxes * batch_size);
num_per_batch.reserve(batch_size);
for (int i = 0; i < batch_size; ++i) {
scores_slice = scores.Slice(i, i + 1);
scores_slice.Resize({score_dims[1], score_dims[2]});
boxes_slice = bboxes.Slice(i, i + 1);
boxes_slice.Resize({score_dims[2], box_dim});
int start = i * score_dims[2];
num_out = MultiClassMatrixNMS(scores_slice,
boxes_slice,
&detections,
&indices,
start,
background_label,
nms_top_k,
keep_top_k,
normalized,
static_cast<T>(score_threshold),
static_cast<T>(post_threshold),
use_gaussian,
gaussian_sigma);
offsets.push_back(offsets.back() + num_out);
num_per_batch.emplace_back(num_out);
}
int64_t num_kept = offsets.back();
if (num_kept == 0) {
out->Resize(phi::make_ddim({0, out_dim}));
ctx.template Alloc<T>(out);
index->Resize(phi::make_ddim({0, 1}));
ctx.template Alloc<int>(index);
} else {
out->Resize(phi::make_ddim({num_kept, out_dim}));
ctx.template Alloc<T>(out);
index->Resize(phi::make_ddim({num_kept, 1}));
ctx.template Alloc<int>(index);
std::copy(detections.begin(), detections.end(), out->data<T>());
std::copy(indices.begin(), indices.end(), index->data<int>());
}
if (roisnum != nullptr) {
roisnum->Resize(phi::make_ddim({batch_size}));
ctx.template Alloc<int>(roisnum);
std::copy(num_per_batch.begin(), num_per_batch.end(), roisnum->data<int>());
}
}
} // namespace phi
PD_REGISTER_KERNEL(
matrix_nms, CPU, ALL_LAYOUT, phi::MatrixNMSKernel, float, double) {}
paddle/phi/kernels/matrix_nms_kernel.h 0 → 100644
浏览文件 @ 02414aac
// 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/core/dense_tensor.h"
namespace phi {
template <typename T, typename Context>
void MatrixNMSKernel(const Context& ctx,
const DenseTensor& bboxes,
const DenseTensor& scores,
float score_threshold,
int nms_top_k,
int keep_top_k,
float post_threshold,
bool use_gaussian,
float gaussian_sigma,
int background_label,
bool normalized,
DenseTensor* out,
DenseTensor* index,
DenseTensor* roisnum);
} // namespace phi
paddle/phi/ops/compat/matrix_nms_sig.cc 0 → 100644
浏览文件 @ 02414aac
// 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 MatrixNMSOpArgumentMapping(const ArgumentMappingContext& ctx) {
return KernelSignature("matrix_nms",
{"BBoxes", "Scores"},
{"score_threshold",
"nms_top_k",
"keep_top_k",
"post_threshold",
"use_gaussian",
"gaussian_sigma",
"background_label",
"normalized"},
{"Out", "Index", "RoisNum"});
}
} // namespace phi
PD_REGISTER_ARG_MAPPING_FN(matrix_nms, phi::MatrixNMSOpArgumentMapping);
python/paddle/fluid/layers/detection.py
浏览文件 @ 02414aac
......@@ -3642,6 +3642,16 @@ def matrix_nms(bboxes,
keep_top_k=200,
normalized=False)
"""
if in_dygraph_mode():
attrs = (score_threshold, nms_top_k, keep_top_k, post_threshold,
use_gaussian, gaussian_sigma, background_label, normalized)
out, index = _C_ops.final_state_matrix_nms(bboxes, scores, *attrs)
if return_index:
return out, index
else:
return out
check_variable_and_dtype(bboxes, 'BBoxes', ['float32', 'float64'],
'matrix_nms')
check_variable_and_dtype(scores, 'Scores', ['float32', 'float64'],
......@@ -3664,13 +3674,13 @@ def matrix_nms(bboxes,
'Scores': scores
},
attrs={
'background_label': background_label,
'score_threshold': score_threshold,
'post_threshold': post_threshold,
'nms_top_k': nms_top_k,
'gaussian_sigma': gaussian_sigma,
'use_gaussian': use_gaussian,
'keep_top_k': keep_top_k,
'use_gaussian': use_gaussian,
'gaussian_sigma': gaussian_sigma,
'background_label': background_label,
'normalized': normalized
},
outputs={
......
python/paddle/fluid/tests/unittests/test_matrix_nms_op.py
浏览文件 @ 02414aac
......@@ -22,6 +22,29 @@ from paddle.fluid import Program, program_guard
import paddle
def python_matrix_nms(bboxes,
scores,
score_threshold,
nms_top_k,
keep_top_k,
post_threshold,
use_gaussian=False,
gaussian_sigma=2.,
background_label=0,
normalized=True,
return_index=True,
return_rois_num=True):
out, rois_num, index = paddle.vision.ops.matrix_nms(
bboxes, scores, score_threshold, post_threshold, nms_top_k, keep_top_k,
use_gaussian, gaussian_sigma, background_label, normalized,
return_index, return_rois_num)
if not return_index:
index = None
if not return_rois_num:
rois_num = None
return out, index, rois_num
def softmax(x):
# clip to shiftx, otherwise, when calc loss with
# log(exp(shiftx)), may get log(0)=INF
......@@ -167,6 +190,7 @@ class TestMatrixNMSOp(OpTest):
def setUp(self):
self.set_argument()
self.python_api = python_matrix_nms
N = 7
M = 1200
C = 21
......@@ -203,23 +227,23 @@ class TestMatrixNMSOp(OpTest):
self.op_type = 'matrix_nms'
self.inputs = {'BBoxes': boxes, 'Scores': scores}
self.outputs = {
'Out': (nmsed_outs, [lod]),
'Index': (index_outs[:, None], [lod]),
'Out': nmsed_outs,
'Index': index_outs[:, None],
'RoisNum': np.array(lod).astype('int32')
}
self.attrs = {
'background_label': 0,
'score_threshold': score_threshold,
'nms_top_k': nms_top_k,
'keep_top_k': keep_top_k,
'score_threshold': score_threshold,
'post_threshold': post_threshold,
'use_gaussian': use_gaussian,
'gaussian_sigma': gaussian_sigma,
'background_label': 0,
'normalized': True,
}
def test_check_output(self):
self.check_output()
self.check_output(check_eager=True)
class TestMatrixNMSOpNoOutput(TestMatrixNMSOp):
......@@ -265,50 +289,51 @@ class TestMatrixNMSError(unittest.TestCase):
# the bboxes type must be Variable
fluid.layers.matrix_nms(bboxes=boxes_np,
scores=scores_data,
nms_top_k=nms_top_k,
keep_top_k=keep_top_k,
score_threshold=score_threshold,
post_threshold=post_threshold)
post_threshold=post_threshold,
nms_top_k=nms_top_k,
keep_top_k=keep_top_k)
paddle.vision.ops.matrix_nms(bboxes=boxes_np,
scores=scores_data,
nms_top_k=nms_top_k,
keep_top_k=keep_top_k,
score_threshold=score_threshold,
post_threshold=post_threshold)
post_threshold=post_threshold,
nms_top_k=nms_top_k,
keep_top_k=keep_top_k)
def test_scores_Variable():
# the scores type must be Variable
fluid.layers.matrix_nms(bboxes=boxes_data,
scores=scores_np,
nms_top_k=nms_top_k,
keep_top_k=keep_top_k,
score_threshold=score_threshold,
post_threshold=post_threshold)
post_threshold=post_threshold,
nms_top_k=nms_top_k,
keep_top_k=keep_top_k)
paddle.vision.ops.matrix_nms(bboxes=boxes_data,
scores=scores_np,
nms_top_k=nms_top_k,
keep_top_k=keep_top_k,
score_threshold=score_threshold,
post_threshold=post_threshold)
post_threshold=post_threshold,
nms_top_k=nms_top_k,
keep_top_k=keep_top_k)
def test_empty():
# when all score are lower than threshold
try:
fluid.layers.matrix_nms(bboxes=boxes_data,
scores=scores_data,
score_threshold=score_threshold,
post_threshold=post_threshold,
nms_top_k=nms_top_k,
keep_top_k=keep_top_k,
score_threshold=10.,
post_threshold=post_threshold)
keep_top_k=keep_top_k)
except Exception as e:
self.fail(e)
try:
paddle.vision.ops.matrix_nms(bboxes=boxes_data,
scores=scores_data,
nms_top_k=nms_top_k,
keep_top_k=keep_top_k,
score_threshold=10.,
post_threshold=post_threshold)
paddle.vision.ops.matrix_nms(
bboxes=boxes_data,
scores=scores_data,
score_threshold=score_threshold,
post_threshold=post_threshold,
nms_top_k=nms_top_k,
keep_top_k=keep_top_k)
except Exception as e:
self.fail(e)
......@@ -317,20 +342,20 @@ class TestMatrixNMSError(unittest.TestCase):
try:
fluid.layers.matrix_nms(bboxes=boxes_data,
scores=scores_data,
nms_top_k=nms_top_k,
keep_top_k=keep_top_k,
score_threshold=score_threshold,
post_threshold=post_threshold)
post_threshold=post_threshold,
nms_top_k=nms_top_k,
keep_top_k=keep_top_k)
except Exception as e:
self.fail(e)
try:
paddle.vision.ops.matrix_nms(
bboxes=boxes_data,
scores=scores_data,
nms_top_k=nms_top_k,
keep_top_k=keep_top_k,
score_threshold=score_threshold,
post_threshold=post_threshold)
post_threshold=post_threshold,
nms_top_k=nms_top_k,
keep_top_k=keep_top_k)
except Exception as e:
self.fail(e)
......@@ -340,4 +365,5 @@ class TestMatrixNMSError(unittest.TestCase):
if __name__ == '__main__':
paddle.enable_static()
unittest.main()
python/paddle/vision/ops.py
浏览文件 @ 02414aac
......@@ -1891,6 +1891,16 @@ def matrix_nms(bboxes,
check_type(background_label, 'background_label', int, 'matrix_nms')
if in_dygraph_mode():
out, index, rois_num = _C_ops.final_state_matrix_nms(
bboxes, scores, score_threshold, nms_top_k, keep_top_k,
post_threshold, use_gaussian, gaussian_sigma, background_label,
normalized)
if not return_index:
index = None
if not return_rois_num:
rois_num = None
return out, rois_num, index
elif _in_legacy_dygraph():
attrs = ('background_label', background_label, 'score_threshold',
score_threshold, 'post_threshold', post_threshold, 'nms_top_k',
nms_top_k, 'gaussian_sigma', gaussian_sigma, 'use_gaussian',
......
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