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PaddleDetection
提交 26f03ea1 编写于 作者: W wanghaox
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update detection_map operator

上级 67cbb3e3
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paddle/operators/detection_map_op.cc
浏览文件 @ 26f03ea1
......@@ -24,6 +24,29 @@ class DetectionMAPOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("Detection"),
"Input(Detection) of DetectionMAPOp should not be null.");
PADDLE_ENFORCE(ctx->HasInput("Label"),
"Input(Label) of DetectionMAPOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("MAP"),
"Output(MAP) of DetectionMAPOp should not be null.");
auto det_dims = ctx->GetInputDim("Detection");
PADDLE_ENFORCE_EQ(det_dims.size(), 2UL,
"The rank of Input(Detection) must be 2, "
"the shape is [N, 6].");
PADDLE_ENFORCE_EQ(det_dims[1], 6UL,
"The shape is of Input(Detection) [N, 6].");
auto label_dims = ctx->GetInputDim("Label");
PADDLE_ENFORCE_EQ(label_dims.size(), 2UL,
"The rank of Input(Label) must be 2, "
"the shape is [N, 6].");
PADDLE_ENFORCE_EQ(label_dims[1], 6UL,
"The shape is of Input(Label) [N, 6].");
auto ap_type = GetAPType(ctx->Attrs().Get<std::string>("ap_type"));
PADDLE_ENFORCE_NE(ap_type, APType::kNone,
"The ap_type should be 'integral' or '11point.");
auto map_dim = framework::make_ddim({1});
ctx->SetOutputDim("MAP", map_dim);
}
......@@ -42,25 +65,49 @@ class DetectionMAPOpMaker : public framework::OpProtoAndCheckerMaker {
DetectionMAPOpMaker(framework::OpProto* proto,
framework::OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("Detect", "The detection output.");
AddInput("Label", "The label data.");
AddOutput("MAP", "The MAP evaluate result of the detection.");
AddAttr<float>("overlap_threshold", "The overlap threshold.")
AddInput("Label",
"(LoDTensor) A 2-D LoDTensor with shape[N, 6] represents the"
"Labeled ground-truth data. Each row has 6 values: "
"[label, is_difficult, xmin, ymin, xmax, ymax], N is the total "
"number of ground-truth data in this mini-batch. For each "
"instance, the offsets in first dimension are called LoD, "
"the number of offset is N + 1, if LoD[i + 1] - LoD[i] == 0, "
"means there is no ground-truth data.");
AddInput("Detection",
"(LoDTensor) A 2-D LoDTensor with shape [M, 6] represents the "
"detections. Each row has 6 values: "
"[label, confidence, xmin, ymin, xmax, ymax], M is the total "
"number of detections in this mini-batch. For each instance, "
"the offsets in first dimension are called LoD, the number of "
"offset is N + 1, if LoD[i + 1] - LoD[i] == 0, means there is "
"no detected data.");
AddOutput("MAP",
"(Tensor) A tensor with shape [1], store the mAP evaluate "
"result of the detection.");
AddAttr<float>("overlap_threshold",
"(float) "
"The jaccard overlap threshold of detection output and "
"ground-truth data.")
.SetDefault(.3f);
AddAttr<bool>("evaluate_difficult",
"(bool, default true) "
"Switch to control whether the difficult data is evaluated.")
.SetDefault(true);
AddAttr<std::string>("ap_type",
"The AP algorithm type, 'Integral' or '11point'.")
.SetDefault("Integral");
"(string, default 'integral') "
"The AP algorithm type, 'integral' or '11point'.")
.SetDefault("integral")
.InEnum({"integral", "11point"});
AddComment(R"DOC(
Detection MAP Operator.
Detection MAP evaluator for SSD(Single Shot MultiBox Detector) algorithm.
Please get more information from the following papers:
https://arxiv.org/abs/1512.02325.
Detection mAP evaluate operator.
The general steps are as follows. First, calculate the true positive and
false positive according to the input of detection and labels, then
calculate the mAP evaluate value.
Supporting '11 point' and 'integral' mAP algorithm. Please get more information
from the following articles:
https://sanchom.wordpress.com/tag/average-precision/
https://arxiv.org/abs/1512.02325
)DOC");
}
......
paddle/operators/detection_map_op.cu 已删除 100644 → 0
浏览文件 @ 67cbb3e3
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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/operators/detection_map_op.h"
namespace ops = paddle::operators;
REGISTER_OP_CUDA_KERNEL(
detection_map, ops::DetectionMAPOpKernel<paddle::platform::GPUPlace, float>,
ops::DetectionMAPOpKernel<paddle::platform::GPUPlace, double>);
paddle/operators/detection_map_op.h
浏览文件 @ 26f03ea1
......@@ -13,22 +13,37 @@ See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include "paddle/framework/eigen.h"
#include "paddle/framework/op_registry.h"
#include "paddle/operators/math/detection_util.h"
#include "paddle/operators/math/math_function.h"
namespace paddle {
namespace operators {
enum APType { kNone = 0, kIntegral, k11point };
APType GetAPType(std::string str) {
if (str == "integral") {
return APType::kIntegral;
} else if (str == "11point") {
return APType::k11point;
} else {
return APType::kNone;
}
}
template <typename T>
inline bool SortScorePairDescend(const std::pair<float, T>& pair1,
const std::pair<float, T>& pair2) {
return pair1.first > pair2.first;
}
template <typename T>
inline void GetAccumulation(std::vector<std::pair<T, int>> in_pairs,
std::vector<int>* accu_vec) {
std::stable_sort(in_pairs.begin(), in_pairs.end(),
math::SortScorePairDescend<int>);
std::stable_sort(in_pairs.begin(), in_pairs.end(), SortScorePairDescend<int>);
accu_vec->clear();
size_t sum = 0;
for (size_t i = 0; i < in_pairs.size(); ++i) {
// auto score = in_pairs[i].first;
auto count = in_pairs[i].second;
sum += count;
accu_vec->push_back(sum);
......@@ -39,126 +54,125 @@ template <typename Place, typename T>
class DetectionMAPOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* input_label = ctx.Input<framework::LoDTensor>("Label");
auto* input_detect = ctx.Input<framework::Tensor>("Detect");
auto* map_out = ctx.Output<framework::Tensor>("MAP");
auto* in_detect = ctx.Input<framework::LoDTensor>("Detection");
auto* in_label = ctx.Input<framework::LoDTensor>("Label");
auto* out_map = ctx.Output<framework::Tensor>("MAP");
float overlap_threshold = ctx.Attr<float>("overlap_threshold");
float evaluate_difficult = ctx.Attr<bool>("evaluate_difficult");
std::string ap_type = ctx.Attr<std::string>("ap_type");
auto ap_type = GetAPType(ctx.Attr<std::string>("ap_type"));
auto label_lod = input_label->lod();
auto label_lod = in_label->lod();
auto detect_lod = in_detect->lod();
PADDLE_ENFORCE_EQ(label_lod.size(), 1UL,
"Only support one level sequence now.");
auto batch_size = label_lod[0].size() - 1;
std::vector<std::map<int, std::vector<operators::math::BBox<T>>>> gt_bboxes;
std::vector<
std::map<int, std::vector<std::pair<T, operators::math::BBox<T>>>>>
detect_bboxes;
if (platform::is_gpu_place(ctx.GetPlace())) {
framework::LoDTensor input_label_cpu;
framework::Tensor input_detect_cpu;
input_label_cpu.set_lod(input_label->lod());
input_label_cpu.Resize(input_label->dims());
input_detect_cpu.Resize(input_detect->dims());
input_label_cpu.mutable_data<T>(platform::CPUPlace());
input_detect_cpu.mutable_data<T>(platform::CPUPlace());
framework::CopyFrom(*input_label, platform::CPUPlace(),
ctx.device_context(), &input_label_cpu);
framework::CopyFrom(*input_detect, platform::CPUPlace(),
ctx.device_context(), &input_detect_cpu);
GetBBoxes(input_label_cpu, input_detect_cpu, gt_bboxes, detect_bboxes);
} else {
GetBBoxes(*input_label, *input_detect, gt_bboxes, detect_bboxes);
}
PADDLE_ENFORCE_EQ(label_lod[0].size(), detect_lod[0].size(),
"The batch_size of input(Label) and input(Detection) "
"must be the same.");
std::vector<std::map<int, std::vector<Box>>> gt_boxes;
std::vector<std::map<int, std::vector<std::pair<T, Box>>>> detect_boxes;
GetBoxes(*in_label, *in_detect, gt_boxes, detect_boxes);
std::map<int, int> label_pos_count;
std::map<int, std::vector<std::pair<T, int>>> true_pos;
std::map<int, std::vector<std::pair<T, int>>> false_pos;
CalcTrueAndFalsePositive(batch_size, evaluate_difficult, overlap_threshold,
gt_bboxes, detect_bboxes, label_pos_count,
true_pos, false_pos);
CalcTrueAndFalsePositive(gt_boxes, detect_boxes, evaluate_difficult,
overlap_threshold, label_pos_count, true_pos,
false_pos);
T map = CalcMAP(ap_type, label_pos_count, true_pos, false_pos);
T* map_data = nullptr;
framework::Tensor map_cpu;
map_out->mutable_data<T>(ctx.GetPlace());
if (platform::is_gpu_place(ctx.GetPlace())) {
map_data = map_cpu.mutable_data<T>(map_out->dims(), platform::CPUPlace());
T* map_data = out_map->mutable_data<T>(ctx.GetPlace());
map_data[0] = map;
framework::CopyFrom(map_cpu, platform::CPUPlace(), ctx.device_context(),
map_out);
}
protected:
struct Box {
Box(T xmin, T ymin, T xmax, T ymax)
: xmin(xmin), ymin(ymin), xmax(xmax), ymax(ymax), is_difficult(false) {}
T xmin, ymin, xmax, ymax;
bool is_difficult;
};
inline T JaccardOverlap(const Box& box1, const Box& box2) const {
if (box2.xmin > box1.xmax || box2.xmax < box1.xmin ||
box2.ymin > box1.ymax || box2.ymax < box1.ymin) {
return 0.0;
} else {
map_data = map_out->mutable_data<T>(ctx.GetPlace());
map_data[0] = map;
T inter_xmin = std::max(box1.xmin, box2.xmin);
T inter_ymin = std::max(box1.ymin, box2.ymin);
T inter_xmax = std::min(box1.xmax, box2.xmax);
T inter_ymax = std::min(box1.ymax, box2.ymax);
T inter_width = inter_xmax - inter_xmin;
T inter_height = inter_ymax - inter_ymin;
T inter_area = inter_width * inter_height;
T bbox_area1 = (box1.xmax - box1.xmin) * (box1.ymax - box1.ymin);
T bbox_area2 = (box2.xmax - box2.xmin) * (box2.ymax - box2.ymin);
return inter_area / (bbox_area1 + bbox_area2 - inter_area);
}
}
protected:
void GetBBoxes(
const framework::LoDTensor& input_label,
const framework::Tensor& input_detect,
std::vector<std::map<int, std::vector<operators::math::BBox<T>>>>&
gt_bboxes,
std::vector<
std::map<int, std::vector<std::pair<T, operators::math::BBox<T>>>>>&
detect_bboxes) const {
const T* label_data = input_label.data<T>();
const T* detect_data = input_detect.data<T>();
void GetBoxes(const framework::LoDTensor& input_label,
const framework::LoDTensor& input_detect,
std::vector<std::map<int, std::vector<Box>>>& gt_boxes,
std::vector<std::map<int, std::vector<std::pair<T, Box>>>>&
detect_boxes) const {
auto labels = framework::EigenTensor<T, 2>::From(input_label);
auto detect = framework::EigenTensor<T, 2>::From(input_detect);
auto label_lod = input_label.lod();
auto batch_size = label_lod[0].size() - 1;
auto detect_lod = input_detect.lod();
int batch_size = label_lod[0].size() - 1;
auto label_index = label_lod[0];
for (size_t n = 0; n < batch_size; ++n) {
std::map<int, std::vector<operators::math::BBox<T>>> bboxes;
for (int n = 0; n < batch_size; ++n) {
std::map<int, std::vector<Box>> boxes;
for (int i = label_index[n]; i < label_index[n + 1]; ++i) {
std::vector<operators::math::BBox<T>> bbox;
math::GetBBoxFromLabelData<T>(label_data + i * 6, 1, bbox);
int label = static_cast<int>(label_data[i * 6]);
bboxes[label].push_back(bbox[0]);
Box box(labels(i, 2), labels(i, 3), labels(i, 4), labels(i, 5));
int label = labels(i, 0);
auto is_difficult = labels(i, 1);
if (std::abs(is_difficult - 0.0) < 1e-6)
box.is_difficult = false;
else
box.is_difficult = true;
boxes[label].push_back(box);
}
gt_bboxes.push_back(bboxes);
gt_boxes.push_back(boxes);
}
size_t n = 0;
size_t detect_box_count = input_detect.dims()[0];
for (size_t img_id = 0; img_id < batch_size; ++img_id) {
std::map<int, std::vector<std::pair<T, operators::math::BBox<T>>>> bboxes;
size_t cur_img_id = static_cast<size_t>((detect_data + n * 7)[0]);
while (cur_img_id == img_id && n < detect_box_count) {
std::vector<T> label;
std::vector<T> score;
std::vector<operators::math::BBox<T>> bbox;
math::GetBBoxFromDetectData<T>(detect_data + n * 7, 1, label, score,
bbox);
bboxes[label[0]].push_back(std::make_pair(score[0], bbox[0]));
++n;
cur_img_id = static_cast<size_t>((detect_data + n * 7)[0]);
auto detect_index = detect_lod[0];
for (int n = 0; n < batch_size; ++n) {
std::map<int, std::vector<std::pair<T, Box>>> boxes;
for (int i = detect_index[n]; i < detect_index[n + 1]; ++i) {
Box box(detect(i, 2), detect(i, 3), detect(i, 4), detect(i, 5));
int label = detect(i, 0);
auto score = detect(i, 1);
boxes[label].push_back(std::make_pair(score, box));
}
detect_bboxes.push_back(bboxes);
detect_boxes.push_back(boxes);
}
}
void CalcTrueAndFalsePositive(
size_t batch_size, bool evaluate_difficult, float overlap_threshold,
const std::vector<std::map<int, std::vector<operators::math::BBox<T>>>>&
gt_bboxes,
const std::vector<
std::map<int, std::vector<std::pair<T, operators::math::BBox<T>>>>>&
detect_bboxes,
const std::vector<std::map<int, std::vector<Box>>>& gt_boxes,
const std::vector<std::map<int, std::vector<std::pair<T, Box>>>>&
detect_boxes,
bool evaluate_difficult, float overlap_threshold,
std::map<int, int>& label_pos_count,
std::map<int, std::vector<std::pair<T, int>>>& true_pos,
std::map<int, std::vector<std::pair<T, int>>>& false_pos) const {
for (size_t n = 0; n < batch_size; ++n) {
auto image_gt_bboxes = gt_bboxes[n];
for (auto it = image_gt_bboxes.begin(); it != image_gt_bboxes.end();
++it) {
int batch_size = gt_boxes.size();
for (int n = 0; n < batch_size; ++n) {
auto image_gt_boxes = gt_boxes[n];
for (auto it = image_gt_boxes.begin(); it != image_gt_boxes.end(); ++it) {
size_t count = 0;
auto labeled_bboxes = it->second;
if (evaluate_difficult) {
......@@ -179,16 +193,16 @@ class DetectionMAPOpKernel : public framework::OpKernel<T> {
}
}
for (size_t n = 0; n < detect_bboxes.size(); ++n) {
auto image_gt_bboxes = gt_bboxes[n];
auto detections = detect_bboxes[n];
for (size_t n = 0; n < detect_boxes.size(); ++n) {
auto image_gt_boxes = gt_boxes[n];
auto detections = detect_boxes[n];
if (image_gt_bboxes.size() == 0) {
if (image_gt_boxes.size() == 0) {
for (auto it = detections.begin(); it != detections.end(); ++it) {
auto pred_bboxes = it->second;
auto pred_boxes = it->second;
int label = it->first;
for (size_t i = 0; i < pred_bboxes.size(); ++i) {
auto score = pred_bboxes[i].first;
for (size_t i = 0; i < pred_boxes.size(); ++i) {
auto score = pred_boxes[i].first;
true_pos[label].push_back(std::make_pair(score, 0));
false_pos[label].push_back(std::make_pair(score, 1));
}
......@@ -198,28 +212,27 @@ class DetectionMAPOpKernel : public framework::OpKernel<T> {
for (auto it = detections.begin(); it != detections.end(); ++it) {
int label = it->first;
auto pred_bboxes = it->second;
if (image_gt_bboxes.find(label) == image_gt_bboxes.end()) {
for (size_t i = 0; i < pred_bboxes.size(); ++i) {
auto score = pred_bboxes[i].first;
auto pred_boxes = it->second;
if (image_gt_boxes.find(label) == image_gt_boxes.end()) {
for (size_t i = 0; i < pred_boxes.size(); ++i) {
auto score = pred_boxes[i].first;
true_pos[label].push_back(std::make_pair(score, 0));
false_pos[label].push_back(std::make_pair(score, 1));
}
continue;
}
auto matched_bboxes = image_gt_bboxes.find(label)->second;
auto matched_bboxes = image_gt_boxes.find(label)->second;
std::vector<bool> visited(matched_bboxes.size(), false);
// Sort detections in descend order based on scores
std::sort(pred_bboxes.begin(), pred_bboxes.end(),
math::SortScorePairDescend<operators::math::BBox<T>>);
for (size_t i = 0; i < pred_bboxes.size(); ++i) {
float max_overlap = -1.0;
std::sort(pred_boxes.begin(), pred_boxes.end(),
SortScorePairDescend<Box>);
for (size_t i = 0; i < pred_boxes.size(); ++i) {
T max_overlap = -1.0;
size_t max_idx = 0;
auto score = pred_bboxes[i].first;
auto score = pred_boxes[i].first;
for (size_t j = 0; j < matched_bboxes.size(); ++j) {
float overlap =
JaccardOverlap(pred_bboxes[i].second, matched_bboxes[j]);
T overlap = JaccardOverlap(pred_boxes[i].second, matched_bboxes[j]);
if (overlap > max_overlap) {
max_overlap = overlap;
max_idx = j;
......@@ -249,7 +262,7 @@ class DetectionMAPOpKernel : public framework::OpKernel<T> {
}
T CalcMAP(
std::string ap_type, const std::map<int, int>& label_pos_count,
APType ap_type, const std::map<int, int>& label_pos_count,
const std::map<int, std::vector<std::pair<T, int>>>& true_pos,
const std::map<int, std::vector<std::pair<T, int>>>& false_pos) const {
T mAP = 0.0;
......@@ -266,18 +279,18 @@ class DetectionMAPOpKernel : public framework::OpKernel<T> {
GetAccumulation<T>(label_true_pos, &tp_sum);
std::vector<int> fp_sum;
GetAccumulation<T>(label_false_pos, &fp_sum);
std::vector<float> precision, recall;
std::vector<T> precision, recall;
size_t num = tp_sum.size();
// Compute Precision.
for (size_t i = 0; i < num; ++i) {
// CHECK_LE(tpCumSum[i], labelNumPos);
precision.push_back(static_cast<float>(tp_sum[i]) /
static_cast<float>(tp_sum[i] + fp_sum[i]));
recall.push_back(static_cast<float>(tp_sum[i]) / label_num_pos);
precision.push_back(static_cast<T>(tp_sum[i]) /
static_cast<T>(tp_sum[i] + fp_sum[i]));
recall.push_back(static_cast<T>(tp_sum[i]) / label_num_pos);
}
// VOC2007 style
if (ap_type == "11point") {
std::vector<float> max_precisions(11, 0.0);
if (ap_type == APType::k11point) {
std::vector<T> max_precisions(11, 0.0);
int start_idx = num - 1;
for (int j = 10; j >= 0; --j)
for (int i = start_idx; i >= 0; --i) {
......@@ -292,7 +305,7 @@ class DetectionMAPOpKernel : public framework::OpKernel<T> {
}
for (int j = 10; j >= 0; --j) mAP += max_precisions[j] / 11;
++count;
} else if (ap_type == "Integral") {
} else if (ap_type == APType::kIntegral) {
// Nature integral
float average_precisions = 0.;
float prev_recall = 0.;
......
paddle/operators/math/detection_util.cc 已删除 100644 → 0
浏览文件 @ 67cbb3e3
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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/operators/math/detection_util.h"
#include "paddle/operators/math/math_function.h"
namespace paddle {
namespace operators {
namespace math {} // namespace math
} // namespace operators
} // namespace paddle
paddle/operators/math/detection_util.cu 已删除 100644 → 0
浏览文件 @ 67cbb3e3
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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/operators/math/detection_util.h"
#include "paddle/operators/math/math_function.h"
#include "paddle/platform/cuda_helper.h"
namespace paddle {
namespace operators {
namespace math {} // namespace math
} // namespace operators
} // namespace paddle
paddle/operators/math/detection_util.h 已删除 100644 → 0
浏览文件 @ 67cbb3e3
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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/framework/selected_rows.h"
#include "paddle/platform/device_context.h"
namespace paddle {
namespace operators {
namespace math {
template <typename T>
struct BBox {
BBox(T x_min, T y_min, T x_max, T y_max)
: x_min(x_min),
y_min(y_min),
x_max(x_max),
y_max(y_max),
is_difficult(false) {}
BBox() {}
T get_width() const { return x_max - x_min; }
T get_height() const { return y_max - y_min; }
T get_center_x() const { return (x_min + x_max) / 2; }
T get_center_y() const { return (y_min + y_max) / 2; }
T get_area() const { return get_width() * get_height(); }
// coordinate of bounding box
T x_min;
T y_min;
T x_max;
T y_max;
// whether difficult object (e.g. object with heavy occlusion is difficult)
bool is_difficult;
};
template <typename T>
void GetBBoxFromDetectData(const T* detect_data, const size_t num_bboxes,
std::vector<T>& labels, std::vector<T>& scores,
std::vector<BBox<T>>& bboxes) {
size_t out_offset = bboxes.size();
labels.resize(out_offset + num_bboxes);
scores.resize(out_offset + num_bboxes);
bboxes.resize(out_offset + num_bboxes);
for (size_t i = 0; i < num_bboxes; ++i) {
labels[out_offset + i] = *(detect_data + i * 7 + 1);
scores[out_offset + i] = *(detect_data + i * 7 + 2);
BBox<T> bbox;
bbox.x_min = *(detect_data + i * 7 + 3);
bbox.y_min = *(detect_data + i * 7 + 4);
bbox.x_max = *(detect_data + i * 7 + 5);
bbox.y_max = *(detect_data + i * 7 + 6);
bboxes[out_offset + i] = bbox;
};
}
template <typename T>
void GetBBoxFromLabelData(const T* label_data, const size_t num_bboxes,
std::vector<BBox<T>>& bboxes) {
size_t out_offset = bboxes.size();
bboxes.resize(bboxes.size() + num_bboxes);
for (size_t i = 0; i < num_bboxes; ++i) {
BBox<T> bbox;
bbox.x_min = *(label_data + i * 6 + 1);
bbox.y_min = *(label_data + i * 6 + 2);
bbox.x_max = *(label_data + i * 6 + 3);
bbox.y_max = *(label_data + i * 6 + 4);
T is_difficult = *(label_data + i * 6 + 5);
if (std::abs(is_difficult - 0.0) < 1e-6)
bbox.is_difficult = false;
else
bbox.is_difficult = true;
bboxes[out_offset + i] = bbox;
}
}
template <typename T>
inline float JaccardOverlap(const BBox<T>& bbox1, const BBox<T>& bbox2) {
if (bbox2.x_min > bbox1.x_max || bbox2.x_max < bbox1.x_min ||
bbox2.y_min > bbox1.y_max || bbox2.y_max < bbox1.y_min) {
return 0.0;
} else {
float inter_x_min = std::max(bbox1.x_min, bbox2.x_min);
float inter_y_min = std::max(bbox1.y_min, bbox2.y_min);
float inter_x_max = std::min(bbox1.x_max, bbox2.x_max);
float inter_y_max = std::min(bbox1.y_max, bbox2.y_max);
float inter_width = inter_x_max - inter_x_min;
float inter_height = inter_y_max - inter_y_min;
float inter_area = inter_width * inter_height;
float bbox_area1 = bbox1.get_area();
float bbox_area2 = bbox2.get_area();
return inter_area / (bbox_area1 + bbox_area2 - inter_area);
}
}
template <typename T>
bool SortScorePairDescend(const std::pair<float, T>& pair1,
const std::pair<float, T>& pair2) {
return pair1.first > pair2.first;
}
// template <>
// bool SortScorePairDescend(const std::pair<float, NormalizedBBox>& pair1,
// const std::pair<float, NormalizedBBox>& pair2) {
// return pair1.first > pair2.first;
// }
} // namespace math
} // namespace operators
} // namespace paddle
python/paddle/v2/fluid/tests/test_detection_map_op.py
浏览文件 @ 26f03ea1
......@@ -10,14 +10,14 @@ class TestDetectionMAPOp(OpTest):
def set_data(self):
self.init_test_case()
self.mAP = [self.calc_map(self.tf_pos)]
self.mAP = [self.calc_map(self.tf_pos, self.tf_pos_lod)]
self.label = np.array(self.label).astype('float32')
self.detect = np.array(self.detect).astype('float32')
self.mAP = np.array(self.mAP).astype('float32')
self.inputs = {
'Label': (self.label, self.label_lod),
'Detect': self.detect
'Detection': (self.detect, self.detect_lod)
}
self.attrs = {
......@@ -31,29 +31,29 @@ class TestDetectionMAPOp(OpTest):
def init_test_case(self):
self.overlap_threshold = 0.3
self.evaluate_difficult = True
self.ap_type = "Integral"
self.ap_type = "integral"
self.label_lod = [[0, 2, 4]]
# label xmin ymin xmax ymax difficult
self.label = [[1, 0.1, 0.1, 0.3, 0.3, 0], [1, 0.6, 0.6, 0.8, 0.8, 1],
[2, 0.3, 0.3, 0.6, 0.5, 0], [1, 0.7, 0.1, 0.9, 0.3, 0]]
# label difficult xmin ymin xmax ymax
self.label = [[1, 0, 0.1, 0.1, 0.3, 0.3], [1, 1, 0.6, 0.6, 0.8, 0.8],
[2, 0, 0.3, 0.3, 0.6, 0.5], [1, 0, 0.7, 0.1, 0.9, 0.3]]
# image_id label score xmin ymin xmax ymax difficult
# label score xmin ymin xmax ymax difficult
self.detect_lod = [[0, 3, 7]]
self.detect = [
[0, 1, 0.3, 0.1, 0.0, 0.4, 0.3], [0, 1, 0.7, 0.0, 0.1, 0.2, 0.3],
[0, 1, 0.9, 0.7, 0.6, 0.8, 0.8], [1, 2, 0.8, 0.2, 0.1, 0.4, 0.4],
[1, 2, 0.1, 0.4, 0.3, 0.7, 0.5], [1, 1, 0.2, 0.8, 0.1, 1.0, 0.3],
[1, 3, 0.2, 0.8, 0.1, 1.0, 0.3]
[1, 0.3, 0.1, 0.0, 0.4, 0.3], [1, 0.7, 0.0, 0.1, 0.2, 0.3],
[1, 0.9, 0.7, 0.6, 0.8, 0.8], [2, 0.8, 0.2, 0.1, 0.4, 0.4],
[2, 0.1, 0.4, 0.3, 0.7, 0.5], [1, 0.2, 0.8, 0.1, 1.0, 0.3],
[3, 0.2, 0.8, 0.1, 1.0, 0.3]
]
# image_id label score false_pos false_pos
# [-1, 1, 3, -1, -1],
# [-1, 2, 1, -1, -1]
self.tf_pos = [[0, 1, 0.9, 1, 0], [0, 1, 0.7, 1, 0], [0, 1, 0.3, 0, 1],
[1, 1, 0.2, 1, 0], [1, 2, 0.8, 0, 1], [1, 2, 0.1, 1, 0],
[1, 3, 0.2, 0, 1]]
# label score true_pos false_pos
self.tf_pos_lod = [[0, 3, 7]]
self.tf_pos = [[1, 0.9, 1, 0], [1, 0.7, 1, 0], [1, 0.3, 0, 1],
[1, 0.2, 1, 0], [2, 0.8, 0, 1], [2, 0.1, 1, 0],
[3, 0.2, 0, 1]]
def calc_map(self, tf_pos):
def calc_map(self, tf_pos, tf_pos_lod):
mAP = 0.0
count = 0
......@@ -71,7 +71,7 @@ class TestDetectionMAPOp(OpTest):
return accu_list
label_count = collections.Counter()
for (label, xmin, ymin, xmax, ymax, difficult) in self.label:
for (label, difficult, xmin, ymin, xmax, ymax) in self.label:
if self.evaluate_difficult:
label_count[label] += 1
elif not difficult:
......@@ -79,7 +79,7 @@ class TestDetectionMAPOp(OpTest):
true_pos = collections.defaultdict(list)
false_pos = collections.defaultdict(list)
for (image_id, label, score, tp, fp) in tf_pos:
for (label, score, tp, fp) in tf_pos:
true_pos[label].append([score, tp])
false_pos[label].append([score, fp])
......@@ -103,22 +103,22 @@ class TestDetectionMAPOp(OpTest):
recall.append(float(accu_tp_sum[i]) / label_pos_num)
if self.ap_type == "11point":
max_precisions = [11.0, 0.0]
max_precisions = [0.0] * 11
start_idx = len(accu_tp_sum) - 1
for j in range(10, 0, -1):
for i in range(start_idx, 0, -1):
if recall[i] < j / 10.0:
for j in range(10, -1, -1):
for i in range(start_idx, -1, -1):
if recall[i] < float(j) / 10.0:
start_idx = i
if j > 0:
max_precisions[j - 1] = max_precisions[j]
break
else:
if max_precisions[j] < accu_precision[i]:
max_precisions[j] = accu_precision[i]
for j in range(10, 0, -1):
if max_precisions[j] < precision[i]:
max_precisions[j] = precision[i]
for j in range(10, -1, -1):
mAP += max_precisions[j] / 11
count += 1
elif self.ap_type == "Integral":
elif self.ap_type == "integral":
average_precisions = 0.0
prev_recall = 0.0
for i in range(len(accu_tp_sum)):
......@@ -147,8 +147,17 @@ class TestDetectionMAPOpSkipDiff(TestDetectionMAPOp):
self.evaluate_difficult = False
self.tf_pos = [[0, 1, 0.7, 1, 0], [0, 1, 0.3, 0, 1], [1, 1, 0.2, 1, 0],
[1, 2, 0.8, 0, 1], [1, 2, 0.1, 1, 0], [1, 3, 0.2, 0, 1]]
self.tf_pos_lod = [[0, 2, 6]]
# label score true_pos false_pos
self.tf_pos = [[1, 0.7, 1, 0], [1, 0.3, 0, 1], [1, 0.2, 1, 0],
[2, 0.8, 0, 1], [2, 0.1, 1, 0], [3, 0.2, 0, 1]]
class TestDetectionMAPOp11Point(TestDetectionMAPOp):
def init_test_case(self):
super(TestDetectionMAPOp11Point, self).init_test_case()
self.ap_type = "11point"
if __name__ == '__main__':
......
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