detection map evaluator for SSD #6588
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| namespace operators { | ||
| namespace math {} // namespace math | ||
| } // namespace operators | ||
| } // namespace paddle |
| namespace operators { | ||
| namespace math {} // namespace math | ||
| } // namespace operators | ||
| } // namespace paddle |
| return pair1.first > pair2.first; | ||
| } | ||
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| // template <> |
paddle/operators/detection_map_op.cc
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| Detection MAP evaluator for SSD(Single Shot MultiBox Detector) algorithm. | ||
| Please get more information from the following papers: | ||
| https://arxiv.org/abs/1512.02325. | ||
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paddle/operators/detection_map_op.cc
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| framework::OpKernelType GetExpectedKernelType( | ||
| const framework::ExecutionContext& ctx) const override { | ||
| return framework::OpKernelType( | ||
| framework::ToDataType(ctx.Input<framework::Tensor>("Label")->type()), |
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The kernel type (float or double) should be deduced by Detection.
paddle/operators/detection_map_op.cc
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| public: | ||
| DetectionMAPOpMaker(OpProto* proto, OpAttrChecker* op_checker) | ||
| : OpProtoAndCheckerMaker(proto, op_checker) { | ||
| AddInput("Label", |
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在box_coder_op, target_assign_op中，假定：ground-truth box和ground-truth label是两个输入。 这里是concat到一起，合成了一个输入。这点需要讨论下？
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建议在python封装中concat在一起
paddle/operators/detection_map_op.cc
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| 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."); |
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Put the check for attr in the DetectionMAPOpMaker.
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done，moved to DetectionMAPOpMaker.
paddle/operators/detection_map_op.h
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| size_t num = tp_sum.size(); | ||
| // Compute Precision. | ||
| for (size_t i = 0; i < num; ++i) { | ||
| // CHECK_LE(tpCumSum[i], labelNumPos); |
paddle/operators/detection_map_op.h
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| } | ||
| } | ||
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| T CalcMAP( |
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这个函数只计算了当前mini-batch的mAP。 多mini-batch累积计算mAP时需要label_pos_count, true_pos, false_pos，目前Fluid是在Python里用其他op组合计算多mini-batch累积的。 因此这3个是需要输出的，但目前std::map<int, std::vector<std::pair<T, int>>> 数据结构无法作为op的输出。
这块需要讨论下~
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done，增加了OutPosCount、OutTruePos、OutFalsePos的输出
paddle/operators/detection_map_op.cc
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| @@ -0,0 +1,123 @@ | |||
| /* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. | |||
paddle/operators/detection_map_op.h
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| @@ -0,0 +1,329 @@ | |||
| /* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. | |||
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| if (false_pos_data[j * 2 + 1] < kEPS) flag = 0; | ||
| false_pos[i].push_back(std::make_pair(score, flag)); | ||
| } | ||
| } |
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true_pos 和 false_pos赋值代码一样，可以用lambda函数写，简化代码。
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| GetBoxes(*in_label, *in_detect, gt_boxes, detect_boxes); | ||
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| std::map<int, int> label_pos_count; |
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label_pos_count没有必要定义成 std::map<int, int>，可以直接用Tensor。
| continue; | ||
| } | ||
| int label = it->first; | ||
| if (label_pos_count.find(label) == label_pos_count.end()) { |
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label_pos_count改成Tensor，先初始化下，这里就不用if分支。
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label_pos_count改成Tensor代码改动量较大，作为后续优化点改进
| "no detected data."); | ||
| AddInput("PosCount", | ||
| "(Tensor) A tensor with shape [Ncls, 1], store the " | ||
| "input positive example count of each class.") |
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不够好理解，看doc并不知道这个input是干啥的。
下面doc注释存在同样问题。
| "(LodTensor) A LodTensor with shape [Nfp', 2], store the " | ||
| "false positive example of each class. It combines the " | ||
| "input(FalsePos) and the false positive examples computed from " | ||
| "input(Detection) and input(Label)."); |
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- OutPosCount, OutTruePos, OutFalsePos 看注释这3个输出，包含previous mini-batch的状态。
- 这个op只能算累计的mAP，无法算当前mini-batch的mAP?
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1，包含previous mini-batch的状态
2，当PosCount等传入为空，即只计算当前mini-batch的mAP
已增加注释
| using framework::OperatorWithKernel::OperatorWithKernel; | ||
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| void InferShape(framework::InferShapeContext* ctx) const override { | ||
| PADDLE_ENFORCE(ctx->HasInput("Detection"), |
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Seems DetectRes more suitable ?
| "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", |
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Please exchange the position of Detection and Label.
| "no detected data."); | ||
| AddInput("PosCount", | ||
| "(Tensor) A tensor with shape [Ncls, 1], store the " | ||
| "input positive example count of each class.") |
| "input positive example count of each class.") | ||
| .AsDispensable(); | ||
| AddInput("TruePos", | ||
| "(LodTensor) A 2-D LodTensor with shape [Ntp, 2], store the " |
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I think InTruePos is better?
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done,
好像其他op的输入参数，较少用In开头的, 没有修改为InTruePos
| "(LodTensor) A 2-D LodTensor with shape [Nfp, 2], store the " | ||
| "input false positive example of each class.") | ||
| .AsDispensable(); | ||
| AddOutput("OutPosCount", |
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Seems AccumPosCount more suitable?
| .SetDefault(.3f); | ||
| AddAttr<bool>("evaluate_difficult", | ||
| "(bool, default true) " | ||
| "Switch to control whether the difficult data is evaluated.") |
| "(string, default 'integral') " | ||
| "The AP algorithm type, 'integral' or '11point'.") | ||
| .SetDefault("integral") | ||
| .InEnum({"integral", "11point"}) |
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Maybe it's better to declare two const variables ?
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我看头文件里面有声明enum类型的APType，直接用GetAPType(APType. kIntegral)可以避免"integral","11point"四处出现，因为程序里面用到的type是enum型的
| AddComment(R"DOC( | ||
| 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 |
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true positive you mean count of true positive ?
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not the count of true positive
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| mAP += average_precisions | ||
| count += 1 | ||
| self.out_class_pos_count, self.out_true_pos, self.out_true_pos_lod, self.out_false_pos, self.out_false_pos_lod = get_output_pos( |
| mAP += average_precisions; | ||
| ++count; | ||
| } else { | ||
| LOG(FATAL) << "Unkown ap version: " << ap_type; |
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Maybe better to throw a exception here
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已经进行过参数检查，不会运行到这里，这个else可以删除，保留只是为了代码分枝完备
resolve #6266