detection map evaluator for SSD

paddle/fluid/operators/detection_map_op.cc

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+/* Copyright (c) 2018 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/fluid/operators/detection_map_op.h"
+
+namespace paddle {
+namespace operators {
+
+using Tensor = framework::Tensor;
+
+class DetectionMAPOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+  void InferShape(framework::InferShapeContext* ctx) const override {
+    PADDLE_ENFORCE(ctx->HasInput("DetectRes"),
+                   "Input(DetectRes) of DetectionMAPOp should not be null.");
+    PADDLE_ENFORCE(ctx->HasInput("Label"),
+                   "Input(Label) of DetectionMAPOp should not be null.");
+    PADDLE_ENFORCE(
+        ctx->HasOutput("AccumPosCount"),
+        "Output(AccumPosCount) of DetectionMAPOp should not be null.");
+    PADDLE_ENFORCE(
+        ctx->HasOutput("AccumTruePos"),
+        "Output(AccumTruePos) of DetectionMAPOp should not be null.");
+    PADDLE_ENFORCE(
+        ctx->HasOutput("AccumFalsePos"),
+        "Output(AccumFalsePos) of DetectionMAPOp should not be null.");
+    PADDLE_ENFORCE(ctx->HasOutput("MAP"),
+                   "Output(MAP) of DetectionMAPOp should not be null.");
+
+    auto det_dims = ctx->GetInputDim("DetectRes");
+    PADDLE_ENFORCE_EQ(det_dims.size(), 2UL,
+                      "The rank of Input(DetectRes) must be 2, "
+                      "the shape is [N, 6].");
+    PADDLE_ENFORCE_EQ(det_dims[1], 6UL,
+                      "The shape is of Input(DetectRes) [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].");
+
+    if (ctx->HasInput("PosCount")) {
+      PADDLE_ENFORCE(ctx->HasInput("TruePos"),
+                     "Input(TruePos) of DetectionMAPOp should not be null when "
+                     "Input(TruePos) is not null.");
+      PADDLE_ENFORCE(
+          ctx->HasInput("FalsePos"),
+          "Input(FalsePos) of DetectionMAPOp should not be null when "
+          "Input(FalsePos) is not null.");
+    }
+
+    ctx->SetOutputDim("MAP", framework::make_ddim({1}));
+  }
+
+ protected:
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext& ctx) const override {
+    return framework::OpKernelType(
+        framework::ToDataType(
+            ctx.Input<framework::Tensor>("DetectRes")->type()),
+        ctx.device_context());
+  }
+};
+
+class DetectionMAPOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  DetectionMAPOpMaker(OpProto* proto, OpAttrChecker* op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddInput("DetectRes",
+             "(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 detect results 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.");
+    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("PosCount",
+             "(Tensor) A tensor with shape [Ncls, 1], store the "
+             "input positive example count of each class, Ncls is the count of "
+             "input classification. "
+             "This input is used to pass the AccumPosCount generated by the "
+             "previous mini-batch when the multi mini-batches cumulative "
+             "calculation carried out. "
+             "When the input(PosCount) is empty, the cumulative "
+             "calculation is not carried out, and only the results of the "
+             "current mini-batch are calculated.")
+        .AsDispensable();
+    AddInput("TruePos",
+             "(LoDTensor) A 2-D LoDTensor with shape [Ntp, 2], store the "
+             "input true positive example of each class."
+             "This input is used to pass the AccumTruePos generated by the "
+             "previous mini-batch when the multi mini-batches cumulative "
+             "calculation carried out. ")
+        .AsDispensable();
+    AddInput("FalsePos",
+             "(LoDTensor) A 2-D LoDTensor with shape [Nfp, 2], store the "
+             "input false positive example of each class."
+             "This input is used to pass the AccumFalsePos generated by the "
+             "previous mini-batch when the multi mini-batches cumulative "
+             "calculation carried out. ")
+        .AsDispensable();
+    AddOutput("AccumPosCount",
+              "(Tensor) A tensor with shape [Ncls, 1], store the "
+              "positive example count of each class. It combines the input "
+              "input(PosCount) and the positive example count computed from "
+              "input(Detection) and input(Label).");
+    AddOutput("AccumTruePos",
+              "(LoDTensor) A LoDTensor with shape [Ntp', 2], store the "
+              "true positive example of each class. It combines the "
+              "input(TruePos) and the true positive examples computed from "
+              "input(Detection) and input(Label).");
+    AddOutput("AccumFalsePos",
+              "(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).");
+    AddOutput("MAP",
+              "(Tensor) A tensor with shape [1], store the mAP evaluate "
+              "result of the detection.");
+
+    AddAttr<float>(
+        "overlap_threshold",
+        "(float) "
+        "The lower bound 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",
+                         "(string, default 'integral') "
+                         "The AP algorithm type, 'integral' or '11point'.")
+        .SetDefault("integral")
+        .InEnum({"integral", "11point"})
+        .AddCustomChecker([](const std::string& ap_type) {
+          PADDLE_ENFORCE_NE(GetAPType(ap_type), APType::kNone,
+                            "The ap_type should be 'integral' or '11point.");
+        });
+    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
+ 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");
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OP_WITHOUT_GRADIENT(detection_map, ops::DetectionMAPOp,
+                             ops::DetectionMAPOpMaker);
+REGISTER_OP_CPU_KERNEL(
+    detection_map, ops::DetectionMAPOpKernel<paddle::platform::CPUPlace, float>,
+    ops::DetectionMAPOpKernel<paddle::platform::CPUPlace, double>);