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7 years ago
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/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License. */
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#include "paddle/fluid/operators/detection_map_op.h"
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namespace paddle {
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namespace operators {
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using Tensor = framework::Tensor;
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class DetectionMAPOp : public framework::OperatorWithKernel {
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public:
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using framework::OperatorWithKernel::OperatorWithKernel;
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void InferShape(framework::InferShapeContext* ctx) const override {
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PADDLE_ENFORCE(ctx->HasInput("DetectRes"),
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"Input(DetectRes) of DetectionMAPOp should not be null.");
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PADDLE_ENFORCE(ctx->HasInput("Label"),
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"Input(Label) of DetectionMAPOp should not be null.");
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PADDLE_ENFORCE(
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ctx->HasOutput("AccumPosCount"),
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"Output(AccumPosCount) of DetectionMAPOp should not be null.");
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PADDLE_ENFORCE(
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ctx->HasOutput("AccumTruePos"),
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"Output(AccumTruePos) of DetectionMAPOp should not be null.");
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PADDLE_ENFORCE(
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ctx->HasOutput("AccumFalsePos"),
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"Output(AccumFalsePos) of DetectionMAPOp should not be null.");
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PADDLE_ENFORCE(ctx->HasOutput("MAP"),
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"Output(MAP) of DetectionMAPOp should not be null.");
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auto det_dims = ctx->GetInputDim("DetectRes");
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PADDLE_ENFORCE_EQ(det_dims.size(), 2UL,
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"The rank of Input(DetectRes) must be 2, "
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"the shape is [N, 6].");
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PADDLE_ENFORCE_EQ(det_dims[1], 6UL,
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"The shape is of Input(DetectRes) [N, 6].");
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auto label_dims = ctx->GetInputDim("Label");
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PADDLE_ENFORCE_EQ(label_dims.size(), 2UL,
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"The rank of Input(Label) must be 2, "
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"the shape is [N, 6].");
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PADDLE_ENFORCE_EQ(label_dims[1], 6UL,
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"The shape is of Input(Label) [N, 6].");
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if (ctx->HasInput("PosCount")) {
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PADDLE_ENFORCE(ctx->HasInput("TruePos"),
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"Input(TruePos) of DetectionMAPOp should not be null when "
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"Input(TruePos) is not null.");
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PADDLE_ENFORCE(
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ctx->HasInput("FalsePos"),
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"Input(FalsePos) of DetectionMAPOp should not be null when "
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"Input(FalsePos) is not null.");
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}
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ctx->SetOutputDim("MAP", framework::make_ddim({1}));
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}
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protected:
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framework::OpKernelType GetExpectedKernelType(
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const framework::ExecutionContext& ctx) const override {
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return framework::OpKernelType(
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framework::ToDataType(
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ctx.Input<framework::Tensor>("DetectRes")->type()),
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ctx.device_context());
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}
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};
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class DetectionMAPOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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DetectionMAPOpMaker(OpProto* proto, OpAttrChecker* op_checker)
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: OpProtoAndCheckerMaker(proto, op_checker) {
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AddInput("DetectRes",
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"(LoDTensor) A 2-D LoDTensor with shape [M, 6] represents the "
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"detections. Each row has 6 values: "
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"[label, confidence, xmin, ymin, xmax, ymax], M is the total "
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"number of detect results in this mini-batch. For each instance, "
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"the offsets in first dimension are called LoD, the number of "
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"offset is N + 1, if LoD[i + 1] - LoD[i] == 0, means there is "
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"no detected data.");
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AddInput("Label",
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"(LoDTensor) A 2-D LoDTensor with shape[N, 6] represents the"
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"Labeled ground-truth data. Each row has 6 values: "
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"[label, is_difficult, xmin, ymin, xmax, ymax], N is the total "
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"number of ground-truth data in this mini-batch. For each "
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"instance, the offsets in first dimension are called LoD, "
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"the number of offset is N + 1, if LoD[i + 1] - LoD[i] == 0, "
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"means there is no ground-truth data.");
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AddInput("PosCount",
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"(Tensor) A tensor with shape [Ncls, 1], store the "
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"input positive example count of each class, Ncls is the count of "
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"input classification. "
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"This input is used to pass the AccumPosCount generated by the "
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"previous mini-batch when the multi mini-batches cumulative "
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"calculation carried out. "
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"When the input(PosCount) is empty, the cumulative "
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"calculation is not carried out, and only the results of the "
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"current mini-batch are calculated.")
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.AsDispensable();
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AddInput("TruePos",
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"(LoDTensor) A 2-D LoDTensor with shape [Ntp, 2], store the "
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"input true positive example of each class."
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"This input is used to pass the AccumTruePos generated by the "
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"previous mini-batch when the multi mini-batches cumulative "
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"calculation carried out. ")
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.AsDispensable();
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AddInput("FalsePos",
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"(LoDTensor) A 2-D LoDTensor with shape [Nfp, 2], store the "
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"input false positive example of each class."
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"This input is used to pass the AccumFalsePos generated by the "
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"previous mini-batch when the multi mini-batches cumulative "
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"calculation carried out. ")
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.AsDispensable();
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AddOutput("AccumPosCount",
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"(Tensor) A tensor with shape [Ncls, 1], store the "
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"positive example count of each class. It combines the input "
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"input(PosCount) and the positive example count computed from "
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"input(Detection) and input(Label).");
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AddOutput("AccumTruePos",
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"(LoDTensor) A LoDTensor with shape [Ntp', 2], store the "
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"true positive example of each class. It combines the "
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"input(TruePos) and the true positive examples computed from "
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"input(Detection) and input(Label).");
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AddOutput("AccumFalsePos",
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"(LoDTensor) A LoDTensor with shape [Nfp', 2], store the "
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"false positive example of each class. It combines the "
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"input(FalsePos) and the false positive examples computed from "
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"input(Detection) and input(Label).");
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AddOutput("MAP",
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"(Tensor) A tensor with shape [1], store the mAP evaluate "
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"result of the detection.");
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AddAttr<float>(
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"overlap_threshold",
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"(float) "
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"The lower bound jaccard overlap threshold of detection output and "
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"ground-truth data.")
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.SetDefault(.3f);
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AddAttr<bool>("evaluate_difficult",
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"(bool, default true) "
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"Switch to control whether the difficult data is evaluated.")
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.SetDefault(true);
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AddAttr<std::string>("ap_type",
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"(string, default 'integral') "
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"The AP algorithm type, 'integral' or '11point'.")
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.SetDefault("integral")
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.InEnum({"integral", "11point"})
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.AddCustomChecker([](const std::string& ap_type) {
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PADDLE_ENFORCE_NE(GetAPType(ap_type), APType::kNone,
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"The ap_type should be 'integral' or '11point.");
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});
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AddComment(R"DOC(
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Detection mAP evaluate operator.
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The general steps are as follows. First, calculate the true positive and
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false positive according to the input of detection and labels, then
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calculate the mAP evaluate value.
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Supporting '11 point' and 'integral' mAP algorithm. Please get more information
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from the following articles:
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https://sanchom.wordpress.com/tag/average-precision/
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https://arxiv.org/abs/1512.02325
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)DOC");
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}
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};
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} // namespace operators
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} // namespace paddle
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namespace ops = paddle::operators;
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REGISTER_OP_WITHOUT_GRADIENT(detection_map, ops::DetectionMAPOp,
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ops::DetectionMAPOpMaker);
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REGISTER_OP_CPU_KERNEL(
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detection_map, ops::DetectionMAPOpKernel<paddle::platform::CPUPlace, float>,
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ops::DetectionMAPOpKernel<paddle::platform::CPUPlace, double>);
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@ -0,0 +1,76 @@
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# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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import numpy as np
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import paddle.v2.fluid.layers as layers
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import paddle.v2.fluid.framework as framework
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import paddle.v2.fluid as fluid
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class TestPythonOperatorOverride(unittest.TestCase):
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def check_result(self, fn, place, dtype):
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shape = [9, 10]
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x_data = np.random.random(size=shape).astype(dtype)
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y_data = np.random.random(size=shape).astype(dtype)
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python_out = fn(x_data, y_data)
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x_var = layers.create_global_var(
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name='x', shape=shape, value=0.0, dtype=dtype, persistable=True)
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y_var = layers.create_global_var(
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name='y', shape=shape, value=0.0, dtype=dtype, persistable=True)
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out = fn(x_var, y_var)
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exe = fluid.Executor(place)
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exe.run(fluid.default_startup_program())
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fluid_out = exe.run(fluid.default_main_program(),
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feed={'x': x_data,
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'y': y_data},
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fetch_list=[out])
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np.testing.assert_array_equal(python_out, fluid_out[0])
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def test_override(self):
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# compare func to check
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compare_fns = [
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lambda _a, _b: _a == _b,
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lambda _a, _b: _a != _b,
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lambda _a, _b: _a < _b,
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lambda _a, _b: _a <= _b,
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lambda _a, _b: _a > _b,
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lambda _a, _b: _a >= _b,
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]
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# places to check
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places = [fluid.CPUPlace()]
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if fluid.core.is_compiled_with_cuda():
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places.append(fluid.CUDAPlace(0))
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# dtypes to check
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dtypes = ['int32', 'float32']
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for place in places:
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for dtype in dtypes:
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for compare_fn in compare_fns:
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with framework.program_guard(framework.Program(),
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framework.Program()):
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self.check_result(compare_fn, place, dtype)
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if __name__ == '__main__':
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unittest.main()
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