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111 lines
4.2 KiB
111 lines
4.2 KiB
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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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/mean_iou_op.h"
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namespace paddle {
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namespace operators {
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class MeanIoUOp : 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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OP_INOUT_CHECK(ctx->HasInput("Predictions"), "Input", "Predictions",
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"MeanIoU");
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OP_INOUT_CHECK(ctx->HasInput("Labels"), "Input", "Labels", "MeanIoU");
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OP_INOUT_CHECK(ctx->HasOutput("OutMeanIou"), "Output", "OutMeanIou",
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"MeanIoU");
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OP_INOUT_CHECK(ctx->HasOutput("OutWrong"), "Output", "OutWrong", "MeanIoU");
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OP_INOUT_CHECK(ctx->HasOutput("OutCorrect"), "Output", "OutCorrect",
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"MeanIoU");
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int64_t num_classes =
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static_cast<int64_t>(ctx->Attrs().Get<int>("num_classes"));
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ctx->SetOutputDim("OutMeanIou", {1});
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ctx->SetOutputDim("OutWrong", {num_classes});
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ctx->SetOutputDim("OutCorrect", {num_classes});
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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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OperatorWithKernel::IndicateVarDataType(ctx, "Predictions"),
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ctx.GetPlace());
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}
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};
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class MeanIoUOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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void Make() override {
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AddInput("Predictions",
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"(Tensor), A Tensor of prediction results for semantic labels"
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" with type int32 or int64. The rank should be greater than 1.");
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AddInput(
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"Labels",
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"(Tensor), A Tensor of ground truth labels with type int32 or int64."
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"Its shape should be the same as Input(Predictions).");
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AddInput("InWrongs",
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"(vector<Tensor>), A list of Tensor with shape "
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"[num_classes]. They are used to collect wrong number among "
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"batches. Empty list is also valid here.")
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.AsDuplicable()
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.AsDispensable();
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AddInput(
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"InCorrects",
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"(vector<Tensor>), A list of Tensor with shape "
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"[num_classes]. They are used to collect correct number among batches. "
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"Empty list is also valid here.")
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.AsDuplicable()
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.AsDispensable();
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AddInput("InMeanIou",
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"(vector<Tensor>), A list of Tensor that Output(mean_iou) should "
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"be added to. Empty list is also valid here.")
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.AsDuplicable()
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.AsDispensable();
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AddOutput("OutMeanIou",
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"(vector<Tensor>), A Tensor representing the"
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" mean intersection-over-union with shape [1].");
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AddOutput("OutWrong", "(Tensor), A Tensor with shape [num_classes]. ");
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AddOutput("OutCorrect", "(Tensor), A Tensor with shape [num_classes]. ");
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AddAttr<int>("num_classes", "(int), The possible number of labels.");
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AddComment(R"DOC(
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mean-IOU Operator.
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Mean Intersection-Over-Union is a common evaluation metric for
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semantic image segmentation, which first computes the IOU for each
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semantic class and then computes the average over classes.
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IOU is defined as follows:
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IOU = true_positive / (true_positive + false_positive + false_negative).
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It is based on pixel level area while "IOU Similarity Operator"
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is based on area of rectangle.
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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_OPERATOR(
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mean_iou, ops::MeanIoUOp, ops::MeanIoUOpMaker,
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paddle::framework::EmptyGradOpMaker<paddle::framework::OpDesc>,
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paddle::framework::EmptyGradOpMaker<paddle::imperative::OpBase>);
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REGISTER_OP_CPU_KERNEL(mean_iou, ops::MeanIoUKernel<int>,
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ops::MeanIoUKernel<int32_t>,
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ops::MeanIoUKernel<int64_t>);
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