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125 lines
4.4 KiB
125 lines
4.4 KiB
// Copyright (c) 2019 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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#include "paddle/fluid/operators/one_hot_v2_op.h"
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#include <string>
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#include <vector>
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#include "paddle/fluid/framework/framework.pb.h"
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namespace paddle {
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namespace operators {
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class OneHotV2Op : 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("X"), "Input", "X", "one_hot_v2");
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OP_INOUT_CHECK(ctx->HasOutput("Out"), "Output", "Out", "one_hot_v2");
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auto x_dims = ctx->GetInputDim("X");
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PADDLE_ENFORCE_GE(x_dims.size(), 1,
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platform::errors::InvalidArgument(
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"Rank of Input(X) should be at least 1."));
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int depth = ctx->Attrs().Get<int>("depth");
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if (ctx->HasInput("depth_tensor")) {
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depth = -1;
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}
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auto out_dims_vec = framework::vectorize(x_dims);
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out_dims_vec.push_back(depth);
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auto out_dims = framework::make_ddim(out_dims_vec);
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ctx->SetOutputDim("Out", out_dims);
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ctx->ShareLoD("X", /* --> */ "Out");
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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, "X"),
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ctx.device_context());
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}
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framework::OpKernelType GetKernelTypeForVar(
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const std::string& var_name, const Tensor& tensor,
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const framework::OpKernelType& expected_kernel_type) const override {
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if (var_name == "depth_tensor") {
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return expected_kernel_type;
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}
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return framework::OpKernelType(expected_kernel_type.data_type_,
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tensor.place(), tensor.layout());
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}
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};
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class OneHotV2OpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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void Make() override {
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AddInput("X",
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"(LoDTensor, LoDTensor<int>) Input variable with rank at least 2. "
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"The last dimension of X should be 1. Each value of X is an index "
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"to indicate the position.");
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AddInput("depth_tensor", "(Tensor, Tensor<int>), Length of one-hot vector")
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.AsDispensable();
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AddOutput("Out",
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"(Tensor, Tensor<float>) Output tensor with same rank as X. "
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"The tensor consists of one-hot representations of values in X.");
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AddAttr<int>("depth",
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"A positive integer to specify the length of one-hot vector.")
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.SetDefault(-1);
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AddAttr<int>("dtype",
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"An integer to specify the data type of one-hot "
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"vector. The default value is FP32.")
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.SetDefault(paddle::framework::proto::VarType::FP32);
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AddAttr<bool>("allow_out_of_range",
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"If it is set true and the input data is out of range, "
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"the output tensor will be filled zeros. The default value "
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"is false.")
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.SetDefault(false);
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AddComment(R"DOC(
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One Hot Operator. This operator creates the one-hot representations for input
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index values. The following example will help to explain the function of this
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operator:
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X is a LoDTensor:
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X.lod = [[0, 1, 4]]
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X.shape = [4]
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X.data = [1, 1, 3, 0]
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set depth = 4
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Out is a LoDTensor:
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Out.lod = [[0, 1, 4]]
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Out.shape = [4, 4]
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Out.data = [[0., 1., 0., 0.],
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[0., 1., 0., 0.],
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[0., 0., 0., 1.],
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[1., 0., 0., 0.]]
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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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one_hot_v2, ops::OneHotV2Op, ops::OneHotV2OpMaker,
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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(
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one_hot_v2, ops::OneHotV2Kernel<paddle::platform::CPUDeviceContext, int>,
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ops::OneHotV2Kernel<paddle::platform::CPUDeviceContext, int64_t>);
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