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Paddle/paddle/fluid/operators/one_hot_v2_op.cc

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4.4 KiB

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