Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into add-merge-splited-ids
qiaolongfei
7 years ago
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/* 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/slice_op.h"
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#include <algorithm>
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#include <vector>
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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 SliceOp : 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("Input"),
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"Input (Input) of slice op should not be null.");
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PADDLE_ENFORCE(ctx->HasOutput("Out"),
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"Output (Out) of slice op should not be null.");
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auto in_dims = ctx->GetInputDim("Input");
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PADDLE_ENFORCE(in_dims.size() < 7,
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"The rank of input should be less than 7.");
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framework::DDim out_dims(in_dims);
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auto axes = ctx->Attrs().Get<std::vector<int>>("axes");
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auto starts = ctx->Attrs().Get<std::vector<int>>("starts");
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auto ends = ctx->Attrs().Get<std::vector<int>>("ends");
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PADDLE_ENFORCE_EQ(starts.size(), ends.size());
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PADDLE_ENFORCE_EQ(starts.size(), axes.size());
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int dim_value, start, end;
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for (size_t i = 0; i < axes.size(); ++i) {
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dim_value = out_dims[axes[i]];
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start = starts[i] < 0 ? (starts[i] + dim_value) : starts[i];
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end = ends[i] < 0 ? (ends[i] + dim_value) : ends[i];
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start = std::max(start, 0);
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end = std::max(end, 0);
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start = std::min(start, dim_value);
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end = std::min(end, dim_value);
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start = std::min(start, end);
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out_dims[axes[i]] = end - start;
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}
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ctx->SetOutputDim("Out", out_dims);
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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(ctx.Input<Tensor>("Input")->type()),
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ctx.GetPlace());
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}
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};
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class SliceOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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void Make() override {
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AddInput("Input", "Tensor of data to extract slices from.");
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AddOutput("Out", "Sliced data tensor.");
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AddAttr<std::vector<int>>(
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"axes",
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"(list<int>) Axes that `starts` and `ends` apply to. It's optional."
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"If not present, will be treated as [0, 1, ..., len(`starts`) - 1].");
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AddAttr<std::vector<int>>(
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"starts",
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"(list<int>) Starting indices of corresponding axis in `axes`");
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AddAttr<std::vector<int>>(
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"ends",
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"(list<int>) Starting indices of corresponding axis in `axes`.");
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AddComment(R"DOC(
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Slice Operator.
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Produces a slice of the input tensor along multiple axes. Similar to numpy:
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https://docs.scipy.org/doc/numpy/reference/arrays.indexing.html
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Slice uses `axes`, `starts` and `ends` attributes to specify the start and
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end dimension for each axis in the list of axes, it uses this information
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to slice the input data tensor. If a negative value is passed for any of
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the start or end indices, it represents number of elements before the end
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of that dimension. If the value passed to start or end is larger than
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the n (the number of elements in this dimension), it represents n.
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For slicing to the end of a dimension with unknown size, it is recommended
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to pass in INT_MAX. If axes are omitted, they are set to [0, ..., ndim-1].
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Example 1:
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Given:
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data = [ [1, 2, 3, 4], [5, 6, 7, 8], ]
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axes = [0, 1]
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starts = [1, 0]
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ends = [2, 3]
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Then:
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result = [ [5, 6, 7], ]
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Example 2:
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Given:
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data = [ [1, 2, 3, 4], [5, 6, 7, 8], ]
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starts = [0, 1]
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ends = [-1, 1000]
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Then:
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result = [ [2, 3, 4], ]
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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(slice, ops::SliceOp, ops::SliceOpMaker,
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paddle::framework::EmptyGradOpMaker);
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REGISTER_OP_CPU_KERNEL(
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slice, ops::SliceKernel<paddle::platform::CPUDeviceContext, int>,
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ops::SliceKernel<paddle::platform::CPUDeviceContext, int64_t>,
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ops::SliceKernel<paddle::platform::CPUDeviceContext, float>,
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ops::SliceKernel<paddle::platform::CPUDeviceContext, double>);
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@ -0,0 +1,22 @@
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/* 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/slice_op.h"
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namespace ops = paddle::operators;
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REGISTER_OP_CUDA_KERNEL(
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slice, ops::SliceKernel<paddle::platform::CUDADeviceContext, float>,
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ops::SliceKernel<paddle::platform::CUDADeviceContext, double>,
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ops::SliceKernel<paddle::platform::CUDADeviceContext, int>,
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ops::SliceKernel<paddle::platform::CUDADeviceContext, int64_t>);
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@ -0,0 +1,88 @@
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/* 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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#pragma once
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#include <algorithm>
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#include <vector>
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#include "paddle/fluid/framework/op_registry.h"
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namespace paddle {
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namespace operators {
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template <typename DeviceContext, typename T>
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class SliceKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& ctx) const override {
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int rank = ctx.Input<framework::Tensor>("Input")->dims().size();
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switch (rank) {
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case 1:
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SliceCompute<1>(ctx);
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break;
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case 2:
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SliceCompute<2>(ctx);
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break;
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case 3:
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SliceCompute<3>(ctx);
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break;
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case 4:
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SliceCompute<4>(ctx);
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break;
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case 5:
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SliceCompute<5>(ctx);
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break;
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case 6:
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SliceCompute<6>(ctx);
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break;
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}
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}
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private:
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template <size_t D>
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void SliceCompute(const framework::ExecutionContext& context) const {
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auto& place =
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*context.template device_context<DeviceContext>().eigen_device();
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auto in = context.Input<framework::Tensor>("Input");
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auto out = context.Output<framework::Tensor>("Out");
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out->mutable_data<T>(context.GetPlace());
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auto out_dims = out->dims();
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auto in_dims = in->dims();
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auto axes = context.Attr<std::vector<int>>("axes");
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auto starts = context.Attr<std::vector<int>>("starts");
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auto offsets = Eigen::array<int, D>();
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auto extents = Eigen::array<int, D>();
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for (size_t i = 0; i < D; ++i) {
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offsets[i] = 0;
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extents[i] = out_dims[i];
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}
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int start;
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for (size_t i = 0; i < axes.size(); ++i) {
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start = starts[i];
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if (start < 0) {
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start = (start + in_dims[axes[i]]);
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}
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start = std::max(start, 0);
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offsets[axes[i]] = start;
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}
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auto in_t =
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framework::EigenTensor<T, D, Eigen::RowMajor, Eigen::DenseIndex>::From(
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*in);
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auto out_t =
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framework::EigenTensor<T, D, Eigen::RowMajor, Eigen::DenseIndex>::From(
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*out);
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out_t.device(place) = in_t.slice(offsets, extents);
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}
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};
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} // namespace operators
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} // namespace paddle
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