You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
89 lines
2.6 KiB
89 lines
2.6 KiB
7 years ago
|
/* Copyright (c) 2018 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. */
|
||
|
|
|
||
|
|
#pragma once
|
||
|
|
#include <algorithm>
|
||
|
|
#include <vector>
|
||
|
|
#include "paddle/fluid/framework/op_registry.h"
|
||
|
|
|
||
|
|
namespace paddle {
|
||
|
|
namespace operators {
|
||
|
|
|
||
|
|
template <typename DeviceContext, typename T>
|
||
|
|
class SliceKernel : public framework::OpKernel<T> {
|
||
|
|
public:
|
||
|
|
void Compute(const framework::ExecutionContext& ctx) const override {
|
||
|
|
int rank = ctx.Input<framework::Tensor>("Input")->dims().size();
|
||
|
|
switch (rank) {
|
||
|
|
case 1:
|
||
|
|
SliceCompute<1>(ctx);
|
||
|
|
break;
|
||
|
|
case 2:
|
||
|
|
SliceCompute<2>(ctx);
|
||
|
|
break;
|
||
|
|
case 3:
|
||
|
|
SliceCompute<3>(ctx);
|
||
|
|
break;
|
||
|
|
case 4:
|
||
|
|
SliceCompute<4>(ctx);
|
||
|
|
break;
|
||
|
|
case 5:
|
||
|
|
SliceCompute<5>(ctx);
|
||
|
|
break;
|
||
|
|
case 6:
|
||
|
|
SliceCompute<6>(ctx);
|
||
|
|
break;
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
private:
|
||
|
|
template <size_t D>
|
||
|
|
void SliceCompute(const framework::ExecutionContext& context) const {
|
||
|
|
auto& place =
|
||
|
|
*context.template device_context<DeviceContext>().eigen_device();
|
||
|
|
auto in = context.Input<framework::Tensor>("Input");
|
||
|
|
auto out = context.Output<framework::Tensor>("Out");
|
||
|
|
out->mutable_data<T>(context.GetPlace());
|
||
|
|
auto out_dims = out->dims();
|
||
|
|
auto in_dims = in->dims();
|
||
|
|
auto axes = context.Attr<std::vector<int>>("axes");
|
||
|
|
auto starts = context.Attr<std::vector<int>>("starts");
|
||
|
|
|
||
|
|
auto offsets = Eigen::array<int, D>();
|
||
|
|
auto extents = Eigen::array<int, D>();
|
||
|
|
for (size_t i = 0; i < D; ++i) {
|
||
|
|
offsets[i] = 0;
|
||
|
|
extents[i] = out_dims[i];
|
||
|
|
}
|
||
|
|
int start;
|
||
|
|
for (size_t i = 0; i < axes.size(); ++i) {
|
||
|
|
start = starts[i];
|
||
|
|
if (start < 0) {
|
||
|
|
start = (start + in_dims[axes[i]]);
|
||
|
|
}
|
||
|
|
start = std::max(start, 0);
|
||
|
|
offsets[axes[i]] = start;
|
||
|
|
}
|
||
|
|
auto in_t =
|
||
|
|
framework::EigenTensor<T, D, Eigen::RowMajor, Eigen::DenseIndex>::From(
|
||
|
|
*in);
|
||
|
|
auto out_t =
|
||
|
|
framework::EigenTensor<T, D, Eigen::RowMajor, Eigen::DenseIndex>::From(
|
||
|
|
*out);
|
||
|
|
out_t.device(place) = in_t.slice(offsets, extents);
|
||
|
|
}
|
||
|
|
};
|
||
|
|
} // namespace operators
|
||
|
|
} // namespace paddle
|