| /* Copyright 2017 The TensorFlow 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. |
| ==============================================================================*/ |
| |
| #define EIGEN_USE_THREADS |
| |
| #include "tensorflow/core/framework/op_kernel.h" |
| #include "tensorflow/core/framework/register_types.h" |
| #include "tensorflow/core/framework/tensor.h" |
| #include "tensorflow/core/framework/types.h" |
| #include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor" |
| |
| namespace tensorflow { |
| |
| namespace { |
| template <typename T> |
| struct mod_op { |
| const T operator()(const T& a, const T& b) const { return a % b; } |
| }; |
| } // namespace |
| |
| typedef Eigen::ThreadPoolDevice CPUDevice; |
| |
| template <typename Tidx> |
| class UnravelIndexOp : public OpKernel { |
| public: |
| explicit UnravelIndexOp(OpKernelConstruction* ctx) : OpKernel(ctx) {} |
| |
| void Compute(OpKernelContext* ctx) override { |
| const Tensor& indices_tensor = ctx->input(0); |
| OP_REQUIRES(ctx, |
| TensorShapeUtils::IsVector(indices_tensor.shape()) || |
| TensorShapeUtils::IsScalar(indices_tensor.shape()), |
| errors::InvalidArgument( |
| "The indices can only be scalar or vector, got \"", |
| indices_tensor.shape().DebugString(), "\"")); |
| |
| const Tensor& dims_tensor = ctx->input(1); |
| OP_REQUIRES( |
| ctx, TensorShapeUtils::IsVector(dims_tensor.shape()), |
| errors::InvalidArgument("The indices can only be 1-D, got \"", |
| dims_tensor.shape().DebugString(), "\"")); |
| |
| auto dims = dims_tensor.vec<Tidx>(); |
| |
| Eigen::array<bool, 1> reverse({true}); |
| |
| Tensor strides_tensor; |
| OP_REQUIRES_OK(ctx, |
| ctx->allocate_temp(DataTypeToEnum<Tidx>::value, |
| TensorShape({dims_tensor.NumElements()}), |
| &strides_tensor)); |
| |
| auto strides = strides_tensor.vec<Tidx>(); |
| strides = dims.reverse(reverse) |
| .scan(0, Eigen::internal::ProdReducer<Tidx>(), false) |
| .reverse(reverse); |
| |
| Tensor strides_shifted_tensor; |
| OP_REQUIRES_OK(ctx, |
| ctx->allocate_temp(DataTypeToEnum<Tidx>::value, |
| TensorShape({dims_tensor.NumElements()}), |
| &strides_shifted_tensor)); |
| |
| auto strides_shifted = strides_shifted_tensor.vec<Tidx>(); |
| strides_shifted = dims.reverse(reverse) |
| .scan(0, Eigen::internal::ProdReducer<Tidx>(), true) |
| .reverse(reverse); |
| |
| Tensor* output_tensor = nullptr; |
| if (TensorShapeUtils::IsScalar(indices_tensor.shape())) { |
| OP_REQUIRES_OK( |
| ctx, ctx->allocate_output(0, TensorShape({dims_tensor.NumElements()}), |
| &output_tensor)); |
| |
| auto output = output_tensor->vec<Tidx>(); |
| |
| output = output.constant(indices_tensor.scalar<Tidx>()()); |
| output = output.binaryExpr(strides, mod_op<Tidx>()) / strides_shifted; |
| } else { |
| OP_REQUIRES_OK( |
| ctx, ctx->allocate_output(0, |
| TensorShape({dims_tensor.NumElements(), |
| indices_tensor.NumElements()}), |
| &output_tensor)); |
| |
| auto output = output_tensor->matrix<Tidx>(); |
| |
| Eigen::array<Eigen::Index, 2> reshape{{dims_tensor.NumElements(), 1}}; |
| Eigen::array<Eigen::Index, 2> bcast({1, indices_tensor.NumElements()}); |
| Eigen::array<Eigen::Index, 2> indices_reshape{ |
| {1, indices_tensor.NumElements()}}; |
| Eigen::array<Eigen::Index, 2> indices_bcast( |
| {dims_tensor.NumElements(), 1}); |
| |
| output = indices_tensor.vec<Tidx>() |
| .reshape(indices_reshape) |
| .broadcast(indices_bcast); |
| output = output.binaryExpr(strides.reshape(reshape).broadcast(bcast), |
| mod_op<Tidx>()) / |
| strides_shifted.reshape(reshape).broadcast(bcast); |
| } |
| } |
| }; |
| |
| #define REGISTER_KERNEL(type) \ |
| REGISTER_KERNEL_BUILDER( \ |
| Name("UnravelIndex").Device(DEVICE_CPU).TypeConstraint<type>("Tidx"), \ |
| UnravelIndexOp<type>); |
| TF_CALL_int32(REGISTER_KERNEL) TF_CALL_int64(REGISTER_KERNEL) |
| #undef REGISTER_KERNEL |
| |
| } // namespace tensorflow |