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      1 /* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
      2 
      3 Licensed under the Apache License, Version 2.0 (the "License");
      4 you may not use this file except in compliance with the License.
      5 You may obtain a copy of the License at
      6 
      7     http://www.apache.org/licenses/LICENSE-2.0
      8 
      9 Unless required by applicable law or agreed to in writing, software
     10 distributed under the License is distributed on an "AS IS" BASIS,
     11 WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
     12 See the License for the specific language governing permissions and
     13 limitations under the License.
     14 ==============================================================================*/
     15 
     16 #ifndef TENSORFLOW_KERNELS_PAD_OP_H_
     17 #define TENSORFLOW_KERNELS_PAD_OP_H_
     18 // Functor definition for PadOp, must be compilable by nvcc.
     19 
     20 #include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
     21 #include "tensorflow/core/framework/tensor_types.h"
     22 #include "tensorflow/core/platform/types.h"
     23 
     24 namespace tensorflow {
     25 namespace functor {
     26 
     27 // Functor used by PadOp to do the computations.
     28 template <typename Device, typename T, typename Tpadding, int Dims>
     29 struct Pad {
     30   // Pad "input" into "output", as specified by "paddings" and "pad_value".
     31   // See pad_op.cc for details.
     32   void operator()(const Device& d, typename TTypes<T, Dims>::Tensor output,
     33                   typename TTypes<T, Dims>::ConstTensor input,
     34                   Eigen::array<Eigen::IndexPair<Tpadding>, Dims> paddings,
     35                   T pad_value) {
     36     if (Eigen::internal::is_same<Device, Eigen::GpuDevice>::value &&
     37         (output.size() <= std::numeric_limits<int32>::max())) {
     38       To32Bit(output).device(d) = To32Bit(input).pad(paddings, pad_value);
     39     } else {
     40       output.device(d) = input.pad(paddings, pad_value);
     41     }
     42   }
     43 };
     44 
     45 template <typename Device, typename T, typename Tpadding>
     46 struct Pad<Device, T, Tpadding, 0> {
     47   // In the scalar case we simply copy the input.
     48   void operator()(const Device& d, typename TTypes<T, 0>::Tensor output,
     49                   typename TTypes<T, 0>::ConstTensor input,
     50                   Eigen::array<Eigen::IndexPair<Tpadding>, 0>, T) {
     51     output.device(d) = input;
     52   }
     53 };
     54 }  // namespace functor
     55 }  // namespace tensorflow
     56 
     57 #endif  // TENSORFLOW_KERNELS_PAD_OP_H_
     58