TensorFlow实现自定义Op方式

『写在前面』

以CTC Beam search decoder为例，简单整理一下TensorFlow实现自定义Op的操作流程。

基本的流程

1. 定义Op接口

#include "tensorflow/core/framework/op.h"
 
REGISTER_OP("Custom")  
  .Input("custom_input: int32")
  .Output("custom_output: int32");

2. 为Op实现Compute操作(CPU)或实现kernel(GPU)

#include "tensorflow/core/framework/op_kernel.h"
 
using namespace tensorflow;
 
class CustomOp : public OpKernel{
  public:
  explicit CustomOp(OpKernelConstruction* context) : OpKernel(context) {}
  void Compute(OpKernelContext* context) override {
  // 获取输入 tensor.
  const Tensor& input_tensor = context->input(0);
  auto input = input_tensor.flat<int32>();
  // 创建一个输出 tensor.
  Tensor* output_tensor = NULL;
  OP_REQUIRES_OK(context, context->allocate_output(0, input_tensor.shape(),
                           &output_tensor));
  auto output = output_tensor->template flat<int32>();
  //进行具体的运算，操作input和output
  //……
 }
};

3. 将实现的kernel注册到TensorFlow系统中

REGISTER_KERNEL_BUILDER(Name("Custom").Device(DEVICE_CPU), CustomOp);

CTCBeamSearchDecoder自定义

该Op对应TensorFlow中的源码部分

Op接口的定义：

tensorflow-master/tensorflow/core/ops/ctc_ops.cc

CTCBeamSearchDecoder本身的定义：

tensorflow-master/tensorflow/core/util/ctc/ctc_beam_search.cc

Op-Class的封装与Op注册：

tensorflow-master/tensorflow/core/kernels/ctc_decoder_ops.cc

基于源码修改的Op

#include <algorithm>
#include <vector>
#include <cmath>
 
#include "tensorflow/core/util/ctc/ctc_beam_search.h"
 
#include "tensorflow/core/framework/op.h"
#include "tensorflow/core/framework/op_kernel.h"
#include "tensorflow/core/framework/shape_inference.h"
#include "tensorflow/core/kernels/bounds_check.h"
 
namespace tf = tensorflow;
using tf::shape_inference::DimensionHandle;
using tf::shape_inference::InferenceContext;
using tf::shape_inference::ShapeHandle;
 
using namespace tensorflow;
 
REGISTER_OP("CTCBeamSearchDecoderWithParam")
  .Input("inputs: float")
  .Input("sequence_length: int32")
  .Attr("beam_width: int >= 1")
  .Attr("top_paths: int >= 1")
  .Attr("merge_repeated: bool = true")
  //新添加了两个参数
  .Attr("label_selection_size: int >= 0 = 0") 
  .Attr("label_selection_margin: float") 
  .Output("decoded_indices: top_paths * int64")
  .Output("decoded_values: top_paths * int64")
  .Output("decoded_shape: top_paths * int64")
  .Output("log_probability: float")
  .SetShapeFn([](InferenceContext* c) {
   ShapeHandle inputs;
   ShapeHandle sequence_length;
 
   TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 3, &inputs));
   TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 1, &sequence_length));
 
   // Get batch size from inputs and sequence_length.
   DimensionHandle batch_size;
   TF_RETURN_IF_ERROR(
     c->Merge(c->Dim(inputs, 1), c->Dim(sequence_length, 0), &batch_size));
 
   int32 top_paths;
   TF_RETURN_IF_ERROR(c->GetAttr("top_paths", &top_paths));
 
   // Outputs.
   int out_idx = 0;
   for (int i = 0; i < top_paths; ++i) { // decoded_indices
    c->set_output(out_idx++, c->Matrix(InferenceContext::kUnknownDim, 2));
   }
   for (int i = 0; i < top_paths; ++i) { // decoded_values
    c->set_output(out_idx++, c->Vector(InferenceContext::kUnknownDim));
   }
   ShapeHandle shape_v = c->Vector(2);
   for (int i = 0; i < top_paths; ++i) { // decoded_shape
    c->set_output(out_idx++, shape_v);
   }
   c->set_output(out_idx++, c->Matrix(batch_size, top_paths));
   return Status::OK();
  });
 
typedef Eigen::ThreadPoolDevice CPUDevice;
 
inline float RowMax(const TTypes<float>::UnalignedConstMatrix& m, int r,
          int* c) {
 *c = 0;
 CHECK_LT(0, m.dimension(1));
 float p = m(r, 0);
 for (int i = 1; i < m.dimension(1); ++i) {
  if (m(r, i) > p) {
   p = m(r, i);
   *c = i;
  }
 }
 return p;
}
 
class CTCDecodeHelper {
 public:
 CTCDecodeHelper() : top_paths_(1) {}
 
 inline int GetTopPaths() const { return top_paths_; }
 void SetTopPaths(int tp) { top_paths_ = tp; }
 
 Status ValidateInputsGenerateOutputs(
   OpKernelContext* ctx, const Tensor** inputs, const Tensor** seq_len,
   Tensor** log_prob, OpOutputList* decoded_indices,
   OpOutputList* decoded_values, OpOutputList* decoded_shape) const {
  Status status = ctx->input("inputs", inputs);
  if (!status.ok()) return status;
  status = ctx->input("sequence_length", seq_len);
  if (!status.ok()) return status;
 
  const TensorShape& inputs_shape = (*inputs)->shape();
 
  if (inputs_shape.dims() != 3) {
   return errors::InvalidArgument("inputs is not a 3-Tensor");
  }
 
  const int64 max_time = inputs_shape.dim_size(0);
  const int64 batch_size = inputs_shape.dim_size(1);
 
  if (max_time == 0) {
   return errors::InvalidArgument("max_time is 0");
  }
  if (!TensorShapeUtils::IsVector((*seq_len)->shape())) {
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TensorFlow实现自定义Op方式

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