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())) {
return errors::InvalidArgument("sequence_length is not a vector");
}
if (!(batch_size == (*seq_len)->dim_size(0))) {
return errors::FailedPrecondition(
"len(sequence_length) != batch_size. ", "len(sequence_length): ",
(*seq_len)->dim_size(0), " batch_size: ", batch_size);
}
auto seq_len_t = (*seq_len)->vec<int32>();
for (int b = 0; b < batch_size; ++b) {
if (!(seq_len_t(b) <= max_time)) {
return errors::FailedPrecondition("sequence_length(", b, ") <= ",
max_time);
}
}
Status s = ctx->allocate_output(
"log_probability", TensorShape({batch_size, top_paths_}), log_prob);
if (!s.ok()) return s;
s = ctx->output_list("decoded_indices", decoded_indices);
if (!s.ok()) return s;
s = ctx->output_list("decoded_values", decoded_values);
if (!s.ok()) return s;
s = ctx->output_list("decoded_shape", decoded_shape);
if (!s.ok()) return s;
return Status::OK();
}
// sequences[b][p][ix] stores decoded value "ix" of path "p" for batch "b".
Status StoreAllDecodedSequences(
const std::vector<std::vector<std::vector<int> > >& sequences,
OpOutputList* decoded_indices, OpOutputList* decoded_values,
OpOutputList* decoded_shape) const {
// Calculate the total number of entries for each path
const int64 batch_size = sequences.size();
std::vector<int64> num_entries(top_paths_, 0);
// Calculate num_entries per path
for (const auto& batch_s : sequences) {
CHECK_EQ(batch_s.size(), top_paths_);
for (int p = 0; p < top_paths_; ++p) {
num_entries[p] += batch_s[p].size();
}
}
for (int p = 0; p < top_paths_; ++p) {
Tensor* p_indices = nullptr;
Tensor* p_values = nullptr;
Tensor* p_shape = nullptr;
const int64 p_num = num_entries[p];
Status s =
decoded_indices->allocate(p, TensorShape({p_num, 2}), &p_indices);
if (!s.ok()) return s;
s = decoded_values->allocate(p, TensorShape({p_num}), &p_values);
if (!s.ok()) return s;
s = decoded_shape->allocate(p, TensorShape({2}), &p_shape);
if (!s.ok()) return s;
auto indices_t = p_indices->matrix<int64>();
auto values_t = p_values->vec<int64>();
auto shape_t = p_shape->vec<int64>();
int64 max_decoded = 0;
int64 offset = 0;
for (int64 b = 0; b < batch_size; ++b) {
auto& p_batch = sequences[b][p];
int64 num_decoded = p_batch.size();
max_decoded = std::max(max_decoded, num_decoded);
std::copy_n(p_batch.begin(), num_decoded, &values_t(offset));
for (int64 t = 0; t < num_decoded; ++t, ++offset) {
indices_t(offset, 0) = b;
indices_t(offset, 1) = t;
}
}
shape_t(0) = batch_size;
shape_t(1) = max_decoded;
}
return Status::OK();
}
private:
int top_paths_;
TF_DISALLOW_COPY_AND_ASSIGN(CTCDecodeHelper);
};
// CTC beam search
class CTCBeamSearchDecoderWithParamOp : public OpKernel {
public:
explicit CTCBeamSearchDecoderWithParamOp(OpKernelConstruction* ctx) : OpKernel(ctx) {
OP_REQUIRES_OK(ctx, ctx->GetAttr("merge_repeated", &merge_repeated_));
OP_REQUIRES_OK(ctx, ctx->GetAttr("beam_width", &beam_width_));
//从参数列表中读取新添的两个参数
OP_REQUIRES_OK(ctx, ctx->GetAttr("label_selection_size", &label_selection_size));
OP_REQUIRES_OK(ctx, ctx->GetAttr("label_selection_margin", &label_selection_margin));
int top_paths;
OP_REQUIRES_OK(ctx, ctx->GetAttr("top_paths", &top_paths));
decode_helper_.SetTopPaths(top_paths);
}
void Compute(OpKernelContext* ctx) override {
const Tensor* inputs;
const Tensor* seq_len;
Tensor* log_prob = nullptr;
OpOutputList decoded_indices;
OpOutputList decoded_values;
OpOutputList decoded_shape;
OP_REQUIRES_OK(ctx, decode_helper_.ValidateInputsGenerateOutputs(
ctx, &inputs, &seq_len, &log_prob, &decoded_indices,
&decoded_values, &decoded_shape));
auto inputs_t = inputs->tensor<float, 3>();
auto seq_len_t = seq_len->vec<int32>();
auto log_prob_t = log_prob->matrix<float>();
const TensorShape& inputs_shape = inputs->shape();
const int64 max_time = inputs_shape.dim_size(0);
const int64 batch_size = inputs_shape.dim_size(1);
const int64 num_classes_raw = inputs_shape.dim_size(2);
OP_REQUIRES(
ctx, FastBoundsCheck(num_classes_raw, std::numeric_limits<int>::max()),
errors::InvalidArgument("num_classes cannot exceed max int"));
const int num_classes = static_cast<const int>(num_classes_raw);
log_prob_t.setZero();
std::vector<TTypes<float>::UnalignedConstMatrix> input_list_t;
for (std::size_t t = 0; t < max_time; ++t) {
input_list_t.emplace_back(inputs_t.data() + t * batch_size * num_classes,
batch_size, num_classes);
}
ctc::CTCBeamSearchDecoder<> beam_search(num_classes, beam_width_,
&beam_scorer_, 1 /* batch_size */,
merge_repeated_);
//使用传入的两个参数进行Set
beam_search.SetLabelSelectionParameters(label_selection_size, label_selection_margin);
Tensor input_chip(DT_FLOAT, TensorShape({num_classes}));
auto input_chip_t = input_chip.flat<float>();
std::vector<std::vector<std::vector<int> > > best_paths(batch_size);
std::vector<float> log_probs;
// Assumption: the blank index is num_classes - 1
for (int b = 0; b < batch_size; ++b) {
auto& best_paths_b = best_paths[b];
best_paths_b.resize(decode_helper_.GetTopPaths());
for (int t = 0; t < seq_len_t(b); ++t) {
input_chip_t = input_list_t[t].chip(b, 0);
auto input_bi =
Eigen::Map<const Eigen::ArrayXf>(input_chip_t.data(), num_classes);
beam_search.Step(input_bi);
}
OP_REQUIRES_OK(
ctx, beam_search.TopPaths(decode_helper_.GetTopPaths(), &best_paths_b,
&log_probs, merge_repeated_));
beam_search.Reset();
for (int bp = 0; bp < decode_helper_.GetTopPaths(); ++bp) {
log_prob_t(b, bp) = log_probs[bp];
}
}
OP_REQUIRES_OK(ctx, decode_helper_.StoreAllDecodedSequences(
best_paths, &decoded_indices, &decoded_values,
&decoded_shape));
}
private:
CTCDecodeHelper decode_helper_;
ctc::CTCBeamSearchDecoder<>::DefaultBeamScorer beam_scorer_;
bool merge_repeated_;
int beam_width_;
//新添两个数据成员，用于存储新加的参数
int label_selection_size;
float label_selection_margin;
TF_DISALLOW_COPY_AND_ASSIGN(CTCBeamSearchDecoderWithParamOp);
};
REGISTER_KERNEL_BUILDER(Name("CTCBeamSearchDecoderWithParam").Device(DEVICE_CPU),
CTCBeamSearchDecoderWithParamOp);
将自定义的Op编译成.so文件
在tensorflow-master目录下新建一个文件夹custom_op
cd custom_op
新建一个BUILD文件，并在其中添加如下代码：
cc_library(
name = "ctc_decoder_with_param",
srcs = [
"new_beamsearch.cc"
] +
glob(["boost_locale/**/*.hpp"]),
includes = ["boost_locale"],
copts = ["-std=c++11"],
deps = ["//tensorflow/core:core",
"//tensorflow/core/util/ctc",
"//third_party/eigen3",
],
)
编译过程:
1. cd 到 tensorflow-master 目录下
2. bazel build -c opt --copt=-O3 //tensorflow:libtensorflow_cc.so //custom_op:ctc_decoder_with_param
3. bazel-bin/custom_op 目录下生成 libctc_decoder_with_param.so
在训练（预测）程序中使用自定义的Op
在程序中定义如下的方法：
decode_param_op_module = tf.load_op_library('libctc_decoder_with_param.so')
def decode_with_param(inputs, sequence_length, beam_width=100,
top_paths=1, merge_repeated=True):
decoded_ixs, decoded_vals, decoded_shapes, log_probabilities = (
decode_param_op_module.ctc_beam_search_decoder_with_param(
inputs, sequence_length, beam_width=beam_width,
top_paths=top_paths, merge_repeated=merge_repeated,
label_selection_size=40, label_selection_margin=0.99))
return (
[tf.SparseTensor(ix, val, shape) for (ix, val, shape)
in zip(decoded_ixs, decoded_vals, decoded_shapes)],
log_probabilities)
然后就可以像使用tf.nn.ctc_beam_search_decoder一样使用该Op了。
以上这篇TensorFlow实现自定义Op方式就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持。
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