EVOLUTION-MANAGER

Edit File: example_proto_helper.h

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
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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,
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==============================================================================*/

#ifndef TENSORFLOW_CORE_UTIL_EXAMPLE_PROTO_HELPER_H_
#define TENSORFLOW_CORE_UTIL_EXAMPLE_PROTO_HELPER_H_

#include <string>
#include <unordered_set>
#include <vector>

#include "tensorflow/core/example/example.pb.h"
#include "tensorflow/core/example/feature.pb.h"
#include "tensorflow/core/framework/allocator.h"
#include "tensorflow/core/framework/graph.pb.h"
#include "tensorflow/core/framework/partial_tensor_shape.h"
#include "tensorflow/core/framework/tensor.h"
#include "tensorflow/core/framework/types.h"
#include "tensorflow/core/lib/core/errors.h"
#include "tensorflow/core/platform/types.h"
#include "tensorflow/core/util/sparse/sparse_tensor.h"

// This is a set of helper methods that will make it possible to share
// tensorflow::Example proto Tensor conversion code inside the ExampleParserOp
// OpKernel as well as in external code.
namespace tensorflow {

// "Dense" feature configuration.
struct FixedLenFeature {
  string key;
  DataType dtype;
  TensorShape shape;
  Tensor default_value;
  string values_output_tensor_name;
};

// "Sparse" feature configuration.
struct VarLenFeature {
  string key;
  DataType dtype;
  string values_output_tensor_name;
  string indices_output_tensor_name;
  string shapes_output_tensor_name;
};

// Given a single tensorflow::Example, with an optional example name
// at a particular index within a batch, and dense and sparse feature
// configurations from fixed_len_features, var_len_features, this method
// updates the dense value tensor and the sparse values temporary vector
// of tensors. The indexing of the output vectors correspond 1:1 to the
// indexing of the feature configuration vectors.
//
// The fixed_len_features and var_len_features maps are assume to be
// have disjoint key fields from the Feature map in the tensorflow.Example
// proto.
//
// For each sparse feature, the sparse values temporary vector holds a
// tensor for each Example. Each tensor is either empty or filled, depending
// on if the sparse feature value is set for the Example. This
// temporary structure is needed because we need to know the total number
// of filled elements in the batch to get the proper final sparse tensor
// shapes allocated.  After the entire batch is processed,
// GetSparseTensorShape can be used to calculate the final shapes and
// CopyIntoSparseTensor can be used to copy from the temporary vector
// into the final allocated tensors.
Status SingleExampleProtoToTensors(
    const Example& example, const string& name, const int batch_index,
    const std::vector<FixedLenFeature>& fixed_len_features,
    const std::vector<VarLenFeature>& var_len_features,
    std::vector<Tensor*>* dense_values,
    std::vector<std::vector<Tensor>>* sparse_values_temporary_vector);

// The shape of the indices and values tensors associated with a SparseTensor
// are dependent on the contents of the batch.
struct VarLenFeatureBatchShapes {
  TensorShape indices_shape;
  TensorShape values_shape;
  int max_num_features;
};

// Get the shape of the sparse values and indices tensors for the batch,
// given how many of the tensors in the temporary sparse values vector
// are actually filled.
Status GetSparseTensorShapes(const VarLenFeature& var_len_feature,
                             const std::vector<Tensor>& sparse_values_tmp,
                             const int batch_size,
                             VarLenFeatureBatchShapes* output_shapes);

// A method to convert a batch of tensorflow::Example protos into output
// tensors. This method is useful if there already is a batch of deserialized
// Example protos in memory (such as a serving use-case) and we do not wish
// to incur an extraneous serialize/deserialize.  It is intended
// as an outside of OpKernel compatible replacement for the functionality of
// ExampleParserOp. In a serving setting, this method could be used to produce
// a feed_dict of Tensors that could bypass the ExampleParserOp.
//
// Note that unlike SingleExampleProtoToTensors, output tensors are
// allocated using a provided Allocator within this method.
Status BatchExampleProtoToTensors(
    const std::vector<const Example*>& examples,
    const std::vector<string>& names,
    const std::vector<FixedLenFeature>& fixed_len_features,
    const std::vector<VarLenFeature>& var_len_features, Allocator* allocator,
    std::vector<Tensor>* output_dense_values_tensor,
    std::vector<Tensor>* output_sparse_indices_tensor,
    std::vector<Tensor>* output_sparse_values_tensor,
    std::vector<Tensor>* output_sparse_shapes_tensor);

// Check that the given dtype is one that is compatible with
// tensorflow::Example protocol buffer feature values.
Status CheckValidType(const DataType& dtype);

// Check that the provided Feature proto message's oneof value
// matches that of the provided dtype.
Status CheckTypesMatch(const Feature& feature, const DataType& dtype,
                       bool* match);

// For a single Example, copy a dense feature value into an output
// dense value tensor Out at the provided out_index offset.
Status FeatureDenseCopy(const std::size_t out_index, const string& name,
                        const string& key, const DataType& dtype,
                        const TensorShape& shape, const Feature& feature,
                        Tensor* out);

// Copy the value a provided Tensor into an output dense_value tensor Out
// at the provided out_index offset.
void RowDenseCopy(const std::size_t& out_index, const DataType& dtype,
                  const Tensor& in, Tensor* out);

// For a single Example, and given sparse feature return a temporary output
// Tensor suitable for being collected in the temporary sparse value vector.
Tensor FeatureSparseCopy(const std::size_t batch, const string& key,
                         const DataType& dtype, const Feature& feature);

// Copy a temporary Tensor into the final sparse indices and values
// tensor at a given batch index and element offset. This method
// assumes that the indices/values Tensors have been properly allocated
// for the batch.
int64 CopyIntoSparseTensor(const Tensor& in, const int batch,
                           const int64 offset, Tensor* indices, Tensor* values);

// Check that each dense_shape has known rank and inner dimensions; and
// update variable_length (whether the outer dimension is None) and
// elements_per_stride for each denes_shape.
Status GetDenseShapes(const std::vector<PartialTensorShape>& dense_shapes,
                      std::vector<bool>* variable_length,
                      std::vector<std::size_t>* elements_per_stride);

// Parses the attributes passed to ParseExample.
// REQUIRES: Init must be called after construction.
struct ParseExampleAttrs {
 public:
  template <typename ContextType>
  Status Init(ContextType* ctx, int op_version = 1) {
    TF_RETURN_IF_ERROR(ctx->GetAttr("sparse_types", &sparse_types));
    TF_RETURN_IF_ERROR(ctx->GetAttr("Tdense", &dense_types));
    TF_RETURN_IF_ERROR(ctx->GetAttr("dense_shapes", &dense_shapes));
    TF_RETURN_IF_ERROR(
        GetDenseShapes(dense_shapes, &variable_length, &elements_per_stride));
    switch (op_version) {
      case 1:
        TF_RETURN_IF_ERROR(ctx->GetAttr("Nsparse", &num_sparse));
        TF_RETURN_IF_ERROR(ctx->GetAttr("Ndense", &num_dense));
        break;
      case 2:
        TF_RETURN_IF_ERROR(
            ctx->GetAttr("ragged_value_types", &ragged_value_types));
        TF_RETURN_IF_ERROR(ctx->GetAttr("num_sparse", &num_sparse));
        TF_RETURN_IF_ERROR(
            ctx->GetAttr("ragged_split_types", &ragged_split_types));
        break;
      default:
        return errors::InvalidArgument("Unexpected op_version", op_version);
    }
    return FinishInit(op_version);
  }

int64 num_sparse;
  int64 num_dense;
  int64 num_ragged;
  std::vector<DataType> sparse_types;
  std::vector<DataType> dense_types;
  std::vector<DataType> ragged_value_types;
  std::vector<DataType> ragged_split_types;
  std::vector<PartialTensorShape> dense_shapes;
  std::vector<bool> variable_length;
  std::vector<std::size_t> elements_per_stride;

private:
  Status FinishInit(int op_version);  // for context-independent parts of Init.
};

// Parses the attributes passed to ParseSingleExample.
// REQUIRES: Init must be called after construction.
struct ParseSingleExampleAttrs {
 public:
  template <typename ContextType>
  Status Init(ContextType* ctx) {
    TF_RETURN_IF_ERROR(ctx->GetAttr("sparse_keys", &sparse_keys));
    TF_RETURN_IF_ERROR(ctx->GetAttr("sparse_types", &sparse_types));
    TF_RETURN_IF_ERROR(ctx->GetAttr("dense_keys", &dense_keys));
    TF_RETURN_IF_ERROR(ctx->GetAttr("Tdense", &dense_types));
    TF_RETURN_IF_ERROR(ctx->GetAttr("dense_shapes", &dense_shapes));

int num_sparse;
    TF_RETURN_IF_ERROR(ctx->GetAttr("num_sparse", &num_sparse));
    if (num_sparse != sparse_keys.size() || num_sparse != sparse_types.size()) {
      return errors::InvalidArgument(
          "num_sparse (", num_sparse, ") must match the size of sparse_keys (",
          sparse_keys.size(), ") and sparse_types (", sparse_types.size(), ")");
    }

TF_RETURN_IF_ERROR(
        GetDenseShapes(dense_shapes, &variable_length, &elements_per_stride));
    return FinishInit();
  }

std::vector<tstring> sparse_keys;
  std::vector<DataType> sparse_types;
  std::vector<tstring> dense_keys;
  std::vector<DataType> dense_types;
  std::vector<PartialTensorShape> dense_shapes;
  std::vector<bool> variable_length;
  std::vector<std::size_t> elements_per_stride;

private:
  Status FinishInit();  // for context-independent parts of Init.
};

// Parses the attributes passed to ParseSequenceExample.
// REQUIRES: Init must be called after construction.
struct ParseSequenceExampleAttrs {
 public:
  template <typename ContextType>
  Status Init(ContextType* ctx, int op_version = 1) {
    switch (op_version) {
      case 1: {
        std::vector<string> missing_empty_vector;
        TF_RETURN_IF_ERROR(ctx->GetAttr(
            "feature_list_dense_missing_assumed_empty", &missing_empty_vector));
        for (const string& feature : missing_empty_vector) {
          feature_list_dense_missing_assumed_empty.insert(feature);
        }
      }
        TF_RETURN_IF_ERROR(
            ctx->GetAttr("context_sparse_keys", &context_sparse_keys));
        TF_RETURN_IF_ERROR(
            ctx->GetAttr("context_dense_keys", &context_dense_keys));
        TF_RETURN_IF_ERROR(ctx->GetAttr("feature_list_sparse_keys",
                                        &feature_list_sparse_keys));
        TF_RETURN_IF_ERROR(
            ctx->GetAttr("feature_list_dense_keys", &feature_list_dense_keys));
        TF_RETURN_IF_ERROR(ctx->GetAttr("Ncontext_dense", &num_context_dense));
        break;
      case 2:
        TF_RETURN_IF_ERROR(ctx->GetAttr("context_ragged_value_types",
                                        &context_ragged_value_types));
        TF_RETURN_IF_ERROR(ctx->GetAttr("context_ragged_split_types",
                                        &context_ragged_split_types));
        TF_RETURN_IF_ERROR(ctx->GetAttr("feature_list_ragged_value_types",
                                        &feature_list_ragged_value_types));
        TF_RETURN_IF_ERROR(ctx->GetAttr("feature_list_ragged_split_types",
                                        &feature_list_ragged_split_types));
        break;
      default:
        return errors::InvalidArgument("Unexpected op_version", op_version);
    }
    TF_RETURN_IF_ERROR(
        ctx->GetAttr("context_sparse_types", &context_sparse_types));
    TF_RETURN_IF_ERROR(
        ctx->GetAttr("Nfeature_list_dense", &num_feature_list_dense));
    TF_RETURN_IF_ERROR(ctx->GetAttr("Ncontext_sparse", &num_context_sparse));
    TF_RETURN_IF_ERROR(ctx->GetAttr("Tcontext_dense", &context_dense_types));
    TF_RETURN_IF_ERROR(
        ctx->GetAttr("feature_list_sparse_types", &feature_list_sparse_types));
    TF_RETURN_IF_ERROR(
        ctx->GetAttr("feature_list_dense_types", &feature_list_dense_types));
    TF_RETURN_IF_ERROR(
        ctx->GetAttr("Nfeature_list_sparse", &num_feature_list_sparse));
    TF_RETURN_IF_ERROR(
        ctx->GetAttr("context_dense_shapes", &context_dense_shapes));
    TF_RETURN_IF_ERROR(
        ctx->GetAttr("feature_list_dense_shapes", &feature_list_dense_shapes));
    return FinishInit(op_version);
  }

std::unordered_set<string> feature_list_dense_missing_assumed_empty;
  int64 num_context_sparse;
  int64 num_context_dense;
  int64 num_context_ragged;
  int64 num_feature_list_sparse;
  int64 num_feature_list_dense;
  int64 num_feature_list_ragged;
  std::vector<tstring> context_sparse_keys;
  std::vector<tstring> context_dense_keys;
  std::vector<tstring> feature_list_sparse_keys;
  std::vector<tstring> feature_list_dense_keys;
  std::vector<DataType> context_sparse_types;
  std::vector<DataType> context_dense_types;
  std::vector<TensorShape> context_dense_shapes;
  std::vector<DataType> feature_list_sparse_types;
  std::vector<DataType> feature_list_dense_types;
  std::vector<TensorShape> feature_list_dense_shapes;
  std::vector<DataType> context_ragged_value_types;
  std::vector<DataType> context_ragged_split_types;
  std::vector<DataType> feature_list_ragged_value_types;
  std::vector<DataType> feature_list_ragged_split_types;

private:
  Status FinishInit(int op_version);  // for context-independent parts of Init.
};

// Parses the attributes passed to ParseSingleSequenceExample.
// REQUIRES: Init must be called after construction.
struct ParseSingleSequenceExampleAttrs {
 public:
  template <typename ContextType>
  Status Init(ContextType* ctx) {
    TF_RETURN_IF_ERROR(
        ctx->GetAttr("context_sparse_types", &context_sparse_types));
    TF_RETURN_IF_ERROR(ctx->GetAttr("Ncontext_dense", &num_context_dense));
    TF_RETURN_IF_ERROR(
        ctx->GetAttr("Nfeature_list_dense", &num_feature_list_dense));
    TF_RETURN_IF_ERROR(ctx->GetAttr("Ncontext_sparse", &num_context_sparse));
    TF_RETURN_IF_ERROR(ctx->GetAttr("Tcontext_dense", &context_dense_types));
    TF_RETURN_IF_ERROR(
        ctx->GetAttr("feature_list_sparse_types", &feature_list_sparse_types));
    TF_RETURN_IF_ERROR(
        ctx->GetAttr("feature_list_dense_types", &feature_list_dense_types));
    TF_RETURN_IF_ERROR(
        ctx->GetAttr("Nfeature_list_sparse", &num_feature_list_sparse));
    TF_RETURN_IF_ERROR(
        ctx->GetAttr("context_dense_shapes", &context_dense_shapes));
    TF_RETURN_IF_ERROR(
        ctx->GetAttr("feature_list_dense_shapes", &feature_list_dense_shapes));
    return FinishInit();
  }

int64 num_context_sparse;
  int64 num_context_dense;
  int64 num_feature_list_sparse;
  int64 num_feature_list_dense;
  std::vector<DataType> context_sparse_types;
  std::vector<DataType> context_dense_types;
  std::vector<TensorShape> context_dense_shapes;
  std::vector<DataType> feature_list_sparse_types;
  std::vector<DataType> feature_list_dense_types;
  std::vector<TensorShape> feature_list_dense_shapes;

private:
  Status FinishInit();  // for context-independent parts of Init.
};

}  // namespace tensorflow

#endif  // TENSORFLOW_CORE_UTIL_EXAMPLE_PROTO_HELPER_H_