diff options
Diffstat (limited to 'tensorflow/docs_src/extend/new_data_formats.md')
| -rw-r--r-- | tensorflow/docs_src/extend/new_data_formats.md | 60 |
1 files changed, 33 insertions, 27 deletions
diff --git a/tensorflow/docs_src/extend/new_data_formats.md b/tensorflow/docs_src/extend/new_data_formats.md index d1d1f69766..abbf47910e 100644 --- a/tensorflow/docs_src/extend/new_data_formats.md +++ b/tensorflow/docs_src/extend/new_data_formats.md @@ -77,18 +77,24 @@ can be used as a starting point for your implementation: #include "tensorflow/core/framework/op.h" #include "tensorflow/core/framework/shape_inference.h" -namespace tensorflow { +namespace myproject { namespace { -class MyReaderDatasetOp : public DatasetOpKernel { +using ::tensorflow::DT_STRING; +using ::tensorflow::PartialTensorShape; +using ::tensorflow::Status; + +class MyReaderDatasetOp : public tensorflow::DatasetOpKernel { public: - MyReaderDatasetOp(OpKernelConstruction* ctx) : DatasetOpKernel(ctx) { + MyReaderDatasetOp(tensorflow::OpKernelConstruction* ctx) + : DatasetOpKernel(ctx) { // Parse and validate any attrs that define the dataset using // `ctx->GetAttr()`, and store them in member variables. } - void MakeDataset(OpKernelContext* ctx, DatasetBase** output) override { + void MakeDataset(tensorflow::OpKernelContext* ctx, + tensorflow::DatasetBase** output) override { // Parse and validate any input tensors 0that define the dataset using // `ctx->input()` or the utility function // `ParseScalarArgument<T>(ctx, &arg)`. @@ -99,14 +105,14 @@ class MyReaderDatasetOp : public DatasetOpKernel { } private: - class Dataset : public GraphDatasetBase { + class Dataset : public tensorflow::GraphDatasetBase { public: - Dataset(OpKernelContext* ctx) : GraphDatasetBase(ctx) {} + Dataset(tensorflow::OpKernelContext* ctx) : GraphDatasetBase(ctx) {} - std::unique_ptr<IteratorBase> MakeIteratorInternal( + std::unique_ptr<tensorflow::IteratorBase> MakeIteratorInternal( const string& prefix) const override { - return std::unique_ptr<IteratorBase>( - new Iterator({this, strings::StrCat(prefix, "::MyReader")})); + return std::unique_ptr<tensorflow::IteratorBase>(new Iterator( + {this, tensorflow::strings::StrCat(prefix, "::MyReader")})); } // Record structure: Each record is represented by a scalar string tensor. @@ -114,8 +120,8 @@ class MyReaderDatasetOp : public DatasetOpKernel { // Dataset elements can have a fixed number of components of different // types and shapes; replace the following two methods to customize this // aspect of the dataset. - const DataTypeVector& output_dtypes() const override { - static DataTypeVector* dtypes = new DataTypeVector({DT_STRING}); + const tensorflow::DataTypeVector& output_dtypes() const override { + static auto* const dtypes = new tensorflow::DataTypeVector({DT_STRING}); return *dtypes; } const std::vector<PartialTensorShape>& output_shapes() const override { @@ -132,16 +138,16 @@ class MyReaderDatasetOp : public DatasetOpKernel { // Implement this method if you want to be able to save and restore // instances of this dataset (and any iterators over it). Status AsGraphDefInternal(DatasetGraphDefBuilder* b, - Node** output) const override { + tensorflow::Node** output) const override { // Construct nodes to represent any of the input tensors from this // object's member variables using `b->AddScalar()` and `b->AddVector()`. - std::vector<Node*> input_tensors; + std::vector<tensorflow::Node*> input_tensors; TF_RETURN_IF_ERROR(b->AddDataset(this, input_tensors, output)); return Status::OK(); } private: - class Iterator : public DatasetIterator<Dataset> { + class Iterator : public tensorflow::DatasetIterator<Dataset> { public: explicit Iterator(const Params& params) : DatasetIterator<Dataset>(params), i_(0) {} @@ -158,15 +164,15 @@ class MyReaderDatasetOp : public DatasetOpKernel { // return `Status::OK()`. // 3. If an error occurs, return an error status using one of the helper // functions from "tensorflow/core/lib/core/errors.h". - Status GetNextInternal(IteratorContext* ctx, - std::vector<Tensor>* out_tensors, + Status GetNextInternal(tensorflow::IteratorContext* ctx, + std::vector<tensorflow::Tensor>* out_tensors, bool* end_of_sequence) override { // NOTE: `GetNextInternal()` may be called concurrently, so it is // recommended that you protect the iterator state with a mutex. - mutex_lock l(mu_); + tensorflow::mutex_lock l(mu_); if (i_ < 10) { // Create a scalar string tensor and add it to the output. - Tensor record_tensor(ctx->allocator({}), DT_STRING, {}); + tensorflow::Tensor record_tensor(ctx->allocator({}), DT_STRING, {}); record_tensor.scalar<string>()() = "MyReader!"; out_tensors->emplace_back(std::move(record_tensor)); ++i_; @@ -183,20 +189,20 @@ class MyReaderDatasetOp : public DatasetOpKernel { // // Implement these two methods if you want to be able to save and restore // instances of this iterator. - Status SaveInternal(IteratorStateWriter* writer) override { - mutex_lock l(mu_); + Status SaveInternal(tensorflow::IteratorStateWriter* writer) override { + tensorflow::mutex_lock l(mu_); TF_RETURN_IF_ERROR(writer->WriteScalar(full_name("i"), i_)); return Status::OK(); } - Status RestoreInternal(IteratorContext* ctx, - IteratorStateReader* reader) override { - mutex_lock l(mu_); + Status RestoreInternal(tensorflow::IteratorContext* ctx, + tensorflow::IteratorStateReader* reader) override { + tensorflow::mutex_lock l(mu_); TF_RETURN_IF_ERROR(reader->ReadScalar(full_name("i"), &i_)); return Status::OK(); } private: - mutex mu_; + tensorflow::mutex mu_; int64 i_ GUARDED_BY(mu_); }; }; @@ -211,14 +217,14 @@ class MyReaderDatasetOp : public DatasetOpKernel { REGISTER_OP("MyReaderDataset") .Output("handle: variant") .SetIsStateful() - .SetShapeFn(shape_inference::ScalarShape); + .SetShapeFn(tensorflow::shape_inference::ScalarShape); // Register the kernel implementation for MyReaderDataset. -REGISTER_KERNEL_BUILDER(Name("MyReaderDataset").Device(DEVICE_CPU), +REGISTER_KERNEL_BUILDER(Name("MyReaderDataset").Device(tensorflow::DEVICE_CPU), MyReaderDatasetOp); } // namespace -} // namespace tensorflow +} // namespace myproject ``` The last step is to build the C++ code and add a Python wrapper. The easiest way |