diff options
Diffstat (limited to 'tensorflow/core/ops/dataset_ops.cc')
| -rw-r--r-- | tensorflow/core/ops/dataset_ops.cc | 197 |
1 files changed, 0 insertions, 197 deletions
diff --git a/tensorflow/core/ops/dataset_ops.cc b/tensorflow/core/ops/dataset_ops.cc index f512213964..8f5d8308a3 100644 --- a/tensorflow/core/ops/dataset_ops.cc +++ b/tensorflow/core/ops/dataset_ops.cc @@ -141,16 +141,6 @@ count: A scalar representing the number of elements from the `input_dataset` that should be skipped. If count is -1, skips everything. )doc"); -REGISTER_OP("IgnoreErrorsDataset") - .Input("input_dataset: variant") - .Output("handle: variant") - .Attr("output_types: list(type) >= 1") - .Attr("output_shapes: list(shape) >= 1") - .SetShapeFn(shape_inference::ScalarShape) - .Doc(R"doc( -Creates a dataset that contains the elements of `input_dataset` ignoring errors. -)doc"); - REGISTER_OP("MapDataset") .Input("input_dataset: variant") .Input("other_arguments: Targuments") @@ -184,32 +174,6 @@ num_parallel_calls: The number of concurrent invocations of `f` that process elements from `input_dataset` in parallel. )doc"); -REGISTER_OP("MapAndBatchDataset") - .Input("input_dataset: variant") - .Input("other_arguments: Targuments") - .Input("batch_size: int64") - .Input("num_parallel_batches: int64") - .Output("handle: variant") - .Attr("f: func") - .Attr("Targuments: list(type) >= 0") - .Attr("output_types: list(type) >= 1") - .Attr("output_shapes: list(shape) >= 1") - .SetShapeFn(shape_inference::ScalarShape) - .Doc(R"doc( -Creates a dataset that applies `f` to the outputs of `input_dataset` and then -batches `batch_size` of them. - -Unlike a "MapDataset", which applies `f` sequentially, this dataset invokes up -to `batch_size * num_parallel_batches` copies of `f` in parallel. - -batch_size: A scalar representing the number of elements to accumulate in a - batch. It determines the number of concurrent invocations of `f` that process - elements from `input_dataset` in parallel. -num_parallel_batches: A scalar representing the number of batches to create in - parallel. Processing multiple batches in parallel benefits workloads prone to - stragglers. -)doc"); - REGISTER_OP("PrefetchDataset") .Input("input_dataset: variant") .Input("buffer_size: int64") @@ -224,21 +188,6 @@ buffer_size: The maximum number of elements to buffer in an iterator over this dataset. )doc"); -REGISTER_OP("ScanDataset") - .Input("input_dataset: variant") - .Input("initial_state: Tstate") - .Input("other_arguments: Targuments") - .Output("handle: variant") - .Attr("f: func") - .Attr("Tstate: list(type) >= 1") - .Attr("Targuments: list(type) >= 0") - .Attr("output_types: list(type) >= 1") - .Attr("output_shapes: list(shape) >= 1") - .SetShapeFn(shape_inference::ScalarShape) - .Doc(R"doc( -Creates a dataset successively reduces `f` over the elements of `input_dataset`. -)doc"); - REGISTER_OP("FlatMapDataset") .Input("input_dataset: variant") .Input("other_arguments: Targuments") @@ -285,59 +234,6 @@ f: A function mapping elements of `input_dataset`, concatenated with `output_types` and `output_shapes`. )doc"); -REGISTER_OP("ParallelInterleaveDataset") - .Input("input_dataset: variant") - .Input("other_arguments: Targuments") - .Input("cycle_length: int64") - .Input("block_length: int64") - .Input("sloppy: bool") - .Output("handle: variant") - .Attr("f: func") - .Attr("Targuments: list(type) >= 0") - .Attr("output_types: list(type) >= 1") - .Attr("output_shapes: list(shape) >= 1") - .SetShapeFn(shape_inference::ScalarShape) - .Doc(R"doc( -Creates a dataset that applies `f` to the outputs of `input_dataset`. - -The resulting dataset is similar to the `InterleaveDataset`, with the exception -that if retrieving the next value from a dataset would cause the requester to -block, it will skip that input dataset. This dataset is especially useful -when loading data from a variable-latency datastores (e.g. HDFS, GCS), as it -allows the training step to proceed so long as some data is available. - -!! WARNING !! This dataset is not deterministic! - -f: A function mapping elements of `input_dataset`, concatenated with - `other_arguments`, to a Dataset variant that contains elements matching - `output_types` and `output_shapes`. -)doc"); - -REGISTER_OP("GroupByWindowDataset") - .Input("input_dataset: variant") - .Input("key_func_other_arguments: Tkey_func_other_arguments") - .Input("reduce_func_other_arguments: Treduce_func_other_arguments") - .Input( - "window_size_func_other_arguments: Twindow_size_func_other_arguments") - .Output("handle: variant") - .Attr("key_func: func") - .Attr("reduce_func: func") - .Attr("window_size_func: func") - .Attr("Tkey_func_other_arguments: list(type) >= 0") - .Attr("Treduce_func_other_arguments: list(type) >= 0") - .Attr("Twindow_size_func_other_arguments: list(type) >= 0") - .Attr("output_types: list(type) >= 1") - .Attr("output_shapes: list(shape) >= 1") - .SetShapeFn(shape_inference::ScalarShape) - .Doc(R"doc( -Creates a dataset that computes a windowed group-by on `input_dataset`. - -// TODO(mrry): Support non-int64 keys. - -key_func: A function mapping an element of `input_dataset`, concatenated - with `key_func_other_arguments` to a scalar value of type DT_INT64. -)doc"); - REGISTER_OP("FilterDataset") .Input("input_dataset: variant") .Input("other_arguments: Targuments") @@ -408,27 +304,6 @@ padding_values: A list of scalars containing the padding value to use for each of the outputs. )doc"); -REGISTER_OP("DenseToSparseBatchDataset") - .Input("input_dataset: variant") - .Input("batch_size: int64") - .Input("row_shape: int64") - .Output("handle: variant") - // NOTE(mrry): the 0th and 2nd elements will be DT_INT64. - .Attr("output_types: list(type) >= 1") - // NOTE(mrry): the 1st and 2nd elements will be vectors. - .Attr("output_shapes: list(shape) >= 1") - .SetShapeFn(shape_inference::ScalarShape) - .Doc(R"doc( -Creates a dataset that yields a SparseTensor for each element of the input. - -input_dataset: A handle to an input dataset. Must have a single component. -batch_size: A scalar representing the number of elements to accumulate in a - batch. -row_shape: A vector representing the dense shape of each row in the produced - SparseTensor. The shape may be partially specified, using `-1` to indicate - that a particular dimension should use the maximum size of all batch elements. -)doc"); - REGISTER_OP("RangeDataset") .Input("start: int64") .Input("stop: int64") @@ -514,24 +389,6 @@ compression_type: A scalar containing either (i) the empty string (no buffer_size: A scalar containing the number of bytes to buffer. )doc"); -REGISTER_OP("SqlDataset") - .Input("driver_name: string") - .Input("data_source_name: string") - .Input("query: string") - .Output("handle: variant") - .Attr("output_types: list(type) >= 1") - .Attr("output_shapes: list(shape) >= 1") - .SetIsStateful() // TODO(b/65524810): Source dataset ops must be marked - // stateful to inhibit constant folding. - .SetShapeFn(shape_inference::ScalarShape) - .Doc(R"doc( -Creates a dataset that executes a SQL query and emits rows of the result set. - -driver_name: The database type. Currently, the only supported type is 'sqlite'. -data_source_name: A connection string to connect to the database. -query: A SQL query to execute. -)doc"); - REGISTER_OP("FixedLengthRecordDataset") .Input("filenames: string") .Input("header_bytes: int64") @@ -662,36 +519,6 @@ REGISTER_OP("IteratorGetNext") Gets the next output from the given iterator. )doc"); -REGISTER_OP("DatasetToSingleElement") - .Input("dataset: variant") - .Output("components: output_types") - .Attr("output_types: list(type) >= 1") - .Attr("output_shapes: list(shape) >= 1") - .SetShapeFn([](shape_inference::InferenceContext* c) { - shape_inference::ShapeHandle unused; - TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 0, &unused)); - std::vector<PartialTensorShape> output_shapes; - TF_RETURN_IF_ERROR(c->GetAttr("output_shapes", &output_shapes)); - if (output_shapes.size() != c->num_outputs()) { - return errors::InvalidArgument( - "`output_shapes` must be the same length as `output_types` (", - output_shapes.size(), " vs. ", c->num_outputs()); - } - for (size_t i = 0; i < output_shapes.size(); ++i) { - shape_inference::ShapeHandle output_shape_handle; - TF_RETURN_IF_ERROR(c->MakeShapeFromPartialTensorShape( - output_shapes[i], &output_shape_handle)); - c->set_output(static_cast<int>(i), output_shape_handle); - } - return Status::OK(); - }) - .Doc(R"doc( -Outputs the single element from the given dataset. - -dataset: A handle to a dataset that contains a single element. -components: The components of the single element of `input`. -)doc"); - REGISTER_OP("IteratorToStringHandle") .Input("resource_handle: resource") .Output("string_handle: string") @@ -720,28 +547,4 @@ output_shapes: If specified, defines the shape of each tuple component in an element produced by the resulting iterator. )doc"); -REGISTER_OP("SerializeIterator") - .Input("resource_handle: resource") - .Output("serialized: variant") - .SetShapeFn(shape_inference::ScalarShape) - .Doc(R"doc( -Converts the given `resource_handle` representing an iterator to a variant tensor. - -resource_handle: A handle to an iterator resource. -serialized: A variant tensor storing the state of the iterator contained in the - resource. -)doc"); - -REGISTER_OP("DeserializeIterator") - .Input("resource_handle: resource") - .Input("serialized: variant") - .SetShapeFn(shape_inference::NoOutputs) - .Doc(R"doc( -Converts the given variant tensor to an iterator and stores it in the given resource. - -resource_handle: A handle to an iterator resource. -serialized: A variant tensor storing the state of the iterator contained in the - resource. -)doc"); - } // namespace tensorflow |