1 files changed, 104 insertions, 0 deletions
diff --git a/tensorflow/core/ops/parsing_ops.cc b/tensorflow/core/ops/parsing_ops.cc
new file mode 100644
index 0000000000..7fcaa3abf1
--- /dev/null
+++ b/tensorflow/core/ops/parsing_ops.cc
@@ -0,0 +1,104 @@
+#include "tensorflow/core/framework/op.h"
+
+namespace tensorflow {
+
+REGISTER_OP("DecodeRaw")
+    .Input("bytes: string")
+    .Output("output: out_type")
+    .Attr("out_type: {float,double,int32,uint8,int16,int8,int64}")
+    .Attr("little_endian: bool = true")
+    .Doc(R"doc(
+Reinterpret the bytes of a string as a vector of numbers.
+
+bytes: All the elements must have the same length.
+little_endian: Whether the input bytes are in little-endian order.
+  Ignored for out_types that are stored in a single byte like uint8.
+output: A Tensor with one more dimension than the input bytes.  The
+  added dimension will have size equal to the length of the elements
+  of bytes divided by the number of bytes to represent out_type.
+)doc");
+
+REGISTER_OP("ParseExample")
+    .Input("serialized: string")
+    .Input("names: string")
+    .Input("sparse_keys: Nsparse * string")
+    .Input("dense_keys: Ndense * string")
+    .Input("dense_defaults: Tdense")
+    .Output("sparse_indices: Nsparse * int64")
+    .Output("sparse_values: sparse_types")
+    .Output("sparse_shapes: Nsparse * int64")
+    .Output("dense_values: Tdense")
+    .Attr("Nsparse: int >= 0")  // Inferred from sparse_keys
+    .Attr("Ndense: int >= 0")   // Inferred from dense_keys
+    .Attr("sparse_types: list({float,int64,string}) >= 0")
+    .Attr("Tdense: list({float,int64,string}) >= 0")
+    .Attr("dense_shapes: list(shape) >= 0")
+    .Doc(R"doc(
+Transforms a vector of brain.Example protos (as strings) into typed tensors.
+
+serialized: A vector containing a batch of binary serialized Example protos.
+names: A vector containing the names of the serialized protos.
+  May contain, for example, table key (descriptive) names for the
+  corresponding serialized protos.  These are purely useful for debugging
+  purposes, and the presence of values here has no effect on the output.
+  May also be an empty vector if no names are available.
+  If non-empty, this vector must be the same length as "serialized".
+dense_keys: A list of Ndense string Tensors (scalars).
+  The keys expected in the Examples' features associated with dense values.
+dense_defaults: A list of Ndense Tensors (some may be empty).
+  dense_defaults[j] provides default values
+  when the example's feature_map lacks dense_key[j].  If an empty Tensor is
+  provided for dense_defaults[j], then the Feature dense_keys[j] is required.
+  The input type is inferred from dense_defaults[j], even when it's empty.
+  If dense_defaults[j] is not empty, its shape must match dense_shapes[j].
+dense_shapes: A list of Ndense shapes; the shapes of data in each Feature
+  given in dense_keys.
+  The number of elements in the Feature corresponding to dense_key[j]
+  must always equal dense_shapes[j].NumEntries().
+  If dense_shapes[j] == (D0, D1, ..., DN) then the the shape of output
+  Tensor dense_values[j] will be (|serialized|, D0, D1, ..., DN):
+  The dense outputs are just the inputs row-stacked by batch.
+sparse_keys: A list of Nsparse string Tensors (scalars).
+  The keys expected in the Examples' features associated with sparse values.
+sparse_types: A list of Nsparse types; the data types of data in each Feature
+  given in sparse_keys.
+  Currently the ParseExample supports DT_FLOAT (FloatList),
+  DT_INT64 (Int64List), and DT_STRING (BytesList).
+)doc");
+
+REGISTER_OP("DecodeCSV")
+    .Input("records: string")
+    .Input("record_defaults: OUT_TYPE")
+    .Output("output: OUT_TYPE")
+    .Attr("OUT_TYPE: list({float,int32,int64,string})")
+    .Attr("field_delim: string = ','")
+    .Doc(R"doc(
+Convert CSV records to tensors. Each column maps to one tensor.
+
+RFC 4180 format is expected for the CSV records.
+(https://tools.ietf.org/html/rfc4180)
+Note that we allow leading and trailing spaces with int or float field.
+
+records: Each string is a record/row in the csv and all records should have
+  the same format.
+record_defaults: One tensor per column of the input record, with either a
+  scalar default value for that column or empty if the column is required.
+field_delim: delimiter to separate fields in a record.
+output: Each tensor will have the same shape as records.
+)doc");
+
+REGISTER_OP("StringToNumber")
+    .Input("string_tensor: string")
+    .Output("output: out_type")
+    .Attr("out_type: {float, int32} = DT_FLOAT")
+    .Doc(R"doc(
+Converts each string in the input Tensor to the specified numeric type.
+
+(Note that int32 overflow results in an error while float overflow
+results in a rounded value.)
+
+out_type: The numeric type to interpret each string in string_tensor as.
+output: A Tensor of the same shape as the input string_tensor.
+)doc");
+
+}  // namespace tensorflow