1 /* Copyright 2015 The TensorFlow Authors. All Rights Reserved. 2 3 Licensed under the Apache License, Version 2.0 (the "License"); 4 you may not use this file except in compliance with the License. 5 You may obtain a copy of the License at 6 7 http://www.apache.org/licenses/LICENSE-2.0 8 9 Unless required by applicable law or agreed to in writing, software 10 distributed under the License is distributed on an "AS IS" BASIS, 11 WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 See the License for the specific language governing permissions and 13 limitations under the License. 14 ==============================================================================*/ 15 16 #include "tensorflow/core/framework/common_shape_fns.h" 17 #include "tensorflow/core/framework/op.h" 18 #include "tensorflow/core/framework/shape_inference.h" 19 20 namespace tensorflow { 21 22 using shape_inference::DimensionHandle; 23 using shape_inference::InferenceContext; 24 using shape_inference::ShapeHandle; 25 26 REGISTER_OP("StringToHashBucketFast") 27 .Input("input: string") 28 .Output("output: int64") 29 .Attr("num_buckets: int >= 1") 30 .SetShapeFn(shape_inference::UnchangedShape); 31 32 REGISTER_OP("StringToHashBucketStrong") 33 .Input("input: string") 34 .Output("output: int64") 35 .Attr("num_buckets: int >= 1") 36 .Attr("key: list(int)") 37 .SetShapeFn(shape_inference::UnchangedShape); 38 39 REGISTER_OP("StringToHashBucket") 40 .Input("string_tensor: string") 41 .Output("output: int64") 42 .Attr("num_buckets: int >= 1") 43 .SetShapeFn(shape_inference::UnchangedShape); 44 45 REGISTER_OP("ReduceJoin") 46 .Input("inputs: string") 47 .Input("reduction_indices: int32") 48 .Attr("keep_dims: bool = false") 49 .Attr("separator: string = ''") 50 .Output("output: string") 51 .SetShapeFn(shape_inference::ReductionShape); 52 53 REGISTER_OP("AsString") 54 .Input("input: T") 55 .Output("output: string") 56 .Attr("T: {int32, int64, complex64, float, double, bool, int8}") 57 .Attr("precision: int = -1") 58 .Attr("scientific: bool = false") 59 .Attr("shortest: bool = false") 60 .Attr("width: int = -1") 61 .Attr("fill: string = ''") 62 .SetShapeFn(shape_inference::UnchangedShape); 63 64 REGISTER_OP("StringJoin") 65 .Input("inputs: N * string") 66 .Attr("N: int") 67 .Attr("separator: string = ''") 68 .Output("output: string") __anon5bd2fe870102(InferenceContext* c) 69 .SetShapeFn([](InferenceContext* c) { 70 // If all inputs are scalars, then return a scalar. 71 bool all_scalar = true; 72 for (int i = 0; i < c->num_inputs(); ++i) { 73 if (c->Rank(c->input(i)) != 0) all_scalar = false; 74 } 75 if (all_scalar) { 76 c->set_output(0, c->Scalar()); 77 return Status::OK(); 78 } 79 80 // At least one input is unknown or a scalar. 81 // Merge the non-scalars to find the output shape. 82 // Don't merge inputs with unknown rank, as they can actually be scalars 83 // or the output shape. 84 ShapeHandle out = c->UnknownShape(); 85 for (int i = 0; i < c->num_inputs(); ++i) { 86 if (c->RankKnown(c->input(i)) && c->Rank(c->input(i)) != 0) { 87 TF_RETURN_IF_ERROR(c->Merge(out, c->input(i), &out)); 88 } 89 } 90 c->set_output(0, out); 91 return Status::OK(); 92 }); 93 94 REGISTER_OP("StringSplit") 95 .Input("input: string") 96 .Input("delimiter: string") 97 .Output("indices: int64") 98 .Output("values: string") 99 .Output("shape: int64") 100 .Attr("skip_empty: bool = true") __anon5bd2fe870202(InferenceContext* c) 101 .SetShapeFn([](InferenceContext* c) { 102 ShapeHandle unused; 103 TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 1, &unused)); 104 TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 0, &unused)); 105 106 c->set_output(0, c->Matrix(InferenceContext::kUnknownDim, 2)); 107 c->set_output(1, c->Vector(InferenceContext::kUnknownDim)); 108 c->set_output(2, c->Vector(2)); 109 return Status::OK(); 110 }); 111 112 REGISTER_OP("EncodeBase64") 113 .Input("input: string") 114 .Output("output: string") 115 .Attr("pad: bool = false") 116 .SetShapeFn(shape_inference::UnchangedShape); 117 118 REGISTER_OP("DecodeBase64") 119 .Input("input: string") 120 .Output("output: string") 121 .SetShapeFn(shape_inference::UnchangedShape); 122 123 REGISTER_OP("Substr") 124 .Input("input: string") 125 .Input("pos: T") 126 .Input("len: T") 127 .Output("output: string") 128 .Attr("T: {int32, int64}") __anon5bd2fe870302(InferenceContext* c) 129 .SetShapeFn([](InferenceContext* c) { 130 ShapeHandle pos_shape = c->input(1); 131 ShapeHandle len_shape = c->input(2); 132 ShapeHandle unused; 133 // Check that pos/len have same rank 134 TF_RETURN_IF_ERROR(c->WithRank(pos_shape, c->Rank(len_shape), &unused)); 135 // Check that dimensions are equal 136 for (int32 i = 0; i < c->Rank(pos_shape); ++i) { 137 DimensionHandle pos_dim = c->Dim(pos_shape, i); 138 DimensionHandle len_dim = c->Dim(len_shape, i); 139 if (c->Value(pos_dim) != c->Value(len_dim)) { 140 return errors::InvalidArgument( 141 "pos and len shapes must match: ", c->DebugString(pos_shape), 142 " vs. ", c->DebugString(len_shape)); 143 } 144 } 145 // c->input(0) is the ShapeHandle to input strings 146 // BroadcastBinaryOpShapeFn infers shape from c->input(0) and c->input(1). 147 return shape_inference::BroadcastBinaryOpShapeFn(c); 148 }); 149 150 } // namespace tensorflow 151