1 /* Copyright 2016 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 // See docs in ../ops/string_ops.cc. 17 18 #include <string> 19 20 #include "tensorflow/core/framework/kernel_def_builder.h" 21 #include "tensorflow/core/framework/op_kernel.h" 22 #include "tensorflow/core/framework/tensor.h" 23 #include "tensorflow/core/lib/core/errors.h" 24 #include "tensorflow/core/lib/core/status.h" 25 #include "tensorflow/core/lib/strings/stringprintf.h" 26 27 namespace tensorflow { 28 29 class AsStringOp : public OpKernel { 30 public: 31 using OpKernel::OpKernel; 32 33 explicit AsStringOp(OpKernelConstruction* ctx) : OpKernel(ctx) { 34 int32 precision; 35 bool scientific; 36 bool shortest; 37 int32 width; 38 string fill_string; 39 DataType dtype; 40 OP_REQUIRES_OK(ctx, ctx->GetAttr("T", &dtype)); 41 OP_REQUIRES_OK(ctx, ctx->GetAttr("precision", &precision)); 42 OP_REQUIRES_OK(ctx, ctx->GetAttr("scientific", &scientific)); 43 OP_REQUIRES_OK(ctx, ctx->GetAttr("shortest", &shortest)); 44 OP_REQUIRES_OK(ctx, ctx->GetAttr("width", &width)); 45 OP_REQUIRES_OK(ctx, ctx->GetAttr("fill", &fill_string)); 46 switch (dtype) { 47 case DT_FLOAT: 48 case DT_DOUBLE: 49 case DT_COMPLEX64: 50 break; 51 default: 52 OP_REQUIRES(ctx, !(scientific || shortest), 53 errors::InvalidArgument("scientific and shortest format " 54 "not supported for datatype ", 55 DataTypeString(dtype))); 56 OP_REQUIRES(ctx, precision < 0, 57 errors::InvalidArgument("precision not supported " 58 "for datatype ", 59 DataTypeString(dtype))); 60 } 61 OP_REQUIRES( 62 ctx, fill_string.size() <= 1, 63 errors::InvalidArgument("Fill string must be one or fewer characters")); 64 OP_REQUIRES(ctx, !(scientific && shortest), 65 errors::InvalidArgument( 66 "Cannot select both scientific and shortest notation")); 67 format_ = "%"; 68 if (width > -1) { 69 strings::Appendf(&format_, "%s%d", fill_string.c_str(), width); 70 } 71 if (precision > -1) { 72 strings::Appendf(&format_, ".%d", precision); 73 } 74 switch (dtype) { 75 case DT_INT8: 76 case DT_INT32: 77 strings::Appendf(&format_, "d"); 78 break; 79 case DT_INT64: 80 strings::Appendf(&format_, "lld"); 81 break; 82 case DT_FLOAT: 83 case DT_DOUBLE: 84 case DT_COMPLEX64: 85 if (shortest) { 86 strings::Appendf(&format_, "g"); 87 } else if (scientific) { 88 strings::Appendf(&format_, "e"); 89 } else { 90 strings::Appendf(&format_, "f"); 91 } 92 break; 93 case DT_BOOL: 94 break; 95 default: 96 bool type_not_supported = true; 97 OP_REQUIRES(ctx, !type_not_supported, 98 errors::InvalidArgument("Type not supported: ", 99 DataTypeString(dtype))); 100 } 101 102 if (dtype == DT_COMPLEX64) { 103 format_ = strings::Printf("(%s,%s)", format_.c_str(), format_.c_str()); 104 } 105 } 106 107 void Compute(OpKernelContext* context) override { 108 const Tensor* input_tensor; 109 OP_REQUIRES_OK(context, context->input("input", &input_tensor)); 110 const DataType& dtype = input_tensor->dtype(); 111 112 Tensor* output_tensor = nullptr; 113 OP_REQUIRES_OK(context, 114 context->allocate_output("output", input_tensor->shape(), 115 &output_tensor)); 116 auto output_flat = output_tensor->flat<string>(); 117 118 #define ENCODE_TYPE(type, T, enc_str) \ 119 case (type): { \ 120 const auto& input_flat = input_tensor->flat<T>(); \ 121 for (int i = 0; i < input_flat.size(); ++i) { \ 122 output_flat(i) = strings::Printf((enc_str.c_str()), input_flat(i)); \ 123 } \ 124 } break 125 126 switch (dtype) { 127 ENCODE_TYPE(DT_INT32, int32, format_); 128 ENCODE_TYPE(DT_INT64, int64, format_); 129 ENCODE_TYPE(DT_FLOAT, float, format_); 130 ENCODE_TYPE(DT_DOUBLE, double, format_); 131 ENCODE_TYPE(DT_INT8, int8, format_); 132 case (DT_BOOL): { 133 const auto& input_flat = input_tensor->flat<bool>(); 134 for (int i = 0; i < input_flat.size(); ++i) { 135 output_flat(i) = (input_flat(i)) ? "true" : "false"; 136 } 137 } break; 138 case (DT_COMPLEX64): { 139 const auto& input_flat = input_tensor->flat<complex64>(); 140 for (int i = 0; i < input_flat.size(); ++i) { 141 output_flat(i) = strings::Printf( 142 format_.c_str(), input_flat(i).real(), input_flat(i).imag()); 143 } 144 } break; 145 default: 146 bool can_encode_type = false; 147 OP_REQUIRES(context, can_encode_type, 148 errors::InvalidArgument("Cannot encode input of type ", 149 DataTypeString(dtype))); 150 } 151 152 #undef ENCODE_TYPE 153 } 154 155 private: 156 string format_; 157 }; 158 159 REGISTER_KERNEL_BUILDER(Name("AsString").Device(DEVICE_CPU), AsStringOp); 160 161 } // namespace tensorflow 162
