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 #include "tensorflow/core/framework/op.h" 17 #include "tensorflow/core/framework/shape_inference.h" 18 19 namespace tensorflow { 20 21 using shape_inference::DimensionHandle; 22 using shape_inference::InferenceContext; 23 using shape_inference::ShapeHandle; 24 25 // CTC is Connectionist Temporal Classification. See util/ctc/ for details. 26 27 REGISTER_OP("CTCLoss") 28 .Input("inputs: T") 29 .Input("labels_indices: int64") 30 .Input("labels_values: int32") 31 .Input("sequence_length: int32") 32 .Attr("preprocess_collapse_repeated: bool = false") 33 .Attr("ctc_merge_repeated: bool = true") 34 .Attr("ignore_longer_outputs_than_inputs: bool = false") 35 .Output("loss: T") 36 .Output("gradient: T") 37 .Attr("T: {float, double} = DT_FLOAT") __anon8bbafe030102(InferenceContext* c) 38 .SetShapeFn([](InferenceContext* c) { 39 ShapeHandle inputs; 40 ShapeHandle labels_indices; 41 ShapeHandle labels_values; 42 ShapeHandle sequence_length; 43 44 TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 3, &inputs)); 45 TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 2, &labels_indices)); 46 TF_RETURN_IF_ERROR(c->WithRank(c->input(2), 1, &labels_values)); 47 TF_RETURN_IF_ERROR(c->WithRank(c->input(3), 1, &sequence_length)); 48 49 DimensionHandle unused; 50 TF_RETURN_IF_ERROR(c->Merge(c->Dim(labels_indices, 0), 51 c->Dim(labels_values, 0), &unused)); 52 53 // Get batch size from inputs and sequence_length, and update inputs 54 // with the merged batch_size since it is returned. 55 DimensionHandle batch_size; 56 TF_RETURN_IF_ERROR( 57 c->Merge(c->Dim(inputs, 1), c->Dim(sequence_length, 0), &batch_size)); 58 TF_RETURN_IF_ERROR(c->ReplaceDim(inputs, 1, batch_size, &inputs)); 59 60 c->set_output(0, c->Vector(batch_size)); 61 c->set_output(1, inputs); 62 return OkStatus(); 63 }); 64 65 REGISTER_OP("CTCLossV2") 66 .Input("inputs: float") 67 .Input("labels_indices: int64") 68 .Input("labels_values: int32") 69 .Input("sequence_length: int32") 70 .Attr("preprocess_collapse_repeated: bool = false") 71 .Attr("ctc_merge_repeated: bool = true") 72 .Attr("ignore_longer_outputs_than_inputs: bool = false") 73 .Output("loss: float") 74 .Output("gradient: float") __anon8bbafe030202(InferenceContext* c) 75 .SetShapeFn([](InferenceContext* c) { 76 ShapeHandle inputs; 77 ShapeHandle labels_indices; 78 ShapeHandle labels_values; 79 ShapeHandle sequence_length; 80 81 TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 3, &inputs)); 82 TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 2, &labels_indices)); 83 TF_RETURN_IF_ERROR(c->WithRank(c->input(2), 1, &labels_values)); 84 TF_RETURN_IF_ERROR(c->WithRank(c->input(3), 1, &sequence_length)); 85 86 DimensionHandle unused; 87 TF_RETURN_IF_ERROR(c->Merge(c->Dim(labels_indices, 0), 88 c->Dim(labels_values, 0), &unused)); 89 90 // Get batch size from inputs and sequence_length, and update inputs 91 // with the merged batch_size since it is returned. 92 DimensionHandle batch_size; 93 TF_RETURN_IF_ERROR( 94 c->Merge(c->Dim(inputs, 1), c->Dim(sequence_length, 0), &batch_size)); 95 TF_RETURN_IF_ERROR(c->ReplaceDim(inputs, 1, batch_size, &inputs)); 96 97 c->set_output(0, c->Vector(batch_size)); 98 c->set_output(1, inputs); 99 return OkStatus(); 100 }); 101 102 REGISTER_OP("CTCGreedyDecoder") 103 .Input("inputs: T") 104 .Input("sequence_length: int32") 105 .Attr("merge_repeated: bool = false") 106 .Attr("blank_index: int = -1") 107 .Output("decoded_indices: int64") 108 .Output("decoded_values: int64") 109 .Output("decoded_shape: int64") 110 .Output("log_probability: T") 111 .Attr("T: {float, double} = DT_FLOAT") __anon8bbafe030302(InferenceContext* c) 112 .SetShapeFn([](InferenceContext* c) { 113 ShapeHandle inputs; 114 ShapeHandle sequence_length; 115 116 TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 3, &inputs)); 117 TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 1, &sequence_length)); 118 119 // Get batch size from inputs and sequence_length. 120 DimensionHandle batch_size; 121 TF_RETURN_IF_ERROR( 122 c->Merge(c->Dim(inputs, 1), c->Dim(sequence_length, 0), &batch_size)); 123 124 DimensionHandle total_decoded_outputs = c->UnknownDim(); 125 c->set_output(0, c->Matrix(total_decoded_outputs, 2)); 126 c->set_output(1, c->Vector(total_decoded_outputs)); 127 c->set_output(2, c->Vector(2)); 128 c->set_output(3, c->Matrix(batch_size, 1)); 129 return OkStatus(); 130 }); 131 132 REGISTER_OP("CTCBeamSearchDecoder") 133 .Input("inputs: T") 134 .Input("sequence_length: int32") 135 .Attr("beam_width: int >= 1") 136 .Attr("top_paths: int >= 1") 137 .Attr("merge_repeated: bool = true") 138 .Output("decoded_indices: top_paths * int64") 139 .Output("decoded_values: top_paths * int64") 140 .Output("decoded_shape: top_paths * int64") 141 .Output("log_probability: T") 142 .Attr("T: {float, double} = DT_FLOAT") __anon8bbafe030402(InferenceContext* c) 143 .SetShapeFn([](InferenceContext* c) { 144 ShapeHandle inputs; 145 ShapeHandle sequence_length; 146 147 TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 3, &inputs)); 148 TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 1, &sequence_length)); 149 150 // Get batch size from inputs and sequence_length. 151 DimensionHandle batch_size; 152 TF_RETURN_IF_ERROR( 153 c->Merge(c->Dim(inputs, 1), c->Dim(sequence_length, 0), &batch_size)); 154 155 int32_t top_paths; 156 TF_RETURN_IF_ERROR(c->GetAttr("top_paths", &top_paths)); 157 158 // Outputs. 159 int out_idx = 0; 160 for (int i = 0; i < top_paths; ++i) { // decoded_indices 161 c->set_output(out_idx++, c->Matrix(InferenceContext::kUnknownDim, 2)); 162 } 163 for (int i = 0; i < top_paths; ++i) { // decoded_values 164 c->set_output(out_idx++, c->Vector(InferenceContext::kUnknownDim)); 165 } 166 ShapeHandle shape_v = c->Vector(2); 167 for (int i = 0; i < top_paths; ++i) { // decoded_shape 168 c->set_output(out_idx++, shape_v); 169 } 170 c->set_output(out_idx++, c->Matrix(batch_size, top_paths)); 171 return OkStatus(); 172 }); 173 174 } // namespace tensorflow 175