1 /* Copyright 2017 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 #include <algorithm> 16 #include <memory> 17 #include <string> 18 #include <unordered_map> 19 #include <vector> 20 21 #include "tensorflow/contrib/lite/toco/graph_transformations/graph_transformations.h" 22 #include "tensorflow/contrib/lite/toco/model.h" 23 #include "tensorflow/contrib/lite/toco/tooling_util.h" 24 #include "tensorflow/core/platform/logging.h" 25 26 namespace toco { 27 28 // Reorder the elements of an input_array according to the input_axes_order and 29 // output_axes_order. Then adjust the shapes of the input and output arrays 30 // accordingly. Note that input_array must have a buffer (that is, it is a 31 // constant array). 32 template <typename T, ArrayDataType DataType> 33 void ReorderAxes(AxesOrder input_axes_order, AxesOrder output_axes_order, 34 Array* input_array, Array* output_array) { 35 CHECK(input_array->buffer->type == DataType); 36 CHECK(!output_array->buffer); 37 auto& input_data = input_array->GetMutableBuffer<DataType>().data; 38 std::vector<T> reordered_data; 39 reordered_data.resize(RequiredBufferSizeForShape(output_array->shape())); 40 // TODO(b/62904716) Shapes should be used directly. 41 Shape input_shape = input_array->shape(); 42 Shape output_shape = output_array->shape(); 43 if (AxesCount(input_axes_order) == 2) { 44 UnextendShape(&input_shape, 2); 45 UnextendShape(&output_shape, 2); 46 } 47 ShuffleArray(input_shape, input_axes_order, output_axes_order, output_shape, 48 input_data.data(), reordered_data.data()); 49 input_data = reordered_data; 50 input_array->copy_shape(output_array->shape()); 51 } 52 53 bool ResolveReorderAxes::Run(Model* model, std::size_t op_index) { 54 auto reorder_it = model->operators.begin() + op_index; 55 auto* reorder_op = static_cast<ReorderAxesOperator*>(reorder_it->get()); 56 if (reorder_op->type != OperatorType::kReorderAxes) { 57 return false; 58 } 59 const auto& input_array_name = reorder_op->inputs[0]; 60 const auto& output_array_name = reorder_op->outputs[0]; 61 auto& input_array = model->GetArray(input_array_name); 62 auto& output_array = model->GetArray(output_array_name); 63 if (!input_array.buffer) { 64 return false; 65 } 66 // Yield until output dims have been resolved. 67 if (!output_array.has_shape()) { 68 return false; 69 } 70 // Reorder the input array dims and buffer data 71 if (input_array.buffer->type == ArrayDataType::kFloat) { 72 ReorderAxes<float, ArrayDataType::kFloat>(reorder_op->input_axes_order, 73 reorder_op->output_axes_order, 74 &input_array, &output_array); 75 } else if (input_array.buffer->type == ArrayDataType::kInt32) { 76 ReorderAxes<uint8, ArrayDataType::kUint8>(reorder_op->input_axes_order, 77 reorder_op->output_axes_order, 78 &input_array, &output_array); 79 } else { 80 LOG(FATAL) << "Cannot ReorderAxes unless input buffer is float or uint8."; 81 } 82 83 input_array.copy_shape(output_array.shape()); 84 85 // Update the edges of the graph to point to the input array 86 for (const auto& other_op : model->operators) { 87 for (auto& input : other_op->inputs) { 88 if (input == output_array_name) { 89 input = input_array_name; 90 } 91 } 92 } 93 94 AddMessageF("Reordered axes for array %s", input_array_name); 95 96 // Remove the op and output array. 97 model->EraseArray(output_array_name); 98 model->operators.erase(reorder_it); 99 return true; 100 } 101 102 } // namespace toco 103
