| /* Copyright 2022 The TensorFlow Authors. All Rights Reserved. |
| |
| Licensed under the Apache License, Version 2.0 (the "License"); |
| you may not use this file except in compliance with the License. |
| You may obtain a copy of the License at |
| |
| http://www.apache.org/licenses/LICENSE-2.0 |
| |
| Unless required by applicable law or agreed to in writing, software |
| distributed under the License is distributed on an "AS IS" BASIS, |
| WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| See the License for the specific language governing permissions and |
| limitations under the License. |
| ==============================================================================*/ |
| |
| #include "tensorflow/lite/c/builtin_op_data.h" |
| #include "tensorflow/lite/c/common.h" |
| #include "tensorflow/lite/kernels/internal/tensor_ctypes.h" |
| #include "tensorflow/lite/micro/kernels/kernel_util.h" |
| #include "tensorflow/lite/micro/micro_log.h" |
| |
| namespace tflite { |
| |
| namespace { |
| |
| constexpr int kOutputTensor = 0; |
| |
| template <typename T> |
| TfLiteStatus PackImpl(TfLiteContext* context, TfLiteNode* node, |
| TfLiteEvalTensor* output, int values_count, int axis) { |
| const TfLiteEvalTensor* input0 = |
| tflite::micro::GetEvalInput(context, node, 0); |
| |
| const int dimensions = output->dims->size; |
| const TfLiteIntArray* input_dims = input0->dims; |
| const TfLiteIntArray* output_dims = output->dims; |
| |
| if (axis < 0) { |
| axis += dimensions; |
| } |
| |
| int outer_size = 1; |
| for (int i = 0; i < axis; ++i) { |
| outer_size *= output_dims->data[i]; |
| } |
| int copy_size = 1; |
| for (int i = axis + 1; i < dimensions; ++i) { |
| copy_size *= output_dims->data[i]; |
| } |
| int input_size = 1; |
| for (int i = 0; i < input_dims->size; ++i) { |
| input_size *= input_dims->data[i]; |
| } |
| TFLITE_DCHECK_EQ(input_size, copy_size * outer_size); |
| |
| T* output_data = tflite::micro::GetTensorData<T>(output); |
| |
| for (int i = 0; i < values_count; ++i) { |
| const TfLiteEvalTensor* t = tflite::micro::GetEvalInput(context, node, i); |
| const T* input_data = tflite::micro::GetTensorData<T>(t); |
| for (int k = 0; k < outer_size; ++k) { |
| const T* input_ptr = input_data + copy_size * k; |
| int loc = k * values_count * copy_size + i * copy_size; |
| T* output_ptr = output_data + loc; |
| for (int j = 0; j < copy_size; ++j) output_ptr[j] = input_ptr[j]; |
| } |
| } |
| |
| return kTfLiteOk; |
| } |
| |
| TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { |
| const TfLitePackParams* data = |
| reinterpret_cast<TfLitePackParams*>(node->builtin_data); |
| |
| TfLiteEvalTensor* output = |
| tflite::micro::GetEvalOutput(context, node, kOutputTensor); |
| |
| switch (output->type) { |
| case kTfLiteFloat32: { |
| return PackImpl<float>(context, node, output, data->values_count, |
| data->axis); |
| } |
| case kTfLiteInt8: { |
| return PackImpl<int8_t>(context, node, output, data->values_count, |
| data->axis); |
| } |
| case kTfLiteInt32: { |
| return PackImpl<int32_t>(context, node, output, data->values_count, |
| data->axis); |
| } |
| case kTfLiteInt64: { |
| return PackImpl<int64_t>(context, node, output, data->values_count, |
| data->axis); |
| } |
| default: { |
| MicroPrintf("Type '%s' is not supported by pack.", |
| TfLiteTypeGetName(output->type)); |
| return kTfLiteError; |
| } |
| } |
| |
| return kTfLiteOk; |
| } |
| |
| } // namespace |
| |
| TFLMRegistration Register_PACK() { |
| return tflite::micro::RegisterOp(nullptr, nullptr, Eval); |
| } |
| |
| } // namespace tflite |