mirror of
https://github.com/vee1e/tflite-micro.git
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7a7a3de092
@tensorflow/micro Update PACK with INT16 support. Add INT16 unit tests. This is a copy of PR #2737 bug=fixes #2736
111 lines
3.5 KiB
C++
111 lines
3.5 KiB
C++
/* Copyright 2025 The TensorFlow Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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==============================================================================*/
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#include "tensorflow/lite/c/builtin_op_data.h"
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#include "tensorflow/lite/c/common.h"
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#include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
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#include "tensorflow/lite/kernels/kernel_util.h"
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#include "tensorflow/lite/micro/kernels/kernel_util.h"
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#include "tensorflow/lite/micro/micro_log.h"
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namespace tflite {
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namespace {
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constexpr int kInputTensor = 0;
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template <typename T>
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TfLiteStatus UnpackImpl(TfLiteContext* context, TfLiteNode* node,
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const TfLiteEvalTensor* input, int output_count,
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int axis) {
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const TfLiteEvalTensor* output0 =
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tflite::micro::GetEvalOutput(context, node, 0);
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const TfLiteIntArray* input_dims = input->dims;
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const TfLiteIntArray* output_dims = output0->dims;
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const int dimensions = input_dims->size;
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if (axis < 0) {
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axis += input->dims->size;
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}
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TFLITE_DCHECK_LT(axis, dimensions);
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int outer_size = 1;
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for (int i = 0; i < axis; ++i) {
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outer_size *= input_dims->data[i];
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}
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int copy_size = 1;
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for (int i = axis + 1; i < dimensions; ++i) {
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copy_size *= input_dims->data[i];
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}
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int output_size = 1;
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for (int i = 0; i < output_dims->size; ++i) {
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output_size *= output_dims->data[i];
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}
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TFLITE_DCHECK_EQ(output_size, copy_size * outer_size);
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const T* input_data = tflite::micro::GetTensorData<T>(input);
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for (int i = 0; i < output_count; ++i) {
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TfLiteEvalTensor* t = tflite::micro::GetEvalOutput(context, node, i);
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T* output_data = tflite::micro::GetTensorData<T>(t);
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for (int k = 0; k < outer_size; ++k) {
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T* output_ptr = output_data + copy_size * k;
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int loc = k * output_count * copy_size + i * copy_size;
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const T* input_ptr = input_data + loc;
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for (int j = 0; j < copy_size; ++j) output_ptr[j] = input_ptr[j];
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}
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}
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return kTfLiteOk;
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}
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TfLiteStatus UnpackEval(TfLiteContext* context, TfLiteNode* node) {
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TfLiteUnpackParams* data =
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reinterpret_cast<TfLiteUnpackParams*>(node->builtin_data);
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const TfLiteEvalTensor* input =
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tflite::micro::GetEvalInput(context, node, kInputTensor);
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switch (input->type) {
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case kTfLiteFloat32: {
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return UnpackImpl<float>(context, node, input, data->num, data->axis);
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}
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case kTfLiteInt32: {
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return UnpackImpl<int32_t>(context, node, input, data->num, data->axis);
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}
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case kTfLiteInt16: {
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return UnpackImpl<int16_t>(context, node, input, data->num, data->axis);
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}
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case kTfLiteInt8: {
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return UnpackImpl<int8_t>(context, node, input, data->num, data->axis);
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}
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default: {
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MicroPrintf("Type '%s' is not supported by unpack.",
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TfLiteTypeGetName(input->type));
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return kTfLiteError;
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}
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}
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return kTfLiteOk;
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}
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} // namespace
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TFLMRegistration Register_UNPACK() {
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return tflite::micro::RegisterOp(nullptr, nullptr, UnpackEval);
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}
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} // namespace tflite
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