a0422e404e
The interpreter doesn't call it if a nullptr is presented in the registration. This can slightly reduce code size. PiperOrigin-RevId: 304284594 Change-Id: I7fa092aa9cf5142ee30982d07655971fd1626630
126 lines
4.0 KiB
C++
126 lines
4.0 KiB
C++
/* Copyright 2019 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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namespace tflite {
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namespace ops {
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namespace micro {
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namespace pack {
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namespace {
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constexpr int kOutputTensor = 0;
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template <typename T>
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TfLiteStatus PackImpl(TfLiteContext* context, TfLiteNode* node,
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TfLiteTensor* output, int values_count, int axis) {
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const int dimensions = output->dims->size;
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const TfLiteTensor* input0 = GetInput(context, node, 0);
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const TfLiteIntArray* input_dims = input0->dims;
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const TfLiteIntArray* output_dims = output->dims;
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if (axis < 0) {
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axis += dimensions;
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}
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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 *= output_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 *= output_dims->data[i];
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}
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int input_size = 1;
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for (int i = 0; i < input_dims->size; ++i) {
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input_size *= input_dims->data[i];
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}
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TFLITE_DCHECK_EQ(input_size, copy_size * outer_size);
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T* output_data = GetTensorData<T>(output);
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for (int i = 0; i < values_count; ++i) {
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const TfLiteTensor* t = GetInput(context, node, i);
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const T* input_data = GetTensorData<T>(t);
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for (int k = 0; k < outer_size; ++k) {
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const T* input_ptr = input_data + copy_size * k;
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int loc = k * values_count * copy_size + i * copy_size;
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T* output_ptr = output_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 Eval(TfLiteContext* context, TfLiteNode* node) {
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const TfLitePackParams* data =
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reinterpret_cast<TfLitePackParams*>(node->builtin_data);
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TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
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switch (output->type) {
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case kTfLiteFloat32: {
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return PackImpl<float>(context, node, output, data->values_count,
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data->axis);
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}
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case kTfLiteUInt8: {
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return PackImpl<uint8_t>(context, node, output, data->values_count,
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data->axis);
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}
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case kTfLiteInt8: {
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return PackImpl<int8_t>(context, node, output, data->values_count,
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data->axis);
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}
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case kTfLiteInt32: {
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return PackImpl<int32_t>(context, node, output, data->values_count,
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data->axis);
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}
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case kTfLiteInt64: {
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return PackImpl<int64_t>(context, node, output, data->values_count,
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data->axis);
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}
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default: {
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TF_LITE_KERNEL_LOG(context, "Type '%s' is not supported by pack.",
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TfLiteTypeGetName(output->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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} // namespace pack
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TfLiteRegistration* Register_PACK() {
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static TfLiteRegistration r = {/*init=*/nullptr,
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/*free=*/nullptr,
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/*prepare=*/nullptr,
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/*invoke=*/pack::Eval,
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/*profiling_string=*/nullptr,
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/*builtin_code=*/0,
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/*custom_name=*/nullptr,
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/*version=*/0};
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return &r;
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}
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} // namespace micro
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} // namespace ops
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} // namespace tflite
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