| /* Copyright 2024 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/kernels/internal/reference/resize_nearest_neighbor.h" |
| |
| #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/kernels/kernel_util.h" |
| #include "tensorflow/lite/kernels/op_macros.h" |
| #include "tensorflow/lite/micro/kernels/kernel_util.h" |
| #include "tensorflow/lite/micro/micro_log.h" |
| #include "tflm/opt/opt.h" |
| |
| namespace tflite { |
| |
| namespace { |
| |
| constexpr int kInputTensor = 0; |
| constexpr int kSizeTensor = 1; |
| constexpr int kOutputTensor = 0; |
| |
| TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { |
| MicroContext* micro_context = GetMicroContext(context); |
| |
| TF_LITE_ENSURE_EQ(context, NumInputs(node), 2); |
| TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1); |
| |
| TfLiteTensor* input = |
| micro_context->AllocateTempInputTensor(node, kInputTensor); |
| TfLiteTensor* size = |
| micro_context->AllocateTempInputTensor(node, kSizeTensor); |
| TfLiteTensor* output = |
| micro_context->AllocateTempOutputTensor(node, kOutputTensor); |
| |
| // Our current implementations rely on the input being 4D, |
| // and the size being 1D tensor with exactly 2 elements. |
| TF_LITE_ENSURE_EQ(context, NumDimensions(input), 4); |
| TF_LITE_ENSURE_EQ(context, NumDimensions(size), 1); |
| TF_LITE_ENSURE_EQ(context, size->type, kTfLiteInt32); |
| TF_LITE_ENSURE_EQ(context, size->dims->data[0], 2); |
| |
| output->type = input->type; |
| |
| if (!IsConstantTensor(size)) { |
| MicroPrintf("Dynamic tensors are unsupported in tfmicro."); |
| return kTfLiteError; |
| } |
| |
| micro_context->DeallocateTempTfLiteTensor(input); |
| micro_context->DeallocateTempTfLiteTensor(size); |
| micro_context->DeallocateTempTfLiteTensor(output); |
| |
| return kTfLiteOk; |
| } |
| |
| TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { |
| auto* params = |
| reinterpret_cast<TfLiteResizeNearestNeighborParams*>(node->builtin_data); |
| |
| const TfLiteEvalTensor* input = |
| tflite::micro::GetEvalInput(context, node, kInputTensor); |
| const TfLiteEvalTensor* size = |
| tflite::micro::GetEvalInput(context, node, kSizeTensor); |
| TfLiteEvalTensor* output = |
| tflite::micro::GetEvalOutput(context, node, kOutputTensor); |
| |
| tflite::ResizeNearestNeighborParams op_params; |
| op_params.align_corners = params->align_corners; |
| op_params.half_pixel_centers = false; |
| |
| if (output->type == kTfLiteFloat32) { |
| reference_ops::ResizeNearestNeighbor( |
| op_params, tflite::micro::GetTensorShape(input), |
| tflite::micro::GetTensorData<int32_t>(input), |
| tflite::micro::GetTensorShape(size), |
| tflite::micro::GetTensorData<int32_t>(size), |
| tflite::micro::GetTensorShape(output), |
| tflite::micro::GetTensorData<int32_t>(output)); |
| } else if (output->type == kTfLiteInt8) { |
| kelvin::opt::ResizeNearestNeighborS8( |
| op_params, tflite::micro::GetTensorShape(input), |
| tflite::micro::GetTensorData<int8_t>(input), |
| tflite::micro::GetTensorShape(size), |
| tflite::micro::GetTensorData<int32_t>(size), |
| tflite::micro::GetTensorShape(output), |
| tflite::micro::GetTensorData<int8_t>(output)); |
| } else if (output->type == kTfLiteInt16) { |
| reference_ops::ResizeNearestNeighbor( |
| op_params, tflite::micro::GetTensorShape(input), |
| tflite::micro::GetTensorData<int16_t>(input), |
| tflite::micro::GetTensorShape(size), |
| tflite::micro::GetTensorData<int32_t>(size), |
| tflite::micro::GetTensorShape(output), |
| tflite::micro::GetTensorData<int16_t>(output)); |
| } else { |
| MicroPrintf("Output tensor type %s (%d) not supported.", |
| TfLiteTypeGetName(output->type), output->type); |
| |
| return kTfLiteError; |
| } |
| |
| return kTfLiteOk; |
| } |
| |
| } // namespace |
| |
| TFLMRegistration Register_RESIZE_NEAREST_NEIGHBOR() { |
| return tflite::micro::RegisterOp(nullptr, Prepare, Eval); |
| } |
| |
| } // namespace tflite |