| /* Copyright 2023 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. |
| ==============================================================================*/ |
| #if defined(VISION_P6) |
| |
| #include "tensorflow/lite/c/builtin_op_data.h" |
| #include "tensorflow/lite/kernels/internal/reference/pooling.h" |
| #include "tensorflow/lite/kernels/kernel_util.h" |
| #include "tensorflow/lite/micro/kernels/kernel_util.h" |
| #include "tensorflow/lite/micro/kernels/pooling.h" |
| #include "tensorflow/lite/micro/kernels/xtensa/xtensa.h" |
| #include "tensorflow/lite/micro/kernels/xtensa/xtensa_pooling.h" |
| |
| #define MAX_POOLING 0 |
| #define AVG_POOLING 1 |
| |
| namespace tflite { |
| |
| TfLiteStatus PoolingPrepareVision(TfLiteContext* context, TfLiteNode* node, |
| uint8_t pool_type) { |
| TF_LITE_ENSURE_STATUS(PoolingPrepare(context, node)); |
| TFLITE_DCHECK(node->user_data != nullptr); |
| TFLITE_DCHECK(node->builtin_data != nullptr); |
| |
| XtensaOpDataPooling* data = |
| reinterpret_cast<XtensaOpDataPooling*>(node->user_data); |
| const auto& params = |
| *(reinterpret_cast<const TfLitePoolParams*>(node->builtin_data)); |
| |
| MicroContext* micro_context = GetMicroContext(context); |
| TfLiteTensor* output = |
| micro_context->AllocateTempOutputTensor(node, kPoolingOutputTensor); |
| TF_LITE_ENSURE(context, output != nullptr); |
| TfLiteTensor* input = |
| micro_context->AllocateTempInputTensor(node, kPoolingInputTensor); |
| TF_LITE_ENSURE(context, input != nullptr); |
| |
| if (input->type == kTfLiteInt8) { |
| uint32_t context_size = 0; |
| uint32_t status = xiPoolGetMemReqd_Context(&context_size); |
| TFLITE_DCHECK(status == 0); |
| if (context_size) { |
| void* context_data = |
| context->AllocatePersistentBuffer(context, context_size); |
| if (context_data == nullptr) { |
| return kTfLiteError; |
| } |
| data->p_context = reinterpret_cast<uint8_t*>(context_data); |
| data->context_size = context_size; |
| } |
| |
| uint32_t input_dims[4] = {1, 1, 1, 1}; |
| uint32_t output_dims[4] = {1, 1, 1, 1}; |
| for (int i = 0; i < NumDimensions(input); i++) { |
| input_dims[i] = |
| std::max(1, SizeOfDimension(input, NumDimensions(input) - 1 - i)); |
| } |
| for (int i = 0; i < NumDimensions(output); i++) { |
| output_dims[i] = |
| std::max(1, SizeOfDimension(output, NumDimensions(output) - 1 - i)); |
| } |
| |
| status = xiPoolSetContext( |
| data->p_context, data->context_size, input_dims[0], input_dims[1], |
| input_dims[2], input_dims[3], output_dims[0], output_dims[1], |
| output_dims[2], params.filter_width, params.filter_height, |
| params.stride_width, params.stride_height, |
| data->reference_op_data.padding.width, |
| data->reference_op_data.padding.height, input->params.zero_point, |
| output->params.zero_point, data->reference_op_data.activation_min, |
| data->reference_op_data.activation_max, pool_type); |
| if (status) { |
| return kTfLiteError; |
| } |
| } |
| |
| micro_context->DeallocateTempTfLiteTensor(output); |
| micro_context->DeallocateTempTfLiteTensor(input); |
| |
| return kTfLiteOk; |
| } |
| |
| TfLiteStatus AvgPoolingPrepareVision(TfLiteContext* context, TfLiteNode* node) { |
| return PoolingPrepareVision(context, node, AVG_POOLING); |
| } |
| |
| TfLiteStatus MaxPoolingPrepareVision(TfLiteContext* context, TfLiteNode* node) { |
| return PoolingPrepareVision(context, node, MAX_POOLING); |
| } |
| |
| TfLiteStatus PoolEvalVision(TfLiteContext* context, TfLiteNode* node, |
| const TfLitePoolParams& params, |
| const XtensaOpDataPooling& data, |
| const TfLiteEvalTensor* input, |
| TfLiteEvalTensor* output) { |
| const uint32_t input_size = NumElements(input->dims); |
| const uint32_t output_size = NumElements(output->dims); |
| |
| xiPool(data.p_context, data.context_size, |
| const_cast<int8_t*>(tflite::micro::GetTensorData<int8_t>(input)), |
| input_size, tflite::micro::GetTensorData<int8_t>(output), output_size); |
| return kTfLiteOk; |
| } |
| } // namespace tflite |
| |
| #endif // VISIONP6 |