| /* Copyright 2021 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/common.h" |
| #include "tensorflow/lite/kernels/internal/quantization_util.h" |
| #include "tensorflow/lite/kernels/internal/reference/integer_ops/conv.h" |
| #include "tensorflow/lite/kernels/internal/tensor_ctypes.h" |
| #include "tensorflow/lite/kernels/kernel_util.h" |
| #include "tensorflow/lite/kernels/padding.h" |
| #include "tensorflow/lite/micro/kernels/conv.h" |
| #include "tensorflow/lite/micro/kernels/kernel_util.h" |
| |
| namespace tflite { |
| namespace { |
| |
| void* Init(TfLiteContext* context, const char* buffer, size_t length) { |
| TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr); |
| return context->AllocatePersistentBuffer(context, sizeof(OpDataConv)); |
| } |
| |
| } // namespace. |
| |
| TfLiteStatus ConvReferenceEvalInt16(TfLiteContext* context, TfLiteNode* node) { |
| TFLITE_DCHECK(node->user_data != nullptr); |
| TFLITE_DCHECK(node->builtin_data != nullptr); |
| const auto& params = |
| *(reinterpret_cast<TfLiteConvParams*>(node->builtin_data)); |
| const auto& op_data = *(reinterpret_cast<OpDataConv*>(node->user_data)); |
| |
| TfLiteEvalTensor* output = |
| tflite::micro::GetEvalOutput(context, node, kConvOutputTensor); |
| const TfLiteEvalTensor* input = |
| tflite::micro::GetEvalInput(context, node, kConvInputTensor); |
| const TfLiteEvalTensor* filter = |
| tflite::micro::GetEvalInput(context, node, kConvWeightsTensor); |
| const TfLiteEvalTensor* bias = |
| (NumInputs(node) == 3) |
| ? tflite::micro::GetEvalInput(context, node, kConvBiasTensor) |
| : nullptr; |
| |
| reference_integer_ops::ConvPerChannel( |
| ConvParamsQuantized(params, op_data), |
| op_data.per_channel_output_multiplier, op_data.per_channel_output_shift, |
| tflite::micro::GetTensorShape(input), |
| tflite::micro::GetTensorData<int16_t>(input), |
| tflite::micro::GetTensorShape(filter), |
| tflite::micro::GetTensorData<int8_t>(filter), |
| tflite::micro::GetTensorShape(bias), |
| tflite::micro::GetTensorData<std::int64_t>(bias), |
| tflite::micro::GetTensorShape(output), |
| tflite::micro::GetTensorData<int16_t>(output)); |
| return kTfLiteOk; |
| } |
| |
| // TODO(b/189981943): This variant can be used for a smaller binary |
| // since the optimized conv implementation currently adds a lot to |
| // the binary size (~30KB to text section). |
| TFLMRegistration Register_CONV_2D_INT16REF() { |
| return tflite::micro::RegisterOp(Init, ConvPrepare, ConvReferenceEvalInt16); |
| } |
| |
| } // namespace tflite |