| /* Copyright 2017 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/micro/kernels/depthwise_conv.h" |
| |
| #include "tensorflow/lite/c/builtin_op_data.h" |
| #include "tensorflow/lite/c/common.h" |
| #include "tensorflow/lite/kernels/internal/portable_tensor_utils.h" |
| #include "tensorflow/lite/kernels/internal/reference/depthwiseconv_float.h" |
| #include "tensorflow/lite/kernels/internal/reference/integer_ops/depthwise_conv.h" |
| #include "tensorflow/lite/kernels/kernel_util.h" |
| #include "tensorflow/lite/micro/kernels/kernel_util.h" |
| #include "tensorflow/lite/micro/micro_log.h" |
| |
| namespace tflite { |
| namespace { |
| |
| void* DepthwiseConvInit(TfLiteContext* context, const char* buffer, |
| size_t length) { |
| TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr); |
| return context->AllocatePersistentBuffer(context, sizeof(OpDataConv)); |
| } |
| |
| TfLiteStatus DepthwiseConvEval(TfLiteContext* context, TfLiteNode* node) { |
| TFLITE_DCHECK(node->user_data != nullptr); |
| TFLITE_DCHECK(node->builtin_data != nullptr); |
| |
| auto& params = |
| *(reinterpret_cast<TfLiteDepthwiseConvParams*>(node->builtin_data)); |
| const OpDataConv& data = *(static_cast<const OpDataConv*>(node->user_data)); |
| |
| TfLiteEvalTensor* output = |
| tflite::micro::GetEvalOutput(context, node, kDepthwiseConvOutputTensor); |
| const TfLiteEvalTensor* input = |
| tflite::micro::GetEvalInput(context, node, kDepthwiseConvInputTensor); |
| const TfLiteEvalTensor* filter = |
| tflite::micro::GetEvalInput(context, node, kDepthwiseConvWeightsTensor); |
| const TfLiteEvalTensor* bias = |
| (NumInputs(node) == 3) |
| ? tflite::micro::GetEvalInput(context, node, kDepthwiseConvBiasTensor) |
| : nullptr; |
| |
| switch (input->type) { // Already know in/out types are same. |
| case kTfLiteFloat32: { |
| tflite::reference_ops::DepthwiseConv( |
| DepthwiseConvParamsFloat(params, data), |
| tflite::micro::GetTensorShape(input), |
| tflite::micro::GetTensorData<float>(input), |
| tflite::micro::GetTensorShape(filter), |
| tflite::micro::GetTensorData<float>(filter), |
| tflite::micro::GetTensorShape(bias), |
| tflite::micro::GetOptionalTensorData<float>(bias), |
| tflite::micro::GetTensorShape(output), |
| tflite::micro::GetTensorData<float>(output)); |
| break; |
| } |
| case kTfLiteInt8: { |
| switch (filter->type) { |
| case kTfLiteInt4: { |
| int8_t* unpacked_filter_data = static_cast<int8_t*>( |
| context->GetScratchBuffer(context, data.filter_buffer_index)); |
| tflite::tensor_utils::UnpackDenseInt4IntoInt8( |
| tflite::micro::GetTensorData<int8_t>(filter), |
| tflite::micro::GetTensorShape(filter).FlatSize(), |
| unpacked_filter_data); |
| reference_integer_ops::DepthwiseConvPerChannel( |
| DepthwiseConvParamsQuantized(params, data), |
| data.per_channel_output_multiplier, data.per_channel_output_shift, |
| tflite::micro::GetTensorShape(input), |
| tflite::micro::GetTensorData<int8_t>(input), |
| tflite::micro::GetTensorShape(filter), unpacked_filter_data, |
| tflite::micro::GetTensorShape(bias), |
| tflite::micro::GetOptionalTensorData<int32_t>(bias), |
| tflite::micro::GetTensorShape(output), |
| tflite::micro::GetTensorData<int8_t>(output)); |
| break; |
| } |
| case kTfLiteInt8: { |
| reference_integer_ops::DepthwiseConvPerChannel( |
| DepthwiseConvParamsQuantized(params, data), |
| data.per_channel_output_multiplier, data.per_channel_output_shift, |
| tflite::micro::GetTensorShape(input), |
| tflite::micro::GetTensorData<int8_t>(input), |
| tflite::micro::GetTensorShape(filter), |
| tflite::micro::GetTensorData<int8_t>(filter), |
| tflite::micro::GetTensorShape(bias), |
| tflite::micro::GetOptionalTensorData<int32_t>(bias), |
| tflite::micro::GetTensorShape(output), |
| tflite::micro::GetTensorData<int8_t>(output)); |
| break; |
| } |
| default: |
| MicroPrintf("Filter type %s (%d) for input type %s not supported.", |
| TfLiteTypeGetName(filter->type), filter->type, |
| TfLiteTypeGetName(input->type)); |
| return kTfLiteError; |
| } |
| break; |
| } |
| case kTfLiteInt16: { |
| switch (filter->type) { |
| case kTfLiteInt8: { |
| reference_integer_ops::DepthwiseConvPerChannel( |
| DepthwiseConvParamsQuantized(params, data), |
| data.per_channel_output_multiplier, 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::GetOptionalTensorData<int64_t>(bias), |
| tflite::micro::GetTensorShape(output), |
| tflite::micro::GetTensorData<int16_t>(output)); |
| break; |
| } |
| default: |
| MicroPrintf("Filter type %s (%d) for input type %s not supported.", |
| TfLiteTypeGetName(filter->type), filter->type, |
| TfLiteTypeGetName(input->type)); |
| return kTfLiteError; |
| } |
| break; |
| } |
| default: |
| MicroPrintf("Input type %s (%d) not supported.", |
| TfLiteTypeGetName(input->type), input->type); |
| return kTfLiteError; |
| } |
| return kTfLiteOk; |
| } |
| |
| } // namespace |
| |
| TFLMRegistration Register_DEPTHWISE_CONV_2D() { |
| return tflite::micro::RegisterOp(DepthwiseConvInit, DepthwiseConvPrepare, |
| DepthwiseConvEval); |
| } |
| |
| } // namespace tflite |