| /* 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/kernels/internal/reference/integer_ops/logistic.h" |
| |
| #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/logistic.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/kernels/logistic.h" |
| #include "tensorflow/lite/micro/kernels/xtensa/xtensa.h" |
| #include "tensorflow/lite/micro/micro_log.h" |
| |
| namespace tflite { |
| namespace { |
| |
| void* LogisticInit(TfLiteContext* context, const char* buffer, size_t length) { |
| TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr); |
| return context->AllocatePersistentBuffer(context, sizeof(OpDataLogistic)); |
| } |
| |
| TfLiteStatus LogisticEval(TfLiteContext* context, TfLiteNode* node) { |
| const TfLiteEvalTensor* input = |
| tflite::micro::GetEvalInput(context, node, kLogisticInputTensor); |
| TfLiteEvalTensor* output = |
| tflite::micro::GetEvalOutput(context, node, kLogisticOutputTensor); |
| |
| TFLITE_DCHECK(node->user_data != nullptr); |
| OpDataLogistic* data = static_cast<OpDataLogistic*>(node->user_data); |
| |
| if (input->type != output->type) { |
| MicroPrintf( |
| "Input and output types must be identical. Input %s, output %s.", |
| TfLiteTypeGetName(input->type), TfLiteTypeGetName(output->type)); |
| return kTfLiteError; |
| } |
| |
| switch (input->type) { |
| case kTfLiteFloat32: { |
| #if HIFI_VFPU && (defined(HIFI4) || defined(HIFI5)) |
| const RuntimeShape& input_shape = tflite::micro::GetTensorShape(input); |
| const RuntimeShape& output_shape = tflite::micro::GetTensorShape(output); |
| const int flat_size = MatchingFlatSize(input_shape, output_shape); |
| |
| const float* inp_data_ptr = tflite::micro::GetTensorData<float>(input); |
| float* out_data_ptr = tflite::micro::GetTensorData<float>(output); |
| |
| TF_LITE_ENSURE_EQ( |
| context, |
| xa_nn_vec_sigmoid_f32_f32(out_data_ptr, inp_data_ptr, flat_size), 0); |
| #else |
| reference_ops::Logistic(tflite::micro::GetTensorShape(input), |
| tflite::micro::GetTensorData<float>(input), |
| tflite::micro::GetTensorShape(output), |
| tflite::micro::GetTensorData<float>(output)); |
| #endif // HIFI_VFPU && (defined(HIFI4) || defined(HIFI5)) |
| break; |
| } |
| case kTfLiteInt8: { |
| #if defined(HIFI4) || defined(HIFI5) |
| const RuntimeShape& input_shape = tflite::micro::GetTensorShape(input); |
| const RuntimeShape& output_shape = tflite::micro::GetTensorShape(output); |
| const int flat_size = MatchingFlatSize(input_shape, output_shape); |
| |
| const int8_t* input_data_ptr = |
| tflite::micro::GetTensorData<int8_t>(input); |
| int8_t* output_data_ptr = tflite::micro::GetTensorData<int8_t>(output); |
| |
| TF_LITE_ENSURE_EQ( |
| context, |
| xa_nn_vec_sigmoid_asym8s_asym8s( |
| output_data_ptr, input_data_ptr, data->input_zero_point, |
| data->input_range_radius, data->input_multiplier, |
| data->input_left_shift, flat_size), |
| 0); |
| #else |
| reference_integer_ops::Logistic( |
| data->input_zero_point, data->input_range_radius, |
| data->input_multiplier, data->input_left_shift, |
| NumElements(input->dims), tflite::micro::GetTensorData<int8_t>(input), |
| tflite::micro::GetTensorData<int8_t>(output)); |
| #endif // defined(HIFI4) || defined(HIFI5) |
| break; |
| } |
| case kTfLiteInt16: { |
| switch (output->type) { |
| case kTfLiteInt16: |
| reference_integer_ops::Logistic( |
| data->input_multiplier, data->input_left_shift, |
| NumElements(input->dims), |
| tflite::micro::GetTensorData<int16_t>(input), |
| tflite::micro::GetTensorData<int16_t>(output)); |
| break; |
| default: |
| MicroPrintf("Input %s, output %s not supported.", |
| TfLiteTypeGetName(input->type), |
| TfLiteTypeGetName(output->type)); |
| return kTfLiteError; |
| } |
| break; |
| } |
| default: { |
| MicroPrintf("Input %s, output %s not supported.", |
| TfLiteTypeGetName(input->type), |
| TfLiteTypeGetName(output->type)); |
| return kTfLiteError; |
| } |
| } |
| return kTfLiteOk; |
| } |
| |
| } // namespace |
| |
| TFLMRegistration Register_LOGISTIC() { |
| return tflite::micro::RegisterOp(LogisticInit, LogisticPrepare, LogisticEval); |
| } |
| } // namespace tflite |