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* Moves common functions to new maximum_minimum.h * Creates cmsis-nn/maximum_minimum.cc Change-Id: Ifbb3fedf53043b2f8d4c48d73c2ca44c7f0f87ca Signed-off-by: Ryan O'Shea <[email protected]>
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tensorflow/lite/micro/kernels/cmsis_nn/maximum_minimum.cc
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,159 @@ | ||
| /* 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. | ||
| ==============================================================================*/ | ||
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|
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| #include "tensorflow/lite/micro/kernels/maximum_minimum.h" | ||
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| #include "Include/arm_nnfunctions.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/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" | ||
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| namespace tflite { | ||
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|
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| namespace { | ||
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| cmsis_nn_dims FillVariableShape(int32_t rank, int32_t* tensor_dims) { | ||
| if (rank == 4) { | ||
| return {tensor_dims[0], tensor_dims[1], tensor_dims[2], tensor_dims[3]}; | ||
| } else if (rank == 3) { | ||
| return {1, tensor_dims[0], tensor_dims[1], tensor_dims[2]}; | ||
| } else if (rank == 2) { | ||
| return {1, 1, tensor_dims[0], tensor_dims[1]}; | ||
| } else { | ||
| return {1, 1, 1, 1}; | ||
| } | ||
| } | ||
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| TfLiteStatus EvalMaximum(TfLiteContext* context, TfLiteNode* node) { | ||
| OpContext op_context(context, node); | ||
| const TfLiteEvalTensor* input1 = | ||
| tflite::micro::GetEvalInput(context, node, kInputTensor1); | ||
| const TfLiteEvalTensor* input2 = | ||
| tflite::micro::GetEvalInput(context, node, kInputTensor2); | ||
| TfLiteEvalTensor* output = | ||
| tflite::micro::GetEvalOutput(context, node, kOutputTensor); | ||
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| RuntimeShape input_1_shape = tflite::micro::GetTensorShape(input1); | ||
| RuntimeShape input_2_shape = tflite::micro::GetTensorShape(input2); | ||
| RuntimeShape output_shape = tflite::micro::GetTensorShape(output); | ||
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| cmsis_nn_dims input_1_dims = FillVariableShape( | ||
| input_1_shape.DimensionsCount(), input_1_shape.DimsData()); | ||
| cmsis_nn_dims input_2_dims = FillVariableShape( | ||
| input_2_shape.DimensionsCount(), input_2_shape.DimsData()); | ||
| cmsis_nn_dims output_dims = FillVariableShape(output_shape.DimensionsCount(), | ||
| output_shape.DimsData()); | ||
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| switch (op_context.output->type) { | ||
| case kTfLiteInt8: | ||
| cmsis_nn_context ctx; | ||
| ctx.buf = nullptr; | ||
| ctx.size = 0; | ||
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| arm_maximum_s8( | ||
| &ctx, tflite::micro::GetTensorData<int8_t>(input1), &input_1_dims, | ||
| tflite::micro::GetTensorData<int8_t>(input2), &input_2_dims, | ||
| tflite::micro::GetTensorData<int8_t>(output), &output_dims); | ||
| break; | ||
| case kTfLiteFloat32: | ||
| TFLiteOperation<float, MaximumOp>(context, node, op_context); | ||
| break; | ||
| case kTfLiteInt16: | ||
| TFLiteOperation<int16_t, MaximumOp>(context, node, op_context); | ||
| break; | ||
| case kTfLiteInt32: | ||
| TFLiteOperation<int32_t, MaximumOp>(context, node, op_context); | ||
| break; | ||
| case kTfLiteInt64: | ||
| TFLiteOperation<int64_t, MaximumOp>(context, node, op_context); | ||
| break; | ||
| default: | ||
| MicroPrintf("Type %s (%d) is not supported by Maximum/Minimum.", | ||
| TfLiteTypeGetName(op_context.output->type), | ||
| op_context.output->type); | ||
| return kTfLiteError; | ||
| } | ||
| return kTfLiteOk; | ||
| } | ||
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| TfLiteStatus EvalMinimum(TfLiteContext* context, TfLiteNode* node) { | ||
| OpContext op_context(context, node); | ||
| const TfLiteEvalTensor* input1 = | ||
| tflite::micro::GetEvalInput(context, node, kInputTensor1); | ||
| const TfLiteEvalTensor* input2 = | ||
| tflite::micro::GetEvalInput(context, node, kInputTensor2); | ||
| TfLiteEvalTensor* output = | ||
| tflite::micro::GetEvalOutput(context, node, kOutputTensor); | ||
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| RuntimeShape input_1_shape = tflite::micro::GetTensorShape(input1); | ||
| RuntimeShape input_2_shape = tflite::micro::GetTensorShape(input2); | ||
| RuntimeShape output_shape = tflite::micro::GetTensorShape(output); | ||
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| cmsis_nn_dims input_1_dims = FillVariableShape( | ||
| input_1_shape.DimensionsCount(), input_1_shape.DimsData()); | ||
| cmsis_nn_dims input_2_dims = FillVariableShape( | ||
| input_2_shape.DimensionsCount(), input_2_shape.DimsData()); | ||
| cmsis_nn_dims output_dims = FillVariableShape(output_shape.DimensionsCount(), | ||
| output_shape.DimsData()); | ||
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| switch (op_context.output->type) { | ||
| case kTfLiteInt8: | ||
| cmsis_nn_context ctx; | ||
| ctx.buf = nullptr; | ||
| ctx.size = 0; | ||
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| arm_minimum_s8( | ||
| &ctx, tflite::micro::GetTensorData<int8_t>(input1), &input_1_dims, | ||
| tflite::micro::GetTensorData<int8_t>(input2), &input_2_dims, | ||
| tflite::micro::GetTensorData<int8_t>(output), &output_dims); | ||
| break; | ||
| case kTfLiteFloat32: | ||
| TFLiteOperation<float, MinimumOp>(context, node, op_context); | ||
| break; | ||
| case kTfLiteInt16: | ||
| TFLiteOperation<int16_t, MinimumOp>(context, node, op_context); | ||
| break; | ||
| case kTfLiteInt32: | ||
| TFLiteOperation<int32_t, MinimumOp>(context, node, op_context); | ||
| break; | ||
| case kTfLiteInt64: | ||
| TFLiteOperation<int64_t, MinimumOp>(context, node, op_context); | ||
| break; | ||
| default: | ||
| MicroPrintf("Type %s (%d) is not supported by Maximum/Minimum.", | ||
| TfLiteTypeGetName(op_context.output->type), | ||
| op_context.output->type); | ||
| return kTfLiteError; | ||
| } | ||
| return kTfLiteOk; | ||
| } | ||
|
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| } // namespace | ||
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| TFLMRegistration Register_MAXIMUM() { | ||
| return tflite::micro::RegisterOp(nullptr, nullptr, EvalMaximum); | ||
| } | ||
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| TFLMRegistration Register_MINIMUM() { | ||
| return tflite::micro::RegisterOp(nullptr, nullptr, EvalMinimum); | ||
| } | ||
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| } // namespace tflite |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,85 @@ | ||
| /* 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. | ||
| ==============================================================================*/ | ||
|
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| #ifndef TENSORFLOW_LITE_MICRO_KERNELS_MAXIMUM_MINIMUM_H_ | ||
| #define TENSORFLOW_LITE_MICRO_KERNELS_MAXIMUM_MINIMUM_H_ | ||
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| #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/maximum_minimum.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" | ||
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| namespace tflite { | ||
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| // This file has a reference implementation of TFMaximum/TFMinimum. | ||
| enum KernelType { | ||
| kReference, | ||
| }; | ||
|
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| constexpr int kInputTensor1 = 0; | ||
| constexpr int kInputTensor2 = 1; | ||
| constexpr int kOutputTensor = 0; | ||
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| struct OpContext { | ||
| OpContext(TfLiteContext* context, TfLiteNode* node) { | ||
| input1 = tflite::micro::GetEvalInput(context, node, kInputTensor1); | ||
| input2 = tflite::micro::GetEvalInput(context, node, kInputTensor2); | ||
| output = tflite::micro::GetEvalOutput(context, node, kOutputTensor); | ||
| } | ||
| const TfLiteEvalTensor* input1; | ||
| const TfLiteEvalTensor* input2; | ||
| TfLiteEvalTensor* output; | ||
| }; | ||
|
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| struct MaximumOp { | ||
| template <typename data_type> | ||
| static data_type op(data_type el1, data_type el2) { | ||
| return el1 > el2 ? el1 : el2; | ||
| } | ||
| }; | ||
|
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| struct MinimumOp { | ||
| template <typename data_type> | ||
| static data_type op(data_type el1, data_type el2) { | ||
| return el1 < el2 ? el1 : el2; | ||
| } | ||
| }; | ||
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| template <typename data_type, typename op_type> | ||
| void TFLiteOperation(TfLiteContext* context, TfLiteNode* node, | ||
| const OpContext& op_context) { | ||
| reference_ops::MaximumMinimumBroadcastSlow( | ||
| tflite::micro::GetTensorShape(op_context.input1), | ||
| tflite::micro::GetTensorData<data_type>(op_context.input1), | ||
| tflite::micro::GetTensorShape(op_context.input2), | ||
| tflite::micro::GetTensorData<data_type>(op_context.input2), | ||
| tflite::micro::GetTensorShape(op_context.output), | ||
| tflite::micro::GetTensorData<data_type>(op_context.output), | ||
| op_type::template op<data_type>); | ||
| } | ||
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| TFLMRegistration Register_MAXIMUM(); | ||
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| TFLMRegistration Register_MINIMUM(); | ||
|
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| } // namespace tflite | ||
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| #endif // TENSORFLOW_LITE_MICRO_KERNELS_MAXIMUM_MINIMUM_H_ |
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