tensorflow/lite/micro/kernels/strided_slice.cc - 3p/tensorflow/tflite-micro - Git at Google

 /* 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.
 ==============================================================================*/
 #include "tensorflow/lite/kernels/internal/reference/strided_slice.h"

 #include <cmath>
 #include <cstring>

 #include "tensorflow/lite/c/builtin_op_data.h"
 #include "tensorflow/lite/c/common.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"

 namespace tflite {

 namespace {

 constexpr int kInputTensor = 0;
 constexpr int kBeginTensor = 1;
 constexpr int kEndTensor = 2;
 constexpr int kStridesTensor = 3;
 constexpr int kOutputTensor = 0;

 struct StridedSliceContext {
   StridedSliceContext(TfLiteContext* context, TfLiteNode* node) {
     params = reinterpret_cast<TfLiteStridedSliceParams*>(node->builtin_data);
     micro_context = GetMicroContext(context);
     input = micro_context->AllocateTempInputTensor(node, kInputTensor);
     begin = micro_context->AllocateTempInputTensor(node, kBeginTensor);
     end = micro_context->AllocateTempInputTensor(node, kEndTensor);
     strides = micro_context->AllocateTempInputTensor(node, kStridesTensor);
     output = micro_context->AllocateTempOutputTensor(node, kOutputTensor);
     dims = NumDimensions(input);
   }
   ~StridedSliceContext() {
     micro_context->DeallocateTempTfLiteTensor(input);
     micro_context->DeallocateTempTfLiteTensor(begin);
     micro_context->DeallocateTempTfLiteTensor(end);
     micro_context->DeallocateTempTfLiteTensor(strides);
     micro_context->DeallocateTempTfLiteTensor(output);
   }
   const TfLiteStridedSliceParams* params;
   MicroContext* micro_context;
   TfLiteTensor* input;
   TfLiteTensor* begin;
   TfLiteTensor* end;
   TfLiteTensor* strides;
   TfLiteTensor* output;
   int dims;
 };

 // This Op only supports 1-4D cases and since we use the reference 4D
 // implementation, the 1-3D tensors are mapped to 4D.
 const int kMaxDim = 4;

 tflite::StridedSliceParams BuildStridedSliceParams(
     StridedSliceContext* op_context) {
   tflite::StridedSliceParams op_params{};
   op_params.start_indices_count = op_context->dims;
   op_params.stop_indices_count = op_context->dims;
   op_params.strides_count = op_context->dims;

   for (int i = 0; i < op_context->dims; ++i) {
     op_params.start_indices[i] = GetTensorData<int32_t>(op_context->begin)[i];
     op_params.stop_indices[i] = GetTensorData<int32_t>(op_context->end)[i];
     op_params.strides[i] = GetTensorData<int32_t>(op_context->strides)[i];
   }

   op_params.begin_mask = op_context->params->begin_mask;
   op_params.ellipsis_mask = 0;
   op_params.end_mask = op_context->params->end_mask;
   op_params.new_axis_mask = 0;
   op_params.shrink_axis_mask = op_context->params->shrink_axis_mask;
   return op_params;
 }

 // Processes the indexing tensors (begin, end and strides) to resize the
 // output tensor. This function is callable from both Prepare() and Eval() as
 // long as the caller ensures the indexing tensors are present.
 TfLiteStatus CheckOutputSize(TfLiteContext* context,
                              StridedSliceContext* op_context) {
   using ::tflite::strided_slice::StartForAxis;
   using ::tflite::strided_slice::StopForAxis;
   TfLiteIntArray* output_shape = op_context->output->dims;
   int shape_size = 0;
   auto op_params = BuildStridedSliceParams(op_context);
   auto input_shape = GetTensorShape(op_context->input);
   for (int idx = 0; idx < op_context->dims; ++idx) {
     int32_t stride = GetTensorData<int32_t>(op_context->strides)[idx];
     TF_LITE_ENSURE_MSG(context, stride != 0, "stride value has to be non-zero");
     int32_t begin = StartForAxis(op_params, input_shape, idx);
     int32_t end = StopForAxis(op_params, input_shape, idx, begin);

     // When shrinking an axis, the end position does not matter (and can be
     // incorrect when negative indexing is used, see Issue #19260). Always use
     // begin + 1 to generate a length 1 slice, since begin has
     // already been adjusted for negative indices by StartForAxis.
     const bool shrink_axis = op_context->params->shrink_axis_mask & (1 << idx);
     if (shrink_axis) {
       end = begin + 1;
     }

     // This is valid for both positive and negative strides
     int32_t dim_shape = std::ceil((end - begin) / static_cast<float>(stride));
     dim_shape = dim_shape < 0 ? 0 : dim_shape;
     if (!shrink_axis) {
       TF_LITE_ENSURE_EQ(context, output_shape->data[shape_size], dim_shape);
       shape_size++;
     }
   }
   TF_LITE_ENSURE_EQ(context, output_shape->size, shape_size);
   return kTfLiteOk;
 }

 void* Init(TfLiteContext* context, const char* buffer, size_t length) {
   TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr);
   return context->AllocatePersistentBuffer(context, sizeof(StridedSliceParams));
 }

 TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
   TFLITE_DCHECK(node->user_data != nullptr);
   StridedSliceParams* op_params =
       static_cast<StridedSliceParams*>(node->user_data);
   TF_LITE_ENSURE_EQ(context, NumInputs(node), 4);
   TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
   StridedSliceContext op_context(context, node);
   TF_LITE_ENSURE_MSG(context, op_context.dims <= kMaxDim,
                      "input dim should not exceed 4");
   auto params = BuildStridedSliceParams(&op_context);
   memcpy(op_params, &params, sizeof(StridedSliceParams));
   return CheckOutputSize(context, &op_context);
 }

 TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
   TFLITE_DCHECK(node->user_data != nullptr);
   const StridedSliceParams& op_params =
       *(static_cast<const StridedSliceParams*>(node->user_data));

   const TfLiteEvalTensor* input =
       tflite::micro::GetEvalInput(context, node, kInputTensor);
   TfLiteEvalTensor* output =
       tflite::micro::GetEvalOutput(context, node, kOutputTensor);
   switch (output->type) {
     case kTfLiteFloat32:
       reference_ops::StridedSlice(op_params,
                                   tflite::micro::GetTensorShape(input),
                                   tflite::micro::GetTensorData<float>(input),
                                   tflite::micro::GetTensorShape(output),
                                   tflite::micro::GetTensorData<float>(output));
       break;
     case kTfLiteInt8:
       reference_ops::StridedSlice(op_params,
                                   tflite::micro::GetTensorShape(input),
                                   tflite::micro::GetTensorData<int8_t>(input),
                                   tflite::micro::GetTensorShape(output),
                                   tflite::micro::GetTensorData<int8_t>(output));
       break;
     case kTfLiteInt16:
       reference_ops::StridedSlice(
           op_params, tflite::micro::GetTensorShape(input),
           tflite::micro::GetTensorData<int16_t>(input),
           tflite::micro::GetTensorShape(output),
           tflite::micro::GetTensorData<int16_t>(output));
       break;
     case kTfLiteInt32:
       reference_ops::StridedSlice(
           op_params, tflite::micro::GetTensorShape(input),
           tflite::micro::GetTensorData<int32_t>(input),
           tflite::micro::GetTensorShape(output),
           tflite::micro::GetTensorData<int32_t>(output));
       break;
     case kTfLiteBool:
       reference_ops::StridedSlice(op_params,
                                   tflite::micro::GetTensorShape(input),
                                   tflite::micro::GetTensorData<bool>(input),
                                   tflite::micro::GetTensorShape(output),
                                   tflite::micro::GetTensorData<bool>(output));
       break;
     default:
       MicroPrintf("Type %s (%d) not supported.", TfLiteTypeGetName(input->type),
                   input->type);
       return kTfLiteError;
   }
   return kTfLiteOk;
 }

 }  // namespace

 TFLMRegistration Register_STRIDED_SLICE() {
   return tflite::micro::RegisterOp(Init, Prepare, Eval);
 }

 }  // namespace tflite
	/* 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.
	==============================================================================*/
	#include "tensorflow/lite/kernels/internal/reference/strided_slice.h"

	#include <cmath>
	#include <cstring>

	#include "tensorflow/lite/c/builtin_op_data.h"
	#include "tensorflow/lite/c/common.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"

	namespace tflite {

	namespace {

	constexpr int kInputTensor = 0;
	constexpr int kBeginTensor = 1;
	constexpr int kEndTensor = 2;
	constexpr int kStridesTensor = 3;
	constexpr int kOutputTensor = 0;

	struct StridedSliceContext {
	StridedSliceContext(TfLiteContext* context, TfLiteNode* node) {
	params = reinterpret_cast<TfLiteStridedSliceParams*>(node->builtin_data);
	micro_context = GetMicroContext(context);
	input = micro_context->AllocateTempInputTensor(node, kInputTensor);
	begin = micro_context->AllocateTempInputTensor(node, kBeginTensor);
	end = micro_context->AllocateTempInputTensor(node, kEndTensor);
	strides = micro_context->AllocateTempInputTensor(node, kStridesTensor);
	output = micro_context->AllocateTempOutputTensor(node, kOutputTensor);
	dims = NumDimensions(input);
	}
	~StridedSliceContext() {
	micro_context->DeallocateTempTfLiteTensor(input);
	micro_context->DeallocateTempTfLiteTensor(begin);
	micro_context->DeallocateTempTfLiteTensor(end);
	micro_context->DeallocateTempTfLiteTensor(strides);
	micro_context->DeallocateTempTfLiteTensor(output);
	}
	const TfLiteStridedSliceParams* params;
	MicroContext* micro_context;
	TfLiteTensor* input;
	TfLiteTensor* begin;
	TfLiteTensor* end;
	TfLiteTensor* strides;
	TfLiteTensor* output;
	int dims;
	};

	// This Op only supports 1-4D cases and since we use the reference 4D
	// implementation, the 1-3D tensors are mapped to 4D.
	const int kMaxDim = 4;

	tflite::StridedSliceParams BuildStridedSliceParams(
	StridedSliceContext* op_context) {
	tflite::StridedSliceParams op_params{};
	op_params.start_indices_count = op_context->dims;
	op_params.stop_indices_count = op_context->dims;
	op_params.strides_count = op_context->dims;

	for (int i = 0; i < op_context->dims; ++i) {
	op_params.start_indices[i] = GetTensorData<int32_t>(op_context->begin)[i];
	op_params.stop_indices[i] = GetTensorData<int32_t>(op_context->end)[i];
	op_params.strides[i] = GetTensorData<int32_t>(op_context->strides)[i];
	}

	op_params.begin_mask = op_context->params->begin_mask;
	op_params.ellipsis_mask = 0;
	op_params.end_mask = op_context->params->end_mask;
	op_params.new_axis_mask = 0;
	op_params.shrink_axis_mask = op_context->params->shrink_axis_mask;
	return op_params;
	}

	// Processes the indexing tensors (begin, end and strides) to resize the
	// output tensor. This function is callable from both Prepare() and Eval() as
	// long as the caller ensures the indexing tensors are present.
	TfLiteStatus CheckOutputSize(TfLiteContext* context,
	StridedSliceContext* op_context) {
	using ::tflite::strided_slice::StartForAxis;
	using ::tflite::strided_slice::StopForAxis;
	TfLiteIntArray* output_shape = op_context->output->dims;
	int shape_size = 0;
	auto op_params = BuildStridedSliceParams(op_context);
	auto input_shape = GetTensorShape(op_context->input);
	for (int idx = 0; idx < op_context->dims; ++idx) {
	int32_t stride = GetTensorData<int32_t>(op_context->strides)[idx];
	TF_LITE_ENSURE_MSG(context, stride != 0, "stride value has to be non-zero");
	int32_t begin = StartForAxis(op_params, input_shape, idx);
	int32_t end = StopForAxis(op_params, input_shape, idx, begin);

	// When shrinking an axis, the end position does not matter (and can be
	// incorrect when negative indexing is used, see Issue #19260). Always use
	// begin + 1 to generate a length 1 slice, since begin has
	// already been adjusted for negative indices by StartForAxis.
	const bool shrink_axis = op_context->params->shrink_axis_mask & (1 << idx);
	if (shrink_axis) {
	end = begin + 1;
	}

	// This is valid for both positive and negative strides
	int32_t dim_shape = std::ceil((end - begin) / static_cast<float>(stride));
	dim_shape = dim_shape < 0 ? 0 : dim_shape;
	if (!shrink_axis) {
	TF_LITE_ENSURE_EQ(context, output_shape->data[shape_size], dim_shape);
	shape_size++;
	}
	}
	TF_LITE_ENSURE_EQ(context, output_shape->size, shape_size);
	return kTfLiteOk;
	}

	void* Init(TfLiteContext* context, const char* buffer, size_t length) {
	TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr);
	return context->AllocatePersistentBuffer(context, sizeof(StridedSliceParams));
	}

	TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
	TFLITE_DCHECK(node->user_data != nullptr);
	StridedSliceParams* op_params =
	static_cast<StridedSliceParams*>(node->user_data);
	TF_LITE_ENSURE_EQ(context, NumInputs(node), 4);
	TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
	StridedSliceContext op_context(context, node);
	TF_LITE_ENSURE_MSG(context, op_context.dims <= kMaxDim,
	"input dim should not exceed 4");
	auto params = BuildStridedSliceParams(&op_context);
	memcpy(op_params, &params, sizeof(StridedSliceParams));
	return CheckOutputSize(context, &op_context);
	}

	TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
	TFLITE_DCHECK(node->user_data != nullptr);
	const StridedSliceParams& op_params =
	(static_cast<const StridedSliceParams>(node->user_data));

	const TfLiteEvalTensor* input =
	tflite::micro::GetEvalInput(context, node, kInputTensor);
	TfLiteEvalTensor* output =
	tflite::micro::GetEvalOutput(context, node, kOutputTensor);
	switch (output->type) {
	case kTfLiteFloat32:
	reference_ops::StridedSlice(op_params,
	tflite::micro::GetTensorShape(input),
	tflite::micro::GetTensorData<float>(input),
	tflite::micro::GetTensorShape(output),
	tflite::micro::GetTensorData<float>(output));
	break;
	case kTfLiteInt8:
	reference_ops::StridedSlice(op_params,
	tflite::micro::GetTensorShape(input),
	tflite::micro::GetTensorData<int8_t>(input),
	tflite::micro::GetTensorShape(output),
	tflite::micro::GetTensorData<int8_t>(output));
	break;
	case kTfLiteInt16:
	reference_ops::StridedSlice(
	op_params, tflite::micro::GetTensorShape(input),
	tflite::micro::GetTensorData<int16_t>(input),
	tflite::micro::GetTensorShape(output),
	tflite::micro::GetTensorData<int16_t>(output));
	break;
	case kTfLiteInt32:
	reference_ops::StridedSlice(
	op_params, tflite::micro::GetTensorShape(input),
	tflite::micro::GetTensorData<int32_t>(input),
	tflite::micro::GetTensorShape(output),
	tflite::micro::GetTensorData<int32_t>(output));
	break;
	case kTfLiteBool:
	reference_ops::StridedSlice(op_params,
	tflite::micro::GetTensorShape(input),
	tflite::micro::GetTensorData<bool>(input),
	tflite::micro::GetTensorShape(output),
	tflite::micro::GetTensorData<bool>(output));
	break;
	default:
	MicroPrintf("Type %s (%d) not supported.", TfLiteTypeGetName(input->type),
	input->type);
	return kTfLiteError;
	}
	return kTfLiteOk;
	}

	} // namespace

	TFLMRegistration Register_STRIDED_SLICE() {
	return tflite::micro::RegisterOp(Init, Prepare, Eval);
	}

	} // namespace tflite