nn/common/operations/StridedSlice.cpp - platform/frameworks/ml - Git at Google

 /*
  * Copyright (C) 2018 The Android Open Source Project
  *
  * 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.
  */

 // Contains the implementation of the operations.

 #define LOG_TAG "Operations"

 #include <tensorflow/lite/kernels/internal/reference/legacy_reference_ops.h>

 #include <vector>

 #include "CpuOperationUtils.h"
 #include "OperationResolver.h"
 #include "Operations.h"
 #include "Tracing.h"

 namespace android {
 namespace nn {
 namespace strided_slice {

 constexpr uint32_t kNumInputs = 7;
 constexpr uint32_t kInputTensor = 0;
 constexpr uint32_t kBeginTensor = 1;
 constexpr uint32_t kEndTensor = 2;
 constexpr uint32_t kStridesTensor = 3;
 constexpr uint32_t kBeginMask = 4;
 constexpr uint32_t kEndMask = 5;
 constexpr uint32_t kShrinkAxisMask = 6;

 constexpr uint32_t kNumOutputs = 1;
 constexpr uint32_t kOutputTensor = 0;

 namespace {

 template <typename T>
 bool compute(const T* inputData, const Shape& inputShape, const int32_t* beginData,
              const int32_t* endData, const int32_t* stridesData, int32_t beginMask, int32_t endMask,
              int32_t shrinkAxisMask, T* outputData, const Shape& outputShape) {
     NNTRACE_TRANS("stridedSlice");
     // 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;

     std::vector<int> starts;
     std::vector<int> stops;
     std::vector<int> strides;

     int32_t numInputDims = static_cast<int32_t>(getNumberOfDimensions(inputShape));
     for (int32_t idx = numInputDims - 1; idx >= 0; --idx) {
         starts.emplace_back(beginData[idx]);
         stops.emplace_back(endData[idx]);
         strides.emplace_back(stridesData[idx]);
     }

     for (int i = numInputDims; i < kMaxDim; i++) {
         starts.emplace_back(0);
         stops.emplace_back(1);
         strides.emplace_back(1);
     }

     beginMask = ReverseMaskBits(beginMask, numInputDims);
     endMask = ReverseMaskBits(endMask, numInputDims);
     shrinkAxisMask = ReverseMaskBits(shrinkAxisMask, numInputDims);

     tflite::reference_ops::StridedSlice(inputData, convertShapeToDims(inputShape), beginMask,
                                         endMask, shrinkAxisMask, starts, stops, strides, outputData,
                                         convertShapeToDims(outputShape));

     return true;
 }

 template <typename T>
 bool executeTyped(IOperationExecutionContext* context) {
     return compute<T>(
             context->getInputBuffer<T>(kInputTensor), context->getInputShape(kInputTensor),
             context->getInputBuffer<int32_t>(kBeginTensor),
             context->getInputBuffer<int32_t>(kEndTensor),
             context->getInputBuffer<int32_t>(kStridesTensor),
             context->getInputValue<int32_t>(kBeginMask), context->getInputValue<int32_t>(kEndMask),
             context->getInputValue<int32_t>(kShrinkAxisMask),
             context->getOutputBuffer<T>(kOutputTensor), context->getOutputShape(kOutputTensor));
 }

 }  // namespace

 Result<Version> validate(const IOperationValidationContext* context) {
     NN_RET_CHECK_EQ(context->getNumInputs(), kNumInputs);
     NN_RET_CHECK_EQ(context->getNumOutputs(), kNumOutputs);
     OperandType inputType = context->getInputType(kInputTensor);
     NN_RET_CHECK(inputType == OperandType::TENSOR_FLOAT16 ||
                  inputType == OperandType::TENSOR_FLOAT32 ||
                  inputType == OperandType::TENSOR_QUANT8_ASYMM ||
                  inputType == OperandType::TENSOR_QUANT8_ASYMM_SIGNED)
             << "Unsupported input operand type for STRIDED_SLICE op: " << inputType;

     Version minSupportedVersion;
     if (inputType == OperandType::TENSOR_QUANT8_ASYMM_SIGNED) {
         minSupportedVersion = Version::ANDROID_R;
     } else if (inputType == OperandType::TENSOR_FLOAT16) {
         minSupportedVersion = Version::ANDROID_Q;
     } else {
         minSupportedVersion = Version::ANDROID_P;
     }

     NN_RET_CHECK(validateInputTypes(context, {
                                                      inputType,
                                                      OperandType::TENSOR_INT32,
                                                      OperandType::TENSOR_INT32,
                                                      OperandType::TENSOR_INT32,
                                                      OperandType::INT32,
                                                      OperandType::INT32,
                                                      OperandType::INT32,
                                              }));
     NN_RET_CHECK(validateOutputTypes(context, {inputType}));
     const Shape& input = context->getInputShape(kInputTensor);
     if (hasKnownRank(input)) {
         NN_RET_CHECK_LE(getNumberOfDimensions(input), 4);
     }
     return minSupportedVersion;
 }

 bool prepare(IOperationExecutionContext* context) {
     // StridedSlice op only supports 1D-4D input arrays.
     const Shape& inputShape = context->getInputShape(kInputTensor);
     uint32_t numInputDims = getNumberOfDimensions(inputShape);
     NN_OPS_CHECK(numInputDims <= 4);

     const Shape& beginShape = context->getInputShape(kBeginTensor);
     const Shape& endShape = context->getInputShape(kEndTensor);
     const Shape& stridesShape = context->getInputShape(kStridesTensor);

     NN_OPS_CHECK(getNumberOfDimensions(beginShape) == 1);
     NN_OPS_CHECK(getNumberOfDimensions(endShape) == 1);
     NN_OPS_CHECK(getNumberOfDimensions(stridesShape) == 1);

     NN_OPS_CHECK(getSizeOfDimension(beginShape, 0) == numInputDims);
     NN_OPS_CHECK(getSizeOfDimension(endShape, 0) == numInputDims);
     NN_OPS_CHECK(getSizeOfDimension(stridesShape, 0) == numInputDims);

     NN_OPS_CHECK(beginShape.type == OperandType::TENSOR_INT32);
     NN_OPS_CHECK(endShape.type == OperandType::TENSOR_INT32);
     NN_OPS_CHECK(stridesShape.type == OperandType::TENSOR_INT32);

     const int32_t* beginData = context->getInputBuffer<int32_t>(kBeginTensor);
     const int32_t* endData = context->getInputBuffer<int32_t>(kEndTensor);
     const int32_t* stridesData = context->getInputBuffer<int32_t>(kStridesTensor);

     const int32_t beginMask = context->getInputValue<int32_t>(kBeginMask);
     const int32_t endMask = context->getInputValue<int32_t>(kEndMask);
     const int32_t shrinkAxisMask = context->getInputValue<int32_t>(kShrinkAxisMask);

     // Determine size of output tensor and map indices
     std::vector<uint32_t> outDims;
     for (int32_t idx = 0; idx < static_cast<int32_t>(numInputDims); idx++) {
         int32_t dim = static_cast<int32_t>(getSizeOfDimension(inputShape, idx));
         int32_t stride = stridesData[idx];
         // stride value has to be non-zero
         NN_OPS_CHECK(stride != 0);
         bool positiveStride = stride > 0;

         int32_t begin = beginMask & (1 << idx) ? positiveStride ? 0 : dim - 1
                                                : ClampedIndex(beginData[idx], dim, positiveStride);
         int32_t end = endMask & (1 << idx) ? positiveStride ? dim : -1
                                            : ClampedIndex(endData[idx], dim, positiveStride);

         // This is valid for both positive and negative strides
         int32_t outDim = ceil((end - begin) / static_cast<float>(stride));
         outDim = outDim < 0 ? 0 : static_cast<uint32_t>(outDim);
         if (!(shrinkAxisMask & (1 << idx))) {
             outDims.push_back(outDim);
         } else {
             // Only positive stride is allowed on non-range indexing (i.e. shrinkMask is set).
             NN_RET_CHECK_GT(stride, 0) << "index = " << idx;
             NN_RET_CHECK_EQ(outDim, 1) << "index = " << idx;
         }
     }

     // Handle the case when all dimensions are removed
     if (outDims.empty()) {
         outDims.push_back(1);
     }

     Shape outputShape = context->getOutputShape(kOutputTensor);
     NN_RET_CHECK(SetShape(inputShape, &outputShape));
     outputShape.dimensions = outDims;
     return context->setOutputShape(kOutputTensor, outputShape);
 }

 bool execute(IOperationExecutionContext* context) {
     switch (context->getInputType(kInputTensor)) {
         case OperandType::TENSOR_FLOAT16:
             return executeTyped<_Float16>(context);
         case OperandType::TENSOR_FLOAT32:
             return executeTyped<float>(context);
         case OperandType::TENSOR_QUANT8_ASYMM:
             return executeTyped<uint8_t>(context);
         case OperandType::TENSOR_QUANT8_ASYMM_SIGNED:
             return executeTyped<int8_t>(context);
         default:
             NN_RET_CHECK_FAIL() << "Unsupported tensor type for STRIDED_SLICE op.";
     }
 }
 }  // namespace strided_slice

 NN_REGISTER_OPERATION(STRIDED_SLICE, "STRIDED_SLICE", strided_slice::validate,
                       strided_slice::prepare, strided_slice::execute);

 }  // namespace nn
 }  // namespace android
	/*
	* Copyright (C) 2018 The Android Open Source Project
	*
	* 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.
	*/

	// Contains the implementation of the operations.

	#define LOG_TAG "Operations"

	#include <tensorflow/lite/kernels/internal/reference/legacy_reference_ops.h>

	#include <vector>

	#include "CpuOperationUtils.h"
	#include "OperationResolver.h"
	#include "Operations.h"
	#include "Tracing.h"

	namespace android {
	namespace nn {
	namespace strided_slice {

	constexpr uint32_t kNumInputs = 7;
	constexpr uint32_t kInputTensor = 0;
	constexpr uint32_t kBeginTensor = 1;
	constexpr uint32_t kEndTensor = 2;
	constexpr uint32_t kStridesTensor = 3;
	constexpr uint32_t kBeginMask = 4;
	constexpr uint32_t kEndMask = 5;
	constexpr uint32_t kShrinkAxisMask = 6;

	constexpr uint32_t kNumOutputs = 1;
	constexpr uint32_t kOutputTensor = 0;

	namespace {

	template <typename T>
	bool compute(const T* inputData, const Shape& inputShape, const int32_t* beginData,
	const int32_t* endData, const int32_t* stridesData, int32_t beginMask, int32_t endMask,
	int32_t shrinkAxisMask, T* outputData, const Shape& outputShape) {
	NNTRACE_TRANS("stridedSlice");
	// 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;

	std::vector<int> starts;
	std::vector<int> stops;
	std::vector<int> strides;

	int32_t numInputDims = static_cast<int32_t>(getNumberOfDimensions(inputShape));
	for (int32_t idx = numInputDims - 1; idx >= 0; --idx) {
	starts.emplace_back(beginData[idx]);
	stops.emplace_back(endData[idx]);
	strides.emplace_back(stridesData[idx]);
	}

	for (int i = numInputDims; i < kMaxDim; i++) {
	starts.emplace_back(0);
	stops.emplace_back(1);
	strides.emplace_back(1);
	}

	beginMask = ReverseMaskBits(beginMask, numInputDims);
	endMask = ReverseMaskBits(endMask, numInputDims);
	shrinkAxisMask = ReverseMaskBits(shrinkAxisMask, numInputDims);

	tflite::reference_ops::StridedSlice(inputData, convertShapeToDims(inputShape), beginMask,
	endMask, shrinkAxisMask, starts, stops, strides, outputData,
	convertShapeToDims(outputShape));

	return true;
	}

	template <typename T>
	bool executeTyped(IOperationExecutionContext* context) {
	return compute<T>(
	context->getInputBuffer<T>(kInputTensor), context->getInputShape(kInputTensor),
	context->getInputBuffer<int32_t>(kBeginTensor),
	context->getInputBuffer<int32_t>(kEndTensor),
	context->getInputBuffer<int32_t>(kStridesTensor),
	context->getInputValue<int32_t>(kBeginMask), context->getInputValue<int32_t>(kEndMask),
	context->getInputValue<int32_t>(kShrinkAxisMask),
	context->getOutputBuffer<T>(kOutputTensor), context->getOutputShape(kOutputTensor));
	}

	} // namespace

	Result<Version> validate(const IOperationValidationContext* context) {
	NN_RET_CHECK_EQ(context->getNumInputs(), kNumInputs);
	NN_RET_CHECK_EQ(context->getNumOutputs(), kNumOutputs);
	OperandType inputType = context->getInputType(kInputTensor);
	NN_RET_CHECK(inputType == OperandType::TENSOR_FLOAT16 \|\|
	inputType == OperandType::TENSOR_FLOAT32 \|\|
	inputType == OperandType::TENSOR_QUANT8_ASYMM \|\|
	inputType == OperandType::TENSOR_QUANT8_ASYMM_SIGNED)
	<< "Unsupported input operand type for STRIDED_SLICE op: " << inputType;

	Version minSupportedVersion;
	if (inputType == OperandType::TENSOR_QUANT8_ASYMM_SIGNED) {
	minSupportedVersion = Version::ANDROID_R;
	} else if (inputType == OperandType::TENSOR_FLOAT16) {
	minSupportedVersion = Version::ANDROID_Q;
	} else {
	minSupportedVersion = Version::ANDROID_P;
	}

	NN_RET_CHECK(validateInputTypes(context, {
	inputType,
	OperandType::TENSOR_INT32,
	OperandType::TENSOR_INT32,
	OperandType::TENSOR_INT32,
	OperandType::INT32,
	OperandType::INT32,
	OperandType::INT32,
	}));
	NN_RET_CHECK(validateOutputTypes(context, {inputType}));
	const Shape& input = context->getInputShape(kInputTensor);
	if (hasKnownRank(input)) {
	NN_RET_CHECK_LE(getNumberOfDimensions(input), 4);
	}
	return minSupportedVersion;
	}

	bool prepare(IOperationExecutionContext* context) {
	// StridedSlice op only supports 1D-4D input arrays.
	const Shape& inputShape = context->getInputShape(kInputTensor);
	uint32_t numInputDims = getNumberOfDimensions(inputShape);
	NN_OPS_CHECK(numInputDims <= 4);

	const Shape& beginShape = context->getInputShape(kBeginTensor);
	const Shape& endShape = context->getInputShape(kEndTensor);
	const Shape& stridesShape = context->getInputShape(kStridesTensor);

	NN_OPS_CHECK(getNumberOfDimensions(beginShape) == 1);
	NN_OPS_CHECK(getNumberOfDimensions(endShape) == 1);
	NN_OPS_CHECK(getNumberOfDimensions(stridesShape) == 1);

	NN_OPS_CHECK(getSizeOfDimension(beginShape, 0) == numInputDims);
	NN_OPS_CHECK(getSizeOfDimension(endShape, 0) == numInputDims);
	NN_OPS_CHECK(getSizeOfDimension(stridesShape, 0) == numInputDims);

	NN_OPS_CHECK(beginShape.type == OperandType::TENSOR_INT32);
	NN_OPS_CHECK(endShape.type == OperandType::TENSOR_INT32);
	NN_OPS_CHECK(stridesShape.type == OperandType::TENSOR_INT32);

	const int32_t* beginData = context->getInputBuffer<int32_t>(kBeginTensor);
	const int32_t* endData = context->getInputBuffer<int32_t>(kEndTensor);
	const int32_t* stridesData = context->getInputBuffer<int32_t>(kStridesTensor);

	const int32_t beginMask = context->getInputValue<int32_t>(kBeginMask);
	const int32_t endMask = context->getInputValue<int32_t>(kEndMask);
	const int32_t shrinkAxisMask = context->getInputValue<int32_t>(kShrinkAxisMask);

	// Determine size of output tensor and map indices
	std::vector<uint32_t> outDims;
	for (int32_t idx = 0; idx < static_cast<int32_t>(numInputDims); idx++) {
	int32_t dim = static_cast<int32_t>(getSizeOfDimension(inputShape, idx));
	int32_t stride = stridesData[idx];
	// stride value has to be non-zero
	NN_OPS_CHECK(stride != 0);
	bool positiveStride = stride > 0;

	int32_t begin = beginMask & (1 << idx) ? positiveStride ? 0 : dim - 1
	: ClampedIndex(beginData[idx], dim, positiveStride);
	int32_t end = endMask & (1 << idx) ? positiveStride ? dim : -1
	: ClampedIndex(endData[idx], dim, positiveStride);

	// This is valid for both positive and negative strides
	int32_t outDim = ceil((end - begin) / static_cast<float>(stride));
	outDim = outDim < 0 ? 0 : static_cast<uint32_t>(outDim);
	if (!(shrinkAxisMask & (1 << idx))) {
	outDims.push_back(outDim);
	} else {
	// Only positive stride is allowed on non-range indexing (i.e. shrinkMask is set).
	NN_RET_CHECK_GT(stride, 0) << "index = " << idx;
	NN_RET_CHECK_EQ(outDim, 1) << "index = " << idx;
	}
	}

	// Handle the case when all dimensions are removed
	if (outDims.empty()) {
	outDims.push_back(1);
	}

	Shape outputShape = context->getOutputShape(kOutputTensor);
	NN_RET_CHECK(SetShape(inputShape, &outputShape));
	outputShape.dimensions = outDims;
	return context->setOutputShape(kOutputTensor, outputShape);
	}

	bool execute(IOperationExecutionContext* context) {
	switch (context->getInputType(kInputTensor)) {
	case OperandType::TENSOR_FLOAT16:
	return executeTyped<_Float16>(context);
	case OperandType::TENSOR_FLOAT32:
	return executeTyped<float>(context);
	case OperandType::TENSOR_QUANT8_ASYMM:
	return executeTyped<uint8_t>(context);
	case OperandType::TENSOR_QUANT8_ASYMM_SIGNED:
	return executeTyped<int8_t>(context);
	default:
	NN_RET_CHECK_FAIL() << "Unsupported tensor type for STRIDED_SLICE op.";
	}
	}
	} // namespace strided_slice

	NN_REGISTER_OPERATION(STRIDED_SLICE, "STRIDED_SLICE", strided_slice::validate,
	strided_slice::prepare, strided_slice::execute);

	} // namespace nn
	} // namespace android