+++ /dev/null
-/*
- * Copyright (c) 2018 Samsung Electronics Co., Ltd. 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/contrib/lite/context.h"
-#include "tensorflow/contrib/lite/kernels/kernel_util.h"
-#include "tensorflow/contrib/lite/kernels/internal/tensor.h"
-
-#include <iostream>
-
-//
-// Cast
-//
-namespace
-{
-
-struct CASTOp
-{
-};
-
-void *InitCAST(TfLiteContext *context, const char *buffer, size_t length)
-{
- std::cerr << __FUNCTION__ << "(length: " << length << ")" << std::endl;
- // TODO Implement this!
- return new CASTOp;
-}
-
-void FreeCAST(TfLiteContext *context, void *buffer)
-{
- // TODO Implement this!
- delete static_cast<CASTOp *>(buffer);
-}
-
-TfLiteStatus PrepareCAST(TfLiteContext *context, TfLiteNode *node)
-{
- std::cerr << __FUNCTION__ << "(...)" << std::endl;
- // TODO Implement this!
- return kTfLiteOk;
-}
-
-TfLiteStatus EvalCAST(TfLiteContext *context, TfLiteNode *node)
-{
- std::cerr << __FUNCTION__ << "(...)" << std::endl;
- // TODO Implement this!
- return kTfLiteError;
-}
-}
-
-//
-// Stack
-//
-namespace
-{
-
-struct StackOp
-{
-};
-
-void *InitStack(TfLiteContext *context, const char *buffer, size_t length)
-{
- std::cerr << __FUNCTION__ << "(length: " << length << ")" << std::endl;
- // TODO Implement this!
- return new StackOp;
-}
-
-void FreeStack(TfLiteContext *context, void *buffer)
-{
- // TODO Implement this!
- delete static_cast<StackOp *>(buffer);
-}
-
-TfLiteStatus PrepareStack(TfLiteContext *context, TfLiteNode *node)
-{
- std::cerr << __FUNCTION__ << "(...)" << std::endl;
- // TODO Implement this!
- return kTfLiteOk;
-}
-
-TfLiteStatus EvalStack(TfLiteContext *context, TfLiteNode *node)
-{
- std::cerr << __FUNCTION__ << "(...)" << std::endl;
- // TODO Implement this!
- return kTfLiteError;
-}
-}
-
-//
-// ArgMax
-//
-namespace
-{
-
-struct ArgMaxOp
-{
-};
-
-void *InitArgMax(TfLiteContext *context, const char *buffer, size_t length)
-{
- std::cerr << __FUNCTION__ << "(length: " << length << ")" << std::endl;
- // TODO Implement this!
- return new ArgMaxOp;
-}
-
-void FreeArgMax(TfLiteContext *context, void *buffer)
-{
- // TODO Implement this!
- delete static_cast<ArgMaxOp *>(buffer);
-}
-
-TfLiteStatus PrepareArgMax(TfLiteContext *context, TfLiteNode *node)
-{
- std::cerr << __FUNCTION__ << "(...)" << std::endl;
- // TODO Implement this!
- return kTfLiteOk;
-}
-
-TfLiteStatus EvalArgMax(TfLiteContext *context, TfLiteNode *node)
-{
- std::cerr << __FUNCTION__ << "(...)" << std::endl;
- // TODO Implement this!
- return kTfLiteError;
-}
-}
-
-//
-// TensorFlowMax
-//
-namespace
-{
-
-struct TensorFlowMaxOp
-{
- TensorFlowMaxOp(TfLiteContext *context, TfLiteNode *node)
- {
- input = tflite::GetInput(context, node, 0);
- axis = tflite::GetInput(context, node, 1);
- output = tflite::GetOutput(context, node, 0);
- }
- TfLiteTensor *input;
- TfLiteTensor *axis;
- TfLiteTensor *output;
-};
-
-void *InitTensorFlowMax(TfLiteContext *context, const char *buffer, size_t length)
-{
- // Creates two temp tensors to store index and axis for internal
- // implementation only.
- auto *scratch_tensor_index = new int;
- context->AddTensors(context, 2, scratch_tensor_index);
- return scratch_tensor_index;
-}
-
-void FreeTensorFlowMax(TfLiteContext *context, void *buffer)
-{
- delete static_cast<TensorFlowMaxOp *>(buffer);
-}
-
-// Resizes the temp tensor that stores resolved axis.
-TfLiteStatus ResizeTempAxis(TfLiteContext *context, TensorFlowMaxOp *op_context,
- TfLiteTensor *resolved_axis)
-{
- TfLiteIntArray *axis_size = TfLiteIntArrayCreate(1);
- axis_size->data[0] = static_cast<int>(tflite::NumElements(op_context->axis));
- return context->ResizeTensor(context, resolved_axis, axis_size);
-}
-
-// Resizes output array based on the input size and resolved axis.
-TfLiteStatus ResizeOutputTensor(TfLiteContext *context, TensorFlowMaxOp *op_context)
-{
- size_t num_axis = tflite::NumElements(op_context->axis);
- const TfLiteIntArray *input_dims = op_context->input->dims;
- int input_num_dims = tflite::NumDimensions(op_context->input);
- const int *axis = tflite::GetTensorData<int>(op_context->axis);
-
- {
- // Calculates size of reducing axis.
- int num_reduce_axis = num_axis;
- for (int i = 0; i < num_axis; ++i)
- {
- int current = axis[i];
- if (current < 0)
- {
- current += input_num_dims;
- }
- TF_LITE_ENSURE(context, current >= 0 && current < input_num_dims);
- for (int j = 0; j < i; ++j)
- {
- int previous = axis[j];
- if (previous < 0)
- {
- previous += input_num_dims;
- }
- if (current == previous)
- {
- --num_reduce_axis;
- break;
- }
- }
- }
- // Determines output dimensions.
- TfLiteIntArray *output_dims = TfLiteIntArrayCreate(input_num_dims - num_reduce_axis);
- int num_skip_axis = 0;
- for (int idx = 0; idx < input_num_dims; ++idx)
- {
- bool is_axis = false;
- for (int axis_idx = 0; axis_idx < num_axis; ++axis_idx)
- {
- if (axis[axis_idx] == idx || axis[axis_idx] + input_num_dims == idx)
- {
- ++num_skip_axis;
- is_axis = true;
- break;
- }
- }
- if (!is_axis)
- {
- output_dims->data[idx - num_skip_axis] = input_dims->data[idx];
- }
- }
- return context->ResizeTensor(context, op_context->output, output_dims);
- }
-}
-
-// Initializes temp tensors to store index and resolved axis.
-TfLiteStatus InitializeTemporaries(TfLiteContext *context, TfLiteNode *node,
- TensorFlowMaxOp *op_context)
-{
- // Creates a temp index to iterate through input data.
- int *scratch_tensor_index = reinterpret_cast<int *>(node->user_data);
- TfLiteIntArrayFree(node->temporaries);
- node->temporaries = TfLiteIntArrayCreate(2);
- node->temporaries->data[0] = *scratch_tensor_index;
- TfLiteTensor *scratch_tensor = &context->tensors[node->temporaries->data[0]];
- scratch_tensor->type = kTfLiteInt32;
- scratch_tensor->allocation_type = kTfLiteArenaRw;
- TfLiteIntArray *index_size = TfLiteIntArrayCreate(1);
- index_size->data[0] = tflite::NumDimensions(op_context->input);
- TF_LITE_ENSURE_OK(context, context->ResizeTensor(context, scratch_tensor, index_size));
-
- // Creates a temp tensor to store resolved axis given input data.
- node->temporaries->data[1] = *scratch_tensor_index + 1;
- TfLiteTensor *resolved_axis = &context->tensors[node->temporaries->data[1]];
- resolved_axis->type = kTfLiteInt32;
- return kTfLiteOk;
-}
-
-TfLiteStatus PrepareTensorFlowMax(TfLiteContext *context, TfLiteNode *node)
-{
- TF_LITE_ENSURE_EQ(context, tflite::NumInputs(node), 2);
- TF_LITE_ENSURE_EQ(context, tflite::NumOutputs(node), 1);
-
- TensorFlowMaxOp op_context(context, node);
- TF_LITE_ENSURE_OK(context, InitializeTemporaries(context, node, &op_context));
-
- TfLiteTensor *resolved_axis = &context->tensors[node->temporaries->data[1]];
- // Leaves work to Eval if axis is not constant; else resizes output.
- if (!tflite::IsConstantTensor(op_context.axis))
- {
- tflite::SetTensorToDynamic(op_context.output);
- tflite::SetTensorToDynamic(resolved_axis);
- return kTfLiteOk;
- }
- resolved_axis->allocation_type = kTfLiteArenaRw;
- TF_LITE_ENSURE_OK(context, ResizeTempAxis(context, &op_context, resolved_axis));
- return ResizeOutputTensor(context, &op_context);
-}
-
-// Gets offset of index if expanded on axis. When expanded, the flattened offset
-// will not change, if the output index changes on the given axis. For example,
-// if you have a 2D tensor and you are expanding to 3D on axis 0,
-// then index (0, 1, 2) and index (1, 1, 2) will map from the same flattened
-// offset.
-inline size_t ExpandedInputOffset(const int num_dims, const int *dims, const int *index,
- const int num_axis, const int *axis)
-{
- TFLITE_DCHECK_GT(num_dims, 0);
- TFLITE_DCHECK(dims != nullptr);
- TFLITE_DCHECK(index != nullptr);
- size_t offset = 0;
- int out_idx = 0;
- for (int in_idx = 0; in_idx < num_dims; ++in_idx)
- {
- // if we need to expand this axis
- bool is_axis = false;
- if (axis != nullptr)
- {
- for (int axis_idx = 0; axis_idx < num_axis; ++axis_idx)
- {
- if (in_idx == axis[axis_idx])
- {
- is_axis = true;
- break;
- }
- }
- }
- if (!is_axis)
- {
- offset = offset * static_cast<size_t>(dims[in_idx]) + static_cast<size_t>(index[out_idx]);
- out_idx++;
- }
- else
- {
- offset = offset * static_cast<size_t>(dims[in_idx]);
- }
- }
- return offset;
-}
-
-template <typename T>
-inline void CustomMax(T *input_data, const int *input_dims, const int input_num_dims,
- T *output_data, const int *output_dims, const int output_num_dims,
- const int *axis, const int num_axis_dimensions, bool keep_dims,
- int *temp_index, int *resolved_axis)
-{
- // resolves axis.
- int num_resolved_axis = 0;
- for (int idx = 0; idx < num_axis_dimensions; ++idx)
- {
- int current = axis[idx];
- TFLITE_DCHECK(current < input_num_dims && current + input_num_dims >= 0);
- if (current < 0)
- {
- current += input_num_dims;
- }
- bool is_dup = false;
- for (int j = 0; j < num_resolved_axis; ++j)
- {
- if (resolved_axis[j] == current)
- {
- is_dup = true;
- break;
- }
- }
- if (!is_dup)
- {
- resolved_axis[num_resolved_axis++] = current;
- }
- }
-
- // resets output data.
- for (int idx = 0; idx < output_num_dims; ++idx)
- {
- temp_index[idx] = 0;
- }
- for (bool has_next = true; has_next;
- has_next = tflite::NextIndex(output_num_dims, output_dims, temp_index))
- {
- size_t output_offset =
- tflite::ReducedOutputOffset(output_num_dims, output_dims, temp_index, 0, nullptr);
- size_t input_offset = ExpandedInputOffset(input_num_dims, input_dims, temp_index,
- num_resolved_axis, resolved_axis);
- output_data[output_offset] = input_data[input_offset];
- }
-
- // resets temp index.
- for (int idx = 0; idx < input_num_dims; ++idx)
- {
- temp_index[idx] = 0;
- }
-
- // iterates through input_data.
- for (bool has_next = true; has_next;
- has_next = tflite::NextIndex(input_num_dims, input_dims, temp_index))
- {
- size_t input_offset =
- tflite::ReducedOutputOffset(input_num_dims, input_dims, temp_index, 0, nullptr);
- size_t output_offset = tflite::ReducedOutputOffset(input_num_dims, input_dims, temp_index,
- num_resolved_axis, resolved_axis);
- if (output_data[output_offset] < input_data[input_offset])
- {
- output_data[output_offset] = input_data[input_offset];
- }
- }
-}
-
-TfLiteStatus EvalTensorFlowMax(TfLiteContext *context, TfLiteNode *node)
-{
-
- TensorFlowMaxOp op_context(context, node);
- int num_axis = static_cast<int>(tflite::NumElements(op_context.axis));
- TfLiteTensor *temp_index = &context->tensors[node->temporaries->data[0]];
- TfLiteTensor *resolved_axis = &context->tensors[node->temporaries->data[1]];
- // Resize the output tensor if the output tensor is dynamic.
- if (tflite::IsDynamicTensor(op_context.output))
- {
- TF_LITE_ENSURE_OK(context, ResizeTempAxis(context, &op_context, resolved_axis));
- TF_LITE_ENSURE_OK(context, ResizeOutputTensor(context, &op_context));
- }
-
-#define CUSTOM_MAX(data_type) \
- CustomMax<>(tflite::GetTensorData<data_type>(op_context.input), op_context.input->dims->data, \
- op_context.input->dims->size, tflite::GetTensorData<data_type>(op_context.output), \
- op_context.output->dims->data, op_context.output->dims->size, \
- tflite::GetTensorData<int>(op_context.axis), num_axis, false, \
- tflite::GetTensorData<int>(temp_index), tflite::GetTensorData<int>(resolved_axis))
-
- switch (op_context.input->type)
- {
- case kTfLiteFloat32:
- CUSTOM_MAX(float);
- break;
- case kTfLiteInt32:
- CUSTOM_MAX(int);
- break;
- case kTfLiteUInt8:
- CUSTOM_MAX(uint8_t);
- break;
- case kTfLiteInt64:
- CUSTOM_MAX(int64_t);
- break;
- default:
- return kTfLiteError;
- }
-#undef CUSTOM_MAX
-
- return kTfLiteOk;
-}
-}
-
-namespace
-{
-
-struct TensorFlowSqrtOp
-{
-};
-
-void *InitTensorFlowSqrt(TfLiteContext *constext, const char *buffer, size_t length)
-{
- std::cerr << __FUNCTION__ << "(length: " << length << ")" << std::endl;
- // TODO Implement this!
- return new TensorFlowSqrtOp;
-}
-
-void FreeTensorFlowSqrt(TfLiteContext *context, void *buffer)
-{
- // TODO Implement this!
- delete static_cast<TensorFlowSqrtOp *>(buffer);
-}
-
-TfLiteStatus PrepareTensorFlowSqrt(TfLiteContext *context, TfLiteNode *node)
-{
- std::cerr << __FUNCTION__ << "(...)" << std::endl;
- // TODO Implement this!
- return kTfLiteOk;
-}
-
-TfLiteStatus EvalTensorFlowSqrt(TfLiteContext *context, TfLiteNode *node)
-{
- std::cerr << __FUNCTION__ << "(...)" << std::endl;
- // TODO Implement this!
- return kTfLiteError;
-}
-}
-
-namespace
-{
-
-struct ExpandDimsOp
-{
-};
-
-void *InitExpandDims(TfLiteContext *constext, const char *buffer, size_t length)
-{
- std::cerr << __FUNCTION__ << "(length: " << length << ")" << std::endl;
- // TODO Implement this!
- return new ExpandDimsOp;
-}
-
-void FreeExpandDims(TfLiteContext *context, void *buffer)
-{
- // TODO Implement this!
- delete static_cast<ExpandDimsOp *>(buffer);
-}
-
-TfLiteStatus PrepareExpandDims(TfLiteContext *context, TfLiteNode *node)
-{
- std::cerr << __FUNCTION__ << "(...)" << std::endl;
- // TODO Implement this!
- return kTfLiteOk;
-}
-
-TfLiteStatus EvalExpandDims(TfLiteContext *context, TfLiteNode *node)
-{
- std::cerr << __FUNCTION__ << "(...)" << std::endl;
- // TODO Implement this!
- return kTfLiteError;
-}
-}
-
-namespace
-{
-
-struct RSQRTOp
-{
-};
-
-void *InitRSQRT(TfLiteContext *constext, const char *buffer, size_t length)
-{
- std::cerr << __FUNCTION__ << "(length: " << length << ")" << std::endl;
- // TODO Implement this!
- return new RSQRTOp;
-}
-
-void FreeRSQRT(TfLiteContext *context, void *buffer)
-{
- // TODO Implement this!
- delete static_cast<RSQRTOp *>(buffer);
-}
-
-TfLiteStatus PrepareRSQRT(TfLiteContext *context, TfLiteNode *node)
-{
- std::cerr << __FUNCTION__ << "(...)" << std::endl;
- // TODO Implement this!
- return kTfLiteOk;
-}
-
-TfLiteStatus EvalRSQRT(TfLiteContext *context, TfLiteNode *node)
-{
- std::cerr << __FUNCTION__ << "(...)" << std::endl;
- // TODO Implement this!
- return kTfLiteError;
-}
-}
-
-namespace
-{
-
-struct SquaredDifferenceOp
-{
-};
-
-void *InitSquaredDifference(TfLiteContext *constext, const char *buffer, size_t length)
-{
- std::cerr << __FUNCTION__ << "(length: " << length << ")" << std::endl;
- // TODO Implement this!
- return new SquaredDifferenceOp;
-}
-
-void FreeSquaredDifference(TfLiteContext *context, void *buffer)
-{
- // TODO Implement this!
- delete static_cast<SquaredDifferenceOp *>(buffer);
-}
-
-TfLiteStatus PrepareSquaredDifference(TfLiteContext *context, TfLiteNode *node)
-{
- std::cerr << __FUNCTION__ << "(...)" << std::endl;
- // TODO Implement this!
- return kTfLiteOk;
-}
-
-TfLiteStatus EvalSquaredDifference(TfLiteContext *context, TfLiteNode *node)
-{
- std::cerr << __FUNCTION__ << "(...)" << std::endl;
- // TODO Implement this!
- return kTfLiteError;
-}
-}
-
-#define REGISTER_FUNCTION(Name) \
- TfLiteRegistration *Register_##Name(void) \
- { \
- static TfLiteRegistration r = {::Init##Name, ::Free##Name, ::Prepare##Name, ::Eval##Name, 0, \
- #Name}; \
- return &r; \
- }
-REGISTER_FUNCTION(CAST)
-REGISTER_FUNCTION(Stack)
-REGISTER_FUNCTION(ArgMax)
-REGISTER_FUNCTION(TensorFlowMax)
-REGISTER_FUNCTION(TensorFlowSqrt)
-REGISTER_FUNCTION(ExpandDims)
-REGISTER_FUNCTION(RSQRT)
-REGISTER_FUNCTION(SquaredDifference)
-#undef REGISTER_FUNCTION
projects / platform / core / ml / nnfw.git / commitdiff