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// =============================================================================
// Copyright 2016 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.
// =============================================================================
#ifndef TENSORFLOW_KERNELS_PERIODICRESAMPLE_OP_H_
#define TENSORFLOW_KERNELS_PERIODICRESAMPLE_OP_H_
#include <cmath>
#include <type_traits>
#include <vector>
#include "tensorflow/core/framework/op.h"
#include "tensorflow/core/framework/op_kernel.h"
#include "tensorflow/core/framework/shape_inference.h"
#include "tensorflow/core/framework/tensor_shape.h"
#include "tensorflow/core/lib/core/status.h"
namespace {
template <class IndexVecT, class IndexT>
IndexT compute_input_index(
IndexVecT* target_dimensions, const IndexT& output_index,
const IndexVecT& original_dimensions, const int& adjustable_dimension,
const std::vector<tensorflow::int64>& dimension_ceiling,
const std::vector<tensorflow::int64>& cumulative_dimensions, IndexT* result,
std::vector<IndexT>* output_indices, const int& rank) {
*result = 0;
output_indices->clear();
// un-rasterize the output index
auto last_reduced_i = output_index;
for (auto r = rank - 1; r >= 0; --r) {
(*output_indices)[r] = last_reduced_i % (*target_dimensions)[r];
last_reduced_i =
(last_reduced_i - (*output_indices)[r]) / (*target_dimensions)[r];
}
// rasterize the input index
IndexT last_index_factor = 1;
for (auto r = rank - 1; r >= 0; --r) {
IndexT index = 0;
if (r != adjustable_dimension)
index = (*output_indices)[r] / dimension_ceiling[r];
else {
for (int qi = 0; qi < rank; ++qi) {
if (qi == adjustable_dimension) continue;
index += cumulative_dimensions[qi] *
((*output_indices)[qi] % dimension_ceiling[qi]);
}
index *= (*target_dimensions)[adjustable_dimension];
index += (*output_indices)[r];
}
*result += last_index_factor * index;
last_index_factor *= original_dimensions[r];
}
return *result;
}
template <class InputDataT,
class IndexVecT> // both types are needed here b/c IndexVecT and
// InputDataT are not related
void
fill_periodic_tensor(
tensorflow::OpKernelContext* context,
const IndexVecT& desired_shape,
const tensorflow::Tensor& input_tensor) {
// input is a strided array (last index is fastest, C-ordered)
auto input = input_tensor.flat<InputDataT>();
const int rank = input_tensor.dims();
// original and target dimensions
std::vector<tensorflow::int64> original_dimensions(rank),
target_dimensions(rank);
tensorflow::int64 total_size(input_tensor.NumElements()), new_sliced_size(1);
// factors by which original_dimensions increases/decreases w.r.t.
// target_dimensions
std::vector<tensorflow::int64> dimension_ceiling(rank),
cumulative_dimensions(rank);
// index of adjustable dimension
int adjustable_dimension;
tensorflow::TensorShape output_shape;
// requires that the rank of the input tensor and length of the desired shape
// are equal
OP_REQUIRES(context, rank == desired_shape.size(),
tensorflow::errors::InvalidArgument(
"periodic_resample expects the rank of the input tensor, ",
rank, ", to be the same as the length of the desired shape, ",
desired_shape.size(), "."));
bool found = false;
for (int i = 0; i < rank; ++i) {
// if (desired_shape(i) < 1) {
if (desired_shape[i] < 1) {
// only one index can be adjustable
OP_REQUIRES(context, !found,
tensorflow::errors::InvalidArgument(
"periodic_resample expects only "
"one index to be marked as adjustable."));
adjustable_dimension = i;
found = true;
} else {
// target_dimensions[i] = desired_shape(i);
target_dimensions[i] = desired_shape[i];
new_sliced_size *= target_dimensions[i];
}
}
// at least one index needs to be adjustable
OP_REQUIRES(context, found,
tensorflow::errors::InvalidArgument(
"periodic_resample expects at least "
"one index to be marked as adjustable."));
int count = 0;
for (const auto dim_info : input_tensor.shape()) {
original_dimensions[count] = dim_info.size;
++count;
}
target_dimensions[adjustable_dimension] = total_size / new_sliced_size;
count = 0;
for (int i = 0; i < input_tensor.shape().dims(); ++i) {
dimension_ceiling[count] = tensorflow::int64(std::ceil(
float(target_dimensions[count]) / float(original_dimensions[count])));
if (count == 0)
cumulative_dimensions[count] = 1;
else
cumulative_dimensions[count] =
cumulative_dimensions[count - 1] * dimension_ceiling[count - 1];
++count;
}
// ensure that the new dimension is greater than zero
OP_REQUIRES(context, target_dimensions[adjustable_dimension] > 0,
tensorflow::errors::InvalidArgument(
"periodic_resample found that the "
"adjustable dimension, ",
adjustable_dimension, ", isn't greater than zero, ",
target_dimensions[adjustable_dimension], "."));
for (int i = 0; i < rank; ++i) {
output_shape.AddDim(target_dimensions[i]);
}
const auto new_size =
new_sliced_size * target_dimensions[adjustable_dimension];
// Create an output tensor and attach it to the current context
tensorflow::Tensor* output_tensor = nullptr;
OP_REQUIRES_OK(context,
context->allocate_output(0, output_shape, &output_tensor));
auto output = output_tensor->flat<InputDataT>();
// memory is allocated for these variables outside the inner loop for
// efficiency (although, I could create a separate class scope for
// this purpose instead)
tensorflow::int64 result = 0;
std::vector<tensorflow::int64> output_indices(target_dimensions.size());
// Fill output tensor with periodically resampled input tensor values
for (tensorflow::int64 output_index = 0; output_index < new_size;
++output_index) {
output(output_index) = input(compute_input_index(
&target_dimensions, output_index, original_dimensions,
adjustable_dimension, dimension_ceiling, cumulative_dimensions, &result,
&output_indices, rank));
}
}
void create_output_tensor(
tensorflow::OpKernelContext* context,
const tensorflow::Tensor& input_tensor,
const tensorflow::DataType& input_tensor_type,
const tensorflow::PartialTensorShape& desired_shape_tensor) {
auto desired_shape = desired_shape_tensor.dim_sizes();
// obligatory type switch
switch (input_tensor_type) {
case tensorflow::DataTypeToEnum<float>::value:
fill_periodic_tensor<float>(context, desired_shape, input_tensor);
break;
case tensorflow::DataTypeToEnum<double>::value:
fill_periodic_tensor<double>(context, desired_shape, input_tensor);
break;
case tensorflow::DataTypeToEnum<tensorflow::int32>::value:
fill_periodic_tensor<tensorflow::int32>(context, desired_shape,
input_tensor);
break;
case tensorflow::DataTypeToEnum<tensorflow::int64>::value:
fill_periodic_tensor<tensorflow::int64>(context, desired_shape,
input_tensor);
break;
default:;
}
}
} // namespace
class PeriodicResampleOp : public tensorflow::OpKernel {
public:
explicit PeriodicResampleOp(tensorflow::OpKernelConstruction* context)
: tensorflow::OpKernel(context) {
// Get the desired shape
OP_REQUIRES_OK(context, context->GetAttr("shape", &desired_shape));
}
void Compute(tensorflow::OpKernelContext* context) override {
// Grab the input tensor
const tensorflow::Tensor& input_tensor = context->input(0);
const tensorflow::DataType input_tensor_type = context->input_dtype(0);
create_output_tensor(context, input_tensor, input_tensor_type,
desired_shape);
}
private:
tensorflow::PartialTensorShape desired_shape;
};
#endif // TENSORFLOW_KERNELS_PERIODICRESAMPLE_OP_H_
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