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/*
* Copyright (c) 2017 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to
* deal in the Software without restriction, including without limitation the
* rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
* sell copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in all
* copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*/
#pragma once
#include <cstdio>
#include <cstdlib>
#include <random>
#include "alloc.hpp"
/*****************************************************************************/
/* Padding definitions */
enum PaddingType {
PADDING_SAME, PADDING_VALID
};
/*****************************************************************************/
/* Shape of a kernel */
struct KernelShape {
int n_output_channels, n_rows, n_cols, n_input_channels;
int size(void) const {
return n_output_channels * n_rows * n_cols * n_input_channels;
}
};
struct Tensor4DShape {
int n_batches,
n_rows,
n_cols,
n_channels;
int size() const {
return n_batches * n_rows * n_cols * n_channels;
}
bool TestEq(const Tensor4DShape& other) const {
return (n_batches == other.n_batches &&
n_rows == other.n_rows &&
n_cols == other.n_cols &&
n_channels == other.n_channels);
}
};
template <typename ShapeT, typename T>
class Tensor4D final {
public:
Tensor4D(ShapeT shape) :
_shape(shape),
_data(reinterpret_cast<T*>(ALLOCATE(size_bytes()))) {
Clear();
}
~Tensor4D() {
free(_data);
}
T* ptr() const {
return _data;
}
const ShapeT& shape() const {
return _shape;
}
size_t size_bytes() const {
return _shape.size() * sizeof(T);
}
bool TestEq(Tensor4D<ShapeT, T>& other) const;
T& element(int, int, int, int) const;
void Print() const;
void Clear() {
Fill(static_cast<T>(0));
}
void Fill(T val) {
for (int i = 0; i < _shape.size(); i++)
_data[i] = val;
}
void TestPattern() {
for (int i = 0; i < _shape.size(); i++)
_data[i] = static_cast<T>(i);
}
void Rand(const int seed=2311) {
std::mt19937 gen(seed);
std::uniform_int_distribution<> dis(-50, +50);
for (int i = 0; i < _shape.size(); i++) {
_data[i] = static_cast<T>(dis(gen));
}
}
Tensor4D(const Tensor4D &) = delete;
/** Prevent instances of this class from being copied (As this class contains pointers) */
Tensor4D &operator=(const Tensor4D &) = delete;
/** Allow instances of this class to be moved */
Tensor4D(Tensor4D &&) = default;
/** Allow instances of this class to be moved */
Tensor4D &operator=(Tensor4D &&) = default;
private:
const ShapeT _shape;
T* const _data;
};
template <>
inline float& Tensor4D<Tensor4DShape, float>::element(int n, int i, int j, int c) const {
int index = ((n*_shape.n_rows + i)*_shape.n_cols + j)*_shape.n_channels + c;
return _data[index];
}
template <>
inline float& Tensor4D<KernelShape, float>::element(int oc, int i, int j, int ic) const {
int index = ((i*_shape.n_cols + j)*_shape.n_input_channels + ic)*_shape.n_output_channels + oc;
return _data[index];
}
template <>
inline bool Tensor4D<Tensor4DShape, float>::TestEq(Tensor4D<Tensor4DShape, float>& other) const {
// Test equivalence, printing errors
// First test the shapes are the same
if (!_shape.TestEq(other.shape())) {
printf("Tensors have different shapes.\n");
return false;
} else {
int incorrects = 0;
for (int n = 0; n < _shape.n_batches; n++) {
for (int i = 0; i < _shape.n_rows; i++) {
for (int j = 0; j < _shape.n_cols; j++) {
for (int c = 0; c < _shape.n_channels; c++) {
// Check elements for equivalence
const auto a = this->element(n, i, j, c);
const auto b = other.element(n, i, j, c);
if (a != b) {
printf("Difference at element {%d, %d, %d, %d}: %.3f != %.3f\n", n, i, j, c, a, b);
if (++incorrects > 100) {
printf("More than 100 incorrect values, stopping test.\n");
return false;
}
}
}
}
}
}
return incorrects == 0;
}
}
template <>
inline void Tensor4D<Tensor4DShape, float>::Print() const {
for (int n = 0; n < _shape.n_batches; n++) {
for (int c = 0; c < _shape.n_channels; c++) {
for (int i = 0; i < _shape.n_rows; i++) {
for (int j = 0; j < _shape.n_cols; j++) {
printf("%5.2f ", element(n, i, j, c));
}
printf("\n");
}
printf("\n");
}
}
}
template <>
inline void Tensor4D<KernelShape, float>::Print() const {
for (int oc = 0; oc < _shape.n_output_channels; oc++) {
for (int ic = 0; ic < _shape.n_input_channels; ic++) {
for (int i = 0; i < _shape.n_rows; i++) {
for (int j = 0; j < _shape.n_cols; j++) {
printf("%5.2f ", element(oc, i, j, ic));
}
printf("\n");
}
printf("\n");
}
}
}
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