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325 lines
13 KiB
C++
325 lines
13 KiB
C++
#ifndef MLPP_TENSOR3_H
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#define MLPP_TENSOR3_H
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#ifndef GDNATIVE
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/*************************************************************************/
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/* mlpp_tensor3.h */
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/*************************************************************************/
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/* This file is part of: */
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/* PMLPP Machine Learning Library */
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/* https://github.com/Relintai/pmlpp */
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/*************************************************************************/
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/* Copyright (c) 2023-present Péter Magyar. */
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/* Copyright (c) 2022-2023 Marc Melikyan */
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/* */
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/* Permission is hereby granted, free of charge, to any person obtaining */
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/* a copy of this software and associated documentation files (the */
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/* "Software"), to deal in the Software without restriction, including */
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/* without limitation the rights to use, copy, modify, merge, publish, */
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/* distribute, sublicense, and/or sell copies of the Software, and to */
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/* permit persons to whom the Software is furnished to do so, subject to */
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/* the following conditions: */
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/* */
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/* The above copyright notice and this permission notice shall be */
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/* included in all copies or substantial portions of the Software. */
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/* */
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/* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, */
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/* EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF */
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/* MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.*/
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/* IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY */
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/* CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, */
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/* TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE */
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/* SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. */
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/*************************************************************************/
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#include "core/math/math_defs.h"
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#include "core/containers/pool_vector.h"
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#include "core/containers/sort_array.h"
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#include "core/containers/vector.h"
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#include "core/error/error_macros.h"
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#include "core/math/vector2i.h"
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#include "core/os/memory.h"
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#include "core/object/resource.h"
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#else
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#include "core/containers/vector.h"
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#include "core/defs.h"
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#include "core/math_funcs.h"
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#include "core/os/memory.h"
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#include "core/pool_arrays.h"
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#include "gen/resource.h"
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#endif
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#include "mlpp_matrix.h"
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#include "mlpp_vector.h"
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class Image;
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class MLPPTensor3 : public Resource {
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GDCLASS(MLPPTensor3, Resource);
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public:
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Array get_data();
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void set_data(const Array &p_from);
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_FORCE_INLINE_ real_t *ptrw() {
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return _data;
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}
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_FORCE_INLINE_ const real_t *ptr() const {
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return _data;
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}
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void z_slice_add(const Vector<real_t> &p_row);
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void z_slice_add_pool_vector(const PoolRealArray &p_row);
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void z_slice_add_mlpp_vector(const Ref<MLPPVector> &p_row);
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void z_slice_add_mlpp_matrix(const Ref<MLPPMatrix> &p_matrix);
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void z_slice_remove(int p_index);
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// Removes the item copying the last value into the position of the one to
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// remove. It's generally faster than `remove`.
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void z_slice_remove_unordered(int p_index);
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void z_slice_swap(int p_index_1, int p_index_2);
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_FORCE_INLINE_ void clear() { resize(Size3i()); }
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_FORCE_INLINE_ void reset() {
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if (_data) {
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memfree(_data);
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_data = NULL;
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_size = Size3i();
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}
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}
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_FORCE_INLINE_ bool empty() const { return _size == Size3i(); }
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_FORCE_INLINE_ int z_slice_data_size() const { return _size.x * _size.y; }
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_FORCE_INLINE_ Size2i z_slice_size() const { return Size2i(_size.x, _size.y); }
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_FORCE_INLINE_ int data_size() const { return _size.x * _size.y * _size.z; }
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_FORCE_INLINE_ Size3i size() const { return _size; }
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void resize(const Size3i &p_size);
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void shape_set(const Size3i &p_size);
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_FORCE_INLINE_ int calculate_index(int p_index_z, int p_index_y, int p_index_x) const {
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return p_index_y * _size.x + p_index_x + _size.x * _size.y * p_index_z;
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}
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_FORCE_INLINE_ int calculate_z_slice_index(int p_index_z) const {
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return _size.x * _size.y * p_index_z;
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}
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_FORCE_INLINE_ const real_t &operator[](int p_index) const {
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CRASH_BAD_INDEX(p_index, data_size());
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return _data[p_index];
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}
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_FORCE_INLINE_ real_t &operator[](int p_index) {
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CRASH_BAD_INDEX(p_index, data_size());
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return _data[p_index];
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}
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_FORCE_INLINE_ real_t element_get_index(int p_index) const {
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ERR_FAIL_INDEX_V(p_index, data_size(), 0);
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return _data[p_index];
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}
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_FORCE_INLINE_ void element_set_index(int p_index, real_t p_val) {
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ERR_FAIL_INDEX(p_index, data_size());
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_data[p_index] = p_val;
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}
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_FORCE_INLINE_ real_t element_get(int p_index_z, int p_index_y, int p_index_x) const {
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ERR_FAIL_INDEX_V(p_index_x, _size.x, 0);
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ERR_FAIL_INDEX_V(p_index_y, _size.y, 0);
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ERR_FAIL_INDEX_V(p_index_z, _size.z, 0);
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return _data[p_index_y * _size.x + p_index_x + _size.x * _size.y * p_index_z];
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}
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_FORCE_INLINE_ void element_set(int p_index_z, int p_index_y, int p_index_x, real_t p_val) {
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ERR_FAIL_INDEX(p_index_x, _size.x);
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ERR_FAIL_INDEX(p_index_y, _size.y);
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ERR_FAIL_INDEX(p_index_z, _size.z);
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_data[p_index_y * _size.x + p_index_x + _size.x * _size.y * p_index_z] = p_val;
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}
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Vector<real_t> row_get_vector(int p_index_z, int p_index_y) const;
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PoolRealArray row_get_pool_vector(int p_index_z, int p_index_y) const;
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Ref<MLPPVector> row_get_mlpp_vector(int p_index_z, int p_index_y) const;
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void row_get_into_mlpp_vector(int p_index_z, int p_index_y, Ref<MLPPVector> target) const;
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void row_set_vector(int p_index_z, int p_index_y, const Vector<real_t> &p_row);
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void row_set_pool_vector(int p_index_z, int p_index_y, const PoolRealArray &p_row);
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void row_set_mlpp_vector(int p_index_z, int p_index_y, const Ref<MLPPVector> &p_row);
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Vector<real_t> z_slice_get_vector(int p_index_z) const;
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PoolRealArray z_slice_get_pool_vector(int p_index_z) const;
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Ref<MLPPVector> z_slice_get_mlpp_vector(int p_index_z) const;
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void z_slice_get_into_mlpp_vector(int p_index_z, Ref<MLPPVector> target) const;
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Ref<MLPPMatrix> z_slice_get_mlpp_matrix(int p_index_z) const;
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void z_slice_get_into_mlpp_matrix(int p_index_z, Ref<MLPPMatrix> target) const;
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void z_slice_set_vector(int p_index_z, const Vector<real_t> &p_row);
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void z_slice_set_pool_vector(int p_index_z, const PoolRealArray &p_row);
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void z_slice_set_mlpp_vector(int p_index_z, const Ref<MLPPVector> &p_row);
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void z_slice_set_mlpp_matrix(int p_index_z, const Ref<MLPPMatrix> &p_mat);
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//TODO resize() need to be reworked for add and remove to work, in any other direction than z
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//void x_slice_add(const Ref<MLPPMatrix> &p_matrix);
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//void x_slice_remove(int p_index);
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void x_slice_get_into(int p_index_x, Ref<MLPPMatrix> target) const;
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Ref<MLPPMatrix> x_slice_get(int p_index_x) const;
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void x_slice_set(int p_index_x, const Ref<MLPPMatrix> &p_mat);
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//void y_slice_add(const Ref<MLPPMatrix> &p_matrix);
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//void y_slice_remove(int p_index);
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void y_slice_get_into(int p_index_y, Ref<MLPPMatrix> target) const;
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Ref<MLPPMatrix> y_slice_get(int p_index_y) const;
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void y_slice_set(int p_index_y, const Ref<MLPPMatrix> &p_mat);
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public:
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//Image api
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enum ImageChannelFlags {
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IMAGE_CHANNEL_FLAG_R = 1 << 0,
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IMAGE_CHANNEL_FLAG_G = 1 << 1,
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IMAGE_CHANNEL_FLAG_B = 1 << 2,
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IMAGE_CHANNEL_FLAG_A = 1 << 3,
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IMAGE_CHANNEL_FLAG_NONE = 0,
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IMAGE_CHANNEL_FLAG_RG = IMAGE_CHANNEL_FLAG_R | IMAGE_CHANNEL_FLAG_G,
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IMAGE_CHANNEL_FLAG_RGB = IMAGE_CHANNEL_FLAG_R | IMAGE_CHANNEL_FLAG_G | IMAGE_CHANNEL_FLAG_B,
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IMAGE_CHANNEL_FLAG_GB = IMAGE_CHANNEL_FLAG_G | IMAGE_CHANNEL_FLAG_B,
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IMAGE_CHANNEL_FLAG_GBA = IMAGE_CHANNEL_FLAG_G | IMAGE_CHANNEL_FLAG_B | IMAGE_CHANNEL_FLAG_A,
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IMAGE_CHANNEL_FLAG_BA = IMAGE_CHANNEL_FLAG_B | IMAGE_CHANNEL_FLAG_A,
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IMAGE_CHANNEL_FLAG_RGBA = IMAGE_CHANNEL_FLAG_R | IMAGE_CHANNEL_FLAG_G | IMAGE_CHANNEL_FLAG_B | IMAGE_CHANNEL_FLAG_A,
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};
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void z_slices_add_image(const Ref<Image> &p_img, const int p_channels = IMAGE_CHANNEL_FLAG_RGBA);
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Ref<Image> z_slice_get_image(const int p_index_z) const;
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Ref<Image> z_slices_get_image(const int p_index_r = -1, const int p_index_g = -1, const int p_index_b = -1, const int p_index_a = -1) const;
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void z_slice_get_into_image(Ref<Image> p_target, const int p_index_z, const int p_target_channels = IMAGE_CHANNEL_FLAG_RGB) const;
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void z_slices_get_into_image(Ref<Image> p_target, const int p_index_r = -1, const int p_index_g = -1, const int p_index_b = -1, const int p_index_a = -1) const;
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void z_slice_set_image(const Ref<Image> &p_img, const int p_index_z, const int p_image_channel_flag = IMAGE_CHANNEL_FLAG_R);
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void z_slices_set_image(const Ref<Image> &p_img, const int p_index_r = -1, const int p_index_g = -1, const int p_index_b = -1, const int p_index_a = -1);
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void set_from_image(const Ref<Image> &p_img, const int p_channels = IMAGE_CHANNEL_FLAG_RGBA);
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//void x_slices_add_image(const Ref<Image> &p_img, const int p_channels = IMAGE_CHANNEL_FLAG_RGBA);
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Ref<Image> x_slice_get_image(const int p_index_x) const;
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void x_slice_get_into_image(Ref<Image> p_target, const int p_index_x, const int p_target_channels = IMAGE_CHANNEL_FLAG_RGB) const;
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void x_slice_set_image(const Ref<Image> &p_img, const int p_index_x, const int p_image_channel_flag = IMAGE_CHANNEL_FLAG_R);
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//void y_slices_add_image(const Ref<Image> &p_img, const int p_channels = IMAGE_CHANNEL_FLAG_RGBA);
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Ref<Image> y_slice_get_image(const int p_index_y) const;
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void y_slice_get_into_image(Ref<Image> p_target, const int p_index_y, const int p_target_channels = IMAGE_CHANNEL_FLAG_RGB) const;
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void y_slice_set_image(const Ref<Image> &p_img, const int p_index_y, const int p_image_channel_flag = IMAGE_CHANNEL_FLAG_R);
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public:
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//math api
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void add(const Ref<MLPPTensor3> &B);
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Ref<MLPPTensor3> addn(const Ref<MLPPTensor3> &B) const;
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void addb(const Ref<MLPPTensor3> &A, const Ref<MLPPTensor3> &B);
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void sub(const Ref<MLPPTensor3> &B);
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Ref<MLPPTensor3> subn(const Ref<MLPPTensor3> &B) const;
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void subb(const Ref<MLPPTensor3> &A, const Ref<MLPPTensor3> &B);
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void division_element_wise(const Ref<MLPPTensor3> &B);
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Ref<MLPPTensor3> division_element_wisen(const Ref<MLPPTensor3> &B) const;
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void division_element_wiseb(const Ref<MLPPTensor3> &A, const Ref<MLPPTensor3> &B);
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void sqrt();
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Ref<MLPPTensor3> sqrtn() const;
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void sqrtb(const Ref<MLPPTensor3> &A);
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void exponentiate(real_t p);
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Ref<MLPPTensor3> exponentiaten(real_t p) const;
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void exponentiateb(const Ref<MLPPTensor3> &A, real_t p);
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void scalar_multiply(const real_t scalar);
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Ref<MLPPTensor3> scalar_multiplyn(const real_t scalar) const;
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void scalar_multiplyb(const real_t scalar, const Ref<MLPPTensor3> &A);
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void scalar_add(const real_t scalar);
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Ref<MLPPTensor3> scalar_addn(const real_t scalar) const;
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void scalar_addb(const real_t scalar, const Ref<MLPPTensor3> &A);
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void hadamard_product(const Ref<MLPPTensor3> &B);
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Ref<MLPPTensor3> hadamard_productn(const Ref<MLPPTensor3> &B) const;
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void hadamard_productb(const Ref<MLPPTensor3> &A, const Ref<MLPPTensor3> &B);
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void max(const Ref<MLPPTensor3> &B);
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Ref<MLPPTensor3> maxn(const Ref<MLPPTensor3> &B) const;
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void maxb(const Ref<MLPPTensor3> &A, const Ref<MLPPTensor3> &B);
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void abs();
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Ref<MLPPTensor3> absn() const;
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void absb(const Ref<MLPPTensor3> &A);
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Ref<MLPPVector> flatten() const;
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void flatteno(Ref<MLPPVector> out) const;
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//real_t norm_2(std::vector<std::vector<std::vector<real_t>>> A);
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Ref<MLPPMatrix> tensor_vec_mult(const Ref<MLPPVector> &b);
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//std::vector<std::vector<std::vector<real_t>>> vector_wise_tensor_product(std::vector<std::vector<std::vector<real_t>>> A, std::vector<std::vector<real_t>> B);
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public:
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void fill(real_t p_val);
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Vector<real_t> to_flat_vector() const;
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PoolRealArray to_flat_pool_vector() const;
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Vector<uint8_t> to_flat_byte_array() const;
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Ref<MLPPTensor3> duplicate_fast() const;
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void set_from_mlpp_tensor3(const Ref<MLPPTensor3> &p_from);
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void set_from_mlpp_tensor3r(const MLPPTensor3 &p_from);
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void set_from_mlpp_matrix(const Ref<MLPPMatrix> &p_from);
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void set_from_mlpp_matrixr(const MLPPMatrix &p_from);
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void set_from_mlpp_vectors(const Vector<Ref<MLPPVector>> &p_from);
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void set_from_mlpp_matricess(const Vector<Ref<MLPPMatrix>> &p_from);
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void set_from_mlpp_vectors_array(const Array &p_from);
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void set_from_mlpp_matrices_array(const Array &p_from);
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bool is_equal_approx(const Ref<MLPPTensor3> &p_with, real_t tolerance = static_cast<real_t>(CMP_EPSILON)) const;
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String to_string();
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MLPPTensor3();
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MLPPTensor3(const MLPPMatrix &p_from);
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MLPPTensor3(const Array &p_from);
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~MLPPTensor3();
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// TODO: These are temporary
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std::vector<real_t> to_flat_std_vector() const;
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void set_from_std_vectors(const std::vector<std::vector<std::vector<real_t>>> &p_from);
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std::vector<std::vector<std::vector<real_t>>> to_std_vector();
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MLPPTensor3(const std::vector<std::vector<std::vector<real_t>>> &p_from);
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protected:
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static void _bind_methods();
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protected:
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Size3i _size;
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real_t *_data;
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};
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VARIANT_ENUM_CAST(MLPPTensor3::ImageChannelFlags);
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#endif
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