mirror of
https://github.com/Relintai/pmlpp.git
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1000 lines
24 KiB
C++
1000 lines
24 KiB
C++
#ifndef MLPP_TENSOR3_H
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#define MLPP_TENSOR3_H
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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/reference.h"
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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 Reference {
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GDCLASS(MLPPTensor3, Reference);
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public:
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real_t *ptrw() {
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return _data;
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}
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const real_t *ptr() const {
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return _data;
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}
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// TODO: Need to double check whether it's right to call the z axis feature map (probably not)
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// TODO: Add helper methods for the other axes aswell (probably shouldn't have as extensive of a coverage as z),
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// Only MLPPMatrix: get, add, set.
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// TODO: Add Image get, set helper methods to MLPPMatrix -> so the other axis helper methods can use them.
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// TODO: _FORCE_INLINE_ less big methods (Also do this in MLPPVEctor and MLPPMatrix)
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_FORCE_INLINE_ void add_feature_map(const Vector<real_t> &p_row) {
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if (p_row.size() == 0) {
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return;
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}
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int fms = feature_map_data_size();
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ERR_FAIL_COND(fms != p_row.size());
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int ci = data_size();
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++_size.z;
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_data = (real_t *)memrealloc(_data, data_size() * sizeof(real_t));
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CRASH_COND_MSG(!_data, "Out of memory");
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const real_t *row_arr = p_row.ptr();
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for (int i = 0; i < p_row.size(); ++i) {
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_data[ci + i] = row_arr[i];
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}
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}
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_FORCE_INLINE_ void add_feature_map_pool_vector(const PoolRealArray &p_row) {
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if (p_row.size() == 0) {
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return;
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}
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int fms = feature_map_data_size();
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ERR_FAIL_COND(fms != p_row.size());
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int ci = data_size();
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++_size.z;
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_data = (real_t *)memrealloc(_data, data_size() * sizeof(real_t));
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CRASH_COND_MSG(!_data, "Out of memory");
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PoolRealArray::Read rread = p_row.read();
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const real_t *row_arr = rread.ptr();
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for (int i = 0; i < p_row.size(); ++i) {
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_data[ci + i] = row_arr[i];
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}
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}
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_FORCE_INLINE_ void add_feature_map_mlpp_vector(const Ref<MLPPVector> &p_row) {
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ERR_FAIL_COND(!p_row.is_valid());
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int p_row_size = p_row->size();
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if (p_row_size == 0) {
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return;
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}
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int fms = feature_map_data_size();
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ERR_FAIL_COND(fms != p_row_size);
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int ci = data_size();
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++_size.z;
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_data = (real_t *)memrealloc(_data, data_size() * sizeof(real_t));
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CRASH_COND_MSG(!_data, "Out of memory");
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const real_t *row_ptr = p_row->ptr();
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for (int i = 0; i < p_row_size; ++i) {
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_data[ci + i] = row_ptr[i];
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}
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}
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_FORCE_INLINE_ void add_feature_map_mlpp_matrix(const Ref<MLPPMatrix> &p_matrix) {
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ERR_FAIL_COND(!p_matrix.is_valid());
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int other_data_size = p_matrix->data_size();
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if (other_data_size == 0) {
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return;
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}
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Size2i matrix_size = p_matrix->size();
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Size2i fms = feature_map_size();
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ERR_FAIL_COND(fms != matrix_size);
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int start_offset = data_size();
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++_size.z;
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_data = (real_t *)memrealloc(_data, data_size() * sizeof(real_t));
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CRASH_COND_MSG(!_data, "Out of memory");
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const real_t *other_ptr = p_matrix->ptr();
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for (int i = 0; i < other_data_size; ++i) {
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_data[start_offset + i] = other_ptr[i];
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}
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}
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void remove_feature_map(int p_index) {
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ERR_FAIL_INDEX(p_index, _size.z);
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--_size.z;
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int ds = data_size();
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if (ds == 0) {
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memfree(_data);
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_data = NULL;
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return;
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}
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int fmds = feature_map_data_size();
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for (int i = calculate_feature_map_index(p_index); i < ds; ++i) {
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_data[i] = _data[i + fmds];
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}
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_data = (real_t *)memrealloc(_data, data_size() * sizeof(real_t));
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CRASH_COND_MSG(!_data, "Out of memory");
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}
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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 remove_feature_map_unordered(int p_index) {
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ERR_FAIL_INDEX(p_index, _size.z);
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--_size.z;
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int ds = data_size();
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if (ds == 0) {
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memfree(_data);
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_data = NULL;
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return;
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}
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int start_ind = calculate_feature_map_index(p_index);
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int end_ind = calculate_feature_map_index(p_index + 1);
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for (int i = start_ind; i < end_ind; ++i) {
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_data[i] = _data[ds + i];
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}
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_data = (real_t *)memrealloc(_data, data_size() * sizeof(real_t));
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CRASH_COND_MSG(!_data, "Out of memory");
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}
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void swap_feature_map(int p_index_1, int p_index_2) {
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ERR_FAIL_INDEX(p_index_1, _size.z);
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ERR_FAIL_INDEX(p_index_2, _size.z);
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int ind1_start = calculate_feature_map_index(p_index_1);
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int ind2_start = calculate_feature_map_index(p_index_2);
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int fmds = feature_map_data_size();
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for (int i = 0; i < fmds; ++i) {
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SWAP(_data[ind1_start + i], _data[ind2_start + i]);
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}
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}
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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 feature_map_data_size() const { return _size.x * _size.y; }
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_FORCE_INLINE_ Size2i feature_map_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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_size = p_size;
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int ds = data_size();
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if (ds == 0) {
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if (_data) {
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memfree(_data);
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_data = NULL;
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}
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return;
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}
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_data = (real_t *)memrealloc(_data, ds * sizeof(real_t));
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CRASH_COND_MSG(!_data, "Out of memory");
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}
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void set_shape(const Size3i &p_size) {
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int ds = data_size();
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int new_data_size = p_size.x * p_size.y * p_size.z;
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ERR_FAIL_COND_MSG(ds != new_data_size, "The new size has a different volume than the old. If this is intended use resize()!");
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_size = p_size;
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}
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_FORCE_INLINE_ int calculate_index(int p_index_y, int p_index_x, int p_index_z) 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_feature_map_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 get_element_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 set_element_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 get_element(int p_index_y, int p_index_x, int p_index_z) 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 set_element(int p_index_y, int p_index_x, int p_index_z, 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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_FORCE_INLINE_ Vector<real_t> get_row_vector(int p_index_y, int p_index_z) {
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ERR_FAIL_INDEX_V(p_index_y, _size.y, Vector<real_t>());
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ERR_FAIL_INDEX_V(p_index_z, _size.z, Vector<real_t>());
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Vector<real_t> ret;
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if (unlikely(_size.x == 0)) {
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return ret;
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}
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ret.resize(_size.x);
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int ind_start = p_index_y * _size.x;
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real_t *row_ptr = ret.ptrw();
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for (int i = 0; i < _size.x; ++i) {
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row_ptr[i] = _data[ind_start + i];
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}
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return ret;
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}
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_FORCE_INLINE_ PoolRealArray get_row_pool_vector(int p_index_y, int p_index_z) {
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ERR_FAIL_INDEX_V(p_index_y, _size.y, PoolRealArray());
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ERR_FAIL_INDEX_V(p_index_z, _size.z, PoolRealArray());
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PoolRealArray ret;
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if (unlikely(_size.x == 0)) {
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return ret;
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}
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ret.resize(_size.x);
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int ind_start = p_index_y * _size.x + _size.x * _size.y * p_index_z;
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PoolRealArray::Write w = ret.write();
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real_t *row_ptr = w.ptr();
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for (int i = 0; i < _size.x; ++i) {
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row_ptr[i] = _data[ind_start + i];
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}
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return ret;
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}
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_FORCE_INLINE_ Ref<MLPPVector> get_row_mlpp_vector(int p_index_y, int p_index_z) {
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ERR_FAIL_INDEX_V(p_index_y, _size.y, Ref<MLPPVector>());
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ERR_FAIL_INDEX_V(p_index_z, _size.z, Ref<MLPPVector>());
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Ref<MLPPVector> ret;
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ret.instance();
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if (unlikely(_size.x == 0)) {
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return ret;
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}
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ret->resize(_size.x);
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int ind_start = p_index_y * _size.x + _size.x * _size.y * p_index_z;
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real_t *row_ptr = ret->ptrw();
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for (int i = 0; i < _size.x; ++i) {
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row_ptr[i] = _data[ind_start + i];
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}
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return ret;
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}
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_FORCE_INLINE_ void get_row_into_mlpp_vector(int p_index_y, int p_index_z, Ref<MLPPVector> target) const {
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ERR_FAIL_COND(!target.is_valid());
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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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if (unlikely(target->size() != _size.x)) {
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target->resize(_size.x);
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}
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int ind_start = p_index_y * _size.x + _size.x * _size.y * p_index_z;
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real_t *row_ptr = target->ptrw();
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for (int i = 0; i < _size.x; ++i) {
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row_ptr[i] = _data[ind_start + i];
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}
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}
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_FORCE_INLINE_ void set_row_vector(int p_index_y, int p_index_z, const Vector<real_t> &p_row) {
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ERR_FAIL_COND(p_row.size() != _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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int ind_start = p_index_y * _size.x + _size.x * _size.y * p_index_z;
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const real_t *row_ptr = p_row.ptr();
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for (int i = 0; i < _size.x; ++i) {
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_data[ind_start + i] = row_ptr[i];
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}
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}
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_FORCE_INLINE_ void set_row_pool_vector(int p_index_y, int p_index_z, const PoolRealArray &p_row) {
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ERR_FAIL_COND(p_row.size() != _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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int ind_start = p_index_y * _size.x + _size.x * _size.y * p_index_z;
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PoolRealArray::Read r = p_row.read();
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const real_t *row_ptr = r.ptr();
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for (int i = 0; i < _size.x; ++i) {
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_data[ind_start + i] = row_ptr[i];
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}
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}
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_FORCE_INLINE_ void set_row_mlpp_vector(int p_index_y, int p_index_z, const Ref<MLPPVector> &p_row) {
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ERR_FAIL_COND(!p_row.is_valid());
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ERR_FAIL_COND(p_row->size() != _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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int ind_start = p_index_y * _size.x + _size.x * _size.y * p_index_z;
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const real_t *row_ptr = p_row->ptr();
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for (int i = 0; i < _size.x; ++i) {
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_data[ind_start + i] = row_ptr[i];
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}
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}
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_FORCE_INLINE_ Vector<real_t> get_feature_map_vector(int p_index_z) {
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ERR_FAIL_INDEX_V(p_index_z, _size.z, Vector<real_t>());
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Vector<real_t> ret;
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int fmds = feature_map_data_size();
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if (unlikely(fmds == 0)) {
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return ret;
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}
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ret.resize(fmds);
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int ind_start = calculate_feature_map_index(p_index_z);
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real_t *row_ptr = ret.ptrw();
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for (int i = 0; i < fmds; ++i) {
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row_ptr[i] = _data[ind_start + i];
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}
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return ret;
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}
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_FORCE_INLINE_ PoolRealArray get_feature_map_pool_vector(int p_index_z) {
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ERR_FAIL_INDEX_V(p_index_z, _size.z, PoolRealArray());
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PoolRealArray ret;
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int fmds = feature_map_data_size();
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if (unlikely(fmds == 0)) {
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return ret;
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}
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ret.resize(fmds);
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int ind_start = calculate_feature_map_index(p_index_z);
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PoolRealArray::Write w = ret.write();
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real_t *row_ptr = w.ptr();
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for (int i = 0; i < fmds; ++i) {
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row_ptr[i] = _data[ind_start + i];
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}
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return ret;
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}
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_FORCE_INLINE_ Ref<MLPPVector> get_feature_map_mlpp_vector(int p_index_z) {
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ERR_FAIL_INDEX_V(p_index_z, _size.z, Ref<MLPPVector>());
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Ref<MLPPVector> ret;
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ret.instance();
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int fmds = feature_map_data_size();
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if (unlikely(fmds == 0)) {
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return ret;
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}
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ret->resize(fmds);
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int ind_start = calculate_feature_map_index(p_index_z);
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real_t *row_ptr = ret->ptrw();
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for (int i = 0; i < fmds; ++i) {
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row_ptr[i] = _data[ind_start + i];
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}
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return ret;
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}
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_FORCE_INLINE_ void get_feature_map_into_mlpp_vector(int p_index_z, Ref<MLPPVector> target) const {
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ERR_FAIL_INDEX(p_index_z, _size.z);
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int fmds = feature_map_data_size();
|
|
|
|
if (unlikely(target->size() != fmds)) {
|
|
target->resize(fmds);
|
|
}
|
|
|
|
int ind_start = calculate_feature_map_index(p_index_z);
|
|
|
|
real_t *row_ptr = target->ptrw();
|
|
|
|
for (int i = 0; i < fmds; ++i) {
|
|
row_ptr[i] = _data[ind_start + i];
|
|
}
|
|
}
|
|
|
|
_FORCE_INLINE_ Ref<MLPPMatrix> get_feature_map_mlpp_matrix(int p_index_z) {
|
|
ERR_FAIL_INDEX_V(p_index_z, _size.z, Ref<MLPPMatrix>());
|
|
|
|
Ref<MLPPMatrix> ret;
|
|
ret.instance();
|
|
|
|
int fmds = feature_map_data_size();
|
|
|
|
if (unlikely(fmds == 0)) {
|
|
return ret;
|
|
}
|
|
|
|
ret->resize(feature_map_size());
|
|
|
|
int ind_start = calculate_feature_map_index(p_index_z);
|
|
|
|
real_t *row_ptr = ret->ptrw();
|
|
|
|
for (int i = 0; i < fmds; ++i) {
|
|
row_ptr[i] = _data[ind_start + i];
|
|
}
|
|
|
|
return ret;
|
|
}
|
|
|
|
_FORCE_INLINE_ void get_feature_map_into_mlpp_matrix(int p_index_z, Ref<MLPPMatrix> target) const {
|
|
ERR_FAIL_INDEX(p_index_z, _size.z);
|
|
|
|
int fmds = feature_map_data_size();
|
|
Size2i fms = feature_map_size();
|
|
|
|
if (unlikely(target->size() != fms)) {
|
|
target->resize(fms);
|
|
}
|
|
|
|
int ind_start = calculate_feature_map_index(p_index_z);
|
|
|
|
real_t *row_ptr = target->ptrw();
|
|
|
|
for (int i = 0; i < fmds; ++i) {
|
|
row_ptr[i] = _data[ind_start + i];
|
|
}
|
|
}
|
|
|
|
_FORCE_INLINE_ void set_feature_map_vector(int p_index_z, const Vector<real_t> &p_row) {
|
|
ERR_FAIL_INDEX(p_index_z, _size.z);
|
|
|
|
int fmds = feature_map_data_size();
|
|
|
|
ERR_FAIL_COND(p_row.size() != fmds);
|
|
|
|
int ind_start = calculate_feature_map_index(p_index_z);
|
|
|
|
const real_t *row_ptr = p_row.ptr();
|
|
|
|
for (int i = 0; i < fmds; ++i) {
|
|
_data[ind_start + i] = row_ptr[i];
|
|
}
|
|
}
|
|
|
|
_FORCE_INLINE_ void set_feature_map_pool_vector(int p_index_z, const PoolRealArray &p_row) {
|
|
ERR_FAIL_INDEX(p_index_z, _size.z);
|
|
|
|
int fmds = feature_map_data_size();
|
|
|
|
ERR_FAIL_COND(p_row.size() != fmds);
|
|
|
|
int ind_start = calculate_feature_map_index(p_index_z);
|
|
|
|
PoolRealArray::Read r = p_row.read();
|
|
const real_t *row_ptr = r.ptr();
|
|
|
|
for (int i = 0; i < fmds; ++i) {
|
|
_data[ind_start + i] = row_ptr[i];
|
|
}
|
|
}
|
|
|
|
_FORCE_INLINE_ void set_feature_map_mlpp_vector(int p_index_z, const Ref<MLPPVector> &p_row) {
|
|
ERR_FAIL_INDEX(p_index_z, _size.z);
|
|
ERR_FAIL_COND(!p_row.is_valid());
|
|
|
|
int fmds = feature_map_data_size();
|
|
|
|
ERR_FAIL_COND(p_row->size() != fmds);
|
|
|
|
int ind_start = calculate_feature_map_index(p_index_z);
|
|
|
|
const real_t *row_ptr = p_row->ptr();
|
|
|
|
for (int i = 0; i < fmds; ++i) {
|
|
_data[ind_start + i] = row_ptr[i];
|
|
}
|
|
}
|
|
|
|
_FORCE_INLINE_ void set_feature_map_mlpp_matrix(int p_index_z, const Ref<MLPPMatrix> &p_mat) {
|
|
ERR_FAIL_INDEX(p_index_z, _size.z);
|
|
ERR_FAIL_COND(!p_mat.is_valid());
|
|
|
|
int fmds = feature_map_data_size();
|
|
|
|
ERR_FAIL_COND(p_mat->size() != feature_map_size());
|
|
|
|
int ind_start = calculate_feature_map_index(p_index_z);
|
|
|
|
const real_t *row_ptr = p_mat->ptr();
|
|
|
|
for (int i = 0; i < fmds; ++i) {
|
|
_data[ind_start + i] = row_ptr[i];
|
|
}
|
|
}
|
|
|
|
public:
|
|
//Image api
|
|
|
|
enum ImageChannelFlags {
|
|
IMAGE_CHANNEL_FLAG_R = 1 << 0,
|
|
IMAGE_CHANNEL_FLAG_G = 1 << 1,
|
|
IMAGE_CHANNEL_FLAG_B = 1 << 2,
|
|
IMAGE_CHANNEL_FLAG_A = 1 << 3,
|
|
|
|
IMAGE_CHANNEL_FLAG_NONE = 0,
|
|
IMAGE_CHANNEL_FLAG_RG = IMAGE_CHANNEL_FLAG_R | IMAGE_CHANNEL_FLAG_G,
|
|
IMAGE_CHANNEL_FLAG_RGB = IMAGE_CHANNEL_FLAG_R | IMAGE_CHANNEL_FLAG_G | IMAGE_CHANNEL_FLAG_B,
|
|
IMAGE_CHANNEL_FLAG_GB = IMAGE_CHANNEL_FLAG_G | IMAGE_CHANNEL_FLAG_B,
|
|
IMAGE_CHANNEL_FLAG_GBA = IMAGE_CHANNEL_FLAG_G | IMAGE_CHANNEL_FLAG_B | IMAGE_CHANNEL_FLAG_A,
|
|
IMAGE_CHANNEL_FLAG_BA = IMAGE_CHANNEL_FLAG_B | IMAGE_CHANNEL_FLAG_A,
|
|
IMAGE_CHANNEL_FLAG_RGBA = IMAGE_CHANNEL_FLAG_R | IMAGE_CHANNEL_FLAG_G | IMAGE_CHANNEL_FLAG_B | IMAGE_CHANNEL_FLAG_A,
|
|
};
|
|
|
|
void add_feature_maps_image(const Ref<Image> &p_img, const int p_channels = IMAGE_CHANNEL_FLAG_RGBA);
|
|
|
|
Ref<Image> get_feature_map_image(const int p_index_z);
|
|
Ref<Image> get_feature_maps_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);
|
|
|
|
void get_feature_map_into_image(Ref<Image> p_target, const int p_index_z, const int p_target_channels = IMAGE_CHANNEL_FLAG_RGB) const;
|
|
void get_feature_maps_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;
|
|
|
|
void set_feature_map_image(const Ref<Image> &p_img, const int p_index_z, const int p_image_channel_flag = IMAGE_CHANNEL_FLAG_R);
|
|
void set_feature_maps_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);
|
|
|
|
void set_from_image(const Ref<Image> &p_img, const int p_channels = IMAGE_CHANNEL_FLAG_RGBA);
|
|
|
|
public:
|
|
//math api
|
|
|
|
//Vector<Ref<MLPPMatrix>> additionnvt(const Vector<Ref<MLPPMatrix>> &A, const Vector<Ref<MLPPMatrix>> &B);
|
|
|
|
//Vector<Ref<MLPPMatrix>> element_wise_divisionnvnvt(const Vector<Ref<MLPPMatrix>> &A, const Vector<Ref<MLPPMatrix>> &B);
|
|
|
|
//Vector<Ref<MLPPMatrix>> sqrtnvt(const Vector<Ref<MLPPMatrix>> &A);
|
|
|
|
//Vector<Ref<MLPPMatrix>> exponentiatenvt(const Vector<Ref<MLPPMatrix>> &A, real_t p);
|
|
|
|
//std::vector<std::vector<real_t>> tensor_vec_mult(std::vector<std::vector<std::vector<real_t>>> A, std::vector<real_t> b);
|
|
|
|
//std::vector<real_t> flatten(std::vector<std::vector<std::vector<real_t>>> A);
|
|
|
|
//Vector<Ref<MLPPMatrix>> scalar_multiplynvt(real_t scalar, Vector<Ref<MLPPMatrix>> A);
|
|
//Vector<Ref<MLPPMatrix>> scalar_addnvt(real_t scalar, Vector<Ref<MLPPMatrix>> A);
|
|
|
|
//Vector<Ref<MLPPMatrix>> resizenvt(const Vector<Ref<MLPPMatrix>> &A, const Vector<Ref<MLPPMatrix>> &B);
|
|
|
|
//std::vector<std::vector<std::vector<real_t>>> hadamard_product(std::vector<std::vector<std::vector<real_t>>> A, std::vector<std::vector<std::vector<real_t>>> B);
|
|
|
|
//Vector<Ref<MLPPMatrix>> maxnvt(const Vector<Ref<MLPPMatrix>> &A, const Vector<Ref<MLPPMatrix>> &B);
|
|
//Vector<Ref<MLPPMatrix>> absnvt(const Vector<Ref<MLPPMatrix>> &A);
|
|
|
|
//real_t norm_2(std::vector<std::vector<std::vector<real_t>>> A);
|
|
|
|
//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);
|
|
|
|
public:
|
|
void fill(real_t p_val) {
|
|
if (!_data) {
|
|
return;
|
|
}
|
|
|
|
int ds = data_size();
|
|
for (int i = 0; i < ds; ++i) {
|
|
_data[i] = p_val;
|
|
}
|
|
}
|
|
|
|
Vector<real_t> to_flat_vector() const {
|
|
Vector<real_t> ret;
|
|
ret.resize(data_size());
|
|
real_t *w = ret.ptrw();
|
|
memcpy(w, _data, sizeof(real_t) * data_size());
|
|
return ret;
|
|
}
|
|
|
|
PoolRealArray to_flat_pool_vector() const {
|
|
PoolRealArray pl;
|
|
if (data_size()) {
|
|
pl.resize(data_size());
|
|
typename PoolRealArray::Write w = pl.write();
|
|
real_t *dest = w.ptr();
|
|
|
|
for (int i = 0; i < data_size(); ++i) {
|
|
dest[i] = static_cast<real_t>(_data[i]);
|
|
}
|
|
}
|
|
return pl;
|
|
}
|
|
|
|
Vector<uint8_t> to_flat_byte_array() const {
|
|
Vector<uint8_t> ret;
|
|
ret.resize(data_size() * sizeof(real_t));
|
|
uint8_t *w = ret.ptrw();
|
|
memcpy(w, _data, sizeof(real_t) * data_size());
|
|
return ret;
|
|
}
|
|
|
|
Ref<MLPPTensor3> duplicate() const {
|
|
Ref<MLPPTensor3> ret;
|
|
ret.instance();
|
|
|
|
ret->set_from_mlpp_tensor3r(*this);
|
|
|
|
return ret;
|
|
}
|
|
|
|
_FORCE_INLINE_ void set_from_mlpp_tensor3(const Ref<MLPPTensor3> &p_from) {
|
|
ERR_FAIL_COND(!p_from.is_valid());
|
|
|
|
resize(p_from->size());
|
|
|
|
int ds = p_from->data_size();
|
|
const real_t *ptr = p_from->ptr();
|
|
|
|
for (int i = 0; i < ds; ++i) {
|
|
_data[i] = ptr[i];
|
|
}
|
|
}
|
|
|
|
_FORCE_INLINE_ void set_from_mlpp_tensor3r(const MLPPTensor3 &p_from) {
|
|
resize(p_from.size());
|
|
|
|
int ds = p_from.data_size();
|
|
const real_t *ptr = p_from.ptr();
|
|
|
|
for (int i = 0; i < ds; ++i) {
|
|
_data[i] = ptr[i];
|
|
}
|
|
}
|
|
|
|
_FORCE_INLINE_ void set_from_mlpp_matrix(const Ref<MLPPMatrix> &p_from) {
|
|
ERR_FAIL_COND(!p_from.is_valid());
|
|
|
|
Size2i mat_size = p_from->size();
|
|
resize(Size3i(mat_size.x, mat_size.y, 1));
|
|
|
|
int ds = p_from->data_size();
|
|
const real_t *ptr = p_from->ptr();
|
|
|
|
for (int i = 0; i < ds; ++i) {
|
|
_data[i] = ptr[i];
|
|
}
|
|
}
|
|
|
|
_FORCE_INLINE_ void set_from_mlpp_matrixr(const MLPPMatrix &p_from) {
|
|
Size2i mat_size = p_from.size();
|
|
resize(Size3i(mat_size.x, mat_size.y, 1));
|
|
|
|
int ds = p_from.data_size();
|
|
const real_t *ptr = p_from.ptr();
|
|
|
|
for (int i = 0; i < ds; ++i) {
|
|
_data[i] = ptr[i];
|
|
}
|
|
}
|
|
|
|
_FORCE_INLINE_ void set_from_mlpp_vectors(const Vector<Ref<MLPPVector>> &p_from) {
|
|
if (p_from.size() == 0) {
|
|
reset();
|
|
return;
|
|
}
|
|
|
|
if (!p_from[0].is_valid()) {
|
|
reset();
|
|
return;
|
|
}
|
|
|
|
resize(Size3i(p_from[0]->size(), p_from.size(), 1));
|
|
|
|
if (data_size() == 0) {
|
|
reset();
|
|
return;
|
|
}
|
|
|
|
for (int i = 0; i < p_from.size(); ++i) {
|
|
const Ref<MLPPVector> &r = p_from[i];
|
|
|
|
ERR_CONTINUE(!r.is_valid());
|
|
ERR_CONTINUE(r->size() != _size.x);
|
|
|
|
int start_index = i * _size.x;
|
|
|
|
const real_t *from_ptr = r->ptr();
|
|
for (int j = 0; j < _size.x; j++) {
|
|
_data[start_index + j] = from_ptr[j];
|
|
}
|
|
}
|
|
}
|
|
|
|
_FORCE_INLINE_ void set_from_mlpp_matricess(const Vector<Ref<MLPPMatrix>> &p_from) {
|
|
if (p_from.size() == 0) {
|
|
reset();
|
|
return;
|
|
}
|
|
|
|
if (!p_from[0].is_valid()) {
|
|
reset();
|
|
return;
|
|
}
|
|
|
|
resize(Size3i(p_from[0]->size().x, p_from[0]->size().y, p_from.size()));
|
|
|
|
if (data_size() == 0) {
|
|
reset();
|
|
return;
|
|
}
|
|
|
|
Size2i fms = feature_map_size();
|
|
int fmds = feature_map_data_size();
|
|
|
|
for (int i = 0; i < p_from.size(); ++i) {
|
|
const Ref<MLPPMatrix> &r = p_from[i];
|
|
|
|
ERR_CONTINUE(!r.is_valid());
|
|
ERR_CONTINUE(r->size() != fms);
|
|
|
|
int start_index = calculate_feature_map_index(i);
|
|
|
|
const real_t *from_ptr = r->ptr();
|
|
for (int j = 0; j < fmds; j++) {
|
|
_data[start_index + j] = from_ptr[j];
|
|
}
|
|
}
|
|
}
|
|
|
|
_FORCE_INLINE_ void set_from_mlpp_vectors_array(const Array &p_from) {
|
|
if (p_from.size() == 0) {
|
|
reset();
|
|
return;
|
|
}
|
|
|
|
Ref<MLPPVector> v0 = p_from[0];
|
|
|
|
if (!v0.is_valid()) {
|
|
reset();
|
|
return;
|
|
}
|
|
|
|
resize(Size3i(v0->size(), p_from.size(), 1));
|
|
|
|
if (data_size() == 0) {
|
|
reset();
|
|
return;
|
|
}
|
|
|
|
for (int i = 0; i < p_from.size(); ++i) {
|
|
Ref<MLPPVector> r = p_from[i];
|
|
|
|
ERR_CONTINUE(!r.is_valid());
|
|
ERR_CONTINUE(r->size() != _size.x);
|
|
|
|
int start_index = i * _size.x;
|
|
|
|
const real_t *from_ptr = r->ptr();
|
|
for (int j = 0; j < _size.x; j++) {
|
|
_data[start_index + j] = from_ptr[j];
|
|
}
|
|
}
|
|
}
|
|
|
|
_FORCE_INLINE_ void set_from_mlpp_matrices_array(const Array &p_from) {
|
|
if (p_from.size() == 0) {
|
|
reset();
|
|
return;
|
|
}
|
|
|
|
Ref<MLPPMatrix> v0 = p_from[0];
|
|
|
|
if (!v0.is_valid()) {
|
|
reset();
|
|
return;
|
|
}
|
|
|
|
resize(Size3i(v0->size().x, v0->size().y, p_from.size()));
|
|
|
|
if (data_size() == 0) {
|
|
reset();
|
|
return;
|
|
}
|
|
|
|
Size2i fms = feature_map_size();
|
|
int fmds = feature_map_data_size();
|
|
|
|
for (int i = 0; i < p_from.size(); ++i) {
|
|
Ref<MLPPMatrix> r = p_from[i];
|
|
|
|
ERR_CONTINUE(!r.is_valid());
|
|
ERR_CONTINUE(r->size() != fms);
|
|
|
|
int start_index = calculate_feature_map_index(i);
|
|
|
|
const real_t *from_ptr = r->ptr();
|
|
for (int j = 0; j < fmds; j++) {
|
|
_data[start_index + j] = from_ptr[j];
|
|
}
|
|
}
|
|
}
|
|
|
|
_FORCE_INLINE_ bool is_equal_approx(const Ref<MLPPTensor3> &p_with, real_t tolerance = static_cast<real_t>(CMP_EPSILON)) const {
|
|
ERR_FAIL_COND_V(!p_with.is_valid(), false);
|
|
|
|
if (unlikely(this == p_with.ptr())) {
|
|
return true;
|
|
}
|
|
|
|
if (_size != p_with->size()) {
|
|
return false;
|
|
}
|
|
|
|
int ds = data_size();
|
|
|
|
for (int i = 0; i < ds; ++i) {
|
|
if (!Math::is_equal_approx(_data[i], p_with->_data[i], tolerance)) {
|
|
return false;
|
|
}
|
|
}
|
|
|
|
return true;
|
|
}
|
|
|
|
String to_string();
|
|
|
|
_FORCE_INLINE_ MLPPTensor3() {
|
|
_data = NULL;
|
|
}
|
|
|
|
_FORCE_INLINE_ MLPPTensor3(const MLPPMatrix &p_from) {
|
|
_data = NULL;
|
|
|
|
Size2i mat_size = p_from.size();
|
|
resize(Size3i(mat_size.x, mat_size.y, 1));
|
|
|
|
int ds = p_from.data_size();
|
|
const real_t *ptr = p_from.ptr();
|
|
|
|
for (int i = 0; i < ds; ++i) {
|
|
_data[i] = ptr[i];
|
|
}
|
|
}
|
|
|
|
MLPPTensor3(const Array &p_from) {
|
|
_data = NULL;
|
|
|
|
set_from_mlpp_matrices_array(p_from);
|
|
}
|
|
|
|
_FORCE_INLINE_ ~MLPPTensor3() {
|
|
if (_data) {
|
|
reset();
|
|
}
|
|
}
|
|
|
|
// TODO: These are temporary
|
|
std::vector<real_t> to_flat_std_vector() const;
|
|
void set_from_std_vectors(const std::vector<std::vector<std::vector<real_t>>> &p_from);
|
|
std::vector<std::vector<std::vector<real_t>>> to_std_vector();
|
|
MLPPTensor3(const std::vector<std::vector<std::vector<real_t>>> &p_from);
|
|
|
|
protected:
|
|
static void _bind_methods();
|
|
|
|
protected:
|
|
Size3i _size;
|
|
real_t *_data;
|
|
};
|
|
|
|
VARIANT_ENUM_CAST(MLPPTensor3::ImageChannelFlags);
|
|
|
|
#endif
|