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https://github.com/Relintai/pmlpp.git
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Moved lots of methods in MLPPTensor3's header to the .cpp file.
This commit is contained in:
parent
3108274c09
commit
2e6094cf03
@ -3,6 +3,534 @@
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#include "core/io/image.h"
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void MLPPTensor3::add_z_slice(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 = z_slice_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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void MLPPTensor3::add_z_slice_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 = z_slice_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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void MLPPTensor3::add_z_slice_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 = z_slice_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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void MLPPTensor3::add_z_slice_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 = z_slice_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 MLPPTensor3::remove_z_slice(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 = z_slice_data_size();
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for (int i = calculate_z_slice_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 MLPPTensor3::remove_z_slice_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_z_slice_index(p_index);
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int end_ind = calculate_z_slice_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 MLPPTensor3::swap_z_slice(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_z_slice_index(p_index_1);
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int ind2_start = calculate_z_slice_index(p_index_2);
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int fmds = z_slice_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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void MLPPTensor3::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 MLPPTensor3::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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Vector<real_t> MLPPTensor3::get_row_vector(int p_index_y, int p_index_z) const {
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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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PoolRealArray MLPPTensor3::get_row_pool_vector(int p_index_y, int p_index_z) const {
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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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Ref<MLPPVector> MLPPTensor3::get_row_mlpp_vector(int p_index_y, int p_index_z) const {
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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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void MLPPTensor3::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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void MLPPTensor3::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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void MLPPTensor3::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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void MLPPTensor3::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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Vector<real_t> MLPPTensor3::get_z_slice_vector(int p_index_z) const {
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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 = z_slice_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_z_slice_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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PoolRealArray MLPPTensor3::get_z_slice_pool_vector(int p_index_z) const {
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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 = z_slice_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_z_slice_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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Ref<MLPPVector> MLPPTensor3::get_z_slice_mlpp_vector(int p_index_z) const {
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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 = z_slice_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_z_slice_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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void MLPPTensor3::get_z_slice_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 = z_slice_data_size();
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if (unlikely(target->size() != fmds)) {
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target->resize(fmds);
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}
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int ind_start = calculate_z_slice_index(p_index_z);
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real_t *row_ptr = target->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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}
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Ref<MLPPMatrix> MLPPTensor3::get_z_slice_mlpp_matrix(int p_index_z) const {
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ERR_FAIL_INDEX_V(p_index_z, _size.z, Ref<MLPPMatrix>());
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Ref<MLPPMatrix> ret;
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ret.instance();
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int fmds = z_slice_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(z_slice_size());
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int ind_start = calculate_z_slice_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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void MLPPTensor3::get_z_slice_into_mlpp_matrix(int p_index_z, Ref<MLPPMatrix> target) const {
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ERR_FAIL_INDEX(p_index_z, _size.z);
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int fmds = z_slice_data_size();
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Size2i fms = z_slice_size();
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if (unlikely(target->size() != fms)) {
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target->resize(fms);
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}
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int ind_start = calculate_z_slice_index(p_index_z);
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real_t *row_ptr = target->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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}
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void MLPPTensor3::set_z_slice_vector(int p_index_z, const Vector<real_t> &p_row) {
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ERR_FAIL_INDEX(p_index_z, _size.z);
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int fmds = z_slice_data_size();
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ERR_FAIL_COND(p_row.size() != fmds);
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int ind_start = calculate_z_slice_index(p_index_z);
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const real_t *row_ptr = p_row.ptr();
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for (int i = 0; i < fmds; ++i) {
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_data[ind_start + i] = row_ptr[i];
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}
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}
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void MLPPTensor3::set_z_slice_pool_vector(int p_index_z, const PoolRealArray &p_row) {
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ERR_FAIL_INDEX(p_index_z, _size.z);
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|
||||
int fmds = z_slice_data_size();
|
||||
|
||||
ERR_FAIL_COND(p_row.size() != fmds);
|
||||
|
||||
int ind_start = calculate_z_slice_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];
|
||||
}
|
||||
}
|
||||
|
||||
void MLPPTensor3::set_z_slice_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 = z_slice_data_size();
|
||||
|
||||
ERR_FAIL_COND(p_row->size() != fmds);
|
||||
|
||||
int ind_start = calculate_z_slice_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];
|
||||
}
|
||||
}
|
||||
|
||||
void MLPPTensor3::set_z_slice_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 = z_slice_data_size();
|
||||
|
||||
ERR_FAIL_COND(p_mat->size() != z_slice_size());
|
||||
|
||||
int ind_start = calculate_z_slice_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];
|
||||
}
|
||||
}
|
||||
|
||||
void MLPPTensor3::add_z_slices_image(const Ref<Image> &p_img, const int p_channels) {
|
||||
ERR_FAIL_COND(!p_img.is_valid());
|
||||
|
||||
@ -975,6 +1503,270 @@ std::vector<std::vector<std::vector<real_t>>> MLPPTensor3::vector_wise_tensor_pr
|
||||
}
|
||||
*/
|
||||
|
||||
void MLPPTensor3::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> MLPPTensor3::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 MLPPTensor3::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> MLPPTensor3::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> MLPPTensor3::duplicate() const {
|
||||
Ref<MLPPTensor3> ret;
|
||||
ret.instance();
|
||||
|
||||
ret->set_from_mlpp_tensor3r(*this);
|
||||
|
||||
return ret;
|
||||
}
|
||||
|
||||
void MLPPTensor3::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];
|
||||
}
|
||||
}
|
||||
|
||||
void MLPPTensor3::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];
|
||||
}
|
||||
}
|
||||
|
||||
void MLPPTensor3::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];
|
||||
}
|
||||
}
|
||||
|
||||
void MLPPTensor3::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];
|
||||
}
|
||||
}
|
||||
|
||||
void MLPPTensor3::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];
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
void MLPPTensor3::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 = z_slice_size();
|
||||
int fmds = z_slice_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_z_slice_index(i);
|
||||
|
||||
const real_t *from_ptr = r->ptr();
|
||||
for (int j = 0; j < fmds; j++) {
|
||||
_data[start_index + j] = from_ptr[j];
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
void MLPPTensor3::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];
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
void MLPPTensor3::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 = z_slice_size();
|
||||
int fmds = z_slice_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_z_slice_index(i);
|
||||
|
||||
const real_t *from_ptr = r->ptr();
|
||||
for (int j = 0; j < fmds; j++) {
|
||||
_data[start_index + j] = from_ptr[j];
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
bool MLPPTensor3::is_equal_approx(const Ref<MLPPTensor3> &p_with, real_t tolerance) 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 MLPPTensor3::to_string() {
|
||||
String str;
|
||||
|
||||
@ -1004,6 +1796,36 @@ String MLPPTensor3::to_string() {
|
||||
return str;
|
||||
}
|
||||
|
||||
MLPPTensor3::MLPPTensor3() {
|
||||
_data = NULL;
|
||||
}
|
||||
|
||||
MLPPTensor3::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::MLPPTensor3(const Array &p_from) {
|
||||
_data = NULL;
|
||||
|
||||
set_from_mlpp_matrices_array(p_from);
|
||||
}
|
||||
|
||||
MLPPTensor3::~MLPPTensor3() {
|
||||
if (_data) {
|
||||
reset();
|
||||
}
|
||||
}
|
||||
|
||||
std::vector<real_t> MLPPTensor3::to_flat_std_vector() const {
|
||||
std::vector<real_t> ret;
|
||||
ret.resize(data_size());
|
||||
|
||||
@ -29,170 +29,17 @@ public:
|
||||
return _data;
|
||||
}
|
||||
|
||||
_FORCE_INLINE_ void add_z_slice(const Vector<real_t> &p_row) {
|
||||
if (p_row.size() == 0) {
|
||||
return;
|
||||
}
|
||||
|
||||
int fms = z_slice_data_size();
|
||||
|
||||
ERR_FAIL_COND(fms != p_row.size());
|
||||
|
||||
int ci = data_size();
|
||||
|
||||
++_size.z;
|
||||
|
||||
_data = (real_t *)memrealloc(_data, data_size() * sizeof(real_t));
|
||||
CRASH_COND_MSG(!_data, "Out of memory");
|
||||
|
||||
const real_t *row_arr = p_row.ptr();
|
||||
|
||||
for (int i = 0; i < p_row.size(); ++i) {
|
||||
_data[ci + i] = row_arr[i];
|
||||
}
|
||||
}
|
||||
|
||||
_FORCE_INLINE_ void add_z_slice_pool_vector(const PoolRealArray &p_row) {
|
||||
if (p_row.size() == 0) {
|
||||
return;
|
||||
}
|
||||
|
||||
int fms = z_slice_data_size();
|
||||
|
||||
ERR_FAIL_COND(fms != p_row.size());
|
||||
|
||||
int ci = data_size();
|
||||
|
||||
++_size.z;
|
||||
|
||||
_data = (real_t *)memrealloc(_data, data_size() * sizeof(real_t));
|
||||
CRASH_COND_MSG(!_data, "Out of memory");
|
||||
|
||||
PoolRealArray::Read rread = p_row.read();
|
||||
const real_t *row_arr = rread.ptr();
|
||||
|
||||
for (int i = 0; i < p_row.size(); ++i) {
|
||||
_data[ci + i] = row_arr[i];
|
||||
}
|
||||
}
|
||||
|
||||
_FORCE_INLINE_ void add_z_slice_mlpp_vector(const Ref<MLPPVector> &p_row) {
|
||||
ERR_FAIL_COND(!p_row.is_valid());
|
||||
|
||||
int p_row_size = p_row->size();
|
||||
|
||||
if (p_row_size == 0) {
|
||||
return;
|
||||
}
|
||||
|
||||
int fms = z_slice_data_size();
|
||||
|
||||
ERR_FAIL_COND(fms != p_row_size);
|
||||
|
||||
int ci = data_size();
|
||||
|
||||
++_size.z;
|
||||
|
||||
_data = (real_t *)memrealloc(_data, data_size() * sizeof(real_t));
|
||||
CRASH_COND_MSG(!_data, "Out of memory");
|
||||
|
||||
const real_t *row_ptr = p_row->ptr();
|
||||
|
||||
for (int i = 0; i < p_row_size; ++i) {
|
||||
_data[ci + i] = row_ptr[i];
|
||||
}
|
||||
}
|
||||
|
||||
_FORCE_INLINE_ void add_z_slice_mlpp_matrix(const Ref<MLPPMatrix> &p_matrix) {
|
||||
ERR_FAIL_COND(!p_matrix.is_valid());
|
||||
|
||||
int other_data_size = p_matrix->data_size();
|
||||
|
||||
if (other_data_size == 0) {
|
||||
return;
|
||||
}
|
||||
|
||||
Size2i matrix_size = p_matrix->size();
|
||||
Size2i fms = z_slice_size();
|
||||
|
||||
ERR_FAIL_COND(fms != matrix_size);
|
||||
|
||||
int start_offset = data_size();
|
||||
|
||||
++_size.z;
|
||||
|
||||
_data = (real_t *)memrealloc(_data, data_size() * sizeof(real_t));
|
||||
CRASH_COND_MSG(!_data, "Out of memory");
|
||||
|
||||
const real_t *other_ptr = p_matrix->ptr();
|
||||
|
||||
for (int i = 0; i < other_data_size; ++i) {
|
||||
_data[start_offset + i] = other_ptr[i];
|
||||
}
|
||||
}
|
||||
|
||||
void remove_z_slice(int p_index) {
|
||||
ERR_FAIL_INDEX(p_index, _size.z);
|
||||
|
||||
--_size.z;
|
||||
|
||||
int ds = data_size();
|
||||
|
||||
if (ds == 0) {
|
||||
memfree(_data);
|
||||
_data = NULL;
|
||||
return;
|
||||
}
|
||||
|
||||
int fmds = z_slice_data_size();
|
||||
|
||||
for (int i = calculate_z_slice_index(p_index); i < ds; ++i) {
|
||||
_data[i] = _data[i + fmds];
|
||||
}
|
||||
|
||||
_data = (real_t *)memrealloc(_data, data_size() * sizeof(real_t));
|
||||
CRASH_COND_MSG(!_data, "Out of memory");
|
||||
}
|
||||
void add_z_slice(const Vector<real_t> &p_row);
|
||||
void add_z_slice_pool_vector(const PoolRealArray &p_row);
|
||||
void add_z_slice_mlpp_vector(const Ref<MLPPVector> &p_row);
|
||||
void add_z_slice_mlpp_matrix(const Ref<MLPPMatrix> &p_matrix);
|
||||
void remove_z_slice(int p_index);
|
||||
|
||||
// Removes the item copying the last value into the position of the one to
|
||||
// remove. It's generally faster than `remove`.
|
||||
void remove_z_slice_unordered(int p_index) {
|
||||
ERR_FAIL_INDEX(p_index, _size.z);
|
||||
void remove_z_slice_unordered(int p_index);
|
||||
|
||||
--_size.z;
|
||||
|
||||
int ds = data_size();
|
||||
|
||||
if (ds == 0) {
|
||||
memfree(_data);
|
||||
_data = NULL;
|
||||
return;
|
||||
}
|
||||
|
||||
int start_ind = calculate_z_slice_index(p_index);
|
||||
int end_ind = calculate_z_slice_index(p_index + 1);
|
||||
|
||||
for (int i = start_ind; i < end_ind; ++i) {
|
||||
_data[i] = _data[ds + i];
|
||||
}
|
||||
|
||||
_data = (real_t *)memrealloc(_data, data_size() * sizeof(real_t));
|
||||
CRASH_COND_MSG(!_data, "Out of memory");
|
||||
}
|
||||
|
||||
void swap_z_slice(int p_index_1, int p_index_2) {
|
||||
ERR_FAIL_INDEX(p_index_1, _size.z);
|
||||
ERR_FAIL_INDEX(p_index_2, _size.z);
|
||||
|
||||
int ind1_start = calculate_z_slice_index(p_index_1);
|
||||
int ind2_start = calculate_z_slice_index(p_index_2);
|
||||
|
||||
int fmds = z_slice_data_size();
|
||||
|
||||
for (int i = 0; i < fmds; ++i) {
|
||||
SWAP(_data[ind1_start + i], _data[ind2_start + i]);
|
||||
}
|
||||
}
|
||||
void swap_z_slice(int p_index_1, int p_index_2);
|
||||
|
||||
_FORCE_INLINE_ void clear() { resize(Size3i()); }
|
||||
_FORCE_INLINE_ void reset() {
|
||||
@ -209,32 +56,8 @@ public:
|
||||
_FORCE_INLINE_ int data_size() const { return _size.x * _size.y * _size.z; }
|
||||
_FORCE_INLINE_ Size3i size() const { return _size; }
|
||||
|
||||
void resize(const Size3i &p_size) {
|
||||
_size = p_size;
|
||||
|
||||
int ds = data_size();
|
||||
|
||||
if (ds == 0) {
|
||||
if (_data) {
|
||||
memfree(_data);
|
||||
_data = NULL;
|
||||
}
|
||||
|
||||
return;
|
||||
}
|
||||
|
||||
_data = (real_t *)memrealloc(_data, ds * sizeof(real_t));
|
||||
CRASH_COND_MSG(!_data, "Out of memory");
|
||||
}
|
||||
|
||||
void set_shape(const Size3i &p_size) {
|
||||
int ds = data_size();
|
||||
int new_data_size = p_size.x * p_size.y * p_size.z;
|
||||
|
||||
ERR_FAIL_COND_MSG(ds != new_data_size, "The new size has a different volume than the old. If this is intended use resize()!");
|
||||
|
||||
_size = p_size;
|
||||
}
|
||||
void resize(const Size3i &p_size);
|
||||
void set_shape(const Size3i &p_size);
|
||||
|
||||
_FORCE_INLINE_ int calculate_index(int p_index_y, int p_index_x, int p_index_z) const {
|
||||
return p_index_y * _size.x + p_index_x + _size.x * _size.y * p_index_z;
|
||||
@ -281,341 +104,26 @@ public:
|
||||
_data[p_index_y * _size.x + p_index_x + _size.x * _size.y * p_index_z] = p_val;
|
||||
}
|
||||
|
||||
_FORCE_INLINE_ Vector<real_t> get_row_vector(int p_index_y, int p_index_z) const {
|
||||
ERR_FAIL_INDEX_V(p_index_y, _size.y, Vector<real_t>());
|
||||
ERR_FAIL_INDEX_V(p_index_z, _size.z, Vector<real_t>());
|
||||
|
||||
Vector<real_t> ret;
|
||||
|
||||
if (unlikely(_size.x == 0)) {
|
||||
return ret;
|
||||
}
|
||||
|
||||
ret.resize(_size.x);
|
||||
|
||||
int ind_start = p_index_y * _size.x;
|
||||
|
||||
real_t *row_ptr = ret.ptrw();
|
||||
|
||||
for (int i = 0; i < _size.x; ++i) {
|
||||
row_ptr[i] = _data[ind_start + i];
|
||||
}
|
||||
|
||||
return ret;
|
||||
}
|
||||
|
||||
_FORCE_INLINE_ PoolRealArray get_row_pool_vector(int p_index_y, int p_index_z) const {
|
||||
ERR_FAIL_INDEX_V(p_index_y, _size.y, PoolRealArray());
|
||||
ERR_FAIL_INDEX_V(p_index_z, _size.z, PoolRealArray());
|
||||
|
||||
PoolRealArray ret;
|
||||
|
||||
if (unlikely(_size.x == 0)) {
|
||||
return ret;
|
||||
}
|
||||
|
||||
ret.resize(_size.x);
|
||||
|
||||
int ind_start = p_index_y * _size.x + _size.x * _size.y * p_index_z;
|
||||
|
||||
PoolRealArray::Write w = ret.write();
|
||||
real_t *row_ptr = w.ptr();
|
||||
|
||||
for (int i = 0; i < _size.x; ++i) {
|
||||
row_ptr[i] = _data[ind_start + i];
|
||||
}
|
||||
|
||||
return ret;
|
||||
}
|
||||
|
||||
_FORCE_INLINE_ Ref<MLPPVector> get_row_mlpp_vector(int p_index_y, int p_index_z) const {
|
||||
ERR_FAIL_INDEX_V(p_index_y, _size.y, Ref<MLPPVector>());
|
||||
ERR_FAIL_INDEX_V(p_index_z, _size.z, Ref<MLPPVector>());
|
||||
|
||||
Ref<MLPPVector> ret;
|
||||
ret.instance();
|
||||
|
||||
if (unlikely(_size.x == 0)) {
|
||||
return ret;
|
||||
}
|
||||
|
||||
ret->resize(_size.x);
|
||||
|
||||
int ind_start = p_index_y * _size.x + _size.x * _size.y * p_index_z;
|
||||
|
||||
real_t *row_ptr = ret->ptrw();
|
||||
|
||||
for (int i = 0; i < _size.x; ++i) {
|
||||
row_ptr[i] = _data[ind_start + i];
|
||||
}
|
||||
|
||||
return ret;
|
||||
}
|
||||
|
||||
_FORCE_INLINE_ void get_row_into_mlpp_vector(int p_index_y, int p_index_z, Ref<MLPPVector> target) const {
|
||||
ERR_FAIL_COND(!target.is_valid());
|
||||
ERR_FAIL_INDEX(p_index_y, _size.y);
|
||||
ERR_FAIL_INDEX(p_index_z, _size.z);
|
||||
|
||||
if (unlikely(target->size() != _size.x)) {
|
||||
target->resize(_size.x);
|
||||
}
|
||||
|
||||
int ind_start = p_index_y * _size.x + _size.x * _size.y * p_index_z;
|
||||
|
||||
real_t *row_ptr = target->ptrw();
|
||||
|
||||
for (int i = 0; i < _size.x; ++i) {
|
||||
row_ptr[i] = _data[ind_start + i];
|
||||
}
|
||||
}
|
||||
|
||||
_FORCE_INLINE_ void set_row_vector(int p_index_y, int p_index_z, const Vector<real_t> &p_row) {
|
||||
ERR_FAIL_COND(p_row.size() != _size.x);
|
||||
ERR_FAIL_INDEX(p_index_y, _size.y);
|
||||
ERR_FAIL_INDEX(p_index_z, _size.z);
|
||||
|
||||
int ind_start = p_index_y * _size.x + _size.x * _size.y * p_index_z;
|
||||
|
||||
const real_t *row_ptr = p_row.ptr();
|
||||
|
||||
for (int i = 0; i < _size.x; ++i) {
|
||||
_data[ind_start + i] = row_ptr[i];
|
||||
}
|
||||
}
|
||||
|
||||
_FORCE_INLINE_ void set_row_pool_vector(int p_index_y, int p_index_z, const PoolRealArray &p_row) {
|
||||
ERR_FAIL_COND(p_row.size() != _size.x);
|
||||
ERR_FAIL_INDEX(p_index_y, _size.y);
|
||||
ERR_FAIL_INDEX(p_index_z, _size.z);
|
||||
|
||||
int ind_start = p_index_y * _size.x + _size.x * _size.y * p_index_z;
|
||||
|
||||
PoolRealArray::Read r = p_row.read();
|
||||
const real_t *row_ptr = r.ptr();
|
||||
|
||||
for (int i = 0; i < _size.x; ++i) {
|
||||
_data[ind_start + i] = row_ptr[i];
|
||||
}
|
||||
}
|
||||
|
||||
_FORCE_INLINE_ void set_row_mlpp_vector(int p_index_y, int p_index_z, const Ref<MLPPVector> &p_row) {
|
||||
ERR_FAIL_COND(!p_row.is_valid());
|
||||
ERR_FAIL_COND(p_row->size() != _size.x);
|
||||
ERR_FAIL_INDEX(p_index_y, _size.y);
|
||||
ERR_FAIL_INDEX(p_index_z, _size.z);
|
||||
|
||||
int ind_start = p_index_y * _size.x + _size.x * _size.y * p_index_z;
|
||||
|
||||
const real_t *row_ptr = p_row->ptr();
|
||||
|
||||
for (int i = 0; i < _size.x; ++i) {
|
||||
_data[ind_start + i] = row_ptr[i];
|
||||
}
|
||||
}
|
||||
|
||||
_FORCE_INLINE_ Vector<real_t> get_z_slice_vector(int p_index_z) const {
|
||||
ERR_FAIL_INDEX_V(p_index_z, _size.z, Vector<real_t>());
|
||||
|
||||
Vector<real_t> ret;
|
||||
|
||||
int fmds = z_slice_data_size();
|
||||
|
||||
if (unlikely(fmds == 0)) {
|
||||
return ret;
|
||||
}
|
||||
|
||||
ret.resize(fmds);
|
||||
|
||||
int ind_start = calculate_z_slice_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_ PoolRealArray get_z_slice_pool_vector(int p_index_z) const {
|
||||
ERR_FAIL_INDEX_V(p_index_z, _size.z, PoolRealArray());
|
||||
|
||||
PoolRealArray ret;
|
||||
|
||||
int fmds = z_slice_data_size();
|
||||
|
||||
if (unlikely(fmds == 0)) {
|
||||
return ret;
|
||||
}
|
||||
|
||||
ret.resize(fmds);
|
||||
|
||||
int ind_start = calculate_z_slice_index(p_index_z);
|
||||
|
||||
PoolRealArray::Write w = ret.write();
|
||||
real_t *row_ptr = w.ptr();
|
||||
|
||||
for (int i = 0; i < fmds; ++i) {
|
||||
row_ptr[i] = _data[ind_start + i];
|
||||
}
|
||||
|
||||
return ret;
|
||||
}
|
||||
|
||||
_FORCE_INLINE_ Ref<MLPPVector> get_z_slice_mlpp_vector(int p_index_z) const {
|
||||
ERR_FAIL_INDEX_V(p_index_z, _size.z, Ref<MLPPVector>());
|
||||
|
||||
Ref<MLPPVector> ret;
|
||||
ret.instance();
|
||||
|
||||
int fmds = z_slice_data_size();
|
||||
|
||||
if (unlikely(fmds == 0)) {
|
||||
return ret;
|
||||
}
|
||||
|
||||
ret->resize(fmds);
|
||||
|
||||
int ind_start = calculate_z_slice_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_z_slice_into_mlpp_vector(int p_index_z, Ref<MLPPVector> target) const {
|
||||
ERR_FAIL_INDEX(p_index_z, _size.z);
|
||||
|
||||
int fmds = z_slice_data_size();
|
||||
|
||||
if (unlikely(target->size() != fmds)) {
|
||||
target->resize(fmds);
|
||||
}
|
||||
|
||||
int ind_start = calculate_z_slice_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_z_slice_mlpp_matrix(int p_index_z) const {
|
||||
ERR_FAIL_INDEX_V(p_index_z, _size.z, Ref<MLPPMatrix>());
|
||||
|
||||
Ref<MLPPMatrix> ret;
|
||||
ret.instance();
|
||||
|
||||
int fmds = z_slice_data_size();
|
||||
|
||||
if (unlikely(fmds == 0)) {
|
||||
return ret;
|
||||
}
|
||||
|
||||
ret->resize(z_slice_size());
|
||||
|
||||
int ind_start = calculate_z_slice_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_z_slice_into_mlpp_matrix(int p_index_z, Ref<MLPPMatrix> target) const {
|
||||
ERR_FAIL_INDEX(p_index_z, _size.z);
|
||||
|
||||
int fmds = z_slice_data_size();
|
||||
Size2i fms = z_slice_size();
|
||||
|
||||
if (unlikely(target->size() != fms)) {
|
||||
target->resize(fms);
|
||||
}
|
||||
|
||||
int ind_start = calculate_z_slice_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_z_slice_vector(int p_index_z, const Vector<real_t> &p_row) {
|
||||
ERR_FAIL_INDEX(p_index_z, _size.z);
|
||||
|
||||
int fmds = z_slice_data_size();
|
||||
|
||||
ERR_FAIL_COND(p_row.size() != fmds);
|
||||
|
||||
int ind_start = calculate_z_slice_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_z_slice_pool_vector(int p_index_z, const PoolRealArray &p_row) {
|
||||
ERR_FAIL_INDEX(p_index_z, _size.z);
|
||||
|
||||
int fmds = z_slice_data_size();
|
||||
|
||||
ERR_FAIL_COND(p_row.size() != fmds);
|
||||
|
||||
int ind_start = calculate_z_slice_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_z_slice_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 = z_slice_data_size();
|
||||
|
||||
ERR_FAIL_COND(p_row->size() != fmds);
|
||||
|
||||
int ind_start = calculate_z_slice_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_z_slice_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 = z_slice_data_size();
|
||||
|
||||
ERR_FAIL_COND(p_mat->size() != z_slice_size());
|
||||
|
||||
int ind_start = calculate_z_slice_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];
|
||||
}
|
||||
}
|
||||
Vector<real_t> get_row_vector(int p_index_y, int p_index_z) const;
|
||||
PoolRealArray get_row_pool_vector(int p_index_y, int p_index_z) const;
|
||||
Ref<MLPPVector> get_row_mlpp_vector(int p_index_y, int p_index_z) const;
|
||||
void get_row_into_mlpp_vector(int p_index_y, int p_index_z, Ref<MLPPVector> target) const;
|
||||
|
||||
void set_row_vector(int p_index_y, int p_index_z, const Vector<real_t> &p_row);
|
||||
void set_row_pool_vector(int p_index_y, int p_index_z, const PoolRealArray &p_row);
|
||||
void set_row_mlpp_vector(int p_index_y, int p_index_z, const Ref<MLPPVector> &p_row);
|
||||
|
||||
Vector<real_t> get_z_slice_vector(int p_index_z) const;
|
||||
PoolRealArray get_z_slice_pool_vector(int p_index_z) const;
|
||||
Ref<MLPPVector> get_z_slice_mlpp_vector(int p_index_z) const;
|
||||
void get_z_slice_into_mlpp_vector(int p_index_z, Ref<MLPPVector> target) const;
|
||||
Ref<MLPPMatrix> get_z_slice_mlpp_matrix(int p_index_z) const;
|
||||
void get_z_slice_into_mlpp_matrix(int p_index_z, Ref<MLPPMatrix> target) const;
|
||||
|
||||
void set_z_slice_vector(int p_index_z, const Vector<real_t> &p_row);
|
||||
void set_z_slice_pool_vector(int p_index_z, const PoolRealArray &p_row);
|
||||
void set_z_slice_mlpp_vector(int p_index_z, const Ref<MLPPVector> &p_row);
|
||||
void set_z_slice_mlpp_matrix(int p_index_z, const Ref<MLPPMatrix> &p_mat);
|
||||
|
||||
public:
|
||||
//Image api
|
||||
@ -700,301 +208,33 @@ public:
|
||||
//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;
|
||||
}
|
||||
void fill(real_t p_val);
|
||||
|
||||
int ds = data_size();
|
||||
for (int i = 0; i < ds; ++i) {
|
||||
_data[i] = p_val;
|
||||
}
|
||||
}
|
||||
Vector<real_t> to_flat_vector() const;
|
||||
PoolRealArray to_flat_pool_vector() const;
|
||||
Vector<uint8_t> to_flat_byte_array() const;
|
||||
|
||||
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;
|
||||
}
|
||||
Ref<MLPPTensor3> duplicate() const;
|
||||
|
||||
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();
|
||||
void set_from_mlpp_tensor3(const Ref<MLPPTensor3> &p_from);
|
||||
void set_from_mlpp_tensor3r(const MLPPTensor3 &p_from);
|
||||
|
||||
for (int i = 0; i < data_size(); ++i) {
|
||||
dest[i] = static_cast<real_t>(_data[i]);
|
||||
}
|
||||
}
|
||||
return pl;
|
||||
}
|
||||
void set_from_mlpp_matrix(const Ref<MLPPMatrix> &p_from);
|
||||
void set_from_mlpp_matrixr(const MLPPMatrix &p_from);
|
||||
void set_from_mlpp_vectors(const Vector<Ref<MLPPVector>> &p_from);
|
||||
void set_from_mlpp_matricess(const Vector<Ref<MLPPMatrix>> &p_from);
|
||||
|
||||
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;
|
||||
}
|
||||
void set_from_mlpp_vectors_array(const Array &p_from);
|
||||
void set_from_mlpp_matrices_array(const Array &p_from);
|
||||
|
||||
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 = z_slice_size();
|
||||
int fmds = z_slice_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_z_slice_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 = z_slice_size();
|
||||
int fmds = z_slice_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_z_slice_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;
|
||||
}
|
||||
bool is_equal_approx(const Ref<MLPPTensor3> &p_with, real_t tolerance = static_cast<real_t>(CMP_EPSILON)) const;
|
||||
|
||||
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();
|
||||
}
|
||||
}
|
||||
MLPPTensor3();
|
||||
MLPPTensor3(const MLPPMatrix &p_from);
|
||||
MLPPTensor3(const Array &p_from);
|
||||
~MLPPTensor3();
|
||||
|
||||
// TODO: These are temporary
|
||||
std::vector<real_t> to_flat_std_vector() const;
|
||||
|
||||
Loading…
Reference in New Issue
Block a user