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86 lines
3.4 KiB
86 lines
3.4 KiB
cimport cython
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import numpy as np
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cimport numpy as np
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from libc.stdlib cimport free, malloc
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from libcpp cimport bool
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from libcpp.string cimport string
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from cpython cimport PyObject, Py_INCREF
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CREATE_INIT = True # workaround, so cython builds a init function
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np.import_array()
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ctypedef unsigned char uint8_t
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cdef extern from "rf/train.h":
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cdef cppclass TrainParameters:
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int n_trees;
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int max_tree_depth;
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int n_test_split_functions;
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int n_test_thresholds;
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int n_test_samples;
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int min_samples_to_split;
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int min_samples_for_leaf;
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int print_node_info;
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TrainParameters();
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cdef extern from "hyperdepth.h":
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void train(int row_from, int row_to, TrainParameters params, const uint8_t* ims, const float* disps, int n, int h, int w, int n_disp_bins, int depth_switch, int n_threads, string forest_prefix);
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void eval(int row_from, int row_to, const uint8_t* ims, const float* disps, int n, int h, int w, int n_disp_bins, int depth_switch, int n_threads, string forest_prefix, float* out);
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cdef class TrainParams:
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cdef TrainParameters params;
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def __cinit__(self, int n_trees=6, int max_tree_depth=8, int n_test_split_functions=50, int n_test_thresholds=10, int n_test_samples=4096, int min_samples_to_split=16, int min_samples_for_leaf=8, int print_node_info=100):
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self.params.n_trees = n_trees
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self.params.max_tree_depth = max_tree_depth
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self.params.n_test_split_functions = n_test_split_functions
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self.params.n_test_thresholds = n_test_thresholds
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self.params.n_test_samples = n_test_samples
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self.params.min_samples_to_split = min_samples_to_split
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self.params.min_samples_for_leaf = min_samples_for_leaf
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self.params.print_node_info = print_node_info
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def __str__(self):
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return f'n_trees={self.params.n_trees}, max_tree_depth={self.params.max_tree_depth}, n_test_split_functions={self.params.n_test_split_functions}, n_test_thresholds={self.params.n_test_thresholds}, n_test_samples={self.params.n_test_samples}, min_samples_to_split={self.params.min_samples_to_split}, min_samples_for_leaf={self.params.min_samples_for_leaf}'
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def train_forest(TrainParams params, uint8_t[:,:,::1] ims, float[:,:,::1] disps, int n_disp_bins=10, int depth_switch=0, int n_threads=18, str forest_prefix='forest', int row_from=-1, int row_to=-1):
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cdef int n = ims.shape[0]
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cdef int h = ims.shape[1]
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cdef int w = ims.shape[2]
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if row_from < 0:
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row_from = 0
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if row_to > h or row_to < 0:
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row_to = h
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if n != disps.shape[0] or h != disps.shape[1] or w != disps.shape[2]:
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raise Exception('ims.shape != disps.shape')
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train(row_from, row_to, params.params, &ims[0,0,0], &disps[0,0,0], n, h, w, n_disp_bins, depth_switch, n_threads, forest_prefix.encode())
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def eval_forest(uint8_t[:,:,::1] ims, float[:,:,::1] disps, int n_disp_bins=10, int depth_switch=0, int n_threads=18, str forest_prefix='forest', int row_from=-1, int row_to=-1):
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cdef int n = ims.shape[0]
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cdef int h = ims.shape[1]
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cdef int w = ims.shape[2]
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if n != disps.shape[0] or h != disps.shape[1] or w != disps.shape[2]:
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raise Exception('ims.shape != disps.shape')
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if row_from < 0:
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row_from = 0
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if row_to > h or row_to < 0:
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row_to = h
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out = np.empty((n, h, w, 3), dtype=np.float32)
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cdef float[:,:,:,::1] out_view = out
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eval(row_from, row_to, &ims[0,0,0], &disps[0,0,0], n, h, w, n_disp_bins, depth_switch, n_threads, forest_prefix.encode(), &out_view[0,0,0,0])
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return out
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