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dataset_multi_scale.py
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dataset_multi_scale.py
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from __future__ import print_function
import os
import os.path
import sys
import torch
import torch.utils.data as data
import numpy as np
import scipy.spatial as spatial
# do NOT modify the returned points! kdtree uses a reference, not a copy of these points,
# so modifying the points would make the kdtree give incorrect results
def load_shape(point_filename, normals_filename, curv_filename, pidx_filename):
pts = np.load(point_filename+'.npy')
if normals_filename != None:
normals = np.load(normals_filename+'.npy')
else:
normals = None
if curv_filename != None:
curvatures = np.load(curv_filename+'.npy')
else:
curvatures = None
if pidx_filename != None:
patch_indices = np.load(pidx_filename+'.npy')
else:
patch_indices = None
sys.setrecursionlimit(int(max(1000, round(pts.shape[0]/10)))) # otherwise KDTree construction may run out of recursions
kdtree = spatial.cKDTree(pts, 10)
return Shape(pts=pts, kdtree=kdtree, normals=normals, curv=curvatures, pidx=patch_indices)
class SequentialPointcloudPatchSampler(data.sampler.Sampler):
def __init__(self, data_source):
self.data_source = data_source
self.total_patch_count = None
self.total_patch_count = 0
for shape_ind, _ in enumerate(self.data_source.shape_names):
self.total_patch_count = self.total_patch_count + self.data_source.shape_patch_count[shape_ind]
def __iter__(self):
return iter(range(self.total_patch_count))
def __len__(self):
return self.total_patch_count
class SequentialShapeRandomPointcloudPatchSampler(data.sampler.Sampler):
def __init__(self, data_source, patches_per_shape, seed=None, sequential_shapes=False, identical_epochs=False):
self.data_source = data_source
self.patches_per_shape = patches_per_shape
self.sequential_shapes = sequential_shapes
self.seed = seed
self.identical_epochs = identical_epochs
self.total_patch_count = None
self.shape_patch_inds = None
if self.seed is None:
self.seed = np.random.random_integers(0, 2**32-1, 1)[0]
self.rng = np.random.RandomState(self.seed)
self.total_patch_count = 0
for shape_ind, _ in enumerate(self.data_source.shape_names):
self.total_patch_count = self.total_patch_count + min(self.patches_per_shape, self.data_source.shape_patch_count[shape_ind])
def __iter__(self):
# optionally always pick the same permutation (mainly for debugging)
if self.identical_epochs:
self.rng.seed(self.seed)
# global point index offset for each shape
shape_patch_offset = list(np.cumsum(self.data_source.shape_patch_count))
shape_patch_offset.insert(0, 0)
shape_patch_offset.pop()
shape_inds = range(len(self.data_source.shape_names))
if not self.sequential_shapes:
shape_inds = self.rng.permutation(shape_inds)
# return a permutation of the points in the dataset where all points in the same shape are adjacent (for performance reasons):
# first permute shapes, then concatenate a list of permuted points in each shape
self.shape_patch_inds = [[]]*len(self.data_source.shape_names)
point_permutation = []
for shape_ind in shape_inds:
start = shape_patch_offset[shape_ind]
end = shape_patch_offset[shape_ind]+self.data_source.shape_patch_count[shape_ind]
global_patch_inds = self.rng.choice(range(start, end), size=min(self.patches_per_shape, end-start), replace=False)
point_permutation.extend(global_patch_inds)
# save indices of shape point subset
self.shape_patch_inds[shape_ind] = global_patch_inds - start
return iter(point_permutation)
def __len__(self):
return self.total_patch_count
class RandomPointcloudPatchSampler(data.sampler.Sampler):
def __init__(self, data_source, patches_per_shape, seed=None, identical_epochs=False):
self.data_source = data_source
self.patches_per_shape = patches_per_shape
self.seed = seed
self.identical_epochs = identical_epochs
self.total_patch_count = None
if self.seed is None:
self.seed = np.random.random_integers(0, 2**32-1, 1)[0]
self.rng = np.random.RandomState(self.seed)
self.total_patch_count = 0
for shape_ind, _ in enumerate(self.data_source.shape_names):
self.total_patch_count = self.total_patch_count + min(self.patches_per_shape, self.data_source.shape_patch_count[shape_ind])
def __iter__(self):
# optionally always pick the same permutation (mainly for debugging)
if self.identical_epochs:
self.rng.seed(self.seed)
return iter(self.rng.choice(sum(self.data_source.shape_patch_count), size=self.total_patch_count, replace=False))
def __len__(self):
return self.total_patch_count
class Shape():
def __init__(self, pts, kdtree, normals=None, curv=None, pidx=None):
self.pts = pts
self.kdtree = kdtree
self.normals = normals
self.curv = curv
self.pidx = pidx # patch center points indices (None means all points are potential patch centers)
class Cache():
def __init__(self, capacity, loader, loadfunc):
self.elements = {}
self.used_at = {}
self.capacity = capacity
self.loader = loader
self.loadfunc = loadfunc
self.counter = 0
def get(self, element_id):
if element_id not in self.elements:
# cache miss
# if at capacity, throw out least recently used item
if len(self.elements) >= self.capacity:
remove_id = min(self.used_at, key=self.used_at.get)
del self.elements[remove_id]
del self.used_at[remove_id]
# load element
self.elements[element_id] = self.loadfunc(self.loader, element_id)
self.used_at[element_id] = self.counter
self.counter += 1
return self.elements[element_id]
class PointcloudPatchDataset(data.Dataset):
# patch radius as fraction of the bounding box diagonal of a shape
def __init__(self, root, shape_list_filename, patch_radius, points_per_patch, patch_features,
seed=None, identical_epochs=False, use_pca=False, center='point', point_tuple=1, cache_capacity=1,
point_count_std=0.0, sparse_patches=False, neighbor_search_method='r',final_patch_size=175):
# initialize parameters
self.root = root
self.shape_list_filename = shape_list_filename
self.patch_features = patch_features
self.patch_radius = patch_radius
self.points_per_patch = points_per_patch
self.identical_epochs = identical_epochs
self.use_pca = use_pca
self.sparse_patches = sparse_patches
self.center = center
self.point_tuple = point_tuple
self.point_count_std = point_count_std
self.seed = seed
self.neighbor_search_method = neighbor_search_method
self.include_normals = False
self.include_curvatures = False
self.include_neighbor_normals = False
self.final_patch_size = final_patch_size
for pfeat in self.patch_features:
if pfeat == 'normal':
self.include_normals = True
elif pfeat == 'max_curvature' or pfeat == 'min_curvature':
self.include_curvatures = True
elif pfeat == 'neighbor_normals':
self.include_neighbor_normals = True
else:
raise ValueError('Unknown patch feature: %s' % (pfeat))
# self.loaded_shape = None
self.load_iteration = 0
self.shape_cache = Cache(cache_capacity, self, PointcloudPatchDataset.load_shape_by_index)
# get all shape names in the dataset
self.shape_names = []
with open(os.path.join(root, self.shape_list_filename)) as f:
self.shape_names = f.readlines()
self.shape_names = [x.strip() for x in self.shape_names]
self.shape_names = list(filter(None, self.shape_names))
# initialize rng for picking points in a patch
if self.seed is None:
self.seed = np.random.random_integers(0, 2**32-1, 1)[0]
self.rng = np.random.RandomState(self.seed)
# get basic information for each shape in the dataset
self.shape_patch_count = []
self.patch_radius_absolute = []
for shape_ind, shape_name in enumerate(self.shape_names):
print('getting information for shape %s' % (shape_name))
# load from text file and save in more efficient numpy format
point_filename = os.path.join(self.root, shape_name+'.xyz')
pts = np.loadtxt(point_filename).astype('float32')
np.save(point_filename+'.npy', pts)
if self.include_normals:
normals_filename = os.path.join(self.root, shape_name+'.normals')
normals = np.loadtxt(normals_filename).astype('float32')
np.save(normals_filename+'.npy', normals)
else:
normals_filename = None
if self.include_curvatures:
curv_filename = os.path.join(self.root, shape_name+'.curv')
curvatures = np.loadtxt(curv_filename).astype('float32')
np.save(curv_filename+'.npy', curvatures)
else:
curv_filename = None
if self.sparse_patches:
pidx_filename = os.path.join(self.root, shape_name+'.pidx')
patch_indices = np.loadtxt(pidx_filename).astype('int')
np.save(pidx_filename+'.npy', patch_indices)
else:
pidx_filename = None
shape = self.shape_cache.get(shape_ind)
if shape.pidx is None:
self.shape_patch_count.append(shape.pts.shape[0])
else:
self.shape_patch_count.append(len(shape.pidx))
bbdiag = float(np.linalg.norm(shape.pts.max(0) - shape.pts.min(0), 2))
self.patch_radius_absolute.append([bbdiag * rad for rad in self.patch_radius])
# returns a patch centered at the point with the given global index
# and the ground truth normal the the patch center
def __getitem__(self, index):
# find shape that contains the point with given global index
shape_ind, patch_ind = self.shape_index(index)
shape = self.shape_cache.get(shape_ind)
if shape.pidx is None:
center_point_ind = patch_ind
else:
center_point_ind = shape.pidx[patch_ind]
# get neighboring points (within euclidean distance patch_radius)
patch_pts = torch.zeros(self.points_per_patch*len(self.patch_radius_absolute[shape_ind]), 3, dtype=torch.float)
neighbor_normals = torch.zeros(self.final_patch_size * len(self.patch_radius_absolute[shape_ind]), 3,
dtype=torch.float)
patch_pts_valid = []
scale_ind_range = np.zeros([len(self.patch_radius_absolute[shape_ind]), 2], dtype='int')
effective_points_num = np.array([], dtype=np.int)
for s, rad in enumerate(self.patch_radius_absolute[shape_ind]):
if self.neighbor_search_method == 'r':
patch_point_inds = np.array(shape.kdtree.query_ball_point(shape.pts[center_point_ind, :], rad))
patch_scale = rad
elif self.neighbor_search_method == 'k':
point_distances, patch_point_inds = shape.kdtree.query(shape.pts[center_point_ind, :], k=self.points_per_patch)
rad = max(point_distances)
patch_scale = rad
# optionally always pick the same points for a given patch index (mainly for debugging)
if self.identical_epochs:
self.rng.seed((self.seed + index) % (2**32))
point_count = int(min(self.points_per_patch, len(patch_point_inds)))
effective_points_num = np.append(effective_points_num, point_count)
# randomly decrease the number of points to get patches with different point densities
if self.point_count_std > 0:
point_count = max(5, round(point_count * self.rng.uniform(1.0-self.point_count_std*2)))
point_count = min(point_count, len(patch_point_inds))
# if there are too many neighbors, pick a random subset
if point_count < len(patch_point_inds):
patch_point_inds = patch_point_inds[self.rng.choice(len(patch_point_inds), point_count, replace=False)]
start = s*self.points_per_patch
end = start+point_count
scale_ind_range[s, :] = [start, end]
patch_pts_valid += list(range(start, end))
# convert points to torch tensors
patch_pts[start:end, :] = torch.from_numpy(shape.pts[patch_point_inds, :])
# center patch (central point at origin - but avoid changing padded zeros)
if self.center == 'mean':
patch_pts[start:end, :] = patch_pts[start:end, :] - patch_pts[start:end, :].mean(0)
elif self.center == 'point':
patch_pts[start:end, :] = patch_pts[start:end, :] - torch.from_numpy(shape.pts[center_point_ind, :])
elif self.center == 'none':
pass # no centering
else:
raise ValueError('Unknown patch centering option: %s' % (self.center))
# normalize size of patch (scale with 1 / patch radius)
# if self.neighbor_search_method == 'r':
patch_pts[start:end, :] = patch_pts[start:end, :] / rad
# elif self.neighbor_search_method == 'k':
# patch_pts[start:end, :] = patch_pts[start:end, :] / torch.max(torch.norm(patch_pts[start:end, :], p=2, dim=1))
if self.include_normals:
patch_normal = torch.from_numpy(shape.normals[center_point_ind, :])
if self.include_neighbor_normals:
neighbor_normals[start:end, :] = torch.from_numpy(shape.normals[patch_point_inds[:175], :])
if self.include_curvatures:
patch_curv = torch.from_numpy(shape.curv[center_point_ind, :])
# scale curvature to match the scaled vertices (curvature*s matches position/s):
# if self.neighbor_search_method == 'r':
patch_curv = patch_curv * self.patch_radius_absolute[shape_ind][0]
# elif self.neighbor_search_method == 'k':
# patch_curv = patch_curv / torch.max(torch.norm(patch_pts[start:end, :], p=2, dim=1))
if self.use_pca:
# compute pca of points in the patch:
# center the patch around the mean:
pts_mean = patch_pts[patch_pts_valid, :].mean(0)
patch_pts[patch_pts_valid, :] = patch_pts[patch_pts_valid, :] - pts_mean
trans, _, _ = torch.svd(torch.t(patch_pts[patch_pts_valid, :]))
patch_pts[patch_pts_valid, :] = torch.mm(patch_pts[patch_pts_valid, :], trans)
cp_new = -pts_mean # since the patch was originally centered, the original cp was at (0,0,0)
cp_new = torch.matmul(cp_new, trans)
# re-center on original center point
patch_pts[patch_pts_valid, :] = patch_pts[patch_pts_valid, :] - cp_new
if self.include_normals:
patch_normal = torch.matmul(patch_normal, trans)
if self.include_neighbor_normals:
neighbor_normals = torch.matmul(neighbor_normals, trans)
else:
trans = torch.eye(3).float()
# get point tuples from the current patch
if self.point_tuple > 1:
patch_tuples = torch.zeros(self.points_per_patch*len(self.patch_radius_absolute[shape_ind]), 3*self.point_tuple, dtype=torch.float)
for s, rad in enumerate(self.patch_radius_absolute[shape_ind]):
start = scale_ind_range[s, 0]
end = scale_ind_range[s, 1]
point_count = end - start
tuple_count = point_count**self.point_tuple
# get linear indices of the tuples
if tuple_count > self.points_per_patch:
patch_tuple_inds = self.rng.choice(tuple_count, self.points_per_patch, replace=False)
tuple_count = self.points_per_patch
else:
patch_tuple_inds = np.arange(tuple_count)
# linear tuple index to index for each tuple element
patch_tuple_inds = np.unravel_index(patch_tuple_inds, (point_count,)*self.point_tuple)
for t in range(self.point_tuple):
patch_tuples[start:start+tuple_count, t*3:(t+1)*3] = patch_pts[start+patch_tuple_inds[t], :]
patch_pts = patch_tuples
patch_feats = ()
for pfeat in self.patch_features:
if pfeat == 'normal':
patch_feats = patch_feats + (patch_normal,)
elif pfeat == 'max_curvature':
patch_feats = patch_feats + (patch_curv[0:1],)
elif pfeat == 'min_curvature':
patch_feats = patch_feats + (patch_curv[1:2],)
elif pfeat == 'neighbor_normals':
patch_feats = patch_feats + (neighbor_normals,)
else:
raise ValueError('Unknown patch feature: %s' % (pfeat))
return (patch_pts,) + patch_feats + (trans,) + (patch_scale,) #+ (effective_points_num,)
def __len__(self):
return sum(self.shape_patch_count)
# translate global (dataset-wide) point index to shape index & local (shape-wide) point index
def shape_index(self, index):
shape_patch_offset = 0
shape_ind = None
for shape_ind, shape_patch_count in enumerate(self.shape_patch_count):
if index >= shape_patch_offset and index < shape_patch_offset + shape_patch_count:
shape_patch_ind = index - shape_patch_offset
break
shape_patch_offset = shape_patch_offset + shape_patch_count
return shape_ind, shape_patch_ind
# load shape from a given shape index
def load_shape_by_index(self, shape_ind):
point_filename = os.path.join(self.root, self.shape_names[shape_ind]+'.xyz')
normals_filename = os.path.join(self.root, self.shape_names[shape_ind]+'.normals') if self.include_normals else None
curv_filename = os.path.join(self.root, self.shape_names[shape_ind]+'.curv') if self.include_curvatures else None
pidx_filename = os.path.join(self.root, self.shape_names[shape_ind]+'.pidx') if self.sparse_patches else None
return load_shape(point_filename, normals_filename, curv_filename, pidx_filename)