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dataloaders.py
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dataloaders.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import Dataset
from torch.utils.data import DataLoader
import numpy as np
import os
import h5py
import subprocess
import shlex
import json
import glob
from .. ops import transform_functions, se3
from sklearn.neighbors import NearestNeighbors
from scipy.spatial.distance import minkowski
from scipy.spatial import cKDTree
from torch.utils.data import Dataset
def download_modelnet40():
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
DATA_DIR = os.path.join(BASE_DIR, os.pardir, 'data')
if not os.path.exists(DATA_DIR):
os.mkdir(DATA_DIR)
if not os.path.exists(os.path.join(DATA_DIR, 'modelnet40_ply_hdf5_2048')):
www = 'https://shapenet.cs.stanford.edu/media/modelnet40_ply_hdf5_2048.zip'
zipfile = os.path.basename(www)
os.system('wget --no-check-certificate %s; unzip %s' % (www, zipfile))
os.system('mv %s %s' % (zipfile[:-4], DATA_DIR))
os.system('rm %s' % (zipfile))
def load_data(train, use_normals):
if train: partition = 'train'
else: partition = 'test'
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
DATA_DIR = os.path.join(BASE_DIR, os.pardir, 'data')
all_data = []
all_label = []
for h5_name in glob.glob(os.path.join(DATA_DIR, 'modelnet40_ply_hdf5_2048', 'ply_data_%s*.h5' % partition)):
f = h5py.File(h5_name)
if use_normals: data = np.concatenate([f['data'][:], f['normal'][:]], axis=-1).astype('float32')
else: data = f['data'][:].astype('float32')
label = f['label'][:].astype('int64')
f.close()
all_data.append(data)
all_label.append(label)
all_data = np.concatenate(all_data, axis=0)
all_label = np.concatenate(all_label, axis=0)
return all_data, all_label
def deg_to_rad(deg):
return np.pi / 180 * deg
def create_random_transform(dtype, max_rotation_deg, max_translation):
max_rotation = deg_to_rad(max_rotation_deg)
rot = np.random.uniform(-max_rotation, max_rotation, [1, 3])
trans = np.random.uniform(-max_translation, max_translation, [1, 3])
quat = transform_functions.euler_to_quaternion(rot, "xyz")
vec = np.concatenate([quat, trans], axis=1)
vec = torch.tensor(vec, dtype=dtype)
return vec
def jitter_pointcloud(pointcloud, sigma=0.04, clip=0.05):
# N, C = pointcloud.shape
sigma = 0.04*np.random.random_sample()
pointcloud += torch.empty(pointcloud.shape).normal_(mean=0, std=sigma).clamp(-clip, clip)
return pointcloud
def farthest_subsample_points(pointcloud1, num_subsampled_points=768):
pointcloud1 = pointcloud1
num_points = pointcloud1.shape[0]
nbrs1 = NearestNeighbors(n_neighbors=num_subsampled_points, algorithm='auto',
metric=lambda x, y: minkowski(x, y)).fit(pointcloud1[:, :3])
random_p1 = np.random.random(size=(1, 3)) + np.array([[500, 500, 500]]) * np.random.choice([1, -1, 1, -1])
idx1 = nbrs1.kneighbors(random_p1, return_distance=False).reshape((num_subsampled_points,))
gt_mask = torch.zeros(num_points).scatter_(0, torch.tensor(idx1), 1)
return pointcloud1[idx1, :], gt_mask
def uniform_2_sphere(num: int = None):
"""Uniform sampling on a 2-sphere
Source: https://gist.github.com/andrewbolster/10274979
Args:
num: Number of vectors to sample (or None if single)
Returns:
Random Vector (np.ndarray) of size (num, 3) with norm 1.
If num is None returned value will have size (3,)
"""
if num is not None:
phi = np.random.uniform(0.0, 2 * np.pi, num)
cos_theta = np.random.uniform(-1.0, 1.0, num)
else:
phi = np.random.uniform(0.0, 2 * np.pi)
cos_theta = np.random.uniform(-1.0, 1.0)
theta = np.arccos(cos_theta)
x = np.sin(theta) * np.cos(phi)
y = np.sin(theta) * np.sin(phi)
z = np.cos(theta)
return np.stack((x, y, z), axis=-1)
def planar_crop(points, p_keep= 0.7):
p_keep = np.array(p_keep, dtype=np.float32)
rand_xyz = uniform_2_sphere()
pts = points.numpy()
centroid = np.mean(pts[:, :3], axis=0)
points_centered = pts[:, :3] - centroid
dist_from_plane = np.dot(points_centered, rand_xyz)
mask = dist_from_plane > np.percentile(dist_from_plane, (1.0 - p_keep) * 100)
idx_x = torch.Tensor(np.nonzero(mask))
return torch.Tensor(pts[mask, :3]), idx_x
def knn_idx(pts, k):
kdt = cKDTree(pts)
_, idx = kdt.query(pts, k=k+1)
return idx[:, 1:]
def get_rri(pts, k):
# pts: N x 3, original points
# q: N x K x 3, nearest neighbors
q = pts[knn_idx(pts, k)]
p = np.repeat(pts[:, None], k, axis=1)
# rp, rq: N x K x 1, norms
rp = np.linalg.norm(p, axis=-1, keepdims=True)
rq = np.linalg.norm(q, axis=-1, keepdims=True)
pn = p / rp
qn = q / rq
dot = np.sum(pn * qn, -1, keepdims=True)
# theta: N x K x 1, angles
theta = np.arccos(np.clip(dot, -1, 1))
T_q = q - dot * p
sin_psi = np.sum(np.cross(T_q[:, None], T_q[:, :, None]) * pn[:, None], -1)
cos_psi = np.sum(T_q[:, None] * T_q[:, :, None], -1)
psi = np.arctan2(sin_psi, cos_psi) % (2*np.pi)
idx = np.argpartition(psi, 1)[:, :, 1:2]
# phi: N x K x 1, projection angles
phi = np.take_along_axis(psi, idx, axis=-1)
feat = np.concatenate([rp, rq, theta, phi], axis=-1)
return feat.reshape(-1, k * 4)
def get_rri_cuda(pts, k, npts_per_block=1):
try:
import pycuda.autoinit
from pycuda import gpuarray
from pycuda.compiler import SourceModule
except Exception as e:
print("Error raised in pycuda modules! pycuda only works with GPU, ", e)
raise
mod_rri = SourceModule(open('rri.cu').read() % (k, npts_per_block))
rri_cuda = mod_rri.get_function('get_rri_feature')
N = len(pts)
pts_gpu = gpuarray.to_gpu(pts.astype(np.float32).ravel())
k_idx = knn_idx(pts, k)
k_idx_gpu = gpuarray.to_gpu(k_idx.astype(np.int32).ravel())
feat_gpu = gpuarray.GPUArray((N * k * 4,), np.float32)
rri_cuda(pts_gpu, np.int32(N), k_idx_gpu, feat_gpu,
grid=(((N-1) // npts_per_block)+1, 1),
block=(npts_per_block, k, 1))
feat = feat_gpu.get().reshape(N, k * 4).astype(np.float32)
return feat
class UnknownDataTypeError(Exception):
def __init__(self, *args):
if args: self.message = args[0]
else: self.message = 'Datatype not understood for dataset.'
def __str__(self):
return self.message
class ModelNet40Data(Dataset):
def __init__(
self,
train=True,
num_points=1024,
download=True,
randomize_data=False,
use_normals=False
):
super(ModelNet40Data, self).__init__()
if download: download_modelnet40()
self.data, self.labels = load_data(train, use_normals)
if not train: self.shapes = self.read_classes_ModelNet40()
self.num_points = num_points
self.randomize_data = randomize_data
def __getitem__(self, idx):
if self.randomize_data: current_points = self.randomize(idx)
else: current_points = self.data[idx].copy()
current_points = torch.from_numpy(current_points[:self.num_points, :]).float()
label = torch.from_numpy(self.labels[idx]).type(torch.LongTensor)
return current_points, label
def __len__(self):
return self.data.shape[0]
def randomize(self, idx):
pt_idxs = np.arange(0, self.num_points)
np.random.shuffle(pt_idxs)
return self.data[idx, pt_idxs].copy()
def get_shape(self, label):
return self.shapes[label]
def read_classes_ModelNet40(self):
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
DATA_DIR = os.path.join(BASE_DIR, os.pardir, 'data')
file = open(os.path.join(DATA_DIR, 'modelnet40_ply_hdf5_2048', 'shape_names.txt'), 'r')
shape_names = file.read()
shape_names = np.array(shape_names.split('\n')[:-1])
return shape_names
class ClassificationData(Dataset):
def __init__(self, data_class=ModelNet40Data()):
super(ClassificationData, self).__init__()
self.set_class(data_class)
def __len__(self):
return len(self.data_class)
def set_class(self, data_class):
self.data_class = data_class
def get_shape(self, label):
try:
return self.data_class.get_shape(label)
except:
return -1
def __getitem__(self, index):
return self.data_class[index]
class RegistrationData(Dataset):
def __init__(self, algorithm, data_class=ModelNet40Data(), partial_source=False, partial_template=False, noise=False, additional_params={}):
super(RegistrationData, self).__init__()
available_algorithms = ['PCRNet', 'PointNetLK', 'DCP', 'PRNet', 'iPCRNet', 'RPMNet', 'DeepGMR']
if algorithm in available_algorithms: self.algorithm = algorithm
else: raise Exception("Algorithm not available for registration.")
self.set_class(data_class)
self.partial_template = partial_template
self.partial_source = partial_source
self.noise = noise
self.additional_params = additional_params
self.use_rri = False
if self.algorithm == 'PCRNet' or self.algorithm == 'iPCRNet':
from .. ops.transform_functions import PCRNetTransform
self.transforms = PCRNetTransform(len(data_class), angle_range=45, translation_range=1)
if self.algorithm == 'PointNetLK':
from .. ops.transform_functions import PNLKTransform
self.transforms = PNLKTransform(0.8, True)
if self.algorithm == 'RPMNet':
from .. ops.transform_functions import RPMNetTransform
self.transforms = RPMNetTransform(0.8, True)
if self.algorithm == 'DCP' or self.algorithm == 'PRNet':
from .. ops.transform_functions import DCPTransform
self.transforms = DCPTransform(angle_range=45, translation_range=1)
if self.algorithm == 'DeepGMR':
self.get_rri = get_rri_cuda if torch.cuda.is_available() else get_rri
from .. ops.transform_functions import DeepGMRTransform
self.transforms = DeepGMRTransform(angle_range=90, translation_range=1)
if 'nearest_neighbors' in self.additional_params.keys() and self.additional_params['nearest_neighbors'] > 0:
self.use_rri = True
self.nearest_neighbors = self.additional_params['nearest_neighbors']
def __len__(self):
return len(self.data_class)
def set_class(self, data_class):
self.data_class = data_class
def __getitem__(self, index):
template, label = self.data_class[index]
self.transforms.index = index # for fixed transformations in PCRNet.
source = self.transforms(template)
# Check for Partial Data.
if self.additional_params.get('partial_point_cloud_method', None) == 'planar_crop':
source, gt_idx_source = planar_crop(source)
template, gt_idx_template = planar_crop(template)
intersect_mask, intersect_x, intersect_y = np.intersect1d(gt_idx_source, gt_idx_template, return_indices=True)
self.template_mask = torch.zeros(template.shape[0])
self.source_mask = torch.zeros(source.shape[0])
self.template_mask[intersect_y] = 1
self.source_mask[intersect_x] = 1
else:
if self.partial_source: source, self.source_mask = farthest_subsample_points(source)
if self.partial_template: template, self.template_mask = farthest_subsample_points(template)
# Check for Noise in Source Data.
if self.noise: source = jitter_pointcloud(source)
if self.use_rri:
template, source = template.numpy(), source.numpy()
template = np.concatenate([template, self.get_rri(template - template.mean(axis=0), self.nearest_neighbors)], axis=1)
source = np.concatenate([source, self.get_rri(source - source.mean(axis=0), self.nearest_neighbors)], axis=1)
template, source = torch.tensor(template).float(), torch.tensor(source).float()
igt = self.transforms.igt
if self.additional_params.get('use_masknet', False):
if self.partial_source and self.partial_template:
return template, source, igt, self.template_mask, self.source_mask
elif self.partial_source:
return template, source, igt, self.source_mask
elif self.partial_template:
return template, source, igt, self.template_mask
else:
return template, source, igt
class SegmentationData(Dataset):
def __init__(self):
super(SegmentationData, self).__init__()
def __len__(self):
pass
def __getitem__(self, index):
pass
class FlowData(Dataset):
def __init__(self):
super(FlowData, self).__init__()
self.pc1, self.pc2, self.flow = self.read_data()
def __len__(self):
if isinstance(self.pc1, np.ndarray):
return self.pc1.shape[0]
elif isinstance(self.pc1, list):
return len(self.pc1)
else:
raise UnknownDataTypeError
def read_data(self):
pass
def __getitem__(self, index):
return self.pc1[index], self.pc2[index], self.flow[index]
class SceneflowDataset(Dataset):
def __init__(self, npoints=1024, root='', partition='train'):
if root == '':
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
DATA_DIR = os.path.join(BASE_DIR, os.pardir, 'data')
root = os.path.join(DATA_DIR, 'data_processed_maxcut_35_20k_2k_8192')
if not os.path.exists(root):
print("To download dataset, click here: https://drive.google.com/file/d/1CMaxdt-Tg1Wct8v8eGNwuT7qRSIyJPY-/view")
exit()
else:
print("SceneflowDataset Found Successfully!")
self.npoints = npoints
self.partition = partition
self.root = root
if self.partition=='train':
self.datapath = glob.glob(os.path.join(self.root, 'TRAIN*.npz'))
else:
self.datapath = glob.glob(os.path.join(self.root, 'TEST*.npz'))
self.cache = {}
self.cache_size = 30000
###### deal with one bad datapoint with nan value
self.datapath = [d for d in self.datapath if 'TRAIN_C_0140_left_0006-0' not in d]
######
print(self.partition, ': ',len(self.datapath))
def __getitem__(self, index):
if index in self.cache:
pos1, pos2, color1, color2, flow, mask1 = self.cache[index]
else:
fn = self.datapath[index]
with open(fn, 'rb') as fp:
data = np.load(fp)
pos1 = data['points1'].astype('float32')
pos2 = data['points2'].astype('float32')
color1 = data['color1'].astype('float32')
color2 = data['color2'].astype('float32')
flow = data['flow'].astype('float32')
mask1 = data['valid_mask1']
if len(self.cache) < self.cache_size:
self.cache[index] = (pos1, pos2, color1, color2, flow, mask1)
if self.partition == 'train':
n1 = pos1.shape[0]
sample_idx1 = np.random.choice(n1, self.npoints, replace=False)
n2 = pos2.shape[0]
sample_idx2 = np.random.choice(n2, self.npoints, replace=False)
pos1 = pos1[sample_idx1, :]
pos2 = pos2[sample_idx2, :]
color1 = color1[sample_idx1, :]
color2 = color2[sample_idx2, :]
flow = flow[sample_idx1, :]
mask1 = mask1[sample_idx1]
else:
pos1 = pos1[:self.npoints, :]
pos2 = pos2[:self.npoints, :]
color1 = color1[:self.npoints, :]
color2 = color2[:self.npoints, :]
flow = flow[:self.npoints, :]
mask1 = mask1[:self.npoints]
pos1_center = np.mean(pos1, 0)
pos1 -= pos1_center
pos2 -= pos1_center
return pos1, pos2, color1, color2, flow, mask1
def __len__(self):
return len(self.datapath)
if __name__ == '__main__':
class Data():
def __init__(self):
super(Data, self).__init__()
self.data, self.label = self.read_data()
def read_data(self):
return [4,5,6], [4,5,6]
def __len__(self):
return len(self.data)
def __getitem__(self, idx):
return self.data[idx], self.label[idx]
cd = RegistrationData('abc')
import ipdb; ipdb.set_trace()