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import os
import torch
import numpy as np
import pickle
from torch.utils.data import Dataset
from pointllm.utils import *
from pointllm.data.utils import *
class ModelNet(Dataset):
def __init__(self, config_path, split, subset_nums=-1, use_color=False):
"""
Args:
data_args:
split: train or test
"""
super(ModelNet, self).__init__()
if config_path is None:
# * use the default config file in the same dir
config_path = os.path.join(os.path.dirname(__file__), "modelnet_config", "ModelNet40.yaml")
config = cfg_from_yaml_file(config_path)
# * check data path
self.root = config["DATA_PATH"]
if not os.path.exists(self.root):
print(f"Data path {self.root} does not exist. Please check your data path.")
exit()
self.npoints = config.npoints
self.num_category = config.NUM_CATEGORY # * should be 40
self.random_sample = config.random_sampling
self.use_height = config.use_height
self.use_normals = config.USE_NORMALS
self.subset_nums = subset_nums
self.normalize_pc = True
self.use_color = use_color
if self.use_height or self.use_normals:
print(f"Warning: Usually we don't use height or normals for shapenet but use_height: {self.use_height} and \
use_normals: {self.use_normals}.")
self.split = split
assert (self.split == 'train' or self.split == 'test')
self.catfile = os.path.join(os.path.dirname(__file__), "modelnet_config", 'modelnet40_shape_names_modified.txt')
# "tv_stand" -> "tv stand"
self.categories = [line.rstrip() for line in open(self.catfile)] # * list of category names
self.save_path = os.path.join(self.root,
'modelnet%d_%s_%dpts_fps.dat' % (self.num_category, self.split, self.npoints))
print('Load processed data from %s...' % self.save_path)
with open(self.save_path, 'rb') as f:
self.list_of_points, self.list_of_labels = pickle.load(f) # * ndarray of N, C: (8192, 6) (xyz and normals)
if self.subset_nums > 0:
# * set random seed
import random
random.seed(0)
# * random choose subset_nums
idxs = random.sample(range(len(self.list_of_labels)), self.subset_nums)
self.list_of_labels = [self.list_of_labels[idx] for idx in idxs]
self.list_of_points = [self.list_of_points[idx] for idx in idxs]
# * print len
print(f"Load {len(self.list_of_points)} data from {self.save_path}.")
def __len__(self):
return len(self.list_of_labels)
def _get_item(self, index):
point_set, label = self.list_of_points[index], self.list_of_labels[index]
if self.npoints < point_set.shape[0]:
if self.random_sample:
# * random sample
point_set = point_set[np.random.choice(point_set.shape[0], self.npoints, replace=False)]
else:
point_set = farthest_point_sample(point_set, self.npoints)
point_set[:, 0:3] = pc_normalize(point_set[:, 0:3])
if not self.use_normals:
point_set = point_set[:, 0:3]
if self.use_height:
self.gravity_dim = 1
height_array = point_set[:, self.gravity_dim:self.gravity_dim + 1] - point_set[:,
self.gravity_dim:self.gravity_dim + 1].min()
point_set = np.concatenate((point_set, height_array), axis=1)
point_set = np.concatenate((point_set, np.zeros_like(point_set)), axis=-1) if self.use_color else point_set
return point_set, label.item() # * ndarray, int
def pc_norm(self, pc):
""" pc: NxC, return NxC """
xyz = pc[:, :3]
other_feature = pc[:, 3:]
centroid = np.mean(xyz, axis=0)
xyz = xyz - centroid
m = np.max(np.sqrt(np.sum(xyz ** 2, axis=1)))
xyz = xyz / m
pc = np.concatenate((xyz, other_feature), axis=1)
return pc
def __getitem__(self, index):
points, label = self._get_item(index)
pt_idxs = np.arange(0, points.shape[0]) # 2048
if self.split == 'train':
np.random.shuffle(pt_idxs)
current_points = points[pt_idxs].copy()
if self.normalize_pc:
# * modelnet point cloud is already normalized
current_points = self.pc_norm(current_points)
current_points = torch.from_numpy(current_points).float() # * N, C tensors
label_name = self.categories[int(label)]
data_dict = {
"indice": index, # * int
"point_clouds": current_points, # * tensor of N, C
"labels": label, # * int
"label_names": label_name # * str
}
return data_dict
if __name__ == '__main__':
import argparse
parser = argparse.ArgumentParser(description='ModelNet Dataset')
parser.add_argument("--config_path", type=str, default=None, help="config file path.")
parser.add_argument("--split", type=str, default="test", help="train or test.")
parser.add_argument("--subset_nums", type=int, default=200)
args = parser.parse_args()
dataset = ModelNet(config_path=args.config_path, split=args.split, subset_nums=args.subset_nums)
# * get the first item
print(dataset[0]) |