109 lines
3.6 KiB
Python
109 lines
3.6 KiB
Python
import glob
|
|
import os
|
|
|
|
import h5py
|
|
import numpy as np
|
|
from torch.utils.data import Dataset
|
|
|
|
os.environ["HDF5_USE_FILE_LOCKING"] = "FALSE"
|
|
|
|
|
|
def download():
|
|
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
|
|
DATA_DIR = os.path.join(BASE_DIR, "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(f"wget {www} --no-check-certificate; unzip {zipfile}")
|
|
os.system(f"mv {zipfile[:-4]} {DATA_DIR}")
|
|
os.system("rm %s" % (zipfile))
|
|
|
|
|
|
def load_data(partition):
|
|
download()
|
|
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
|
|
DATA_DIR = os.path.join(BASE_DIR, "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)):
|
|
# print(f"h5_name: {h5_name}")
|
|
f = h5py.File(h5_name, "r")
|
|
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 random_point_dropout(pc, max_dropout_ratio=0.875):
|
|
"""batch_pc: BxNx3."""
|
|
# for b in range(batch_pc.shape[0]):
|
|
dropout_ratio = np.random.random() * max_dropout_ratio # 0~0.875
|
|
drop_idx = np.where(np.random.random(pc.shape[0]) <= dropout_ratio)[0]
|
|
# print ('use random drop', len(drop_idx))
|
|
|
|
if len(drop_idx) > 0:
|
|
pc[drop_idx, :] = pc[0, :] # set to the first point
|
|
return pc
|
|
|
|
|
|
def translate_pointcloud(pointcloud):
|
|
xyz1 = np.random.uniform(low=2.0 / 3.0, high=3.0 / 2.0, size=[3])
|
|
xyz2 = np.random.uniform(low=-0.2, high=0.2, size=[3])
|
|
|
|
return np.add(np.multiply(pointcloud, xyz1), xyz2).astype("float32")
|
|
|
|
|
|
def jitter_pointcloud(pointcloud, sigma=0.01, clip=0.02):
|
|
N, C = pointcloud.shape
|
|
pointcloud += np.clip(sigma * np.random.randn(N, C), -1 * clip, clip)
|
|
return pointcloud
|
|
|
|
|
|
class ModelNet40(Dataset):
|
|
def __init__(self, num_points, partition="train"):
|
|
self.data, self.label = load_data(partition)
|
|
self.num_points = num_points
|
|
self.partition = partition
|
|
|
|
def __getitem__(self, item):
|
|
pointcloud = self.data[item][: self.num_points]
|
|
label = self.label[item]
|
|
if self.partition == "train":
|
|
# pointcloud = random_point_dropout(pointcloud) # open for dgcnn not for our idea for all
|
|
pointcloud = translate_pointcloud(pointcloud)
|
|
np.random.shuffle(pointcloud)
|
|
return pointcloud, label
|
|
|
|
def __len__(self):
|
|
return self.data.shape[0]
|
|
|
|
|
|
if __name__ == "__main__":
|
|
train = ModelNet40(1024)
|
|
test = ModelNet40(1024, "test")
|
|
# for data, label in train:
|
|
# print(data.shape)
|
|
# print(label.shape)
|
|
from torch.utils.data import DataLoader
|
|
|
|
train_loader = DataLoader(
|
|
ModelNet40(partition="train", num_points=1024),
|
|
num_workers=4,
|
|
batch_size=32,
|
|
shuffle=True,
|
|
drop_last=True,
|
|
)
|
|
for batch_idx, (data, label) in enumerate(train_loader):
|
|
print(f"batch_idx: {batch_idx} | data shape: {data.shape} | ;lable shape: {label.shape}")
|
|
|
|
train_set = ModelNet40(partition="train", num_points=1024)
|
|
test_set = ModelNet40(partition="test", num_points=1024)
|
|
print(f"train_set size {train_set.__len__()}")
|
|
print(f"test_set size {test_set.__len__()}")
|