PointMLP/classification_ModelNet40/data.py

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__()}")