178 lines
6.4 KiB
Python
Executable file
178 lines
6.4 KiB
Python
Executable file
import glob
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import json
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import os
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import h5py
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import numpy as np
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from torch.utils.data import Dataset
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os.environ["HDF5_USE_FILE_LOCKING"] = "FALSE"
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def load_data(partition):
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all_data = []
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all_label = []
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for h5_name in glob.glob("./data/modelnet40_ply_hdf5_2048/ply_data_%s*.h5" % partition):
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f = h5py.File(h5_name)
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data = f["data"][:].astype("float32")
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label = f["label"][:].astype("int64")
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f.close()
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all_data.append(data)
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all_label.append(label)
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all_data = np.concatenate(all_data, axis=0)
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all_label = np.concatenate(all_label, axis=0)
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return all_data, all_label
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def pc_normalize(pc):
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centroid = np.mean(pc, axis=0)
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pc = pc - centroid
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m = np.max(np.sqrt(np.sum(pc**2, axis=1)))
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return pc / m
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def translate_pointcloud(pointcloud):
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xyz1 = np.random.uniform(low=2.0 / 3.0, high=3.0 / 2.0, size=[3])
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xyz2 = np.random.uniform(low=-0.2, high=0.2, size=[3])
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return np.add(np.multiply(pointcloud, xyz1), xyz2).astype("float32")
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def jitter_pointcloud(pointcloud, sigma=0.01, clip=0.02):
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N, C = pointcloud.shape
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pointcloud += np.clip(sigma * np.random.randn(N, C), -1 * clip, clip)
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return pointcloud
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# =========== ModelNet40 =================
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class ModelNet40(Dataset):
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def __init__(self, num_points, partition="train"):
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self.data, self.label = load_data(partition)
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self.num_points = num_points
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self.partition = partition # Here the new given partition will cover the 'train'
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def __getitem__(self, item): # indice of the pts or label
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pointcloud = self.data[item][: self.num_points]
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label = self.label[item]
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if self.partition == "train":
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# pointcloud = pc_normalize(pointcloud) # you can try to add it or not to train our model
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pointcloud = translate_pointcloud(pointcloud)
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np.random.shuffle(pointcloud) # shuffle the order of pts
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return pointcloud, label
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def __len__(self):
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return self.data.shape[0]
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# =========== ShapeNet Part =================
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class PartNormalDataset(Dataset):
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def __init__(self, npoints=2500, split="train", normalize=False):
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self.npoints = npoints
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self.root = "./data/shapenetcore_partanno_segmentation_benchmark_v0_normal"
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self.catfile = os.path.join(self.root, "synsetoffset2category.txt")
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self.cat = {}
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self.normalize = normalize
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with open(self.catfile) as f:
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for line in f:
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ls = line.strip().split()
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self.cat[ls[0]] = ls[1]
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self.cat = {k: v for k, v in self.cat.items()}
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self.meta = {}
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with open(os.path.join(self.root, "train_test_split", "shuffled_train_file_list.json")) as f:
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train_ids = set([str(d.split("/")[2]) for d in json.load(f)])
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with open(os.path.join(self.root, "train_test_split", "shuffled_val_file_list.json")) as f:
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val_ids = set([str(d.split("/")[2]) for d in json.load(f)])
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with open(os.path.join(self.root, "train_test_split", "shuffled_test_file_list.json")) as f:
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test_ids = set([str(d.split("/")[2]) for d in json.load(f)])
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for item in self.cat:
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self.meta[item] = []
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dir_point = os.path.join(self.root, self.cat[item])
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fns = sorted(os.listdir(dir_point))
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if split == "trainval":
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fns = [fn for fn in fns if ((fn[0:-4] in train_ids) or (fn[0:-4] in val_ids))]
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elif split == "train":
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fns = [fn for fn in fns if fn[0:-4] in train_ids]
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elif split == "val":
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fns = [fn for fn in fns if fn[0:-4] in val_ids]
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elif split == "test":
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fns = [fn for fn in fns if fn[0:-4] in test_ids]
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else:
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print("Unknown split: %s. Exiting.." % (split))
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exit(-1)
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for fn in fns:
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token = os.path.splitext(os.path.basename(fn))[0]
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self.meta[item].append(os.path.join(dir_point, token + ".txt"))
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self.datapath = []
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for item in self.cat:
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for fn in self.meta[item]:
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self.datapath.append((item, fn))
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self.classes = dict(zip(self.cat, range(len(self.cat))))
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# Mapping from category ('Chair') to a list of int [10,11,12,13] as segmentation labels
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self.seg_classes = {
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"Earphone": [16, 17, 18],
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"Motorbike": [30, 31, 32, 33, 34, 35],
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"Rocket": [41, 42, 43],
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"Car": [8, 9, 10, 11],
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"Laptop": [28, 29],
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"Cap": [6, 7],
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"Skateboard": [44, 45, 46],
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"Mug": [36, 37],
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"Guitar": [19, 20, 21],
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"Bag": [4, 5],
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"Lamp": [24, 25, 26, 27],
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"Table": [47, 48, 49],
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"Airplane": [0, 1, 2, 3],
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"Pistol": [38, 39, 40],
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"Chair": [12, 13, 14, 15],
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"Knife": [22, 23],
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}
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self.cache = {} # from index to (point_set, cls, seg) tuple
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self.cache_size = 20000
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def __getitem__(self, index):
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if index in self.cache:
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point_set, normal, seg, cls = self.cache[index]
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else:
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fn = self.datapath[index]
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cat = self.datapath[index][0]
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cls = self.classes[cat]
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cls = np.array([cls]).astype(np.int32)
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data = np.loadtxt(fn[1]).astype(np.float32)
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point_set = data[:, 0:3]
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normal = data[:, 3:6]
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seg = data[:, -1].astype(np.int32)
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if len(self.cache) < self.cache_size:
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self.cache[index] = (point_set, normal, seg, cls)
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if self.normalize:
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point_set = pc_normalize(point_set)
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choice = np.random.choice(len(seg), self.npoints, replace=True)
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# resample
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# note that the number of points in some points clouds is less than 2048, thus use random.choice
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# remember to use the same seed during train and test for a getting stable result
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point_set = point_set[choice, :]
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seg = seg[choice]
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normal = normal[choice, :]
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return point_set, cls, seg, normal
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def __len__(self):
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return len(self.datapath)
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if __name__ == "__main__":
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train = PartNormalDataset(npoints=2048, split="trainval", normalize=False)
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test = PartNormalDataset(npoints=2048, split="test", normalize=False)
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for data, label, _, _ in train:
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print(data.shape)
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print(label.shape)
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