97 lines
3.6 KiB
Python
97 lines
3.6 KiB
Python
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import os
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import glob
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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 download():
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BASE_DIR = os.path.dirname(os.path.abspath(__file__))
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DATA_DIR = os.path.join(BASE_DIR, 'data')
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if not os.path.exists(DATA_DIR):
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os.mkdir(DATA_DIR)
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if not os.path.exists(os.path.join(DATA_DIR, 'modelnet40_ply_hdf5_2048')):
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www = 'https://shapenet.cs.stanford.edu/media/modelnet40_ply_hdf5_2048.zip'
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zipfile = os.path.basename(www)
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os.system('wget %s --no-check-certificate; unzip %s' % (www, zipfile))
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os.system('mv %s %s' % (zipfile[:-4], DATA_DIR))
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os.system('rm %s' % (zipfile))
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def load_data(partition):
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download()
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BASE_DIR = os.path.dirname(os.path.abspath(__file__))
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DATA_DIR = os.path.join(BASE_DIR, 'data')
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all_data = []
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all_label = []
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for h5_name in glob.glob(os.path.join(DATA_DIR, 'modelnet40_ply_hdf5_2048', 'ply_data_%s*.h5'%partition)):
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# print(f"h5_name: {h5_name}")
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f = h5py.File(h5_name,'r')
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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 random_point_dropout(pc, max_dropout_ratio=0.875):
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''' batch_pc: BxNx3 '''
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# for b in range(batch_pc.shape[0]):
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dropout_ratio = np.random.random()*max_dropout_ratio # 0~0.875
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drop_idx = np.where(np.random.random((pc.shape[0]))<=dropout_ratio)[0]
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# print ('use random drop', len(drop_idx))
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if len(drop_idx)>0:
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pc[drop_idx,:] = pc[0,:] # set to the first point
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return pc
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def translate_pointcloud(pointcloud):
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xyz1 = np.random.uniform(low=2./3., high=3./2., size=[3])
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xyz2 = np.random.uniform(low=-0.2, high=0.2, size=[3])
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translated_pointcloud = np.add(np.multiply(pointcloud, xyz1), xyz2).astype('float32')
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return translated_pointcloud
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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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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
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def __getitem__(self, item):
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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 = random_point_dropout(pointcloud) # open for dgcnn not for our idea for all
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pointcloud = translate_pointcloud(pointcloud)
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np.random.shuffle(pointcloud)
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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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if __name__ == '__main__':
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train = ModelNet40(1024)
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test = ModelNet40(1024, 'test')
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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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from torch.utils.data import DataLoader
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train_loader = DataLoader(ModelNet40(partition='train', num_points=1024), num_workers=4,
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batch_size=32, shuffle=True, drop_last=True)
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for batch_idx, (data, label) in enumerate(train_loader):
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print(f"batch_idx: {batch_idx} | data shape: {data.shape} | ;lable shape: {label.shape}")
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train_set = ModelNet40(partition='train', num_points=1024)
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test_set = ModelNet40(partition='test', num_points=1024)
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print(f"train_set size {train_set.__len__()}")
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print(f"test_set size {test_set.__len__()}")
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