dmlc--dgl
Quan (Andy) Gan
dc19cd5687
* initial commit * second commit * another commit * change docstring * migrating to dgl.nn * fixes * docs * lint * multiple fixes * doc
56 行
1.8 KiB
Python
56 行
1.8 KiB
Python
import numpy as np
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from torch.utils.data import Dataset
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class ModelNet(object):
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def __init__(self, path, num_points):
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import h5py
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self.f = h5py.File(path)
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self.num_points = num_points
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self.n_train = self.f['train/data'].shape[0]
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self.n_valid = int(self.n_train / 5)
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self.n_train -= self.n_valid
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self.n_test = self.f['test/data'].shape[0]
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def train(self):
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return ModelNetDataset(self, 'train')
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def valid(self):
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return ModelNetDataset(self, 'valid')
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def test(self):
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return ModelNetDataset(self, 'test')
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class ModelNetDataset(Dataset):
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def __init__(self, modelnet, mode):
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super(ModelNetDataset, self).__init__()
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self.num_points = modelnet.num_points
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self.mode = mode
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if mode == 'train':
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self.data = modelnet.f['train/data'][:modelnet.n_train]
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self.label = modelnet.f['train/label'][:modelnet.n_train]
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elif mode == 'valid':
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self.data = modelnet.f['train/data'][modelnet.n_train:]
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self.label = modelnet.f['train/label'][modelnet.n_train:]
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elif mode == 'test':
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self.data = modelnet.f['test/data'].value
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self.label = modelnet.f['test/label'].value
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def translate(self, x, scale=(2/3, 3/2), shift=(-0.2, 0.2)):
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xyz1 = np.random.uniform(low=scale[0], high=scale[1], size=[3])
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xyz2 = np.random.uniform(low=shift[0], high=shift[1], size=[3])
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x = np.add(np.multiply(x, xyz1), xyz2).astype('float32')
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return x
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def __len__(self):
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return self.data.shape[0]
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def __getitem__(self, i):
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x = self.data[i][:self.num_points]
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y = self.label[i]
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if self.mode == 'train':
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x = self.translate(x)
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np.random.shuffle(x)
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return x, y
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