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- #!/usr/bin/env python
- import os
- import glob
- import h5py
- import numpy as np
- from torch.utils.data import Dataset
-
-
- def download(data_dir):
- if not os.path.exists(data_dir):
- os.makedirs(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('wget %s; unzip %s' % (www, zipfile))
- os.system('mv %s %s' % (zipfile[:-4], data_dir))
- os.system('rm %s' % (zipfile))
-
-
- def load_data(data_dir, partition):
- download(data_dir)
- all_data = []
- all_label = []
- for h5_name in glob.glob(os.path.join(data_dir, 'modelnet40_ply_hdf5_2048', 'ply_data_%s*.h5'%partition)):
- with h5py.File(h5_name, 'r') as f:
- data = f['data'][:].astype('float32')
- label = f['label'][:].astype('int64')
- 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 translate_pointcloud(pointcloud):
- """
- for scaling and shifting the point cloud
- :param pointcloud:
- :return:
- """
- scale = np.random.uniform(low=2. / 3., high=3. / 2., size=[3])
- shift = np.random.uniform(low=-0.2, high=0.2, size=[3])
- translated_pointcloud = np.add(np.multiply(pointcloud, scale), shift).astype('float32')
- return translated_pointcloud
-
-
- class ModelNet40(Dataset):
- """
- This is the data loader for ModelNet 40
- ModelNet40 contains 12,311 meshed CAD models from 40 categories.
-
- num_points: 1024 by default
- data_dir
- paritition: train or test
- """
- def __init__(self, num_points=1024, data_dir="/data/deepgcn/modelnet40", partition='train'):
- self.data, self.label = load_data(data_dir, 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 = translate_pointcloud(pointcloud)
- np.random.shuffle(pointcloud)
- return pointcloud,item, label
-
- def __len__(self):
- return self.data.shape[0]
-
- def num_classes(self):
- return np.max(self.label) + 1
-
- import random
- class ModelNet40_LR(Dataset):
- """
- This is the data loader for ModelNet 40
- ModelNet40 contains 12,311 meshed CAD models from 40 categories.
-
- num_points: 1024 by default
- data_dir
- paritition: train or test
- """
- def __init__(self, num_points=1024, data_dir="/data/deepgcn/modelnet40", partition='train'):
- self.data, self.label = load_data(data_dir, 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 = translate_pointcloud(pointcloud)
- np.random.shuffle(pointcloud)
- return pointcloud, label
-
- def __len__(self):
- return self.data.shape[0]
-
- def num_classes(self):
- return np.max(self.label) + 1
-
- class ModelNet40_DA(Dataset):
- """
- This is the data loader for ModelNet 40
- ModelNet40 contains 12,311 meshed CAD models from 40 categories.
-
- num_points: 1024 by default
- data_dir
- paritition: train or test
- """
- def __init__(self, num_points=1024, data_dir="/data/deepgcn/modelnet40", partition='train'):
- self.data, self.label = load_data(data_dir, partition)
- self.num_points = num_points
- self.partition = partition
-
- def __getitem__(self, item):
- if self.partition == 'train':
- pointcloud = self.data[item][:self.num_points]
- label = self.label[item]
- pointcloud = translate_pointcloud(pointcloud)
- np.random.shuffle(pointcloud)
- return pointcloud,label
-
- else:
- pointcloud = self.data[item][:self.num_points]
- label = self.label[item]
- return pointcloud, label
-
-
-
- def __len__(self):
- return self.data.shape[0]
-
- def num_classes(self):
- return np.max(self.label) + 1
-
-
- if __name__ == '__main__':
- train = ModelNet40(1024)
- test = ModelNet40(1024, 'test')
- for data, label in train:
- print(data.max)
- print(label.shape)
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