datasets/colored_mnist_dataset.py
- 1 F401: 'torch.nn.functional as F' imported but unused
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E501: line too long (114 > 79 characters)
(and 15 similar)
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21 def __init__(self, version=None, root_dir='data', download=False, split_scheme='official', random_split=False, -
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25 self.ogb_dataset = PyGColoredMNISTDataset(name='ColoredMNIST', root=root_dir) -
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32 self._y_type = 'float' # although the task is binary classification, the prediction target contains nan value, thus we need float -
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42 metadata_file_path = os.path.join(self.ogb_dataset.raw_dir, 'ColoredMNIST_group.npy') -
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45 'https://www.dropbox.com/s/ax1ek3yc6n1q739/ColoredMNIST_group.zip?dl=1', self.ogb_dataset.raw_dir) -
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49 self._metadata_array_wo_y = torch.from_numpy(np.load(metadata_file_path)).reshape(-1, 1).long() -
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51 torch.unsqueeze(self.ogb_dataset.data.y, dim=1)), 1) -
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58 val_split_idx = random_index[int(3 / 6 * dataset_size):int(4 / 6 * dataset_size)] -
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63 train_val_group_idx, test_group_idx = torch.tensor(train_val_group_idx), torch.tensor(test_group_idx) -
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66 split_idx = torch.zeros(len(torch.squeeze(self._metadata_array_wo_y)), dtype=torch.bool) -
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68 split_idx += (torch.squeeze(self._metadata_array_wo_y) == idx) -
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71 train_val_split_idx, test_split_idx = split_idx(train_val_group_idx), split_idx(test_group_idx) -
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73 train_val_split_idx = torch.arange(dataset_size, dtype=torch.int64)[train_val_split_idx] -
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76 train_split_idx = train_val_split_idx[random_index[:int(3 / 4 * train_val_sets_size)]] -
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77 val_split_idx = train_val_split_idx[random_index[int(3 / 4 * train_val_sets_size):]] -
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104 - prediction_fn (function): A function that turns y_pred into predicted labels. -
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105 Only None is supported because OGB Evaluators accept binary logits -
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135 adj = self._sym_normalize_adj(to_dense_adj(graph.edge_index).squeeze()) -
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147 # adj_node_feat[3:, node, node] = F.one_hot(node_feat[2].long(), 8) -
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156 deg_inv = torch.where(deg > 0, 1. / torch.sqrt(deg), torch.zeros(deg.size()))
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- 1 E302: expected 2 blank lines, found 1