dmlc--dgl
kitaev-chen
a3febc061b
* add gin model * convert dataset.py to data_ont_the_fly way and put it into dgl.data module * convert dataset.py to data_ont_the_fly way and put it into dgl.data module python code checked * modified document and reference TUDataset; checked python part and bypass cpp part due to error * change tensor to numpy in dataset and transform in collate@Dataloader * Change minor format issue Change minor format issue * moved logging; adjusted tqdm etc
87 行
3.0 KiB
Python
87 行
3.0 KiB
Python
"""Parser for arguments
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Put all arguments in one file and group similar arguments
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"""
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import argparse
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class Parser():
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def __init__(self, description):
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'''
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arguments parser
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'''
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self.parser = argparse.ArgumentParser(description=description)
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self.args = None
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self._parse()
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def _parse(self):
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# dataset
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self.parser.add_argument(
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'--dataset', type=str, default="MUTAG",
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help='name of dataset (default: MUTAG)')
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self.parser.add_argument(
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'--batch_size', type=int, default=32,
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help='batch size for training and validation (default: 32)')
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self.parser.add_argument(
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'--fold_idx', type=int, default=0,
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help='the index(<10) of fold in 10-fold validation.')
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self.parser.add_argument(
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'--filename', type=str, default="",
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help='output file')
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# device
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self.parser.add_argument(
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'--disable-cuda', action='store_true',
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help='Disable CUDA')
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self.parser.add_argument(
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'--device', type=int, default=0,
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help='which gpu device to use (default: 0)')
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# net
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self.parser.add_argument(
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'--net', type=str, default="gin",
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help='gnn net (default: gin)')
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self.parser.add_argument(
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'--num_layers', type=int, default=5,
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help='number of layers (default: 5)')
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self.parser.add_argument(
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'--num_mlp_layers', type=int, default=2,
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help='number of MLP layers(default: 2). 1 means linear model.')
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self.parser.add_argument(
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'--hidden_dim', type=int, default=64,
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help='number of hidden units (default: 64)')
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# graph
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self.parser.add_argument(
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'--graph_pooling_type', type=str,
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default="sum", choices=["sum", "mean", "max"],
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help='type of graph pooling: sum, mean or max')
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self.parser.add_argument(
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'--neighbor_pooling_type', type=str,
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default="sum", choices=["sum", "mean", "max"],
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help='type of neighboring pooling: sum, mean or max')
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self.parser.add_argument(
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'--learn_eps', action="store_true",
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help='learn the epsilon weighting')
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self.parser.add_argument(
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'--degree_as_tag', action="store_true",
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help='take the degree of nodes as input feature')
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# learning
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self.parser.add_argument(
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'--seed', type=int, default=0,
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help='random seed (default: 0)')
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self.parser.add_argument(
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'--epochs', type=int, default=350,
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help='number of epochs to train (default: 350)')
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self.parser.add_argument(
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'--lr', type=float, default=0.01,
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help='learning rate (default: 0.01)')
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self.parser.add_argument(
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'--final_dropout', type=float, default=0.5,
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help='final layer dropout (default: 0.5)')
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# done
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self.args = self.parser.parse_args()
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