dmlc--dgl
Da Zheng
924efc6520
* fix. * use inplace. * move to shared memory graph store. * fix. * add more unit tests. * fix. * fix test. * fix test. * disable test. * fix.
85 行
3.4 KiB
Python
85 行
3.4 KiB
Python
from multiprocessing import Process
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import argparse, time, math
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import numpy as np
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import os
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os.environ['OMP_NUM_THREADS'] = '16'
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import mxnet as mx
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from mxnet import gluon
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import dgl
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from dgl import DGLGraph
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from dgl.data import register_data_args, load_data
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from gcn_ns_sc import gcn_ns_train
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from gcn_cv_sc import gcn_cv_train
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from graphsage_cv import graphsage_cv_train
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def main(args):
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g = dgl.contrib.graph_store.create_graph_from_store(args.graph_name, "shared_mem")
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# We need to set random seed here. Otherwise, all processes have the same mini-batches.
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mx.random.seed(g.worker_id)
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features = g.nodes[:].data['features']
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labels = g.nodes[:].data['labels']
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train_mask = g.nodes[:].data['train_mask']
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val_mask = g.nodes[:].data['val_mask']
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test_mask = g.nodes[:].data['test_mask']
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if args.num_gpus > 0:
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ctx = mx.gpu(g.worker_id % args.num_gpus)
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else:
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ctx = mx.cpu()
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train_nid = mx.nd.array(np.nonzero(train_mask.asnumpy())[0]).astype(np.int64)
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test_nid = mx.nd.array(np.nonzero(test_mask.asnumpy())[0]).astype(np.int64)
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n_classes = len(np.unique(labels.asnumpy()))
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n_train_samples = train_mask.sum().asscalar()
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n_val_samples = val_mask.sum().asscalar()
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n_test_samples = test_mask.sum().asscalar()
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if args.model == "gcn_ns":
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gcn_ns_train(g, ctx, args, n_classes, train_nid, test_nid, n_test_samples)
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elif args.model == "gcn_cv":
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gcn_cv_train(g, ctx, args, n_classes, train_nid, test_nid, n_test_samples, True)
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elif args.model == "graphsage_cv":
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graphsage_cv_train(g, ctx, args, n_classes, train_nid, test_nid, n_test_samples, True)
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else:
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print("unknown model. Please choose from gcn_ns, gcn_cv, graphsage_cv")
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print("parent ends")
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='GCN')
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register_data_args(parser)
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parser.add_argument("--model", type=str,
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help="select a model. Valid models: gcn_ns, gcn_cv, graphsage_cv")
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parser.add_argument("--graph-name", type=str, default="",
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help="graph name")
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parser.add_argument("--num-feats", type=int, default=100,
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help="the number of features")
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parser.add_argument("--dropout", type=float, default=0.5,
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help="dropout probability")
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parser.add_argument("--num-gpus", type=int, default=0,
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help="the number of GPUs to train")
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parser.add_argument("--lr", type=float, default=3e-2,
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help="learning rate")
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parser.add_argument("--n-epochs", type=int, default=200,
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help="number of training epochs")
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parser.add_argument("--batch-size", type=int, default=1000,
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help="batch size")
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parser.add_argument("--test-batch-size", type=int, default=1000,
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help="test batch size")
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parser.add_argument("--num-neighbors", type=int, default=3,
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help="number of neighbors to be sampled")
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parser.add_argument("--n-hidden", type=int, default=16,
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help="number of hidden gcn units")
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parser.add_argument("--n-layers", type=int, default=1,
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help="number of hidden gcn layers")
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parser.add_argument("--self-loop", action='store_true',
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help="graph self-loop (default=False)")
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parser.add_argument("--weight-decay", type=float, default=5e-4,
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help="Weight for L2 loss")
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args = parser.parse_args()
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print(args)
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main(args)
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