dmlc--dgl
Chao Ma
96 行
3.5 KiB
Python
96 行
3.5 KiB
Python
import argparse, time, math
|
|
import numpy as np
|
|
import torch
|
|
import torch.nn as nn
|
|
import torch.nn.functional as F
|
|
from functools import partial
|
|
import dgl
|
|
import dgl.function as fn
|
|
from dgl import DGLGraph
|
|
from dgl.data import register_data_args, load_data
|
|
from dgl.contrib.sampling import SamplerPool
|
|
|
|
class MySamplerPool(SamplerPool):
|
|
def worker(self, args):
|
|
|
|
number_hops = 1
|
|
if args.model == "gcn_ns":
|
|
number_hops = args.n_layers + 1
|
|
elif args.model == "gcn_cv":
|
|
number_hops = args.n_layers
|
|
else:
|
|
print("unknown model. Please choose from gcn_ns and gcn_cv")
|
|
|
|
# Start sender
|
|
namebook = { 0:args.ip }
|
|
sender = dgl.contrib.sampling.SamplerSender(namebook)
|
|
|
|
# load and preprocess dataset
|
|
data = load_data(args)
|
|
|
|
if args.self_loop and not args.dataset.startswith('reddit'):
|
|
data.graph.add_edges_from([(i,i) for i in range(len(data.graph))])
|
|
|
|
train_nid = np.nonzero(data.train_mask)[0].astype(np.int64)
|
|
test_nid = np.nonzero(data.test_mask)[0].astype(np.int64)
|
|
|
|
# create GCN model
|
|
g = DGLGraph(data.graph, readonly=True)
|
|
|
|
while True:
|
|
idx = 0
|
|
for nf in dgl.contrib.sampling.NeighborSampler(g, args.batch_size,
|
|
args.num_neighbors,
|
|
neighbor_type='in',
|
|
shuffle=True,
|
|
num_workers=32,
|
|
num_hops=number_hops,
|
|
seed_nodes=train_nid):
|
|
print("send train nodeflow: %d" % (idx))
|
|
sender.send(nf, 0)
|
|
idx += 1
|
|
sender.signal(0)
|
|
|
|
def main(args):
|
|
pool = MySamplerPool()
|
|
pool.start(args.num_sampler, args)
|
|
|
|
if __name__ == '__main__':
|
|
parser = argparse.ArgumentParser(description='GCN')
|
|
register_data_args(parser)
|
|
parser.add_argument("--model", type=str,
|
|
help="select a model. Valid models: gcn_ns, gcn_cv")
|
|
parser.add_argument("--dropout", type=float, default=0.5,
|
|
help="dropout probability")
|
|
parser.add_argument("--gpu", type=int, default=-1,
|
|
help="gpu")
|
|
parser.add_argument("--lr", type=float, default=3e-2,
|
|
help="learning rate")
|
|
parser.add_argument("--n-epochs", type=int, default=200,
|
|
help="number of training epochs")
|
|
parser.add_argument("--batch-size", type=int, default=1000,
|
|
help="batch size")
|
|
parser.add_argument("--test-batch-size", type=int, default=1000,
|
|
help="test batch size")
|
|
parser.add_argument("--num-neighbors", type=int, default=3,
|
|
help="number of neighbors to be sampled")
|
|
parser.add_argument("--n-hidden", type=int, default=16,
|
|
help="number of hidden gcn units")
|
|
parser.add_argument("--n-layers", type=int, default=1,
|
|
help="number of hidden gcn layers")
|
|
parser.add_argument("--self-loop", action='store_true',
|
|
help="graph self-loop (default=False)")
|
|
parser.add_argument("--weight-decay", type=float, default=5e-4,
|
|
help="Weight for L2 loss")
|
|
parser.add_argument("--ip", type=str, default='127.0.0.1:50051',
|
|
help="IP address")
|
|
parser.add_argument("--num-sampler", type=int, default=1,
|
|
help="number of sampler")
|
|
args = parser.parse_args()
|
|
|
|
print(args)
|
|
|
|
main(args)
|
|
|
|
|