dmlc--dgl
Da Zheng
7c47d8c9b4
* simplify shared memory graph index. * fix. * remove edge_dir in SharedMemGraphStore. * avoid creating shared-mem graph store with from_csr. * simplify from_csr. * add comments. * fix lint. * remove the test. * fix compilation error. * fix a bug. * fix a bug. Co-authored-by: Ubuntu <ubuntu@ip-172-31-16-150.us-west-2.compute.internal>
92 行
3.5 KiB
Python
92 行
3.5 KiB
Python
import os
|
|
import argparse, time, math
|
|
import numpy as np
|
|
from scipy import sparse as spsp
|
|
import mxnet as mx
|
|
import dgl
|
|
from dgl import DGLGraph
|
|
from dgl.data import register_data_args, load_data
|
|
|
|
class GraphData:
|
|
def __init__(self, csr, num_feats, graph_name):
|
|
num_nodes = csr.shape[0]
|
|
num_edges = mx.nd.contrib.getnnz(csr).asnumpy()[0]
|
|
self.graph = dgl.graph_index.from_csr(csr.indptr, csr.indices, False, 'in')
|
|
self.graph = self.graph.copyto_shared_mem(dgl.contrib.graph_store._get_graph_path(graph_name))
|
|
self.features = mx.nd.random.normal(shape=(csr.shape[0], num_feats))
|
|
self.num_labels = 10
|
|
self.labels = mx.nd.floor(mx.nd.random.uniform(low=0, high=self.num_labels,
|
|
shape=(csr.shape[0])))
|
|
self.train_mask = np.zeros((num_nodes,))
|
|
self.train_mask[np.arange(0, int(num_nodes/2), dtype=np.int64)] = 1
|
|
self.val_mask = np.zeros((num_nodes,))
|
|
self.val_mask[np.arange(int(num_nodes/2), int(num_nodes/4*3), dtype=np.int64)] = 1
|
|
self.test_mask = np.zeros((num_nodes,))
|
|
self.test_mask[np.arange(int(num_nodes/4*3), int(num_nodes), dtype=np.int64)] = 1
|
|
|
|
def main(args):
|
|
# load and preprocess dataset
|
|
if args.graph_file != '':
|
|
csr = mx.nd.load(args.graph_file)[0]
|
|
n_edges = csr.shape[0]
|
|
graph_name = os.path.basename(args.graph_file)
|
|
data = GraphData(csr, args.num_feats, graph_name)
|
|
csr = None
|
|
else:
|
|
data = load_data(args)
|
|
n_edges = data.graph.number_of_edges()
|
|
graph_name = args.dataset
|
|
|
|
if args.self_loop and not args.dataset.startswith('reddit'):
|
|
data.graph.add_edges_from([(i,i) for i in range(len(data.graph))])
|
|
|
|
mem_ctx = mx.cpu()
|
|
|
|
features = mx.nd.array(data.features, ctx=mem_ctx)
|
|
labels = mx.nd.array(data.labels, ctx=mem_ctx)
|
|
train_mask = mx.nd.array(data.train_mask, ctx=mem_ctx)
|
|
val_mask = mx.nd.array(data.val_mask, ctx=mem_ctx)
|
|
test_mask = mx.nd.array(data.test_mask, ctx=mem_ctx)
|
|
n_classes = data.num_labels
|
|
|
|
n_train_samples = train_mask.sum().asscalar()
|
|
n_val_samples = val_mask.sum().asscalar()
|
|
n_test_samples = test_mask.sum().asscalar()
|
|
|
|
print("""----Data statistics------'
|
|
#Edges %d
|
|
#Classes %d
|
|
#Train samples %d
|
|
#Val samples %d
|
|
#Test samples %d""" %
|
|
(n_edges, n_classes,
|
|
n_train_samples,
|
|
n_val_samples,
|
|
n_test_samples))
|
|
|
|
# create GCN model
|
|
print('graph name: ' + graph_name)
|
|
g = dgl.contrib.graph_store.create_graph_store_server(data.graph, graph_name, "shared_mem",
|
|
args.num_workers, False, edge_dir='in')
|
|
g.ndata['features'] = features
|
|
g.ndata['labels'] = labels
|
|
g.ndata['train_mask'] = train_mask
|
|
g.ndata['val_mask'] = val_mask
|
|
g.ndata['test_mask'] = test_mask
|
|
g.run()
|
|
|
|
if __name__ == '__main__':
|
|
parser = argparse.ArgumentParser(description='GCN')
|
|
register_data_args(parser)
|
|
parser.add_argument("--graph-file", type=str, default="",
|
|
help="graph file")
|
|
parser.add_argument("--num-feats", type=int, default=100,
|
|
help="the number of features")
|
|
parser.add_argument("--self-loop", action='store_true',
|
|
help="graph self-loop (default=False)")
|
|
parser.add_argument("--num-workers", type=int, default=1,
|
|
help="the number of workers")
|
|
args = parser.parse_args()
|
|
|
|
main(args)
|