def testGraphasc(file_location):
with open(file_location) as f:
G = graph_io.load_graph(f)
asc = sortAsc(G)
greedyG, chromaticnr, time = Greedy(G, asc)
writedotgraph(greedyG)
print("chromaticnumber ascending order =" + str(chromaticnr) +
" time elapsed: " + str(time))
return chromaticnr, time
def testGraphrndm(file_location):
with open(file_location) as f:
G = graph_io.load_graph(f)
random = shuffle(G)
greedyG, chromaticnr, time = Greedy(G, random)
print("chromaticnumber random order =" + str(chromaticnr) +
" time elapsed: " + str(time))
writedotgraph(greedyG)
return chromaticnr, time
def testGraphdesc(file_location):
with open(file_location) as f:
G = graph_io.load_graph(f)
print("Graph " + str(file_location))
desc = sortDesc(G)
greedyG, chromaticnr, time = Greedy(G, desc)
print("chromaticnumber descending order =" + str(chromaticnr) +
" time elapsed: " + str(time))
return chromaticnr, time
def testfile(filename):
"""Check if results for the given file are correct"""
with open(PATH + "/" + filename) as f:
graphs = load_graph(f, read_list=True)
graphs = graphs[0]
results = []
for i in range(len(graphs)):
for j in range(len(graphs)):
if j > i:
result = tree_isomorphism(graphs[i], graphs[j])
expected = get_expected_result(filename, graphs[i].name,
graphs[j].name)
message = "Expected " + str(expected) + " for " + graphs[
i].name + " and " + graphs[j].name + " in " + filename
results.append([expected, result, message])
return results
for FileName in TestInstances:
# Tuple arguments below:
# First: printable string
# Second: Boolean value
# Third: Function
# Fourth: Filename
# Fifth: Whether output should be directed
for testalg in [
("Bellman-Ford, undirected", TEST_BELLMAN_FORD_UNDIRECTED,
bellman_ford_undirected, "BellmanFordUndirected", False),
("Bellman-Ford, directed", TEST_BELLMAN_FORD_DIRECTED,
bellman_ford_directed, "BellmanFordDirected", True),
("Dijkstra, undirected", TEST_DIJKSTRA_UNDIRECTED,
dijkstra_undirected, "DijkstraUndirected", False),
("Dijkstra, directed", TEST_DIJKSTRA_DIRECTED, dijkstra_directed,
"DijkstraDirected", True)
]:
if USE_UNSAFE_GRAPH:
print("\n\nLoading graph", FileName, "(Fast graph)")
with open(os.path.join(os.getcwd(), FileName)) as f:
G = load_graph(f, graph.Graph)
else:
print("\n\nLoading graph", FileName)
with open(os.path.join(os.getcwd(), FileName)) as f:
G = load_graph(f)
for i, vertex in enumerate(list(G.vertices)):
vertex.colornum = i
do_testalg(testalg, G)
def run(proc_id, n_gpus, devices):
th.manual_seed(1234)
np.random.seed(1234)
th.cuda.manual_seed_all(1234)
dev_id = devices[proc_id]
if n_gpus > 1:
dist_init_method = 'tcp://{master_ip}:{master_port}'.format(
master_ip='127.0.0.1', master_port='12345')
world_size = n_gpus
th.distributed.init_process_group(backend="nccl",
init_method=dist_init_method,
world_size=world_size,
rank=dev_id)
csr = load_graph('er100000')
g = dgl.DGLGraph(csr, readonly=True)
if proc_id == 0:
print(g)
in_feats = 300
n_classes = 10
features = th.rand(g.number_of_nodes(), in_feats).to(dev_id)
labels = th.randint(n_classes, (g.number_of_nodes(), )).to(dev_id)
# number of GCN layers
L = 2
# number of hidden units of a fully connected layer
n_hidden = 64
# dropout probability
dropout = 0.2
# batch size
batch_size = 1000
# number of neighbors to sample
num_neighbors = 4
# number of epochs
num_epochs = 10
model = GraphSAGE(in_feats, n_hidden, n_classes, L, F.relu, dropout, 'gcn')
model = model.to(dev_id)
loss_fcn = nn.CrossEntropyLoss()
loss_fcn = loss_fcn.to(dev_id)
optimizer = optim.Adam(model.parameters(), lr=0.03)
th.cuda.synchronize()
avg = 0
for epoch in range(num_epochs):
i = 0
elapsed_time = 0
tic = time.time()
if n_gpus > 1:
th.distributed.barrier()
# forward
pred = model(g, features)
# compute loss
loss = loss_fcn(pred, labels)
# backward
optimizer.zero_grad()
loss.backward()
if n_gpus > 1:
for param in model.parameters():
if param.requires_grad and param.grad is not None:
th.distributed.all_reduce(param.grad.data,
op=th.distributed.ReduceOp.SUM)
param.grad.data /= n_gpus
optimizer.step()
toc = time.time()
elapsed_time += toc - tic
if n_gpus > 1:
th.distributed.barrier()
if proc_id == 0: print('epoch time: ', elapsed_time)
if epoch >= 5:
avg += elapsed_time
if n_gpus > 1:
th.distributed.barrier()
if proc_id == 0:
print('avg time: ', avg / (epoch - 4))
else:
return MAYBE
def verify_colors(G, H):
"""
Verify the colors of graph G and H
:param G: The graph G
:param H: The graph H
:return: Whether the resulting coloring is a bijection or not
"""
for G_v in G.vertices:
for H_v in H.vertices:
if G_v.colornum == H_v.colornum:
if not get_neighbourhood_colors(G_v) == get_neighbourhood_colors(H_v):
return False
return True
if __name__ == "__main__":
"""
Main function
:param 1: The .grl-file
"""
start = time.time()
with open(sys.argv[1]) as f:
graph_list = load_graph(f, read_list=True)
refine_all(graph_list)
end = time.time()
print("Elapsed time (total): " + str(int((end - start) * 1000)) + " ms")
def load_graph_from_file(filename):
"""Check if results for the given file are correct"""
with open(PATH + "/" + filename) as f:
graphs = load_graph(f, read_list=True)
return graphs[0][0]
def get_graphs_from_file(file: str) -> List[Graph]:
with open(file) as f:
graphs = load_graph(f, read_list=True)
return graphs[0]
from graph_io import load_graph, write_dot
def print_dot(filename, G):
"""
Print a dot file with filename and graph
:param filename: The file
:param G: The graph
"""
with open(filename, 'w') as f:
write_dot(G, f)
def print_all(basename, l):
for i in range(len(l)):
print_dot(basename + str(i) + ".dot", l[0][i])
if __name__ == "__main__":
"""
Main function
:param 1: The .grl-file
"""
with open(sys.argv[1]) as f:
if (str(sys.argv[1]).endswith(".grl")):
graph_list = load_graph(f, read_list=True)
print_all(str(sys.argv[1]).split('.grl')[0], graph_list)
else:
graph = load_graph(f, read_list=False)
print_dot(str(sys.argv[1]).split('.gr')[0] + ".dot", graph)
