def test_load_net_should_load_known_net_types(self):
files = ["test.adjlist", "test.gml", "test.edgelist"]
for name in files:
try:
g = nets.load_net(os.path.join(test_net_path, name))
except Exception, e:
raise Exception("'%s' failed to parse: %s" % (name, e))
self.assertEqual(11, g.size())
def test_load_net_should_try_to_load_poorly_name_files(self):
files = ["poorly_named", "a.net.in.some.format", "a"]
for name in files:
g = nets.load_net(os.path.join(test_net_path, name))
self.assertEqual(11, g.size(), "%s should have 11 nodes, has %d" % (name, g.size()))
def _test_should_load_nets_from_file_streams(self):
#:MC: this does not work properly with read_adjlist (at least). it returns 0-sized nets.
f = open(os.path.join(test_net_path, "test.adjlist"))
g = nets.load_net(f)
self.assertEqual(11, g.size())
f.close()
######################
# connectivity
print('Generating fixed adjancency matrix.')
if args.small_world:
connectivity = generate_connectivity(args.img_side, args.num_cn,
args.critical_dist, 'small_world')
else:
connectivity = generate_connectivity(args.img_side, args.num_cn,
args.critical_dist, 'local')
connectivity = torch.tensor(connectivity).long().unsqueeze(0).to('cpu')
batch_connectivity = connectivity.repeat(args.batch_size, 1, 1).to(args.device)
######################
# initialization
if torch.cuda.device_count() > 1:
model = nn.DataParallel(nets.load_net(args,
batch_connectivity)).to(args.device)
criterion = nn.DataParallel(nets.criterion(args.time_weight)).to(
args.device)
else:
model = nets.load_net(args, batch_connectivity).to(args.device)
criterion = nets.criterion(args.time_weight).to(args.device)
print('network contains {} parameters'.format(
nets.count_parameters(model))) # parameter number
time.sleep(2)
loss_history = []
loss_history_test = []
coupling_history = []
displayer = disp.displayer(interactive=args.interactive)
if args.intrinsic_frequencies == 'learned':
