# data parallel with multiple GPUs on a single node
net = nn.DataParallel(net, device_ids=range(torch.cuda.device_count()))
#--------------------------------------------------------------------------
# Setup the direction file and the surface file
#--------------------------------------------------------------------------
dir_file = net_plotter.name_direction_file(args) # name the direction file
if rank == 0:
net_plotter.setup_direction(args, dir_file, net)
surf_file = name_surface_file(args, dir_file)
if rank == 0:
setup_surface_file(args, surf_file, dir_file)
# wait until master has setup the direction file and surface file
mpi4pytorch.barrier(comm)
# load directions
d = net_plotter.load_directions(dir_file)
# calculate the consine similarity of the two directions
if len(d) == 2 and rank == 0:
similarity = proj.cal_angle(proj.nplist_to_tensor(d[0]), proj.nplist_to_tensor(d[1]))
print('cosine similarity between x-axis and y-axis: %f' % similarity)
#--------------------------------------------------------------------------
# Setup dataloader
#--------------------------------------------------------------------------
# download CIFAR10 if it does not exit
if rank == 0 and args.dataset == 'cifar10':
torchvision.datasets.CIFAR10(root=args.dataset + '/data', train=True, download=True)
# data parallel with multiple GPUs on a single node
net = nn.DataParallel(net, device_ids=range(torch.cuda.device_count()))
#--------------------------------------------------------------------------
# Setup the direction file and the surface file
#--------------------------------------------------------------------------
dir_file = net_plotter.name_direction_file(args) # name the direction file
if rank == 0:
net_plotter.setup_direction(args, dir_file, net)
surf_file = name_surface_file(args, dir_file)
if rank == 0:
setup_surface_file(args, surf_file, dir_file)
# wait until master has setup the direction file and surface file
mpi.barrier(comm)
# load directions
d = net_plotter.load_directions(dir_file)
# calculate the consine similarity of the two directions
if len(d) == 2 and rank == 0:
similarity = proj.cal_angle(proj.nplist_to_tensor(d[0]),
proj.nplist_to_tensor(d[1]))
print('cosine similarity between x-axis and y-axis: %f' % similarity)
#--------------------------------------------------------------------------
# Setup dataloader
#--------------------------------------------------------------------------
# download CIFAR10 if it does not exit
if rank == 0 and args.dataset == 'cifar10':
torchvision.datasets.CIFAR10(root=args.dataset + '/data',
device_ids=range(
min(args.ngpu, torch.cuda.device_count())))
#--------------------------------------------------------------------------
# Setup the direction file and the surface file
#--------------------------------------------------------------------------
dir_file = net_plotter.name_direction_file(args) # name the direction file
if rank == 0:
net_plotter.setup_direction(args, dir_file, net)
surf_file = name_surface_file(args, dir_file)
if rank == 0:
setup_surface_file(args, surf_file, dir_file)
# wait until master has setup the direction file and surface file
mpi4pytorch.barrier(comm)
# load directions
d = net_plotter.load_directions(dir_file)
# calculate the consine similarity of the two directions
if len(d) == 2 and rank == 0:
similarity = proj.cal_angle(proj.nplist_to_tensor(d[0]),
proj.nplist_to_tensor(d[1]))
print('cosine similarity between x-axis and y-axis: %f' % similarity)
#--------------------------------------------------------------------------
# Setup dataloader
#--------------------------------------------------------------------------
# download CIFAR10 if it does not exit
if rank == 0 and args.dataset == 'cifar10':
torchvision.datasets.CIFAR10(root=args.dataset + '/data',