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',
train=True,
download=True)
mpi.barrier(comm)
trainloader, testloader = dataloader.load_dataset(
args.dataset, args.datapath, args.batch_size, args.threads,
surf_file += '.h5'
f = h5py.File(surf_file, 'a')
f['dir_file'] = args.dir_file
# Create the coordinates(resolutions) at which the function is evaluated
xcoordinates = np.linspace(args.xmin, args.xmax, num=args.xnum)
f['xcoordinates'] = xcoordinates
ycoordinates = np.linspace(args.ymin, args.ymax, num=args.ynum)
f['ycoordinates'] = ycoordinates
f.close()
directions = [xdirection, ydirection]
similarity = proj.cal_angle(proj.nplist_to_tensor(directions[0]),
proj.nplist_to_tensor(directions[1]))
print('cosine similarity between x-axis and y-axis: %f' % similarity)
f = h5py.File(surf_file, 'r+')
xcoordinates = f['xcoordinates'][:]
ycoordinates = f['ycoordinates'][:]
losses = -np.ones(shape=(len(xcoordinates), len(ycoordinates)))
accuracies = -np.ones(shape=(len(xcoordinates), len(ycoordinates)))
f['train_loss'] = losses
f['train_acc'] = accuracies
inds, coords, inds_nums = scheduler.get_job_indices(
losses, xcoordinates, ycoordinates, None)
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)
mpi4pytorch.barrier(comm)
trainloader, testloader = dataloader.load_dataset(args.dataset, args.datapath,
args.batch_size, args.threads, args.raw_data,
args.data_split, args.split_idx,
args.trainloader, args.testloader)
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.list_to_vec(d[0]),
proj.list_to_vec(d[1]))
print('cosine similarity between x-axis and y-axis: ' +
str(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)
mpi4pytorch.barrier(comm)
trainloader, testloader = dataloader.load_dataset(
