def main():
parser = get_parser_ens()
args = parser.parse_args()
args.method = os.path.basename(__file__).split('-')[1][:-3]
if args.aug_test:
args.method = args.method + '_augment'
torch.backends.cudnn.benchmark = True
compute = {
'CIFAR10':
['VGG16BN', 'PreResNet110', 'PreResNet164', 'WideResNet28x10'],
'CIFAR100':
['VGG16BN', 'PreResNet110', 'PreResNet164', 'WideResNet28x10'],
'ImageNet': ['ResNet50']
}
for model in compute[args.dataset]:
args.model = model
logger = Logger(base='./logs/')
print('-' * 5, 'Computing results of', model, 'on', args.dataset + '.',
'-' * 5)
loaders, num_classes = get_data(args)
targets = get_targets(loaders['test'], args)
args.num_classes = num_classes
model = get_model(args)
for run in range(1, 6):
log_probs = []
fnames = read_models(args,
base=os.path.expanduser(args.models_dir),
run=run if args.dataset != 'ImageNet' else -1)
fnames = sorted(fnames,
key=lambda a: int(a.split('-')[-1].split('.')[0]))
for ns in range(100)[:min(
len(fnames), 100 if args.dataset != 'ImageNet' else 50)]:
start = time.time()
model.load_state_dict(get_sd(fnames[ns], args))
ones_log_prob = one_sample_pred(loaders['test'], model)
log_probs.append(ones_log_prob)
logger.add_metrics_ts(ns,
log_probs,
targets,
args,
time_=start)
logger.save(args)
os.makedirs('.megacache', exist_ok=True)
logits_pth = '.megacache/logits_%s-%s-%s-%s-%s'
logits_pth = logits_pth % (args.dataset, args.model, args.method,
ns + 1, run)
log_prob = logsumexp(np.dstack(log_probs), axis=2) - np.log(ns + 1)
print('Save final logprobs to %s' % logits_pth, end='\n\n')
np.save(logits_pth, log_prob)
def main():
parser = get_parser_ens()
args = parser.parse_args()
args.method = os.path.basename(__file__).split('-')[1][:-3]
torch.backends.cudnn.benchmark = True
if args.aug_test:
args.method = args.method + '_augment'
print('Computing for all datasets!')
compute = {
'CIFAR10': ['VGG16BN', 'WideResNet28x10do'],
'CIFAR100': ['VGG16BN', 'WideResNet28x10do']
}
for model in compute[args.dataset]:
args.model = model
logger = Logger()
print('-' * 5, 'Computing results of', model, 'on', args.dataset + '.',
'-' * 5)
loaders, num_classes = get_data(args)
targets = get_targets(loaders['test'], args)
fnames = read_models(args, base=os.path.expanduser(args.models_dir))
args.num_classes = num_classes
model = get_model(args)
for try_ in range(1, 6):
fnames = np.random.permutation(fnames)
model.load_state_dict(get_sd(fnames[0], args))
log_probs = []
for ns in range(100):
start = time.time()
ones_log_prob = one_sample_pred(loaders['test'], model)
log_probs.append(ones_log_prob)
logger.add_metrics_ts(ns,
log_probs,
targets,
args,
time_=start)
logger.save(args)
os.makedirs('./.megacache', exist_ok=True)
logits_pth = '.megacache/logits_%s-%s-%s-%s-%s'
logits_pth = logits_pth % (args.dataset, args.model, args.method,
ns + 1, try_)
log_prob = logsumexp(np.dstack(log_probs), axis=2) - np.log(ns + 1)
print('Save final logprobs to %s' % logits_pth)
np.save(logits_pth, log_prob)
print('Used weights from %s' % fnames[0], end='\n\n')
def main():
parser = get_parser_ens()
args = parser.parse_args()
args.method = os.path.basename(__file__).split('-')[1][:-3]
torch.backends.cudnn.benchmark = True
if args.aug_test:
args.method = args.method + '_augment'
os.makedirs('./logs', exist_ok=True)
compute = {
'CIFAR10': ['BayesVGG16BN', 'BayesPreResNet110', 'BayesPreResNet164', 'BayesWideResNet28x10'],
'CIFAR100': ['BayesVGG16BN', 'BayesPreResNet110', 'BayesPreResNet164', 'BayesWideResNet28x10'],
'ImageNet': ['BayesResNet50']
}
for model in compute[args.dataset]:
args.model = model
logger = Logger()
print('-'*5, 'Computing results of', model, 'on', args.dataset + '.', '-'*5)
loaders, num_classes = get_data(args)
targets = get_targets(loaders['test'], args)
fnames = read_models(args, base=os.path.expanduser(args.models_dir))
args.num_classes = num_classes
model = get_model(args)
for run in range(1, 6):
print('Repeat num. %s' % run)
log_probs = []
checkpoint = get_sd(fnames[0], args)
model.load_state_dict(checkpoint)
for ns in range(100 if args.dataset != 'ImageNet' else 50):
start = time.time()
ones_log_prob = one_sample_pred(loaders['test'], model)
log_probs.append(ones_log_prob)
logger.add_metrics_ts(ns, log_probs, targets, args, time_=start)
logger.save(args)
os.makedirs('.megacache', exist_ok=True)
logits_pth = '.megacache/logits_%s-%s-%s-%s-%s'
logits_pth = logits_pth % (args.dataset, args.model, args.method, ns+1, run)
log_prob = logsumexp(np.dstack(log_probs), axis=2) - np.log(ns+1)
np.save(logits_pth, log_prob)
lr=args.lr_init,
momentum=args.momentum,
weight_decay=args.wd)
print(optimizer1)
params = [p for n, p in model.named_parameters() if 'wlog_sigma' in n]
optimizer2 = torch.optim.Adam(
params,
lr=args.lr_vars,
weight_decay=0,
)
print(optimizer2)
start_epoch = 0
args.model = args.model.replace('Bayes', '')
fnames = utils.read_models(args,
base=os.path.abspath('/home/aashukha/megares'))
args.model = 'Bayes' + args.model
print('Resume training from %s' % fnames[0])
checkpoint = torch.load(fnames[0])
model.load_state_dict(checkpoint, strict=False)
columns = [
'ep', 'lr', 'tr_loss', 'tr_acc', 'te_loss', 'te_acc', 'time', 'nll', 'kl',
'te_ac', 'te_nll'
]
for epoch in range(start_epoch, args.epochs):
time_ep = time.time()
lr = schedule(epoch)