def main():
parser = argparse.ArgumentParser()
parser.add_argument('--config', '-c', type=str, required=True)
parser.add_argument('--device', '-d', default='cuda')
parser.add_argument('--batch_size', '-b', type=int, default=256)
parser.add_argument('--epochs', '-e', type=int, default=400)
parser.add_argument('--save_iter', '-s', type=int, default=10)
parser.add_argument('--vis_iter', '-v', type=int, default=10)
parser.add_argument('--log_dir', '-l', type=str, default=None)
parser.add_argument('--seed', type=int, default=42)
parser.add_argument('--dataset', '-D', type=str, default='mnist',
choices=['mnist', 'cifar10'])
parser.add_argument('--data_augmentation', '-A', action='store_true', dest='data_augmentation')
parser.set_defaults(data_augmentation=False)
parser.add_argument('--num_train_examples', type=int, default=None)
parser.add_argument('--label_noise_level', '-n', type=float, default=0.0)
parser.add_argument('--label_noise_type', type=str, default='flip',
choices=['flip', 'error', 'cifar10_custom'])
parser.add_argument('--transform_function', type=str, default=None,
choices=[None, 'remove_random_chunks'])
parser.add_argument('--clean_validation', dest='clean_validation', action='store_true')
parser.set_defaults(clean_validation=False)
parser.add_argument('--remove_prob', type=float, default=0.5)
args = parser.parse_args()
print(args)
# Load data
train_loader, val_loader, test_loader, _ = load_data_from_arguments(args)
# Options
optimization_args = {
'optimizer': {
'name': 'adam',
'lr': 1e-3
}
}
with open(args.config, 'r') as f:
architecture_args = json.load(f)
model = VAE(input_shape=train_loader.dataset[0][0].shape,
architecture_args=architecture_args,
device=args.device)
training.train(model=model,
train_loader=train_loader,
val_loader=val_loader,
epochs=args.epochs,
save_iter=args.save_iter,
vis_iter=args.vis_iter,
optimization_args=optimization_args,
log_dir=args.log_dir)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--config', '-c', type=str, required=True)
parser.add_argument('--device', '-d', default='cuda', help='specifies the main device')
parser.add_argument('--all_device_ids', nargs='+', type=str, default=None,
help="If not None, this list specifies devices for multiple GPU training. "
"The first device should match with the main device (args.device).")
parser.add_argument('--batch_size', '-b', type=int, default=256)
parser.add_argument('--epochs', '-e', type=int, default=400)
parser.add_argument('--stopping_param', type=int, default=2**30)
parser.add_argument('--save_iter', '-s', type=int, default=10)
parser.add_argument('--vis_iter', '-v', type=int, default=10)
parser.add_argument('--log_dir', '-l', type=str, default=None)
parser.add_argument('--seed', type=int, default=42)
# data parameters
parser.add_argument('--dataset', '-D', type=str, default='mnist')
parser.add_argument('--data_augmentation', '-A', action='store_true', dest='data_augmentation')
parser.set_defaults(data_augmentation=False)
parser.add_argument('--error_prob', '-n', type=float, default=0.0)
parser.add_argument('--num_train_examples', type=int, default=None)
parser.add_argument('--clean_validation', action='store_true', default=False)
parser.add_argument('--resize_to_imagenet', action='store_true', dest='resize_to_imagenet')
parser.set_defaults(resize_to_imagenet=False)
parser.add_argument('--cache_dataset', action='store_true', dest='cache_dataset')
parser.set_defaults(cache_dataset=False)
parser.add_argument('--num_workers', type=int, default=0, help='number of workers in data loaders')
# hyper-parameters
parser.add_argument('--model_class', '-m', type=str, default='ClassifierL2')
parser.add_argument('--l2_reg_coef', type=float, default=0.0)
parser.add_argument('--lr', type=float, default=1e-3, help='Learning rate')
parser.add_argument('--optimizer', type=str, default='adam', choices=['adam', 'sgd'])
args = parser.parse_args()
print(args)
# Load data
train_data, val_data, test_data, _ = load_data_from_arguments(args, build_loaders=False)
if args.cache_dataset:
train_data = CacheDatasetWrapper(train_data)
val_data = CacheDatasetWrapper(val_data)
test_data = CacheDatasetWrapper(test_data)
train_loader, val_loader, test_loader = get_loaders_from_datasets(train_data, val_data, test_data,
batch_size=args.batch_size,
num_workers=args.num_workers)
# Options
optimization_args = {
'optimizer': {
'name': args.optimizer,
'lr': args.lr,
}
}
with open(args.config, 'r') as f:
architecture_args = json.load(f)
model_class = getattr(methods, args.model_class)
model = model_class(input_shape=train_loader.dataset[0][0].shape,
architecture_args=architecture_args,
l2_reg_coef=args.l2_reg_coef,
device=args.device,
seed=args.seed)
metrics_list = [metrics.Accuracy(output_key='pred')]
if args.dataset == 'imagenet':
metrics_list.append(metrics.TopKAccuracy(k=5, output_key='pred'))
callbacks_list = [callbacks.SaveBestWithMetric(metric=metrics_list[0], partition='val', direction='max')]
stopper = callbacks.EarlyStoppingWithMetric(metric=metrics_list[0], stopping_param=args.stopping_param,
partition='val', direction='max')
training.train(model=model,
train_loader=train_loader,
val_loader=val_loader,
epochs=args.epochs,
save_iter=args.save_iter,
vis_iter=args.vis_iter,
optimization_args=optimization_args,
log_dir=args.log_dir,
args_to_log=args,
stopper=stopper,
metrics=metrics_list,
callbacks=callbacks_list,
device_ids=args.all_device_ids)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--config', '-c', type=str, required=True)
parser.add_argument('--device',
'-d',
default='cuda',
help='specifies the main device')
parser.add_argument(
'--all_device_ids',
nargs='+',
type=str,
default=None,
help=
"If not None, this list specifies devices for multiple GPU training. "
"The first device should match with the main device (args.device).")
parser.add_argument('--batch_size', '-b', type=int, default=256)
parser.add_argument('--epochs', '-e', type=int, default=400)
parser.add_argument('--stopping_param', type=int, default=2**30)
parser.add_argument('--save_iter', '-s', type=int, default=2**30)
parser.add_argument('--vis_iter', '-v', type=int, default=2**30)
parser.add_argument('--seed', type=int, default=42)
parser.add_argument(
'--num_accumulation_steps',
default=1,
type=int,
help='Number of training steps to accumulate before updating weights')
# data parameters
parser.add_argument('--dataset', '-D', type=str, default='mnist')
parser.add_argument('--data_augmentation',
'-A',
action='store_true',
dest='data_augmentation')
parser.set_defaults(data_augmentation=False)
parser.add_argument('--error_prob', '-n', type=float, default=0.0)
parser.add_argument('--num_train_examples', type=int, default=None)
parser.add_argument('--clean_validation',
action='store_true',
default=False)
parser.add_argument('--resize_to_imagenet',
action='store_true',
dest='resize_to_imagenet')
parser.set_defaults(resize_to_imagenet=False)
parser.add_argument('--cache_dataset',
action='store_true',
dest='cache_dataset')
parser.set_defaults(cache_dataset=False)
parser.add_argument(
'--sample_ranking_file',
type=str,
default=None,
help=
'Points to a pickle file that stores an ordering of examples from least to '
'most important. The most important args.exclude_ratio number of samples '
'will be excluded from training.')
parser.add_argument('--exclude_ratio',
type=float,
default=0.0,
help='Fraction of examples to exclude.')
parser.add_argument('--exclude_side',
type=str,
default='top',
choices=['top', 'bottom'],
help='from which side of the order to remove')
parser.add_argument('--num_workers',
type=int,
default=0,
help='number of workers in data loaders')
# hyper-parameters
parser.add_argument('--model_class',
'-m',
type=str,
default='ClassifierL2')
parser.add_argument('--l2_reg_coef', type=float, default=0.0)
parser.add_argument('--lr', type=float, default=1e-3, help='Learning rate')
parser.add_argument('--optimizer',
type=str,
default='adam',
choices=['adam', 'sgd'])
parser.add_argument('--random_baseline_seed', type=int, default=42)
parser.add_argument('--output_dir',
'-o',
type=str,
default='sample_info/results/data-summarization/')
parser.add_argument('--baseline_name', '-B', type=str, required=True)
parser.add_argument('--exp_name', '-E', type=str, required=True)
args = parser.parse_args()
print(args)
# set tensorboard log directory
args.log_dir = os.path.join(args.output_dir, args.baseline_name,
args.exp_name, 'logs')
utils.make_path(args.log_dir)
# Load data
train_data, val_data, test_data, _ = load_data_from_arguments(
args, build_loaders=False)
# exclude samples
np.random.seed(args.random_baseline_seed)
order = np.random.permutation(len(train_data))
# if sample ranking file is given, take the order from there
if args.sample_ranking_file is not None:
with open(args.sample_ranking_file, 'rb') as f:
order = pickle.load(f)
exclude_count = int(args.exclude_ratio * len(train_data))
if exclude_count == 0:
exclude_indices = []
else:
if args.exclude_side == 'top':
exclude_indices = order[-exclude_count:]
else:
exclude_indices = order[:exclude_count]
train_data = SubsetDataWrapper(dataset=train_data,
exclude_indices=exclude_indices)
if args.cache_dataset:
train_data = CacheDatasetWrapper(train_data)
val_data = CacheDatasetWrapper(val_data)
test_data = CacheDatasetWrapper(test_data)
shuffle_train = (args.batch_size * args.num_accumulation_steps <
len(train_data))
train_loader, val_loader, test_loader = get_loaders_from_datasets(
train_data,
val_data,
test_data,
batch_size=args.batch_size,
num_workers=args.num_workers,
shuffle_train=shuffle_train)
# Options
optimization_args = {
'optimizer': {
'name': args.optimizer,
'lr': args.lr,
}
}
with open(args.config, 'r') as f:
architecture_args = json.load(f)
model_class = getattr(methods, args.model_class)
model = model_class(input_shape=train_loader.dataset[0][0].shape,
architecture_args=architecture_args,
l2_reg_coef=args.l2_reg_coef,
device=args.device,
seed=args.seed)
# put the model in always eval mode. This makes sure that in case the network has pretrained BatchNorm
# layers, their running average is fixed.
utils.put_always_eval_mode(model)
metrics_list = [
metrics.Accuracy(output_key='pred',
one_hot=(train_data[0][1].ndim > 0))
]
if args.dataset == 'imagenet':
metrics_list.append(metrics.TopKAccuracy(k=5, output_key='pred'))
stopper = callbacks.EarlyStoppingWithMetric(
metric=metrics_list[0],
stopping_param=args.stopping_param,
partition='val',
direction='max')
training.train(model=model,
train_loader=train_loader,
val_loader=val_loader,
epochs=args.epochs,
save_iter=args.save_iter,
vis_iter=args.vis_iter,
optimization_args=optimization_args,
log_dir=args.log_dir,
args_to_log=args,
stopper=stopper,
metrics=metrics_list,
device_ids=args.all_device_ids,
num_accumulation_steps=args.num_accumulation_steps)
val_preds = utils.apply_on_dataset(model=model,
dataset=val_data,
cpu=True,
partition='val',
batch_size=args.batch_size)['pred']
val_acc = metrics_list[0].value(epoch=args.epochs - 1, partition='val')
file_name = f'results-{args.exclude_ratio:.4f}'
if args.baseline_name == 'random':
file_name += f'-{args.random_baseline_seed}'
file_name += '.pkl'
file_path = os.path.join(args.output_dir, args.baseline_name,
args.exp_name, file_name)
utils.make_path(os.path.dirname(file_path))
with open(file_path, 'wb') as f:
pickle.dump({
'val_preds': val_preds,
'val_acc': val_acc,
'args': args
}, f)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--config', '-c', type=str, required=True)
parser.add_argument('--device',
'-d',
default='cuda',
help='specifies the main device')
parser.add_argument(
'--all_device_ids',
nargs='+',
type=str,
default=None,
help=
"If not None, this list specifies devices for multiple GPU training. "
"The first device should match with the main device (args.device).")
parser.add_argument('--batch_size', '-b', type=int, default=2**20)
parser.add_argument('--epochs', '-e', type=int, default=2000)
parser.add_argument('--stopping_param', type=int, default=2**20)
parser.add_argument('--save_iter', '-s', type=int, default=2**20)
parser.add_argument('--vis_iter', '-v', type=int, default=2**20)
parser.add_argument('--log_dir',
'-l',
type=str,
default='sample_info/logs/junk')
parser.add_argument('--seed', type=int, default=42)
parser.add_argument(
'--num_accumulation_steps',
default=1,
type=int,
help='Number of training steps to accumulate before updating weights')
# data parameters
parser.add_argument(
'--dataset',
'-D',
type=str,
default='mnist4vs9',
choices=[
'mnist4vs9', 'synthetic', 'cifar10-cat-vs-dog', 'cats-and-dogs'
],
help='Which dataset to use. One can add more choices if needed.')
parser.add_argument('--data_augmentation',
'-A',
action='store_true',
dest='data_augmentation')
parser.set_defaults(data_augmentation=False)
parser.add_argument('--error_prob', '-n', type=float, default=0.0)
parser.add_argument('--num_train_examples', type=int, default=None)
parser.add_argument('--clean_validation',
action='store_true',
default=False)
parser.add_argument('--resize_to_imagenet',
action='store_true',
dest='resize_to_imagenet')
parser.set_defaults(resize_to_imagenet=False)
parser.add_argument('--cache_dataset',
action='store_true',
dest='cache_dataset')
parser.set_defaults(cache_dataset=False)
parser.add_argument('--num_workers',
type=int,
default=0,
help='number of workers in data loaders')
parser.add_argument('--exclude_index',
type=int,
default=None,
help='Index of an example to remove.')
# hyper-parameters
parser.add_argument('--model_class',
'-m',
type=str,
default='ClassifierL2')
parser.add_argument('--linearized', dest='linearized', action='store_true')
parser.set_defaults(linearized=False)
parser.add_argument('--l2_reg_coef', type=float, default=0.0)
parser.add_argument('--lr', type=float, default=1e-3, help='Learning rate')
parser.add_argument('--optimizer',
type=str,
default='sgd',
choices=['adam', 'sgd'])
parser.add_argument(
'--output_dir',
'-o',
type=str,
default='sample_info/results/ground-truth/ground-truth/')
parser.add_argument('--exp_name', '-E', type=str, required=True)
args = parser.parse_args()
print(args)
# Build data
train_data, val_data, test_data, _ = load_data_from_arguments(
args, build_loaders=False)
# exclude the example
if args.exclude_index is not None:
train_data = SubsetDataWrapper(dataset=train_data,
exclude_indices=[args.exclude_index])
if args.cache_dataset:
train_data = CacheDatasetWrapper(train_data)
val_data = CacheDatasetWrapper(val_data)
test_data = CacheDatasetWrapper(test_data)
shuffle_train = (args.batch_size * args.num_accumulation_steps <
len(train_data))
train_loader, val_loader, test_loader = get_loaders_from_datasets(
train_data,
val_data,
test_data,
batch_size=args.batch_size,
num_workers=args.num_workers,
shuffle_train=shuffle_train)
# Options
optimization_args = {
'optimizer': {
'name': args.optimizer,
'lr': args.lr,
}
}
with open(args.config, 'r') as f:
architecture_args = json.load(f)
model_class = getattr(methods, args.model_class)
model = model_class(input_shape=train_loader.dataset[0][0].shape,
architecture_args=architecture_args,
l2_reg_coef=args.l2_reg_coef,
seed=args.seed,
device=args.device)
# put the model in always eval mode. This makes sure that in case the network has pretrained BatchNorm
# layers, their running average is fixed.
utils.put_always_eval_mode(model)
if args.linearized:
print("Using a linearized model")
model = LinearizedModelV2(model=model,
train_data=train_data,
val_data=val_data,
l2_reg_coef=args.l2_reg_coef)
if args.dataset == 'synthetic':
model.visualize = (lambda *args, **kwargs: {}
) # no visualization is needed
metrics_list = [metrics.Accuracy(output_key='pred')]
training.train(model=model,
train_loader=train_loader,
val_loader=val_loader,
epochs=args.epochs + 1,
save_iter=args.save_iter,
vis_iter=args.vis_iter,
optimization_args=optimization_args,
log_dir=args.log_dir,
args_to_log=args,
metrics=metrics_list,
device_ids=args.all_device_ids,
num_accumulation_steps=args.num_accumulation_steps)
params = dict(model.named_parameters())
for k in params.keys():
params[k] = utils.to_cpu(params[k])
val_preds = utils.apply_on_dataset(model=model,
dataset=val_data,
cpu=True,
partition='val',
batch_size=args.batch_size)['pred']
val_acc = metrics_list[0].value(epoch=args.epochs, partition='val')
exp_dir = os.path.join(args.output_dir, args.exp_name)
# if it the the full dataset save params and val_preds, otherwise compare to the saved weights/predictions
if args.exclude_index is None:
file_path = os.path.join(exp_dir, 'full-data-training.pkl')
else:
file_path = os.path.join(exp_dir, f'{args.exclude_index}.pkl')
utils.make_path(os.path.dirname(file_path))
with open(file_path, 'wb') as f:
pickle.dump(
{
'weights': params,
'val_preds': val_preds,
'val_acc': val_acc,
'args': args
}, f)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--config', '-c', type=str, required=True)
parser.add_argument('--device',
'-d',
default='cuda',
help='specifies the main device')
parser.add_argument(
'--all_device_ids',
nargs='+',
type=str,
default=None,
help=
"If not None, this list specifies devices for multiple GPU training. "
"The first device should match with the main device (args.device).")
parser.add_argument('--batch_size', '-b', type=int, default=256)
parser.add_argument('--epochs', '-e', type=int, default=400)
parser.add_argument('--stopping_param', type=int, default=2**30)
parser.add_argument('--save_iter', '-s', type=int, default=10)
parser.add_argument('--vis_iter', '-v', type=int, default=10)
parser.add_argument('--log_dir', '-l', type=str, default=None)
parser.add_argument('--seed', type=int, default=42)
# data parameters
parser.add_argument('--dataset', '-D', type=str, default='corrupt4_mnist')
parser.add_argument('--data_augmentation',
'-A',
action='store_true',
dest='data_augmentation')
parser.set_defaults(data_augmentation=False)
parser.add_argument('--error_prob', '-n', type=float, default=0.0)
parser.add_argument('--num_train_examples', type=int, default=None)
parser.add_argument('--clean_validation',
action='store_true',
default=False)
# hyper-parameters
parser.add_argument('--model_class',
'-m',
type=str,
default='ClassifierL2WithGradCollector')
parser.add_argument('--weight_decay', type=float, default=0.0)
parser.add_argument('--lr', type=float, default=1e-3, help='Learning rate')
parser.add_argument('--optimizer',
type=str,
default='adam',
choices=['adam', 'sgd'])
parser.add_argument('--output_dir',
'-o',
type=str,
default='results/stability/mnist-4vs9-1000-samples/')
args = parser.parse_args()
print(args)
# Load data
# TODO: remove hard coding
train_data, val_data, test_data, _ = load_data_from_arguments(
{
'dataset': 'mnist',
'num_train_examples': 10 * 500
},
build_loaders=False)
train_data = BinaryDatasetWrapper(train_data, which_labels=(4, 9))
val_data = BinaryDatasetWrapper(val_data, which_labels=(4, 9))
test_data = BinaryDatasetWrapper(test_data, which_labels=(4, 9))
train_data = ReturnSampleIndexWrapper(train_data)
val_data = ReturnSampleIndexWrapper(val_data)
test_data = ReturnSampleIndexWrapper(test_data)
train_loader, val_loader, test_loader = get_loaders_from_datasets(
train_data,
val_data,
test_data,
batch_size=2**30,
shuffle_train=False,
num_workers=0)
# Options
optimization_args = {
'optimizer': {
'name': args.optimizer,
'lr': args.lr,
'weight_decay': args.weight_decay
}
}
with open(args.config, 'r') as f:
architecture_args = json.load(f)
ts = range(100, 401, 100)
for t in ts:
model_class = getattr(methods, args.model_class)
model = model_class(input_shape=train_loader.dataset[0][0][0].shape,
architecture_args=architecture_args,
device=args.device,
seed=args.seed)
metrics_list = [metrics.Accuracy(output_key='pred')]
training.train(
model=model,
train_loader=train_loader,
val_loader=val_loader,
epochs=t,
save_iter=args.save_iter,
vis_iter=2**30, # NOTE: never visualize
optimization_args=optimization_args,
log_dir=args.log_dir,
args_to_log=args,
metrics=metrics_list,
device_ids=args.all_device_ids)
vectors = model._grad_updates
norms = []
for i in range(len(train_data)):
grad_dict = vectors[i]
norm = 0.0
for k, v in grad_dict.items():
norm += torch.norm(v.flatten())
norms.append(norm)
quantities = norms
meta = {
'description':
'Total gradient update per example. The measures are the norm of total gradient update.',
'time': t,
'continuous': False,
'args': args
}
process_results(vectors=vectors,
quantities=quantities,
meta=meta,
exp_name=f'total-grad-t{t}',
output_dir=args.output_dir,
train_data=train_data.dataset)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--config', '-c', type=str, required=True)
parser.add_argument('--device', '-d', default='cuda')
parser.add_argument(
'--all_device_ids',
nargs='+',
type=str,
default=None,
help=
"If not None, this list specifies devices for multiple GPU training. "
"The first device should match with the main device (args.device).")
parser.add_argument('--batch_size', '-b', type=int, default=256)
parser.add_argument('--epochs', '-e', type=int, default=400)
parser.add_argument('--stopping_param', type=int, default=50)
parser.add_argument('--save_iter', '-s', type=int, default=10)
parser.add_argument('--vis_iter', '-v', type=int, default=10)
parser.add_argument('--log_dir', '-l', type=str, default=None)
parser.add_argument('--seed', type=int, default=42)
parser.add_argument('--dataset',
'-D',
type=str,
default='mnist',
choices=[
'mnist', 'uniform-noise-mnist', 'cifar10',
'uniform-noise-cifar10', 'pair-noise-cifar10',
'cifar100', 'uniform-noise-cifar100', 'clothing1m',
'imagenet'
])
parser.add_argument('--data_augmentation',
'-A',
action='store_true',
dest='data_augmentation')
parser.set_defaults(data_augmentation=False)
parser.add_argument('--num_train_examples', type=int, default=None)
parser.add_argument('--error_prob', '-n', type=float, default=0.0)
parser.add_argument('--clean_validation',
dest='clean_validation',
action='store_true')
parser.set_defaults(clean_validation=False)
parser.add_argument('--model_class',
'-m',
type=str,
default='StandardClassifier')
parser.add_argument(
'--loss_function',
type=str,
default='ce',
choices=['ce', 'mse', 'mae', 'gce', 'dmi', 'fw', 'none'])
parser.add_argument('--loss_function_param', type=float, default=1.0)
parser.add_argument('--load_from', type=str, default=None)
parser.add_argument('--grad_weight_decay', '-L', type=float, default=0.0)
parser.add_argument('--grad_l1_penalty', '-S', type=float, default=0.0)
parser.add_argument('--lamb', type=float, default=1.0)
parser.add_argument('--pretrained_arg', '-r', type=str, default=None)
parser.add_argument('--sample_from_q',
action='store_true',
dest='sample_from_q')
parser.set_defaults(sample_from_q=False)
parser.add_argument('--q_dist',
type=str,
default='Gaussian',
choices=['Gaussian', 'Laplace', 'dot', 'ce'])
parser.add_argument('--no-detach', dest='detach', action='store_false')
parser.set_defaults(detach=True)
parser.add_argument('--warm_up',
type=int,
default=0,
help='Number of epochs to skip before '
'starting to train using predicted gradients')
parser.add_argument('--weight_decay', type=float, default=0.0)
parser.add_argument(
'--add_noise',
action='store_true',
dest='add_noise',
help='add noise to the gradients of a standard classifier.')
parser.set_defaults(add_noise=False)
parser.add_argument('--noise_type',
type=str,
default='Gaussian',
choices=['Gaussian', 'Laplace'])
parser.add_argument('--noise_std', type=float, default=0.0)
parser.add_argument('--lr', type=float, default=1e-3, help='Learning rate')
args = parser.parse_args()
print(args)
# Load data
train_loader, val_loader, test_loader, _ = load_data_from_arguments(args)
# Options
optimization_args = {
'optimizer': {
'name': 'adam',
'lr': args.lr,
'weight_decay': args.weight_decay
}
}
# optimization_args = {
# 'optimizer': {
# 'name': 'sgd',
# 'lr': 1e-3,
# },
# 'scheduler': {
# 'step_size': 15,
# 'gamma': 1.25
# }
# }
with open(args.config, 'r') as f:
architecture_args = json.load(f)
model_class = getattr(methods, args.model_class)
model = model_class(input_shape=train_loader.dataset[0][0].shape,
architecture_args=architecture_args,
pretrained_arg=args.pretrained_arg,
device=args.device,
grad_weight_decay=args.grad_weight_decay,
grad_l1_penalty=args.grad_l1_penalty,
lamb=args.lamb,
sample_from_q=args.sample_from_q,
q_dist=args.q_dist,
load_from=args.load_from,
loss_function=args.loss_function,
loss_function_param=args.loss_function_param,
add_noise=args.add_noise,
noise_type=args.noise_type,
noise_std=args.noise_std,
detach=args.detach,
warm_up=args.warm_up)
metrics_list = [metrics.Accuracy(output_key='pred')]
if args.dataset == 'imagenet':
metrics_list.append(metrics.TopKAccuracy(k=5, output_key='pred'))
callbacks_list = [
callbacks.SaveBestWithMetric(metric=metrics_list[0],
partition='val',
direction='max')
]
stopper = callbacks.EarlyStoppingWithMetric(
metric=metrics_list[0],
stopping_param=args.stopping_param,
partition='val',
direction='max')
training.train(model=model,
train_loader=train_loader,
val_loader=val_loader,
epochs=args.epochs,
save_iter=args.save_iter,
vis_iter=args.vis_iter,
optimization_args=optimization_args,
log_dir=args.log_dir,
args_to_log=args,
stopper=stopper,
metrics=metrics_list,
callbacks=callbacks_list,
device_ids=args.all_device_ids)
# if training finishes successfully, compute the test score
print("Testing the best validation model...")
model = utils.load(os.path.join(args.log_dir, 'checkpoints',
'best_val.mdl'),
methods=methods,
device=args.device)
pred = utils.apply_on_dataset(model,
test_loader.dataset,
batch_size=args.batch_size,
output_keys_regexp='pred',
description='Testing')['pred']
labels = [p[1] for p in test_loader.dataset]
labels = torch.tensor(labels, dtype=torch.long)
labels = utils.to_cpu(labels)
with open(os.path.join(args.log_dir, 'test_predictions.pkl'), 'wb') as f:
pickle.dump({'pred': pred, 'labels': labels}, f)
accuracy = torch.mean((pred.argmax(dim=1) == labels).float())
with open(os.path.join(args.log_dir, 'test_accuracy.txt'), 'w') as f:
f.write("{}\n".format(accuracy))
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--config', '-c', type=str, required=True)
parser.add_argument('--device', '-d', default='cuda')
parser.add_argument(
'--all_device_ids',
nargs='+',
type=str,
default=None,
help=
"If not None, this list specifies devices for multiple GPU training. "
"The first device should match with the main device (args.device).")
parser.add_argument('--batch_size', '-b', type=int, default=128)
parser.add_argument('--epochs', '-e', type=int, default=4000)
parser.add_argument('--stopping_param', type=int, default=2**30)
parser.add_argument('--save_iter', '-s', type=int, default=100)
parser.add_argument('--vis_iter', '-v', type=int, default=10)
parser.add_argument('--log_dir', '-l', type=str, default=None)
parser.add_argument('--seed', type=int, default=42)
parser.add_argument('--dataset',
'-D',
type=str,
default='uniform-noise-cifar10',
choices=['uniform-noise-cifar10'])
parser.add_argument('--data_augmentation',
'-A',
action='store_true',
dest='data_augmentation')
parser.set_defaults(data_augmentation=False)
parser.add_argument('--num_train_examples', type=int, default=None)
parser.add_argument('--error_prob', '-n', type=float, default=0.0)
parser.add_argument('--clean_validation',
dest='clean_validation',
action='store_true')
parser.set_defaults(clean_validation=False)
parser.add_argument('--model_class',
'-m',
type=str,
default='StandardClassifier')
parser.add_argument('--load_from', type=str, default=None)
parser.add_argument('--grad_weight_decay', '-L', type=float, default=0.0)
parser.add_argument('--lamb', type=float, default=1.0)
parser.add_argument('--pretrained_arg', '-r', type=str, default=None)
parser.add_argument('--sample_from_q',
action='store_true',
dest='sample_from_q')
parser.set_defaults(sample_from_q=False)
parser.add_argument('--q_dist',
type=str,
default='Gaussian',
choices=['Gaussian', 'Laplace', 'dot'])
parser.add_argument('--weight_decay', type=float, default=0.0)
parser.add_argument('--lr', type=float, default=1e-4, help='Learning rate')
parser.add_argument('--k',
'-k',
type=int,
required=False,
default=10,
help='width parameter of ResNet18-k')
parser.add_argument('--exclude_percent', type=float,
default=0.0) # TODO: make this argument work
args = parser.parse_args()
print(args)
# Load data
train_loader, val_loader, test_loader, _ = load_data_from_arguments(args)
# Options
optimization_args = {
'optimizer': {
'name': 'adam',
'lr': args.lr,
'weight_decay': args.weight_decay
}
}
with open(args.config, 'r') as f:
architecture_args = json.load(f)
# set the width parameter k
if ('classifier' in architecture_args
and architecture_args['classifier'].get(
'net', '').find('double-descent') != -1):
architecture_args['classifier']['k'] = args.k
if ('q-network' in architecture_args
and architecture_args['classifier'].get(
'net', '').find('double-descent') != -1):
architecture_args['q-network']['k'] = args.k
model_class = getattr(methods, args.model_class)
model = model_class(input_shape=train_loader.dataset[0][0].shape,
architecture_args=architecture_args,
pretrained_arg=args.pretrained_arg,
device=args.device,
grad_weight_decay=args.grad_weight_decay,
lamb=args.lamb,
sample_from_q=args.sample_from_q,
q_dist=args.q_dist,
load_from=args.load_from,
loss_function='ce')
metrics_list = [metrics.Accuracy(output_key='pred')]
if args.dataset == 'imagenet':
metrics_list.append(metrics.TopKAccuracy(k=5, output_key='pred'))
callbacks_list = [
callbacks.SaveBestWithMetric(metric=metrics_list[0],
partition='val',
direction='max')
]
stopper = callbacks.EarlyStoppingWithMetric(
metric=metrics_list[0],
stopping_param=args.stopping_param,
partition='val',
direction='max')
training.train(model=model,
train_loader=train_loader,
val_loader=val_loader,
epochs=args.epochs,
save_iter=args.save_iter,
vis_iter=args.vis_iter,
optimization_args=optimization_args,
log_dir=args.log_dir,
args_to_log=args,
stopper=stopper,
metrics=metrics_list,
callbacks=callbacks_list,
device_ids=args.all_device_ids)
# test the last model and best model
models_to_test = [{
'name': 'best',
'file': 'best_val_accuracy.mdl'
}, {
'name': 'final',
'file': 'final.mdl'
}]
for spec in models_to_test:
print("Testing the {} model...".format(spec['name']))
model = utils.load(os.path.join(args.log_dir, 'checkpoints',
spec['file']),
methods=methods,
device=args.device)
pred = utils.apply_on_dataset(model,
test_loader.dataset,
batch_size=args.batch_size,
output_keys_regexp='pred',
description='Testing')['pred']
labels = [p[1] for p in test_loader.dataset]
labels = torch.tensor(labels, dtype=torch.long)
labels = utils.to_cpu(labels)
with open(
os.path.join(args.log_dir,
'{}_test_predictions.pkl'.format(spec['name'])),
'wb') as f:
pickle.dump({'pred': pred, 'labels': labels}, f)
accuracy = torch.mean((pred.argmax(dim=1) == labels).float())
with open(
os.path.join(args.log_dir,
'{}_test_accuracy.txt'.format(spec['name'])),
'w') as f:
f.write("{}\n".format(accuracy))