def ce_gradient_pair_scatter(
model, data_loader, d1=0, d2=1, max_num_examples=2000, plt=None
):
if plt is None:
plt = matplotlib.pyplot
model.eval()
pred = utils.apply_on_dataset(
model=model,
dataset=data_loader.dataset,
output_keys_regexp="pred",
max_num_examples=max_num_examples,
description="grad-pair-scatter:pred",
)["pred"]
n_examples = min(len(data_loader.dataset), max_num_examples)
labels = []
for idx in range(n_examples):
labels.append(data_loader.dataset[idx][1])
labels = torch.tensor(labels, dtype=torch.long)
labels = F.one_hot(labels, num_classes=model.num_classes).float()
labels = utils.to_cpu(labels)
grad_wrt_logits = torch.softmax(pred, dim=-1) - labels
grad_wrt_logits = utils.to_numpy(grad_wrt_logits)
fig, ax = plt.subplots(1, figsize=(5, 5))
plt.scatter(grad_wrt_logits[:, d1], grad_wrt_logits[:, d2])
ax.set_xlabel(str(d1))
ax.set_ylabel(str(d2))
# L = np.percentile(grad_wrt_logits, q=5, axis=0)
# R = np.percentile(grad_wrt_logits, q=95, axis=0)
# ax.set_xlim(L[d1], R[d1])
# ax.set_ylim(L[d2], R[d2])
ax.set_title("Two coordinates of grad wrt to logits")
return fig, plt
def ce_gradient_norm_histogram(
model, data_loader, tensorboard, epoch, name, max_num_examples=5000
):
model.eval()
pred = utils.apply_on_dataset(
model=model,
dataset=data_loader.dataset,
output_keys_regexp="pred",
description="grad-histogram:pred",
max_num_examples=max_num_examples,
)["pred"]
n_examples = min(len(data_loader.dataset), max_num_examples)
labels = []
for idx in range(n_examples):
labels.append(data_loader.dataset[idx][1])
labels = torch.tensor(labels, dtype=torch.long)
labels = F.one_hot(labels, num_classes=model.num_classes).float()
labels = utils.to_cpu(labels)
grad_wrt_logits = torch.softmax(pred, dim=-1) - labels
grad_norms = torch.sum(grad_wrt_logits ** 2, dim=-1)
grad_norms = utils.to_numpy(grad_norms)
try:
tensorboard.add_histogram(tag=name, values=grad_norms, global_step=epoch)
except ValueError as e:
print("Tensorboard histogram error: {}".format(e))
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('--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', 'cifar10', '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('--label_noise_level', '-n', type=float, default=0.0)
parser.add_argument('--label_noise_type',
type=str,
default='error',
choices=['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)
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'])
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 = datasets.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 = []
if args.dataset == 'imagenet':
metrics_list.append(metrics.TopKAccuracy(k=5, output_key='pred'))
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,
stopping_param=args.stopping_param,
metrics=metrics_list)
# 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'),
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("--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="cifar10",
choices=["mnist", "cifar10", "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("--label_noise_level", "-n", type=float, default=0.0)
parser.add_argument(
"--label_noise_type",
type=str,
default="error",
choices=["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)
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)
args = parser.parse_args()
print(args)
# Load data
train_loader, val_loader, test_loader = datasets.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",
)
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,
stopping_param=args.stopping_param,
)
# test the last model and best model
models_to_test = [
{
"name": "best",
"file": "best_val.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"]),
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))
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--config", "-c", type=str, default=None)
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("--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", "cifar10", "cifar100", "clothing1m", "imagenet", "cover"
],
)
# parser.add_argument("--image-root", metavar="DIR", help="path to images")
# parser.add_argument("--label", metavar="DIR", help="path to label file")
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="error",
choices=["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)
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"])
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
args.image_root = [
"/data/chenlong.1024/5198852-tiktok-1w_images",
"/data/chenlong.1024/5205046-tiktok-10w_images",
"/data/chenlong.1024/5599074-tiktok-impr_cnt20_images",
"/data/chenlong.1024/5600297-tiktok-impr_cnt10_images",
]
args.label = [
"/data/chenlong.1024/5198852-tiktok-1w.csv",
"/data/chenlong.1024/5205046-tiktok-10w.csv",
"/data/chenlong.1024/5599074-tiktok-impr_cnt20.csv",
"/data/chenlong.1024/5600297-tiktok-impr_cnt10.csv",
]
train_loader, val_loader, test_loader = datasets.load_data_from_arguments(
args)
# Options
optimization_args = {
"optimizer": {
"name": "adam",
"lr": args.lr,
"weight_decay": args.weight_decay
},
"scheduler": {
"step_size": 20,
"gamma": 0.3
},
}
# optimization_args = {
# 'optimizer': {
# 'name': 'sgd',
# 'lr': 1e-3,
# },
# 'scheduler': {
# 'step_size': 15,
# 'gamma': 1.25
# }
# }
model_class = getattr(methods, args.model_class)
if "CoverModel" in args.model_class:
model = model_class(
num_classes=2,
pretrained=True,
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,
)
else:
with open(args.config, "r") as f:
architecture_args = json.load(f)
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 = []
if args.dataset == "imagenet":
metrics_list.append(metrics.TopKAccuracy(k=5, output_key="pred"))
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,
stopping_param=args.stopping_param,
metrics=metrics_list,
)
# 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"),
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('--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='cifar10',
choices=['mnist', 'cifar10', '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('--label_noise_level', '-n', type=float, default=0.0)
parser.add_argument('--label_noise_type',
type=str,
default='error',
choices=['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)
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=True,
default=10,
help='width parameter of ResNet18-k')
args = parser.parse_args()
print(args)
# Load data
train_loader, val_loader, test_loader = datasets.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', '') == 'double-descent-cifar10-resnet18'):
architecture_args['classifier']['k'] = args.k
if ('q-network' in architecture_args
and architecture_args['q-network'].get(
'net', '') == 'double-descent-cifar10-resnet18'):
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')
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,
stopping_param=args.stopping_param)
# test the last model and best model
models_to_test = [{
'name': 'best',
'file': 'best_val.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']),
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))