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main.py
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main.py
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import os
import argparse
from importlib import import_module
import shutil
import json
import torch
import torch.nn.functional as F
from torchvision import datasets, transforms, models
import torchsso
from torchsso.optim import SecondOrderOptimizer, VIOptimizer
from torchsso.utils import Logger
DATASET_CIFAR10 = 'CIFAR-10'
DATASET_CIFAR100 = 'CIFAR-100'
DATASET_MNIST = 'MNIST'
def main():
parser = argparse.ArgumentParser()
# Data
parser.add_argument('--dataset', type=str,
choices=[DATASET_CIFAR10, DATASET_CIFAR100, DATASET_MNIST], default=DATASET_CIFAR10,
help='name of dataset')
parser.add_argument('--root', type=str, default='./data',
help='root of dataset')
parser.add_argument('--epochs', type=int, default=10,
help='number of epochs to train')
parser.add_argument('--batch_size', type=int, default=128,
help='input batch size for training')
parser.add_argument('--val_batch_size', type=int, default=128,
help='input batch size for valing')
parser.add_argument('--normalizing_data', action='store_true',
help='[data pre processing] normalizing data')
parser.add_argument('--random_crop', action='store_true',
help='[data augmentation] random crop')
parser.add_argument('--random_horizontal_flip', action='store_true',
help='[data augmentation] random horizontal flip')
# Training Settings
parser.add_argument('--arch_file', type=str, default=None,
help='name of file which defines the architecture')
parser.add_argument('--arch_name', type=str, default='LeNet5',
help='name of the architecture')
parser.add_argument('--arch_args', type=json.loads, default=None,
help='[JSON] arguments for the architecture')
parser.add_argument('--optim_name', type=str, default=SecondOrderOptimizer.__name__,
help='name of the optimizer')
parser.add_argument('--optim_args', type=json.loads, default=None,
help='[JSON] arguments for the optimizer')
parser.add_argument('--curv_args', type=json.loads, default=dict(),
help='[JSON] arguments for the curvature')
parser.add_argument('--fisher_args', type=json.loads, default=dict(),
help='[JSON] arguments for the fisher')
parser.add_argument('--scheduler_name', type=str, default=None,
help='name of the learning rate scheduler')
parser.add_argument('--scheduler_args', type=json.loads, default=None,
help='[JSON] arguments for the scheduler')
# Options
parser.add_argument('--download', action='store_true', default=False,
help='if True, downloads the dataset (CIFAR-10 or 100) from the internet')
parser.add_argument('--create_graph', action='store_true', default=False,
help='create graph of the derivative')
parser.add_argument('--no_cuda', action='store_true', default=False,
help='disables CUDA training')
parser.add_argument('--seed', type=int, default=1,
help='random seed')
parser.add_argument('--num_workers', type=int, default=0,
help='number of sub processes for data loading')
parser.add_argument('--log_interval', type=int, default=50,
help='how many batches to wait before logging training status')
parser.add_argument('--log_file_name', type=str, default='log',
help='log file name')
parser.add_argument('--checkpoint_interval', type=int, default=50,
help='how many epochs to wait before logging training status')
parser.add_argument('--resume', type=str, default=None,
help='checkpoint path for resume training')
parser.add_argument('--out', type=str, default='result',
help='dir to save output files')
parser.add_argument('--config', default='configs/cifar10/lenet_kfac.json',
help='config file path')
args = parser.parse_args()
dict_args = vars(args)
# Load config file
if args.config is not None:
with open(args.config) as f:
config = json.load(f)
dict_args.update(config)
# Set device
use_cuda = not args.no_cuda and torch.cuda.is_available()
device = torch.device('cuda' if use_cuda else 'cpu')
# Set random seed
torch.manual_seed(args.seed)
# Setup data augmentation & data pre processing
train_transforms, val_transforms = [], []
if args.random_crop:
train_transforms.append(transforms.RandomCrop(32, padding=4))
if args.random_horizontal_flip:
train_transforms.append(transforms.RandomHorizontalFlip())
train_transforms.append(transforms.ToTensor())
val_transforms.append(transforms.ToTensor())
if args.normalizing_data:
normalize = transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))
train_transforms.append(normalize)
val_transforms.append(normalize)
train_transform = transforms.Compose(train_transforms)
val_transform = transforms.Compose(val_transforms)
# Setup data loader
if args.dataset == DATASET_CIFAR10:
# CIFAR-10
num_classes = 10
dataset_class = datasets.CIFAR10
elif args.dataset == DATASET_CIFAR100:
# CIFAR-100
num_classes = 100
dataset_class = datasets.CIFAR100
elif args.dataset == DATASET_MNIST:
num_classes = 10
dataset_class = datasets.MNIST
else:
assert False, f'unknown dataset {args.dataset}'
train_dataset = dataset_class(
root=args.root, train=True, download=args.download, transform=train_transform)
val_dataset = dataset_class(
root=args.root, train=False, download=args.download, transform=val_transform)
train_loader = torch.utils.data.DataLoader(
train_dataset, batch_size=args.batch_size, shuffle=True, num_workers=args.num_workers)
val_loader = torch.utils.data.DataLoader(
val_dataset, batch_size=args.val_batch_size, shuffle=False, num_workers=args.num_workers)
# Setup model
if args.arch_file is None:
arch_class = getattr(models, args.arch_name)
else:
_, ext = os.path.splitext(args.arch_file)
dirname = os.path.dirname(args.arch_file)
if dirname == '':
module_path = args.arch_file.replace(ext, '')
elif dirname == '.':
module_path = os.path.basename(args.arch_file).replace(ext, '')
else:
module_path = '.'.join(os.path.split(args.arch_file)).replace(ext, '')
module = import_module(module_path)
arch_class = getattr(module, args.arch_name)
arch_kwargs = {} if args.arch_args is None else args.arch_args
arch_kwargs['num_classes'] = num_classes
model = arch_class(**arch_kwargs)
setattr(model, 'num_classes', num_classes)
model = model.to(device)
optim_kwargs = {} if args.optim_args is None else args.optim_args
# Setup optimizer
if args.optim_name == SecondOrderOptimizer.__name__:
optimizer = SecondOrderOptimizer(model, **optim_kwargs, curv_kwargs=args.curv_args)
elif args.optim_name == VIOptimizer.__name__:
optimizer = VIOptimizer(model, dataset_size=len(train_loader.dataset), seed=args.seed,
**optim_kwargs, curv_kwargs=args.curv_args)
else:
optim_class = getattr(torch.optim, args.optim_name)
optimizer = optim_class(model.parameters(), **optim_kwargs)
# Setup lr scheduler
if args.scheduler_name is None:
scheduler = None
else:
scheduler_class = getattr(torchsso.optim.lr_scheduler, args.scheduler_name, None)
if scheduler_class is None:
scheduler_class = getattr(torch.optim.lr_scheduler, args.scheduler_name)
scheduler_kwargs = {} if args.scheduler_args is None else args.scheduler_args
scheduler = scheduler_class(optimizer, **scheduler_kwargs)
start_epoch = 1
# Load checkpoint
if args.resume is not None:
print('==> Resuming from checkpoint..')
assert os.path.exists(args.resume), 'Error: no checkpoint file found'
checkpoint = torch.load(args.resume)
model.load_state_dict(checkpoint['model'])
start_epoch = checkpoint['epoch']
# All config
print('===========================')
for key, val in vars(args).items():
if key == 'dataset':
print('{}: {}'.format(key, val))
print('train data size: {}'.format(len(train_loader.dataset)))
print('val data size: {}'.format(len(val_loader.dataset)))
else:
print('{}: {}'.format(key, val))
print('===========================')
# Copy this file & config to args.out
if not os.path.isdir(args.out):
os.makedirs(args.out)
shutil.copy(os.path.realpath(__file__), args.out)
if args.config is not None:
shutil.copy(args.config, args.out)
if args.arch_file is not None:
shutil.copy(args.arch_file, args.out)
# Setup logger
logger = Logger(args.out, args.log_file_name)
logger.start()
# Run training
for epoch in range(start_epoch, args.epochs + 1):
# train
accuracy, loss, confidence = train(model, device, train_loader, optimizer, scheduler, epoch, args, logger)
# val
val_accuracy, val_loss = validate(model, device, val_loader, optimizer)
# save log
iteration = epoch * len(train_loader)
log = {'epoch': epoch, 'iteration': iteration,
'accuracy': accuracy, 'loss': loss, 'confidence': confidence,
'val_accuracy': val_accuracy, 'val_loss': val_loss,
'lr': optimizer.param_groups[0]['lr'],
'momentum': optimizer.param_groups[0].get('momentum', 0)}
logger.write(log)
# save checkpoint
if epoch % args.checkpoint_interval == 0 or epoch == args.epochs:
path = os.path.join(args.out, 'epoch{}.ckpt'.format(epoch))
data = {
'model': model.state_dict(),
'optimizer': optimizer.state_dict(),
'epoch': epoch
}
torch.save(data, path)
def train(model, device, train_loader, optimizer, scheduler, epoch, args, logger):
def scheduler_type(_scheduler):
if _scheduler is None:
return 'none'
return getattr(_scheduler, 'scheduler_type', 'epoch')
model.train()
total_correct = 0
loss = None
confidence = {'top1': 0, 'top1_true': 0, 'top1_false': 0, 'true': 0, 'false': 0}
total_data_size = 0
epoch_size = len(train_loader.dataset)
num_iters_in_epoch = len(train_loader)
base_num_iter = (epoch - 1) * num_iters_in_epoch
for batch_idx, (data, target) in enumerate(train_loader):
data, target = data.to(device), target.to(device)
for name, param in model.named_parameters():
attr = 'p_pre_{}'.format(name)
setattr(model, attr, param.detach().clone())
# update params
def closure():
optimizer.zero_grad()
output = model(data)
loss = F.cross_entropy(output, target)
loss.backward(create_graph=args.create_graph)
return loss, output
if isinstance(optimizer, SecondOrderOptimizer) and optimizer.curv_type == 'Fisher':
closure = torchsso.get_closure_for_fisher(optimizer, model, data, target, **args.fisher_args)
loss, output = optimizer.step(closure=closure)
pred = output.argmax(dim=1, keepdim=True)
correct = pred.eq(target.view_as(pred)).sum().item()
loss = loss.item()
total_correct += correct
prob = F.softmax(output, dim=1)
for p, idx in zip(prob, target):
confidence['top1'] += torch.max(p).item()
top1 = torch.argmax(p).item()
if top1 == idx:
confidence['top1_true'] += p[top1].item()
else:
confidence['top1_false'] += p[top1].item()
confidence['true'] += p[idx].item()
confidence['false'] += (1 - p[idx].item())
iteration = base_num_iter + batch_idx + 1
total_data_size += len(data)
if scheduler_type(scheduler) == 'iter':
scheduler.step()
if batch_idx % args.log_interval == 0:
accuracy = 100. * total_correct / total_data_size
elapsed_time = logger.elapsed_time
print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}, '
'Accuracy: {:.0f}/{} ({:.2f}%), '
'Elapsed Time: {:.1f}s'.format(
epoch, total_data_size, epoch_size, 100. * (batch_idx + 1) / num_iters_in_epoch,
loss, total_correct, total_data_size, accuracy, elapsed_time))
# save log
lr = optimizer.param_groups[0]['lr']
log = {'epoch': epoch, 'iteration': iteration, 'elapsed_time': elapsed_time,
'accuracy': accuracy, 'loss': loss, 'lr': lr}
for name, param in model.named_parameters():
attr = 'p_pre_{}'.format(name)
p_pre = getattr(model, attr)
p_norm = param.norm().item()
p_shape = list(param.size())
p_pre_norm = p_pre.norm().item()
g_norm = param.grad.norm().item()
upd_norm = param.sub(p_pre).norm().item()
noise_scale = getattr(param, 'noise_scale', 0)
p_log = {'p_shape': p_shape, 'p_norm': p_norm, 'p_pre_norm': p_pre_norm,
'g_norm': g_norm, 'upd_norm': upd_norm, 'noise_scale': noise_scale}
log[name] = p_log
logger.write(log)
if scheduler_type(scheduler) == 'epoch':
scheduler.step(epoch - 1)
accuracy = 100. * total_correct / epoch_size
confidence['top1'] /= epoch_size
confidence['top1_true'] /= total_correct
confidence['top1_false'] /= (epoch_size - total_correct)
confidence['true'] /= epoch_size
confidence['false'] /= (epoch_size * (model.num_classes - 1))
return accuracy, loss, confidence
def validate(model, device, val_loader, optimizer):
model.eval()
val_loss = 0
correct = 0
with torch.no_grad():
for data, target in val_loader:
data, target = data.to(device), target.to(device)
if isinstance(optimizer, VIOptimizer):
output = optimizer.prediction(data)
else:
output = model(data)
val_loss += F.cross_entropy(output, target, reduction='sum').item() # sum up batch loss
pred = output.argmax(dim=1, keepdim=True) # get the index of the max log-probability
correct += pred.eq(target.view_as(pred)).sum().item()
val_loss /= len(val_loader.dataset)
val_accuracy = 100. * correct / len(val_loader.dataset)
print('\nEval: Average loss: {:.4f}, Accuracy: {:.0f}/{} ({:.2f}%)\n'.format(
val_loss, correct, len(val_loader.dataset), val_accuracy))
return val_accuracy, val_loss
if __name__ == '__main__':
main()