salient_filters[ind] = 999999
# if len(salient_filters) == 0:
# break
return input_gradient
if __name__ == "__main__":
# Choose GPU device and print status information:
setup = inversefed.utils.system_startup(args)
start_time = time.time()
# Prepare for training
# Get data:
loss_fn, trainloader, validloader = inversefed.construct_dataloaders(
args.dataset, defs, data_path=args.data_path)
dm = torch.as_tensor(
getattr(inversefed.consts, f'{args.dataset.lower()}_mean'),
**setup)[:, None, None]
ds = torch.as_tensor(
getattr(inversefed.consts, f'{args.dataset.lower()}_std'),
**setup)[:, None, None]
if args.dataset == 'ImageNet':
if args.model == 'ResNet152':
model = torchvision.models.resnet152(pretrained=args.trained_model)
elif args.model == 'ResNet50':
model = torchvision.models.resnet50(pretrained=args.trained_model)
else:
model = torchvision.models.resnet18(pretrained=args.trained_model)
def preprocess(opt, defs, valid=False):
if opt.data == 'cifar100':
loss_fn, trainloader, validloader = inversefed.construct_dataloaders(
'CIFAR100', defs)
trainset, validset = _build_cifar100('~/data/')
if len(opt.aug_list) > 0:
policy_list = split(opt.aug_list)
else:
policy_list = []
if not valid:
trainset.transform = build_transform(True, policy_list, opt, defs)
trainloader = torch.utils.data.DataLoader(trainset,
batch_size=defs.batch_size,
shuffle=True,
drop_last=False,
num_workers=4,
pin_memory=True)
if valid:
validset.transform = build_transform(True, policy_list, opt, defs)
validloader = torch.utils.data.DataLoader(validset,
batch_size=defs.batch_size,
shuffle=False,
drop_last=False,
num_workers=4,
pin_memory=True)
return loss_fn, trainloader, validloader
elif opt.data == 'FashionMinist':
loss_fn, _, _ = inversefed.construct_dataloaders('CIFAR100', defs)
trainset = torchvision.datasets.FashionMNIST(
'../data',
train=True,
download=True,
transform=transforms.Compose([
lambda x: transforms.functional.to_grayscale(
x, num_output_channels=3),
transforms.Resize(32),
transforms.ToTensor(),
transforms.Normalize((0.1307, ), (0.3081, ))
]))
validset = torchvision.datasets.FashionMNIST(
'../data',
train=False,
download=True,
transform=transforms.Compose([
lambda x: transforms.functional.to_grayscale(
x, num_output_channels=3),
transforms.Resize(32),
transforms.ToTensor(),
transforms.Normalize((0.1307, ), (0.3081, ))
]))
# key
if len(opt.aug_list) > 0:
policy_list = split(
opt.aug_list) # [int(idx) for idx in opt.aug_list.split('-')]
else:
policy_list = []
tlist = policy_list if not valid else list()
trainset.transform = build_transform(True, tlist, opt, defs)
trainloader = torch.utils.data.DataLoader(trainset,
batch_size=defs.batch_size,
shuffle=True,
drop_last=False,
num_workers=4,
pin_memory=True)
tlist = list() if not valid else policy_list
validset.transform = build_transform(True, tlist, opt, defs)
validloader = torch.utils.data.DataLoader(validset,
batch_size=defs.batch_size,
shuffle=False,
drop_last=False,
num_workers=4,
pin_memory=True)
return loss_fn, trainloader, validloader
else:
raise NotImplementedError