def get_args():
parser = argparse.ArgumentParser()
parser.add_argument('--dataset', type=str, default='MiniImageNet', choices=['MiniImageNet', 'TieredImagenet'])
parser.add_argument('--backbone_class', type=str, default='Res12', choices=['ConvNet', 'ConvAttNet', 'Res12',
'Res18', 'ResNest12', 'SAN'])
parser.add_argument('--query', type=int, default=15)
parser.add_argument('--label-smoothing', type=float, default=0.0, help='label-smoothing (default eta: 0.0)')
parser.add_argument('--mixup', type=float, default=0.0, help='mixup (default eta: 0.0)')
parser.add_argument('--rand-aug', action='store_true', default=False, help='random augment')
parser.add_argument('--augment', type=str, default='none')
parser.add_argument('--batch_size', type=int, default=32)
parser.add_argument('--max_epoch', type=int, default=2)
parser.add_argument('--lr', type=float, default=0.0008)
parser.add_argument('--lr_scheduler', type=str, default='cosine', help='learning rate scheduler (default: cos)')
parser.add_argument('--step', type=str, default='[2,3,4,5]', help='Decrease learning rate at these epochs.')
parser.add_argument('--gamma', type=float, default=0.1)
parser.add_argument('--no-bn-wd', action='store_true', default=False, help='no bias decay')
parser.add_argument('--warmup-epochs', type=int, default=0, help='number of warmup epochs (default: 0)')
parser.add_argument('--weight-decay', type=float, default=1e-4, help='SGD weight decay (default: 1e-4)')
parser.add_argument('--gpu', type=str, default='1')
parser.add_argument('--resume', action='store_true', default=False)
parser.add_argument('--finetune', action='store_true', default=False)
parser.add_argument('--samples_per_class', type=int, default=50)
parser.add_argument('--num_test_episodes', type=int, default=2000)
parser.add_argument('--save_dir', type=str, default='./checkpoints')
parser.add_argument('--init_weights', type=str, default='./checkpoints/best/Res12-pre.pth')
parser.add_argument('--test_size', type=int, default=84)
parser.add_argument('--eval_interval', type=int, default=1)
parser.add_argument('--start', type=int, default=84)
parser.add_argument('--end', type=int, default=168)
args = parser.parse_args()
args.orig_imsize = -1
pprint(vars(args))
set_gpu(args.gpu)
return args
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--batch_size', type=int, default=100)
parser.add_argument('--max_epoch', type=int, default=6)
parser.add_argument('--leaky_ReLU', type=bool, default=False)
parser.add_argument('--decay_eta', type=bool, default=False)
parser.add_argument('--cyclical_eta', type=bool, default=True)
parser.add_argument('--rho', type=float, default=0.8)
parser.add_argument('--z_scores', type=bool, default=True)
parser.add_argument('--Lambda', type=float, default=0.01)
parser.add_argument('--dataset',
type=str,
default='CIFAR10',
choices=['CIFAR10'])
args = parser.parse_args()
pprint(vars(args))
dataLoader = None
if args.dataset == 'CIFAR10':
dataLoader = CIFAR10DataLoader()
assert dataLoader != None
X_train, X_test, y_train, y_test = dataLoader.load()
prep = Proprocess(X_train)
X_train, X_val, y_train, y_val = train_test_split(X_train,
y_train,
test_size=0.2,
random_state=42)
if args.z_scores:
X_train = prep.z_score(X_train).T
X_val = prep.z_score(X_val).T
model = MyNet(args)
model.fit(X_train, y_train, X_val, y_val)
model.my_plot()
import numpy as np
import torch
import os
import json
from model.trainer.fsl_trainer import FSLTrainer
from model.utils import (
pprint,
set_gpu,
get_command_line_parser,
postprocess_args,
)
# from ipdb import launch_ipdb_on_exception
if __name__ == '__main__':
parser = get_command_line_parser()
args = postprocess_args(parser.parse_args())
# with launch_ipdb_on_exception():
pprint(vars(args))
print('Number device: ', torch.cuda.device_count())
set_gpu(args.gpu)
trainer = FSLTrainer(args)
# trainer.train()
trainer.evaluate_test()
trainer.final_record()
print(args.save_path)
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument('--dataset',
type=str,
default='MiniImageNet',
choices=['MiniImageNet', 'TieredImagenet'])
parser.add_argument('--backbone_class',
type=str,
default='ConvNet',
choices=[
'ConvNet', 'ConvAttNet', 'Res12', 'Res18',
'ResNest12', 'SAN'
])
parser.add_argument('--query', type=int, default=15)
parser.add_argument('--label-smoothing',
type=float,
default=0.0,
help='label-smoothing (default eta: 0.0)')
parser.add_argument('--mixup',
type=float,
default=0.0,
help='mixup (default eta: 0.0)')
parser.add_argument('--rand-aug',
action='store_true',
default=False,
help='random augment')
parser.add_argument('--augment', type=str, default='none')
parser.add_argument('--batch_size', type=int, default=32)
parser.add_argument('--max_epoch', type=int, default=500)
parser.add_argument('--lr', type=float, default=0.1)
parser.add_argument('--lr_scheduler',
type=str,
default='multistep',
help='learning rate scheduler (default: cos)')
parser.add_argument('--step',
type=str,
default='[2,3,4,5]',
help='Decrease learning rate at these epochs.')
parser.add_argument('--gamma', type=float, default=0.1)
parser.add_argument('--no-bn-wd',
action='store_true',
default=False,
help='no bias decay')
parser.add_argument('--warmup-epochs',
type=int,
default=5,
help='number of warmup epochs (default: 0)')
parser.add_argument('--weight-decay',
type=float,
default=1e-4,
help='SGD weight decay (default: 1e-4)')
parser.add_argument('--gpu', type=str, default='0')
parser.add_argument('--resume', action='store_true', default=False)
parser.add_argument('--finetune', action='store_true', default=False)
parser.add_argument('--samples_per_class', type=int, default=50)
parser.add_argument('--save_dir', type=str, default='./checkpoints')
args = parser.parse_args()
args.orig_imsize = -1
pprint(vars(args))
set_gpu(args.gpu)
save_path1 = '-'.join(['PreTrain', args.dataset, args.backbone_class])
save_path1 += 'LS{}MX{}'.format(args.label_smoothing, args.mixup)
if args.lr_scheduler == 'cosine':
save_path2 = 'Bsz{}Epoch-{}-Cos-lr{}decay{}'.format(
args.batch_size, args.max_epoch, args.lr, args.weight_decay)
elif args.lr_scheduler == 'step':
save_path2 = 'Bsz{}Epoch-{}-Step-lr{}-{}-{}decay{}'.format(
args.batch_size, args.max_epoch, args.lr, args.step, args.gamma,
args.weight_decay)
elif args.lr_scheduler == 'multistep':
save_path2 = 'Bsz{}Epoch-{}-MultiStep-lr{}-{}-{}decay{}'.format(
args.batch_size, args.max_epoch, args.lr, args.step, args.gamma,
args.weight_decay)
else:
raise ValueError('No Such Schedule')
if args.warmup_epochs > 0:
save_path2 += 'Warmup{}'.format(args.warmup_epochs)
if args.rand_aug:
save_path2 += '-RandAug'
if args.no_bn_wd:
save_path2 += '-no-bn-wd'
args.save_path = osp.join(save_path1, save_path2)
if not os.path.exists(os.path.join(args.save_dir, save_path1)):
os.mkdir(os.path.join(args.save_dir, save_path1))
return args
