def main():
parser = ArgumentParser(add_help=False)
parser.add_argument('-c', '--config_path', type=str, default='./src/configs/CIFAR10/ContraGAN.json')
parser.add_argument('--checkpoint_folder', type=str, default=None)
parser.add_argument('-current', '--load_current', action='store_true', help='whether you load the current or best checkpoint')
parser.add_argument('--log_output_path', type=str, default=None)
parser.add_argument('-DDP', '--distributed_data_parallel', action='store_true')
parser.add_argument('-n', '--nodes', default=1, type=int, metavar='N')
parser.add_argument('-nr', '--nr', default=0, type=int, help='ranking within the nodes')
parser.add_argument('--seed', type=int, default=-1, help='seed for generating random numbers')
parser.add_argument('--num_workers', type=int, default=8, help='')
parser.add_argument('-sync_bn', '--synchronized_bn', action='store_true', help='whether turn on synchronized batchnorm')
parser.add_argument('-mpc', '--mixed_precision', action='store_true', help='whether turn on mixed precision training')
parser.add_argument('-LARS', '--LARS_optimizer', action='store_true', help='whether turn on LARS optimizer')
parser.add_argument('-rm_API', '--disable_debugging_API', action='store_true', help='whether disable pytorch autograd debugging mode')
parser.add_argument('--reduce_train_dataset', type=float, default=1.0, help='control the number of train dataset')
parser.add_argument('-stat_otf', '--bn_stat_OnTheFly', action='store_true', help='when evaluating, use the statistics of a batch')
parser.add_argument('-std_stat', '--standing_statistics', action='store_true')
parser.add_argument('--standing_step', type=int, default=-1, help='# of steps for accumulation batchnorm')
parser.add_argument('--freeze_layers', type=int, default=-1, help='# of layers for freezing discriminator')
parser.add_argument('-l', '--load_all_data_in_memory', action='store_true')
parser.add_argument('-t', '--train', action='store_true')
parser.add_argument('-e', '--eval', action='store_true')
parser.add_argument('-s', '--save_images', action='store_true')
parser.add_argument('-iv', '--image_visualization', action='store_true', help='select whether conduct image visualization')
parser.add_argument('-knn', '--k_nearest_neighbor', action='store_true', help='select whether conduct k-nearest neighbor analysis')
parser.add_argument('-itp', '--interpolation', action='store_true', help='whether conduct interpolation analysis')
parser.add_argument('-fa', '--frequency_analysis', action='store_true', help='whether conduct frequency analysis')
parser.add_argument('-tsne', '--tsne_analysis', action='store_true', help='whether conduct tsne analysis')
parser.add_argument('--nrow', type=int, default=10, help='number of rows to plot image canvas')
parser.add_argument('--ncol', type=int, default=8, help='number of cols to plot image canvas')
parser.add_argument('--print_every', type=int, default=100, help='control log interval')
parser.add_argument('--save_every', type=int, default=2000, help='control evaluation and save interval')
parser.add_argument('--eval_type', type=str, default='test', help='[train/valid/test]')
args = parser.parse_args()
if not args.train and \
not args.eval and \
not args.save_images and \
not args.image_visualization and \
not args.k_nearest_neighbor and \
not args.interpolation and \
not args.frequency_analysis and \
not args.tsne_analysis:
parser.print_help(sys.stderr)
sys.exit(1)
if args.config_path is not None:
with open(args.config_path) as f:
model_configs = json.load(f)
train_configs = vars(args)
else:
raise NotImplementedError
hdf5_path_train = make_hdf5(model_configs['data_processing'], train_configs, mode="train") \
if train_configs['load_all_data_in_memory'] else None
if train_configs['seed'] == -1:
train_configs['seed'] = random.randint(1,4096)
cudnn.benchmark, cudnn.deterministic = True, False
else:
cudnn.benchmark, cudnn.deterministic = False, True
fix_all_seed(train_configs['seed'])
gpus_per_node, rank = torch.cuda.device_count(), torch.cuda.current_device()
world_size = gpus_per_node*train_configs['nodes']
if world_size == 1:
warnings.warn('You have chosen a specific GPU. This will completely disable data parallelism.')
run_name = make_run_name(RUN_NAME_FORMAT, framework=train_configs['config_path'].split('/')[-1][:-5], phase='train')
if train_configs['disable_debugging_API']: torch.autograd.set_detect_anomaly(False)
check_flags(train_configs, model_configs, world_size)
if train_configs['distributed_data_parallel'] and world_size > 1:
print("Train the models through DistributedDataParallel (DDP) mode.")
mp.spawn(prepare_train_eval, nprocs=gpus_per_node, args=(gpus_per_node, world_size, run_name,
train_configs, model_configs, hdf5_path_train))
else:
prepare_train_eval(rank, gpus_per_node, world_size, run_name, train_configs, model_configs, hdf5_path_train=hdf5_path_train)
def main():
parser = ArgumentParser(add_help=False)
parser.add_argument('-c', '--config_path', type=str, default='./src/configs/CIFAR10/ContraGAN.json')
parser.add_argument('--checkpoint_folder', type=str, default=None)
parser.add_argument('-current', '--load_current', action='store_true', help='whether you load the current or best checkpoint')
parser.add_argument('--log_output_path', type=str, default=None)
parser.add_argument('--seed', type=int, default=-1, help='seed for generating random numbers')
parser.add_argument('-DDP', '--distributed_data_parallel', action='store_true')
parser.add_argument('--num_workers', type=int, default=8, help='')
parser.add_argument('-sync_bn', '--synchronized_bn', action='store_true', help='whether turn on synchronized batchnorm')
parser.add_argument('-mpc', '--mixed_precision', action='store_true', help='whether turn on mixed precision training')
parser.add_argument('-rm_API', '--disable_debugging_API', action='store_true', help='whether disable pytorch autograd debugging mode')
parser.add_argument('--reduce_train_dataset', type=float, default=1.0, help='control the number of train dataset')
parser.add_argument('-std_stat', '--standing_statistics', action='store_true')
parser.add_argument('--standing_step', type=int, default=-1, help='# of steps for accumulation batchnorm')
parser.add_argument('--freeze_layers', type=int, default=-1, help='# of layers for freezing discriminator')
parser.add_argument('-l', '--load_all_data_in_memory', action='store_true')
parser.add_argument('-t', '--train', action='store_true')
parser.add_argument('-e', '--eval', action='store_true')
parser.add_argument('-s', '--save_images', action='store_true')
parser.add_argument('-iv', '--image_visualization', action='store_true', help='select whether conduct image visualization')
parser.add_argument('-knn', '--k_nearest_neighbor', action='store_true', help='select whether conduct k-nearest neighbor analysis')
parser.add_argument('-itp', '--interpolation', action='store_true', help='whether conduct interpolation analysis')
parser.add_argument('-fa', '--frequency_analysis', action='store_true', help='whether conduct frequency analysis')
parser.add_argument('-tsne', '--tsne_analysis', action='store_true', help='whether conduct tsne analysis')
parser.add_argument('--nrow', type=int, default=10, help='number of rows to plot image canvas')
parser.add_argument('--ncol', type=int, default=8, help='number of cols to plot image canvas')
parser.add_argument('--print_every', type=int, default=100, help='control log interval')
parser.add_argument('--save_every', type=int, default=2000, help='control evaluation and save interval')
parser.add_argument('--eval_type', type=str, default='test', help='[train/valid/test]')
args = parser.parse_args()
if not args.train and \
not args.eval and \
not args.save_images and \
not args.image_visualization and \
not args.k_nearest_neighbor and \
not args.interpolation and \
not args.frequency_analysis and \
not args.tsne_analysis:
parser.print_help(sys.stderr)
sys.exit(1)
if args.config_path is not None:
with open(args.config_path) as f:
model_config = json.load(f)
train_config = vars(args)
else:
raise NotImplementedError
if model_config['data_processing']['dataset_name'] == 'cifar10':
assert train_config['eval_type'] in ['train', 'test'], "Cifar10 does not contain dataset for validation."
elif model_config['data_processing']['dataset_name'] in ['imagenet', 'tiny_imagenet', 'custom']:
assert train_config['eval_type'] == 'train' or train_config['eval_type'] == 'valid', \
"StudioGAN dose not support the evalutation protocol that uses the test dataset on imagenet, tiny imagenet, and custom datasets"
if train_config['distributed_data_parallel']:
msg = "StudioGAN does not support image visualization, k_nearest_neighbor, interpolation, and frequency_analysis with DDP. " +\
"Please change DDP with a single GPU training or DataParallel instead."
assert train_config['image_visualization'] + train_config['k_nearest_neighbor'] + \
train_config['interpolation'] + train_config['frequency_analysis'] + train_config['tsne_analysis'] == 0, msg
hdf5_path_train = make_hdf5(model_config['data_processing'], train_config, mode="train") \
if train_config['load_all_data_in_memory'] else None
if train_config['seed'] == -1:
cudnn.benchmark, cudnn.deterministic = True, False
else:
fix_all_seed(train_config['seed'])
cudnn.benchmark, cudnn.deterministic = False, True
world_size, rank = torch.cuda.device_count(), torch.cuda.current_device()
if world_size == 1: warnings.warn('You have chosen a specific GPU. This will completely disable data parallelism.')
if train_config['disable_debugging_API']: torch.autograd.set_detect_anomaly(False)
check_flag_0(model_config['train']['optimization']['batch_size'], world_size, train_config['freeze_layers'], train_config['checkpoint_folder'],
model_config['train']['model']['architecture'], model_config['data_processing']['img_size'])
run_name = make_run_name(RUN_NAME_FORMAT, framework=train_config['config_path'].split('/')[-1][:-5], phase='train')
if train_config['distributed_data_parallel'] and world_size > 1:
print("Train the models through DistributedDataParallel (DDP) mode.")
mp.spawn(prepare_train_eval, nprocs=world_size, args=(world_size, run_name, train_config, model_config, hdf5_path_train))
else:
prepare_train_eval(rank, world_size, run_name, train_config, model_config, hdf5_path_train=hdf5_path_train)
def main():
parser = ArgumentParser(add_help=False)
parser.add_argument('-c',
'--config_path',
type=str,
default='./configs/CIFAR10/ContraGAN.json')
parser.add_argument('--checkpoint_folder', type=str, default=None)
parser.add_argument('-current',
'--load_current',
action='store_true',
help='whether you load the current or best checkpoint')
parser.add_argument('--log_output_path', type=str, default=None)
parser.add_argument('--seed',
type=int,
default=-1,
help='seed for generating random numbers')
parser.add_argument('--num_workers', type=int, default=8, help='')
parser.add_argument('-sync_bn',
'--synchronized_bn',
action='store_true',
help='whether turn on synchronized batchnorm')
parser.add_argument('-mpc',
'--mixed_precision',
action='store_true',
help='whether turn on mixed precision training')
parser.add_argument('-rm_API',
'--disable_debugging_API',
action='store_true',
help='whether disable pytorch autograd debugging mode')
parser.add_argument('--reduce_train_dataset',
type=float,
default=1.0,
help='control the number of train dataset')
parser.add_argument('-std_stat',
'--standing_statistics',
action='store_true')
parser.add_argument('--standing_step',
type=int,
default=-1,
help='# of steps for accumulation batchnorm')
parser.add_argument('--freeze_layers',
type=int,
default=-1,
help='# of layers for freezing discriminator')
parser.add_argument('-l', '--load_all_data_in_memory', action='store_true')
parser.add_argument('-t', '--train', action='store_true')
parser.add_argument('-e', '--eval', action='store_true')
parser.add_argument('-s', '--save_images', action='store_true')
parser.add_argument('-iv',
'--image_visualization',
action='store_true',
help='select whether conduct image visualization')
parser.add_argument(
'-knn',
'--k_nearest_neighbor',
action='store_true',
help='select whether conduct k-nearest neighbor analysis')
parser.add_argument('-itp',
'--interpolation',
action='store_true',
help='whether conduct interpolation analysis')
parser.add_argument('-fa',
'--frequency_analysis',
action='store_true',
help='whether conduct frequency analysis')
parser.add_argument('--nrow',
type=int,
default=10,
help='number of rows to plot image canvas')
parser.add_argument('--ncol',
type=int,
default=8,
help='number of cols to plot image canvas')
parser.add_argument('--print_every',
type=int,
default=100,
help='control log interval')
parser.add_argument('--save_every',
type=int,
default=2000,
help='control evaluation and save interval')
parser.add_argument('--eval_type',
type=str,
default='test',
help='[train/valid/test]')
args = parser.parse_args()
if not args.train and \
not args.eval and \
not args.save_images and \
not args.image_visualization and \
not args.k_nearest_neighbor and \
not args.interpolation and \
not args.frequency_analysis:
parser.print_help(sys.stderr)
sys.exit(1)
if args.config_path is not None:
with open(args.config_path) as f:
model_config = json.load(f)
train_config = vars(args)
else:
raise NotImplementedError
cfgs = dict2clsattr(train_config, model_config)
if cfgs.dataset_name == 'cifar10':
assert cfgs.eval_type in [
'train', 'test'
], "cifar10 does not contain dataset for validation"
elif cfgs.dataset_name in ['imagenet', 'tiny_imagenet', 'custom']:
assert cfgs.eval_type == 'train' or cfgs.eval_type == 'valid', "not support the evalutation using test dataset"
hdf5_path_train = make_hdf5(
cfgs, mode="train") if cfgs.load_all_data_in_memory else None
prepare_train_eval(cfgs, hdf5_path_train=hdf5_path_train)
def main():
parser = ArgumentParser(add_help=False)
parser.add_argument('-c',
'--config_path',
type=str,
default='./configs/CIFAR10/ContraGAN.json')
parser.add_argument('--checkpoint_folder', type=str, default=None)
parser.add_argument('-current',
'--load_current',
action='store_true',
help='choose whether you load current or best weights')
parser.add_argument('--log_output_path', type=str, default=None)
parser.add_argument('--seed',
type=int,
default=82624,
help='seed for generating random number')
parser.add_argument('--num_workers', type=int, default=8, help='')
parser.add_argument('-sync_bn',
'--synchronized_bn',
action='store_true',
help='select whether turn on synchronized batchnorm')
parser.add_argument('-mpc',
'--mixed_precision',
action='store_true',
help='select whether turn on mixed precision training')
parser.add_argument('-rm_API',
'--disable_debugging_API',
action='store_true',
help='whether disable pytorch autograd debugging mode')
parser.add_argument('--reduce_train_dataset',
type=float,
default=1.0,
help='control the number of train dataset')
parser.add_argument('-std_stat',
'--standing_statistics',
action='store_true')
parser.add_argument('--standing_step',
type=int,
default=-1,
help='# of steps for accumulation batchnorm')
parser.add_argument('--freeze_layers',
type=int,
default=-1,
help='# of layers for freezing discriminator')
parser.add_argument('-l', '--load_all_data_in_memory', action='store_true')
parser.add_argument('-t', '--train', action='store_true')
parser.add_argument('-e', '--eval', action='store_true')
parser.add_argument('-s', '--save_images', action='store_true')
parser.add_argument(
'-knn',
'--k_nearest_neighbor',
action='store_true',
help='select whether conduct k-nearest neighbor analysis')
parser.add_argument('-itp',
'--interpolation',
action='store_true',
help='select whether conduct interpolation analysis')
parser.add_argument('--nrow',
type=int,
default=10,
help='number of rows to plot image canvas')
parser.add_argument('--ncol',
type=int,
default=8,
help='number of cols to plot image canvas')
parser.add_argument('--print_every',
type=int,
default=100,
help='control log interval')
parser.add_argument('--save_every',
type=int,
default=2000,
help='control evaluation and save interval')
parser.add_argument('--eval_type',
type=str,
default='test',
help='[train/valid/test]')
args = parser.parse_args()
if args.config_path is not None:
with open(args.config_path) as f:
model_config = json.load(f)
train_config = vars(args)
else:
raise NotImplementedError
dataset = model_config['data_processing']['dataset_name']
if dataset == 'cifar10':
assert args.eval_type in [
'train', 'test'
], "cifar10 does not contain dataset for validation"
elif dataset in ['imagenet', 'tiny_imagenet', 'custom']:
assert args.eval_type == 'train' or args.eval_type == 'valid',\
"we do not support the evaluation mode using test images in tiny_imagenet/imagenet/custom dataset"
hdf5_path_train = make_hdf5(
**model_config['data_processing'], **train_config,
mode='train') if args.load_all_data_in_memory else None
load_frameowrk(**train_config,
**model_config['data_processing'],
**model_config['train']['model'],
**model_config['train']['optimization'],
**model_config['train']['loss_function'],
**model_config['train']['initialization'],
**model_config['train']['training_and_sampling_setting'],
train_config=train_config,
model_config=model_config['train'],
hdf5_path_train=hdf5_path_train)