102->201 / NAS->autoDL / more configs of TAS / reorganize docs / fix bugs in NAS baselines
This commit is contained in:
D-X-Y
295
exps/NAS-Bench-201/statistics.py
Normal file
295
exps/NAS-Bench-201/statistics.py
Normal file
@@ -0,0 +1,295 @@
|
||||
##################################################
|
||||
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
|
||||
##################################################
|
||||
import os, sys, time, argparse, collections
|
||||
from copy import deepcopy
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from pathlib import Path
|
||||
from collections import defaultdict
|
||||
lib_dir = (Path(__file__).parent / '..' / '..' / 'lib').resolve()
|
||||
if str(lib_dir) not in sys.path: sys.path.insert(0, str(lib_dir))
|
||||
from log_utils import AverageMeter, time_string, convert_secs2time
|
||||
from config_utils import load_config, dict2config
|
||||
from datasets import get_datasets
|
||||
# NAS-Bench-201 related module or function
|
||||
from models import CellStructure, get_cell_based_tiny_net
|
||||
from nas_201_api import ArchResults, ResultsCount
|
||||
from functions import pure_evaluate
|
||||
|
||||
|
||||
|
||||
def create_result_count(used_seed, dataset, arch_config, results, dataloader_dict):
|
||||
xresult = ResultsCount(dataset, results['net_state_dict'], results['train_acc1es'], results['train_losses'], \
|
||||
results['param'], results['flop'], arch_config, used_seed, results['total_epoch'], None)
|
||||
|
||||
net_config = dict2config({'name': 'infer.tiny', 'C': arch_config['channel'], 'N': arch_config['num_cells'], 'genotype': CellStructure.str2structure(arch_config['arch_str']), 'num_classes':arch_config['class_num']}, None)
|
||||
network = get_cell_based_tiny_net(net_config)
|
||||
network.load_state_dict(xresult.get_net_param())
|
||||
if 'train_times' in results: # new version
|
||||
xresult.update_train_info(results['train_acc1es'], results['train_acc5es'], results['train_losses'], results['train_times'])
|
||||
xresult.update_eval(results['valid_acc1es'], results['valid_losses'], results['valid_times'])
|
||||
else:
|
||||
if dataset == 'cifar10-valid':
|
||||
xresult.update_OLD_eval('x-valid' , results['valid_acc1es'], results['valid_losses'])
|
||||
loss, top1, top5, latencies = pure_evaluate(dataloader_dict['{:}@{:}'.format('cifar10', 'test')], network.cuda())
|
||||
xresult.update_OLD_eval('ori-test', {results['total_epoch']-1: top1}, {results['total_epoch']-1: loss})
|
||||
xresult.update_latency(latencies)
|
||||
elif dataset == 'cifar10':
|
||||
xresult.update_OLD_eval('ori-test', results['valid_acc1es'], results['valid_losses'])
|
||||
loss, top1, top5, latencies = pure_evaluate(dataloader_dict['{:}@{:}'.format(dataset, 'test')], network.cuda())
|
||||
xresult.update_latency(latencies)
|
||||
elif dataset == 'cifar100' or dataset == 'ImageNet16-120':
|
||||
xresult.update_OLD_eval('ori-test', results['valid_acc1es'], results['valid_losses'])
|
||||
loss, top1, top5, latencies = pure_evaluate(dataloader_dict['{:}@{:}'.format(dataset, 'valid')], network.cuda())
|
||||
xresult.update_OLD_eval('x-valid', {results['total_epoch']-1: top1}, {results['total_epoch']-1: loss})
|
||||
loss, top1, top5, latencies = pure_evaluate(dataloader_dict['{:}@{:}'.format(dataset, 'test')], network.cuda())
|
||||
xresult.update_OLD_eval('x-test' , {results['total_epoch']-1: top1}, {results['total_epoch']-1: loss})
|
||||
xresult.update_latency(latencies)
|
||||
else:
|
||||
raise ValueError('invalid dataset name : {:}'.format(dataset))
|
||||
return xresult
|
||||
|
||||
|
||||
|
||||
def account_one_arch(arch_index, arch_str, checkpoints, datasets, dataloader_dict):
|
||||
information = ArchResults(arch_index, arch_str)
|
||||
|
||||
for checkpoint_path in checkpoints:
|
||||
checkpoint = torch.load(checkpoint_path, map_location='cpu')
|
||||
used_seed = checkpoint_path.name.split('-')[-1].split('.')[0]
|
||||
for dataset in datasets:
|
||||
assert dataset in checkpoint, 'Can not find {:} in arch-{:} from {:}'.format(dataset, arch_index, checkpoint_path)
|
||||
results = checkpoint[dataset]
|
||||
assert results['finish-train'], 'This {:} arch seed={:} does not finish train on {:} ::: {:}'.format(arch_index, used_seed, dataset, checkpoint_path)
|
||||
arch_config = {'channel': results['channel'], 'num_cells': results['num_cells'], 'arch_str': arch_str, 'class_num': results['config']['class_num']}
|
||||
|
||||
xresult = create_result_count(used_seed, dataset, arch_config, results, dataloader_dict)
|
||||
information.update(dataset, int(used_seed), xresult)
|
||||
return information
|
||||
|
||||
|
||||
|
||||
def GET_DataLoaders(workers):
|
||||
|
||||
torch.set_num_threads(workers)
|
||||
|
||||
root_dir = (Path(__file__).parent / '..' / '..').resolve()
|
||||
torch_dir = Path(os.environ['TORCH_HOME'])
|
||||
# cifar
|
||||
cifar_config_path = root_dir / 'configs' / 'nas-benchmark' / 'CIFAR.config'
|
||||
cifar_config = load_config(cifar_config_path, None, None)
|
||||
print ('{:} Create data-loader for all datasets'.format(time_string()))
|
||||
print ('-'*200)
|
||||
TRAIN_CIFAR10, VALID_CIFAR10, xshape, class_num = get_datasets('cifar10', str(torch_dir/'cifar.python'), -1)
|
||||
print ('original CIFAR-10 : {:} training images and {:} test images : {:} input shape : {:} number of classes'.format(len(TRAIN_CIFAR10), len(VALID_CIFAR10), xshape, class_num))
|
||||
cifar10_splits = load_config(root_dir / 'configs' / 'nas-benchmark' / 'cifar-split.txt', None, None)
|
||||
assert cifar10_splits.train[:10] == [0, 5, 7, 11, 13, 15, 16, 17, 20, 24] and cifar10_splits.valid[:10] == [1, 2, 3, 4, 6, 8, 9, 10, 12, 14]
|
||||
temp_dataset = deepcopy(TRAIN_CIFAR10)
|
||||
temp_dataset.transform = VALID_CIFAR10.transform
|
||||
# data loader
|
||||
trainval_cifar10_loader = torch.utils.data.DataLoader(TRAIN_CIFAR10, batch_size=cifar_config.batch_size, shuffle=True , num_workers=workers, pin_memory=True)
|
||||
train_cifar10_loader = torch.utils.data.DataLoader(TRAIN_CIFAR10, batch_size=cifar_config.batch_size, sampler=torch.utils.data.sampler.SubsetRandomSampler(cifar10_splits.train), num_workers=workers, pin_memory=True)
|
||||
valid_cifar10_loader = torch.utils.data.DataLoader(temp_dataset , batch_size=cifar_config.batch_size, sampler=torch.utils.data.sampler.SubsetRandomSampler(cifar10_splits.valid), num_workers=workers, pin_memory=True)
|
||||
test__cifar10_loader = torch.utils.data.DataLoader(VALID_CIFAR10, batch_size=cifar_config.batch_size, shuffle=False, num_workers=workers, pin_memory=True)
|
||||
print ('CIFAR-10 : trval-loader has {:3d} batch with {:} per batch'.format(len(trainval_cifar10_loader), cifar_config.batch_size))
|
||||
print ('CIFAR-10 : train-loader has {:3d} batch with {:} per batch'.format(len(train_cifar10_loader), cifar_config.batch_size))
|
||||
print ('CIFAR-10 : valid-loader has {:3d} batch with {:} per batch'.format(len(valid_cifar10_loader), cifar_config.batch_size))
|
||||
print ('CIFAR-10 : test--loader has {:3d} batch with {:} per batch'.format(len(test__cifar10_loader), cifar_config.batch_size))
|
||||
print ('-'*200)
|
||||
# CIFAR-100
|
||||
TRAIN_CIFAR100, VALID_CIFAR100, xshape, class_num = get_datasets('cifar100', str(torch_dir/'cifar.python'), -1)
|
||||
print ('original CIFAR-100: {:} training images and {:} test images : {:} input shape : {:} number of classes'.format(len(TRAIN_CIFAR100), len(VALID_CIFAR100), xshape, class_num))
|
||||
cifar100_splits = load_config(root_dir / 'configs' / 'nas-benchmark' / 'cifar100-test-split.txt', None, None)
|
||||
assert cifar100_splits.xvalid[:10] == [1, 3, 4, 5, 8, 10, 13, 14, 15, 16] and cifar100_splits.xtest[:10] == [0, 2, 6, 7, 9, 11, 12, 17, 20, 24]
|
||||
train_cifar100_loader = torch.utils.data.DataLoader(TRAIN_CIFAR100, batch_size=cifar_config.batch_size, shuffle=True, num_workers=workers, pin_memory=True)
|
||||
valid_cifar100_loader = torch.utils.data.DataLoader(VALID_CIFAR100, batch_size=cifar_config.batch_size, sampler=torch.utils.data.sampler.SubsetRandomSampler(cifar100_splits.xvalid), num_workers=workers, pin_memory=True)
|
||||
test__cifar100_loader = torch.utils.data.DataLoader(VALID_CIFAR100, batch_size=cifar_config.batch_size, sampler=torch.utils.data.sampler.SubsetRandomSampler(cifar100_splits.xtest) , num_workers=workers, pin_memory=True)
|
||||
print ('CIFAR-100 : train-loader has {:3d} batch'.format(len(train_cifar100_loader)))
|
||||
print ('CIFAR-100 : valid-loader has {:3d} batch'.format(len(valid_cifar100_loader)))
|
||||
print ('CIFAR-100 : test--loader has {:3d} batch'.format(len(test__cifar100_loader)))
|
||||
print ('-'*200)
|
||||
|
||||
imagenet16_config_path = 'configs/nas-benchmark/ImageNet-16.config'
|
||||
imagenet16_config = load_config(imagenet16_config_path, None, None)
|
||||
TRAIN_ImageNet16_120, VALID_ImageNet16_120, xshape, class_num = get_datasets('ImageNet16-120', str(torch_dir/'cifar.python'/'ImageNet16'), -1)
|
||||
print ('original TRAIN_ImageNet16_120: {:} training images and {:} test images : {:} input shape : {:} number of classes'.format(len(TRAIN_ImageNet16_120), len(VALID_ImageNet16_120), xshape, class_num))
|
||||
imagenet_splits = load_config(root_dir / 'configs' / 'nas-benchmark' / 'imagenet-16-120-test-split.txt', None, None)
|
||||
assert imagenet_splits.xvalid[:10] == [1, 2, 3, 6, 7, 8, 9, 12, 16, 18] and imagenet_splits.xtest[:10] == [0, 4, 5, 10, 11, 13, 14, 15, 17, 20]
|
||||
train_imagenet_loader = torch.utils.data.DataLoader(TRAIN_ImageNet16_120, batch_size=imagenet16_config.batch_size, shuffle=True, num_workers=workers, pin_memory=True)
|
||||
valid_imagenet_loader = torch.utils.data.DataLoader(VALID_ImageNet16_120, batch_size=imagenet16_config.batch_size, sampler=torch.utils.data.sampler.SubsetRandomSampler(imagenet_splits.xvalid), num_workers=workers, pin_memory=True)
|
||||
test__imagenet_loader = torch.utils.data.DataLoader(VALID_ImageNet16_120, batch_size=imagenet16_config.batch_size, sampler=torch.utils.data.sampler.SubsetRandomSampler(imagenet_splits.xtest) , num_workers=workers, pin_memory=True)
|
||||
print ('ImageNet-16-120 : train-loader has {:3d} batch with {:} per batch'.format(len(train_imagenet_loader), imagenet16_config.batch_size))
|
||||
print ('ImageNet-16-120 : valid-loader has {:3d} batch with {:} per batch'.format(len(valid_imagenet_loader), imagenet16_config.batch_size))
|
||||
print ('ImageNet-16-120 : test--loader has {:3d} batch with {:} per batch'.format(len(test__imagenet_loader), imagenet16_config.batch_size))
|
||||
|
||||
# 'cifar10', 'cifar100', 'ImageNet16-120'
|
||||
loaders = {'cifar10@trainval': trainval_cifar10_loader,
|
||||
'cifar10@train' : train_cifar10_loader,
|
||||
'cifar10@valid' : valid_cifar10_loader,
|
||||
'cifar10@test' : test__cifar10_loader,
|
||||
'cifar100@train' : train_cifar100_loader,
|
||||
'cifar100@valid' : valid_cifar100_loader,
|
||||
'cifar100@test' : test__cifar100_loader,
|
||||
'ImageNet16-120@train': train_imagenet_loader,
|
||||
'ImageNet16-120@valid': valid_imagenet_loader,
|
||||
'ImageNet16-120@test' : test__imagenet_loader}
|
||||
return loaders
|
||||
|
||||
|
||||
|
||||
def simplify(save_dir, meta_file, basestr, target_dir):
|
||||
meta_infos = torch.load(meta_file, map_location='cpu')
|
||||
meta_archs = meta_infos['archs'] # a list of architecture strings
|
||||
meta_num_archs = meta_infos['total']
|
||||
meta_max_node = meta_infos['max_node']
|
||||
assert meta_num_archs == len(meta_archs), 'invalid number of archs : {:} vs {:}'.format(meta_num_archs, len(meta_archs))
|
||||
|
||||
sub_model_dirs = sorted(list(save_dir.glob('*-*-{:}'.format(basestr))))
|
||||
print ('{:} find {:} directories used to save checkpoints'.format(time_string(), len(sub_model_dirs)))
|
||||
|
||||
subdir2archs, num_evaluated_arch = collections.OrderedDict(), 0
|
||||
num_seeds = defaultdict(lambda: 0)
|
||||
for index, sub_dir in enumerate(sub_model_dirs):
|
||||
xcheckpoints = list(sub_dir.glob('arch-*-seed-*.pth'))
|
||||
arch_indexes = set()
|
||||
for checkpoint in xcheckpoints:
|
||||
temp_names = checkpoint.name.split('-')
|
||||
assert len(temp_names) == 4 and temp_names[0] == 'arch' and temp_names[2] == 'seed', 'invalid checkpoint name : {:}'.format(checkpoint.name)
|
||||
arch_indexes.add( temp_names[1] )
|
||||
subdir2archs[sub_dir] = sorted(list(arch_indexes))
|
||||
num_evaluated_arch += len(arch_indexes)
|
||||
# count number of seeds for each architecture
|
||||
for arch_index in arch_indexes:
|
||||
num_seeds[ len(list(sub_dir.glob('arch-{:}-seed-*.pth'.format(arch_index)))) ] += 1
|
||||
print('{:} There are {:5d} architectures that have been evaluated ({:} in total).'.format(time_string(), num_evaluated_arch, meta_num_archs))
|
||||
for key in sorted( list( num_seeds.keys() ) ): print ('{:} There are {:5d} architectures that are evaluated {:} times.'.format(time_string(), num_seeds[key], key))
|
||||
|
||||
dataloader_dict = GET_DataLoaders( 6 )
|
||||
|
||||
to_save_simply = save_dir / 'simplifies'
|
||||
to_save_allarc = save_dir / 'simplifies' / 'architectures'
|
||||
if not to_save_simply.exists(): to_save_simply.mkdir(parents=True, exist_ok=True)
|
||||
if not to_save_allarc.exists(): to_save_allarc.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
assert (save_dir / target_dir) in subdir2archs, 'can not find {:}'.format(target_dir)
|
||||
arch2infos, datasets = {}, ('cifar10-valid', 'cifar10', 'cifar100', 'ImageNet16-120')
|
||||
evaluated_indexes = set()
|
||||
target_directory = save_dir / target_dir
|
||||
target_less_dir = save_dir / '{:}-LESS'.format(target_dir)
|
||||
arch_indexes = subdir2archs[ target_directory ]
|
||||
num_seeds = defaultdict(lambda: 0)
|
||||
end_time = time.time()
|
||||
arch_time = AverageMeter()
|
||||
for idx, arch_index in enumerate(arch_indexes):
|
||||
checkpoints = list(target_directory.glob('arch-{:}-seed-*.pth'.format(arch_index)))
|
||||
ckps_less = list(target_less_dir.glob('arch-{:}-seed-*.pth'.format(arch_index)))
|
||||
# create the arch info for each architecture
|
||||
try:
|
||||
arch_info_full = account_one_arch(arch_index, meta_archs[int(arch_index)], checkpoints, datasets, dataloader_dict)
|
||||
arch_info_less = account_one_arch(arch_index, meta_archs[int(arch_index)], ckps_less, ['cifar10-valid'], dataloader_dict)
|
||||
num_seeds[ len(checkpoints) ] += 1
|
||||
except:
|
||||
print('Loading {:} failed, : {:}'.format(arch_index, checkpoints))
|
||||
continue
|
||||
assert int(arch_index) not in evaluated_indexes, 'conflict arch-index : {:}'.format(arch_index)
|
||||
assert 0 <= int(arch_index) < len(meta_archs), 'invalid arch-index {:} (not found in meta_archs)'.format(arch_index)
|
||||
arch_info = {'full': arch_info_full, 'less': arch_info_less}
|
||||
evaluated_indexes.add( int(arch_index) )
|
||||
arch2infos[int(arch_index)] = arch_info
|
||||
torch.save({'full': arch_info_full.state_dict(),
|
||||
'less': arch_info_less.state_dict()}, to_save_allarc / '{:}-FULL.pth'.format(arch_index))
|
||||
arch_info['full'].clear_params()
|
||||
arch_info['less'].clear_params()
|
||||
torch.save({'full': arch_info_full.state_dict(),
|
||||
'less': arch_info_less.state_dict()}, to_save_allarc / '{:}-SIMPLE.pth'.format(arch_index))
|
||||
# measure elapsed time
|
||||
arch_time.update(time.time() - end_time)
|
||||
end_time = time.time()
|
||||
need_time = '{:}'.format( convert_secs2time(arch_time.avg * (len(arch_indexes)-idx-1), True) )
|
||||
print('{:} {:} [{:03d}/{:03d}] : {:} still need {:}'.format(time_string(), target_dir, idx, len(arch_indexes), arch_index, need_time))
|
||||
# measure time
|
||||
xstrs = ['{:}:{:03d}'.format(key, num_seeds[key]) for key in sorted( list( num_seeds.keys() ) ) ]
|
||||
print('{:} {:} done : {:}'.format(time_string(), target_dir, xstrs))
|
||||
final_infos = {'meta_archs' : meta_archs,
|
||||
'total_archs': meta_num_archs,
|
||||
'basestr' : basestr,
|
||||
'arch2infos' : arch2infos,
|
||||
'evaluated_indexes': evaluated_indexes}
|
||||
save_file_name = to_save_simply / '{:}.pth'.format(target_dir)
|
||||
torch.save(final_infos, save_file_name)
|
||||
print ('Save {:} / {:} architecture results into {:}.'.format(len(evaluated_indexes), meta_num_archs, save_file_name))
|
||||
|
||||
|
||||
|
||||
def merge_all(save_dir, meta_file, basestr):
|
||||
meta_infos = torch.load(meta_file, map_location='cpu')
|
||||
meta_archs = meta_infos['archs']
|
||||
meta_num_archs = meta_infos['total']
|
||||
meta_max_node = meta_infos['max_node']
|
||||
assert meta_num_archs == len(meta_archs), 'invalid number of archs : {:} vs {:}'.format(meta_num_archs, len(meta_archs))
|
||||
|
||||
sub_model_dirs = sorted(list(save_dir.glob('*-*-{:}'.format(basestr))))
|
||||
print ('{:} find {:} directories used to save checkpoints'.format(time_string(), len(sub_model_dirs)))
|
||||
for index, sub_dir in enumerate(sub_model_dirs):
|
||||
arch_info_files = sorted( list(sub_dir.glob('arch-*-seed-*.pth') ) )
|
||||
print ('The {:02d}/{:02d}-th directory : {:} : {:} runs.'.format(index, len(sub_model_dirs), sub_dir, len(arch_info_files)))
|
||||
|
||||
arch2infos, evaluated_indexes = dict(), set()
|
||||
for IDX, sub_dir in enumerate(sub_model_dirs):
|
||||
ckp_path = sub_dir.parent / 'simplifies' / '{:}.pth'.format(sub_dir.name)
|
||||
if ckp_path.exists():
|
||||
sub_ckps = torch.load(ckp_path, map_location='cpu')
|
||||
assert sub_ckps['total_archs'] == meta_num_archs and sub_ckps['basestr'] == basestr
|
||||
xarch2infos = sub_ckps['arch2infos']
|
||||
xevalindexs = sub_ckps['evaluated_indexes']
|
||||
for eval_index in xevalindexs:
|
||||
assert eval_index not in evaluated_indexes and eval_index not in arch2infos
|
||||
#arch2infos[eval_index] = xarch2infos[eval_index].state_dict()
|
||||
arch2infos[eval_index] = {'full': xarch2infos[eval_index]['full'].state_dict(),
|
||||
'less': xarch2infos[eval_index]['less'].state_dict()}
|
||||
evaluated_indexes.add( eval_index )
|
||||
print ('{:} [{:03d}/{:03d}] merge data from {:} with {:} models.'.format(time_string(), IDX, len(sub_model_dirs), ckp_path, len(xevalindexs)))
|
||||
else:
|
||||
raise ValueError('Can not find {:}'.format(ckp_path))
|
||||
#print ('{:} [{:03d}/{:03d}] can not find {:}, skip.'.format(time_string(), IDX, len(subdir2archs), ckp_path))
|
||||
|
||||
evaluated_indexes = sorted( list( evaluated_indexes ) )
|
||||
print ('Finally, there are {:} architectures that have been trained and evaluated.'.format(len(evaluated_indexes)))
|
||||
|
||||
to_save_simply = save_dir / 'simplifies'
|
||||
if not to_save_simply.exists(): to_save_simply.mkdir(parents=True, exist_ok=True)
|
||||
final_infos = {'meta_archs' : meta_archs,
|
||||
'total_archs': meta_num_archs,
|
||||
'arch2infos' : arch2infos,
|
||||
'evaluated_indexes': evaluated_indexes}
|
||||
save_file_name = to_save_simply / '{:}-final-infos.pth'.format(basestr)
|
||||
torch.save(final_infos, save_file_name)
|
||||
print ('Save {:} / {:} architecture results into {:}.'.format(len(evaluated_indexes), meta_num_archs, save_file_name))
|
||||
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
|
||||
parser = argparse.ArgumentParser(description='NAS-BENCH-201', formatter_class=argparse.ArgumentDefaultsHelpFormatter)
|
||||
parser.add_argument('--mode' , type=str, choices=['cal', 'merge'], help='The running mode for this script.')
|
||||
parser.add_argument('--base_save_dir', type=str, default='./output/NAS-BENCH-201-4', help='The base-name of folder to save checkpoints and log.')
|
||||
parser.add_argument('--target_dir' , type=str, help='The target directory.')
|
||||
parser.add_argument('--max_node' , type=int, default=4, help='The maximum node in a cell.')
|
||||
parser.add_argument('--channel' , type=int, default=16, help='The number of channels.')
|
||||
parser.add_argument('--num_cells' , type=int, default=5, help='The number of cells in one stage.')
|
||||
args = parser.parse_args()
|
||||
|
||||
save_dir = Path( args.base_save_dir )
|
||||
meta_path = save_dir / 'meta-node-{:}.pth'.format(args.max_node)
|
||||
assert save_dir.exists(), 'invalid save dir path : {:}'.format(save_dir)
|
||||
assert meta_path.exists(), 'invalid saved meta path : {:}'.format(meta_path)
|
||||
print ('start the statistics of our nas-benchmark from {:} using {:}.'.format(save_dir, args.target_dir))
|
||||
basestr = 'C{:}-N{:}'.format(args.channel, args.num_cells)
|
||||
|
||||
if args.mode == 'cal':
|
||||
simplify(save_dir, meta_path, basestr, args.target_dir)
|
||||
elif args.mode == 'merge':
|
||||
merge_all(save_dir, meta_path, basestr)
|
||||
else:
|
||||
raise ValueError('invalid mode : {:}'.format(args.mode))
|
||||
Reference in New Issue
Block a user