Reformulate via black
This commit is contained in:
D-X-Y
341
exps/KD-main.py
341
exps/KD-main.py
@@ -2,161 +2,224 @@
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.01 #
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#####################################################
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import sys, time, torch, random, argparse
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from PIL import ImageFile
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from PIL import ImageFile
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ImageFile.LOAD_TRUNCATED_IMAGES = True
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from copy import deepcopy
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from copy import deepcopy
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from pathlib import Path
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lib_dir = (Path(__file__).parent / '..' / 'lib').resolve()
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if str(lib_dir) not in sys.path: sys.path.insert(0, str(lib_dir))
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lib_dir = (Path(__file__).parent / ".." / "lib").resolve()
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if str(lib_dir) not in sys.path:
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sys.path.insert(0, str(lib_dir))
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from config_utils import load_config, obtain_cls_kd_args as obtain_args
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from procedures import prepare_seed, prepare_logger, save_checkpoint, copy_checkpoint
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from procedures import get_optim_scheduler, get_procedures
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from datasets import get_datasets
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from models import obtain_model, load_net_from_checkpoint
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from utils import get_model_infos
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from log_utils import AverageMeter, time_string, convert_secs2time
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from procedures import prepare_seed, prepare_logger, save_checkpoint, copy_checkpoint
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from procedures import get_optim_scheduler, get_procedures
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from datasets import get_datasets
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from models import obtain_model, load_net_from_checkpoint
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from utils import get_model_infos
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from log_utils import AverageMeter, time_string, convert_secs2time
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def main(args):
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assert torch.cuda.is_available(), 'CUDA is not available.'
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torch.backends.cudnn.enabled = True
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torch.backends.cudnn.benchmark = True
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#torch.backends.cudnn.deterministic = True
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torch.set_num_threads( args.workers )
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prepare_seed(args.rand_seed)
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logger = prepare_logger(args)
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train_data, valid_data, xshape, class_num = get_datasets(args.dataset, args.data_path, args.cutout_length)
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train_loader = torch.utils.data.DataLoader(train_data, batch_size=args.batch_size, shuffle=True , num_workers=args.workers, pin_memory=True)
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valid_loader = torch.utils.data.DataLoader(valid_data, batch_size=args.batch_size, shuffle=False, num_workers=args.workers, pin_memory=True)
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# get configures
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model_config = load_config(args.model_config, {'class_num': class_num}, logger)
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optim_config = load_config(args.optim_config,
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{'class_num': class_num, 'KD_alpha': args.KD_alpha, 'KD_temperature': args.KD_temperature},
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logger)
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assert torch.cuda.is_available(), "CUDA is not available."
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torch.backends.cudnn.enabled = True
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torch.backends.cudnn.benchmark = True
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# torch.backends.cudnn.deterministic = True
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torch.set_num_threads(args.workers)
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# load checkpoint
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teacher_base = load_net_from_checkpoint(args.KD_checkpoint)
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teacher = torch.nn.DataParallel(teacher_base).cuda()
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prepare_seed(args.rand_seed)
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logger = prepare_logger(args)
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base_model = obtain_model(model_config)
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flop, param = get_model_infos(base_model, xshape)
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logger.log('Student ====>>>>:\n{:}'.format(base_model))
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logger.log('Teacher ====>>>>:\n{:}'.format(teacher_base))
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logger.log('model information : {:}'.format(base_model.get_message()))
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logger.log('-'*50)
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logger.log('Params={:.2f} MB, FLOPs={:.2f} M ... = {:.2f} G'.format(param, flop, flop/1e3))
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logger.log('-'*50)
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logger.log('train_data : {:}'.format(train_data))
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logger.log('valid_data : {:}'.format(valid_data))
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optimizer, scheduler, criterion = get_optim_scheduler(base_model.parameters(), optim_config)
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logger.log('optimizer : {:}'.format(optimizer))
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logger.log('scheduler : {:}'.format(scheduler))
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logger.log('criterion : {:}'.format(criterion))
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last_info, model_base_path, model_best_path = logger.path('info'), logger.path('model'), logger.path('best')
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network, criterion = torch.nn.DataParallel(base_model).cuda(), criterion.cuda()
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train_data, valid_data, xshape, class_num = get_datasets(args.dataset, args.data_path, args.cutout_length)
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train_loader = torch.utils.data.DataLoader(
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train_data, batch_size=args.batch_size, shuffle=True, num_workers=args.workers, pin_memory=True
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)
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valid_loader = torch.utils.data.DataLoader(
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valid_data, batch_size=args.batch_size, shuffle=False, num_workers=args.workers, pin_memory=True
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)
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# get configures
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model_config = load_config(args.model_config, {"class_num": class_num}, logger)
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optim_config = load_config(
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args.optim_config,
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{"class_num": class_num, "KD_alpha": args.KD_alpha, "KD_temperature": args.KD_temperature},
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logger,
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)
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if last_info.exists(): # automatically resume from previous checkpoint
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logger.log("=> loading checkpoint of the last-info '{:}' start".format(last_info))
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last_info = torch.load(last_info)
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start_epoch = last_info['epoch'] + 1
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checkpoint = torch.load(last_info['last_checkpoint'])
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base_model.load_state_dict( checkpoint['base-model'] )
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scheduler.load_state_dict ( checkpoint['scheduler'] )
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optimizer.load_state_dict ( checkpoint['optimizer'] )
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valid_accuracies = checkpoint['valid_accuracies']
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max_bytes = checkpoint['max_bytes']
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logger.log("=> loading checkpoint of the last-info '{:}' start with {:}-th epoch.".format(last_info, start_epoch))
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elif args.resume is not None:
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assert Path(args.resume).exists(), 'Can not find the resume file : {:}'.format(args.resume)
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checkpoint = torch.load( args.resume )
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start_epoch = checkpoint['epoch'] + 1
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base_model.load_state_dict( checkpoint['base-model'] )
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scheduler.load_state_dict ( checkpoint['scheduler'] )
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optimizer.load_state_dict ( checkpoint['optimizer'] )
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valid_accuracies = checkpoint['valid_accuracies']
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max_bytes = checkpoint['max_bytes']
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logger.log("=> loading checkpoint from '{:}' start with {:}-th epoch.".format(args.resume, start_epoch))
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elif args.init_model is not None:
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assert Path(args.init_model).exists(), 'Can not find the initialization file : {:}'.format(args.init_model)
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checkpoint = torch.load( args.init_model )
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base_model.load_state_dict( checkpoint['base-model'] )
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start_epoch, valid_accuracies, max_bytes = 0, {'best': -1}, {}
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logger.log('=> initialize the model from {:}'.format( args.init_model ))
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else:
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logger.log("=> do not find the last-info file : {:}".format(last_info))
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start_epoch, valid_accuracies, max_bytes = 0, {'best': -1}, {}
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# load checkpoint
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teacher_base = load_net_from_checkpoint(args.KD_checkpoint)
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teacher = torch.nn.DataParallel(teacher_base).cuda()
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train_func, valid_func = get_procedures(args.procedure)
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total_epoch = optim_config.epochs + optim_config.warmup
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# Main Training and Evaluation Loop
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start_time = time.time()
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epoch_time = AverageMeter()
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for epoch in range(start_epoch, total_epoch):
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scheduler.update(epoch, 0.0)
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need_time = 'Time Left: {:}'.format( convert_secs2time(epoch_time.avg * (total_epoch-epoch), True) )
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epoch_str = 'epoch={:03d}/{:03d}'.format(epoch, total_epoch)
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LRs = scheduler.get_lr()
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find_best = False
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base_model = obtain_model(model_config)
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flop, param = get_model_infos(base_model, xshape)
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logger.log("Student ====>>>>:\n{:}".format(base_model))
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logger.log("Teacher ====>>>>:\n{:}".format(teacher_base))
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logger.log("model information : {:}".format(base_model.get_message()))
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logger.log("-" * 50)
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logger.log("Params={:.2f} MB, FLOPs={:.2f} M ... = {:.2f} G".format(param, flop, flop / 1e3))
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logger.log("-" * 50)
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logger.log("train_data : {:}".format(train_data))
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logger.log("valid_data : {:}".format(valid_data))
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optimizer, scheduler, criterion = get_optim_scheduler(base_model.parameters(), optim_config)
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logger.log("optimizer : {:}".format(optimizer))
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logger.log("scheduler : {:}".format(scheduler))
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logger.log("criterion : {:}".format(criterion))
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logger.log('\n***{:s}*** start {:s} {:s}, LR=[{:.6f} ~ {:.6f}], scheduler={:}'.format(time_string(), epoch_str, need_time, min(LRs), max(LRs), scheduler))
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# train for one epoch
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train_loss, train_acc1, train_acc5 = train_func(train_loader, teacher, network, criterion, scheduler, optimizer, optim_config, epoch_str, args.print_freq, logger)
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# log the results
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logger.log('***{:s}*** TRAIN [{:}] loss = {:.6f}, accuracy-1 = {:.2f}, accuracy-5 = {:.2f}'.format(time_string(), epoch_str, train_loss, train_acc1, train_acc5))
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last_info, model_base_path, model_best_path = logger.path("info"), logger.path("model"), logger.path("best")
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network, criterion = torch.nn.DataParallel(base_model).cuda(), criterion.cuda()
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# evaluate the performance
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if (epoch % args.eval_frequency == 0) or (epoch + 1 == total_epoch):
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logger.log('-'*150)
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valid_loss, valid_acc1, valid_acc5 = valid_func(valid_loader, teacher, network, criterion, optim_config, epoch_str, args.print_freq_eval, logger)
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valid_accuracies[epoch] = valid_acc1
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logger.log('***{:s}*** VALID [{:}] loss = {:.6f}, accuracy@1 = {:.2f}, accuracy@5 = {:.2f} | Best-Valid-Acc@1={:.2f}, Error@1={:.2f}'.format(time_string(), epoch_str, valid_loss, valid_acc1, valid_acc5, valid_accuracies['best'], 100-valid_accuracies['best']))
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if valid_acc1 > valid_accuracies['best']:
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valid_accuracies['best'] = valid_acc1
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find_best = True
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logger.log('Currently, the best validation accuracy found at {:03d}-epoch :: acc@1={:.2f}, acc@5={:.2f}, error@1={:.2f}, error@5={:.2f}, save into {:}.'.format(epoch, valid_acc1, valid_acc5, 100-valid_acc1, 100-valid_acc5, model_best_path))
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num_bytes = torch.cuda.max_memory_cached( next(network.parameters()).device ) * 1.0
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logger.log('[GPU-Memory-Usage on {:} is {:} bytes, {:.2f} KB, {:.2f} MB, {:.2f} GB.]'.format(next(network.parameters()).device, int(num_bytes), num_bytes / 1e3, num_bytes / 1e6, num_bytes / 1e9))
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max_bytes[epoch] = num_bytes
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if epoch % 10 == 0: torch.cuda.empty_cache()
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if last_info.exists(): # automatically resume from previous checkpoint
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logger.log("=> loading checkpoint of the last-info '{:}' start".format(last_info))
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last_info = torch.load(last_info)
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start_epoch = last_info["epoch"] + 1
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checkpoint = torch.load(last_info["last_checkpoint"])
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base_model.load_state_dict(checkpoint["base-model"])
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scheduler.load_state_dict(checkpoint["scheduler"])
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optimizer.load_state_dict(checkpoint["optimizer"])
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valid_accuracies = checkpoint["valid_accuracies"]
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max_bytes = checkpoint["max_bytes"]
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logger.log(
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"=> loading checkpoint of the last-info '{:}' start with {:}-th epoch.".format(last_info, start_epoch)
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)
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elif args.resume is not None:
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assert Path(args.resume).exists(), "Can not find the resume file : {:}".format(args.resume)
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checkpoint = torch.load(args.resume)
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start_epoch = checkpoint["epoch"] + 1
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base_model.load_state_dict(checkpoint["base-model"])
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scheduler.load_state_dict(checkpoint["scheduler"])
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optimizer.load_state_dict(checkpoint["optimizer"])
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valid_accuracies = checkpoint["valid_accuracies"]
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max_bytes = checkpoint["max_bytes"]
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logger.log("=> loading checkpoint from '{:}' start with {:}-th epoch.".format(args.resume, start_epoch))
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elif args.init_model is not None:
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assert Path(args.init_model).exists(), "Can not find the initialization file : {:}".format(args.init_model)
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checkpoint = torch.load(args.init_model)
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base_model.load_state_dict(checkpoint["base-model"])
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start_epoch, valid_accuracies, max_bytes = 0, {"best": -1}, {}
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logger.log("=> initialize the model from {:}".format(args.init_model))
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else:
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logger.log("=> do not find the last-info file : {:}".format(last_info))
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start_epoch, valid_accuracies, max_bytes = 0, {"best": -1}, {}
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# save checkpoint
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save_path = save_checkpoint({
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'epoch' : epoch,
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'args' : deepcopy(args),
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'max_bytes' : deepcopy(max_bytes),
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'FLOP' : flop,
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'PARAM' : param,
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'valid_accuracies': deepcopy(valid_accuracies),
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'model-config' : model_config._asdict(),
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'optim-config' : optim_config._asdict(),
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'base-model' : base_model.state_dict(),
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'scheduler' : scheduler.state_dict(),
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'optimizer' : optimizer.state_dict(),
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}, model_base_path, logger)
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if find_best: copy_checkpoint(model_base_path, model_best_path, logger)
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last_info = save_checkpoint({
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'epoch': epoch,
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'args' : deepcopy(args),
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'last_checkpoint': save_path,
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}, logger.path('info'), logger)
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train_func, valid_func = get_procedures(args.procedure)
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# measure elapsed time
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epoch_time.update(time.time() - start_time)
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total_epoch = optim_config.epochs + optim_config.warmup
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# Main Training and Evaluation Loop
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start_time = time.time()
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epoch_time = AverageMeter()
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for epoch in range(start_epoch, total_epoch):
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scheduler.update(epoch, 0.0)
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need_time = "Time Left: {:}".format(convert_secs2time(epoch_time.avg * (total_epoch - epoch), True))
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epoch_str = "epoch={:03d}/{:03d}".format(epoch, total_epoch)
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LRs = scheduler.get_lr()
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find_best = False
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logger.log('\n' + '-'*200)
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logger.log('||| Params={:.2f} MB, FLOPs={:.2f} M ... = {:.2f} G'.format(param, flop, flop/1e3))
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logger.log('Finish training/validation in {:} with Max-GPU-Memory of {:.2f} MB, and save final checkpoint into {:}'.format(convert_secs2time(epoch_time.sum, True), max(v for k, v in max_bytes.items()) / 1e6, logger.path('info')))
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logger.log('-'*200 + '\n')
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logger.close()
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logger.log(
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"\n***{:s}*** start {:s} {:s}, LR=[{:.6f} ~ {:.6f}], scheduler={:}".format(
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time_string(), epoch_str, need_time, min(LRs), max(LRs), scheduler
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)
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)
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# train for one epoch
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train_loss, train_acc1, train_acc5 = train_func(
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train_loader,
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teacher,
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network,
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criterion,
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scheduler,
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optimizer,
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optim_config,
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epoch_str,
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args.print_freq,
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logger,
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)
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# log the results
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logger.log(
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"***{:s}*** TRAIN [{:}] loss = {:.6f}, accuracy-1 = {:.2f}, accuracy-5 = {:.2f}".format(
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time_string(), epoch_str, train_loss, train_acc1, train_acc5
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)
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)
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# evaluate the performance
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if (epoch % args.eval_frequency == 0) or (epoch + 1 == total_epoch):
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logger.log("-" * 150)
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valid_loss, valid_acc1, valid_acc5 = valid_func(
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valid_loader, teacher, network, criterion, optim_config, epoch_str, args.print_freq_eval, logger
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)
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valid_accuracies[epoch] = valid_acc1
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logger.log(
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"***{:s}*** VALID [{:}] loss = {:.6f}, accuracy@1 = {:.2f}, accuracy@5 = {:.2f} | Best-Valid-Acc@1={:.2f}, Error@1={:.2f}".format(
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time_string(),
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epoch_str,
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valid_loss,
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valid_acc1,
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valid_acc5,
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valid_accuracies["best"],
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100 - valid_accuracies["best"],
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)
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)
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if valid_acc1 > valid_accuracies["best"]:
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valid_accuracies["best"] = valid_acc1
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find_best = True
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logger.log(
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"Currently, the best validation accuracy found at {:03d}-epoch :: acc@1={:.2f}, acc@5={:.2f}, error@1={:.2f}, error@5={:.2f}, save into {:}.".format(
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epoch, valid_acc1, valid_acc5, 100 - valid_acc1, 100 - valid_acc5, model_best_path
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)
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)
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num_bytes = torch.cuda.max_memory_cached(next(network.parameters()).device) * 1.0
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logger.log(
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"[GPU-Memory-Usage on {:} is {:} bytes, {:.2f} KB, {:.2f} MB, {:.2f} GB.]".format(
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next(network.parameters()).device, int(num_bytes), num_bytes / 1e3, num_bytes / 1e6, num_bytes / 1e9
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)
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)
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max_bytes[epoch] = num_bytes
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if epoch % 10 == 0:
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torch.cuda.empty_cache()
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# save checkpoint
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save_path = save_checkpoint(
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{
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"epoch": epoch,
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"args": deepcopy(args),
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"max_bytes": deepcopy(max_bytes),
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"FLOP": flop,
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"PARAM": param,
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"valid_accuracies": deepcopy(valid_accuracies),
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"model-config": model_config._asdict(),
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"optim-config": optim_config._asdict(),
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"base-model": base_model.state_dict(),
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"scheduler": scheduler.state_dict(),
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"optimizer": optimizer.state_dict(),
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},
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model_base_path,
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logger,
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)
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if find_best:
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copy_checkpoint(model_base_path, model_best_path, logger)
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last_info = save_checkpoint(
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{
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"epoch": epoch,
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"args": deepcopy(args),
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"last_checkpoint": save_path,
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},
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logger.path("info"),
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logger,
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)
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# measure elapsed time
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epoch_time.update(time.time() - start_time)
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start_time = time.time()
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logger.log("\n" + "-" * 200)
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logger.log("||| Params={:.2f} MB, FLOPs={:.2f} M ... = {:.2f} G".format(param, flop, flop / 1e3))
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logger.log(
|
||||
"Finish training/validation in {:} with Max-GPU-Memory of {:.2f} MB, and save final checkpoint into {:}".format(
|
||||
convert_secs2time(epoch_time.sum, True), max(v for k, v in max_bytes.items()) / 1e6, logger.path("info")
|
||||
)
|
||||
)
|
||||
logger.log("-" * 200 + "\n")
|
||||
logger.close()
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
args = obtain_args()
|
||||
main(args)
|
||||
if __name__ == "__main__":
|
||||
args = obtain_args()
|
||||
main(args)
|
||||
|
||||
Reference in New Issue
Block a user