203 lines
12 KiB
Python
203 lines
12 KiB
Python
#####################################################
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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 os import path as osp
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ImageFile.LOAD_TRUNCATED_IMAGES = True
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import numpy as np
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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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print ('lib_dir : {:}'.format(lib_dir))
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if str(lib_dir) not in sys.path: sys.path.insert(0, str(lib_dir))
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from config_utils import load_config, configure2str, obtain_search_single_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, SearchDataset
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from models import obtain_search_model, obtain_model, change_key
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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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# prepare dataset
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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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split_file_path = Path(args.split_path)
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assert split_file_path.exists(), '{:} does not exist'.format(split_file_path)
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split_info = torch.load(split_file_path)
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train_split, valid_split = split_info['train'], split_info['valid']
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assert len( set(train_split).intersection( set(valid_split) ) ) == 0, 'There should be 0 element that belongs to both train and valid'
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assert len(train_split) + len(valid_split) == len(train_data), '{:} + {:} vs {:}'.format(len(train_split), len(valid_split), len(train_data))
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search_dataset = SearchDataset(args.dataset, train_data, train_split, valid_split)
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search_train_loader = torch.utils.data.DataLoader(train_data, batch_size=args.batch_size,
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sampler=torch.utils.data.sampler.SubsetRandomSampler(train_split), pin_memory=True, num_workers=args.workers)
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search_valid_loader = torch.utils.data.DataLoader(train_data, batch_size=args.batch_size,
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sampler=torch.utils.data.sampler.SubsetRandomSampler(valid_split), pin_memory=True, num_workers=args.workers)
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search_loader = torch.utils.data.DataLoader(search_dataset, batch_size=args.batch_size, shuffle=True, num_workers=args.workers, pin_memory=True, sampler=None)
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# get configures
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model_config = load_config(args.model_config, {'class_num': class_num, 'search_mode': args.search_shape}, logger)
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# obtain the model
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search_model = obtain_search_model(model_config)
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MAX_FLOP, param = get_model_infos(search_model, xshape)
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optim_config = load_config(args.optim_config, {'class_num': class_num, 'FLOP': MAX_FLOP}, logger)
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logger.log('Model Information : {:}'.format(search_model.get_message()))
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logger.log('MAX_FLOP = {:} M'.format(MAX_FLOP))
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logger.log('Params = {:} M'.format(param))
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logger.log('train_data : {:}'.format(train_data))
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logger.log('search-data: {:}'.format(search_dataset))
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logger.log('search_train_loader : {:} samples'.format( len(train_split) ))
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logger.log('search_valid_loader : {:} samples'.format( len(valid_split) ))
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base_optimizer, scheduler, criterion = get_optim_scheduler(search_model.base_parameters(), optim_config)
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arch_optimizer = torch.optim.Adam(search_model.arch_parameters(), lr=optim_config.arch_LR, betas=(0.5, 0.999), weight_decay=optim_config.arch_decay)
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logger.log('base-optimizer : {:}'.format(base_optimizer))
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logger.log('arch-optimizer : {:}'.format(arch_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(search_model).cuda(), criterion.cuda()
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# load checkpoint
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if last_info.exists() or (args.resume is not None and osp.isfile(args.resume)): # automatically resume from previous checkpoint
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if args.resume is not None and osp.isfile(args.resume):
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resume_path = Path(args.resume)
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elif last_info.exists():
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resume_path = last_info
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else: raise ValueError('Something is wrong.')
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logger.log("=> loading checkpoint of the last-info '{:}' start".format(resume_path))
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checkpoint = torch.load(resume_path)
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if 'last_checkpoint' in checkpoint:
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last_checkpoint_path = checkpoint['last_checkpoint']
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if not last_checkpoint_path.exists():
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logger.log('Does not find {:}, try another path'.format(last_checkpoint_path))
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last_checkpoint_path = resume_path.parent / last_checkpoint_path.parent.name / last_checkpoint_path.name
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assert last_checkpoint_path.exists(), 'can not find the checkpoint from {:}'.format(last_checkpoint_path)
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checkpoint = torch.load( last_checkpoint_path )
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start_epoch = checkpoint['epoch'] + 1
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search_model.load_state_dict( checkpoint['search_model'] )
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scheduler.load_state_dict ( checkpoint['scheduler'] )
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base_optimizer.load_state_dict ( checkpoint['base_optimizer'] )
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arch_optimizer.load_state_dict ( checkpoint['arch_optimizer'] )
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valid_accuracies = checkpoint['valid_accuracies']
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arch_genotypes = checkpoint['arch_genotypes']
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discrepancies = checkpoint['discrepancies']
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logger.log("=> loading checkpoint of the last-info '{:}' start with {:}-th epoch.".format(resume_path, start_epoch))
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else:
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logger.log("=> do not find the last-info file : {:} or resume : {:}".format(last_info, args.resume))
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start_epoch, valid_accuracies, arch_genotypes, discrepancies = 0, {'best': -1}, {}, {}
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# main procedure
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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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start_time, epoch_time = time.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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search_model.set_tau(args.gumbel_tau_max, args.gumbel_tau_min, epoch*1.0/total_epoch)
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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***{:s}*** start {:s} {:s}, LR=[{:.6f} ~ {:.6f}], scheduler={:}, tau={:}, FLOP={:.2f}'.format(time_string(), epoch_str, need_time, min(LRs), max(LRs), scheduler, search_model.tau, MAX_FLOP))
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# train for one epoch
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train_base_loss, train_arch_loss, train_acc1, train_acc5 = train_func(search_loader, network, criterion, scheduler, base_optimizer, arch_optimizer, optim_config, \
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{'epoch-str' : epoch_str, 'FLOP-exp': MAX_FLOP * args.FLOP_ratio,
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'FLOP-weight': args.FLOP_weight, 'FLOP-tolerant': MAX_FLOP * args.FLOP_tolerant}, args.print_freq, logger)
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# log the results
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logger.log('***{:s}*** TRAIN [{:}] base-loss = {:.6f}, arch-loss = {:.6f}, accuracy-1 = {:.2f}, accuracy-5 = {:.2f}'.format(time_string(), epoch_str, train_base_loss, train_arch_loss, train_acc1, train_acc5))
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cur_FLOP, genotype = search_model.get_flop('genotype', model_config._asdict(), None)
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arch_genotypes[epoch] = genotype
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arch_genotypes['last'] = genotype
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logger.log('[{:}] genotype : {:}'.format(epoch_str, genotype))
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arch_info, discrepancy = search_model.get_arch_info()
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logger.log(arch_info)
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discrepancies[epoch] = discrepancy
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logger.log('[{:}] FLOP : {:.2f} MB, ratio : {:.4f}, Expected-ratio : {:.4f}, Discrepancy : {:.3f}'.format(epoch_str, cur_FLOP, cur_FLOP/MAX_FLOP, args.FLOP_ratio, np.mean(discrepancy)))
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#if cur_FLOP/MAX_FLOP > args.FLOP_ratio:
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# init_flop_weight = init_flop_weight * args.FLOP_decay
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#else:
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# init_flop_weight = init_flop_weight / args.FLOP_decay
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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(search_valid_loader, network, criterion, 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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arch_genotypes['best'] = genotype
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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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# 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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'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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'search_model' : search_model.state_dict(),
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'scheduler' : scheduler.state_dict(),
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'base_optimizer': base_optimizer.state_dict(),
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'arch_optimizer': arch_optimizer.state_dict(),
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'arch_genotypes': arch_genotypes,
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'discrepancies' : discrepancies,
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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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# 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('')
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logger.log('-'*100)
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last_config_path = logger.path('log') / 'seed-{:}-last.config'.format(args.rand_seed)
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configure2str(arch_genotypes['last'], str(last_config_path))
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logger.log('save the last config int {:} :\n{:}'.format(last_config_path, arch_genotypes['last']))
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best_arch, valid_acc = arch_genotypes['best'], valid_accuracies['best']
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for key, config in arch_genotypes.items():
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if key == 'last': continue
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FLOP_ratio = config['estimated_FLOP'] / MAX_FLOP
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if abs(FLOP_ratio - args.FLOP_ratio) <= args.FLOP_tolerant:
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if valid_acc < valid_accuracies[key]:
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best_arch, valid_acc = config, valid_accuracies[key]
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print('Best-Arch : {:}\nRatio={:}, Valid-ACC={:}'.format(best_arch, best_arch['estimated_FLOP'] / MAX_FLOP, valid_acc))
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best_config_path = logger.path('log') / 'seed-{:}-best.config'.format(args.rand_seed)
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configure2str(best_arch, str(best_config_path))
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logger.log('save the last config int {:} :\n{:}'.format(best_config_path, best_arch))
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logger.log('\n' + '-'*200)
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logger.log('Finish training/validation in {:}, and save final checkpoint into {:}'.format(convert_secs2time(epoch_time.sum, True), logger.path('info')))
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logger.close()
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if __name__ == '__main__':
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args = obtain_args()
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main(args)
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