313 lines
14 KiB
Python
313 lines
14 KiB
Python
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import os, sys, time, glob, random, argparse
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import numpy as np
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from copy import deepcopy
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import torch
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import torch.nn.functional as F
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import torch.backends.cudnn as cudnn
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import torchvision.transforms as transforms
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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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from datasets import TieredImageNet, MetaBatchSampler
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from utils import AverageMeter, time_string, convert_secs2time
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from utils import print_log, obtain_accuracy
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from utils import Cutout, count_parameters_in_MB
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from meta_nas import return_alphas_str, MetaNetwork
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from train_utils import main_procedure
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from scheduler import load_config
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Networks = {'meta': MetaNetwork}
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parser = argparse.ArgumentParser("cifar")
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parser.add_argument('--data_path', type=str, help='Path to dataset')
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parser.add_argument('--arch', type=str, choices=Networks.keys(), help='Choose networks.')
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parser.add_argument('--n_way', type=int, help='N-WAY.')
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parser.add_argument('--k_shot', type=int, help='K-SHOT.')
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# Learning Parameters
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parser.add_argument('--learning_rate_max', type=float, help='initial learning rate')
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parser.add_argument('--learning_rate_min', type=float, help='minimum learning rate')
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parser.add_argument('--momentum', type=float, help='momentum')
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parser.add_argument('--weight_decay', type=float, help='weight decay')
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parser.add_argument('--epochs', type=int, help='num of training epochs')
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# architecture leraning rate
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parser.add_argument('--arch_learning_rate', type=float, default=3e-4, help='learning rate for arch encoding')
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parser.add_argument('--arch_weight_decay', type=float, default=1e-3, help='weight decay for arch encoding')
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#
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parser.add_argument('--init_channels', type=int, help='num of init channels')
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parser.add_argument('--layers', type=int, help='total number of layers')
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#
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parser.add_argument('--cutout', type=int, help='cutout length, negative means no cutout')
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parser.add_argument('--grad_clip', type=float, help='gradient clipping')
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parser.add_argument('--model_config', type=str , help='the model configuration')
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# resume
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parser.add_argument('--resume', type=str , help='the resume path')
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parser.add_argument('--only_base',action='store_true', default=False, help='only train the searched model')
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# split data
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parser.add_argument('--validate', action='store_true', default=False, help='split train-data int train/val or not')
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parser.add_argument('--train_portion', type=float, default=0.5, help='portion of training data')
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# log
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parser.add_argument('--workers', type=int, default=2, help='number of data loading workers (default: 2)')
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parser.add_argument('--save_path', type=str, help='Folder to save checkpoints and log.')
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parser.add_argument('--print_freq', type=int, help='print frequency (default: 200)')
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parser.add_argument('--manualSeed', type=int, help='manual seed')
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args = parser.parse_args()
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assert torch.cuda.is_available(), 'torch.cuda is not available'
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if args.manualSeed is None:
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args.manualSeed = random.randint(1, 10000)
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random.seed(args.manualSeed)
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cudnn.benchmark = True
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cudnn.enabled = True
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torch.manual_seed(args.manualSeed)
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torch.cuda.manual_seed_all(args.manualSeed)
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def main():
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# Init logger
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args.save_path = os.path.join(args.save_path, 'seed-{:}'.format(args.manualSeed))
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if not os.path.isdir(args.save_path):
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os.makedirs(args.save_path)
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log = open(os.path.join(args.save_path, 'log-seed-{:}.txt'.format(args.manualSeed)), 'w')
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print_log('save path : {}'.format(args.save_path), log)
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state = {k: v for k, v in args._get_kwargs()}
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print_log(state, log)
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print_log("Random Seed: {}".format(args.manualSeed), log)
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print_log("Python version : {}".format(sys.version.replace('\n', ' ')), log)
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print_log("Torch version : {}".format(torch.__version__), log)
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print_log("CUDA version : {}".format(torch.version.cuda), log)
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print_log("cuDNN version : {}".format(cudnn.version()), log)
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print_log("Num of GPUs : {}".format(torch.cuda.device_count()), log)
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# Mean + Std
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means, stds = [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]
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# Data Argumentation
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lists = [transforms.RandomHorizontalFlip(), transforms.RandomCrop(80, padding=4), transforms.ToTensor(),
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transforms.Normalize(means, stds)]
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if args.cutout > 0 : lists += [Cutout(args.cutout)]
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train_transform = transforms.Compose(lists)
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test_transform = transforms.Compose([transforms.CenterCrop(80), transforms.ToTensor(), transforms.Normalize(means, stds)])
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train_data = TieredImageNet(args.data_path, 'train', train_transform)
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test_data = TieredImageNet(args.data_path, 'val' , test_transform )
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train_sampler = MetaBatchSampler(train_data.labels, args.n_way, args.k_shot * 2, len(train_data) // (args.n_way*args.k_shot))
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test_sampler = MetaBatchSampler( test_data.labels, args.n_way, args.k_shot * 2, len( test_data) // (args.n_way*args.k_shot))
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train_loader = torch.utils.data.DataLoader(train_data, batch_sampler=train_sampler)
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test_loader = torch.utils.data.DataLoader( test_data, batch_sampler= test_sampler)
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# network
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basemodel = Networks[args.arch](args.init_channels, args.layers, head='imagenet')
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model = torch.nn.DataParallel(basemodel).cuda()
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print_log("Parameter size = {:.3f} MB".format(count_parameters_in_MB(basemodel.base_parameters())), log)
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print_log("Train-transformation : {:}\nTest--transformation : {:}".format(train_transform, test_transform), log)
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# optimizer and LR-scheduler
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#base_optimizer = torch.optim.SGD (basemodel.base_parameters(), args.learning_rate_max, momentum=args.momentum, weight_decay=args.weight_decay)
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base_optimizer = torch.optim.Adam(basemodel.base_parameters(), lr=args.learning_rate_max, betas=(0.5, 0.999), weight_decay=args.weight_decay)
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base_scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(base_optimizer, float(args.epochs), eta_min=args.learning_rate_min)
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arch_optimizer = torch.optim.Adam(basemodel.arch_parameters(), lr=args.arch_learning_rate, betas=(0.5, 0.999), weight_decay=args.arch_weight_decay)
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# snapshot
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checkpoint_path = os.path.join(args.save_path, 'checkpoint-meta-search.pth')
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if args.resume is not None and os.path.isfile(args.resume):
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checkpoint = torch.load(args.resume)
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start_epoch = checkpoint['epoch']
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basemodel.load_state_dict( checkpoint['state_dict'] )
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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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base_scheduler.load_state_dict( checkpoint['base_scheduler'] )
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genotypes = checkpoint['genotypes']
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print_log('Load resume from {:} with start-epoch = {:}'.format(args.resume, start_epoch), log)
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elif os.path.isfile(checkpoint_path):
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checkpoint = torch.load(checkpoint_path)
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start_epoch = checkpoint['epoch']
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basemodel.load_state_dict( checkpoint['state_dict'] )
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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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base_scheduler.load_state_dict( checkpoint['base_scheduler'] )
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genotypes = checkpoint['genotypes']
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print_log('Load checkpoint from {:} with start-epoch = {:}'.format(checkpoint_path, start_epoch), log)
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else:
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start_epoch, genotypes = 0, {}
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print_log('Train model-search from scratch.', log)
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config = load_config(args.model_config)
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if args.only_base:
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print_log('---- Only Train the Searched Model ----', log)
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CIFAR_DATA_DIR = os.environ['TORCH_HOME'] + '/cifar.python'
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main_procedure(config, 'cifar10', CIFAR_DATA_DIR, args, basemodel.genotype(), 36, 20, log)
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return
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# Main loop
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start_time, epoch_time, total_train_time = time.time(), AverageMeter(), 0
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for epoch in range(start_epoch, args.epochs):
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base_scheduler.step()
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need_time = convert_secs2time(epoch_time.val * (args.epochs-epoch), True)
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print_log('\n==>>{:s} [Epoch={:03d}/{:03d}] {:s} [LR={:6.4f} ~ {:6.4f}]'.format(time_string(), epoch, args.epochs, need_time, min(base_scheduler.get_lr()), max(base_scheduler.get_lr())), log)
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genotype = basemodel.genotype()
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print_log('genotype = {:}'.format(genotype), log)
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print_log('{:03d}/{:03d} alphas :\n{:}'.format(epoch, args.epochs, return_alphas_str(basemodel)), log)
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# training
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train_acc1, train_obj, train_time \
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= train(train_loader, test_loader, model, args.n_way, base_optimizer, arch_optimizer, epoch, log)
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total_train_time += train_time
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# validation
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valid_acc1, valid_obj = infer(test_loader, model, epoch, args.n_way, log)
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print_log('META -> {:}-way {:}-shot : {:03d}/{:03d} : Train Acc : {:.2f}, Test Acc : {:.2f}'.format(args.n_way, args.k_shot, epoch, args.epochs, train_acc1, valid_acc1), log)
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# save genotype
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genotypes[epoch] = basemodel.genotype()
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# save checkpoint
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torch.save({'epoch' : epoch + 1,
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'args' : deepcopy(args),
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'state_dict': basemodel.state_dict(),
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'genotypes' : genotypes,
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'base_optimizer' : base_optimizer.state_dict(),
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'arch_optimizer' : arch_optimizer.state_dict(),
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'base_scheduler' : base_scheduler.state_dict()},
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checkpoint_path)
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print_log('----> Save into {:}'.format(checkpoint_path), log)
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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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print_log('Finish with training time = {:}'.format( convert_secs2time(total_train_time, True) ), log)
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# clear GPU cache
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CIFAR_DATA_DIR = os.environ['TORCH_HOME'] + '/cifar.python'
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print_log('test for CIFAR-10', log)
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torch.cuda.empty_cache()
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main_procedure(config, 'cifar10' , CIFAR_DATA_DIR, args, basemodel.genotype(), 36, 20, log)
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print_log('test for CIFAR-100', log)
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torch.cuda.empty_cache()
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main_procedure(config, 'cifar100', CIFAR_DATA_DIR, args, basemodel.genotype(), 36, 20, log)
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log.close()
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def euclidean_dist(A, B):
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na, da = A.size()
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nb, db = B.size()
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assert da == db, 'invalid feature dim : {:} vs. {:}'.format(da, db)
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X, Y = A.view(na, 1, da), B.view(1, nb, db)
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return torch.pow(X-Y, 2).sum(2)
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def get_loss(features, targets, n_way):
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classes = torch.unique(targets)
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shot = features.size(0) // n_way // 2
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support_index, query_index, labels = [], [], []
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for idx, cls in enumerate( classes.tolist() ):
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indexs = (targets == cls).nonzero().view(-1).tolist()
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support_index.append(indexs[:shot])
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query_index += indexs[shot:]
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labels += [idx] * shot
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query_features = features[query_index, :]
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support_features = features[support_index, :]
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support_features = torch.mean(support_features, dim=1)
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labels = torch.LongTensor(labels).cuda(non_blocking=True)
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logits = -euclidean_dist(query_features, support_features)
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loss = F.cross_entropy(logits, labels)
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accuracy = obtain_accuracy(logits.data, labels.data, topk=(1,))[0]
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return loss, accuracy
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def train(train_queue, valid_queue, model, n_way, base_optimizer, arch_optimizer, epoch, log):
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data_time, batch_time = AverageMeter(), AverageMeter()
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objs, accuracies = AverageMeter(), AverageMeter()
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model.train()
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valid_iter = iter(valid_queue)
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end = time.time()
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for step, (inputs, targets) in enumerate(train_queue):
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batch, C, H, W = inputs.size()
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#inputs, targets = inputs.cuda(), targets.cuda(non_blocking=True)
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#targets = targets.cuda(non_blocking=True)
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data_time.update(time.time() - end)
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# get a random minibatch from the search queue with replacement
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try:
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input_search, target_search = next(valid_iter)
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except:
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valid_iter = iter(valid_queue)
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input_search, target_search = next(valid_iter)
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#target_search = target_search.cuda(non_blocking=True)
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# update the architecture
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arch_optimizer.zero_grad()
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feature_search = model(input_search)
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arch_loss, arch_accuracy = get_loss(feature_search, target_search, n_way)
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arch_loss.backward()
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arch_optimizer.step()
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# update the parameters
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base_optimizer.zero_grad()
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feature_model = model(inputs)
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model_loss, model_accuracy = get_loss(feature_model, targets, n_way)
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model_loss.backward()
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torch.nn.utils.clip_grad_norm_(model.module.base_parameters(), args.grad_clip)
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base_optimizer.step()
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objs.update(model_loss.item() , batch)
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accuracies.update(model_accuracy.item(), batch)
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# measure elapsed time
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batch_time.update(time.time() - end)
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end = time.time()
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if step % args.print_freq == 0 or (step+1) == len(train_queue):
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Sstr = ' TRAIN-SEARCH ' + time_string() + ' Epoch: [{:03d}][{:03d}/{:03d}]'.format(epoch, step, len(train_queue))
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Tstr = 'Time {batch_time.val:.2f} ({batch_time.avg:.2f}) Data {data_time.val:.2f} ({data_time.avg:.2f})'.format(batch_time=batch_time, data_time=data_time)
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Lstr = 'Loss {loss.val:.3f} ({loss.avg:.3f}) Prec@1 {top1.val:.2f} ({top1.avg:.2f})'.format(loss=objs, top1=accuracies)
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Istr = 'I : {:}'.format( list(inputs.size()) )
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print_log(Sstr + ' ' + Tstr + ' ' + Lstr + ' ' + Istr, log)
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return accuracies.avg, objs.avg, batch_time.sum
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def infer(valid_queue, model, epoch, n_way, log):
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objs, accuracies = AverageMeter(), AverageMeter()
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model.eval()
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with torch.no_grad():
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for step, (inputs, targets) in enumerate(valid_queue):
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batch, C, H, W = inputs.size()
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#targets = targets.cuda(non_blocking=True)
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features = model(inputs)
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loss, accuracy = get_loss(features, targets, n_way)
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objs.update(loss.item() , batch)
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accuracies.update(accuracy.item(), batch)
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if step % (args.print_freq*4) == 0 or (step+1) == len(valid_queue):
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Sstr = ' VALID-SEARCH ' + time_string() + ' Epoch: [{:03d}][{:03d}/{:03d}]'.format(epoch, step, len(valid_queue))
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Lstr = 'Loss {loss.val:.3f} ({loss.avg:.3f}) Prec@1 {top1.val:.2f} ({top1.avg:.2f})'.format(loss=objs, top1=accuracies)
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print_log(Sstr + ' ' + Lstr, log)
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return accuracies.avg, objs.avg
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if __name__ == '__main__':
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main()
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