185 lines
8.4 KiB
Python
185 lines
8.4 KiB
Python
import os, sys, time
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from copy import deepcopy
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import torch
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import torchvision.datasets as dset
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import torchvision.transforms as transforms
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from utils import print_log, obtain_accuracy, AverageMeter
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from utils import time_string, convert_secs2time
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from utils import count_parameters_in_MB
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from utils import Cutout
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from nas import NetworkCIFAR as Network
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def obtain_best(accuracies):
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if len(accuracies) == 0: return (0, 0)
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tops = [value for key, value in accuracies.items()]
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s2b = sorted( tops )
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return s2b[-1]
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def main_procedure(config, dataset, data_path, args, genotype, init_channels, layers, log):
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# Mean + Std
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if dataset == 'cifar10':
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mean = [x / 255 for x in [125.3, 123.0, 113.9]]
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std = [x / 255 for x in [63.0, 62.1, 66.7]]
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elif dataset == 'cifar100':
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mean = [x / 255 for x in [129.3, 124.1, 112.4]]
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std = [x / 255 for x in [68.2, 65.4, 70.4]]
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else:
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raise TypeError("Unknow dataset : {:}".format(dataset))
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# Dataset Transformation
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if dataset == 'cifar10' or dataset == 'cifar100':
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lists = [transforms.RandomHorizontalFlip(), transforms.RandomCrop(32, padding=4), transforms.ToTensor(),
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transforms.Normalize(mean, std)]
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if config.cutout > 0 : lists += [Cutout(config.cutout)]
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train_transform = transforms.Compose(lists)
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test_transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize(mean, std)])
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else:
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raise TypeError("Unknow dataset : {:}".format(dataset))
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# Dataset Defination
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if dataset == 'cifar10':
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train_data = dset.CIFAR10(data_path, train= True, transform=train_transform, download=True)
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test_data = dset.CIFAR10(data_path, train=False, transform=test_transform , download=True)
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class_num = 10
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elif dataset == 'cifar100':
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train_data = dset.CIFAR100(data_path, train= True, transform=train_transform, download=True)
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test_data = dset.CIFAR100(data_path, train=False, transform=test_transform , download=True)
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class_num = 100
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else:
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raise TypeError("Unknow dataset : {:}".format(dataset))
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print_log('-------------------------------------- main-procedure', log)
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print_log('config : {:}'.format(config), log)
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print_log('genotype : {:}'.format(genotype), log)
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print_log('init_channels : {:}'.format(init_channels), log)
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print_log('layers : {:}'.format(layers), log)
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print_log('class_num : {:}'.format(class_num), log)
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basemodel = Network(init_channels, class_num, layers, config.auxiliary, genotype)
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model = torch.nn.DataParallel(basemodel).cuda()
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total_param, aux_param = count_parameters_in_MB(basemodel), count_parameters_in_MB(basemodel.auxiliary_param())
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print_log('Network =>\n{:}'.format(basemodel), log)
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print_log('Parameters : {:} - {:} = {:.3f} MB'.format(total_param, aux_param, total_param - aux_param), log)
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print_log('config : {:}'.format(config), log)
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print_log('genotype : {:}'.format(genotype), log)
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print_log('args : {:}'.format(args), log)
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print_log('Train-Dataset : {:}'.format(train_data), log)
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print_log('Train-Trans : {:}'.format(train_transform), log)
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print_log('Test--Dataset : {:}'.format(test_data ), log)
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print_log('Test--Trans : {:}'.format(test_transform ), log)
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train_loader = torch.utils.data.DataLoader(train_data, batch_size=config.batch_size, shuffle=True,
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num_workers=args.workers, pin_memory=True)
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test_loader = torch.utils.data.DataLoader(test_data , batch_size=config.batch_size, shuffle=False,
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num_workers=args.workers, pin_memory=True)
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criterion = torch.nn.CrossEntropyLoss().cuda()
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optimizer = torch.optim.SGD(model.parameters(), config.LR, momentum=config.momentum, weight_decay=config.decay)
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#optimizer = torch.optim.SGD(model.parameters(), config.LR, momentum=config.momentum, weight_decay=config.decay, nestero=True)
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if config.type == 'cosine':
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scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, float(config.epochs))
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else:
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raise ValueError('Can not find the schedular type : {:}'.format(config.type))
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checkpoint_path = os.path.join(args.save_path, 'checkpoint-{:}-model.pth'.format(dataset))
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if 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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optimizer.load_state_dict(checkpoint['optimizer'])
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scheduler.load_state_dict(checkpoint['scheduler'])
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accuracies = checkpoint['accuracies']
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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, accuracies = 0, {}
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print_log('Train model from scratch without pre-trained model or snapshot', log)
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# Main loop
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start_time, epoch_time = time.time(), AverageMeter()
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for epoch in range(start_epoch, config.epochs):
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scheduler.step()
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need_time = convert_secs2time(epoch_time.val * (config.epochs-epoch), True)
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print_log("\n==>>{:s} [Epoch={:03d}/{:03d}] {:s} LR={:6.4f} ~ {:6.4f}, Batch={:d}".format(time_string(), epoch, config.epochs, need_time, min(scheduler.get_lr()), max(scheduler.get_lr()), config.batch_size), log)
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basemodel.update_drop_path(config.drop_path_prob * epoch / config.epochs)
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train_acc1, train_acc5, train_los = _train(train_loader, model, criterion, optimizer, 'train', epoch, config, args.print_freq, log)
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with torch.no_grad():
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valid_acc1, valid_acc5, valid_los = _train(test_loader, model, criterion, optimizer, 'test', epoch, config, args.print_freq, log)
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accuracies[epoch] = (valid_acc1, valid_acc5)
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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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'optimizer' : optimizer.state_dict(),
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'scheduler' : scheduler.state_dict(),
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'accuracies': accuracies},
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checkpoint_path)
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best_acc = obtain_best( accuracies )
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print_log('----> Best Accuracy : Acc@1={:.2f}, Acc@5={:.2f}, Error@1={:.2f}, Error@5={:.2f}'.format(best_acc[0], best_acc[1], 100-best_acc[0], 100-best_acc[1]), log)
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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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def _train(xloader, model, criterion, optimizer, mode, epoch, config, print_freq, log):
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data_time, batch_time, losses, top1, top5 = AverageMeter(), AverageMeter(), AverageMeter(), AverageMeter(), AverageMeter()
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if mode == 'train':
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model.train()
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elif mode == 'test':
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model.eval()
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else: raise ValueError("The mode is not right : {:}".format(mode))
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end = time.time()
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for i, (inputs, targets) in enumerate(xloader):
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# measure data loading time
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data_time.update(time.time() - end)
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# calculate prediction and loss
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targets = targets.cuda(non_blocking=True)
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if mode == 'train': optimizer.zero_grad()
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if config.auxiliary and model.training:
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logits, logits_aux = model(inputs)
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else:
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logits = model(inputs)
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loss = criterion(logits, targets)
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if config.auxiliary and model.training:
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loss_aux = criterion(logits_aux, targets)
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loss += config.auxiliary_weight * loss_aux
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if mode == 'train':
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loss.backward()
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if config.grad_clip > 0:
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torch.nn.utils.clip_grad_norm_(model.parameters(), config.grad_clip)
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optimizer.step()
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# record
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prec1, prec5 = obtain_accuracy(logits.data, targets.data, topk=(1, 5))
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losses.update(loss.item(), inputs.size(0))
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top1.update (prec1.item(), inputs.size(0))
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top5.update (prec5.item(), inputs.size(0))
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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 i % print_freq == 0 or (i+1) == len(xloader):
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Sstr = ' {:5s}'.format(mode) + time_string() + ' Epoch: [{:03d}][{:03d}/{:03d}]'.format(epoch, i, len(xloader))
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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}) Prec@5 {top5.val:.2f} ({top5.avg:.2f})'.format(loss=losses, top1=top1, top5=top5)
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print_log(Sstr + ' ' + Tstr + ' ' + Lstr, log)
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print_log ('{TIME:} **{mode:}** Prec@1 {top1.avg:.2f} Prec@5 {top5.avg:.2f} Error@1 {error1:.2f} Error@5 {error5:.2f} Loss:{loss:.3f}'.format(TIME=time_string(), mode=mode, top1=top1, top5=top5, error1=100-top1.avg, error5=100-top5.avg, loss=losses.avg), log)
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return top1.avg, top5.avg, losses.avg
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