Add more algorithms
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D-X-Y
192
others/GDAS/exps-cnn/train_utils_imagenet.py
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others/GDAS/exps-cnn/train_utils_imagenet.py
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##################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
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##################################################
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import os, sys, time
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from copy import deepcopy
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import torch
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import torch.nn as nn
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import torchvision.transforms as transforms
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from shutil import copyfile
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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 print_FLOPs
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from utils import Cutout
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from nas import NetworkImageNet as Network
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from datasets import get_datasets
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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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class CrossEntropyLabelSmooth(nn.Module):
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def __init__(self, num_classes, epsilon):
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super(CrossEntropyLabelSmooth, self).__init__()
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self.num_classes = num_classes
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self.epsilon = epsilon
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self.logsoftmax = nn.LogSoftmax(dim=1)
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def forward(self, inputs, targets):
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log_probs = self.logsoftmax(inputs)
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targets = torch.zeros_like(log_probs).scatter_(1, targets.unsqueeze(1), 1)
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targets = (1 - self.epsilon) * targets + self.epsilon / self.num_classes
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loss = (-targets * log_probs).mean(0).sum()
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return loss
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def main_procedure_imagenet(config, data_path, args, genotype, init_channels, layers, pure_evaluate, log):
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# training data and testing data
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train_data, valid_data, class_num = get_datasets('imagenet-1k', data_path, -1)
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train_queue = torch.utils.data.DataLoader(
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train_data, batch_size=config.batch_size, shuffle= True, pin_memory=True, num_workers=args.workers)
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valid_queue = torch.utils.data.DataLoader(
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valid_data, batch_size=config.batch_size, shuffle=False, pin_memory=True, num_workers=args.workers)
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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_FLOPs(basemodel, (1,3,224,224), [print_log, 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('Train-Dataset : {:}'.format(train_data), log)
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print_log('Valid--Dataset : {:}'.format(valid_data), log)
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print_log('Args : {:}'.format(args), log)
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criterion = torch.nn.CrossEntropyLoss().cuda()
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criterion_smooth = CrossEntropyLabelSmooth(class_num, config.label_smooth).cuda()
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optimizer = torch.optim.SGD(model.parameters(), config.LR, momentum=config.momentum, weight_decay=config.decay, nesterov=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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elif config.type == 'steplr':
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scheduler = torch.optim.lr_scheduler.StepLR(optimizer, config.decay_period, gamma=config.gamma)
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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, 'seed-{:}-checkpoint-imagenet-model.pth'.format(args.manualSeed))
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checkpoint_best = os.path.join(args.save_path, 'seed-{:}-checkpoint-imagenet-best.pth'.format(args.manualSeed))
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if pure_evaluate:
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print_log('-'*20 + 'Pure Evaluation' + '-'*20, log)
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basemodel.load_state_dict( pure_evaluate )
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with torch.no_grad():
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valid_acc1, valid_acc5, valid_los = _train(valid_queue, model, criterion, None, 'test' , -1, config, args.print_freq, log)
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return (valid_acc1, valid_acc5)
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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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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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basemodel.update_drop_path(config.drop_path_prob * epoch / config.epochs)
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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}, Drop-Path-Prob={:}".format(time_string(), epoch, config.epochs, need_time, min(scheduler.get_lr()), max(scheduler.get_lr()), config.batch_size, basemodel.get_drop_path()), log)
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train_acc1, train_acc5, train_los = _train(train_queue, model, criterion_smooth, 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(valid_queue, model, criterion, None, '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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if accuracies[epoch] == best_acc: copyfile(checkpoint_path, checkpoint_best)
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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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return obtain_best( accuracies )
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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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