76 lines
3.6 KiB
Python
76 lines
3.6 KiB
Python
##################################################
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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, torch
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from log_utils import AverageMeter, time_string
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from utils import obtain_accuracy
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def basic_train(xloader, network, criterion, scheduler, optimizer, optim_config, extra_info, print_freq, logger):
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loss, acc1, acc5 = procedure(xloader, network, criterion, scheduler, optimizer, 'train', optim_config, extra_info, print_freq, logger)
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return loss, acc1, acc5
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def basic_valid(xloader, network, criterion, optim_config, extra_info, print_freq, logger):
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with torch.no_grad():
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loss, acc1, acc5 = procedure(xloader, network, criterion, None, None, 'valid', None, extra_info, print_freq, logger)
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return loss, acc1, acc5
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def procedure(xloader, network, criterion, scheduler, optimizer, mode, config, extra_info, print_freq, logger):
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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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network.train()
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elif mode == 'valid':
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network.eval()
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else: raise ValueError("The mode is not right : {:}".format(mode))
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#logger.log('[{:5s}] config :: auxiliary={:}, message={:}'.format(mode, config.auxiliary if hasattr(config, 'auxiliary') else -1, network.module.get_message()))
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logger.log('[{:5s}] config :: auxiliary={:}'.format(mode, config.auxiliary if hasattr(config, 'auxiliary') else -1))
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end = time.time()
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for i, (inputs, targets) in enumerate(xloader):
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if mode == 'train': scheduler.update(None, 1.0 * i / len(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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features, logits = network(inputs)
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if isinstance(logits, list):
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assert len(logits) == 2, 'logits must has {:} items instead of {:}'.format(2, len(logits))
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logits, logits_aux = logits
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else:
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logits, logits_aux = logits, None
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loss = criterion(logits, targets)
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if config is not None and hasattr(config, 'auxiliary') and config.auxiliary > 0:
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loss_aux = criterion(logits_aux, targets)
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loss += config.auxiliary * loss_aux
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if mode == 'train':
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loss.backward()
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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.upper()) + time_string() + ' [{:}][{:03d}/{:03d}]'.format(extra_info, i, len(xloader))
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if scheduler is not None:
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Sstr += ' {:}'.format(scheduler.get_min_info())
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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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Istr = 'Size={:}'.format(list(inputs.size()))
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logger.log(Sstr + ' ' + Tstr + ' ' + Lstr + ' ' + Istr)
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logger.log(' **{mode:5s}** Prec@1 {top1.avg:.2f} Prec@5 {top5.avg:.2f} Error@1 {error1:.2f} Error@5 {error5:.2f} Loss:{loss:.3f}'.format(mode=mode.upper(), top1=top1, top5=top5, error1=100-top1.avg, error5=100-top5.avg, loss=losses.avg))
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return losses.avg, top1.avg, top5.avg
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