import os, sys, time, torch from log_utils import AverageMeter, time_string from utils import obtain_accuracy from models import change_key def get_flop_loss(expected_flop, flop_cur, flop_need, flop_tolerant): expected_flop = torch.mean( expected_flop ) if flop_cur < flop_need - flop_tolerant: # Too Small FLOP loss = - torch.log( expected_flop ) #elif flop_cur > flop_need + flop_tolerant: # Too Large FLOP elif flop_cur > flop_need: # Too Large FLOP loss = torch.log( expected_flop ) else: # Required FLOP loss = None if loss is None: return 0, 0 else : return loss, loss.item() def search_train(search_loader, network, criterion, scheduler, base_optimizer, arch_optimizer, optim_config, extra_info, print_freq, logger): data_time, batch_time = AverageMeter(), AverageMeter() base_losses, arch_losses, top1, top5 = AverageMeter(), AverageMeter(), AverageMeter(), AverageMeter() arch_cls_losses, arch_flop_losses = AverageMeter(), AverageMeter() epoch_str, flop_need, flop_weight, flop_tolerant = extra_info['epoch-str'], extra_info['FLOP-exp'], extra_info['FLOP-weight'], extra_info['FLOP-tolerant'] network.train() logger.log('[Search] : {:}, FLOP-Require={:.2f} MB, FLOP-WEIGHT={:.2f}'.format(epoch_str, flop_need, flop_weight)) end = time.time() network.apply( change_key('search_mode', 'search') ) for step, (base_inputs, base_targets, arch_inputs, arch_targets) in enumerate(search_loader): scheduler.update(None, 1.0 * step / len(search_loader)) # calculate prediction and loss base_targets = base_targets.cuda(non_blocking=True) arch_targets = arch_targets.cuda(non_blocking=True) # measure data loading time data_time.update(time.time() - end) # update the weights base_optimizer.zero_grad() logits, expected_flop = network(base_inputs) #network.apply( change_key('search_mode', 'basic') ) #features, logits = network(base_inputs) base_loss = criterion(logits, base_targets) base_loss.backward() base_optimizer.step() # record prec1, prec5 = obtain_accuracy(logits.data, base_targets.data, topk=(1, 5)) base_losses.update(base_loss.item(), base_inputs.size(0)) top1.update (prec1.item(), base_inputs.size(0)) top5.update (prec5.item(), base_inputs.size(0)) # update the architecture arch_optimizer.zero_grad() logits, expected_flop = network(arch_inputs) flop_cur = network.module.get_flop('genotype', None, None) flop_loss, flop_loss_scale = get_flop_loss(expected_flop, flop_cur, flop_need, flop_tolerant) acls_loss = criterion(logits, arch_targets) arch_loss = acls_loss + flop_loss * flop_weight arch_loss.backward() arch_optimizer.step() # record arch_losses.update(arch_loss.item(), arch_inputs.size(0)) arch_flop_losses.update(flop_loss_scale, arch_inputs.size(0)) arch_cls_losses.update (acls_loss.item(), arch_inputs.size(0)) # measure elapsed time batch_time.update(time.time() - end) end = time.time() if step % print_freq == 0 or (step+1) == len(search_loader): Sstr = '**TRAIN** ' + time_string() + ' [{:}][{:03d}/{:03d}]'.format(epoch_str, step, len(search_loader)) 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) Lstr = 'Base-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=base_losses, top1=top1, top5=top5) Vstr = 'Acls-loss {aloss.val:.3f} ({aloss.avg:.3f}) FLOP-Loss {floss.val:.3f} ({floss.avg:.3f}) Arch-Loss {loss.val:.3f} ({loss.avg:.3f})'.format(aloss=arch_cls_losses, floss=arch_flop_losses, loss=arch_losses) logger.log(Sstr + ' ' + Tstr + ' ' + Lstr + ' ' + Vstr) #Istr = 'Bsz={:} Asz={:}'.format(list(base_inputs.size()), list(arch_inputs.size())) #logger.log(Sstr + ' ' + Tstr + ' ' + Lstr + ' ' + Vstr + ' ' + Istr) #print(network.module.get_arch_info()) #print(network.module.width_attentions[0]) #print(network.module.width_attentions[1]) logger.log(' **TRAIN** Prec@1 {top1.avg:.2f} Prec@5 {top5.avg:.2f} Error@1 {error1:.2f} Error@5 {error5:.2f} Base-Loss:{baseloss:.3f}, Arch-Loss={archloss:.3f}'.format(top1=top1, top5=top5, error1=100-top1.avg, error5=100-top5.avg, baseloss=base_losses.avg, archloss=arch_losses.avg)) return base_losses.avg, arch_losses.avg, top1.avg, top5.avg def search_valid(xloader, network, criterion, extra_info, print_freq, logger): data_time, batch_time, losses, top1, top5 = AverageMeter(), AverageMeter(), AverageMeter(), AverageMeter(), AverageMeter() network.eval() network.apply( change_key('search_mode', 'search') ) end = time.time() #logger.log('Starting evaluating {:}'.format(epoch_info)) with torch.no_grad(): for i, (inputs, targets) in enumerate(xloader): # measure data loading time data_time.update(time.time() - end) # calculate prediction and loss targets = targets.cuda(non_blocking=True) logits, expected_flop = network(inputs) loss = criterion(logits, targets) # record prec1, prec5 = obtain_accuracy(logits.data, targets.data, topk=(1, 5)) losses.update(loss.item(), inputs.size(0)) top1.update (prec1.item(), inputs.size(0)) top5.update (prec5.item(), inputs.size(0)) # measure elapsed time batch_time.update(time.time() - end) end = time.time() if i % print_freq == 0 or (i+1) == len(xloader): Sstr = '**VALID** ' + time_string() + ' [{:}][{:03d}/{:03d}]'.format(extra_info, i, len(xloader)) 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) 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) Istr = 'Size={:}'.format(list(inputs.size())) logger.log(Sstr + ' ' + Tstr + ' ' + Lstr + ' ' + Istr) logger.log(' **VALID** Prec@1 {top1.avg:.2f} Prec@5 {top5.avg:.2f} Error@1 {error1:.2f} Error@5 {error5:.2f} Loss:{loss:.3f}'.format(top1=top1, top5=top5, error1=100-top1.avg, error5=100-top5.avg, loss=losses.avg)) return losses.avg, top1.avg, top5.avg