text stringlengths 1 93.6k |
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if args.evaluate:
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validate(val_loader, model, criterion, args)
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return
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for epoch in range(args.start_epoch, args.epochs):
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if args.distributed:
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train_sampler.set_epoch(epoch)
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adjust_learning_rate(optimizer, epoch, args)
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# train for one epoch
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train(train_loader, model, criterion, optimizer, epoch, args)
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# evaluate on validation set
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acc1 = validate(val_loader, model, criterion, args)
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# remember best acc@1 and save checkpoint
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is_best = acc1 > best_acc1
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best_acc1 = max(acc1, best_acc1)
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if not args.multiprocessing_distributed or (args.multiprocessing_distributed
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and args.rank % ngpus_per_node == 0):
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save_checkpoint({
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'epoch': epoch + 1,
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'arch': args.arch,
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'state_dict': model.state_dict(),
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'best_acc1': best_acc1,
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'optimizer': optimizer.state_dict(),
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}, is_best)
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def train(train_loader, model, criterion, optimizer, epoch, args):
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batch_time = AverageMeter('Time', ':6.3f')
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data_time = AverageMeter('Data', ':6.3f')
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losses = AverageMeter('Loss', ':.4e')
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top1 = AverageMeter('Acc@1', ':6.2f')
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top5 = AverageMeter('Acc@5', ':6.2f')
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progress = ProgressMeter(
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len(train_loader),
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[batch_time, data_time, losses, top1, top5],
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prefix="Epoch: [{}]".format(epoch))
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# switch to train mode
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model.train()
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end = time.time()
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for i, (images, target) in enumerate(train_loader):
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# measure data loading time
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data_time.update(time.time() - end)
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if args.gpu is not None:
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images = images.cuda(args.gpu, non_blocking=True)
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if torch.cuda.is_available():
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target = target.cuda(args.gpu, non_blocking=True)
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# using Group-CAM to finetune resnet
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model.eval()
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saliency_maps = []
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for idx in range(images.shape[0]):
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image = images[idx].unsqueeze(0)
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# adopt group-cam to fine-tune model,
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# backward_hook of group-cam may be removed to improve the efficiency
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gc = GroupCAM(model)
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if idx == images.shape[0] - 1:
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saliency = gc(image, class_idx=target[idx], retain_graph=False)
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else:
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saliency = gc(image, class_idx=target[idx], retain_graph=True)
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saliency = saliency.to(image.device)
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saliency_maps.append(saliency)
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saliency_maps = torch.cat(saliency_maps, dim=0)
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mean = torch.mean(saliency_maps)
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saliency_maps = torch.where(saliency_maps < mean, 0.0, 1.0)
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images = images * saliency_maps + blur(images) + (1-saliency_maps)
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model.train()
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# compute output
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output = model(images)
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loss = criterion(output, target)
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# measure accuracy and record loss
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acc1, acc5 = accuracy(output, target, topk=(1, 5))
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losses.update(loss.item(), images.size(0))
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top1.update(acc1[0], images.size(0))
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top5.update(acc5[0], images.size(0))
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# compute gradient and do SGD step
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optimizer.zero_grad()
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loss.backward()
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optimizer.step()
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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 % args.print_freq == 0:
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progress.display(i)
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def validate(val_loader, model, criterion, args):
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batch_time = AverageMeter('Time', ':6.3f')
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losses = AverageMeter('Loss', ':.4e')
|
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