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Evaluate on CIFAR10/100-C
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'''
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test_seen_c_loader_list = []
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for corruption in CORRUPTIONS:
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test_c_loader = cifar_c_testloader(corruption=corruption, data_dir=args.data_root_path, num_classes=num_classes,
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test_batch_size=args.test_batch_size, num_workers=args.cpus)
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test_seen_c_loader_list.append(test_c_loader)
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# val corruption:
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print('evaluating corruptions...')
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test_c_losses, test_c_accs = [], []
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for corruption, test_c_loader in zip(CORRUPTIONS, test_seen_c_loader_list):
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test_c_batch_num = len(test_c_loader)
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print(test_c_batch_num) # each corruption has 10k * 5 images, each magnitude has 10k images
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ts = time.time()
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test_c_loss_meter, test_c_acc_meter = AverageMeter(), AverageMeter()
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with torch.no_grad():
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for batch_idx, (images, targets) in enumerate(test_c_loader):
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images, targets = images.cuda(), targets.cuda()
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logits = model(images)
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loss = F.cross_entropy(logits, targets)
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pred = logits.data.max(1)[1]
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acc = pred.eq(targets.data).float().mean()
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# append loss:
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test_c_loss_meter.append(loss.item())
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test_c_acc_meter.append(acc.item())
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print('%s test time: %.2fs' % (corruption, time.time()-ts))
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# test loss and acc of each type of corruptions:
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test_c_losses.append(test_c_loss_meter.avg)
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test_c_accs.append(test_c_acc_meter.avg)
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# print
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corruption_str = '%s: %.4f' % (corruption, test_c_accs[-1])
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print(corruption_str)
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fp.write(corruption_str + '\n')
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fp.flush()
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# mean over 16 types of attacks:
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test_c_loss = np.mean(test_c_losses)
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test_c_acc = np.mean(test_c_accs)
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# print
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avg_str = 'corruption acc: (mean) %.4f' % (test_c_acc)
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print(avg_str)
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fp.write(avg_str + '\n')
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fp.flush()
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def val_cifar10_1():
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'''
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Evaluate on cifar10.1
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'''
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test_v2_loader = cifar10_1_testloader(data_dir=os.path.join(args.data_root_path))
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model.eval()
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ts = time.time()
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test_loss_meter, test_acc_meter = AverageMeter(), AverageMeter()
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with torch.no_grad():
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for images, targets in test_v2_loader:
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images, targets = images.cuda(), targets.cuda()
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logits = model(images)
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loss = F.cross_entropy(logits, targets)
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pred = logits.data.max(1)[1]
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acc = pred.eq(targets.data).float().mean()
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# append loss:
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test_loss_meter.append(loss.item())
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test_acc_meter.append(acc.item())
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print('cifar10.1 test time: %.2fs' % (time.time()-ts))
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# test loss and acc of this epoch:
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test_loss = test_loss_meter.avg
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test_acc = test_acc_meter.avg
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# print:
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clean_str = 'cifar10.1 test acc: %.4f' % test_acc
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print(clean_str)
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fp.write(clean_str + '\n')
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fp.flush()
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## Test on Tiny-ImageNet:
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ResNet18_c_CE_list = [
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0.8037, 0.7597, 0.7758, 0.8426, 0.8274,
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0.7907, 0.8212, 0.7497, 0.7381, 0.7433,
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0.6800, 0.8939, 0.7308, 0.6121, 0.6452
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]
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def find_mCE(target_model_c_CE, anchor_model_c_CE):
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'''
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Args:
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target_model_c_CE: np.ndarray. shape=(15). CE of each corruption type of the target model.
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anchor_model_c_CE: np.ndarray. shape=(15). CE of each corruption type of the anchor model (normally trained ResNet18 as default).
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'''
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assert len(target_model_c_CE) == 15 # a total of 15 types of corruptions
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mCE = 0
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for target_model_CE, anchor_model_CE in zip(target_model_c_CE, anchor_model_c_CE):
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mCE += target_model_CE/anchor_model_CE
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mCE /= len(target_model_c_CE)
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return mCE
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def val_tin():
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'''
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Evaluate on Tiny ImageNet
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