PyTorch如何列出分類錯誤之原始圖片路徑？

check-image-path-in-dataloader.py

class CustomDataset(torch.utils.data.Dataset):
    def __init__(self, dataframe, transform):
        self.dataframe = dataframe
        self.transform = transform
    
    def __len__(self):
        return len(self.dataframe)

    def __getitem__(self, index):
        row = self.dataframe.iloc[index]
        image = self.transform(Image.open((row["file_path"])))
        label = np.asarray(row["class"])
        file_path = row["file_path"]
        return (image, label, file_path)

# val_loader使用CustomDataset這個class
def testing(UnNormalize, writer, val_loader, model, criterion, args, size_val_df, y_pred, y_true):
    batch_time = AverageMeter('Time', ':6.3f')
    losses = AverageMeter('Loss', ':.4e')
    top1 = AverageMeter('Acc@1', ':6.2f')
    top5 = AverageMeter('Acc@5', ':6.2f')
    progress = ProgressMeter(
        len(val_loader),
        [batch_time, losses, top1, top5],
        prefix='Test: ')

    # switch to evaluate mode
    model.eval()
    
    val_running_loss = 0.0
    val_loss_values = []
    val_running_accuracy = 0
    val_accuracy_values = []
    
    incorrect_examples = []
        
    with torch.no_grad():
        end = time.time()
        for i, (images, target, path) in enumerate(val_loader):
            if args.gpu is not None:
                images = images.cuda(args.gpu, non_blocking=True)
            if torch.cuda.is_available():
                target = target.cuda(args.gpu, non_blocking=True)

            # compute output
            output = model(images)
            _, preds = torch.max(output, 1)
            loss = criterion(output, target)
            
            pred_index = output.data.max(1)[1]  # get the index of the max log-probability                       
            if args.batch_size==1 and pred_index.cpu() != target.cpu():
                with open("missclassified_file_path.txt", "a") as f:
                        true_label = str(target.cpu().tolist()[0])
                        pred_label = str(pred_index.cpu().tolist()[0])
                        write_content = ' '.join(path)
                        write_content = write_content + ' ' + true_label + ' ' + pred_label     # 路徑, 實際, 預測
                        
                        f.write(write_content+"\n")

            y_pred.extend(preds.view(-1).detach().cpu().numpy())            
            y_true.extend(target.view(-1).detach().cpu().numpy())
                        
            # measure accuracy and record loss
            acc1, acc5 = accuracy(output, target, topk=(1, 5))
            losses.update(loss.item(), images.size(0))
            top1.update(acc1[0], images.size(0))
            top5.update(acc5[0], images.size(0))
            
            val_running_loss += loss.item() * images.size(0)
            val_loss_values.append(val_running_loss / size_val_df)
            
            val_running_accuracy += torch.sum(preds == target.data)
            val_accuracy_values.append(val_running_accuracy.double() / size_val_df)

            # measure elapsed time
            batch_time.update(time.time() - end)
            end = time.time()
            
            test_data_recorder(UnNormalize, i, pred_index, writer, target, images, output, i, val_loader)
            if i % args.print_freq == 0:
                progress.display(i)
        
        print(' * Acc@1 {top1.avg:.3f} Acc@5 {top5.avg:.3f}'
              .format(top1=top1, top5=top5))
        
    return top1.avg, val_loss_values[-1], val_accuracy_values[-1], y_pred, y_true