Multi scale testing results reproduction

[Xu Mengde]

Thanks for sharing your code. The multi-scale testing result of fold 0 is 52.5 mIOU according to your paper but I can only get 51~ mIOU. Could you give me some advice on how to reproduce the same result as yours? Below is my multi-scale testing code modified from train.py.

with torch.no_grad():
    print ('----Evaluation----')
    model = model.eval()
    valset.history_mask_list=[None] * 1000
    all_inter, all_union, all_predict = [0] * 5, [0] * 5, [0] * 5
    for i_iter, batch in enumerate(valloader):

        query_rgb, query_mask, support_rgb, support_mask, history_mask, sample_class, index = batch
        query_rgb = (query_rgb).cuda(0)
        support_rgb = (support_rgb).cuda(0)
        support_mask = (support_mask).cuda(0)
        query_mask = (query_mask).cuda(0).long()  # change formation for crossentropy use

        query_mask = query_mask[:, 0, :, :]  # remove the second dim,change formation for crossentropy use
        history_mask = (history_mask).cuda(0)
        pred_softmax = torch.zeros(1,2,query_rgb.size(-2),query_rgb.size(-1)).cuda(0)
        for scale in [0.7,1,1.3]:
            query_= nn.functional.interpolate(query_rgb,scale_factor=scale,mode='bilinear',align_corners=True)
            scale_pred = model(query_, support_rgb, support_mask,history_mask)
            scale_pred_softmax = F.softmax(scale_pred, dim=1)
            pred_softmax += nn.functional.interpolate(scale_pred_softmax,size=query_rgb.size()[-2:], mode='bilinear',
                                         align_corners=True)
        pred_softmax/=3.
        # update history mask
        for j in range(support_mask.shape[0]):
            sub_index = index[j]
            valset.history_mask_list[sub_index] = pred_softmax[j]

#         pred = nn.functional.interpolate(pred, size=query_rgb.size()[-2:], mode='bilinear',
#                                          align_corners=True)  #upsample  # upsample

        _, pred_label = torch.max(pred_softmax, 1)
        inter_list, union_list, _, num_predict_list = get_iou_v1(query_mask, pred_label)
        for j in range(query_mask.shape[0]):#batch size
            all_inter[sample_class[j] - (options.fold * 5 + 1)] += inter_list[j]
            all_union[sample_class[j] - (options.fold * 5 + 1)] += union_list[j]

    IOU = [0] * 5

    for j in range(5):
        IOU[j] = all_inter[j] / all_union[j]

    mean_iou = np.mean(IOU)
    print('IOU:%.4f' % (mean_iou))

Thanks very much.

[icoz69]

hello, it seems you do not have iter_time in your code, thus ignoring the IOM module.

[Xu Mengde]

Got it. Thank you very much.