Code issue

[Hydragon516]

Thank you for the interesting research. However, it seems that there are some bugs in the code.

1. datasets/prepare_voc.py

val_list = [osp.join(voc_path, "SegmentationClassAug", f + ".png")
            for f in np.loadtxt(osp.join(voc_path, "ImageSets/Segmentation/val.txt"), dtype=np.str).tolist()]

revised version :

val_list = [osp.join(voc_path, "SegmentationClassAug", f + ".png")
            for f in np.loadtxt(osp.join(voc_path, "ImageSets/Segmentation/val.txt"), dtype=str).tolist()]

2. cat_seg/cat_seg_model.py

def inference_sliding_window(self, batched_inputs, kernel=384, overlap=0.333, out_res=[640, 640]):
        ...

        image = F.interpolate(images[0].unsqueeze(0), size=out_res, mode='bilinear', align_corners=False).squeeze()
        image = rearrange(unfold(image), "(C H W) L-> L C H W", C=3, H=kernel)
        global_image = F.interpolate(images[0].unsqueeze(0), size=(kernel, kernel), mode='bilinear', align_corners=False)
        image = torch.cat((image, global_image), dim=0)
        ...

        global_output = outputs[-1:]
        global_output = F.interpolate(global_output, size=out_res, mode='bilinear', align_corners=False,)
        outputs = outputs[:-1]
        outputs = fold(outputs.flatten(1).T) / fold(unfold(torch.ones([1] + out_res, device=self.device)))
        outputs = (outputs + global_output) / 2.

revised version :

def inference_sliding_window(self, batched_inputs, kernel=384, overlap=0.333, out_res=[640, 640]):
        ...

        image = F.interpolate(images[0].unsqueeze(0), size=out_res, mode='bilinear', align_corners=False).squeeze()
        image = unfold(image).squeeze(0)
        image = rearrange(image, "(C H W) L-> L C H W", C=3, H=kernel)
        global_image = F.interpolate(images[0].unsqueeze(0), size=(kernel, kernel), mode='bilinear', align_corners=False)
        image = torch.cat((image, global_image), dim=0)
        ...

        global_output = outputs[-1:]
        global_output = F.interpolate(global_output, size=out_res, mode='bilinear', align_corners=False,)
        outputs = outputs[:-1]
        outputs = fold(outputs.flatten(1).T)
        outputs = (outputs + global_output) / 2.

These bugs might be due to differences in Python and PyTorch versions.

[hsshin98]

Hi, and thanks for pointing out the issues in our code! These does seem like issues with Pytorch and Python version, and sharing your versions could also help. For our project, we used torch=1.13 and python=3.8.13, and also did not have problems with torch=2.0, so using this configuration might help for now.