SLIC

[rusty1s]

SLIC

[rusty1s]

I believe SLIC does only guarantee equal number of superpixels in the skimage implementation if you set enforce_connectivity to False.

[tonyandsunny]

Yes！I just try it. They can get the equal number of superpixels when set it to false. But the number of superpixels obtained is not equal to the 'num_segments=100' (I just set the parameter).

[rusty1s]

So you have equal number of superpixels, but there are less than 100?

[tonyandsunny]

Yes! And when the 'num_segments=100', the equal number of superpixels is 121 more than 100.
Why the obtained superpixels is not 100?

[rusty1s]

I am not really sure, sorry. Maybe you can consult the sckit-image authors for help

[tonyandsunny]

OK! Thank you very much!

[tonyandsunny]

Is this code a Splineconv model? Sorry, I am not familiar with this code and Splineconv.

[rusty1s]

Not really, it is my masters thesis. You can find the SplineConv implementation here.

[tonyandsunny]

I try use' embedded_gcnn code' to get the cifar10 dataset. And I modified it to the 'pytorch_geometric dataset' format. Then I run the mnist_graculs.py (loading the cifar10 dataset) and the result is just about 0.43. Maybe I was wrong. Could you give some advices?

[rusty1s]

This is actually quite hard to say. It could be anything from wrong data handling to bad hyperparameters.

[tonyandsunny]

while num_left > 0:

            min_batch = min(batch_size, num_left)
            images, labels = dataset.next_batch(min_batch, shuffle=False)
            num_left -= min_batch
            if batch_num == 0:
                label_array = labels
            else:
                label_array = np.concatenate((label_array, labels), axis=0)
            for i in xrange(labels.shape[0]):
                #data = preprocess_algorithm(images[i])
                features, node_slice, edge_index, edge_slice, pos = preprocess_algorithm(images[i])
                #edge_slice_num = edge_slice #每张图片中边的数目
                #node_slice_num = node_slice#每张图片中节点的数目
                if batch_num == 0 and i==0:
                    features_array = features
                    pos_array = pos
                    edge_index_array = edge_index
                else:
                    features_array = np.concatenate((features_array,features),axis=0)
                    pos_array = np.concatenate((pos_array, pos), axis=0)
                    edge_index_array = np.concatenate((edge_index_array, edge_index), axis=1)


                node_slice_list.append(node_slice+node_slice_list[-1])
                edge_slice_list.append(edge_slice+edge_slice_list[-1])
                #data = (features_array, node_slice_list, edge_index_array, edge_slice_list)
                j += 1
                max_index = torch.from_numpy(edge_index)[0,:].max()
                size = torch.from_numpy(pos).size(0) - 1
                assert max_index == size
                assert edge_slice == edge_index.shape[1]
            #_save(data_dir, self._names[j], data)
            _print_status(data_dir,
                          100 * (1 - num_left / dataset.num_examples))
            batch_num+=1

        _print_status(data_dir, 100)
        data = (features_array, node_slice_list, edge_index_array, edge_slice_list,pos_array)

        if isinstance(data, np.ndarray):
            data = (data, label_array)
        else:
            data = data + (label_array,)
        np.save(data_dir, data)

        #torch.save((self.data, self.slices), path)
        print()

The above is the code I modified after dataset.py. In order to be able to change the data format of pytorch-geometric, I feel that there should be no big problem.
One thing is that when I use the slic algorithm to get a super-pixel block, the number of super-pixel nodes obtained for each image is different, so I define node_slice to record the super-pixel block of each image.