Having some issues with running on a custom dataset about superpoint_graph

[lostcolony]

Oh, and I should add, that's with a different value of --ptn_nfeat_stn (but anything less than 6 is set to 8).

[loicland]

Hi,

It is due to an insufficient documentation of my part. This was solved in this issue. I will add it in the readme very soon.

Let me know if this does not fix it for you.

[lostcolony]

So hardcoding to what is at the end of that issue, I get -

RuntimeError: Given groups=1, weight[64, 8, 1], so expected input[69, 7, 128] to have 8 channels, but got 7 channels instead

Just poking and prodding, given how we're indexing, I figured for lpsv, it may have needed to be 4:8 (if we're to pick up 4 indices), which does indeed move me to a different error, mentioned below.

Traceback (most recent call last):
File "learning/main.py", line 383, in
main()
File "learning/main.py", line 286, in main
acc, loss, oacc, avg_iou = train()
File "learning/main.py", line 199, in train
acc_meter.add(o_cpu, t_cpu)
File "/home/ubuntu/anaconda3/lib/python3.6/site-packages/torchnet/meter/classerrormeter.py", line 38, in add
pred = torch.from_numpy(output).topk(maxk, 1, True, True)[1].numpy()
RuntimeError: dimension out of range (expected to be in range of [-1, 0], but got 1)

[loicland]

Can you run main.py in debug mode and tell me the dimensions of o_cpu and t_cpu when at line 199?

[lostcolony]

So they're both empty; label_mode_cpu is an array only of -100 values.

[loicland]

Doo you have ground truth labels available on your small dataset? Are you trying to fine tune?

If you just want to do inference, set the option --epochs to -1.

Ps: a common source for poor quality results is the scaling of the elevation variable. Are you using the retrained weight of semantic3d or s33dis?

[lostcolony]

I largely used the semantic3d dataset as a model for arranging data and such; I have a set of labeled train data, and a few sets of unlabeled test data.

Now, that said, the data itself could be -completely- inappropriate for this. I wanted to see if this would be useful for training/testing against a single full rotational sweep of a 3d lidar, i.e., object recognition in semi-real time. So I'm assuming that rather than an entire 3d scene, I only have point projections from a single location in space. While eventually those would be 360 degrees, to start with (just to get some data to play with), I used a Kinect, so point projections from a single viewport. I snapped pointclouds with that for a number of objects, across a number of scenes/angles, labeled them by hand, and that's what I'm using.

[loicland]

If your shots are indoor I would advise you to use the s3dis model.

It seems that the labels are not correctly seen by the algorithms.
1 - is inference working fine?
2 - is the label field in the h5 file in /features correct?
3 - if not, check what is happening around line 127 of /partition/partition.py

[lostcolony]

Well, I'm not using either model, but looking to train one from scratch. I just meant I used the sema3d structure as a model, since it had unlabeled test data (and the s3dis used k-fold validation, splitting training into train/test sets; the former more closely matches what I'm trying to do). Setting epochs to -1 does execute (with a not unexpected 0% accuracy).

Looking into an object at random from features, I see 939 points in xyz, and 939 labels.

[loicland]

Hi,

can you post the content of the labels value in a /features/ h5 file? or post here a link to a .h5 file?

[lostcolony]

Here's a zip with all the h5s (not many, again, very small data set to start with) - https://s3.amazonaws.com/kinect-toy-data/data.zip

[loicland]

Ok thanks. So it seems like none of the labels of your training dataset are read correctly.

In /partition/partition.plyaround line 128, is has_labels equal to true for files in the training set? If so what are the labels like?

[lostcolony]

I should clarify; I'm actually using the elif condition for custom dataset. Here's what I changed that implementation to -

            elif args.dataset=='custom_dataset':
                #implement in provider.py your own read_custom_format outputing xyz, rgb, labels
                #example for ply files
                if (os.path.isfile(label_file)):
                    xyz, labels = read_ply(data_file, label_file)
                    if(len(list(filter(lambda x: x!=0, labels))) == 0):
                       raise Exception("LABELS ARE ALL ZERO")
                    print("LABELS:", str(labels))
                else:
                    print("HAS NO LABELS:" + data_file)
                    if(data_file.find("test") == -1):
                       raise Exception("DATA FILE" + data_file + " HAS NO LABELS")
                    xyz = read_ply(data_file, label_file)
                    labels = []
                print("XYZ", xyz)
                #another one for las files without rgb
                #xyz = read_las(data_file)
                if args.voxel_width > 0:
                    #an example of pruning without labels
                    xyz, rgb, labels = libply_c.prune(xyz, args.voxel_width, np.zeros(xyz.shape, dtype='u1'), np.array(1,dtype='u1'), 0)
                    #another one without rgb information nor labels
                    #xyz = libply_c.prune(xyz, args.voxel_width, np.zeros(xyz.shape,dtype='u1'), np.array(1,dtype='u1'), 0)[0]
                #if no labels available simply set here labels = []
                #if no rgb available simply set here rgb = [] and make sure to not use it later on
                rgb = []

I also changed read_ply to take in both file and label, and to handle them appropriately, but the raised errors as above should indicate that we have some sort of label info being read in at that stage.

[loicland]

Do the line xyz, labels = read_ply(data_file, label_file) allows you to retrieve nonzeros labels? If so what happens after xyz, rgb, labels = libply_c.prune(xyz, args.voxel_width, np.zeros(xyz.shape, dtype='u1'), np.array(1,dtype='u1'), 0)?

[lostcolony]

Ah-hah. Okay. I added some split logic to handle things differently if it was an unlabeled file, vs a labeled one (trying, again, to use the sema3d provider as an example, since that is a case which has both), and it ran.

I'm now running into a different issue. If I prevent pruning on the test data, I get some mismatched arrays when calculating the confusion matrix; if I allow it, it all works, but attempting to upsample the prediction to the original test files leads to failure for the lack of RGB data in the feature file, specifically -

1 / 3---> test2
reading the subsampled file...
Traceback (most recent call last):
File "partition/write_custom.py", line 69, in
geof, xyz, rgb, graph_nn, l = read_features(fea_file)
File "/home/ubuntu/dev/spgraph/partition/provider.py", line 382, in read_features
rgb = data_file["rgb"][:]
File "h5py/_objects.pyx", line 54, in h5py._objects.with_phil.wrapper
File "h5py/_objects.pyx", line 55, in h5py._objects.with_phil.wrapper
File "/home/ubuntu/anaconda3/lib/python3.6/site-packages/h5py/_hl/group.py", line 167, in getitem
oid = h5o.open(self.id, self._e(name), lapl=self._lapl)
File "h5py/_objects.pyx", line 54, in h5py._objects.with_phil.wrapper
File "h5py/_objects.pyx", line 55, in h5py._objects.with_phil.wrapper
File "h5py/h5o.pyx", line 190, in h5py.h5o.open
KeyError: "Unable to open object (object 'rgb' doesn't exist)"

[lostcolony]

And following that to do the obvious, replacing the rgb line with rgb=[], I'm getting -

1 / 3---> test2
reading the subsampled file...
upsampling...
read lines 0 to 5000000
Traceback (most recent call last):
File "partition/write_custom.py", line 76, in
labels_ups = interpolate_labels_batch(data_file, xyz, labels_full, args.ver_batch)
File "/home/ubuntu/dev/spgraph/partition/provider.py", line 488, in interpolate_labels_batch
, skip_header=i_rows)
File "/home/ubuntu/anaconda3/lib/python3.6/site-packages/numpy/lib/npyio.py", line 1713, in genfromtxt
first_line = _decode_line(next(fhd), encoding)
File "/home/ubuntu/anaconda3/lib/python3.6/codecs.py", line 321, in decode
(result, consumed) = self._buffer_decode(data, self.errors, final)
UnicodeDecodeError: 'utf-8' codec can't decode byte 0xc0 in position 630: invalid start byte

So...maybe it wrote out something wonky?

[loicland]

Right. These are bugs, I'm getting to it now, will push a commit when it's fixed.

[loicland]

Hi,

I did not have time to provide a fully tested solution and I don't want to break the repo just before leaving for 10 days for a conference. Here are some of the (not fully tested) changes I have started to make:

new read_semantic3d_format in \partition\provider.py

def read_semantic3d_format(data_file, n_class, file_label_path, voxel_width, ver_batch):
    """read the format of semantic3d. 
    ver_batch : if ver_batch>0 then load the file ver_batch lines at a time.
                useful for huge files (> 5millions lines)
    voxel_width: if voxel_width>0, voxelize data with a regular grid
    n_class : the number of class; if 0 won't search for labels (test set)
    implements batch-loading for huge files and pruning""" 
    pruning = voxel_width > 0 #subsample the point cloud
    batch_loading = ver_batch>0 #load the point cloud by batch
    has_labels = n_class > 0 #will load the label file
    
    i_rows = 0
    xyz = np.zeros((0, 3), dtype='float32')
    rgb = np.zeros((0, 3), dtype='uint8')
    
    if pruning: #each voxel has an array of points encoding the labels of the points it contains
        labels = np.zeros((0, n_class+1), dtype='uint32') #first column is for unannotaed point
    else: #each point has a label
        labels = np.zeros((0,), dtype='uint32')

    while True: #loop until the whole point cloud is loaded
        
        if batch_loading:
            try:
                vertices = np.genfromtxt(data_file
                         , delimiter=' ', max_rows=ver_batch
                         , skip_header=i_rows)
            except StopIteration:
                #end of file
                break
            if len(vertices)==0:
                 break
        else: #load the whole file at once
            vertices = np.genfromtxt(data_file, delimiter=' ')
                
        xyz_full = np.array(vertices[:, 0:3], dtype='float32')
        rgb_full = np.array(vertices[:, 4:7], dtype='uint8')
        del vertices
        
        if has_labels:
            if batch_loading:
                labels_full = np.genfromtxt(file_label_path, dtype="u1", delimiter=' '
                            , max_rows=ver_batch, skip_header=i_rows)
            else:
                labels_full = np.genfromtxt(file_label_path, dtype="u1", delimiter=' ')
                
        if pruning: #subsampling
            if has_labels:
                xyz_sub, rgb_sub, labels_sub = libply_c.prune(xyz_full, voxel_width
                                             , rgb_full, labels_full, n_class)
                labels = np.vstack((labels, labels_sub))
            else:
                xyz_sub, rgb_sub, l = libply_c.prune(xyz_full, voxel_width
                                    , rgb_full, np.zeros(1, dtype='uint8'), 0)
            del xyz_full, rgb_full
            
            xyz = np.vstack((xyz, xyz_sub))
            rgb = np.vstack((rgb, rgb_sub))
            
        else: #no subsampling
            xyz = np.vstack((xyz, xyz_full))
            rgb = np.vstack((rgb, rgb_full))
            labels = np.hstack((labels, labels_full))
            
        i_rows = i_rows + ver_batch
        
        if not batch_loading: break #no need to loop if no batch loading
        
    if has_labels:
        return xyz, rgb, labels
    else:
        return xyz, rgb

new read_features in \partition\provider.py

def read_features(file_name):
    """read the geometric features, clouds and labels from a h5 file"""
    data_file = h5py.File(file_name, 'r')
    #fist get the number of vertices
    n_ver = len(data_file["linearity"])
    has_labels = len(data_file["labels"])
    #the labels can be empty in the case of a test set
    if has_labels:
        labels = np.array(data_file["labels"])
    else:
        labels = []
    if "rgb" in data_file:
        rgb = data_file["rgb"][:]
    else:
        rgb = []
    #---create the arrays---
    geof = np.zeros((n_ver, 4), dtype='float32')
    #---fill the arrays---
    geof[:, 0] = data_file["linearity"]
    geof[:, 1] = data_file["planarity"]
    geof[:, 2] = data_file["scattering"]
    geof[:, 3] = data_file["verticality"]
    xyz = data_file["xyz"][:]
    source = data_file["source"][:]
    target = data_file["target"][:]
    distances = data_file["distances"][:]
    #---set the graph---
    graph_nn = dict([("is_nn", True)])
    graph_nn["source"] = source
    graph_nn["target"] = target
    graph_nn["distances"] = distances
    return geof, xyz, rgb, graph_nn, labels

change the last lines of partition/vizualize.py to:

   del rgb_up
   #
   if args.ver_batch == 0:
       pred_up = interpolate_labels(xyz_up, xyz, pred_full, args.ver_batch)
   else:
       pred_up = interpolate_labels_batch(xyz_up, xyz, pred_full, args.ver_batch)
   print("writing the upsampled prediction file...")
   prediction2ply(ply_file + "_pred_up.ply", xyz_up, pred_up+1, n_labels, args.dataset)

As for your last error I am not sure. You can try to use partition/vizualize.py with -- upsample 1 to vizualize the result on the full input cloud.

Let me know if this fixes some of your errors. Otherwise I will provide a new patch in about 2 weeks.

cheers,

[loicland]

Hi,

did you have a chance to run those changes, and did it fix your problems?