Python 3.10 (#554)

* TF, Pytorch version updates

Custom PyTorch not needed any more with PyTorch 1.12
TB plugin troubleshooting docs

* torch._six updates for Torch 1.12

Author: ssheorey

docs/tensorboard.md

@@ -252,7 +252,7 @@ order.
 
 Now you can visualize the data in TensorBoard as before. The web interface
 allows showing and hiding points with different classes, changing their colors,
-and exploring predictions and intermediate network features.  Scalar network 
+and exploring predictions and intermediate network features.  Scalar network
 features can be visualized with custom user editable colormaps, and 3D features
 can be visualized as RGB colors. Here is a video showing the different ways in
 which semantic segmentation summary data can be visualized in TensorBoard.
@@ -376,3 +376,14 @@ for step in range(len(val_split)):  # one pointcloud per step
                   step,
                   label_to_names=dset.get_label_to_names())
 ```
+
+Troubleshooting
+---------------
+
+If you cannot interact with the 3D model, or use controls in the WebRTC widget,
+make sure that Allow Autoplay is enabled for the Tensorboard web site and reload.
+
+<img src=https://user-images.githubusercontent.com/41028320/180485249-5233b65e-11b1-44ff-bfc4-35f390ef51f2.png
+title="Allow Autoplay for correct behavior."
+alt="Allow Autoplay for correct behavior."
+style="width:80%;display:block;margin:auto"></img>

ml3d/torch/dataloaders/default_batcher.py

@@ -1,6 +1,7 @@
 import torch
 import re
-from torch._six import container_abcs, string_classes, int_classes
+import collections.abc
+from torch._six import string_classes
 
 np_str_obj_array_pattern = re.compile(r'[SaUO]')
 
@@ -17,11 +18,11 @@ def default_convert(data):
                 and np_str_obj_array_pattern.search(data.dtype.str) is not None:
             return data
         return torch.as_tensor(data)
-    elif isinstance(data, container_abcs.Mapping):
+    elif isinstance(data, collections.abc.Mapping):
         return {key: default_convert(data[key]) for key in data}
     elif isinstance(data, tuple) and hasattr(data, '_fields'):  # namedtuple
         return elem_type(*(default_convert(d) for d in data))
-    elif isinstance(data, container_abcs.Sequence) and not isinstance(
+    elif isinstance(data, collections.abc.Sequence) and not isinstance(
             data, string_classes):
         return [default_convert(d) for d in data]
     else:
@@ -59,15 +60,15 @@ def default_collate(batch):
             return torch.as_tensor(batch)
     elif isinstance(elem, float):
         return torch.tensor(batch, dtype=torch.float64)
-    elif isinstance(elem, int_classes):
+    elif isinstance(elem, int):
         return torch.tensor(batch)
     elif isinstance(elem, string_classes):
         return batch
-    elif isinstance(elem, container_abcs.Mapping):
+    elif isinstance(elem, collections.abc.Mapping):
         return {key: default_collate([d[key] for d in batch]) for key in elem}
     elif isinstance(elem, tuple) and hasattr(elem, '_fields'):  # namedtuple
         return elem_type(*(default_collate(samples) for samples in zip(*batch)))
-    elif isinstance(elem, container_abcs.Sequence):
+    elif isinstance(elem, collections.abc.Sequence):
         # check to make sure that the elements in batch have consistent size
         it = iter(batch)
         elem_size = len(next(it))

requirements-tensorflow.txt

@@ -1 +1 @@
-tensorflow~=2.5.2
+tensorflow~=2.8.2

requirements-torch-cuda.txt

@@ -1,7 +1,5 @@
-https://open3d-downloads.b-cdn.net/torch-1.8.2-cp36-cp36m-linux_x86_64.whl ; python_version == '3.6'
-https://open3d-downloads.b-cdn.net/torch-1.8.2-cp37-cp37m-linux_x86_64.whl ; python_version == '3.7'
-https://open3d-downloads.b-cdn.net/torch-1.8.2-cp38-cp38-linux_x86_64.whl ; python_version == '3.8'
-https://open3d-downloads.b-cdn.net/torch-1.8.2-cp39-cp39-linux_x86_64.whl ; python_version == '3.9'
--f https://download.pytorch.org/whl/lts/1.8/torch_lts.html
-torchvision==0.9.2+cu111
-tensorboard
+-f https://download.pytorch.org/whl/torch/
+torch==1.12.0+cu116
+-f https://download.pytorch.org/whl/torchvision/
+torchvision==0.13.0+cu116
+tensorboard~=2.8.0

requirements-torch.txt

@@ -1,6 +1,6 @@
--f https://download.pytorch.org/whl/lts/1.8/torch_lts.html
-torch==1.8.2+cpu ; sys_platform != 'darwin'
-torchvision==0.9.2+cpu ; sys_platform != 'darwin'
-torch==1.8.1 ; sys_platform == 'darwin'
-torchvision==0.9.1 ; sys_platform == 'darwin'
+--extra-index-url https://download.pytorch.org/whl/torch/
+torch==1.12.0+cpu ; sys_platform != 'darwin'
+torchvision==0.13.0+cpu ; sys_platform != 'darwin'
+torch==1.12.0 ; sys_platform == 'darwin'
+torchvision==0.13.0 ; sys_platform == 'darwin'
 tensorboard