[New game]: Quest For Riches

🎮 Game Request
Quest for Riches is a side-scrolling action-adventure game where players navigate through a fantasy world, battling enemies and solving puzzles to find a hidden treasure. The game includes a health system, enemy AI, player combat mechanics, and level progression.
Point down the features

Movement (run, jump, attack
Health system with health bar
Animations for idle, running, jumping, attacking, hurt, and dying.
NPC's
Enemy detects and chases player
Enemy Attack mechanics
Two levels
Unlockable treasure chest after defeating enemies and solving a puzzle.

Select program in which you are contributing
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Code of Conduct

[X] I follow CONTRIBUTING GUIDELINE of this project.

Hey @peculiap1 !
Thank you for raising an issue 💗
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Make sure you follow CODE OF CONDUCT and CONTRIBUTING GUIDELINES 🚀
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/assign

Hey @peculiap1 ! Thank you so much for your raising the issue💗
It’s all yours, you can come anytime again and make some contributions! 🚀
Alone, we can do little, but together we can do so much! 😇


⚠ Jenkins Plugins Artifactory has degraded performance

In 864c728, Jenkins Plugins Artifactory (https://get.jenkins.io/plugins/artifactory/) experienced degraded performance:

HTTP code: 200
Response time: 1073 ms

Resolved: Jenkins Plugins Artifactory performance has improved in fe38f85 after 12 minutes.


bazel build -c opt --config=android_arm64 mediapipe/examples/android/src/java/com/google/mediapipe/apps/objectdetectiongpu

command： sudo bazel build -c opt --config=android_arm64 mediapipe/examples/android/src/java/com/google/mediapipe/apps/objectdetectiongpu
error：
ERROR: An error occurred during the fetch of repository 'org_tensorflow':
java.io.IOException: Error downloading [https://mirror.bazel.build/github.com/tensorflow/tensorflow/archive/805e47cea96c7e8c6fccf494d40a2392dc99fdd8.tar.gz, https://github.com/tensorflow/tensorflow/archive/805e47cea96c7e8c6fccf494d40a2392dc99fdd8.tar.gz] to /home/alorli/.cache/bazel/_bazel_root/b3741aa3bed2fdd93a89a11ae8270f0c/external/org_tensorflow/805e47cea96c7e8c6fccf494d40a2392dc99fdd8.tar.gz: Tried to reconnect at offset 4,601,424 but server didn't support it
ERROR: no such package '@org_tensorflow//tensorflow': java.io.IOException: Error downloading [https://mirror.bazel.build/github.com/tensorflow/tensorflow/archive/805e47cea96c7e8c6fccf494d40a2392dc99fdd8.tar.gz, https://github.com/tensorflow/tensorflow/archive/805e47cea96c7e8c6fccf494d40a2392dc99fdd8.tar.gz] to /home/alorli/.cache/bazel/_bazel_root/b3741aa3bed2fdd93a89a11ae8270f0c/external/org_tensorflow/805e47cea96c7e8c6fccf494d40a2392dc99fdd8.tar.gz: Tried to reconnect at offset 4,601,424 but server didn't support it
What's the problem?

Some user just solved a similar issue a few days ago. Please see https://github.com/google/mediapipe/issues/581.

I believe the solution I mentioned above should solve this issue. Feel free to reopen this issue if it's not the case. Thanks.


Add Charity Helix endpoints/EventSub events

Type: Feature
Fixes: #422
Adds beta charity Helix endpoints and EventSub events

Changes made as requested.


Support ordering folders / Custom TOC

Currently, the order of the NestedPageMetadata (where we allow to build navigation) uses the order property from frontmatter to order the pages. However, to order thechildren (a group of subsequent pages), we only do an alphabetical order, so there is no way to custom order that. We should add a way to custom order these sections.

cc/ @knownasilya.

I only have folders at the top level, how do I order those?

I only have folders at the top level, how do I order those?

so far, when using auto for urlSchema, I'm placing a number in front of the folder name, and then I made a helper to remove the number prefix from the DocfyOutput node's name.
so,
1-first
2-second
3-third

is shown as
First
Second
Third

in my sidebar

so far, when using auto for urlSchema, I'm placing a number in front of the folder name, and then I made a helper to remove the number prefix from the DocfyOutput node's name.
so,
1-first
2-second
3-third

is shown as
First
Second
Third

in my sidebar

Nice workaround

Nice workaround

Would it be possible to sort pages alphabetically when using the flat output?


fan speed shows 102%

on latest version with midea type ac
https://www.lowes.com/pd/Midea-Midea-8-000-BTU-U-Shaped-Air-Conditioner/5000281553
curious if anyone else is seeing the same issue
everything else seems to work fine so it's not huge deal just visually weird to see in home assistant

Yes, this is more or less expected behavior at the moment.
The setting for auto fan is 102, but there's no way (that I know of) to query the current fan speed.
I could limit the displayed value for the number entity to 100, but I feel it would be misrepresenting the setting.
I'm open to other ideas of course


CUDA out of memory while begin to cluster after epoch 128

Hi, thank you for opening your great work!
I try your source code and have not any modification. I keep batchsize 4 per gpu and use 3 gpu. All hyperparameters are kept default.
I try to set the batchsize to 2 per gpu, however, it run several steps and still go out of memory. I would like to know if you also have such a issue and how can I run the code for 4 batchsize.
While the epoch is lower than 128, it works well. I guess it is because of the score net.
My device is 24G RTX4090, cuda11.3, torch version is 12.0 (I have tried 10.0 and occur a gpu problem).
Thank you!

could you please provide the screenshot of the error?

Thank you for your reply!
Here is my error log:
Traceback (most recent call last):
  File "train.py", line 416, in <module>
    mp.spawn(Distributed_training, nprocs=cfg.gpu_per_node, args=(cfg,))
  File "/data/liuxuexun/miniconda3/envs/pbnet2/lib/python3.8/site-packages/torch/multiprocessing/spawn.py", line 240, in spawn
    return start_processes(fn, args, nprocs, join, daemon, start_method='spawn')
  File "/data/liuxuexun/miniconda3/envs/pbnet2/lib/python3.8/site-packages/torch/multiprocessing/spawn.py", line 198, in start_processes
    while not context.join():
  File "/data/liuxuexun/miniconda3/envs/pbnet2/lib/python3.8/site-packages/torch/multiprocessing/spawn.py", line 160, in join
    raise ProcessRaisedException(msg, error_index, failed_process.pid)
torch.multiprocessing.spawn.ProcessRaisedException:

-- Process 0 terminated with the following error:
Traceback (most recent call last):
  File "/data/liuxuexun/miniconda3/envs/pbnet2/lib/python3.8/site-packages/torch/multiprocessing/spawn.py", line 69, in _wrap
    fn(i, *args)
  File "/data/liuxuexun/code/work2/PBNet/train.py", line 387, in Distributed_training
    train_epoch(dataset.train_data_loader, model, model_fn, optimizer, epoch)
  File "/data/liuxuexun/code/work2/PBNet/train.py", line 54, in train_epoch
    loss, _, visual_dict, meter_dict = model_fn(batch, model, epoch, cfg, task='train')
  File "/data/liuxuexun/code/work2/PBNet/network/PBNet.py", line 357, in model_fn
    ret = model(feat_voxel, xyz_voxel, xyz_original, v2p_index, ins_label, epoch, task)
  File "/data/liuxuexun/miniconda3/envs/pbnet2/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1130, in _call_impl
    return forward_call(*input, **kwargs)
  File "/data/liuxuexun/miniconda3/envs/pbnet2/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1008, in forward
    output = self._run_ddp_forward(*inputs, **kwargs)
  File "/data/liuxuexun/miniconda3/envs/pbnet2/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 969, in _run_ddp_forward
    return module_to_run(*inputs[0], **kwargs[0])
  File "/data/liuxuexun/miniconda3/envs/pbnet2/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1130, in _call_impl
    return forward_call(*input, **kwargs)
  File "/data/liuxuexun/code/work2/PBNet/network/PBNet.py", line 272, in forward
    IOU_feat = self.score_Unet(inputs_v3)
  File "/data/liuxuexun/miniconda3/envs/pbnet2/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1130, in _call_impl
    return forward_call(*input, **kwargs)
  File "/data/liuxuexun/code/work2/PBNet/network/Mink.py", line 348, in forward
    out = self.block8(out)
  File "/data/liuxuexun/miniconda3/envs/pbnet2/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1130, in _call_impl
    return forward_call(*input, **kwargs)
  File "/data/liuxuexun/miniconda3/envs/pbnet2/lib/python3.8/site-packages/torch/nn/modules/container.py", line 139, in forward
    input = module(input)
  File "/data/liuxuexun/miniconda3/envs/pbnet2/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1130, in _call_impl
    return forward_call(*input, **kwargs)
  File "/data/liuxuexun/miniconda3/envs/pbnet2/lib/python3.8/site-packages/MinkowskiEngine-0.5.4-py3.8-linux-x86_64.egg/MinkowskiEngine/modules/resnet_block.py", line 59, in forward
    out = self.conv2(out)
  File "/data/liuxuexun/miniconda3/envs/pbnet2/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1130, in _call_impl
    return forward_call(*input, **kwargs)
  File "/data/liuxuexun/miniconda3/envs/pbnet2/lib/python3.8/site-packages/MinkowskiEngine-0.5.4-py3.8-linux-x86_64.egg/MinkowskiEngine/MinkowskiConvolution.py", line 314, in forward
    outfeat = self.conv.apply(
  File "/data/liuxuexun/miniconda3/envs/pbnet2/lib/python3.8/site-packages/MinkowskiEngine-0.5.4-py3.8-linux-x86_64.egg/MinkowskiEngine/MinkowskiConvolution.py", line 72, in forward
    return fw_fn(
RuntimeError: CUDA out of memory. Tried to allocate 186.00 MiB (GPU 0; 23.64 GiB total capacity; 22.08 GiB already allocated; 68.69 MiB free; 22.48 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF

24G is enough for batchsize 4.

You can check the TensorBoard logs to determine if poor semantic segmentation training has resulted in a large number of floor or wall points being misclassified as instances.
Have you scaled the XYZ coordinates?
Try to reduce the max_crops_p from 300000 to 200000   in the config file

Hi, thank you for your suggest, I am sorry to bother you at weekend.

Here is part of my training los:

[2024-11-15 13:19:32,600  INFO  train.py  line<PHONE_NUMBER>]  >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2024-11-15 13:20:20,313  INFO  train.py  line<PHONE_NUMBER>]  epoch: 96/520, total loss: -0.0652, sem loss: 0.4173, offset norm loss: 0.3825, offset dir loss: -0.8650,,  time: 47.71010708808899s
[2024-11-15 13:20:20,315  INFO  train.py  line<PHONE_NUMBER>]  mIoU/mAcc/allAcc 0.7027/0.8001/0.8883.
[2024-11-15 13:21:56,311  INFO  train.py  line<PHONE_NUMBER>]  epoch: 97/520, train loss: -0.1457,  time: 95.98659133911133s
[2024-11-15 13:21:57,096  INFO  train.py  line<PHONE_NUMBER>]  Saving ./log/2024-11-15_10-24-32/000000097.pth
[2024-11-15 13:23:33,552  INFO  train.py  line<PHONE_NUMBER>]  epoch: 98/520, train loss: -0.1480,  time: 96.4353539943695s
[2024-11-15 13:23:34,410  INFO  train.py  line<PHONE_NUMBER>]  Saving ./log/2024-11-15_10-24-32/000000098.pth
[2024-11-15 13:25:10,009  INFO  train.py  line<PHONE_NUMBER>]  epoch: 99/520, train loss: -0.1527,  time: 95.57884478569031s
[2024-11-15 13:25:10,873  INFO  train.py  line<PHONE_NUMBER>]  Saving ./log/2024-11-15_10-24-32/000000099.pth
[2024-11-15 13:26:46,948  INFO  train.py  line<PHONE_NUMBER>]  epoch: 100/520, train loss: -0.1671,  time: 96.05449438095093s
[2024-11-15 13:26:47,814  INFO  train.py  line<PHONE_NUMBER>]  Saving ./log/2024-11-15_10-24-32/000000100.pth
[2024-11-15 13:26:47,834  INFO  train.py  line<PHONE_NUMBER>]  >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2024-11-15 13:27:35,018  INFO  train.py  line<PHONE_NUMBER>]  epoch: 100/520, total loss: -0.0704, sem loss: 0.4180, offset norm loss: 0.3774, offset dir loss: -0.8658,,  time: 47.18136119842529s
[2024-11-15 13:27:35,021  INFO  train.py  line<PHONE_NUMBER>]  mIoU/mAcc/allAcc 0.6988/0.7957/0.8883.
[2024-11-15 13:29:10,527  INFO  train.py  line<PHONE_NUMBER>]  epoch: 101/520, train loss: -0.1626,  time: 95.49798655509949s
[2024-11-15 13:29:11,275  INFO  train.py  line<PHONE_NUMBER>]  Saving ./log/2024-11-15_10-24-32/000000101.pth
[2024-11-15 13:30:46,213  INFO  train.py  line<PHONE_NUMBER>]  epoch: 102/520, train loss: -0.1545,  time: 94.91780710220337s
[2024-11-15 13:30:47,075  INFO  train.py  line<PHONE_NUMBER>]  Saving ./log/2024-11-15_10-24-32/000000102.pth
[2024-11-15 13:32:22,296  INFO  train.py  line<PHONE_NUMBER>]  epoch: 103/520, train loss: -0.1683,  time: 95.2022352218628s
[2024-11-15 13:32:23,200  INFO  train.py  line<PHONE_NUMBER>]  Saving ./log/2024-11-15_10-24-32/000000103.pth
[2024-11-15 13:33:59,564  INFO  train.py  line<PHONE_NUMBER>]  epoch: 104/520, train loss: -0.1716,  time: 96.3419623374939s
[2024-11-15 13:34:00,615  INFO  train.py  line<PHONE_NUMBER>]  Saving ./log/2024-11-15_10-24-32/000000104.pth
[2024-11-15 13:34:00,635  INFO  train.py  line<PHONE_NUMBER>]  >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2024-11-15 13:34:48,952  INFO  train.py  line<PHONE_NUMBER>]  epoch: 104/520, total loss: -0.0888, sem loss: 0.4067, offset norm loss: 0.3728, offset dir loss: -0.8682,,  time: 48.313562870025635s
[2024-11-15 13:34:48,955  INFO  train.py  line<PHONE_NUMBER>]  mIoU/mAcc/allAcc 0.7038/0.7973/0.8923.
[2024-11-15 13:36:24,895  INFO  train.py  line<PHONE_NUMBER>]  epoch: 105/520, train loss: -0.1775,  time: 95.93347120285034s
[2024-11-15 13:36:25,642  INFO  train.py  line<PHONE_NUMBER>]  Saving ./log/2024-11-15_10-24-32/000000105.pth
[2024-11-15 13:38:02,136  INFO  train.py  line<PHONE_NUMBER>]  epoch: 106/520, train loss: -0.1730,  time: 96.46910905838013s
[2024-11-15 13:38:03,012  INFO  train.py  line<PHONE_NUMBER>]  Saving ./log/2024-11-15_10-24-32/000000106.pth
[2024-11-15 13:39:39,153  INFO  train.py  line<PHONE_NUMBER>]  epoch: 107/520, train loss: -0.1605,  time: 96.11879682540894s
[2024-11-15 13:39:40,038  INFO  train.py  line<PHONE_NUMBER>]  Saving ./log/2024-11-15_10-24-32/000000107.pth
[2024-11-15 13:41:14,805  INFO  train.py  line<PHONE_NUMBER>]  epoch: 108/520, train loss: -0.1704,  time: 94.74788117408752s
[2024-11-15 13:41:15,730  INFO  train.py  line<PHONE_NUMBER>]  Saving ./log/2024-11-15_10-24-32/000000108.pth
[2024-11-15 13:41:15,746  INFO  train.py  line<PHONE_NUMBER>]  >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2024-11-15 13:42:03,220  INFO  train.py  line<PHONE_NUMBER>]  epoch: 108/520, total loss: -0.0860, sem loss: 0.4070, offset norm loss: 0.3749, offset dir loss: -0.8679,,  time: 47.469791650772095s
[2024-11-15 13:42:03,223  INFO  train.py  line<PHONE_NUMBER>]  mIoU/mAcc/allAcc 0.7044/0.8070/0.8903.
[2024-11-15 13:43:40,540  INFO  train.py  line<PHONE_NUMBER>]  epoch: 109/520, train loss: -0.1757,  time: 97.30914211273193s
[2024-11-15 13:43:41,306  INFO  train.py  line<PHONE_NUMBER>]  Saving ./log/2024-11-15_10-24-32/000000109.pth
[2024-11-15 13:45:15,956  INFO  train.py  line<PHONE_NUMBER>]  epoch: 110/520, train loss: -0.1893,  time: 94.62984299659729s
[2024-11-15 13:45:16,905  INFO  train.py  line<PHONE_NUMBER>]  Saving ./log/2024-11-15_10-24-32/000000110.pth
[2024-11-15 13:46:51,912  INFO  train.py  line<PHONE_NUMBER>]  epoch: 111/520, train loss: -0.1691,  time: 94.98392295837402s
[2024-11-15 13:46:52,799  INFO  train.py  line<PHONE_NUMBER>]  Saving ./log/2024-11-15_10-24-32/000000111.pth
[2024-11-15 13:48:27,929  INFO  train.py  line<PHONE_NUMBER>]  epoch: 112/520, train loss: -0.1840,  time: 95.11339521408081s
[2024-11-15 13:48:28,815  INFO  train.py  line<PHONE_NUMBER>]  Saving ./log/2024-11-15_10-24-32/000000112.pth
[2024-11-15 13:48:28,830  INFO  train.py  line<PHONE_NUMBER>]  >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2024-11-15 13:49:16,678  INFO  train.py  line<PHONE_NUMBER>]  epoch: 112/520, total loss: -0.0430, sem loss: 0.4454, offset norm loss: 0.3775, offset dir loss: -0.8659,,  time: 47.844698667526245s
[2024-11-15 13:49:16,680  INFO  train.py  line<PHONE_NUMBER>]  mIoU/mAcc/allAcc 0.6889/0.7954/0.8853.
[2024-11-15 13:50:52,703  INFO  train.py  line<PHONE_NUMBER>]  epoch: 113/520, train loss: -0.1890,  time: 96.01497435569763s
[2024-11-15 13:50:53,460  INFO  train.py  line<PHONE_NUMBER>]  Saving ./log/2024-11-15_10-24-32/000000113.pth
[2024-11-15 13:52:29,559  INFO  train.py  line<PHONE_NUMBER>]  epoch: 114/520, train loss: -0.1800,  time: 96.08232951164246s
[2024-11-15 13:52:30,420  INFO  train.py  line<PHONE_NUMBER>]  Saving ./log/2024-11-15_10-24-32/000000114.pth
[2024-11-15 13:54:05,951  INFO  train.py  line<PHONE_NUMBER>]  epoch: 115/520, train loss: -0.2176,  time: 95.50404930114746s
[2024-11-15 13:54:06,803  INFO  train.py  line<PHONE_NUMBER>]  Saving ./log/2024-11-15_10-24-32/000000115.pth
[2024-11-15 13:55:42,889  INFO  train.py  line<PHONE_NUMBER>]  epoch: 116/520, train loss: -0.2016,  time: 96.065260887146s
[2024-11-15 13:55:43,810  INFO  train.py  line<PHONE_NUMBER>]  Saving ./log/2024-11-15_10-24-32/000000116.pth
[2024-11-15 13:55:43,832  INFO  train.py  line<PHONE_NUMBER>]  >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2024-11-15 13:56:31,374  INFO  train.py  line<PHONE_NUMBER>]  epoch: 116/520, total loss: -0.0713, sem loss: 0.4287, offset norm loss: 0.3688, offset dir loss: -0.8687,,  time: 47.53889322280884s
[2024-11-15 13:56:31,376  INFO  train.py  line<PHONE_NUMBER>]  mIoU/mAcc/allAcc 0.7051/0.7906/0.8911.
[2024-11-15 13:58:07,834  INFO  train.py  line<PHONE_NUMBER>]  epoch: 117/520, train loss: -0.1842,  time: 96.45128226280212s
[2024-11-15 13:58:08,589  INFO  train.py  line<PHONE_NUMBER>]  Saving ./log/2024-11-15_10-24-32/000000117.pth
[2024-11-15 13:59:43,824  INFO  train.py  line<PHONE_NUMBER>]  epoch: 118/520, train loss: -0.1902,  time: 95.21906328201294s
[2024-11-15 13:59:44,683  INFO  train.py  line<PHONE_NUMBER>]  Saving ./log/2024-11-15_10-24-32/000000118.pth
[2024-11-15 14:01:20,664  INFO  train.py  line<PHONE_NUMBER>]  epoch: 119/520, train loss: -0.1771,  time: 95.9618091583252s
[2024-11-15 14:01:21,519  INFO  train.py  line<PHONE_NUMBER>]  Saving ./log/2024-11-15_10-24-32/000000119.pth
[2024-11-15 14:02:57,322  INFO  train.py  line<PHONE_NUMBER>]  epoch: 120/520, train loss: -0.1953,  time: 95.78535604476929s
[2024-11-15 14:02:58,183  INFO  train.py  line<PHONE_NUMBER>]  Saving ./log/2024-11-15_10-24-32/000000120.pth
[2024-11-15 14:02:58,200  INFO  train.py  line<PHONE_NUMBER>]  >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2024-11-15 14:03:45,364  INFO  train.py  line<PHONE_NUMBER>]  epoch: 120/520, total loss: -0.0889, sem loss: 0.4150, offset norm loss: 0.3660, offset dir loss: -0.8700,,  time: 47.160337686538696s
[2024-11-15 14:03:45,367  INFO  train.py  line<PHONE_NUMBER>]  mIoU/mAcc/allAcc 0.7076/0.8074/0.8921.
[2024-11-15 14:05:22,013  INFO  train.py  line<PHONE_NUMBER>]  epoch: 121/520, train loss: -0.1889,  time: 96.63909816741943s
[2024-11-15 14:05:22,770  INFO  train.py  line<PHONE_NUMBER>]  Saving ./log/2024-11-15_10-24-32/000000121.pth
[2024-11-15 14:06:59,120  INFO  train.py  line<PHONE_NUMBER>]  epoch: 122/520, train loss: -0.2014,  time: 96.33055067062378s
[2024-11-15 14:07:00,014  INFO  train.py  line<PHONE_NUMBER>]  Saving ./log/2024-11-15_10-24-32/000000122.pth
[2024-11-15 14:08:35,294  INFO  train.py  line<PHONE_NUMBER>]  epoch: 123/520, train loss: -0.2315,  time: 95.26019167900085s
[2024-11-15 14:08:36,142  INFO  train.py  line<PHONE_NUMBER>]  Saving ./log/2024-11-15_10-24-32/000000123.pth
[2024-11-15 14:10:12,246  INFO  train.py  line<PHONE_NUMBER>]  epoch: 124/520, train loss: -0.2241,  time: 96.0864474773407s
[2024-11-15 14:10:13,107  INFO  train.py  line<PHONE_NUMBER>]  Saving ./log/2024-11-15_10-24-32/000000124.pth
[2024-11-15 14:10:13,125  INFO  train.py  line<PHONE_NUMBER>]  >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2024-11-15 14:11:01,096  INFO  train.py  line<PHONE_NUMBER>]  epoch: 124/520, total loss: -0.0811, sem loss: 0.4127, offset norm loss: 0.3738, offset dir loss: -0.8676,,  time: 47.968700647354126s
[2024-11-15 14:11:01,099  INFO  train.py  line<PHONE_NUMBER>]  mIoU/mAcc/allAcc 0.7019/0.7916/0.8917.
[2024-11-15 14:12:37,530  INFO  train.py  line<PHONE_NUMBER>]  epoch: 125/520, train loss: -0.2452,  time: 96.42434668540955s
[2024-11-15 14:12:38,285  INFO  train.py  line<PHONE_NUMBER>]  Saving ./log/2024-11-15_10-24-32/000000125.pth
[2024-11-15 14:14:13,939  INFO  train.py  line<PHONE_NUMBER>]  epoch: 126/520, train loss: -0.2008,  time: 95.63491559028625s
[2024-11-15 14:14:14,801  INFO  train.py  line<PHONE_NUMBER>]  Saving ./log/2024-11-15_10-24-32/000000126.pth
[2024-11-15 14:15:52,062  INFO  train.py  line<PHONE_NUMBER>]  epoch: 127/520, train loss: -0.2320,  time: 97.24012517929077s
[2024-11-15 14:15:52,918  INFO  train.py  line<PHONE_NUMBER>]  Saving ./log/2024-11-15_10-24-32/000000127.pth
[2024-11-15 14:17:28,987  INFO  train.py  line<PHONE_NUMBER>]  epoch: 128/520, train loss: -0.2037,  time: 96.05051183700562s
[2024-11-15 14:17:29,845  INFO  train.py  line<PHONE_NUMBER>]  Saving ./log/2024-11-15_10-24-32/000000128.pth
[2024-11-15 14:17:29,860  INFO  train.py  line<PHONE_NUMBER>]  >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>
[2024-11-15 14:18:17,398  INFO  train.py  line<PHONE_NUMBER>]  epoch: 128/520, total loss: -0.1032, sem loss: 0.3974, offset norm loss: 0.3690, offset dir loss: -0.8695,,  time: 47.53583002090454s
[2024-11-15 14:18:17,402  INFO  train.py  line<PHONE_NUMBER>]  mIoU/mAcc/allAcc 0.7137/0.8194/0.8942.

I think the iou is high enough.

No any scaled I have done. I have not made any modification of the source code.

I  reduce the max_crops_p from 300000 to 200000 and still out of momery when 4 batchsize, I try 100000 points and it can work, However I think it will cause performance degradation ?

I am wondering if the different version of  Minkowskiengine would cause this problem?

you can try 150000 points and it will cause performance degradation but not too much
Or you can try to train the backbone first about 512 epoch then froze the backbone weight to train the scorenet. In this way，the performance will be par to the original result

Ok, thank you, I will have a try.