spconv1.1 possible? about openpcdet HOT 8 CLOSED

Please refer to here #23 for the issue about spconv 1.1, where you could also find a spconv 1.1 version supported by @codyreading at https://github.com/codyreading/PCDet/tree/spconv-1.1.

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Now PCDet has supported spconv 1.1 as modified here 2244be4.

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Hi, thank you for the amazing work. I tried installing spconv1., but their cuda libs search locations and their build process kept breaking on my lab's servers. I installed spconv1.1 instead, and it just worked. However, I noticed in several places this repo stresses spconv1.0. I am wondering if there is anything that absolutely needs spconv1.0.

The 2 main places this repo uses spconv is the voxel_generator and the convolutions.

I am stuck with the same issue,
I just cannot achieve a successful spconv v1.0 build on our lab's servers.
@monghimng: Were you finally able to build spconv v1.0 ?

See issue #70 for more details.

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I just installed v1.1 and the branch the author provided. It somehow worked for me.

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The repo has support for spconv1.1, the main difference is the GPU memory usage of the PartA2 ( a lot larger in spconv==1.1). Still should work fine from 2244be4.

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I just installed v1.1 and the branch the author provided. It somehow worked for me.

@monghimng:
That's great news, if you are using conda, could you please provide the output of conda list.
Then I could maybe just "clone" your conda environment and all is good.

@codyreading:
Thank you for pointing out the different GPU memory usage.
I tried both SECOND and PartA^2Net with batch_size=1
on a GeForce RTX 2080 Ti with 11GB and also on a TITAN Xp with 12GB GPU memory
and the training in both cases still crashed with unknown device type as mentioned in issue #70.

Now, I think the issue most likely comes from my specific set of versions for

• python
• pytorch
• CUDA
• cudnn.

that I am using. For all details please see issue #70.

Sadly, I found no other combination yet that allowed both,
a successful build of spconv and a successful installation of PCDet.

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Hi @MartinHahner88,

Doesn't look like I've seen that issue. I've been using the following versions which has worked for me, and using pip with virtualenv to install the packages.

• Python: 3.7
• Torch: 1.4
• CUDA: 10.1
• CUDNN: 7.6.5

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@codyreading

Doesn't look like I've seen that issue. I've been using the following versions which has worked for me, and using pip with virtualenv to install the packages.

* Python: 3.7

* Torch: 1.4

* CUDA: 10.1

* CUDNN: 7.6.5

Those versions, unfortunately, let to yet another issue for me, see here.

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