Comments (5)
Hello, @JeffWang987
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You can first use the pre-trained model provided by the author to reproduce the evaluation results.
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You could try to train the network using only one GPU card to see the difference.
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Depth refinement is not used in this project.
from mvsnet_pytorch.
Hello, @JeffWang987 @xy-guo
Where should the code be changed to run on multiple cards?
Traceback (most recent call last):
File "/home/camellia/zyf/MVSNet_pytorch-master/eval.py", line 302, in
save_depth()
File "/home/camellia/zyf/MVSNet_pytorch-master/eval.py", line 113, in save_depth
outputs = model(sample_cuda["imgs"], sample_cuda["proj_matrices"], sample_cuda["depth_values"])
File "/home/camellia/anaconda3/envs/mvsnet-pytorch/lib/python3.8/site-packages/torch/nn/modules/module.py", line 889, in _call_impl
result = self.forward(*input, **kwargs)
File "/home/camellia/anaconda3/envs/mvsnet-pytorch/lib/python3.8/site-packages/torch/nn/parallel/data_parallel.py", line 167, in forward
outputs = self.parallel_apply(replicas, inputs, kwargs)
File "/home/camellia/anaconda3/envs/mvsnet-pytorch/lib/python3.8/site-packages/torch/nn/parallel/data_parallel.py", line 177, in parallel_apply
return parallel_apply(replicas, inputs, kwargs, self.device_ids[:len(replicas)])
File "/home/camellia/anaconda3/envs/mvsnet-pytorch/lib/python3.8/site-packages/torch/nn/parallel/parallel_apply.py", line 86, in parallel_apply
output.reraise()
File "/home/camellia/anaconda3/envs/mvsnet-pytorch/lib/python3.8/site-packages/torch/_utils.py", line 429, in reraise
raise self.exc_type(msg)
RuntimeError: Caught RuntimeError in replica 0 on device 0.
Original Traceback (most recent call last):
File "/home/camellia/anaconda3/envs/mvsnet-pytorch/lib/python3.8/site-packages/torch/nn/parallel/parallel_apply.py", line 61, in _worker
output = module(*input, **kwargs)
File "/home/camellia/anaconda3/envs/mvsnet-pytorch/lib/python3.8/site-packages/torch/nn/modules/module.py", line 889, in _call_impl
result = self.forward(*input, **kwargs)
File "/home/camellia/zyf/MVSNet_pytorch-master/models/mvsnet.py", line 123, in forward
warped_volume = homo_warping(src_fea, src_proj, ref_proj, depth_values)
File "/home/camellia/zyf/MVSNet_pytorch-master/models/module.py", line 127, in homo_warping
warped_src_fea = F.grid_sample(src_fea, grid.view(batch, num_depth * height, width, 2), mode='bilinear',
File "/home/camellia/anaconda3/envs/mvsnet-pytorch/lib/python3.8/site-packages/torch/nn/functional.py", line 3836, in grid_sample
return torch.grid_sampler(input, grid, mode_enum, padding_mode_enum, align_corners)
RuntimeError: CUDA out of memory. Tried to allocate 2.71 GiB (GPU 0; 11.77 GiB total capacity; 6.45 GiB already allocated; 1.92 GiB free; 8.33 GiB reserved in total by PyTorch)
from mvsnet_pytorch.
Hello @zhao-you-fei
You can use the CasMVSNet to achieve the multi-view depth estimation, which achieve more accurate depth estimation and less GPU memory consumption.
Besides, CasMVSNet supports multi GPU training.
from mvsnet_pytorch.
@XYZ-qiyh
谢谢,我知道(虽然casmvsnet这个网络也是卡在了可视化的地方),但我想知道这个mvsnet-pytorch版本的是必须单卡跑吗?我只有两个3060的卡,想直接加载预训练模型测试一下,结果报了以上错误.我想做个对比试验,目前这个问题还没有解决.请问你有什么办法吗 #3 这个问题里的回答我也都试过了,还是会出现同样的错误,我不知道到底是内存不足还是两张卡参数分配不均导致的.请原谅我英文很差只能中文交流了.再次感谢您的回答
from mvsnet_pytorch.
@JeffWang987 @XYZ-qiyh @xy-guo
Hi, have you addressed this problem?
I can reproduce the evaluation results using the pre-trained model. However, when I re-train the model myself (bs=4, a single GPU of 3090Ti, without depth refinement), the results are much worse as follows:
Acc --- Comp --- Overall
0.6212 - 0.4912 - 0.5562
Can you provide some suggestions to solve this issue?
from mvsnet_pytorch.
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