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numpy.core._exceptions._ArrayMemoryError #22

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hefan14 opened this issue Jun 26, 2024 · 6 comments
Open

numpy.core._exceptions._ArrayMemoryError #22

hefan14 opened this issue Jun 26, 2024 · 6 comments

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@hefan14
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hefan14 commented Jun 26, 2024

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Hello. When I ran the evaluation code, the following errors occurred: numpy.core._exceptions._ArrayMemoryError:Unable to allocate 19.1MiB for an array with shape (1537052,) and data type [('vertex_indices', [('f0', 'u1'), ('f1', '<u4', (3, ))])]. I don't understand why it occupies up to 64GB of memory. I would like to know if you have encountered such a problem before.

@BaowenZ
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BaowenZ commented Jun 26, 2024

The function mesh.split costs much memory. Please comment it out and directly output mesh in cull_mesh. And I'll replace it with another function.

@hefan14
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hefan14 commented Jun 27, 2024

Thank you, it has been a great help to me!

@hefan14
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hefan14 commented Jun 27, 2024

May I ask what the specific results are represented by the output of 'vis/vis_201_d2s. ply' and 'vis/vis_201_s2d_ply' in 'eval. py'? How should I obtain valid information from them?

@hefan14
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hefan14 commented Jun 27, 2024

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Hi! The grid results I obtained are not as perfect as those provided on the 'Project page'. Can you provide some possible reasons or suggestions?

@BaowenZ
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BaowenZ commented Jun 27, 2024

Hi! 'vis/vis_201_d2s. ply' and 'vis/vis_201_s2d_ply' represent the closest distances between output and ground truth. Red parts indicate large error. And please check that you have reinstall the package with the right version. And you could print the rendered images during training to check training process.

@hefan14
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hefan14 commented Jul 1, 2024

I retrieved the latest version of the code and conducted the training process, but unfortunately, the grid results I extracted were not ideal and differed significantly from the results provided on the project page.

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