About batch_size and cuda memory about self-supervised-depth-completion HOT 5 CLOSED

Hi. The default model we provide in this code base is indeed memory-consuming. You can start with batch size of 1 or 2.

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@fangchangma Thank you for your reply. When I train the model with batch-size of 1/2, the program will prompt "warning: diff.nelement()==0 in PhotometricLoss (this is expected during early stage of training, try larger batch size" , so I think if a small batch-size will affects the accuracy of the trained model. If it is influential, what do you think is the minimum setting of batch-size?

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What do you mean by batch-size of 1/2?

The warning appears when the inverse-warped rgb image is black (i.e., no warped pixel falls within the field of view). This usually happens at initialization, when the depth prediction is far off from ground truth.

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@fangchangma I mean this warning will appear when the batch size is set to 1 or 2, so I think if the small batch size will lead to a bad result. What do you think the minimum size of the batch size is set to get good results?

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@fangchangma I mean this warning will appear when the batch size is set to 1 or 2, so I think if the small batch size will lead to a bad result. What do you think the minimum size of the batch size is set to get good results?

Hi, I also get the warning. Dose it actually affect the final result? And do you know what is the proper batch size?
Thanks!

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