Train detr3d_vovnet_train exceed the memory of 4*RTX3090

[synsin0]

Environment: 4xRTX3090.
Failure: train detr3d with resnet101 backbone dominates each card with 21GB memory. Train detr3d with vovnet backbone exceeds the memory limit. image_per_gpu is set to 1.
I read from your paper that your experiment uses 8xRTX3090. How should I adjust for adaption of my training process?

[a1600012888]

Hi synsin0.
For vovnet backbone, it is too large to fit in 3090.
If you want to fit it in 3090, you can try:

1. fp16
2. memory checkpoint Training Deep Nets with Sublinear Memory Cost, pytorch provide a checkpoint implementation: torch.utils.checkpoint.checkpoint, see https://pytorch.org/docs/stable/checkpoint.html?highlight=checkpoint
3. Freeze some layers of Vovnet. e.g. first stage, etc.

[cgl-cell]

Environment: 4xRTX3090. Failure: train detr3d with resnet101 backbone dominates each card with 21GB memory. Train detr3d with vovnet backbone exceeds the memory limit. image_per_gpu is set to 1. I read from your paper that your experiment uses 8xRTX3090. How should I adjust for adaption of my training process?

Have you solved it？