Results on MPI-INF-3DHP

[flyyyyer]

Could you provide code on MPI-INF-3DHP?

And how long does it take to train the model？It seems very slow.

[zczcwh]

@flyyyyer Sorry, I use someone else's code and I don't have permission to redistribute. I have to say the training is very slow. I use 2 RTX 3090 GPUs to train the model. For 27 frames it takes 3-4 days and for 81 frames it takes more than 10 days.

[flyyyyer]

@flyyyyer Sorry, I use someone else's code and I don't have permission to redistribute. I have to say the training is very slow. I use 2 RTX 3090 GPUs to train the model. For 27 frames it takes 3-4 days and for 81 frames it takes more than 10 days.

Thanks for your reply ! I want to train my model on MPI-INF-3DHP, could you tell me where can I find the released the code ?

[zczcwh]

@flyyyyer Actually he didn't put the code online. Maybe you can try to find if someone releases the code for preparing MPI-INF-3DHP in their repo.

[Vegetebird]

@flyyyyer Sorry, I use someone else's code and I don't have permission to redistribute. I have to say the training is very slow. I use 2 RTX 3090 GPUs to train the model. For 27 frames it takes 3-4 days and for 81 frames it takes more than 10 days.

I also find the training is very slow. And how to calculate the FLOPs? I calculate the FLOPs 8× higher than you on Table 6.

[zczcwh]

@Vegetebird Yes, I'm trying to adapt my model by using some efficient transformer architecture such as Longformer to speed up. For the FLOPs, I just hand calculate the matrix multiplication FLOPs based on the matrix size in the transformer encoder and other components.

[Vegetebird]

@Vegetebird Yes, I'm trying to adapt my model by using some efficient transformer architecture such as Longformer to speed up. For the FLOPs, I just hand calculate the matrix multiplication FLOPs based on the matrix size in the transformer encoder and other components.

I calculate the FLOPs of your model by 'from thop import profile', it's much higher than you provided in Table 6. I think using thop package get the FLOPs maybe more precise due to the slow traning time and the large size of Transformer (4 layers, 544 dimension).

[zczcwh]

@Vegetebird Yes, I'm trying to adapt my model by using some efficient transformer architecture such as Longformer to speed up. For the FLOPs, I just hand calculate the matrix multiplication FLOPs based on the matrix size in the transformer encoder and other components.

I calculate the FLOPs of your model by 'from thop import profile', it's much higher than you provided in Table 6. I think using thop package get the FLOPs maybe more precise due to the slow traning time and the large size of Transformer (4 layers, 544 dimension).

I see there is an issue for thop says: "MACs value becomes too large if there are shared modules". (Lyken17/pytorch-OpCounter#98). In our case, the spatial transformer is shared weight. This might be the reason that FLOPs will become too large.

[Vegetebird]

@Vegetebird Yes, I'm trying to adapt my model by using some efficient transformer architecture such as Longformer to speed up. For the FLOPs, I just hand calculate the matrix multiplication FLOPs based on the matrix size in the transformer encoder and other components.

I calculate the FLOPs of your model by 'from thop import profile', it's much higher than you provided in Table 6. I think using thop package get the FLOPs maybe more precise due to the slow traning time and the large size of Transformer (4 layers, 544 dimension).

I see there is an issue for thop says: "MACs value becomes too large if there are shared modules". (Lyken17/pytorch-OpCounter#98). In our case, the spatial transformer is shared weight. This might be the reason that FLOPs will become too large.

Sorry, I ingore the detail that you report the FLOPs per frame. But why the FLOPs per frame is different with different input squence length?

[zczcwh]

@Vegetebird the "per frame" means per predicted output frame.

[xobeiotozi]

@flyyyyer Sorry, I use someone else's code and I don't have permission to redistribute. I have to say the training is very slow. I use 2 RTX 3090 GPUs to train the model. For 27 frames it takes 3-4 days and for 81 frames it takes more than 10 days.

Hello! Could you share the code for training the mpi-inf-3dhp dataset? Thank you! xobeiotozi@gmail.com