Comments (4)
your x-axis, is that number of steps? you only did 600 training steps?
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Yes, these are the results after only 600 training steps. I trained magvit in an unconditional manner on UCF101 dataset.
During the training, I noticed the initial recon_loss value was very large (2e+4), so I checked the tensor value ranges when calculating recon_loss between the video and reconstructed video. I found the video values were between 0.0-255.0, while the reconstructed video values were around -1.0 to 1.0.
Therefore, I additionally normalized the data when loading videos to rescale the tensor range to -1.0 to 1.0. With this, the initial recon_loss is around 0.3, but the discr_loss is still around 2.0, much larger than recon_loss. I'm not sure if this will affect training, so I shrink discr_loss a bit by adding discr_weight of 0.1 to balance it with recon_loss. (Then the initial value of losses becomes: recon_loss=0.3, disrc_loss=0.2 around) Here is my new results of 3k steps training with these settings:
I'm retraining as above now - should I increase the training steps to at least 20k? And should I apply this normalization of the loaded video tensor range?
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Fixed
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how did you do it?
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Related Issues (20)
- Large scale training HOT 17
- Running multi-gpu hangs after first step HOT 9
- Is there any requirement on the training images? HOT 3
- object has no attribute 'has_multiscale_discrs' HOT 2
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- Unsuccessful image reconstruction HOT 3
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- pretrained weights
- Pixelated image reconstruction HOT 7
- ‘video_contains_first_frame’ in encoder HOT 1
- recon images is black HOT 9
- Question about casual 3d cnn HOT 1
- The configuration of training
- Is there anyone success to train this model? HOT 15
- Running multi-gpu training HOT 5
- About training steps and correctness. HOT 3
- Error while loading the states of optimizer in Trainer - def load(self, path)
- Is there any pretrained weights for debug? HOT 1
- About training speed.
- Why is magvitv2 different from the description in the paper? Am I understanding it wrong? HOT 7
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