Conversation
…or 2d and 3d flow experiments
| @@ -0,0 +1,43 @@ | |||
| from ml_collections import config_dict | |||
There was a problem hiding this comment.
Can we use a different name than "mod"? Is this the config for the 2D flow?
| loss2 = reduce(loss2, 'b ... -> b (...)', 'mean') | ||
|
|
||
| return loss.mean() + loss1.mean() + loss2.mean() | ||
| return loss.mean()*1.7 + loss1.mean()*1.0 + loss2.mean()*1.0 |
There was a problem hiding this comment.
Interesting, so now the losses are not quite balanced? How did you determine the 1.7? (nit: While it seems like both of the 1.0's are superfluous, at least one of them is because you only need to weigh 2/3 loss terms, or in general k-1 of k loss terms. It's the relative weight that is going to matter).
There was a problem hiding this comment.
Aah, it's a typo, last one was supposed to be 0.3 so that it sums up to 3. But yeah, it's manually chosen hyperparameters, which even I'm not a fan of much. I was thinking of using the SoftAdapt paper to automatically figure out these weights
| flow = Flow( | ||
| dim = config.dim, | ||
| channels = 3 * config.data_config["img_channel"], | ||
| out_dim = 2, |
There was a problem hiding this comment.
Flow is both in 2D and 3D, the out_dim is 2 in 2d, 3 in 3d. The way it is used to warp is using scale_space_warp in 3d and flow_warp in 2d
| config.sampling_steps = 6 | ||
| config.loss = "l1" | ||
| config.sampling_steps = 20 | ||
| config.loss = "l2" |
There was a problem hiding this comment.
This seems like a major change. It would be great if there was a clearly defined experiment behind this choice.
There was a problem hiding this comment.
I ran a couple of experiments with less number of epochs. But it was cluttering the project space of wandb so I deleted them
…el, minipatch, partial rollout
…sr-focal added additionally
2 participants
added a version for 2d flow
only changes in forward, p_losses and sample functions of diffusion model.
3d flow used gaussian pyramids and scale space warp, 2d flow only uses flow warp
compared to cold start problem, both the new versions use swin ir that is instantiated in the diffusion model and used to upsample lowres as ures which gives context instead of hres