cbm_optim's Introduction
cbm_optim's People
cbm_optim's Issues
Add plot_all_losses function
Add CBMConfig
Add a CBMConfig class for opts, opt_names, models, bs muls.
add an example of trainer usage in code.
Add more baseline models.
Add a configuration for SigLIP.Add a configuration for CLIPSeg, other CLIP-like models.Rename model to BaseCBModel.- Create a version with several bottleneck layers and test it.
- Create a version with several bottleneck layers and residual connections.
- Add a CLIP model with hid_size x hid_size CBL which does not depends on the number of concepts. Also add this model to Transformers.
!! try another approach to loss
- sample from gumbel_sfotmax
- add to initial logits and scale by tau
- take log_softmax of it
Test this also.
Make a scale in Gumbel Contrastive Loss trainable.
- Implement GumbelSotmaxLoss as a class.
- Implement GumbelContrastiveLoss as a class.
- In both classes make a tau parameter trainable
change clip_name to backbone_name in data_utils.py and add to cbm.py also
add plot metrics function for all metrics
fix error which sometimes occurs only at the 300 iteration
Fix "Attempting to cast a BatchEncoding to type None. This is not supported." while trained with LoRA
Add cosine similarity loss function
Make optimiser head by default be connected to all layers except of CBL.
Add Wandb and TensorBoard reports for BottleneckTrainer
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