sidak / otfusion Goto Github PK

Hi,

Does the code include fusing multiple (>2) models? Thanks!

Referring to section S5 of the paper: where can I find the parameters for replicating skill transfer experiments?

Dear Sidak,
Thanks again for your code. I was going to run your an example using your resnet checkpoints.
To do this, cifar.zip and resnet_models.zip are extracted and the following command is run (it seems the provided checkpoint are with no BN):

python main.py --gpu-id 0 --model-name resnet18_nobias_nobn --n-epochs 300 --save-result-file sample.csv --sweep-name exp_sample --exact --correction --ground-metric euclidean --weight-stats --activation-histograms --activation-mode raw --geom-ensemble-type acts --sweep-id 21 --act-num-samples 200 --ground-metric-normalize none --activation-seed 21 --prelu-acts --recheck-acc --load-models ./resnet_models/ --ckpt-type best --past-correction --not-squared --dataset Cifar10

However, the code exited with the following error (seems the shortcut is making the trouble):

--------------- At layer index 7 ------------- 
 
Previous layer shape is  torch.Size([128, 128, 3, 3])
let's see the difference in layer names layer2.0.shortcut.0 layer2.0.shortcut.0
torch.Size([200, 1, 128, 16, 16]) shape of activations generally
reorder_dim is  [1, 2, 3, 0]
In layer layer2.0.shortcut.0.weight: getting activation distance statistics
Statistics of the distance from neurons of layer 1 (averaged across nodes of layer 0):
Statistics of the distance from neurons of layer 1 (averaged across nodes of layer 0): 

Max : 8.675606727600098, Mean : 3.544717311859131, Min : 1.0014023780822754, Std: 1.3794620037078857
shape of layer: model 0 torch.Size([128, 64, 1])
shape of layer: model 1 torch.Size([128, 64, 1])
shape of activations: model 0 torch.Size([128, 16, 16, 200])
shape of activations: model 1 torch.Size([128, 16, 16, 200])
shape of previous transport map torch.Size([128, 128])
Traceback (most recent call last):
  File "main.py", line 159, in <module>
    geometric_acc, geometric_model = wasserstein_ensemble.geometric_ensembling_modularized(args, models, train_loader, test_loader, activations)
  File "/home/rahim/NIPS2021/otfusion/wasserstein_ensemble.py", line 893, in geometric_ensembling_modularized
    avg_aligned_layers = get_acts_wassersteinized_layers_modularized(args, networks, activations, train_loader=train_loader, test_loader=test_loader)
  File "/home/rahim/NIPS2021/otfusion/wasserstein_ensemble.py", line 688, in get_acts_wassersteinized_layers_modularized
    aligned_wt = torch.bmm(fc_layer0_weight_data.permute(2, 0, 1), T_var_conv).permute(1, 2, 0)
RuntimeError: batch1 dim 2 must match batch2 dim 1

Secondly, the code is not working with BatchNorm, is that right?

In utils.py line 11, import train as cifar_train. Where is the module 'train'? Thank you!

Hi Sidak,
Thanks for such great work and your code. I tried to run one of the samples codes in the readme, but it seems that a ''train' folder is missing. The error prints as "No module named 'train'".

Hi, Sidak. My name is Terai, and I am an undergraduate student studying informatics engineering.

I have read your paper (titled as Model Fusion via Optimal Transport) published on NIPS'20. After reading through the paper, I have tried to reproduce the simulation results using the codes publicly available on GitHub, but I have two simple questions: How were the two models (used in Figure 2 of the paper) being created? How can I leverage your codes to retrain the models by myself?

I know you are busy, but I would greatly appreciate it if you could help me. Thanks.

Hi expert,
Nice work and novel view to use OT method.
As you mentioned on your paper "The bias of a neuron is set to zero in all of the experiments. It is possible to handle
it as a regular weight by keeping the corresponding input as 1" .
Do you have any new version to handle fusion of bias over this years?
Or How to do a regular weight by keeping the corresponding input as 1?

When trying to reproduce the experiment "MNIST + MLPNet",I encountered this problem:
No module named 'train'.
Which file does this "train" mean?