how to convert sparse model to dense model?

[Mike575]

After finishing pretrain resnet50, we can get a resnet50 weight file in sparse type. I'd like to use resnet50 (dense type) as my backbone in my other projects. But how to convert sparse model to dense model? Is there any convenient function like SparseEncoder.dense_model_to_sparse in encoder.py ?

[keyu-tian]

Actually we don't need a sparse-to-dense conversion (there is no "sparse"-type weights). You can use the pretrained weight directly like the following code (just like an ImageNet-supervised pretrained resnet50):

import torch, timm
res50 = timm.create_model('resnet50')
state = torch.load('resnet50_1kpretrained_timm_style.pth', 'cpu')
missing_keys, unexpected_keys = res50.load_state_dict(state, strict=False)
assert missing_keys == ['fc.weight', 'fc.bias']
assert len(unexpected_keys) == 0

The reason is we use torch builtin operators (nn.Conv2d, nn.LayerNorm, etc.) to simulate those sparse operators (see /pretrain/encoder.py). So it keeps the same weight formats and names.

[puyiwen]

@keyu-tian Sorry to bother you. From your way of loading the model, does it mean that SparK only uses MAE during training, does not change the original model structure, such as 'resnet', on inference? Looking forward to your reply, thank you!