About #Parameters in the model

[prateek-77]

About #Parameters in the model

[JingyunLiang]

#Params: use

 print({sum(map(lambda x: x.numel(), model.parameters())):,d})

# FLOPS: refer to

SwinIR/models/network_swinir.py

Line 862 in 510b756

print(height, width, model.flops() / 1e9)

[prateek-77]

Thanks for the suggestions! The #Flops for the lightweight SwinIR model are matching, however, the #Params are not. It's coming out to be around 910.2K.

I'm using the following model config for lightweight-SwinIR:

height = (1024 // upscale // window_size + 1) * window_size
width = (720 // upscale // window_size + 1) * window_size
model = SwinIR(upscale=2, in_chans=3, img_size=(height, width), window_size=8,
                    img_range=1., depths=[6, 6, 6, 6], embed_dim=60, num_heads=[6, 6, 6, 6],
                    mlp_ratio=2, upsampler='pixelshuffledirect', resi_connection='1conv')

Also, I'm not able to use model.flops() with classic image SR models, since flop calculation is not defined for the Upsample class.

Can you please provide help with these queries? Thanks a lot!

[Auth0rM0rgan]

Im facing the same thing. The number of network parameters is 910K!

[rami0205]

I found that #params in paper excludes "relative_position_bias_table" parameters. These parameters should be included in #params. But this code print({sum(map(lambda x: x.numel(), model.parameters())):,d}) produces right #params that includes "relative_position_bias_table". I think it should be corrected in your paper.