perf and #param

[junhu-svd]

Hi, Thanks for the great works. I have one question about perf and #param mentioned in the paper(Table 7). I use torchsummary to generate the GMACS and #param for MPRNET(2-stage), it turns out that #param is 53.66MB(>>11.3MB in paper), from GMACS side, it is about 10x more than DMPHN(1-2-4). Although torchsummary isn't perfect and there exists a delta between real performance and GMACS, I wonder any hint/idea to explain the gap ?

[adityac8]

Hi @junhu-svd
We use the following snippet to calculate the model parameters.

def params_count(model):
    """Computes the number of parameters."""
    return np.sum([p.numel() for p in model.parameters()]).item()

For our 2-Stage MPRNet Deblurring model, the number of trainable parameters are 11.3 Million. For DMPHN, it is 21.7 Million.

Thanks

[junhu-svd]

Thanks, adityac8,

I can almost reproduce your # after some corrections, and in my latest profiler, MPRNET(2-stages) uses 11.8(not far from 11.3M) Million parameters ~ 45MB (if saved float32)

fyi, DMPHN is 5.4 Million parameters ~ 21.7MB (if saved float32) and this is consistent with the # reported in DMPHN paper

From perf aspect, do you have any official statistics for MPRNET(2-stages) in terms of GMACs? what I got from torch-summary is 231.54G(mult-adds) for 256x256 input ? which is a very higher number.

[swz30]

Hi @junhu-svd

Here is a table from the DMPHN paper.

For evaluation, we use Stack(4)-DMPHN version which is publicly released by the original authors and provides their best results. This Stack(4)-DMPHN has 21.7 Million parameters ~ 86.8 MB.

If you are looking for an efficient model in terms of compute and parameters, you can retrain our MPRNet model by setting
n_feat=40, scale_unetfeats=40, scale_orsnetfeats=32

MPRNet/Deblurring/MPRNet.py

Line 239 in 9edfb1d

def __init__(self, in_c=3, out_c=3, n_feat=96, scale_unetfeats=48, scale_orsnetfeats=32, num_cab=8, kernel_size=3, reduction=4, bias=False):

This will provide you with a 6 Million parameter model that yields ~31.8 dB PSNR on GoPro testset, as shown in Figure 1 of our paper.

Thanks