Inference error using pretrained TinaFace model on my png file

[junwei-h]

Hello,
On Windows WSL, compiled and installed vedadet successfully. When using my own image 128x128 png file to test TinaFace, I got the following error:

Expected number of channels in input to be divisible by num_groups, but got input of shape [64, 560, 560] and num_groups=32

Python 3.8
torch                     1.13.1

No GPU, I ran

python infer.py configs/infer/tinaface/tinaface_r50_fpn_gn_dcn.py test.png

The pre-trained model is tinaface_r50_fpn_gn_dcn.pth and the test.png has 128 x 128 px.

Here is how far I traced the error & the image dimension:

• At

  vedadet/tools/infer.py

  Line 88 in 54731b9

  result = engine.infer(data['img'], data['img_metas'])[0]

  ,
  I have

data['img'].shape 
torch.Size([1, 3, 1120, 1120])

• At

  vedadet/vedadet/engines/infer_engine.py

  Line 57 in 54731b9

  dets = self._get_raw_dets(img, img_metas)

  
  I have

img.shape 
torch.Size([3, 1120, 1120])

• At

  vedadet/vedadet/models/backbones/resnet.py

  Line 624 in 54731b9

  x = self.norm1(x)

  
  I have

x.shape
torch.Size([64, 560, 560])

• At https://github.com/pytorch/pytorch/blob/5913437a40a6e45ab7e164afb7c6ec930dd40b2f/torch/nn/modules/normalization.py#L273
  It triggered the error as I have

input.shape 
torch.Size([64, 560, 560])

Do I need to change 'num_groups=32' in tinaface_r50_fpn_gn_dcn.py?

Any suggestions? Thank you.