Question about model_type, num_classes,image size ?

[bit2001]

Hi,
First, thanks for your excellent product.
I am currently testing your ant-spoofing program.
There are some question.

1. What is the role of "model_type"(MiniFASNetV1/MiniFASNetV2/MiniFASNetV1SE/MiniFASNetV2SE) ?
   There is only 4 items or can be added ?

2. What is the role of "num_classes" ?
   There is 3 or 4 in your source ...
   the 3 or 4 means "0" , "1", "2" ?
   ├── datasets
   └── RGB_Images
   ├── org_1_80x60
   ├── 0
   ├── 1
   └── 2
   What is the meaning 0/1/2 respectively ?

   If I want to 2 kind of classes(Real Face , Fake Face) , I can change 3 or 4 to 2 in your source ?

3. I want to train the 2 kind images(Real Face / Fake Face)
   3.1) I can compose dataset like below ?
   It is reasonable ?
   ├── datasets
   └── RGB_Images
   ├── 1_230x230
   ├── Real
   ├── RealFace1.jpg
   ├── ....
   └── Fake
   ├── FakeFace1.jpg
   ├── FakeFace2.jpg
   ├── ...

        In your source the is only org_1x80x60 , 1_80x80 , 2.7_80x80 , 4_80x80.
        I have image (203x203)
        I should make image size as yoy did ?
          for example,  org_1x203x203 , 1_80x80 , 2.7_80x80 , 4_80x80. ...
   

   3.2) after finishing training, how do I name the output => snapshot .pth and copy it to "Silent-Face-Anti-Spoofing-master\resources\anti_spoof_models"
   Do I name it as "1_80x80_MiniFASNetV2.pth" or another name or "1_230x230_MiniFASNetV2.pth" or new what ?
   Of course , I anlayzed your source but it is a little bit confusing .

Thanks in Advance

[bit2001]

In addition,
I have another question.
I resized image to 80 * 80 * 3(channels) and retrained in linux.
After retrain, I copy 2020-11-10-22-29_Anti_Spoofing_1_80x80_model_iter-4950.pth to resources\anti_spoof_models\1_80x80_MiniFASNetV2SE.pth.

I test it like "python test.py".

The follow error occurred.

Traceback (most recent call last):
File "test.py", line 109, in
test(args.image_name, args.model_dir, args.device_id)
File "test.py", line 59, in test
prediction += model_test.predict(img, os.path.join(model_dir, model_name))
File "D:\WorkSpace2\Silent-Face-Anti-Spoofing-master\Silent-Face-Anti-Spoofing-master\src\anti_spoof_predict.py", line 87, in predict
self._load_model(model_path)
File "D:\WorkSpace2\Silent-Face-Anti-Spoofing-master\Silent-Face-Anti-Spoofing-master\src\anti_spoof_predict.py", line 76, in _load_model
self.model.load_state_dict(new_state_dict)
File "C:\Users\SDS\AppData\Local\Programs\Python\Python36\lib\site-packages\torch\nn\modules\module.py", line 777, in load_state_dict
self.class.name, "\n\t".join(error_msgs)))
RuntimeError: Error(s) in loading state_dict for MiniFASNetSE:
Missing key(s) in state_dict: "conv1.conv.weight", "conv1.bn.weight", "conv1.bn.bias", "conv1.bn.running_mean", "conv1.bn.running_var", "conv1.prelu.weight", "conv2_dw.conv.weight", "conv2_dw.bn.weight", "conv2_dw.bn.bias", "conv2_dw.bn.running_mean", "conv2_dw.bn.running_var", "conv2_dw.prelu.weight", "conv_23.conv.conv.weight", "conv_23.conv.bn.weight", "conv_23.conv.bn.bias", "conv_23.conv.bn.running_mean", "conv_23.conv.bn.running_var", "conv_23.conv.prelu.weight", "conv_23.conv_dw.conv.weight", "conv_23.conv_dw.bn.weight", "conv_23.conv_dw.bn.bias", "conv_23.conv_dw.bn.running_mean", "conv_23.conv_dw.bn.running_var", "conv_23.conv_dw.prelu.weight", "conv_23.project.conv.weight", "conv_23.project.bn.weight", "conv_23.project.bn.bias", "conv_23.project.bn.running_mean", "conv_23.project.bn.running_var", "conv_3.model.0.conv.conv.weight", "conv_3.model.0.conv.bn.weight", "conv_3.model.0.conv.bn.bias", "conv_3.model.0.conv.bn.running_mean", "conv_3.model.0.conv.bn.running_var", "conv_3.model.0.conv.prelu.weight", "conv_3.model.0.conv_dw.conv.weight", "conv_3.model.0.conv_dw.bn.weight", "conv_3.model.0.conv_dw.bn.bias", "conv_3.model.0.conv_dw.bn.running_mean", "conv_3.model.0.conv_dw.bn.running_var", "conv_3.model.0.conv_dw.prelu.weight", "conv_3.model.0.project.conv.weight", "conv_3.model.0.project.bn.weight", "conv_3.model.0.project.bn.bias", "conv_3.model.0.project.bn.running_mean", "conv_3.model.0.project.bn.running_var", "conv_3.model.1.conv.conv.weight", "conv_3.model.1.conv.bn.weight", "conv_3.model.1.conv.bn.bias", "conv_3.model.1.conv.bn.running_mean", "conv_3.model.1.conv.bn.running_var", "conv_3.model.1.conv.prelu.weight", "conv_3.model.1.conv_dw.conv.weight", "conv_3.model.1.conv_dw.bn.weight", "conv_3.model.1.conv_dw.bn.bias", "conv_3.model.1.conv_dw.bn.running_mean", "conv_3.model.1.conv_dw.bn.running_var", "conv_3.model.1.conv_dw.prelu.weight", "conv_3.model.1.project.conv.weight", "conv_3.model.1.project.bn.weight", "conv_3.model.1.project.bn.bias", "conv_3.model.1.project.bn.running_mean", "conv_3.model.1.project.bn.running_var", "conv_3.model.2.conv.conv.weight", "conv_3.model.2.conv.bn.weight", "conv_3.model.2.conv.bn.bias", "conv_3.model.2.conv.bn.running_mean", "conv_3.model.2.conv.bn.running_var", "conv_3.model.2.conv.prelu.weight", "conv_3.model.2.conv_dw.conv.weight", "conv_3.model.2.conv_dw.bn.weight", "conv_3.model.2.conv_dw.bn.bias", "conv_3.model.2.conv_dw.bn.running_mean", "conv_3.model.2.conv_dw.bn.running_var", "conv_3.model.2.conv_dw.prelu.weight", "conv_3.model.2.project.conv.weight", "conv_3.model.2.project.bn.weight", "conv_3.model.2.project.bn.bias", "conv_3.model.2.project.bn.running_mean", "conv_3.model.2.project.bn.running_var", "conv_3.model.3.conv.conv.weight", "conv_3.model.3.conv.bn.weight", "conv_3.model.3.conv.bn.bias", "conv_3.model.3.conv.bn.running_mean", "conv_3.model.3.conv.bn.running_var", "conv_3.model.3.conv.prelu.weight", "conv_3.model.3.conv_dw.conv.weight", "conv_3.model.3.conv_dw.bn.weight", "conv_3.model.3.conv_dw.bn.bias", "conv_3.model.3.conv_dw.bn.running_mean", "conv_3.model.3.conv_dw.bn.running_var", "conv_3.model.3.conv_dw.prelu.weight", "conv_3.model.3.project.conv.weight", "conv_3.model.3.project.bn.weight", "conv_3.model.3.project.bn.bias", "conv_3.model.3.project.bn.running_mean", "conv_3.model.3.project.bn.running_var", "conv_3.model.3.se_module.fc1.weight", "conv_3.model.3.se_module.bn1.weight", "conv_3.model.3.se_module.bn1.bias", "conv_3.model.3.se_module.bn1.running_mean", "conv_3.model.3.se_module.bn1.running_var", "conv_3.model.3.se_module.fc2.weight", "conv_3.model.3.se_module.bn2.weight", "conv_3.model.3.se_module.bn2.bias", "conv_3.model.3.se_module.bn2.running_mean", "conv_3.model.3.se_module.bn2.running_var", "conv_34.conv.conv.weight", "conv_34.conv.bn.weight", "conv_34.conv.bn.bias", "conv_34.conv.bn.running_mean", "conv_34.conv.bn.running_var", "conv_34.conv.prelu.weight", "conv_34.conv_dw.conv.weight", 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Unexpected key(s) in state_dict: "model.conv1.conv.weight", "model.conv1.bn.weight", "model.conv1.bn.bias", "model.conv1.bn.running_mean", "model.conv1.bn.running_var", "model.conv1.bn.num_batches_tracked", "model.conv1.prelu.weight", "model.conv2_dw.conv.weight", "model.conv2_dw.bn.weight", "model.conv2_dw.bn.bias", "model.conv2_dw.bn.running_mean", "model.conv2_dw.bn.running_var", "model.conv2_dw.bn.num_batches_tracked", "model.conv2_dw.prelu.weight", "model.conv_23.conv.conv.weight", "model.conv_23.conv.bn.weight", "model.conv_23.conv.bn.bias", "model.conv_23.conv.bn.running_mean", "model.conv_23.conv.bn.running_var", "model.conv_23.conv.bn.num_batches_tracked", "model.conv_23.conv.prelu.weight", "model.conv_23.conv_dw.conv.weight", "model.conv_23.conv_dw.bn.weight", "model.conv_23.conv_dw.bn.bias", "model.conv_23.conv_dw.bn.running_mean", "model.conv_23.conv_dw.bn.running_var", "model.conv_23.conv_dw.bn.num_batches_tracked", "model.conv_23.conv_dw.prelu.weight", "model.conv_23.project.conv.weight", "model.conv_23.project.bn.weight", "model.conv_23.project.bn.bias", "model.conv_23.project.bn.running_mean", "model.conv_23.project.bn.running_var", "model.conv_23.project.bn.num_batches_tracked", "model.conv_3.model.0.conv.conv.weight", "model.conv_3.model.0.conv.bn.weight", "model.conv_3.model.0.conv.bn.bias", "model.conv_3.model.0.conv.bn.running_mean", "model.conv_3.model.0.conv.bn.running_var", "model.conv_3.model.0.conv.bn.num_batches_tracked", "model.conv_3.model.0.conv.prelu.weight", "model.conv_3.model.0.conv_dw.conv.weight", "model.conv_3.model.0.conv_dw.bn.weight", "model.conv_3.model.0.conv_dw.bn.bias", "model.conv_3.model.0.conv_dw.bn.running_mean", "model.conv_3.model.0.conv_dw.bn.running_var", "model.conv_3.model.0.conv_dw.bn.num_batches_tracked", "model.conv_3.model.0.conv_dw.prelu.weight", "model.conv_3.model.0.project.conv.weight", "model.conv_3.model.0.project.bn.weight", "model.conv_3.model.0.project.bn.bias", "model.conv_3.model.0.project.bn.running_mean", "model.conv_3.model.0.project.bn.running_var", "model.conv_3.model.0.project.bn.num_batches_tracked", "model.conv_3.model.1.conv.conv.weight", "model.conv_3.model.1.conv.bn.weight", "model.conv_3.model.1.conv.bn.bias", "model.conv_3.model.1.conv.bn.running_mean", "model.conv_3.model.1.conv.bn.running_var", "model.conv_3.model.1.conv.bn.num_batches_tracked", "model.conv_3.model.1.conv.prelu.weight", "model.conv_3.model.1.conv_dw.conv.weight", "model.conv_3.model.1.conv_dw.bn.weight", "model.conv_3.model.1.conv_dw.bn.bias", "model.conv_3.model.1.conv_dw.bn.running_mean", "model.conv_3.model.1.conv_dw.bn.running_var", "model.conv_3.model.1.conv_dw.bn.num_batches_tracked", "model.conv_3.model.1.conv_dw.prelu.weight", "model.conv_3.model.1.project.conv.weight", "model.conv_3.model.1.project.bn.weight", "model.conv_3.model.1.project.bn.bias", "model.conv_3.model.1.project.bn.running_mean", "model.conv_3.model.1.project.bn.running_var", 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What is the root cause of error?

Help me .
Thanks.

[hufangjian]

i get the same error.

[zhuyingSeu]

1.We provide four model definitions.
2.You can define the num_classes according to your need.We define the 2D Fake Face as class 0, the True Face as class 1 and the 3D Fake Face as class 2.You can define Fake Face as class 0 and the True Face as class 1.
3.You can ingnore the org_1_80x60, because the size is related to the origin image.
4.You can name the model according to you rule.
5.When we test the model, we do not use the FTGenerator because it is only used to train the model.

[eliayakoumi]

I am getting the same error. after doing the training i am usingthe resulting 2020-12-03-17-48_Anti_Spoofing_2.7_80x80_model_iter-575.pth after renaming it to 2.7_80x80_MiniFASNetV2SE.pth (the same MiniFASNet use in default training code).
How did you solve this error?

[Klawens]

Same error, using MiniFASNetV2SE modified classes=2, got Missing key(s) in state_dict:

[AZ390]

@bit2001 Hello
If you were kind enough and tell me, how did you solve this error, as I am facing the same error?

[Cj00W]

by add some code in anti_spoof_predict.py and rename the pth file as follow, i have run my model successfully.