Parameters/Configurations of the pretrained models

[deciding]

Hi,

I noticed that in the assert/main.py, we have the configuration for resnet.

model_params = {
         'MODEL_SELECT' : 1, # which model 
          'NUM_SPOOF_CLASS' : 7, # x-class classification
          'FOCAL_GAMMA' : None, # gamma parameter for focal loss; if obj is not focal loss, set this to None 
          'NUM_RESNET_BLOCK' : 5, # number of resnet blocks in ResNet 
          'AFN_UPSAMPLE' : 'Bilinear', # upsampling method in AFNet: Conv or Bilinear
          'AFN_ACTIVATION' : 'sigmoid', # activation function in AFNet: sigmoid, softmaxF, softmaxT
          'NUM_HEADS' : 3, # number of heads for multi-head att in SAFNet 
          'SAFN_HIDDEN' : 10, # hidden dim for SAFNet
          'SAFN_DIM' : 'T', # SAFNet attention dim: T or F
          'RNN_HIDDEN' : 128, # hidden dim for RNN
          'RNN_LAYERS' : 4, # number of hidden layers for RNN
          'RNN_BI': True, # bidirecitonal/unidirectional for RNN
          'DROPOUT_R' : 0.0, # dropout rate

but it does not fit for all the 4 pretrained models.

To load the pretrained models successfully, could you provide the configuration/parameters of them? Thanks !