SegFormer Training extremely slow #996
Description
I've been training a series of models implemented by this package for a while, all using 'tu-mobilevit_xxs' as an encoder. However I noticed that the latest addition, segformer, is extremely slow in training compared to the others despite being reported to have the same number of parameters by Pytorch lightning. Here is a visualization from Tensorboard:
And here is the parameter count reported by Lightning:
It's much slower than the rest in training, and considerably slows down my laptop. Why does it take so much resources despite being approximately the same size as the other models? Here are my architecture hyperparameters for reference, for every model I trained:
h_params_Unet = {
'encoder': encoder,
'depth': 5,
'Bnorm': True,
'Attn': 'scse',
'channels': (256, 128, 64, 32, 16),
'lr_init': 1e-3,
'weight_decay': 0,
'temperature': 2,
'Arch': "Unet"
}
h_params_DLV3plus = {
'encoder': encoder,
'depth': 5,
'encoder_output_stride': 16,
'channels': 512,
'decoder_atrous_rates': (16, 32, 128),
'lr_init': 1e-3,
'weight_decay': 0,
'temperature': 2,
'Arch': "DLV3+"
}
h_params_MANet = {
'encoder': encoder, # Backbone encoder
'
```depth': 5, # Number of encoder stages
'Bnorm': True, # Use batch normalization in the decoder
'channels': (256, 128, 64, 32, 16), # Number of channels in each decoder layer
'decoder_pab_channels': 64,
'lr_init': 1e-3, # Initial learning rate
'weight_decay': 0, # Weight decay for regularization
'temperature': 2, # Temperature for distillation
'Arch': "MANet"
}
h_params_PAN = {
'encoder': encoder, # Backbone encoder
'encoder_output_stride': 16,
'channels': 512, # Number of channels in each decoder layer
'lr_init': 1e-3, # Initial learning rate
'weight_decay': 0, # Weight decay for regularization
'temperature': 2, # Temperature for distillation
'Arch': "PAN"
}
h_params_Segformer = {
'encoder': encoder, # Backbone encoder
'depth': 5, # Number of encoder stages
'channels': 512, # Number of channels in each decoder layer
'lr_init': 1e-3, # Initial learning rate
'weight_decay': 0, # Weight decay for regularization
'temperature': 2, # Temperature for distillation
'Arch': "Segformer"
}
All of them have the same number of channels and depth where applicable. What am I doing wrong?