I wanted to work with different models for ensembling purposes. I tried using se_resnext101, but since create_body does arch = model(pretrained), it throws an error saying
TypeError: conv2d(): argument 'input' (position 1) must be Tensor, not bool.
Pytorch model accepts Tensor as input defined in
def forward(self,x)
My model looks like this. I also looked at the docs (#customize model) but the instructions were same.
I am unable to use create_body because of the above reason, create_head isn’t useful either. What is the efficient way to work with custom models with create_cnn?
The method “create_body” have the argument “pretrained”, so you can control it there. Thats why you are getting an error, the method create_body expects not a model, but the method to construct the model.
So how to make create_cnn work ? create_body isn’t useful for custom models which can directly work as: model = se_resnext101(num_classes,pretrained=True) # Can be any model output = model(input)
This is the usual way of working with models.
Yes, I agree… For me too Learner worked, and create_cnn could not make it work. However Jeremy said create_cnn is better. He said: It’s better to use create_cnn, so that fastai will create a version you can use for transfer learning for your problem.
So it seems Learner will not use the pretrained weights and will initialize random weights. I can confirm that there is huge difference between Learner and create_cnn when I passed resnet50 pretrained in both models. The Learner starts with much more error rate and validation loss.
I have listed the different scenarios where it worked or when it did not work with create_cnnin a reply to him.
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