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I am wondering would the number of parameters in the models like ResNet18, Vgg16, and DenseNet201 would change if we change the input size to the model?

I did measure the number of parameters with the following command

pytorch_total_params = sum(p.numel() for p in model.parameters() if p.requires_grad)

Also, I have tried this snippet, and the number of parameters did not change for different input size

import torchvision.models as models
model= models.resnet18(pretrained = False)
model.cuda()
summary(model, (1,64,64))
AI_NA
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2 Answers2

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No it would not. Parameters of a model have the purpose of processing the input as it propagates inside the network pipeline.

The parameters are mostly trained to serve their purpose, which is defined by the training task. Consider a increase in number of parameters based on the input? What would their values be? Would they be random? How would this new parameters with new values affect the inference of the model?

Such a sudden, random change to the fine-tuned, well-trained parameters of the model would be impractical. Maybe there are some other algorithms that I am unaware of, that change their parameter collection based on input. But the architectures that have been mentioned in question do not support such functionality.

Bedir Yilmaz
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Traninable parameters do not change with the change in input. If you see the weights in first layer of the model with the command list(model.parameters())[0].shape you can realize that it does not depend on the height and width of the input, but it depends on the number of channels(e.g Gray, RGB, HyperSpectral), which usually is very insignificant in bigger models. For further information about getting the input shape, you can see this toy example.

Mughees
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