Hello csailnadi,
Take a look at the documentation for Dropout: https://pytorch.org/docs/stable/nn.html#torch.nn.Dropout 4
To keep the same output scale (in expectation), outputs are rescaled by 1/(1-p) during training. You can see this in your example: for instance 0.0041 is mapped to 0.0041 / 0.7 = 0,00585 ~ 0.0059.
Hope this helped