Deconstructing Deep Learning + δeviations
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✎: Post, ⚛ : Deviation, 📖: Book notes, 🚀: Spaceship
by Subhaditya Mukherjee
Continuing the action recognition project.
We first need to define a custom path class which will give us the paths of the original dataset, the preprocessed one and the pretrained weights. I know, we could probably do it without a class but I am reimplementing here so lets just go with it for now. The only new thing here is @staticmethod. This is python syntax for declaring a method which does not need a class to be instantiated and are not dependant on its state. Basically, they would return a static method for any function which is passed as its parameter.
class Path(object):
@staticmethod
def db_dir(database):
root_dir = "/home/subhaditya/Desktop/Datasets/UCF101/"
output_dir = "/home/subhaditya/Desktop/Datasets/UCF101pre/"
return root_dir, output_dir
@staticmethod
def model_dir():
return "/home/subhaditya/Desktop/Datasets/UCF101pre/Models/c3d-pre.pth"
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