The role of the parameter 'scale' in the training mode and the 'scales' in the testing mode

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I still have two questions about the training. 1: what' s the input of the training, fully-sampled HR volume or LR volume? The question stems from the fact that the code has downsampled the h and w directions according to the parameters 'scale' in the 'dataset.py'. xy_inds = torch.meshgrid(torch.linspace(0, self.H - 1, self.H // self.scale), torch.linspace(0, self.W - 1, self.W // self.scale)) 2: what' s the role of the parameter 'scale' in the training mode and the 'scales' in the testing mode? If the input is the LR volume, the model training is converged based on the existing sparse sampling points. It seems that 'scale' is no used in the training process.

Thanks for your fast reply again.