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import numpy as np
import torch
import math
#------------------Denormalization---------------------------------------------
def denormalize_bin(tensor):
tr = torch.clamp(tensor, -1., 1.) # Clamp the values between -1 and 1
tr = tr.add(1).div(2) # Shift to [0, 1]
return tr
def denormalize_tr(tensor):
tr = torch.clamp(tensor, -1., 1.) # Clamp the values between -1 and 1
tr = tr.add(1).div(2).mul(255) # Shift to [0, 1] and scale to [0, 255]
tr = tr.byte() # Convert the tensor to uint8
return tr
def denormalize_ar(tensor):
tr = torch.clamp(tensor, -1., 1.) # Clamp the values between -1 and 1
tr = tr.add(1).div(2).mul(255) # Shift to [0, 1] and scale to [0, 255]
tr = tr.byte() # Convert the tensor to uint8
arr = tr.permute(0, 2, 3, 1).cpu().detach().numpy() # Convert to (N, H, W, C) and numpy array
return arr |