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import numpy as np
import torch
def set_up_normalization(device='cpu', seq_len=16, scale=3, norm_path='../data/norm_scaled.npy'):
assert scale==3, 'Currently only support scale=3'
# return normalizeation function with given device
norm = np.load(norm_path, allow_pickle=True).item()[(seq_len, scale)]
min_val = torch.tensor(norm[0]).to(device, dtype=torch.float32)
max_val = torch.tensor(norm[1]).to(device, dtype=torch.float32)
# input: joints (..., 28*3) or (..., 28, 3)
def denormalize(data: torch.Tensor):
shape_orig = data.shape
data = data.reshape((-1, 3))
data = (data + 1.) * (max_val - min_val) / 2. + min_val
data = data.reshape(shape_orig)
return data
def normalize(data: torch.Tensor):
shape_orig = data.shape
data = data.reshape((-1, 3))
data = -1. + 2. * (data - min_val) / (max_val - min_val)
data = data.reshape(shape_orig)
return data
return normalize, denormalize |