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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 |