Upload src/utils/normalize.py with huggingface_hub

src/utils/normalize.py

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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