I think you should set track_running_stats=False, because you don’t want to use the running stats in any case.
You could calculate the global stats using this example:
class MyDataset(Dataset):
    def __init__(self):
        self.data = torch.randn(100, 3, 24, 24)
        
    def __getitem__(self, index):
        return self.data[index]
    
    def __len__(self):
        return len(self.data)

dataset = MyDataset()
loader = DataLoader(dataset, batch_size=5, shuffle=False, num_workers=1)

tmp_mean = 0
tmp_var = 0
nb_elems = 0
for data in loader:
    b, _, h, w = data.size()
    tmp_mean += data.sum(3).sum(2).sum(0)
    tmp_var += torch.pow(data, 2).sum(3).sum(2).sum(0)
    nb_elems += b*h*w

global_mean = tmp_mean / nb_elems
global_var = tmp_var / (nb_elems - 1) - global_mean**2