even odd extraction without slicing

t = model.swizzle(model.swizzle(y)).clone()
t[0, :, 0, 0] = torch.arange(0, 16)

def even_odd(x, dim=1):
    evens, odds = x.view(x.size()), x.view(x.size())

    odd_offset = np.asscalar(np.prod(x.size()[dim + 1:]))
    shape, stride = list(x.size()), list(x.stride())
    shape[dim] = shape[dim] // 2
    stride[dim] *= 2

    evens.set_(x.storage(), x.storage_offset(),             shape, stride)
    odds.set_(x.storage(), x.storage_offset() + odd_offset, shape, stride)
    
    return evens, odds

t = torch.randn([128, 64, 64, 64])
evens, odds = even_odd(t)
evens[0, :, 0, 0], odds[0, :, 0, 0]

with torch.autograd.profiler.profile(use_cuda=True) as prof:
    for i in range(10):
        evens, odds = even_odd(t)
print(prof.key_averages().table('cpu_time_total'))

with torch.autograd.profiler.profile(use_cuda=True) as prof:
    for i in range(10):
        tevens = t[:,0::2,:,:]
        todds = t[:,1::2,:,:]
print(prof.key_averages().table('cpu_time_total'))

print('even error', torch.abs(tevens - evens).sum())
print('odd error', torch.abs(todds - odds).sum())