def test_quantization_with_bucket(self):
quantization = SQuantization(level=1, norm=2)
quantization.bucket_size = 4
vector = torch.Tensor([1, 2, 3, 4, 11, 2, 30, 4, 8, 1])
bucket_quantization = torch.Tensor([
0.0000, 0.0000, 0.0000, 5.4772257805, 0.0000, 0.0000,
32.2645301819, 32.2645301819, 8.0622577667, 0.0000
])
torch.manual_seed(10)
a = quantization.compress(vector)
print(a)
assert a.equal(
bucket_quantization), "The quantization by bucket is incorrect."
def test_quantization_without_bucket(self):
quantization = SQuantization(level=1, norm=2)
quantization.bucket_size = 100
zeros = torch.zeros(10)
vector = torch.Tensor([1, 2, 3, 4, 5, 6, 9, 1, 2, 3, 2])
assert quantization.__qtzt__(zeros).equal(
zeros), "Compressing a zeros vector must return zeros."
torch.manual_seed(10)
single_qutzt = quantization.__qtzt__(vector)
torch.manual_seed(10)
bucket_qtzt = quantization.compress(vector)
assert bucket_qtzt.equal(
single_qutzt
), "A vector with less element than the bucket size should be quantized as a single vector."