def init(self, C, M, N, dtype, mode):
assert(mode in ('Q', 'D'))
self.input = torch.rand(C, M, N)
self.dtype = dtype
self.op = nnq.Quantize(scale=1.0, zero_point=0, dtype=dtype)
self.set_module_name('QuantizePerTensor')
if mode == 'D':
self.input = self.op(self.input)
self.op = nnq.DeQuantize()
self.set_module_name('DequantizePerTensor')
def test_quant_dequant_api(self):
r = torch.tensor([[1., -1.], [1., -1.]], dtype=torch.float)
scale, zero_point, dtype = 1.0, 2, torch.qint8
# testing Quantize API
qr = torch.quantize_linear(r, scale, zero_point, dtype)
quant_m = nnq.Quantize(scale, zero_point, dtype)
qr2 = quant_m(r)
self.assertEqual(qr, qr2)
# testing Dequantize API
rqr = qr.dequantize()
dequant_m = nnq.DeQuantize()
rqr2 = dequant_m(qr2)
self.assertEqual(rqr, rqr2)
def __init__(self, inputs_to_qparams_map):
super(ConditionalQuantize, self).__init__()
self.quantizers = nn.ModuleDict()
for idx, qparams in inputs_to_qparams_map.items():
self.quantizers[str(idx)] = nnq.Quantize(*qparams)
