def test_dequant_stub():
normal_net = Float.DequantStub()
normal_net.eval()
qat_from_float = QAT.DequantStub.from_float_module(normal_net)
qat_from_float.eval()
disable_fake_quant(qat_from_float)
disable_observer(qat_from_float)
qat_net = QAT.DequantStub()
qat_net.eval()
disable_observer(qat_net)
propagate_qconfig(qat_net, min_max_fakequant_qconfig)
init_qat_net(qat_net)
q_net = Q.DequantStub.from_qat_module(qat_net)
q_net.eval()
x = mge.tensor(np.random.normal(size=(3, 3)).astype("float32"))
x = fake_quant(x, inp_scale)
x.q_dict["scale"] = inp_scale
normal = normal_net(x)
qat_without_fakequant = qat_from_float(x)
fake_quant_normal = normal_net(x)
qat = qat_net(x)
q = q_net(quant(x, inp_scale)).numpy()
np.testing.assert_allclose(qat_without_fakequant, normal)
np.testing.assert_allclose(qat, fake_quant_normal)
np.testing.assert_allclose(q, fake_quant_normal.numpy())
def __init__(self):
super().__init__()
self.quant = QAT.QuantStub()
self.linear = Float.Sequential(QAT.Linear(3, 3), QAT.Linear(3, 3))
self.dequant = QAT.DequantStub()
self.linear[0].bias[...] = Parameter(np.random.rand(3))
self.linear[1].bias[...] = Parameter(np.random.rand(3))
def __init__(self):
super().__init__()
self.quant = QAT.QuantStub()
self.linear = QAT.Linear(3, 3)
self.dequant = QAT.DequantStub()
self.linear.bias.set_value(np.random.rand(3))