def __init__(self):
super().__init__()
self.sigmoid = torch.nn.Sigmoid()
self.hardsigmoid = torch.nn.Hardsigmoid()
self.tanh = torch.nn.Tanh()
self.quant = QuantStub()
self.dequant = DeQuantStub()
def __init__(self):
super(SubModule, self).__init__()
self.qconfig = default_qconfig
self.mod1 = torch.nn.Conv2d(3, 3, 3, bias=False).to(dtype=torch.float)
self.mod2 = nn.ReLU()
self.quant = QuantStub()
self.dequant = DeQuantStub()
def __init__(self, *args: Any, **kwargs: Any) -> None:
"""
MobileNet V3 main class
Args:
Inherits args from floating point MobileNetV3
"""
super().__init__(*args, **kwargs)
self.quant = QuantStub()
self.dequant = DeQuantStub()
def __init__(self):
super(ModelWithFunctionals, self).__init__()
self.mycat = nnq.FloatFunctional()
self.myadd = nnq.FloatFunctional()
self.mymul = nnq.FloatFunctional()
self.myadd_relu = nnq.FloatFunctional()
self.my_scalar_add = nnq.FloatFunctional()
self.my_scalar_mul = nnq.FloatFunctional()
self.quant = QuantStub()
self.dequant = DeQuantStub()
def __init__(self, w, b, m, v):
super(SimpleQuantizedBatchNormRelu, self).__init__()
self.bn = torch.nn.BatchNorm3d(4)
self.relu = torch.nn.ReLU()
self.bn.weight = torch.nn.Parameter(w)
self.bn.bias = torch.nn.Parameter(b)
self.bn.running_mean = m
self.bn.running_var = v
self.q = QuantStub()
self.dq = DeQuantStub()
def __init__(self):
super().__init__()
self.act = Act()
self.quant = QuantStub()
self.dequant = DeQuantStub()