def __init__(self, add_stub=False): super().__init__() self.quant = QuantStub() self.dequant = DeQuantStub() self.add_stub = add_stub self.hswish = nn.Hardswish()
def __init__(self, inputsize=(128, 128)): super(QuantizableBackbone, self).__init__() self.quant = QuantStub() self.dequant = DeQuantStub() self.backbone = Backbone()
def __init__(self): super().__init__() self.quant = QuantStub() self.dequant = DeQuantStub()
def __init__(self): super().__init__() self.float_op = nn.quantized.FloatFunctional() self.quant = QuantStub() self.dequant = DeQuantStub()
def __init__(self): super(QuantStubModel, self).__init__() self.qconfig = default_qconfig self.quant = QuantStub() self.dequant = DeQuantStub() self.fc = torch.nn.Linear(5, 5).to(dtype=torch.float)
def __init__(self, num_classes=1000, width_mult=1.0, inverted_residual_setting=None, round_nearest=8): """ MobileNet V2 main class Args: num_classes (int): Number of classes width_mult (float): Width multiplier - adjusts number of channels in each layer by this amount inverted_residual_setting: Network structure round_nearest (int): Round the number of channels in each layer to be a multiple of this number Set to 1 to turn off rounding """ super(MobileNetV2, self).__init__() block = InvertedResidual input_channel = 32 last_channel = 1280 if inverted_residual_setting is None: inverted_residual_setting = [ # t, c, n, s [1, 16, 1, 1], [6, 24, 2, 2], [6, 32, 3, 2], [6, 64, 4, 2], [6, 96, 3, 1], [6, 160, 3, 2], [6, 320, 1, 1], ] # only check the first element, assuming user knows t,c,n,s are required if len(inverted_residual_setting) == 0 or len( inverted_residual_setting[0]) != 4: raise ValueError("inverted_residual_setting should be non-empty " "or a 4-element list, got {}".format( inverted_residual_setting)) # building first layer input_channel = _make_divisible(input_channel * width_mult, round_nearest) self.last_channel = _make_divisible( last_channel * max(1.0, width_mult), round_nearest) features = [ConvBNReLU(3, input_channel, stride=2)] # building inverted residual blocks for t, c, n, s in inverted_residual_setting: output_channel = _make_divisible(c * width_mult, round_nearest) for i in range(n): stride = s if i == 0 else 1 features.append( block(input_channel, output_channel, stride, expand_ratio=t)) input_channel = output_channel # building last several layers features.append( ConvBNReLU(input_channel, self.last_channel, kernel_size=1)) # make it nn.Sequential self.features = nn.Sequential(*features) self.quant = QuantStub() self.dequant = DeQuantStub() # building classifier self.classifier = nn.Sequential( nn.Dropout(0.2), nn.Linear(self.last_channel, num_classes), ) # weight initialization for m in self.modules(): if isinstance(m, nn.Conv2d): nn.init.kaiming_normal_(m.weight, mode='fan_out') if m.bias is not None: nn.init.zeros_(m.bias) elif isinstance(m, nn.BatchNorm2d): nn.init.ones_(m.weight) nn.init.zeros_(m.bias) elif isinstance(m, nn.Linear): nn.init.normal_(m.weight, 0, 0.01) nn.init.zeros_(m.bias)