def __init__( self, block: nn.Module, layers: List[int], output_dims: List[int], groups: int = 1, width_per_group: int = 64, replace_stride_with_dilation: Optional[Tuple[bool, bool, bool]] = None, normalization: Normalization = None, blocks: int = 4, pretrained_settings: Optional[Dict[str, Union[str, int, float, List[Union[int, float]]]]] = None, pretrained: bool = False, progress: bool = False, ): ResNet.__init__( self, block, layers, groups=groups, width_per_group=width_per_group, norm_layer=normalization, replace_stride_with_dilation=replace_stride_with_dilation, ) Encoder.__init__(self, output_dims, pretrained_settings, pretrained, progress) self.blocks = blocks
def __init__(self, pretrained=True, cuda=True): #super(ResNet, self).__init__( block=BasicBlock, layers=[2, 2, 2, 2] ) ResNet.__init__(self, block=BasicBlock, layers=[2, 2, 2, 2]) if pretrained: self.load_state_dict(model_zoo.load_url(resnet_urls['resnet18'])) self.filter = USM(in_channels=3, kernel_size=5, fixed_coeff=True, sigma=1.667, cuda=cuda, requires_grad=True) #self.filter.assign_weight(1.33) self.filter_conv1 = USM(in_channels=64, kernel_size=5, fixed_coeff=True, sigma=1.667, cuda=cuda)
def __init__(self, block, layers, num_classes=1000, kw=4, ka=4, fp_layers=None, align_zero=True, use_channel_quant=False, use_ckpt=False, use_multi_domain=False): ResNet.__init__(self, block, layers, num_classes) IDQ.__init__(self, ResNet.forward, kw, ka, fp_layers, align_zero, use_channel_quant, use_ckpt, use_multi_domain)
def __init__(self, block, layers, num_coarse_classes, num_classes=1000, zero_init_residual=True, groups=1, width_per_group=64, replace_stride_with_dilation=None, norm_layer=None, mean=None, std=None): _ResNet.__init__( self, block, layers, num_classes=num_classes, zero_init_residual=zero_init_residual, groups=groups, width_per_group=width_per_group, replace_stride_with_dilation=replace_stride_with_dilation, norm_layer=norm_layer) if mean is None: self.register_buffer( 'mean', torch.tensor(IMAGENET_MEAN).view(1, -1, 1, 1) / 255) else: self.register_buffer('mean', torch.tensor(mean).view(1, -1, 1, 1)) if std is None: self.register_buffer( 'std', torch.tensor(IMAGENET_STD).view(1, -1, 1, 1) / 255) else: self.register_buffer('std', torch.tensor(std).view(1, -1, 1, 1)) self.aux1 = InceptionAux(in_channels=4 * 64, num_coarse_classes=num_coarse_classes[0], pool=2) self.aux2 = InceptionAux(in_channels=4 * 128, num_coarse_classes=num_coarse_classes[1], pool=1) self.aux3 = InceptionAux(in_channels=4 * 256, num_coarse_classes=num_coarse_classes[2])
def __init__(self, ngpu): ResNet.__init__(self, BasicBlock, [3, 4, 6, 3]) self.load_state_dict(model_zoo.load_url(model_urls['resnet34'])) self.ngpu = ngpu self.output = nn.Sequential(nn.Linear(512, 1), nn.Sigmoid())