def __init__(self, n_classes): super(HighOrder, self).__init__() self.backbone = resnet(101, 16) self.kernelrep = Kernel_Representation() self.featurefusion = Feature_Fusion() self.low_2x = lowpath() self.low_4x = lowpath() self.low_8x = lowpath() self.conv_low1 = nn.Conv2d(384, 48, kernel_size=1, stride=1, padding=0) self.bn1 = nn.BatchNorm2d(48) self.conv_low2 = nn.Conv2d(640, 48, kernel_size=1, stride=1, padding=0) self.bn2 = nn.BatchNorm2d(48) self.conv_low3 = nn.Conv2d(1152, 48, kernel_size=1, stride=1, padding=0) self.bn3 = nn.BatchNorm2d(48) self.conv_cat = nn.Sequential(ConvBNReLU(400, 256), ConvBNReLU(256, 256)) self.conv_out = nn.Conv2d(256, n_classes, kernel_size=1, bias=False) self.relu = nn.ReLU(inplace=True) self.init_weight()
def __init__(self, classes): super(PANet, self).__init__() self.backbone = resnet(50, 16) self.PAModule = PAmodule(classes) self.DIGModule1 = DIGModule(3, 0, 1024) self.DIGModule2 = DIGModule(2, 1, 512) self.DIGModule3 = DIGModule(1, 1, 256) # self.DIGModule4 = DIGModule(0, 1, 128) self.feat1_conv = nn.Conv2d(256, 64, kernel_size=1, stride=1, padding=0) self.feat2_conv = nn.Conv2d(256, 64, kernel_size=1, stride=1, padding=0) self.feat3_conv = nn.Conv2d(256, 64, kernel_size=1, stride=1, padding=0) self.conv_cat = nn.Sequential(ConvBNReLU(448, 256), ConvBNReLU(256, 256)) self.conv_out = nn.Conv2d(256, classes, kernel_size=1, bias=False) self.init_weight()
def __init__(self): super(Origin_Res, self).__init__() self.resnet = resnet(101, 16) # self.resnet = resnet101(pretrained=True) self.conv1 = ConvBNReLU(2048, 256, 3, 1, 1, 1) self.bn1 = nn.BatchNorm2d(256) self.conv2 = ConvBNReLU(256, 19, 1, 1, 1, 1)
def __init__(self, classes): super(PANet, self).__init__() self.backbone = resnet(101, 16) self.PAModule = PAmodule() self.DIGModule1 = DIGModule(3, 0, 1024) self.DIGModule2 = DIGModule(2, 1, 512) self.DIGModule3 = DIGModule(1, 1, 256) # self.DIGModule4 = DIGModule(0, 1, 128) self.conv_out = nn.Conv2d(256, classes, kernel_size=1, bias=False)
def __init__(self): super(Origin_Res, self).__init__() self.resnet = resnet(101, 16) self.low_2x = low_path_2x() self.low_4x = low_path_4x() self.low_8x = low_path_8x() self.conv_8x = ConvBNReLU(3072, 256, 3, 1, 1, 1) self.conv_4x = ConvBNReLU(1024, 256, 3, 1, 1, 1) self.conv_2x = ConvBNReLU(512, 256, 3, 1, 1, 1) self.conv_all = nn.Sequential(ConvBNReLU(768, 256, 3, 1, 1, 1), ConvBNReLU(256, 256, 3, 1, 1, 1)) self.conv_out = nn.Conv2d(256, 19, kernel_size=1, bias=False)
def __init__(self, n_classes): super(HighOrder, self).__init__() self.backbone = resnet(101, 16) self.featurefusion = Feature_Fusion() self.conv_low = nn.Conv2d(256, 48, kernel_size=1, stride=1, padding=0) self.bn1 = nn.BatchNorm2d(48) self.conv_cat = nn.Sequential(ConvBNReLU(304, 256), ConvBNReLU(256, 256)) self.conv_out = nn.Conv2d(256, n_classes, kernel_size=1, bias=False) self.relu = nn.ReLU(inplace=True) self.init_weight()
def __init__(self, classes): super(PANet, self).__init__() self.backbone = resnet(101, 16) self.PAModule = PAmodule() # self.DIGModule1 = DIGModule(3, 0, 1024) # self.DIGModule2 = DIGModule(2, 1, 512) # self.DIGModule3 = DIGModule(1, 1, 256) # self.DIGModule4 = DIGModule(0, 1, 128) self.conv_low = nn.Conv2d(256, 48, kernel_size=1, stride=1, padding=0) self.bn1 = nn.BatchNorm2d(48) self.conv_cat = nn.Sequential(ConvBNReLU(304, 256), ConvBNReLU(256, 256)) self.conv_out = nn.Conv2d(256, classes, kernel_size=1, bias=False) self.relu = nn.ReLU(inplace=True) self.init_weight()
def __init__(self, classes): super(PANet, self).__init__() self.backbone = resnet(101, 16) self.x4_low_conv = nn.Conv2d(2048, 256, kernel_size=1, stride=1, padding=0) self.PAModule = PAmodule() self.DIGModule1 = DIGModule(3, 0, 1024) self.DIGModule2 = DIGModule(2, 1, 512) self.DIGModule3 = DIGModule(1, 1, 256) # self.DIGModule4 = DIGModule(0, 1, 128) self.low_2x = lowpath() self.low_4x = lowpath() self.low_8x = lowpath() self.conv_low1 = nn.Conv2d(384, 48, kernel_size=1, stride=1, padding=0) self.bn1 = nn.BatchNorm2d(48) self.conv_low2 = nn.Conv2d(640, 48, kernel_size=1, stride=1, padding=0) self.bn2 = nn.BatchNorm2d(48) self.conv_low3 = nn.Conv2d(1152, 48, kernel_size=1, stride=1, padding=0) self.bn3 = nn.BatchNorm2d(48) self.conv_cat = nn.Sequential(ConvBNReLU(400, 256), ConvBNReLU(256, 256)) self.conv_out = nn.Conv2d(256, classes, kernel_size=1, bias=False) self.relu = nn.ReLU(inplace=True) self.init_weight()
def __init__(self, *args, **kwargs): super(Deeplab_v3plus, self).__init__() self.backbone = resnet(101, 16) self.aspp = ASPP(in_chan=2048, out_chan=256, with_gp=False) self.decoder = Decoder(config_CS.classes, low_chan=256)