def __init__(self):
super(RFRNet, self).__init__()
self.Pconv1 = PartialConv2d(3, 64, 7, 2, 3,
multi_channel=True,
bias=False)
self.bn1 = nn.BatchNorm2d(64)
self.Pconv2 = PartialConv2d(64, 64, 7, 1, 3,
multi_channel=True,
bias=False)
self.bn20 = nn.BatchNorm2d(64)
self.Pconv21 = PartialConv2d(64, 64, 7, 1, 3,
multi_channel=True,
bias=False)
self.Pconv22 = PartialConv2d(64, 64, 7, 1, 3,
multi_channel=True,
bias=False)
self.bn2 = nn.BatchNorm2d(64)
self.RFRModule = RFRModule()
self.Tconv = nn.ConvTranspose2d(64, 64, 4, 2, 1,
bias=False)
self.bn3 = nn.BatchNorm2d(64)
self.tail1 = PartialConv2d(67, 32, 3, 1, 1,
multi_channel=True,
bias=False)
self.tail2 = Bottleneck(32, 8)
self.out = nn.Conv2d(64, 3, 3, 1, 1, bias=False)
def __init__(self, in_channels_feature, kernel_s = 3, add_last_edge = True):
super(EdgeGenerator, self).__init__()
self.p_conv = PartialConv2d(in_channels_feature + 1, 64, kernel_size = kernel_s, stride = 1, padding = kernel_s // 2, multi_channel = True, bias = False)
self.edge_resolver = Bottleneck(64, 16)
self.out_layer = nn.Conv2d(64, 1, 1, bias = False)