def __init__(self):
super(CPLMINet_WSGNRes50, self).__init__()
self.upsample_add = upsample_add
self.upsample = cus_sample
self.dummy_tensor = torch.ones(1,
dtype=torch.float32,
requires_grad=True)
self.div_2, self.div_4, self.div_8, self.div_16, self.div_32 = Backbone_ResNet50_in3(
)
self.upsample_add = upsample_add
self.upsample = cus_sample
self.trans = LightAIM(iC_list=(64, 256, 512, 1024, 2048),
oC_list=(64, 64, 64, 64, 64))
self.sim32 = SIM(64, 32)
self.sim16 = SIM(64, 32)
self.sim8 = SIM(64, 32)
self.sim4 = SIM(64, 32)
self.sim2 = SIM(64, 32)
self.upconv32 = BasicConv2d(64, 64, kernel_size=3, stride=1, padding=1)
self.upconv16 = BasicConv2d(64, 64, kernel_size=3, stride=1, padding=1)
self.upconv8 = BasicConv2d(64, 64, kernel_size=3, stride=1, padding=1)
self.upconv4 = BasicConv2d(64, 64, kernel_size=3, stride=1, padding=1)
self.upconv2 = BasicConv2d(64, 32, kernel_size=3, stride=1, padding=1)
self.upconv1 = BasicConv2d(32, 32, kernel_size=3, stride=1, padding=1)
self.classifier = nn.Conv2d(32, 1, 1)
def __init__(self):
super(CPLightMINet_VGG16, self).__init__()
self.upsample_add = upsample_add
self.upsample = cus_sample
self.dummy_tensor = torch.ones(1,
dtype=torch.float32,
requires_grad=True)
(
self.encoder1,
self.encoder2,
self.encoder4,
self.encoder8,
self.encoder16,
) = Backbone_VGG16_in3()
self.trans = LightAIM((64, 128, 256, 512, 512), (32, 64, 64, 64, 64))
self.sim16 = SIM(64, 32)
self.sim8 = SIM(64, 32)
self.sim4 = SIM(64, 32)
self.sim2 = SIM(64, 32)
self.sim1 = SIM(32, 16)
self.upconv16 = BasicConv2d(64, 64, kernel_size=3, stride=1, padding=1)
self.upconv8 = BasicConv2d(64, 64, kernel_size=3, stride=1, padding=1)
self.upconv4 = BasicConv2d(64, 64, kernel_size=3, stride=1, padding=1)
self.upconv2 = BasicConv2d(64, 32, kernel_size=3, stride=1, padding=1)
self.upconv1 = BasicConv2d(32, 32, kernel_size=3, stride=1, padding=1)
self.classifier = nn.Conv2d(32, 1, 1)
def __init__(self):
super(cp_res50, self).__init__()
self.upsample_add = upsample_add
self.upsample = cus_sample
self.dummy_tensor = torch.ones(1,
dtype=torch.float32,
requires_grad=True)
self.div_2, self.div_4, self.div_8, self.div_16, self.div_32 = Backbone_ResNet50_in3(
)
self.upsample_add = upsample_add
self.upsample = cus_sample
self.trans32 = nn.Conv2d(2048, 64, 1, 1)
self.trans16 = nn.Conv2d(1024, 64, 1, 1)
self.trans8 = nn.Conv2d(512, 64, 1, 1)
self.trans4 = nn.Conv2d(256, 64, 1, 1)
self.trans2 = nn.Conv2d(64, 64, 1, 1)
self.sim32 = SIM(64, 32)
self.sim16 = SIM(64, 32)
self.sim8 = SIM(64, 32)
self.sim4 = SIM(64, 32)
self.sim2 = SIM(64, 32)
self.upconv32 = BasicConv2d(64, 64, kernel_size=3, stride=1, padding=1)
self.upconv16 = BasicConv2d(64, 64, kernel_size=3, stride=1, padding=1)
self.upconv8 = BasicConv2d(64, 64, kernel_size=3, stride=1, padding=1)
self.upconv4 = BasicConv2d(64, 64, kernel_size=3, stride=1, padding=1)
self.upconv2 = BasicConv2d(64, 32, kernel_size=3, stride=1, padding=1)
self.upconv1 = BasicConv2d(32, 32, kernel_size=3, stride=1, padding=1)
self.classifier = nn.Conv2d(32, 1, 1)
def __init__(self):
super(LightMINet_VGG16, self).__init__()
self.upsample_add = upsample_add
self.upsample = cus_sample
(
self.encoder1,
self.encoder2,
self.encoder4,
self.encoder8,
self.encoder16,
) = Backbone_VGG16_in3()
self.trans = LightAIM((64, 128, 256, 512, 512), (32, 64, 64, 64, 64))
self.sim16 = SIM(64, 32)
self.sim8 = SIM(64, 32)
self.sim4 = SIM(64, 32)
self.sim2 = SIM(64, 32)
self.sim1 = SIM(32, 16)
self.upconv16 = BasicConv2d(64, 64, kernel_size=3, stride=1, padding=1)
self.upconv8 = BasicConv2d(64, 64, kernel_size=3, stride=1, padding=1)
self.upconv4 = BasicConv2d(64, 64, kernel_size=3, stride=1, padding=1)
self.upconv2 = BasicConv2d(64, 32, kernel_size=3, stride=1, padding=1)
self.upconv1 = BasicConv2d(32, 32, kernel_size=3, stride=1, padding=1)
self.classifier = nn.Conv2d(32, 1, 1)