def __init__(self,
n_classes=1,
sizes=(1, 2, 3, 6),
psp_size=1024,
backend='resnet34',
pretrained=True,
reduction=2):
super().__init__()
self.densenet = mextractors2.densenet121(pretrained=pretrained)
self.conv1 = nn.Sequential(
self.densenet.features.conv0,
self.densenet.features.norm0,
self.densenet.features.relu0,
#self.densenet.features.pool0
) #64,64
self.encoder2 = nn.Sequential(
self.densenet.features.denseblock1,
self.densenet.features.transition1) #32,32
self.encoder3 = nn.Sequential(
self.densenet.features.denseblock2,
self.densenet.features.transition2) #16,16
self.encoder4 = nn.Sequential(
self.densenet.features.denseblock3,
self.densenet.features.transition3) #16,16
self.encoder5 = nn.Sequential(
self.densenet.features.denseblock4) #16,16
self.scse1 = ModifiedSCSEBlock(64)
self.scse2 = ModifiedSCSEBlock(128)
self.scse3 = ModifiedSCSEBlock(256)
self.scse4 = ModifiedSCSEBlock(512)
self.scse5 = ModifiedSCSEBlock(1024)
self.scse6 = ModifiedSCSEBlock(1024)
self.scse_center = ModifiedSCSEBlock(256)
self.aspp = ASPP(depth=256) # 16,16
self.center = nn.Sequential(
ConvBn2d(1024, 512, kernel_size=3,
padding=1), nn.ReLU(inplace=True),
ConvBn2d(512, 256, kernel_size=3, padding=1),
nn.ReLU(inplace=True), nn.MaxPool2d(kernel_size=2, stride=2)) #8,8
self.decoder5 = Decoder(256 + 256, 256, 64, reduction)
self.decoder4 = Decoder(64 + 128, 128, 64, reduction)
self.decoder3 = Decoder(64 + 64, 64, 64, reduction)
self.decoder2 = Decoder(64, 32, 64, reduction)
self.drop_out2 = nn.Dropout2d(p=0.5)
self.logit = nn.Sequential(
nn.Conv2d(256, 64, kernel_size=3, padding=1),
nn.ReLU(inplace=True), nn.Conv2d(64, 1, kernel_size=1, padding=0))
def __init__(self, pretrained=True):
super().__init__()
self.densenet = mextractors2.densenet121(pretrained=pretrained)
self.conv1 = nn.Sequential(
self.densenet.features.conv0,
self.densenet.features.norm0,
self.densenet.features.relu0,
#self.densenet.features.pool0
) #64,64
self.encoder2 = nn.Sequential(
self.densenet.features.denseblock1,
self.densenet.features.transition1) #32,32
self.encoder3 = nn.Sequential(
self.densenet.features.denseblock2,
self.densenet.features.transition2) #16,16
self.encoder4 = nn.Sequential(
self.densenet.features.denseblock3,
self.densenet.features.transition3) #16,16
self.encoder5 = nn.Sequential(
self.densenet.features.denseblock4) #16,16
# self.scse1 = ModifiedSCSEBlock(64)
# self.scse2 = ModifiedSCSEBlock(128)
# self.scse3 = ModifiedSCSEBlock(256)
# self.scse4 = ModifiedSCSEBlock(512)
# self.scse5 = ModifiedSCSEBlock(1024)
#
# self.scse_center = ModifiedSCSEBlock(256)
#self.psp = PSPModule(psp_size, 1024, sizes)#16,16
self.context = nn.Sequential(ASP_OC_Module(1024, 512), )
# self.center = nn.Sequential(
# ConvBn2d(1024,512,kernel_size=3,padding=1),
# nn.ReLU(inplace=True),
# ConvBn2d(512,256,kernel_size=3,padding=1),
# nn.ReLU(inplace=True),
# nn.MaxPool2d(kernel_size=2,stride=2)
# )#8,8
#
#
#
self.decoder5 = Decoder(512 + 128, 128, 64)
self.decoder4 = Decoder(64 + 64, 64, 64)
self.decoder3 = Decoder(64, 32, 64)
#
self.drop_out2 = nn.Dropout2d(p=0.5)
self.logit = nn.Sequential(
nn.Conv2d(64 * 3, 64, kernel_size=3, padding=1),
nn.ReLU(inplace=True), nn.Conv2d(64, 1, kernel_size=1, padding=0))
def __init__(self,
n_classes=1,
sizes=(1, 2, 3, 6),
psp_size=1024,
backend='resnet34',
pretrained=True):
super().__init__()
self.densenet = mextractors2.densenet121(pretrained=pretrained)
self.conv1 = nn.Sequential(
self.densenet.features.conv0,
self.densenet.features.norm0,
self.densenet.features.relu0,
#self.densenet.features.pool0
) #64,64
self.encoder2 = nn.Sequential(
self.densenet.features.denseblock1,
self.densenet.features.transition1) #32,32
self.encoder3 = nn.Sequential(
self.densenet.features.denseblock2,
self.densenet.features.transition2) #16,16
self.encoder4 = nn.Sequential(
self.densenet.features.denseblock3,
self.densenet.features.transition3) #16,16
self.encoder5 = nn.Sequential(
self.densenet.features.denseblock4) #16,16
self.psp = PSPModule(psp_size, 1024, sizes) #16,16
# self.center = nn.Sequential(
# ConvBn2d(1024,512,kernel_size=3,padding=1),
# nn.ReLU(inplace=True),
# ConvBn2d(512,256,kernel_size=3,padding=1),
# nn.ReLU(inplace=True),
# nn.MaxPool2d(kernel_size=2,stride=2)
# )#8,8
self.decoder5 = Decoder(64 + 128, 128, 64)
self.decoder4 = Decoder(64 + 64, 64, 64)
self.decoder3 = Decoder(64, 32, 64)
self.decoder_p1 = Decoder_plain(1024 + 512, 512, 256)
self.decoder_p2 = Decoder_plain(256 + 256, 256, 64)
self.drop_out2 = nn.Dropout2d(p=0.5)
self.logit = nn.Sequential(
nn.Conv2d(256, 64, kernel_size=3, padding=1),
nn.ReLU(inplace=True), nn.Conv2d(64, 1, kernel_size=1, padding=0))