def __init__(self, num_classes, trunk=None, criterion=None):
super(GSCNN, self).__init__()
self.criterion = criterion
self.num_classes = num_classes
wide_resnet = wider_resnet38_a2(classes=1000, dilation=True)
wide_resnet = torch.nn.DataParallel(wide_resnet)
wide_resnet = wide_resnet.module
self.mod1 = wide_resnet.mod1
self.mod2 = wide_resnet.mod2
self.mod3 = wide_resnet.mod3
self.mod4 = wide_resnet.mod4
self.mod5 = wide_resnet.mod5
self.mod6 = wide_resnet.mod6
self.mod7 = wide_resnet.mod7
self.pool2 = wide_resnet.pool2
self.pool3 = wide_resnet.pool3
self.interpolate = F.interpolate
del wide_resnet
self.dsn1 = nn.Conv2d(64, 1, 1)
self.dsn3 = nn.Conv2d(256, 1, 1)
self.dsn4 = nn.Conv2d(512, 1, 1)
self.dsn7 = nn.Conv2d(4096, 1, 1)
self.res1 = Resnet.BasicBlock(64, 64, stride=1, downsample=None)
self.d1 = nn.Conv2d(64, 32, 1)
self.res2 = Resnet.BasicBlock(32, 32, stride=1, downsample=None)
self.d2 = nn.Conv2d(32, 16, 1)
self.res3 = Resnet.BasicBlock(16, 16, stride=1, downsample=None)
self.d3 = nn.Conv2d(16, 8, 1)
self.fuse = nn.Conv2d(8, 1, kernel_size=1, padding=0, bias=False)
self.cw = nn.Conv2d(2, 1, kernel_size=1, padding=0, bias=False)
self.gate1 = gsc.GatedSpatialConv2d(32, 32)
self.gate2 = gsc.GatedSpatialConv2d(16, 16)
self.gate3 = gsc.GatedSpatialConv2d(8, 8)
self.aspp = _AtrousSpatialPyramidPoolingModule(4096, 256,
output_stride=8)
self.bot_fine = nn.Conv2d(128, 48, kernel_size=1, bias=False)
self.bot_aspp = nn.Conv2d(1280 + 256, 256, kernel_size=1, bias=False)
self.final_seg = nn.Sequential(
nn.Conv2d(256 + 48, 256, kernel_size=3, padding=1, bias=False),
Norm2d(256),
nn.ReLU(inplace=True),
nn.Conv2d(256, 256, kernel_size=3, padding=1, bias=False),
Norm2d(256),
nn.ReLU(inplace=True),
nn.Conv2d(256, num_classes, kernel_size=1, bias=False))
self.sigmoid = nn.Sigmoid()
initialize_weights(self.final_seg)
def __init__(self, num_classes, trunk=None, criterion=None):
super(GSCNN, self).__init__()
self.criterion = criterion
self.num_classes = num_classes
wide_resnet = wider_resnet38_a2(classes=1000, dilation=True)
wide_resnet = torch.nn.DataParallel(wide_resnet)
try:
checkpoint = torch.load(
'./network/pretrained_models/wider_resnet38.pth.tar',
map_location='cpu')
wide_resnet.load_state_dict(checkpoint['state_dict'])
del checkpoint
except:
print(
"Please download the ImageNet weights of WideResNet38 in our repo to ./pretrained_models/wider_resnet38.pth.tar."
)
raise RuntimeError(
"=====================Could not load ImageNet weights of WideResNet38 network.======================="
)
wide_resnet = wide_resnet.module
self.mod1 = wide_resnet.mod1
self.mod2 = wide_resnet.mod2
self.mod3 = wide_resnet.mod3
self.mod4 = wide_resnet.mod4
self.mod5 = wide_resnet.mod5
self.mod6 = wide_resnet.mod6
self.mod7 = wide_resnet.mod7
self.pool2 = wide_resnet.pool2
self.pool3 = wide_resnet.pool3
self.interpolate = F.interpolate
del wide_resnet
self.dsn1 = nn.Conv2d(64, 1, 1)
self.dsn3 = nn.Conv2d(256, 1, 1)
self.dsn4 = nn.Conv2d(512, 1, 1)
self.dsn7 = nn.Conv2d(4096, 1, 1)
self.res1 = Resnet.BasicBlock(64, 64, stride=1, downsample=None)
self.d1 = nn.Conv2d(64, 32, 1)
self.res2 = Resnet.BasicBlock(32, 32, stride=1, downsample=None)
self.d2 = nn.Conv2d(32, 16, 1)
self.res3 = Resnet.BasicBlock(16, 16, stride=1, downsample=None)
self.d3 = nn.Conv2d(16, 8, 1)
self.fuse = nn.Conv2d(8, 1, kernel_size=1, padding=0, bias=False)
self.cw = nn.Conv2d(2, 1, kernel_size=1, padding=0, bias=False)
self.gate1 = gsc.GatedSpatialConv2d(32, 32)
self.gate2 = gsc.GatedSpatialConv2d(16, 16)
self.gate3 = gsc.GatedSpatialConv2d(8, 8)
self.aspp = _AtrousSpatialPyramidPoolingModule(4096,
256,
output_stride=8)
self.bot_fine = nn.Conv2d(128, 48, kernel_size=1, bias=False)
self.bot_aspp = nn.Conv2d(1280 + 256, 256, kernel_size=1, bias=False)
self.final_seg = nn.Sequential(
nn.Conv2d(256 + 48, 256, kernel_size=3, padding=1, bias=False),
Norm2d(256), nn.ReLU(inplace=True),
nn.Conv2d(256, 256, kernel_size=3, padding=1, bias=False),
Norm2d(256), nn.ReLU(inplace=True),
nn.Conv2d(256, num_classes, kernel_size=1, bias=False))
self.sigmoid = nn.Sigmoid()
initialize_weights(self.final_seg)