def UNetWithPadding(layer):
x = layer
depth = 32
x = LBC(x, depth, [3, 3], [1, 1], "same")
x = LBC(x, depth, [3, 3], [1, 1], "same")
x2 = eddl.MaxPool(x, [2, 2], [2, 2])
x2 = LBC(x2, 2*depth, [3, 3], [1, 1], "same")
x2 = LBC(x2, 2*depth, [3, 3], [1, 1], "same")
x3 = eddl.MaxPool(x2, [2, 2], [2, 2])
x3 = LBC(x3, 4*depth, [3, 3], [1, 1], "same")
x3 = LBC(x3, 4*depth, [3, 3], [1, 1], "same")
x4 = eddl.MaxPool(x3, [2, 2], [2, 2])
x4 = LBC(x4, 8*depth, [3, 3], [1, 1], "same")
x4 = LBC(x4, 8*depth, [3, 3], [1, 1], "same")
x5 = eddl.MaxPool(x4, [2, 2], [2, 2])
x5 = LBC(x5, 8*depth, [3, 3], [1, 1], "same")
x5 = LBC(x5, 8*depth, [3, 3], [1, 1], "same")
x5 = eddl.BatchNormalization(eddl.Conv(
eddl.UpSampling(x5, [2, 2]), 8*depth, [3, 3], [1, 1], "same"
), True)
x4 = eddl.Concat([x4, x5]) if USE_CONCAT else eddl.Add([x4, x5])
x4 = LBC(x4, 8*depth, [3, 3], [1, 1], "same")
x4 = LBC(x4, 8*depth, [3, 3], [1, 1], "same")
x4 = eddl.BatchNormalization(eddl.Conv(
eddl.UpSampling(x4, [2, 2]), 4*depth, [3, 3], [1, 1], "same"
), True)
x3 = eddl.Concat([x3, x4]) if USE_CONCAT else eddl.Add([x3, x4])
x3 = LBC(x3, 4*depth, [3, 3], [1, 1], "same")
x3 = LBC(x3, 4*depth, [3, 3], [1, 1], "same")
x3 = eddl.Conv(
eddl.UpSampling(x3, [2, 2]), 2*depth, [3, 3], [1, 1], "same"
)
x2 = eddl.Concat([x2, x3]) if USE_CONCAT else eddl.Add([x2, x3])
x2 = LBC(x2, 2*depth, [3, 3], [1, 1], "same")
x2 = LBC(x2, 2*depth, [3, 3], [1, 1], "same")
x2 = eddl.BatchNormalization(eddl.Conv(
eddl.UpSampling(x2, [2, 2]), depth, [3, 3], [1, 1], "same"
), True)
x = eddl.Concat([x, x2]) if USE_CONCAT else eddl.Add([x, x2])
x = LBC(x, depth, [3, 3], [1, 1], "same")
x = LBC(x, depth, [3, 3], [1, 1], "same")
x = eddl.BatchNormalization(eddl.Conv(x, 1, [1, 1]), True)
return x
def SegNet(in_layer, num_classes):
x = in_layer
x = eddl.ReLu(eddl.Conv(x, 64, [3, 3], [1, 1], "same"))
x = eddl.ReLu(eddl.Conv(x, 64, [3, 3], [1, 1], "same"))
x = eddl.MaxPool(x, [2, 2], [2, 2])
x = eddl.ReLu(eddl.Conv(x, 128, [3, 3], [1, 1], "same"))
x = eddl.ReLu(eddl.Conv(x, 128, [3, 3], [1, 1], "same"))
x = eddl.MaxPool(x, [2, 2], [2, 2])
x = eddl.ReLu(eddl.Conv(x, 256, [3, 3], [1, 1], "same"))
x = eddl.ReLu(eddl.Conv(x, 256, [3, 3], [1, 1], "same"))
x = eddl.ReLu(eddl.Conv(x, 256, [3, 3], [1, 1], "same"))
x = eddl.MaxPool(x, [2, 2], [2, 2])
x = eddl.ReLu(eddl.Conv(x, 512, [3, 3], [1, 1], "same"))
x = eddl.ReLu(eddl.Conv(x, 512, [3, 3], [1, 1], "same"))
x = eddl.ReLu(eddl.Conv(x, 512, [3, 3], [1, 1], "same"))
x = eddl.MaxPool(x, [2, 2], [2, 2])
x = eddl.ReLu(eddl.Conv(x, 512, [3, 3], [1, 1], "same"))
x = eddl.ReLu(eddl.Conv(x, 512, [3, 3], [1, 1], "same"))
x = eddl.ReLu(eddl.Conv(x, 512, [3, 3], [1, 1], "same"))
x = eddl.MaxPool(x, [2, 2], [2, 2])
x = eddl.UpSampling(x, [2, 2])
x = eddl.ReLu(eddl.Conv(x, 512, [3, 3], [1, 1], "same"))
x = eddl.ReLu(eddl.Conv(x, 512, [3, 3], [1, 1], "same"))
x = eddl.ReLu(eddl.Conv(x, 512, [3, 3], [1, 1], "same"))
x = eddl.UpSampling(x, [2, 2])
x = eddl.ReLu(eddl.Conv(x, 512, [3, 3], [1, 1], "same"))
x = eddl.ReLu(eddl.Conv(x, 512, [3, 3], [1, 1], "same"))
x = eddl.ReLu(eddl.Conv(x, 256, [3, 3], [1, 1], "same"))
x = eddl.UpSampling(x, [2, 2])
x = eddl.ReLu(eddl.Conv(x, 256, [3, 3], [1, 1], "same"))
x = eddl.ReLu(eddl.Conv(x, 256, [3, 3], [1, 1], "same"))
x = eddl.ReLu(eddl.Conv(x, 128, [3, 3], [1, 1], "same"))
x = eddl.UpSampling(x, [2, 2])
x = eddl.ReLu(eddl.Conv(x, 128, [3, 3], [1, 1], "same"))
x = eddl.ReLu(eddl.Conv(x, 64, [3, 3], [1, 1], "same"))
x = eddl.UpSampling(x, [2, 2])
x = eddl.ReLu(eddl.Conv(x, 64, [3, 3], [1, 1], "same"))
x = eddl.Conv(x, num_classes, [3, 3], [1, 1], "same")
return x
def SegNetBN(x, num_classes):
x = eddl.ReLu(
eddl.BatchNormalization(eddl.Conv(x, 64, [3, 3], [1, 1], "same")))
x = eddl.ReLu(
eddl.BatchNormalization(eddl.Conv(x, 64, [3, 3], [1, 1], "same")))
x = eddl.MaxPool(x, [2, 2], [2, 2])
x = eddl.ReLu(
eddl.BatchNormalization(eddl.Conv(x, 128, [3, 3], [1, 1], "same")))
x = eddl.ReLu(
eddl.BatchNormalization(eddl.Conv(x, 128, [3, 3], [1, 1], "same")))
x = eddl.MaxPool(x, [2, 2], [2, 2])
x = eddl.ReLu(
eddl.BatchNormalization(eddl.Conv(x, 256, [3, 3], [1, 1], "same")))
x = eddl.ReLu(
eddl.BatchNormalization(eddl.Conv(x, 256, [3, 3], [1, 1], "same")))
x = eddl.ReLu(
eddl.BatchNormalization(eddl.Conv(x, 256, [3, 3], [1, 1], "same")))
x = eddl.MaxPool(x, [2, 2], [2, 2])
x = eddl.ReLu(
eddl.BatchNormalization(eddl.Conv(x, 512, [3, 3], [1, 1], "same")))
x = eddl.ReLu(
eddl.BatchNormalization(eddl.Conv(x, 512, [3, 3], [1, 1], "same")))
x = eddl.ReLu(
eddl.BatchNormalization(eddl.Conv(x, 512, [3, 3], [1, 1], "same")))
x = eddl.MaxPool(x, [2, 2], [2, 2])
x = eddl.ReLu(
eddl.BatchNormalization(eddl.Conv(x, 512, [3, 3], [1, 1], "same")))
x = eddl.ReLu(
eddl.BatchNormalization(eddl.Conv(x, 512, [3, 3], [1, 1], "same")))
x = eddl.ReLu(
eddl.BatchNormalization(eddl.Conv(x, 512, [3, 3], [1, 1], "same")))
x = eddl.MaxPool(x, [2, 2], [2, 2])
x = eddl.UpSampling(x, [2, 2])
x = eddl.ReLu(
eddl.BatchNormalization(eddl.Conv(x, 512, [3, 3], [1, 1], "same")))
x = eddl.ReLu(
eddl.BatchNormalization(eddl.Conv(x, 512, [3, 3], [1, 1], "same")))
x = eddl.ReLu(
eddl.BatchNormalization(eddl.Conv(x, 512, [3, 3], [1, 1], "same")))
x = eddl.UpSampling(x, [2, 2])
x = eddl.ReLu(
eddl.BatchNormalization(eddl.Conv(x, 512, [3, 3], [1, 1], "same")))
x = eddl.ReLu(
eddl.BatchNormalization(eddl.Conv(x, 512, [3, 3], [1, 1], "same")))
x = eddl.ReLu(
eddl.BatchNormalization(eddl.Conv(x, 256, [3, 3], [1, 1], "same")))
x = eddl.UpSampling(x, [2, 2])
x = eddl.ReLu(
eddl.BatchNormalization(eddl.Conv(x, 256, [3, 3], [1, 1], "same")))
x = eddl.ReLu(
eddl.BatchNormalization(eddl.Conv(x, 256, [3, 3], [1, 1], "same")))
x = eddl.ReLu(
eddl.BatchNormalization(eddl.Conv(x, 128, [3, 3], [1, 1], "same")))
x = eddl.UpSampling(x, [2, 2])
x = eddl.ReLu(
eddl.BatchNormalization(eddl.Conv(x, 128, [3, 3], [1, 1], "same")))
x = eddl.ReLu(
eddl.BatchNormalization(eddl.Conv(x, 64, [3, 3], [1, 1], "same")))
x = eddl.UpSampling(x, [2, 2])
x = eddl.ReLu(
eddl.BatchNormalization(eddl.Conv(x, 64, [3, 3], [1, 1], "same")))
x = eddl.Conv(x, num_classes, [3, 3], [1, 1], "same")
return x