Add caffe prototxt for cunet/upcunet

appendix/caffe_prototxt/cunet.prototxt

@@ -0,0 +1,794 @@
+layer {
+  name: "input"
+  type: "Input"
+  top: "Input1"
+  input_param {
+    shape {
+      dim: 1
+      dim: 3
+      dim: 312
+      dim: 312
+    }
+  }
+}
+layer {
+  name: "Convolution1"
+  type: "Convolution"
+  bottom: "Input1"
+  top: "Convolution1"
+  convolution_param {
+    num_output: 32
+    pad: 0
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "ReLU1"
+  type: "ReLU"
+  bottom: "Convolution1"
+  top: "Convolution1"
+  relu_param {
+    negative_slope: 0.1
+  }
+}
+layer {
+  name: "Convolution2"
+  type: "Convolution"
+  bottom: "Convolution1"
+  top: "Convolution2"
+  convolution_param {
+    num_output: 64
+    pad: 0
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "ReLU2"
+  type: "ReLU"
+  bottom: "Convolution2"
+  top: "Convolution2"
+  relu_param {
+    negative_slope: 0.1
+  }
+}
+layer {
+  name: "Convolution3"
+  type: "Convolution"
+  bottom: "Convolution2"
+  top: "Convolution3"
+  convolution_param {
+    num_output: 64
+    pad: 0
+    kernel_size: 2
+    stride: 2
+  }
+}
+layer {
+  name: "ReLU3"
+  type: "ReLU"
+  bottom: "Convolution3"
+  top: "Convolution3"
+  relu_param {
+    negative_slope: 0.1
+  }
+}
+layer {
+  name: "Convolution4"
+  type: "Convolution"
+  bottom: "Convolution3"
+  top: "Convolution4"
+  convolution_param {
+    num_output: 128
+    pad: 0
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "ReLU4"
+  type: "ReLU"
+  bottom: "Convolution4"
+  top: "Convolution4"
+  relu_param {
+    negative_slope: 0.1
+  }
+}
+layer {
+  name: "Convolution5"
+  type: "Convolution"
+  bottom: "Convolution4"
+  top: "Convolution5"
+  convolution_param {
+    num_output: 64
+    pad: 0
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "ReLU5"
+  type: "ReLU"
+  bottom: "Convolution5"
+  top: "Convolution5"
+  relu_param {
+    negative_slope: 0.1
+  }
+}
+layer {
+  name: "Pooling1"
+  type: "Pooling"
+  bottom: "Convolution5"
+  top: "Pooling1"
+  pooling_param {
+    pool: AVE
+    global_pooling: true
+  }
+}
+layer {
+  name: "Convolution6"
+  type: "Convolution"
+  bottom: "Pooling1"
+  top: "Convolution6"
+  convolution_param {
+    num_output: 8
+    pad: 0
+    kernel_size: 1
+    stride: 1
+  }
+}
+layer {
+  name: "ReLU6"
+  type: "ReLU"
+  bottom: "Convolution6"
+  top: "Convolution6"
+}
+layer {
+  name: "Convolution7"
+  type: "Convolution"
+  bottom: "Convolution6"
+  top: "Convolution7"
+  convolution_param {
+    num_output: 64
+    pad: 0
+    kernel_size: 1
+    stride: 1
+  }
+}
+layer {
+  name: "Sigmoid1"
+  type: "Sigmoid"
+  bottom: "Convolution7"
+  top: "Convolution7"
+}
+layer {
+  name: "Flatten1"
+  type: "Flatten"
+  bottom: "Convolution7"
+  top: "Flatten1"
+}
+layer {
+  name: "Scale1"
+  type: "Scale"
+  bottom: "Convolution5"
+  bottom: "Flatten1"
+  top: "Scale1"
+  scale_param {
+    axis: 0
+    bias_term: false
+  }
+}
+layer {
+  name: "Deconvolution1"
+  type: "Deconvolution"
+  bottom: "Scale1"
+  top: "Deconvolution1"
+  convolution_param {
+    num_output: 64
+    pad: 0
+    kernel_size: 2
+    stride: 2
+  }
+}
+layer {
+  name: "ReLU7"
+  type: "ReLU"
+  bottom: "Deconvolution1"
+  top: "Deconvolution1"
+  relu_param {
+    negative_slope: 0.1
+  }
+}
+layer {
+  name: "Crop1"
+  type: "Crop"
+  bottom: "Convolution2"
+  bottom: "Deconvolution1"
+  top: "Crop1"
+  crop_param {
+    axis: 2
+    offset: 4
+  }
+}
+layer {
+  name: "Eltwise1"
+  type: "Eltwise"
+  bottom: "Crop1"
+  bottom: "Deconvolution1"
+  top: "Eltwise1"
+  eltwise_param {
+    operation: SUM
+  }
+}
+layer {
+  name: "Convolution8"
+  type: "Convolution"
+  bottom: "Eltwise1"
+  top: "Convolution8"
+  convolution_param {
+    num_output: 64
+    pad: 0
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "ReLU8"
+  type: "ReLU"
+  bottom: "Convolution8"
+  top: "Convolution8"
+  relu_param {
+    negative_slope: 0.1
+  }
+}
+layer {
+  name: "Convolution9"
+  type: "Convolution"
+  bottom: "Convolution8"
+  top: "Convolution9"
+  convolution_param {
+    num_output: 3
+    pad: 0
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "Convolution10"
+  type: "Convolution"
+  bottom: "Convolution9"
+  top: "Convolution10"
+  convolution_param {
+    num_output: 32
+    pad: 0
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "ReLU9"
+  type: "ReLU"
+  bottom: "Convolution10"
+  top: "Convolution10"
+  relu_param {
+    negative_slope: 0.1
+  }
+}
+layer {
+  name: "Convolution11"
+  type: "Convolution"
+  bottom: "Convolution10"
+  top: "Convolution11"
+  convolution_param {
+    num_output: 64
+    pad: 0
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "ReLU10"
+  type: "ReLU"
+  bottom: "Convolution11"
+  top: "Convolution11"
+  relu_param {
+    negative_slope: 0.1
+  }
+}
+layer {
+  name: "Convolution12"
+  type: "Convolution"
+  bottom: "Convolution11"
+  top: "Convolution12"
+  convolution_param {
+    num_output: 64
+    pad: 0
+    kernel_size: 2
+    stride: 2
+  }
+}
+layer {
+  name: "ReLU11"
+  type: "ReLU"
+  bottom: "Convolution12"
+  top: "Convolution12"
+  relu_param {
+    negative_slope: 0.1
+  }
+}
+layer {
+  name: "Convolution13"
+  type: "Convolution"
+  bottom: "Convolution12"
+  top: "Convolution13"
+  convolution_param {
+    num_output: 64
+    pad: 0
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "ReLU12"
+  type: "ReLU"
+  bottom: "Convolution13"
+  top: "Convolution13"
+  relu_param {
+    negative_slope: 0.1
+  }
+}
+layer {
+  name: "Convolution14"
+  type: "Convolution"
+  bottom: "Convolution13"
+  top: "Convolution14"
+  convolution_param {
+    num_output: 128
+    pad: 0
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "ReLU13"
+  type: "ReLU"
+  bottom: "Convolution14"
+  top: "Convolution14"
+  relu_param {
+    negative_slope: 0.1
+  }
+}
+layer {
+  name: "Pooling2"
+  type: "Pooling"
+  bottom: "Convolution14"
+  top: "Pooling2"
+  pooling_param {
+    pool: AVE
+    global_pooling: true
+  }
+}
+layer {
+  name: "Convolution15"
+  type: "Convolution"
+  bottom: "Pooling2"
+  top: "Convolution15"
+  convolution_param {
+    num_output: 16
+    pad: 0
+    kernel_size: 1
+    stride: 1
+  }
+}
+layer {
+  name: "ReLU14"
+  type: "ReLU"
+  bottom: "Convolution15"
+  top: "Convolution15"
+}
+layer {
+  name: "Convolution16"
+  type: "Convolution"
+  bottom: "Convolution15"
+  top: "Convolution16"
+  convolution_param {
+    num_output: 128
+    pad: 0
+    kernel_size: 1
+    stride: 1
+  }
+}
+layer {
+  name: "Sigmoid2"
+  type: "Sigmoid"
+  bottom: "Convolution16"
+  top: "Convolution16"
+}
+layer {
+  name: "Flatten2"
+  type: "Flatten"
+  bottom: "Convolution16"
+  top: "Flatten2"
+}
+layer {
+  name: "Scale2"
+  type: "Scale"
+  bottom: "Convolution14"
+  bottom: "Flatten2"
+  top: "Scale2"
+  scale_param {
+    axis: 0
+    bias_term: false
+  }
+}
+layer {
+  name: "Convolution17"
+  type: "Convolution"
+  bottom: "Scale2"
+  top: "Convolution17"
+  convolution_param {
+    num_output: 128
+    pad: 0
+    kernel_size: 2
+    stride: 2
+  }
+}
+layer {
+  name: "ReLU15"
+  type: "ReLU"
+  bottom: "Convolution17"
+  top: "Convolution17"
+  relu_param {
+    negative_slope: 0.1
+  }
+}
+layer {
+  name: "Convolution18"
+  type: "Convolution"
+  bottom: "Convolution17"
+  top: "Convolution18"
+  convolution_param {
+    num_output: 256
+    pad: 0
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "ReLU16"
+  type: "ReLU"
+  bottom: "Convolution18"
+  top: "Convolution18"
+  relu_param {
+    negative_slope: 0.1
+  }
+}
+layer {
+  name: "Convolution19"
+  type: "Convolution"
+  bottom: "Convolution18"
+  top: "Convolution19"
+  convolution_param {
+    num_output: 128
+    pad: 0
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "ReLU17"
+  type: "ReLU"
+  bottom: "Convolution19"
+  top: "Convolution19"
+  relu_param {
+    negative_slope: 0.1
+  }
+}
+layer {
+  name: "Pooling3"
+  type: "Pooling"
+  bottom: "Convolution19"
+  top: "Pooling3"
+  pooling_param {
+    pool: AVE
+    global_pooling: true
+  }
+}
+layer {
+  name: "Convolution20"
+  type: "Convolution"
+  bottom: "Pooling3"
+  top: "Convolution20"
+  convolution_param {
+    num_output: 16
+    pad: 0
+    kernel_size: 1
+    stride: 1
+  }
+}
+layer {
+  name: "ReLU18"
+  type: "ReLU"
+  bottom: "Convolution20"
+  top: "Convolution20"
+}
+layer {
+  name: "Convolution21"
+  type: "Convolution"
+  bottom: "Convolution20"
+  top: "Convolution21"
+  convolution_param {
+    num_output: 128
+    pad: 0
+    kernel_size: 1
+    stride: 1
+  }
+}
+layer {
+  name: "Sigmoid3"
+  type: "Sigmoid"
+  bottom: "Convolution21"
+  top: "Convolution21"
+}
+layer {
+  name: "Flatten3"
+  type: "Flatten"
+  bottom: "Convolution21"
+  top: "Flatten3"
+}
+layer {
+  name: "Scale3"
+  type: "Scale"
+  bottom: "Convolution19"
+  bottom: "Flatten3"
+  top: "Scale3"
+  scale_param {
+    axis: 0
+    bias_term: false
+  }
+}
+layer {
+  name: "Deconvolution2"
+  type: "Deconvolution"
+  bottom: "Scale3"
+  top: "Deconvolution2"
+  convolution_param {
+    num_output: 128
+    pad: 0
+    kernel_size: 2
+    stride: 2
+  }
+}
+layer {
+  name: "ReLU19"
+  type: "ReLU"
+  bottom: "Deconvolution2"
+  top: "Deconvolution2"
+  relu_param {
+    negative_slope: 0.1
+  }
+}
+layer {
+  name: "Crop2"
+  type: "Crop"
+  bottom: "Scale2"
+  bottom: "Deconvolution2"
+  top: "Crop2"
+  crop_param {
+    axis: 2
+    offset: 4
+  }
+}
+layer {
+  name: "Eltwise2"
+  type: "Eltwise"
+  bottom: "Crop2"
+  bottom: "Deconvolution2"
+  top: "Eltwise2"
+  eltwise_param {
+    operation: SUM
+  }
+}
+layer {
+  name: "Convolution22"
+  type: "Convolution"
+  bottom: "Eltwise2"
+  top: "Convolution22"
+  convolution_param {
+    num_output: 64
+    pad: 0
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "ReLU20"
+  type: "ReLU"
+  bottom: "Convolution22"
+  top: "Convolution22"
+  relu_param {
+    negative_slope: 0.1
+  }
+}
+layer {
+  name: "Convolution23"
+  type: "Convolution"
+  bottom: "Convolution22"
+  top: "Convolution23"
+  convolution_param {
+    num_output: 64
+    pad: 0
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "ReLU21"
+  type: "ReLU"
+  bottom: "Convolution23"
+  top: "Convolution23"
+  relu_param {
+    negative_slope: 0.1
+  }
+}
+layer {
+  name: "Pooling4"
+  type: "Pooling"
+  bottom: "Convolution23"
+  top: "Pooling4"
+  pooling_param {
+    pool: AVE
+    global_pooling: true
+  }
+}
+layer {
+  name: "Convolution24"
+  type: "Convolution"
+  bottom: "Pooling4"
+  top: "Convolution24"
+  convolution_param {
+    num_output: 8
+    pad: 0
+    kernel_size: 1
+    stride: 1
+  }
+}
+layer {
+  name: "ReLU22"
+  type: "ReLU"
+  bottom: "Convolution24"
+  top: "Convolution24"
+}
+layer {
+  name: "Convolution25"
+  type: "Convolution"
+  bottom: "Convolution24"
+  top: "Convolution25"
+  convolution_param {
+    num_output: 64
+    pad: 0
+    kernel_size: 1
+    stride: 1
+  }
+}
+layer {
+  name: "Sigmoid4"
+  type: "Sigmoid"
+  bottom: "Convolution25"
+  top: "Convolution25"
+}
+layer {
+  name: "Flatten4"
+  type: "Flatten"
+  bottom: "Convolution25"
+  top: "Flatten4"
+}
+layer {
+  name: "Scale4"
+  type: "Scale"
+  bottom: "Convolution23"
+  bottom: "Flatten4"
+  top: "Scale4"
+  scale_param {
+    axis: 0
+    bias_term: false
+  }
+}
+layer {
+  name: "Deconvolution3"
+  type: "Deconvolution"
+  bottom: "Scale4"
+  top: "Deconvolution3"
+  convolution_param {
+    num_output: 64
+    pad: 0
+    kernel_size: 2
+    stride: 2
+  }
+}
+layer {
+  name: "ReLU23"
+  type: "ReLU"
+  bottom: "Deconvolution3"
+  top: "Deconvolution3"
+  relu_param {
+    negative_slope: 0.1
+  }
+}
+layer {
+  name: "Crop3"
+  type: "Crop"
+  bottom: "Convolution11"
+  bottom: "Deconvolution3"
+  top: "Crop3"
+  crop_param {
+    axis: 2
+    offset: 16
+  }
+}
+layer {
+  name: "Eltwise3"
+  type: "Eltwise"
+  bottom: "Crop3"
+  bottom: "Deconvolution3"
+  top: "Eltwise3"
+  eltwise_param {
+    operation: SUM
+  }
+}
+layer {
+  name: "Convolution26"
+  type: "Convolution"
+  bottom: "Eltwise3"
+  top: "Convolution26"
+  convolution_param {
+    num_output: 64
+    pad: 0
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "ReLU24"
+  type: "ReLU"
+  bottom: "Convolution26"
+  top: "Convolution26"
+  relu_param {
+    negative_slope: 0.1
+  }
+}
+layer {
+  name: "Convolution27"
+  type: "Convolution"
+  bottom: "Convolution26"
+  top: "Convolution27"
+  convolution_param {
+    num_output: 3
+    pad: 0
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "Crop4"
+  type: "Crop"
+  bottom: "Convolution9"
+  bottom: "Convolution27"
+  top: "Crop4"
+  crop_param {
+    axis: 2
+    offset: 20
+  }
+}
+layer {
+  name: "Eltwise4"
+  type: "Eltwise"
+  bottom: "Crop4"
+  bottom: "Convolution27"
+  top: "Eltwise4"
+  eltwise_param {
+    operation: SUM
+  }
+}
+

appendix/caffe_prototxt/make_cunet.py

@@ -0,0 +1,110 @@
+# Generate prototxt of waifu2x's cunet/upcunet arch. Training is not possible.
+from __future__ import print_function
+import sys
+sys.path.insert(0, "../python") # pycaffe path
+from caffe import layers as L, params as P, to_proto
+from caffe.proto import caffe_pb2
+import caffe
+
+def seblock(bottom, o, r):
+    m = int(o / r)
+    gap = L.Pooling(bottom, pool=P.Pooling.AVE, global_pooling=True)
+    linear1 = L.Convolution(gap,  kernel_size=1, pad=0, stride=1, num_output=m)
+    relu1 = L.ReLU(linear1, in_place=True)
+    linear2 = L.Convolution(relu1,  kernel_size=1, pad=0, stride=1, num_output=o)
+    sigmoid1 = L.Sigmoid(linear2, in_place=True)
+    flatten1 = L.Flatten(sigmoid1)
+    return flatten1
+
+def conv_relu(bottom, o, stride=1, pad=0):
+    conv = L.Convolution(bottom, kernel_size=3, stride=stride, num_output=o, pad=pad)
+    relu = L.ReLU(conv, in_place=True, negative_slope=0.1)
+    return relu
+
+def unet_conv(bottom, m, o, se=True):
+    conv1 = L.Convolution(bottom, kernel_size=3, stride=1,
+                                num_output=m, pad=0)
+    relu1 = L.ReLU(conv1, in_place=True, negative_slope=0.1)
+    conv2 = L.Convolution(relu1, kernel_size=3, stride=1,
+                                num_output=o, pad=0)
+    relu2 = L.ReLU(conv2, in_place=True, negative_slope=0.1)
+
+    if se:
+        se1 = seblock(relu2, o, 8)
+        return L.Scale(relu2, se1, axis=0, bias_term=False)
+    else:
+        return relu2
+
+def unet_branch(bottom, insert_f, i, o, depad):
+    pool = L.Convolution(bottom, kernel_size=2, stride=2, num_output=i, pad=0)
+    relu1 = L.ReLU(pool, in_place=True, negative_slope=0.1)
+    feat = insert_f(relu1)
+    unpool = L.Deconvolution(feat, convolution_param=dict(num_output=o, kernel_size=2, pad=0, stride=2))
+    relu2 = L.ReLU(unpool, in_place=True, negative_slope=0.1)
+    crop = L.Crop(bottom, relu2, crop_param=dict(axis=2, offset=depad))
+    cadd = L.Eltwise(crop, relu2, operation=P.Eltwise.SUM)
+    return cadd
+
+def unet1(bottom, ch, deconv):
+    block1 = lambda bottom: unet_conv(bottom, 128, 64, True)
+    conv1 = unet_conv(bottom, 32, 64, se=False)
+    ub1 = unet_branch(conv1, block1, 64, 64, 4)
+    conv2 = conv_relu(ub1, 64)
+    if deconv:
+        return L.Deconvolution(conv2, convolution_param=dict(num_output=ch, kernel_size=4, pad=3, stride=2))
+    else:
+        return L.Convolution(conv2, kernel_size=3, stride=1, num_output=ch, pad=0)
+
+def unet2(bottom, ch, deconv):
+    def block1(bottom):
+        return unet_conv(bottom, 256, 128, se=True)
+    def block2(bottom):
+        conv1 = unet_conv(bottom, 64, 128, se=True)
+        ub1 = unet_branch(conv1, block1, 128, 128, 4)
+        conv2 = unet_conv(ub1, 64, 64, se=True)
+        return conv2
+    conv1 = unet_conv(bottom, 32, 64, se=False)
+    ub1 = unet_branch(conv1, block2, 64, 64, 16)
+    conv2 = conv_relu(ub1, 64)
+    if deconv:
+        return L.Deconvolution(conv2, convolution_param=dict(num_output=ch, kernel_size=4, pad=3, stride=2))
+    else:
+        return L.Convolution(conv2, kernel_size=3, stride=1, num_output=ch, pad=0)
+
+def make_upcunet():
+    netoffset = 36
+    ch = 3
+    input_size = (256 / 2) + netoffset * 2
+    assert(input_size % 4 == 0)
+
+    data = L.Input(name="input", shape=dict(dim=[1, ch, input_size, input_size]))
+    u1 = unet1(data, ch=ch, deconv=True)
+    u2 = unet2(u1, ch=ch, deconv=False)
+    crop = L.Crop(u1, u2, crop_param=dict(axis=2, offset=20))
+    cadd = L.Eltwise(crop, u2, operation=P.Eltwise.SUM)
+    return to_proto(cadd)
+
+def make_cunet():
+    netoffset = 28
+    ch = 3
+    input_size = 256 + netoffset * 2
+    assert(input_size % 4 == 0)
+
+    data = L.Input(name="input", shape=dict(dim=[1, ch, input_size, input_size]))
+    u1 = unet1(data, ch=ch, deconv=False)
+    u2 = unet2(u1, ch=ch, deconv=False)
+    crop = L.Crop(u1, u2, crop_param=dict(axis=2, offset=20))
+    cadd = L.Eltwise(crop, u2, operation=P.Eltwise.SUM)
+    return to_proto(cadd)
+
+def make_net():
+    with open('upcunet.prototxt', 'w') as f:
+        print(make_upcunet(), file=f)
+    with open('cunet.prototxt', 'w') as f:
+        print(make_cunet(), file=f)
+
+if __name__ == '__main__':
+    make_net()
+    # test loading the net
+    caffe.Net('upcunet.prototxt', caffe.TEST)
+    caffe.Net('cunet.prototxt', caffe.TEST)

appendix/caffe_prototxt/upcunet.prototxt

@@ -0,0 +1,794 @@
+layer {
+  name: "input"
+  type: "Input"
+  top: "Input1"
+  input_param {
+    shape {
+      dim: 1
+      dim: 3
+      dim: 200
+      dim: 200
+    }
+  }
+}
+layer {
+  name: "Convolution1"
+  type: "Convolution"
+  bottom: "Input1"
+  top: "Convolution1"
+  convolution_param {
+    num_output: 32
+    pad: 0
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "ReLU1"
+  type: "ReLU"
+  bottom: "Convolution1"
+  top: "Convolution1"
+  relu_param {
+    negative_slope: 0.1
+  }
+}
+layer {
+  name: "Convolution2"
+  type: "Convolution"
+  bottom: "Convolution1"
+  top: "Convolution2"
+  convolution_param {
+    num_output: 64
+    pad: 0
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "ReLU2"
+  type: "ReLU"
+  bottom: "Convolution2"
+  top: "Convolution2"
+  relu_param {
+    negative_slope: 0.1
+  }
+}
+layer {
+  name: "Convolution3"
+  type: "Convolution"
+  bottom: "Convolution2"
+  top: "Convolution3"
+  convolution_param {
+    num_output: 64
+    pad: 0
+    kernel_size: 2
+    stride: 2
+  }
+}
+layer {
+  name: "ReLU3"
+  type: "ReLU"
+  bottom: "Convolution3"
+  top: "Convolution3"
+  relu_param {
+    negative_slope: 0.1
+  }
+}
+layer {
+  name: "Convolution4"
+  type: "Convolution"
+  bottom: "Convolution3"
+  top: "Convolution4"
+  convolution_param {
+    num_output: 128
+    pad: 0
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "ReLU4"
+  type: "ReLU"
+  bottom: "Convolution4"
+  top: "Convolution4"
+  relu_param {
+    negative_slope: 0.1
+  }
+}
+layer {
+  name: "Convolution5"
+  type: "Convolution"
+  bottom: "Convolution4"
+  top: "Convolution5"
+  convolution_param {
+    num_output: 64
+    pad: 0
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "ReLU5"
+  type: "ReLU"
+  bottom: "Convolution5"
+  top: "Convolution5"
+  relu_param {
+    negative_slope: 0.1
+  }
+}
+layer {
+  name: "Pooling1"
+  type: "Pooling"
+  bottom: "Convolution5"
+  top: "Pooling1"
+  pooling_param {
+    pool: AVE
+    global_pooling: true
+  }
+}
+layer {
+  name: "Convolution6"
+  type: "Convolution"
+  bottom: "Pooling1"
+  top: "Convolution6"
+  convolution_param {
+    num_output: 8
+    pad: 0
+    kernel_size: 1
+    stride: 1
+  }
+}
+layer {
+  name: "ReLU6"
+  type: "ReLU"
+  bottom: "Convolution6"
+  top: "Convolution6"
+}
+layer {
+  name: "Convolution7"
+  type: "Convolution"
+  bottom: "Convolution6"
+  top: "Convolution7"
+  convolution_param {
+    num_output: 64
+    pad: 0
+    kernel_size: 1
+    stride: 1
+  }
+}
+layer {
+  name: "Sigmoid1"
+  type: "Sigmoid"
+  bottom: "Convolution7"
+  top: "Convolution7"
+}
+layer {
+  name: "Flatten1"
+  type: "Flatten"
+  bottom: "Convolution7"
+  top: "Flatten1"
+}
+layer {
+  name: "Scale1"
+  type: "Scale"
+  bottom: "Convolution5"
+  bottom: "Flatten1"
+  top: "Scale1"
+  scale_param {
+    axis: 0
+    bias_term: false
+  }
+}
+layer {
+  name: "Deconvolution1"
+  type: "Deconvolution"
+  bottom: "Scale1"
+  top: "Deconvolution1"
+  convolution_param {
+    num_output: 64
+    pad: 0
+    kernel_size: 2
+    stride: 2
+  }
+}
+layer {
+  name: "ReLU7"
+  type: "ReLU"
+  bottom: "Deconvolution1"
+  top: "Deconvolution1"
+  relu_param {
+    negative_slope: 0.1
+  }
+}
+layer {
+  name: "Crop1"
+  type: "Crop"
+  bottom: "Convolution2"
+  bottom: "Deconvolution1"
+  top: "Crop1"
+  crop_param {
+    axis: 2
+    offset: 4
+  }
+}
+layer {
+  name: "Eltwise1"
+  type: "Eltwise"
+  bottom: "Crop1"
+  bottom: "Deconvolution1"
+  top: "Eltwise1"
+  eltwise_param {
+    operation: SUM
+  }
+}
+layer {
+  name: "Convolution8"
+  type: "Convolution"
+  bottom: "Eltwise1"
+  top: "Convolution8"
+  convolution_param {
+    num_output: 64
+    pad: 0
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "ReLU8"
+  type: "ReLU"
+  bottom: "Convolution8"
+  top: "Convolution8"
+  relu_param {
+    negative_slope: 0.1
+  }
+}
+layer {
+  name: "Deconvolution2"
+  type: "Deconvolution"
+  bottom: "Convolution8"
+  top: "Deconvolution2"
+  convolution_param {
+    num_output: 3
+    pad: 3
+    kernel_size: 4
+    stride: 2
+  }
+}
+layer {
+  name: "Convolution9"
+  type: "Convolution"
+  bottom: "Deconvolution2"
+  top: "Convolution9"
+  convolution_param {
+    num_output: 32
+    pad: 0
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "ReLU9"
+  type: "ReLU"
+  bottom: "Convolution9"
+  top: "Convolution9"
+  relu_param {
+    negative_slope: 0.1
+  }
+}
+layer {
+  name: "Convolution10"
+  type: "Convolution"
+  bottom: "Convolution9"
+  top: "Convolution10"
+  convolution_param {
+    num_output: 64
+    pad: 0
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "ReLU10"
+  type: "ReLU"
+  bottom: "Convolution10"
+  top: "Convolution10"
+  relu_param {
+    negative_slope: 0.1
+  }
+}
+layer {
+  name: "Convolution11"
+  type: "Convolution"
+  bottom: "Convolution10"
+  top: "Convolution11"
+  convolution_param {
+    num_output: 64
+    pad: 0
+    kernel_size: 2
+    stride: 2
+  }
+}
+layer {
+  name: "ReLU11"
+  type: "ReLU"
+  bottom: "Convolution11"
+  top: "Convolution11"
+  relu_param {
+    negative_slope: 0.1
+  }
+}
+layer {
+  name: "Convolution12"
+  type: "Convolution"
+  bottom: "Convolution11"
+  top: "Convolution12"
+  convolution_param {
+    num_output: 64
+    pad: 0
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "ReLU12"
+  type: "ReLU"
+  bottom: "Convolution12"
+  top: "Convolution12"
+  relu_param {
+    negative_slope: 0.1
+  }
+}
+layer {
+  name: "Convolution13"
+  type: "Convolution"
+  bottom: "Convolution12"
+  top: "Convolution13"
+  convolution_param {
+    num_output: 128
+    pad: 0
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "ReLU13"
+  type: "ReLU"
+  bottom: "Convolution13"
+  top: "Convolution13"
+  relu_param {
+    negative_slope: 0.1
+  }
+}
+layer {
+  name: "Pooling2"
+  type: "Pooling"
+  bottom: "Convolution13"
+  top: "Pooling2"
+  pooling_param {
+    pool: AVE
+    global_pooling: true
+  }
+}
+layer {
+  name: "Convolution14"
+  type: "Convolution"
+  bottom: "Pooling2"
+  top: "Convolution14"
+  convolution_param {
+    num_output: 16
+    pad: 0
+    kernel_size: 1
+    stride: 1
+  }
+}
+layer {
+  name: "ReLU14"
+  type: "ReLU"
+  bottom: "Convolution14"
+  top: "Convolution14"
+}
+layer {
+  name: "Convolution15"
+  type: "Convolution"
+  bottom: "Convolution14"
+  top: "Convolution15"
+  convolution_param {
+    num_output: 128
+    pad: 0
+    kernel_size: 1
+    stride: 1
+  }
+}
+layer {
+  name: "Sigmoid2"
+  type: "Sigmoid"
+  bottom: "Convolution15"
+  top: "Convolution15"
+}
+layer {
+  name: "Flatten2"
+  type: "Flatten"
+  bottom: "Convolution15"
+  top: "Flatten2"
+}
+layer {
+  name: "Scale2"
+  type: "Scale"
+  bottom: "Convolution13"
+  bottom: "Flatten2"
+  top: "Scale2"
+  scale_param {
+    axis: 0
+    bias_term: false
+  }
+}
+layer {
+  name: "Convolution16"
+  type: "Convolution"
+  bottom: "Scale2"
+  top: "Convolution16"
+  convolution_param {
+    num_output: 128
+    pad: 0
+    kernel_size: 2
+    stride: 2
+  }
+}
+layer {
+  name: "ReLU15"
+  type: "ReLU"
+  bottom: "Convolution16"
+  top: "Convolution16"
+  relu_param {
+    negative_slope: 0.1
+  }
+}
+layer {
+  name: "Convolution17"
+  type: "Convolution"
+  bottom: "Convolution16"
+  top: "Convolution17"
+  convolution_param {
+    num_output: 256
+    pad: 0
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "ReLU16"
+  type: "ReLU"
+  bottom: "Convolution17"
+  top: "Convolution17"
+  relu_param {
+    negative_slope: 0.1
+  }
+}
+layer {
+  name: "Convolution18"
+  type: "Convolution"
+  bottom: "Convolution17"
+  top: "Convolution18"
+  convolution_param {
+    num_output: 128
+    pad: 0
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "ReLU17"
+  type: "ReLU"
+  bottom: "Convolution18"
+  top: "Convolution18"
+  relu_param {
+    negative_slope: 0.1
+  }
+}
+layer {
+  name: "Pooling3"
+  type: "Pooling"
+  bottom: "Convolution18"
+  top: "Pooling3"
+  pooling_param {
+    pool: AVE
+    global_pooling: true
+  }
+}
+layer {
+  name: "Convolution19"
+  type: "Convolution"
+  bottom: "Pooling3"
+  top: "Convolution19"
+  convolution_param {
+    num_output: 16
+    pad: 0
+    kernel_size: 1
+    stride: 1
+  }
+}
+layer {
+  name: "ReLU18"
+  type: "ReLU"
+  bottom: "Convolution19"
+  top: "Convolution19"
+}
+layer {
+  name: "Convolution20"
+  type: "Convolution"
+  bottom: "Convolution19"
+  top: "Convolution20"
+  convolution_param {
+    num_output: 128
+    pad: 0
+    kernel_size: 1
+    stride: 1
+  }
+}
+layer {
+  name: "Sigmoid3"
+  type: "Sigmoid"
+  bottom: "Convolution20"
+  top: "Convolution20"
+}
+layer {
+  name: "Flatten3"
+  type: "Flatten"
+  bottom: "Convolution20"
+  top: "Flatten3"
+}
+layer {
+  name: "Scale3"
+  type: "Scale"
+  bottom: "Convolution18"
+  bottom: "Flatten3"
+  top: "Scale3"
+  scale_param {
+    axis: 0
+    bias_term: false
+  }
+}
+layer {
+  name: "Deconvolution3"
+  type: "Deconvolution"
+  bottom: "Scale3"
+  top: "Deconvolution3"
+  convolution_param {
+    num_output: 128
+    pad: 0
+    kernel_size: 2
+    stride: 2
+  }
+}
+layer {
+  name: "ReLU19"
+  type: "ReLU"
+  bottom: "Deconvolution3"
+  top: "Deconvolution3"
+  relu_param {
+    negative_slope: 0.1
+  }
+}
+layer {
+  name: "Crop2"
+  type: "Crop"
+  bottom: "Scale2"
+  bottom: "Deconvolution3"
+  top: "Crop2"
+  crop_param {
+    axis: 2
+    offset: 4
+  }
+}
+layer {
+  name: "Eltwise2"
+  type: "Eltwise"
+  bottom: "Crop2"
+  bottom: "Deconvolution3"
+  top: "Eltwise2"
+  eltwise_param {
+    operation: SUM
+  }
+}
+layer {
+  name: "Convolution21"
+  type: "Convolution"
+  bottom: "Eltwise2"
+  top: "Convolution21"
+  convolution_param {
+    num_output: 64
+    pad: 0
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "ReLU20"
+  type: "ReLU"
+  bottom: "Convolution21"
+  top: "Convolution21"
+  relu_param {
+    negative_slope: 0.1
+  }
+}
+layer {
+  name: "Convolution22"
+  type: "Convolution"
+  bottom: "Convolution21"
+  top: "Convolution22"
+  convolution_param {
+    num_output: 64
+    pad: 0
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "ReLU21"
+  type: "ReLU"
+  bottom: "Convolution22"
+  top: "Convolution22"
+  relu_param {
+    negative_slope: 0.1
+  }
+}
+layer {
+  name: "Pooling4"
+  type: "Pooling"
+  bottom: "Convolution22"
+  top: "Pooling4"
+  pooling_param {
+    pool: AVE
+    global_pooling: true
+  }
+}
+layer {
+  name: "Convolution23"
+  type: "Convolution"
+  bottom: "Pooling4"
+  top: "Convolution23"
+  convolution_param {
+    num_output: 8
+    pad: 0
+    kernel_size: 1
+    stride: 1
+  }
+}
+layer {
+  name: "ReLU22"
+  type: "ReLU"
+  bottom: "Convolution23"
+  top: "Convolution23"
+}
+layer {
+  name: "Convolution24"
+  type: "Convolution"
+  bottom: "Convolution23"
+  top: "Convolution24"
+  convolution_param {
+    num_output: 64
+    pad: 0
+    kernel_size: 1
+    stride: 1
+  }
+}
+layer {
+  name: "Sigmoid4"
+  type: "Sigmoid"
+  bottom: "Convolution24"
+  top: "Convolution24"
+}
+layer {
+  name: "Flatten4"
+  type: "Flatten"
+  bottom: "Convolution24"
+  top: "Flatten4"
+}
+layer {
+  name: "Scale4"
+  type: "Scale"
+  bottom: "Convolution22"
+  bottom: "Flatten4"
+  top: "Scale4"
+  scale_param {
+    axis: 0
+    bias_term: false
+  }
+}
+layer {
+  name: "Deconvolution4"
+  type: "Deconvolution"
+  bottom: "Scale4"
+  top: "Deconvolution4"
+  convolution_param {
+    num_output: 64
+    pad: 0
+    kernel_size: 2
+    stride: 2
+  }
+}
+layer {
+  name: "ReLU23"
+  type: "ReLU"
+  bottom: "Deconvolution4"
+  top: "Deconvolution4"
+  relu_param {
+    negative_slope: 0.1
+  }
+}
+layer {
+  name: "Crop3"
+  type: "Crop"
+  bottom: "Convolution10"
+  bottom: "Deconvolution4"
+  top: "Crop3"
+  crop_param {
+    axis: 2
+    offset: 16
+  }
+}
+layer {
+  name: "Eltwise3"
+  type: "Eltwise"
+  bottom: "Crop3"
+  bottom: "Deconvolution4"
+  top: "Eltwise3"
+  eltwise_param {
+    operation: SUM
+  }
+}
+layer {
+  name: "Convolution25"
+  type: "Convolution"
+  bottom: "Eltwise3"
+  top: "Convolution25"
+  convolution_param {
+    num_output: 64
+    pad: 0
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "ReLU24"
+  type: "ReLU"
+  bottom: "Convolution25"
+  top: "Convolution25"
+  relu_param {
+    negative_slope: 0.1
+  }
+}
+layer {
+  name: "Convolution26"
+  type: "Convolution"
+  bottom: "Convolution25"
+  top: "Convolution26"
+  convolution_param {
+    num_output: 3
+    pad: 0
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "Crop4"
+  type: "Crop"
+  bottom: "Deconvolution2"
+  bottom: "Convolution26"
+  top: "Crop4"
+  crop_param {
+    axis: 2
+    offset: 20
+  }
+}
+layer {
+  name: "Eltwise4"
+  type: "Eltwise"
+  bottom: "Crop4"
+  bottom: "Convolution26"
+  top: "Eltwise4"
+  eltwise_param {
+    operation: SUM
+  }
+}
+