wangii · September 27, 2018 09:16 · Sep 27, 2018
diff --git a/openpose hand b/openpose hand
@@ -0,0 +1,1756 @@
+input: "image"
+input_dim: 1 # Original: 2
+input_dim: 3 # It crashes if not left to 3
+input_dim: 1 # Original: 368
+input_dim: 1 # Original: 368
+layer {
+  name: "conv1_1"
+  type: "Convolution"
+  bottom: "image"
+  top: "conv1_1"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 64
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+    }
+    dilation: 1
+  }
+}
+layer {
+  name: "relu1_1"
+  type: "ReLU"
+  bottom: "conv1_1"
+  top: "conv1_1"
+}
+layer {
+  name: "conv1_2"
+  type: "Convolution"
+  bottom: "conv1_1"
+  top: "conv1_2"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 64
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+    }
+    dilation: 1
+  }
+}
+layer {
+  name: "relu1_2"
+  type: "ReLU"
+  bottom: "conv1_2"
+  top: "conv1_2"
+}
+layer {
+  name: "pool1_stage1"
+  type: "Pooling"
+  bottom: "conv1_2"
+  top: "pool1_stage1"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+  }
+}
+layer {
+  name: "conv2_1"
+  type: "Convolution"
+  bottom: "pool1_stage1"
+  top: "conv2_1"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+    }
+    dilation: 1
+  }
+}
+layer {
+  name: "relu2_1"
+  type: "ReLU"
+  bottom: "conv2_1"
+  top: "conv2_1"
+}
+layer {
+  name: "conv2_2"
+  type: "Convolution"
+  bottom: "conv2_1"
+  top: "conv2_2"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+    }
+    dilation: 1
+  }
+}
+layer {
+  name: "relu2_2"
+  type: "ReLU"
+  bottom: "conv2_2"
+  top: "conv2_2"
+}
+layer {
+  name: "pool2_stage1"
+  type: "Pooling"
+  bottom: "conv2_2"
+  top: "pool2_stage1"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+  }
+}
+layer {
+  name: "conv3_1"
+  type: "Convolution"
+  bottom: "pool2_stage1"
+  top: "conv3_1"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+    }
+    dilation: 1
+  }
+}
+layer {
+  name: "relu3_1"
+  type: "ReLU"
+  bottom: "conv3_1"
+  top: "conv3_1"
+}
+layer {
+  name: "conv3_2"
+  type: "Convolution"
+  bottom: "conv3_1"
+  top: "conv3_2"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+    }
+    dilation: 1
+  }
+}
+layer {
+  name: "relu3_2"
+  type: "ReLU"
+  bottom: "conv3_2"
+  top: "conv3_2"
+}
+layer {
+  name: "conv3_3"
+  type: "Convolution"
+  bottom: "conv3_2"
+  top: "conv3_3"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+    }
+    dilation: 1
+  }
+}
+layer {
+  name: "relu3_3"
+  type: "ReLU"
+  bottom: "conv3_3"
+  top: "conv3_3"
+}
+layer {
+  name: "conv3_4"
+  type: "Convolution"
+  bottom: "conv3_3"
+  top: "conv3_4"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+    }
+    dilation: 1
+  }
+}
+layer {
+  name: "relu3_4"
+  type: "ReLU"
+  bottom: "conv3_4"
+  top: "conv3_4"
+}
+layer {
+  name: "pool3_stage1"
+  type: "Pooling"
+  bottom: "conv3_4"
+  top: "pool3_stage1"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+  }
+}
+layer {
+  name: "conv4_1"
+  type: "Convolution"
+  bottom: "pool3_stage1"
+  top: "conv4_1"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 512
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+    }
+    dilation: 1
+  }
+}
+layer {
+  name: "relu4_1"
+  type: "ReLU"
+  bottom: "conv4_1"
+  top: "conv4_1"
+}
+layer {
+  name: "conv4_2"
+  type: "Convolution"
+  bottom: "conv4_1"
+  top: "conv4_2"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 512
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+    }
+    dilation: 1
+  }
+}
+layer {
+  name: "relu4_2"
+  type: "ReLU"
+  bottom: "conv4_2"
+  top: "conv4_2"
+}
+layer {
+  name: "conv4_3"
+  type: "Convolution"
+  bottom: "conv4_2"
+  top: "conv4_3"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 512
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+    }
+    dilation: 1
+  }
+}
+layer {
+  name: "relu4_3"
+  type: "ReLU"
+  bottom: "conv4_3"
+  top: "conv4_3"
+}
+layer {
+  name: "conv4_4"
+  type: "Convolution"
+  bottom: "conv4_3"
+  top: "conv4_4"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 512
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+    }
+    dilation: 1
+  }
+}
+layer {
+  name: "relu4_4"
+  type: "ReLU"
+  bottom: "conv4_4"
+  top: "conv4_4"
+}
+layer {
+  name: "conv5_1"
+  type: "Convolution"
+  bottom: "conv4_4"
+  top: "conv5_1"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 512
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+    }
+    dilation: 1
+  }
+}
+layer {
+  name: "relu5_1"
+  type: "ReLU"
+  bottom: "conv5_1"
+  top: "conv5_1"
+}
+layer {
+  name: "conv5_2"
+  type: "Convolution"
+  bottom: "conv5_1"
+  top: "conv5_2"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 512
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+    }
+    dilation: 1
+  }
+}
+layer {
+  name: "relu5_2"
+  type: "ReLU"
+  bottom: "conv5_2"
+  top: "conv5_2"
+}
+layer {
+  name: "conv5_3_CPM"
+  type: "Convolution"
+  bottom: "conv5_2"
+  top: "conv5_3_CPM"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+    dilation: 1
+  }
+}
+layer {
+  name: "relu5_4_stage1_3"
+  type: "ReLU"
+  bottom: "conv5_3_CPM"
+  top: "conv5_3_CPM"
+}
+layer {
+  name: "conv6_1_CPM"
+  type: "Convolution"
+  bottom: "conv5_3_CPM"
+  top: "conv6_1_CPM"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 512
+    pad: 0
+    kernel_size: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+    dilation: 1
+  }
+}
+layer {
+  name: "relu6_4_stage1_1"
+  type: "ReLU"
+  bottom: "conv6_1_CPM"
+  top: "conv6_1_CPM"
+}
+layer {
+  name: "conv6_2_CPM"
+  type: "Convolution"
+  bottom: "conv6_1_CPM"
+  top: "conv6_2_CPM"
+  param {
+    lr_mult: 1.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 22
+    pad: 0
+    kernel_size: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+    dilation: 1
+  }
+}
+layer {
+  name: "concat_stage2"
+  type: "Concat"
+  bottom: "conv6_2_CPM"
+  bottom: "conv5_3_CPM"
+  top: "concat_stage2"
+  concat_param {
+    axis: 1
+  }
+}
+layer {
+  name: "Mconv1_stage2"
+  type: "Convolution"
+  bottom: "concat_stage2"
+  top: "Mconv1_stage2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+    dilation: 1
+  }
+}
+layer {
+  name: "Mrelu1_2_stage2_1"
+  type: "ReLU"
+  bottom: "Mconv1_stage2"
+  top: "Mconv1_stage2"
+}
+layer {
+  name: "Mconv2_stage2"
+  type: "Convolution"
+  bottom: "Mconv1_stage2"
+  top: "Mconv2_stage2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+    dilation: 1
+  }
+}
+layer {
+  name: "Mrelu1_3_stage2_2"
+  type: "ReLU"
+  bottom: "Mconv2_stage2"
+  top: "Mconv2_stage2"
+}
+layer {
+  name: "Mconv3_stage2"
+  type: "Convolution"
+  bottom: "Mconv2_stage2"
+  top: "Mconv3_stage2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+    dilation: 1
+  }
+}
+layer {
+  name: "Mrelu1_4_stage2_3"
+  type: "ReLU"
+  bottom: "Mconv3_stage2"
+  top: "Mconv3_stage2"
+}
+layer {
+  name: "Mconv4_stage2"
+  type: "Convolution"
+  bottom: "Mconv3_stage2"
+  top: "Mconv4_stage2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+    dilation: 1
+  }
+}
+layer {
+  name: "Mrelu1_5_stage2_4"
+  type: "ReLU"
+  bottom: "Mconv4_stage2"
+  top: "Mconv4_stage2"
+}
+layer {
+  name: "Mconv5_stage2"
+  type: "Convolution"
+  bottom: "Mconv4_stage2"
+  top: "Mconv5_stage2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+    dilation: 1
+  }
+}
+layer {
+  name: "Mrelu1_6_stage2_5"
+  type: "ReLU"
+  bottom: "Mconv5_stage2"
+  top: "Mconv5_stage2"
+}
+layer {
+  name: "Mconv6_stage2"
+  type: "Convolution"
+  bottom: "Mconv5_stage2"
+  top: "Mconv6_stage2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 0
+    kernel_size: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+    dilation: 1
+  }
+}
+layer {
+  name: "Mrelu1_7_stage2_6"
+  type: "ReLU"
+  bottom: "Mconv6_stage2"
+  top: "Mconv6_stage2"
+}
+layer {
+  name: "Mconv7_stage2"
+  type: "Convolution"
+  bottom: "Mconv6_stage2"
+  top: "Mconv7_stage2"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 22
+    pad: 0
+    kernel_size: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+    dilation: 1
+  }
+}
+layer {
+  name: "concat_stage3"
+  type: "Concat"
+  bottom: "Mconv7_stage2"
+  bottom: "conv5_3_CPM"
+  top: "concat_stage3"
+  concat_param {
+    axis: 1
+  }
+}
+layer {
+  name: "Mconv1_stage3"
+  type: "Convolution"
+  bottom: "concat_stage3"
+  top: "Mconv1_stage3"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+    dilation: 1
+  }
+}
+layer {
+  name: "Mrelu1_2_stage3_1"
+  type: "ReLU"
+  bottom: "Mconv1_stage3"
+  top: "Mconv1_stage3"
+}
+layer {
+  name: "Mconv2_stage3"
+  type: "Convolution"
+  bottom: "Mconv1_stage3"
+  top: "Mconv2_stage3"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+    dilation: 1
+  }
+}
+layer {
+  name: "Mrelu1_3_stage3_2"
+  type: "ReLU"
+  bottom: "Mconv2_stage3"
+  top: "Mconv2_stage3"
+}
+layer {
+  name: "Mconv3_stage3"
+  type: "Convolution"
+  bottom: "Mconv2_stage3"
+  top: "Mconv3_stage3"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+    dilation: 1
+  }
+}
+layer {
+  name: "Mrelu1_4_stage3_3"
+  type: "ReLU"
+  bottom: "Mconv3_stage3"
+  top: "Mconv3_stage3"
+}
+layer {
+  name: "Mconv4_stage3"
+  type: "Convolution"
+  bottom: "Mconv3_stage3"
+  top: "Mconv4_stage3"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+    dilation: 1
+  }
+}
+layer {
+  name: "Mrelu1_5_stage3_4"
+  type: "ReLU"
+  bottom: "Mconv4_stage3"
+  top: "Mconv4_stage3"
+}
+layer {
+  name: "Mconv5_stage3"
+  type: "Convolution"
+  bottom: "Mconv4_stage3"
+  top: "Mconv5_stage3"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+    dilation: 1
+  }
+}
+layer {
+  name: "Mrelu1_6_stage3_5"
+  type: "ReLU"
+  bottom: "Mconv5_stage3"
+  top: "Mconv5_stage3"
+}
+layer {
+  name: "Mconv6_stage3"
+  type: "Convolution"
+  bottom: "Mconv5_stage3"
+  top: "Mconv6_stage3"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 0
+    kernel_size: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+    dilation: 1
+  }
+}
+layer {
+  name: "Mrelu1_7_stage3_6"
+  type: "ReLU"
+  bottom: "Mconv6_stage3"
+  top: "Mconv6_stage3"
+}
+layer {
+  name: "Mconv7_stage3"
+  type: "Convolution"
+  bottom: "Mconv6_stage3"
+  top: "Mconv7_stage3"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 22
+    pad: 0
+    kernel_size: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+    dilation: 1
+  }
+}
+layer {
+  name: "concat_stage4"
+  type: "Concat"
+  bottom: "Mconv7_stage3"
+  bottom: "conv5_3_CPM"
+  top: "concat_stage4"
+  concat_param {
+    axis: 1
+  }
+}
+layer {
+  name: "Mconv1_stage4"
+  type: "Convolution"
+  bottom: "concat_stage4"
+  top: "Mconv1_stage4"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+    dilation: 1
+  }
+}
+layer {
+  name: "Mrelu1_2_stage4_1"
+  type: "ReLU"
+  bottom: "Mconv1_stage4"
+  top: "Mconv1_stage4"
+}
+layer {
+  name: "Mconv2_stage4"
+  type: "Convolution"
+  bottom: "Mconv1_stage4"
+  top: "Mconv2_stage4"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+    dilation: 1
+  }
+}
+layer {
+  name: "Mrelu1_3_stage4_2"
+  type: "ReLU"
+  bottom: "Mconv2_stage4"
+  top: "Mconv2_stage4"
+}
+layer {
+  name: "Mconv3_stage4"
+  type: "Convolution"
+  bottom: "Mconv2_stage4"
+  top: "Mconv3_stage4"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+    dilation: 1
+  }
+}
+layer {
+  name: "Mrelu1_4_stage4_3"
+  type: "ReLU"
+  bottom: "Mconv3_stage4"
+  top: "Mconv3_stage4"
+}
+layer {
+  name: "Mconv4_stage4"
+  type: "Convolution"
+  bottom: "Mconv3_stage4"
+  top: "Mconv4_stage4"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+    dilation: 1
+  }
+}
+layer {
+  name: "Mrelu1_5_stage4_4"
+  type: "ReLU"
+  bottom: "Mconv4_stage4"
+  top: "Mconv4_stage4"
+}
+layer {
+  name: "Mconv5_stage4"
+  type: "Convolution"
+  bottom: "Mconv4_stage4"
+  top: "Mconv5_stage4"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+    dilation: 1
+  }
+}
+layer {
+  name: "Mrelu1_6_stage4_5"
+  type: "ReLU"
+  bottom: "Mconv5_stage4"
+  top: "Mconv5_stage4"
+}
+layer {
+  name: "Mconv6_stage4"
+  type: "Convolution"
+  bottom: "Mconv5_stage4"
+  top: "Mconv6_stage4"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 0
+    kernel_size: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+    dilation: 1
+  }
+}
+layer {
+  name: "Mrelu1_7_stage4_6"
+  type: "ReLU"
+  bottom: "Mconv6_stage4"
+  top: "Mconv6_stage4"
+}
+layer {
+  name: "Mconv7_stage4"
+  type: "Convolution"
+  bottom: "Mconv6_stage4"
+  top: "Mconv7_stage4"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 22
+    pad: 0
+    kernel_size: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+    dilation: 1
+  }
+}
+layer {
+  name: "concat_stage5"
+  type: "Concat"
+  bottom: "Mconv7_stage4"
+  bottom: "conv5_3_CPM"
+  top: "concat_stage5"
+  concat_param {
+    axis: 1
+  }
+}
+layer {
+  name: "Mconv1_stage5"
+  type: "Convolution"
+  bottom: "concat_stage5"
+  top: "Mconv1_stage5"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+    dilation: 1
+  }
+}
+layer {
+  name: "Mrelu1_2_stage5_1"
+  type: "ReLU"
+  bottom: "Mconv1_stage5"
+  top: "Mconv1_stage5"
+}
+layer {
+  name: "Mconv2_stage5"
+  type: "Convolution"
+  bottom: "Mconv1_stage5"
+  top: "Mconv2_stage5"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+    dilation: 1
+  }
+}
+layer {
+  name: "Mrelu1_3_stage5_2"
+  type: "ReLU"
+  bottom: "Mconv2_stage5"
+  top: "Mconv2_stage5"
+}
+layer {
+  name: "Mconv3_stage5"
+  type: "Convolution"
+  bottom: "Mconv2_stage5"
+  top: "Mconv3_stage5"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+    dilation: 1
+  }
+}
+layer {
+  name: "Mrelu1_4_stage5_3"
+  type: "ReLU"
+  bottom: "Mconv3_stage5"
+  top: "Mconv3_stage5"
+}
+layer {
+  name: "Mconv4_stage5"
+  type: "Convolution"
+  bottom: "Mconv3_stage5"
+  top: "Mconv4_stage5"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+    dilation: 1
+  }
+}
+layer {
+  name: "Mrelu1_5_stage5_4"
+  type: "ReLU"
+  bottom: "Mconv4_stage5"
+  top: "Mconv4_stage5"
+}
+layer {
+  name: "Mconv5_stage5"
+  type: "Convolution"
+  bottom: "Mconv4_stage5"
+  top: "Mconv5_stage5"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+    dilation: 1
+  }
+}
+layer {
+  name: "Mrelu1_6_stage5_5"
+  type: "ReLU"
+  bottom: "Mconv5_stage5"
+  top: "Mconv5_stage5"
+}
+layer {
+  name: "Mconv6_stage5"
+  type: "Convolution"
+  bottom: "Mconv5_stage5"
+  top: "Mconv6_stage5"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 0
+    kernel_size: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+    dilation: 1
+  }
+}
+layer {
+  name: "Mrelu1_7_stage5_6"
+  type: "ReLU"
+  bottom: "Mconv6_stage5"
+  top: "Mconv6_stage5"
+}
+layer {
+  name: "Mconv7_stage5"
+  type: "Convolution"
+  bottom: "Mconv6_stage5"
+  top: "Mconv7_stage5"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 22
+    pad: 0
+    kernel_size: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+    dilation: 1
+  }
+}
+layer {
+  name: "concat_stage6"
+  type: "Concat"
+  bottom: "Mconv7_stage5"
+  bottom: "conv5_3_CPM"
+  top: "concat_stage6"
+  concat_param {
+    axis: 1
+  }
+}
+layer {
+  name: "Mconv1_stage6"
+  type: "Convolution"
+  bottom: "concat_stage6"
+  top: "Mconv1_stage6"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+    dilation: 1
+  }
+}
+layer {
+  name: "Mrelu1_2_stage6_1"
+  type: "ReLU"
+  bottom: "Mconv1_stage6"
+  top: "Mconv1_stage6"
+}
+layer {
+  name: "Mconv2_stage6"
+  type: "Convolution"
+  bottom: "Mconv1_stage6"
+  top: "Mconv2_stage6"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+    dilation: 1
+  }
+}
+layer {
+  name: "Mrelu1_3_stage6_2"
+  type: "ReLU"
+  bottom: "Mconv2_stage6"
+  top: "Mconv2_stage6"
+}
+layer {
+  name: "Mconv3_stage6"
+  type: "Convolution"
+  bottom: "Mconv2_stage6"
+  top: "Mconv3_stage6"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+    dilation: 1
+  }
+}
+layer {
+  name: "Mrelu1_4_stage6_3"
+  type: "ReLU"
+  bottom: "Mconv3_stage6"
+  top: "Mconv3_stage6"
+}
+layer {
+  name: "Mconv4_stage6"
+  type: "Convolution"
+  bottom: "Mconv3_stage6"
+  top: "Mconv4_stage6"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+    dilation: 1
+  }
+}
+layer {
+  name: "Mrelu1_5_stage6_4"
+  type: "ReLU"
+  bottom: "Mconv4_stage6"
+  top: "Mconv4_stage6"
+}
+layer {
+  name: "Mconv5_stage6"
+  type: "Convolution"
+  bottom: "Mconv4_stage6"
+  top: "Mconv5_stage6"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 3
+    kernel_size: 7
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+    dilation: 1
+  }
+}
+layer {
+  name: "Mrelu1_6_stage6_5"
+  type: "ReLU"
+  bottom: "Mconv5_stage6"
+  top: "Mconv5_stage6"
+}
+layer {
+  name: "Mconv6_stage6"
+  type: "Convolution"
+  bottom: "Mconv5_stage6"
+  top: "Mconv6_stage6"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 128
+    pad: 0
+    kernel_size: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+    dilation: 1
+  }
+}
+layer {
+  name: "Mrelu1_7_stage6_6"
+  type: "ReLU"
+  bottom: "Mconv6_stage6"
+  top: "Mconv6_stage6"
+}
+layer {
+  name: "Mconv7_stage6"
+  type: "Convolution"
+  bottom: "Mconv6_stage6"
+#   top: "Mconv7_stage6"
+  top: "net_output"
+  param {
+    lr_mult: 4.0
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 8.0
+    decay_mult: 0
+  }
+  convolution_param {
+    num_output: 22
+    pad: 0
+    kernel_size: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+    dilation: 1
+  }
+}
+