2976 lines
45 KiB
Plaintext
2976 lines
45 KiB
Plaintext
input: "image"
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input_dim: 1
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input_dim: 3
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input_dim: 1 # This value will be defined at runtime
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input_dim: 1 # This value will be defined at runtime
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layer {
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name: "conv1_1"
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type: "Convolution"
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bottom: "image"
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top: "conv1_1"
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param {
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lr_mult: 1.0
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decay_mult: 1
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}
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param {
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lr_mult: 2.0
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decay_mult: 0
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}
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convolution_param {
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num_output: 64
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pad: 1
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kernel_size: 3
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weight_filler {
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type: "gaussian"
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std: 0.01
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}
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bias_filler {
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type: "constant"
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}
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}
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}
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layer {
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name: "relu1_1"
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type: "ReLU"
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bottom: "conv1_1"
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top: "conv1_1"
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}
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layer {
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name: "conv1_2"
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type: "Convolution"
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bottom: "conv1_1"
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top: "conv1_2"
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param {
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lr_mult: 1.0
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decay_mult: 1
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}
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param {
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lr_mult: 2.0
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decay_mult: 0
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}
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convolution_param {
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num_output: 64
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pad: 1
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kernel_size: 3
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weight_filler {
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type: "gaussian"
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std: 0.01
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}
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bias_filler {
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type: "constant"
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}
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}
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}
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layer {
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name: "relu1_2"
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type: "ReLU"
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bottom: "conv1_2"
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top: "conv1_2"
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}
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layer {
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name: "pool1_stage1"
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type: "Pooling"
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bottom: "conv1_2"
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top: "pool1_stage1"
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pooling_param {
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pool: MAX
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kernel_size: 2
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stride: 2
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}
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}
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layer {
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name: "conv2_1"
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type: "Convolution"
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bottom: "pool1_stage1"
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top: "conv2_1"
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param {
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lr_mult: 1.0
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decay_mult: 1
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}
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param {
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lr_mult: 2.0
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decay_mult: 0
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}
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convolution_param {
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num_output: 128
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pad: 1
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kernel_size: 3
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weight_filler {
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type: "gaussian"
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std: 0.01
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}
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bias_filler {
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type: "constant"
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}
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}
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}
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layer {
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name: "relu2_1"
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type: "ReLU"
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bottom: "conv2_1"
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top: "conv2_1"
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}
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layer {
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name: "conv2_2"
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type: "Convolution"
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bottom: "conv2_1"
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top: "conv2_2"
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param {
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lr_mult: 1.0
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decay_mult: 1
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}
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param {
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lr_mult: 2.0
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decay_mult: 0
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}
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convolution_param {
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num_output: 128
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pad: 1
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kernel_size: 3
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weight_filler {
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type: "gaussian"
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std: 0.01
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}
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bias_filler {
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type: "constant"
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}
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}
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}
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layer {
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name: "relu2_2"
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type: "ReLU"
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bottom: "conv2_2"
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top: "conv2_2"
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}
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layer {
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name: "pool2_stage1"
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type: "Pooling"
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bottom: "conv2_2"
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top: "pool2_stage1"
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pooling_param {
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pool: MAX
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kernel_size: 2
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stride: 2
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}
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}
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layer {
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name: "conv3_1"
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type: "Convolution"
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bottom: "pool2_stage1"
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top: "conv3_1"
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param {
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lr_mult: 1.0
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decay_mult: 1
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}
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param {
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lr_mult: 2.0
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decay_mult: 0
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}
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convolution_param {
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num_output: 256
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pad: 1
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kernel_size: 3
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weight_filler {
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type: "gaussian"
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std: 0.01
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}
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bias_filler {
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type: "constant"
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}
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}
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}
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layer {
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name: "relu3_1"
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type: "ReLU"
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bottom: "conv3_1"
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top: "conv3_1"
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}
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layer {
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name: "conv3_2"
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type: "Convolution"
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bottom: "conv3_1"
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top: "conv3_2"
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param {
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lr_mult: 1.0
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decay_mult: 1
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}
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param {
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lr_mult: 2.0
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decay_mult: 0
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}
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convolution_param {
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num_output: 256
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pad: 1
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kernel_size: 3
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weight_filler {
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type: "gaussian"
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std: 0.01
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}
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bias_filler {
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type: "constant"
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}
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}
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}
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layer {
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name: "relu3_2"
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type: "ReLU"
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bottom: "conv3_2"
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top: "conv3_2"
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}
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layer {
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name: "conv3_3"
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type: "Convolution"
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bottom: "conv3_2"
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top: "conv3_3"
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param {
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lr_mult: 1.0
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decay_mult: 1
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}
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param {
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lr_mult: 2.0
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decay_mult: 0
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}
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convolution_param {
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num_output: 256
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pad: 1
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kernel_size: 3
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weight_filler {
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type: "gaussian"
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std: 0.01
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}
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bias_filler {
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type: "constant"
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}
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}
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}
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layer {
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name: "relu3_3"
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type: "ReLU"
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bottom: "conv3_3"
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top: "conv3_3"
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}
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layer {
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name: "conv3_4"
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type: "Convolution"
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bottom: "conv3_3"
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top: "conv3_4"
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param {
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lr_mult: 1.0
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decay_mult: 1
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}
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param {
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lr_mult: 2.0
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decay_mult: 0
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}
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convolution_param {
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num_output: 256
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pad: 1
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kernel_size: 3
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weight_filler {
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type: "gaussian"
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std: 0.01
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}
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bias_filler {
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type: "constant"
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}
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}
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}
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layer {
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name: "relu3_4"
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type: "ReLU"
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bottom: "conv3_4"
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top: "conv3_4"
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}
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layer {
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name: "pool3_stage1"
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type: "Pooling"
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bottom: "conv3_4"
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top: "pool3_stage1"
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pooling_param {
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pool: MAX
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kernel_size: 2
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stride: 2
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}
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}
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layer {
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name: "conv4_1"
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type: "Convolution"
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bottom: "pool3_stage1"
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top: "conv4_1"
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param {
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lr_mult: 1.0
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decay_mult: 1
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}
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param {
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lr_mult: 2.0
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decay_mult: 0
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}
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convolution_param {
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num_output: 512
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pad: 1
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kernel_size: 3
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weight_filler {
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type: "gaussian"
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std: 0.01
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}
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bias_filler {
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type: "constant"
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}
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}
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}
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layer {
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name: "relu4_1"
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type: "ReLU"
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bottom: "conv4_1"
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top: "conv4_1"
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}
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layer {
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name: "conv4_2"
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type: "Convolution"
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bottom: "conv4_1"
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top: "conv4_2"
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param {
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lr_mult: 1.0
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decay_mult: 1
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}
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param {
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lr_mult: 2.0
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decay_mult: 0
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}
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convolution_param {
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num_output: 512
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pad: 1
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kernel_size: 3
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weight_filler {
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type: "gaussian"
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std: 0.01
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}
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bias_filler {
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type: "constant"
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}
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}
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}
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layer {
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name: "relu4_2"
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type: "ReLU"
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bottom: "conv4_2"
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top: "conv4_2"
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}
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layer {
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name: "conv4_3_CPM"
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type: "Convolution"
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bottom: "conv4_2"
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top: "conv4_3_CPM"
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param {
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lr_mult: 1.0
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decay_mult: 1
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}
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param {
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lr_mult: 2.0
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decay_mult: 0
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}
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convolution_param {
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num_output: 256
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pad: 1
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kernel_size: 3
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weight_filler {
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type: "gaussian"
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std: 0.01
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}
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bias_filler {
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type: "constant"
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}
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}
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}
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layer {
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name: "relu4_3_CPM"
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type: "ReLU"
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bottom: "conv4_3_CPM"
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top: "conv4_3_CPM"
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}
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layer {
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name: "conv4_4_CPM"
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type: "Convolution"
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bottom: "conv4_3_CPM"
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top: "conv4_4_CPM"
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param {
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lr_mult: 1.0
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decay_mult: 1
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}
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param {
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lr_mult: 2.0
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decay_mult: 0
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}
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|
convolution_param {
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num_output: 128
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pad: 1
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kernel_size: 3
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weight_filler {
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|
type: "gaussian"
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std: 0.01
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}
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bias_filler {
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type: "constant"
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}
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}
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}
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layer {
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name: "relu4_4_CPM"
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type: "ReLU"
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bottom: "conv4_4_CPM"
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top: "conv4_4_CPM"
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}
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layer {
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name: "conv5_1_CPM_L1"
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type: "Convolution"
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bottom: "conv4_4_CPM"
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top: "conv5_1_CPM_L1"
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param {
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lr_mult: 1.0
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decay_mult: 1
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}
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param {
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lr_mult: 2.0
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decay_mult: 0
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}
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convolution_param {
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num_output: 128
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pad: 1
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kernel_size: 3
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weight_filler {
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|
type: "gaussian"
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std: 0.01
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}
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bias_filler {
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|
type: "constant"
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}
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}
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}
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layer {
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name: "relu5_1_CPM_L1"
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type: "ReLU"
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bottom: "conv5_1_CPM_L1"
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top: "conv5_1_CPM_L1"
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}
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layer {
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name: "conv5_1_CPM_L2"
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type: "Convolution"
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bottom: "conv4_4_CPM"
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top: "conv5_1_CPM_L2"
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param {
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lr_mult: 1.0
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decay_mult: 1
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}
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param {
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lr_mult: 2.0
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decay_mult: 0
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}
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|
convolution_param {
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num_output: 128
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pad: 1
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kernel_size: 3
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weight_filler {
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|
type: "gaussian"
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std: 0.01
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|
}
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bias_filler {
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|
type: "constant"
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}
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}
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}
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layer {
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name: "relu5_1_CPM_L2"
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type: "ReLU"
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bottom: "conv5_1_CPM_L2"
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top: "conv5_1_CPM_L2"
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}
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layer {
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name: "conv5_2_CPM_L1"
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type: "Convolution"
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bottom: "conv5_1_CPM_L1"
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top: "conv5_2_CPM_L1"
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|
param {
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|
lr_mult: 1.0
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|
decay_mult: 1
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|
}
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param {
|
|
lr_mult: 2.0
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|
decay_mult: 0
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}
|
|
convolution_param {
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|
num_output: 128
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|
pad: 1
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|
kernel_size: 3
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|
weight_filler {
|
|
type: "gaussian"
|
|
std: 0.01
|
|
}
|
|
bias_filler {
|
|
type: "constant"
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|
}
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|
}
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|
}
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|
layer {
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|
name: "relu5_2_CPM_L1"
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|
type: "ReLU"
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|
bottom: "conv5_2_CPM_L1"
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top: "conv5_2_CPM_L1"
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}
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|
layer {
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|
name: "conv5_2_CPM_L2"
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|
type: "Convolution"
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|
bottom: "conv5_1_CPM_L2"
|
|
top: "conv5_2_CPM_L2"
|
|
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
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|
weight_filler {
|
|
type: "gaussian"
|
|
std: 0.01
|
|
}
|
|
bias_filler {
|
|
type: "constant"
|
|
}
|
|
}
|
|
}
|
|
layer {
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|
name: "relu5_2_CPM_L2"
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|
type: "ReLU"
|
|
bottom: "conv5_2_CPM_L2"
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|
top: "conv5_2_CPM_L2"
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|
}
|
|
layer {
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|
name: "conv5_3_CPM_L1"
|
|
type: "Convolution"
|
|
bottom: "conv5_2_CPM_L1"
|
|
top: "conv5_3_CPM_L1"
|
|
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"
|
|
}
|
|
}
|
|
}
|
|
layer {
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|
name: "relu5_3_CPM_L1"
|
|
type: "ReLU"
|
|
bottom: "conv5_3_CPM_L1"
|
|
top: "conv5_3_CPM_L1"
|
|
}
|
|
layer {
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|
name: "conv5_3_CPM_L2"
|
|
type: "Convolution"
|
|
bottom: "conv5_2_CPM_L2"
|
|
top: "conv5_3_CPM_L2"
|
|
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"
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "relu5_3_CPM_L2"
|
|
type: "ReLU"
|
|
bottom: "conv5_3_CPM_L2"
|
|
top: "conv5_3_CPM_L2"
|
|
}
|
|
layer {
|
|
name: "conv5_4_CPM_L1"
|
|
type: "Convolution"
|
|
bottom: "conv5_3_CPM_L1"
|
|
top: "conv5_4_CPM_L1"
|
|
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"
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "relu5_4_CPM_L1"
|
|
type: "ReLU"
|
|
bottom: "conv5_4_CPM_L1"
|
|
top: "conv5_4_CPM_L1"
|
|
}
|
|
layer {
|
|
name: "conv5_4_CPM_L2"
|
|
type: "Convolution"
|
|
bottom: "conv5_3_CPM_L2"
|
|
top: "conv5_4_CPM_L2"
|
|
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"
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "relu5_4_CPM_L2"
|
|
type: "ReLU"
|
|
bottom: "conv5_4_CPM_L2"
|
|
top: "conv5_4_CPM_L2"
|
|
}
|
|
layer {
|
|
name: "conv5_5_CPM_L1"
|
|
type: "Convolution"
|
|
bottom: "conv5_4_CPM_L1"
|
|
top: "conv5_5_CPM_L1"
|
|
param {
|
|
lr_mult: 1.0
|
|
decay_mult: 1
|
|
}
|
|
param {
|
|
lr_mult: 2.0
|
|
decay_mult: 0
|
|
}
|
|
convolution_param {
|
|
num_output: 38
|
|
pad: 0
|
|
kernel_size: 1
|
|
weight_filler {
|
|
type: "gaussian"
|
|
std: 0.01
|
|
}
|
|
bias_filler {
|
|
type: "constant"
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "conv5_5_CPM_L2"
|
|
type: "Convolution"
|
|
bottom: "conv5_4_CPM_L2"
|
|
top: "conv5_5_CPM_L2"
|
|
param {
|
|
lr_mult: 1.0
|
|
decay_mult: 1
|
|
}
|
|
param {
|
|
lr_mult: 2.0
|
|
decay_mult: 0
|
|
}
|
|
convolution_param {
|
|
num_output: 19
|
|
pad: 0
|
|
kernel_size: 1
|
|
weight_filler {
|
|
type: "gaussian"
|
|
std: 0.01
|
|
}
|
|
bias_filler {
|
|
type: "constant"
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "concat_stage2"
|
|
type: "Concat"
|
|
bottom: "conv5_5_CPM_L1"
|
|
bottom: "conv5_5_CPM_L2"
|
|
bottom: "conv4_4_CPM"
|
|
top: "concat_stage2"
|
|
concat_param {
|
|
axis: 1
|
|
}
|
|
}
|
|
layer {
|
|
name: "Mconv1_stage2_L1"
|
|
type: "Convolution"
|
|
bottom: "concat_stage2"
|
|
top: "Mconv1_stage2_L1"
|
|
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"
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "Mrelu1_stage2_L1"
|
|
type: "ReLU"
|
|
bottom: "Mconv1_stage2_L1"
|
|
top: "Mconv1_stage2_L1"
|
|
}
|
|
layer {
|
|
name: "Mconv1_stage2_L2"
|
|
type: "Convolution"
|
|
bottom: "concat_stage2"
|
|
top: "Mconv1_stage2_L2"
|
|
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"
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "Mrelu1_stage2_L2"
|
|
type: "ReLU"
|
|
bottom: "Mconv1_stage2_L2"
|
|
top: "Mconv1_stage2_L2"
|
|
}
|
|
layer {
|
|
name: "Mconv2_stage2_L1"
|
|
type: "Convolution"
|
|
bottom: "Mconv1_stage2_L1"
|
|
top: "Mconv2_stage2_L1"
|
|
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"
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "Mrelu2_stage2_L1"
|
|
type: "ReLU"
|
|
bottom: "Mconv2_stage2_L1"
|
|
top: "Mconv2_stage2_L1"
|
|
}
|
|
layer {
|
|
name: "Mconv2_stage2_L2"
|
|
type: "Convolution"
|
|
bottom: "Mconv1_stage2_L2"
|
|
top: "Mconv2_stage2_L2"
|
|
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"
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "Mrelu2_stage2_L2"
|
|
type: "ReLU"
|
|
bottom: "Mconv2_stage2_L2"
|
|
top: "Mconv2_stage2_L2"
|
|
}
|
|
layer {
|
|
name: "Mconv3_stage2_L1"
|
|
type: "Convolution"
|
|
bottom: "Mconv2_stage2_L1"
|
|
top: "Mconv3_stage2_L1"
|
|
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"
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "Mrelu3_stage2_L1"
|
|
type: "ReLU"
|
|
bottom: "Mconv3_stage2_L1"
|
|
top: "Mconv3_stage2_L1"
|
|
}
|
|
layer {
|
|
name: "Mconv3_stage2_L2"
|
|
type: "Convolution"
|
|
bottom: "Mconv2_stage2_L2"
|
|
top: "Mconv3_stage2_L2"
|
|
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"
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "Mrelu3_stage2_L2"
|
|
type: "ReLU"
|
|
bottom: "Mconv3_stage2_L2"
|
|
top: "Mconv3_stage2_L2"
|
|
}
|
|
layer {
|
|
name: "Mconv4_stage2_L1"
|
|
type: "Convolution"
|
|
bottom: "Mconv3_stage2_L1"
|
|
top: "Mconv4_stage2_L1"
|
|
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"
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "Mrelu4_stage2_L1"
|
|
type: "ReLU"
|
|
bottom: "Mconv4_stage2_L1"
|
|
top: "Mconv4_stage2_L1"
|
|
}
|
|
layer {
|
|
name: "Mconv4_stage2_L2"
|
|
type: "Convolution"
|
|
bottom: "Mconv3_stage2_L2"
|
|
top: "Mconv4_stage2_L2"
|
|
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"
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "Mrelu4_stage2_L2"
|
|
type: "ReLU"
|
|
bottom: "Mconv4_stage2_L2"
|
|
top: "Mconv4_stage2_L2"
|
|
}
|
|
layer {
|
|
name: "Mconv5_stage2_L1"
|
|
type: "Convolution"
|
|
bottom: "Mconv4_stage2_L1"
|
|
top: "Mconv5_stage2_L1"
|
|
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"
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "Mrelu5_stage2_L1"
|
|
type: "ReLU"
|
|
bottom: "Mconv5_stage2_L1"
|
|
top: "Mconv5_stage2_L1"
|
|
}
|
|
layer {
|
|
name: "Mconv5_stage2_L2"
|
|
type: "Convolution"
|
|
bottom: "Mconv4_stage2_L2"
|
|
top: "Mconv5_stage2_L2"
|
|
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"
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "Mrelu5_stage2_L2"
|
|
type: "ReLU"
|
|
bottom: "Mconv5_stage2_L2"
|
|
top: "Mconv5_stage2_L2"
|
|
}
|
|
layer {
|
|
name: "Mconv6_stage2_L1"
|
|
type: "Convolution"
|
|
bottom: "Mconv5_stage2_L1"
|
|
top: "Mconv6_stage2_L1"
|
|
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"
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "Mrelu6_stage2_L1"
|
|
type: "ReLU"
|
|
bottom: "Mconv6_stage2_L1"
|
|
top: "Mconv6_stage2_L1"
|
|
}
|
|
layer {
|
|
name: "Mconv6_stage2_L2"
|
|
type: "Convolution"
|
|
bottom: "Mconv5_stage2_L2"
|
|
top: "Mconv6_stage2_L2"
|
|
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"
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "Mrelu6_stage2_L2"
|
|
type: "ReLU"
|
|
bottom: "Mconv6_stage2_L2"
|
|
top: "Mconv6_stage2_L2"
|
|
}
|
|
layer {
|
|
name: "Mconv7_stage2_L1"
|
|
type: "Convolution"
|
|
bottom: "Mconv6_stage2_L1"
|
|
top: "Mconv7_stage2_L1"
|
|
param {
|
|
lr_mult: 4.0
|
|
decay_mult: 1
|
|
}
|
|
param {
|
|
lr_mult: 8.0
|
|
decay_mult: 0
|
|
}
|
|
convolution_param {
|
|
num_output: 38
|
|
pad: 0
|
|
kernel_size: 1
|
|
weight_filler {
|
|
type: "gaussian"
|
|
std: 0.01
|
|
}
|
|
bias_filler {
|
|
type: "constant"
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "Mconv7_stage2_L2"
|
|
type: "Convolution"
|
|
bottom: "Mconv6_stage2_L2"
|
|
top: "Mconv7_stage2_L2"
|
|
param {
|
|
lr_mult: 4.0
|
|
decay_mult: 1
|
|
}
|
|
param {
|
|
lr_mult: 8.0
|
|
decay_mult: 0
|
|
}
|
|
convolution_param {
|
|
num_output: 19
|
|
pad: 0
|
|
kernel_size: 1
|
|
weight_filler {
|
|
type: "gaussian"
|
|
std: 0.01
|
|
}
|
|
bias_filler {
|
|
type: "constant"
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "concat_stage3"
|
|
type: "Concat"
|
|
bottom: "Mconv7_stage2_L1"
|
|
bottom: "Mconv7_stage2_L2"
|
|
bottom: "conv4_4_CPM"
|
|
top: "concat_stage3"
|
|
concat_param {
|
|
axis: 1
|
|
}
|
|
}
|
|
layer {
|
|
name: "Mconv1_stage3_L1"
|
|
type: "Convolution"
|
|
bottom: "concat_stage3"
|
|
top: "Mconv1_stage3_L1"
|
|
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"
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "Mrelu1_stage3_L1"
|
|
type: "ReLU"
|
|
bottom: "Mconv1_stage3_L1"
|
|
top: "Mconv1_stage3_L1"
|
|
}
|
|
layer {
|
|
name: "Mconv1_stage3_L2"
|
|
type: "Convolution"
|
|
bottom: "concat_stage3"
|
|
top: "Mconv1_stage3_L2"
|
|
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"
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "Mrelu1_stage3_L2"
|
|
type: "ReLU"
|
|
bottom: "Mconv1_stage3_L2"
|
|
top: "Mconv1_stage3_L2"
|
|
}
|
|
layer {
|
|
name: "Mconv2_stage3_L1"
|
|
type: "Convolution"
|
|
bottom: "Mconv1_stage3_L1"
|
|
top: "Mconv2_stage3_L1"
|
|
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"
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "Mrelu2_stage3_L1"
|
|
type: "ReLU"
|
|
bottom: "Mconv2_stage3_L1"
|
|
top: "Mconv2_stage3_L1"
|
|
}
|
|
layer {
|
|
name: "Mconv2_stage3_L2"
|
|
type: "Convolution"
|
|
bottom: "Mconv1_stage3_L2"
|
|
top: "Mconv2_stage3_L2"
|
|
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"
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "Mrelu2_stage3_L2"
|
|
type: "ReLU"
|
|
bottom: "Mconv2_stage3_L2"
|
|
top: "Mconv2_stage3_L2"
|
|
}
|
|
layer {
|
|
name: "Mconv3_stage3_L1"
|
|
type: "Convolution"
|
|
bottom: "Mconv2_stage3_L1"
|
|
top: "Mconv3_stage3_L1"
|
|
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"
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "Mrelu3_stage3_L1"
|
|
type: "ReLU"
|
|
bottom: "Mconv3_stage3_L1"
|
|
top: "Mconv3_stage3_L1"
|
|
}
|
|
layer {
|
|
name: "Mconv3_stage3_L2"
|
|
type: "Convolution"
|
|
bottom: "Mconv2_stage3_L2"
|
|
top: "Mconv3_stage3_L2"
|
|
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"
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "Mrelu3_stage3_L2"
|
|
type: "ReLU"
|
|
bottom: "Mconv3_stage3_L2"
|
|
top: "Mconv3_stage3_L2"
|
|
}
|
|
layer {
|
|
name: "Mconv4_stage3_L1"
|
|
type: "Convolution"
|
|
bottom: "Mconv3_stage3_L1"
|
|
top: "Mconv4_stage3_L1"
|
|
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"
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "Mrelu4_stage3_L1"
|
|
type: "ReLU"
|
|
bottom: "Mconv4_stage3_L1"
|
|
top: "Mconv4_stage3_L1"
|
|
}
|
|
layer {
|
|
name: "Mconv4_stage3_L2"
|
|
type: "Convolution"
|
|
bottom: "Mconv3_stage3_L2"
|
|
top: "Mconv4_stage3_L2"
|
|
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"
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "Mrelu4_stage3_L2"
|
|
type: "ReLU"
|
|
bottom: "Mconv4_stage3_L2"
|
|
top: "Mconv4_stage3_L2"
|
|
}
|
|
layer {
|
|
name: "Mconv5_stage3_L1"
|
|
type: "Convolution"
|
|
bottom: "Mconv4_stage3_L1"
|
|
top: "Mconv5_stage3_L1"
|
|
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"
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "Mrelu5_stage3_L1"
|
|
type: "ReLU"
|
|
bottom: "Mconv5_stage3_L1"
|
|
top: "Mconv5_stage3_L1"
|
|
}
|
|
layer {
|
|
name: "Mconv5_stage3_L2"
|
|
type: "Convolution"
|
|
bottom: "Mconv4_stage3_L2"
|
|
top: "Mconv5_stage3_L2"
|
|
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"
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "Mrelu5_stage3_L2"
|
|
type: "ReLU"
|
|
bottom: "Mconv5_stage3_L2"
|
|
top: "Mconv5_stage3_L2"
|
|
}
|
|
layer {
|
|
name: "Mconv6_stage3_L1"
|
|
type: "Convolution"
|
|
bottom: "Mconv5_stage3_L1"
|
|
top: "Mconv6_stage3_L1"
|
|
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"
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "Mrelu6_stage3_L1"
|
|
type: "ReLU"
|
|
bottom: "Mconv6_stage3_L1"
|
|
top: "Mconv6_stage3_L1"
|
|
}
|
|
layer {
|
|
name: "Mconv6_stage3_L2"
|
|
type: "Convolution"
|
|
bottom: "Mconv5_stage3_L2"
|
|
top: "Mconv6_stage3_L2"
|
|
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"
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "Mrelu6_stage3_L2"
|
|
type: "ReLU"
|
|
bottom: "Mconv6_stage3_L2"
|
|
top: "Mconv6_stage3_L2"
|
|
}
|
|
layer {
|
|
name: "Mconv7_stage3_L1"
|
|
type: "Convolution"
|
|
bottom: "Mconv6_stage3_L1"
|
|
top: "Mconv7_stage3_L1"
|
|
param {
|
|
lr_mult: 4.0
|
|
decay_mult: 1
|
|
}
|
|
param {
|
|
lr_mult: 8.0
|
|
decay_mult: 0
|
|
}
|
|
convolution_param {
|
|
num_output: 38
|
|
pad: 0
|
|
kernel_size: 1
|
|
weight_filler {
|
|
type: "gaussian"
|
|
std: 0.01
|
|
}
|
|
bias_filler {
|
|
type: "constant"
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "Mconv7_stage3_L2"
|
|
type: "Convolution"
|
|
bottom: "Mconv6_stage3_L2"
|
|
top: "Mconv7_stage3_L2"
|
|
param {
|
|
lr_mult: 4.0
|
|
decay_mult: 1
|
|
}
|
|
param {
|
|
lr_mult: 8.0
|
|
decay_mult: 0
|
|
}
|
|
convolution_param {
|
|
num_output: 19
|
|
pad: 0
|
|
kernel_size: 1
|
|
weight_filler {
|
|
type: "gaussian"
|
|
std: 0.01
|
|
}
|
|
bias_filler {
|
|
type: "constant"
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "concat_stage4"
|
|
type: "Concat"
|
|
bottom: "Mconv7_stage3_L1"
|
|
bottom: "Mconv7_stage3_L2"
|
|
bottom: "conv4_4_CPM"
|
|
top: "concat_stage4"
|
|
concat_param {
|
|
axis: 1
|
|
}
|
|
}
|
|
layer {
|
|
name: "Mconv1_stage4_L1"
|
|
type: "Convolution"
|
|
bottom: "concat_stage4"
|
|
top: "Mconv1_stage4_L1"
|
|
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"
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "Mrelu1_stage4_L1"
|
|
type: "ReLU"
|
|
bottom: "Mconv1_stage4_L1"
|
|
top: "Mconv1_stage4_L1"
|
|
}
|
|
layer {
|
|
name: "Mconv1_stage4_L2"
|
|
type: "Convolution"
|
|
bottom: "concat_stage4"
|
|
top: "Mconv1_stage4_L2"
|
|
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"
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "Mrelu1_stage4_L2"
|
|
type: "ReLU"
|
|
bottom: "Mconv1_stage4_L2"
|
|
top: "Mconv1_stage4_L2"
|
|
}
|
|
layer {
|
|
name: "Mconv2_stage4_L1"
|
|
type: "Convolution"
|
|
bottom: "Mconv1_stage4_L1"
|
|
top: "Mconv2_stage4_L1"
|
|
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"
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "Mrelu2_stage4_L1"
|
|
type: "ReLU"
|
|
bottom: "Mconv2_stage4_L1"
|
|
top: "Mconv2_stage4_L1"
|
|
}
|
|
layer {
|
|
name: "Mconv2_stage4_L2"
|
|
type: "Convolution"
|
|
bottom: "Mconv1_stage4_L2"
|
|
top: "Mconv2_stage4_L2"
|
|
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"
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "Mrelu2_stage4_L2"
|
|
type: "ReLU"
|
|
bottom: "Mconv2_stage4_L2"
|
|
top: "Mconv2_stage4_L2"
|
|
}
|
|
layer {
|
|
name: "Mconv3_stage4_L1"
|
|
type: "Convolution"
|
|
bottom: "Mconv2_stage4_L1"
|
|
top: "Mconv3_stage4_L1"
|
|
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"
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "Mrelu3_stage4_L1"
|
|
type: "ReLU"
|
|
bottom: "Mconv3_stage4_L1"
|
|
top: "Mconv3_stage4_L1"
|
|
}
|
|
layer {
|
|
name: "Mconv3_stage4_L2"
|
|
type: "Convolution"
|
|
bottom: "Mconv2_stage4_L2"
|
|
top: "Mconv3_stage4_L2"
|
|
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"
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "Mrelu3_stage4_L2"
|
|
type: "ReLU"
|
|
bottom: "Mconv3_stage4_L2"
|
|
top: "Mconv3_stage4_L2"
|
|
}
|
|
layer {
|
|
name: "Mconv4_stage4_L1"
|
|
type: "Convolution"
|
|
bottom: "Mconv3_stage4_L1"
|
|
top: "Mconv4_stage4_L1"
|
|
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"
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "Mrelu4_stage4_L1"
|
|
type: "ReLU"
|
|
bottom: "Mconv4_stage4_L1"
|
|
top: "Mconv4_stage4_L1"
|
|
}
|
|
layer {
|
|
name: "Mconv4_stage4_L2"
|
|
type: "Convolution"
|
|
bottom: "Mconv3_stage4_L2"
|
|
top: "Mconv4_stage4_L2"
|
|
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"
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "Mrelu4_stage4_L2"
|
|
type: "ReLU"
|
|
bottom: "Mconv4_stage4_L2"
|
|
top: "Mconv4_stage4_L2"
|
|
}
|
|
layer {
|
|
name: "Mconv5_stage4_L1"
|
|
type: "Convolution"
|
|
bottom: "Mconv4_stage4_L1"
|
|
top: "Mconv5_stage4_L1"
|
|
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"
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "Mrelu5_stage4_L1"
|
|
type: "ReLU"
|
|
bottom: "Mconv5_stage4_L1"
|
|
top: "Mconv5_stage4_L1"
|
|
}
|
|
layer {
|
|
name: "Mconv5_stage4_L2"
|
|
type: "Convolution"
|
|
bottom: "Mconv4_stage4_L2"
|
|
top: "Mconv5_stage4_L2"
|
|
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"
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "Mrelu5_stage4_L2"
|
|
type: "ReLU"
|
|
bottom: "Mconv5_stage4_L2"
|
|
top: "Mconv5_stage4_L2"
|
|
}
|
|
layer {
|
|
name: "Mconv6_stage4_L1"
|
|
type: "Convolution"
|
|
bottom: "Mconv5_stage4_L1"
|
|
top: "Mconv6_stage4_L1"
|
|
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"
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "Mrelu6_stage4_L1"
|
|
type: "ReLU"
|
|
bottom: "Mconv6_stage4_L1"
|
|
top: "Mconv6_stage4_L1"
|
|
}
|
|
layer {
|
|
name: "Mconv6_stage4_L2"
|
|
type: "Convolution"
|
|
bottom: "Mconv5_stage4_L2"
|
|
top: "Mconv6_stage4_L2"
|
|
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"
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "Mrelu6_stage4_L2"
|
|
type: "ReLU"
|
|
bottom: "Mconv6_stage4_L2"
|
|
top: "Mconv6_stage4_L2"
|
|
}
|
|
layer {
|
|
name: "Mconv7_stage4_L1"
|
|
type: "Convolution"
|
|
bottom: "Mconv6_stage4_L1"
|
|
top: "Mconv7_stage4_L1"
|
|
param {
|
|
lr_mult: 4.0
|
|
decay_mult: 1
|
|
}
|
|
param {
|
|
lr_mult: 8.0
|
|
decay_mult: 0
|
|
}
|
|
convolution_param {
|
|
num_output: 38
|
|
pad: 0
|
|
kernel_size: 1
|
|
weight_filler {
|
|
type: "gaussian"
|
|
std: 0.01
|
|
}
|
|
bias_filler {
|
|
type: "constant"
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "Mconv7_stage4_L2"
|
|
type: "Convolution"
|
|
bottom: "Mconv6_stage4_L2"
|
|
top: "Mconv7_stage4_L2"
|
|
param {
|
|
lr_mult: 4.0
|
|
decay_mult: 1
|
|
}
|
|
param {
|
|
lr_mult: 8.0
|
|
decay_mult: 0
|
|
}
|
|
convolution_param {
|
|
num_output: 19
|
|
pad: 0
|
|
kernel_size: 1
|
|
weight_filler {
|
|
type: "gaussian"
|
|
std: 0.01
|
|
}
|
|
bias_filler {
|
|
type: "constant"
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "concat_stage5"
|
|
type: "Concat"
|
|
bottom: "Mconv7_stage4_L1"
|
|
bottom: "Mconv7_stage4_L2"
|
|
bottom: "conv4_4_CPM"
|
|
top: "concat_stage5"
|
|
concat_param {
|
|
axis: 1
|
|
}
|
|
}
|
|
layer {
|
|
name: "Mconv1_stage5_L1"
|
|
type: "Convolution"
|
|
bottom: "concat_stage5"
|
|
top: "Mconv1_stage5_L1"
|
|
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"
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "Mrelu1_stage5_L1"
|
|
type: "ReLU"
|
|
bottom: "Mconv1_stage5_L1"
|
|
top: "Mconv1_stage5_L1"
|
|
}
|
|
layer {
|
|
name: "Mconv1_stage5_L2"
|
|
type: "Convolution"
|
|
bottom: "concat_stage5"
|
|
top: "Mconv1_stage5_L2"
|
|
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"
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "Mrelu1_stage5_L2"
|
|
type: "ReLU"
|
|
bottom: "Mconv1_stage5_L2"
|
|
top: "Mconv1_stage5_L2"
|
|
}
|
|
layer {
|
|
name: "Mconv2_stage5_L1"
|
|
type: "Convolution"
|
|
bottom: "Mconv1_stage5_L1"
|
|
top: "Mconv2_stage5_L1"
|
|
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"
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "Mrelu2_stage5_L1"
|
|
type: "ReLU"
|
|
bottom: "Mconv2_stage5_L1"
|
|
top: "Mconv2_stage5_L1"
|
|
}
|
|
layer {
|
|
name: "Mconv2_stage5_L2"
|
|
type: "Convolution"
|
|
bottom: "Mconv1_stage5_L2"
|
|
top: "Mconv2_stage5_L2"
|
|
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"
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "Mrelu2_stage5_L2"
|
|
type: "ReLU"
|
|
bottom: "Mconv2_stage5_L2"
|
|
top: "Mconv2_stage5_L2"
|
|
}
|
|
layer {
|
|
name: "Mconv3_stage5_L1"
|
|
type: "Convolution"
|
|
bottom: "Mconv2_stage5_L1"
|
|
top: "Mconv3_stage5_L1"
|
|
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"
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "Mrelu3_stage5_L1"
|
|
type: "ReLU"
|
|
bottom: "Mconv3_stage5_L1"
|
|
top: "Mconv3_stage5_L1"
|
|
}
|
|
layer {
|
|
name: "Mconv3_stage5_L2"
|
|
type: "Convolution"
|
|
bottom: "Mconv2_stage5_L2"
|
|
top: "Mconv3_stage5_L2"
|
|
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"
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "Mrelu3_stage5_L2"
|
|
type: "ReLU"
|
|
bottom: "Mconv3_stage5_L2"
|
|
top: "Mconv3_stage5_L2"
|
|
}
|
|
layer {
|
|
name: "Mconv4_stage5_L1"
|
|
type: "Convolution"
|
|
bottom: "Mconv3_stage5_L1"
|
|
top: "Mconv4_stage5_L1"
|
|
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"
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "Mrelu4_stage5_L1"
|
|
type: "ReLU"
|
|
bottom: "Mconv4_stage5_L1"
|
|
top: "Mconv4_stage5_L1"
|
|
}
|
|
layer {
|
|
name: "Mconv4_stage5_L2"
|
|
type: "Convolution"
|
|
bottom: "Mconv3_stage5_L2"
|
|
top: "Mconv4_stage5_L2"
|
|
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"
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "Mrelu4_stage5_L2"
|
|
type: "ReLU"
|
|
bottom: "Mconv4_stage5_L2"
|
|
top: "Mconv4_stage5_L2"
|
|
}
|
|
layer {
|
|
name: "Mconv5_stage5_L1"
|
|
type: "Convolution"
|
|
bottom: "Mconv4_stage5_L1"
|
|
top: "Mconv5_stage5_L1"
|
|
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"
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "Mrelu5_stage5_L1"
|
|
type: "ReLU"
|
|
bottom: "Mconv5_stage5_L1"
|
|
top: "Mconv5_stage5_L1"
|
|
}
|
|
layer {
|
|
name: "Mconv5_stage5_L2"
|
|
type: "Convolution"
|
|
bottom: "Mconv4_stage5_L2"
|
|
top: "Mconv5_stage5_L2"
|
|
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"
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "Mrelu5_stage5_L2"
|
|
type: "ReLU"
|
|
bottom: "Mconv5_stage5_L2"
|
|
top: "Mconv5_stage5_L2"
|
|
}
|
|
layer {
|
|
name: "Mconv6_stage5_L1"
|
|
type: "Convolution"
|
|
bottom: "Mconv5_stage5_L1"
|
|
top: "Mconv6_stage5_L1"
|
|
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"
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "Mrelu6_stage5_L1"
|
|
type: "ReLU"
|
|
bottom: "Mconv6_stage5_L1"
|
|
top: "Mconv6_stage5_L1"
|
|
}
|
|
layer {
|
|
name: "Mconv6_stage5_L2"
|
|
type: "Convolution"
|
|
bottom: "Mconv5_stage5_L2"
|
|
top: "Mconv6_stage5_L2"
|
|
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"
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "Mrelu6_stage5_L2"
|
|
type: "ReLU"
|
|
bottom: "Mconv6_stage5_L2"
|
|
top: "Mconv6_stage5_L2"
|
|
}
|
|
layer {
|
|
name: "Mconv7_stage5_L1"
|
|
type: "Convolution"
|
|
bottom: "Mconv6_stage5_L1"
|
|
top: "Mconv7_stage5_L1"
|
|
param {
|
|
lr_mult: 4.0
|
|
decay_mult: 1
|
|
}
|
|
param {
|
|
lr_mult: 8.0
|
|
decay_mult: 0
|
|
}
|
|
convolution_param {
|
|
num_output: 38
|
|
pad: 0
|
|
kernel_size: 1
|
|
weight_filler {
|
|
type: "gaussian"
|
|
std: 0.01
|
|
}
|
|
bias_filler {
|
|
type: "constant"
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "Mconv7_stage5_L2"
|
|
type: "Convolution"
|
|
bottom: "Mconv6_stage5_L2"
|
|
top: "Mconv7_stage5_L2"
|
|
param {
|
|
lr_mult: 4.0
|
|
decay_mult: 1
|
|
}
|
|
param {
|
|
lr_mult: 8.0
|
|
decay_mult: 0
|
|
}
|
|
convolution_param {
|
|
num_output: 19
|
|
pad: 0
|
|
kernel_size: 1
|
|
weight_filler {
|
|
type: "gaussian"
|
|
std: 0.01
|
|
}
|
|
bias_filler {
|
|
type: "constant"
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "concat_stage6"
|
|
type: "Concat"
|
|
bottom: "Mconv7_stage5_L1"
|
|
bottom: "Mconv7_stage5_L2"
|
|
bottom: "conv4_4_CPM"
|
|
top: "concat_stage6"
|
|
concat_param {
|
|
axis: 1
|
|
}
|
|
}
|
|
layer {
|
|
name: "Mconv1_stage6_L1"
|
|
type: "Convolution"
|
|
bottom: "concat_stage6"
|
|
top: "Mconv1_stage6_L1"
|
|
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"
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "Mrelu1_stage6_L1"
|
|
type: "ReLU"
|
|
bottom: "Mconv1_stage6_L1"
|
|
top: "Mconv1_stage6_L1"
|
|
}
|
|
layer {
|
|
name: "Mconv1_stage6_L2"
|
|
type: "Convolution"
|
|
bottom: "concat_stage6"
|
|
top: "Mconv1_stage6_L2"
|
|
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"
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "Mrelu1_stage6_L2"
|
|
type: "ReLU"
|
|
bottom: "Mconv1_stage6_L2"
|
|
top: "Mconv1_stage6_L2"
|
|
}
|
|
layer {
|
|
name: "Mconv2_stage6_L1"
|
|
type: "Convolution"
|
|
bottom: "Mconv1_stage6_L1"
|
|
top: "Mconv2_stage6_L1"
|
|
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"
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "Mrelu2_stage6_L1"
|
|
type: "ReLU"
|
|
bottom: "Mconv2_stage6_L1"
|
|
top: "Mconv2_stage6_L1"
|
|
}
|
|
layer {
|
|
name: "Mconv2_stage6_L2"
|
|
type: "Convolution"
|
|
bottom: "Mconv1_stage6_L2"
|
|
top: "Mconv2_stage6_L2"
|
|
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"
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "Mrelu2_stage6_L2"
|
|
type: "ReLU"
|
|
bottom: "Mconv2_stage6_L2"
|
|
top: "Mconv2_stage6_L2"
|
|
}
|
|
layer {
|
|
name: "Mconv3_stage6_L1"
|
|
type: "Convolution"
|
|
bottom: "Mconv2_stage6_L1"
|
|
top: "Mconv3_stage6_L1"
|
|
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"
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "Mrelu3_stage6_L1"
|
|
type: "ReLU"
|
|
bottom: "Mconv3_stage6_L1"
|
|
top: "Mconv3_stage6_L1"
|
|
}
|
|
layer {
|
|
name: "Mconv3_stage6_L2"
|
|
type: "Convolution"
|
|
bottom: "Mconv2_stage6_L2"
|
|
top: "Mconv3_stage6_L2"
|
|
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"
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "Mrelu3_stage6_L2"
|
|
type: "ReLU"
|
|
bottom: "Mconv3_stage6_L2"
|
|
top: "Mconv3_stage6_L2"
|
|
}
|
|
layer {
|
|
name: "Mconv4_stage6_L1"
|
|
type: "Convolution"
|
|
bottom: "Mconv3_stage6_L1"
|
|
top: "Mconv4_stage6_L1"
|
|
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"
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "Mrelu4_stage6_L1"
|
|
type: "ReLU"
|
|
bottom: "Mconv4_stage6_L1"
|
|
top: "Mconv4_stage6_L1"
|
|
}
|
|
layer {
|
|
name: "Mconv4_stage6_L2"
|
|
type: "Convolution"
|
|
bottom: "Mconv3_stage6_L2"
|
|
top: "Mconv4_stage6_L2"
|
|
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"
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "Mrelu4_stage6_L2"
|
|
type: "ReLU"
|
|
bottom: "Mconv4_stage6_L2"
|
|
top: "Mconv4_stage6_L2"
|
|
}
|
|
layer {
|
|
name: "Mconv5_stage6_L1"
|
|
type: "Convolution"
|
|
bottom: "Mconv4_stage6_L1"
|
|
top: "Mconv5_stage6_L1"
|
|
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"
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "Mrelu5_stage6_L1"
|
|
type: "ReLU"
|
|
bottom: "Mconv5_stage6_L1"
|
|
top: "Mconv5_stage6_L1"
|
|
}
|
|
layer {
|
|
name: "Mconv5_stage6_L2"
|
|
type: "Convolution"
|
|
bottom: "Mconv4_stage6_L2"
|
|
top: "Mconv5_stage6_L2"
|
|
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"
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "Mrelu5_stage6_L2"
|
|
type: "ReLU"
|
|
bottom: "Mconv5_stage6_L2"
|
|
top: "Mconv5_stage6_L2"
|
|
}
|
|
layer {
|
|
name: "Mconv6_stage6_L1"
|
|
type: "Convolution"
|
|
bottom: "Mconv5_stage6_L1"
|
|
top: "Mconv6_stage6_L1"
|
|
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"
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "Mrelu6_stage6_L1"
|
|
type: "ReLU"
|
|
bottom: "Mconv6_stage6_L1"
|
|
top: "Mconv6_stage6_L1"
|
|
}
|
|
layer {
|
|
name: "Mconv6_stage6_L2"
|
|
type: "Convolution"
|
|
bottom: "Mconv5_stage6_L2"
|
|
top: "Mconv6_stage6_L2"
|
|
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"
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "Mrelu6_stage6_L2"
|
|
type: "ReLU"
|
|
bottom: "Mconv6_stage6_L2"
|
|
top: "Mconv6_stage6_L2"
|
|
}
|
|
layer {
|
|
name: "Mconv7_stage6_L1"
|
|
type: "Convolution"
|
|
bottom: "Mconv6_stage6_L1"
|
|
top: "Mconv7_stage6_L1"
|
|
param {
|
|
lr_mult: 4.0
|
|
decay_mult: 1
|
|
}
|
|
param {
|
|
lr_mult: 8.0
|
|
decay_mult: 0
|
|
}
|
|
convolution_param {
|
|
num_output: 38
|
|
pad: 0
|
|
kernel_size: 1
|
|
weight_filler {
|
|
type: "gaussian"
|
|
std: 0.01
|
|
}
|
|
bias_filler {
|
|
type: "constant"
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "Mconv7_stage6_L2"
|
|
type: "Convolution"
|
|
bottom: "Mconv6_stage6_L2"
|
|
top: "Mconv7_stage6_L2"
|
|
param {
|
|
lr_mult: 4.0
|
|
decay_mult: 1
|
|
}
|
|
param {
|
|
lr_mult: 8.0
|
|
decay_mult: 0
|
|
}
|
|
convolution_param {
|
|
num_output: 19
|
|
pad: 0
|
|
kernel_size: 1
|
|
weight_filler {
|
|
type: "gaussian"
|
|
std: 0.01
|
|
}
|
|
bias_filler {
|
|
type: "constant"
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "concat_stage7"
|
|
type: "Concat"
|
|
bottom: "Mconv7_stage6_L2"
|
|
bottom: "Mconv7_stage6_L1"
|
|
# top: "concat_stage7"
|
|
top: "net_output"
|
|
concat_param {
|
|
axis: 1
|
|
}
|
|
} |