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December 27, 2016 00:49
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Caffe Network Visualization
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| name: "CPM" | |
| input: "data" | |
| input_dim: 1 | |
| input_dim: 4 | |
| input_dim: 368 | |
| input_dim: 368 | |
| layer { | |
| name: "image" | |
| type: "Slice" | |
| bottom: "data" | |
| top: "image" | |
| top: "center_map" | |
| slice_param { | |
| slice_point: 3 | |
| axis: 1 | |
| } | |
| } | |
| layer { | |
| name: "pool_center_lower" | |
| type: "Pooling" | |
| bottom: "center_map" | |
| top: "pool_center_lower" | |
| pooling_param { | |
| pool: AVE | |
| kernel_size: 9 | |
| stride: 8 | |
| } | |
| } | |
| layer { | |
| name: "conv1_stage1" | |
| type: "Convolution" | |
| bottom: "image" | |
| top: "conv1_stage1" | |
| param { | |
| lr_mult: 5 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 10 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 4 | |
| kernel_size: 9 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu1_stage1" | |
| type: "ReLU" | |
| bottom: "conv1_stage1" | |
| top: "conv1_stage1" | |
| } | |
| layer { | |
| name: "pool1_stage1" | |
| type: "Pooling" | |
| bottom: "conv1_stage1" | |
| top: "pool1_stage1" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 3 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "conv2_stage1" | |
| type: "Convolution" | |
| bottom: "pool1_stage1" | |
| top: "conv2_stage1" | |
| param { | |
| lr_mult: 5 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 10 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 4 | |
| kernel_size: 9 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu2_stage1" | |
| type: "ReLU" | |
| bottom: "conv2_stage1" | |
| top: "conv2_stage1" | |
| } | |
| layer { | |
| name: "pool2_stage1" | |
| type: "Pooling" | |
| bottom: "conv2_stage1" | |
| top: "pool2_stage1" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 3 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "conv3_stage1" | |
| type: "Convolution" | |
| bottom: "pool2_stage1" | |
| top: "conv3_stage1" | |
| param { | |
| lr_mult: 5 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 10 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 4 | |
| kernel_size: 9 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu3_stage1" | |
| type: "ReLU" | |
| bottom: "conv3_stage1" | |
| top: "conv3_stage1" | |
| } | |
| layer { | |
| name: "pool3_stage1" | |
| type: "Pooling" | |
| bottom: "conv3_stage1" | |
| top: "pool3_stage1" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 3 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "conv4_stage1" | |
| type: "Convolution" | |
| bottom: "pool3_stage1" | |
| top: "conv4_stage1" | |
| param { | |
| lr_mult: 5 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 10 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 32 | |
| pad: 2 | |
| kernel_size: 5 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu4_stage1" | |
| type: "ReLU" | |
| bottom: "conv4_stage1" | |
| top: "conv4_stage1" | |
| } | |
| layer { | |
| name: "conv5_stage1" | |
| type: "Convolution" | |
| bottom: "conv4_stage1" | |
| top: "conv5_stage1" | |
| param { | |
| lr_mult: 5 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 10 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 4 | |
| kernel_size: 9 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu5_stage1" | |
| type: "ReLU" | |
| bottom: "conv5_stage1" | |
| top: "conv5_stage1" | |
| } | |
| layer { | |
| name: "conv6_stage1" | |
| type: "Convolution" | |
| bottom: "conv5_stage1" | |
| top: "conv6_stage1" | |
| param { | |
| lr_mult: 5 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 10 | |
| 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: "relu6_stage1" | |
| type: "ReLU" | |
| bottom: "conv6_stage1" | |
| top: "conv6_stage1" | |
| } | |
| layer { | |
| name: "conv7_stage1" | |
| type: "Convolution" | |
| bottom: "conv6_stage1" | |
| top: "conv7_stage1" | |
| param { | |
| lr_mult: 5 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 10 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 15 | |
| pad: 0 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv1_stage2" | |
| type: "Convolution" | |
| bottom: "image" | |
| top: "conv1_stage2" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 4 | |
| kernel_size: 9 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu1_stage2" | |
| type: "ReLU" | |
| bottom: "conv1_stage2" | |
| top: "conv1_stage2" | |
| } | |
| layer { | |
| name: "pool1_stage2" | |
| type: "Pooling" | |
| bottom: "conv1_stage2" | |
| top: "pool1_stage2" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 3 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "conv2_stage2" | |
| type: "Convolution" | |
| bottom: "pool1_stage2" | |
| top: "conv2_stage2" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 4 | |
| kernel_size: 9 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu2_stage2" | |
| type: "ReLU" | |
| bottom: "conv2_stage2" | |
| top: "conv2_stage2" | |
| } | |
| layer { | |
| name: "pool2_stage2" | |
| type: "Pooling" | |
| bottom: "conv2_stage2" | |
| top: "pool2_stage2" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 3 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "conv3_stage2" | |
| type: "Convolution" | |
| bottom: "pool2_stage2" | |
| top: "conv3_stage2" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 4 | |
| kernel_size: 9 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu3_stage2" | |
| type: "ReLU" | |
| bottom: "conv3_stage2" | |
| top: "conv3_stage2" | |
| } | |
| layer { | |
| name: "pool3_stage2" | |
| type: "Pooling" | |
| bottom: "conv3_stage2" | |
| top: "pool3_stage2" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 3 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "conv4_stage2" | |
| type: "Convolution" | |
| bottom: "pool3_stage2" | |
| top: "conv4_stage2" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 32 | |
| pad: 2 | |
| kernel_size: 5 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu4_stage2" | |
| type: "ReLU" | |
| bottom: "conv4_stage2" | |
| top: "conv4_stage2" | |
| } | |
| layer { | |
| name: "concat_stage2" | |
| type: "Concat" | |
| bottom: "conv4_stage2" | |
| bottom: "conv7_stage1" | |
| bottom: "pool_center_lower" | |
| top: "concat_stage2" | |
| concat_param { | |
| axis: 1 | |
| } | |
| } | |
| layer { | |
| name: "Mconv1_stage2" | |
| type: "Convolution" | |
| bottom: "concat_stage2" | |
| top: "Mconv1_stage2" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 5 | |
| kernel_size: 11 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "Mrelu1_stage2" | |
| type: "ReLU" | |
| bottom: "Mconv1_stage2" | |
| top: "Mconv1_stage2" | |
| } | |
| layer { | |
| name: "Mconv2_stage2" | |
| type: "Convolution" | |
| bottom: "Mconv1_stage2" | |
| top: "Mconv2_stage2" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 5 | |
| kernel_size: 11 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "Mrelu2_stage2" | |
| type: "ReLU" | |
| bottom: "Mconv2_stage2" | |
| top: "Mconv2_stage2" | |
| } | |
| layer { | |
| name: "Mconv3_stage2" | |
| type: "Convolution" | |
| bottom: "Mconv2_stage2" | |
| top: "Mconv3_stage2" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 5 | |
| kernel_size: 11 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "Mrelu3_stage2" | |
| type: "ReLU" | |
| bottom: "Mconv3_stage2" | |
| top: "Mconv3_stage2" | |
| } | |
| layer { | |
| name: "Mconv4_stage2" | |
| type: "Convolution" | |
| bottom: "Mconv3_stage2" | |
| top: "Mconv4_stage2" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| 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: "Mrelu4_stage2" | |
| type: "ReLU" | |
| bottom: "Mconv4_stage2" | |
| top: "Mconv4_stage2" | |
| } | |
| layer { | |
| name: "Mconv5_stage2" | |
| type: "Convolution" | |
| bottom: "Mconv4_stage2" | |
| top: "Mconv5_stage2" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 15 | |
| pad: 0 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv1_stage3" | |
| type: "Convolution" | |
| bottom: "pool3_stage2" | |
| top: "conv1_stage3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 32 | |
| pad: 2 | |
| kernel_size: 5 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu1_stage3" | |
| type: "ReLU" | |
| bottom: "conv1_stage3" | |
| top: "conv1_stage3" | |
| } | |
| layer { | |
| name: "concat_stage3" | |
| type: "Concat" | |
| bottom: "conv1_stage3" | |
| bottom: "Mconv5_stage2" | |
| bottom: "pool_center_lower" | |
| top: "concat_stage3" | |
| concat_param { | |
| axis: 1 | |
| } | |
| } | |
| layer { | |
| name: "Mconv1_stage3" | |
| type: "Convolution" | |
| bottom: "concat_stage3" | |
| top: "Mconv1_stage3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 5 | |
| kernel_size: 11 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "Mrelu1_stage3" | |
| type: "ReLU" | |
| bottom: "Mconv1_stage3" | |
| top: "Mconv1_stage3" | |
| } | |
| layer { | |
| name: "Mconv2_stage3" | |
| type: "Convolution" | |
| bottom: "Mconv1_stage3" | |
| top: "Mconv2_stage3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 5 | |
| kernel_size: 11 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "Mrelu2_stage3" | |
| type: "ReLU" | |
| bottom: "Mconv2_stage3" | |
| top: "Mconv2_stage3" | |
| } | |
| layer { | |
| name: "Mconv3_stage3" | |
| type: "Convolution" | |
| bottom: "Mconv2_stage3" | |
| top: "Mconv3_stage3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 5 | |
| kernel_size: 11 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "Mrelu3_stage3" | |
| type: "ReLU" | |
| bottom: "Mconv3_stage3" | |
| top: "Mconv3_stage3" | |
| } | |
| layer { | |
| name: "Mconv4_stage3" | |
| type: "Convolution" | |
| bottom: "Mconv3_stage3" | |
| top: "Mconv4_stage3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| 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: "Mrelu4_stage3" | |
| type: "ReLU" | |
| bottom: "Mconv4_stage3" | |
| top: "Mconv4_stage3" | |
| } | |
| layer { | |
| name: "Mconv5_stage3" | |
| type: "Convolution" | |
| bottom: "Mconv4_stage3" | |
| top: "Mconv5_stage3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 15 | |
| pad: 0 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv1_stage4" | |
| type: "Convolution" | |
| bottom: "pool3_stage2" | |
| top: "conv1_stage4" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 32 | |
| pad: 2 | |
| kernel_size: 5 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu1_stage4" | |
| type: "ReLU" | |
| bottom: "conv1_stage4" | |
| top: "conv1_stage4" | |
| } | |
| layer { | |
| name: "concat_stage4" | |
| type: "Concat" | |
| bottom: "conv1_stage4" | |
| bottom: "Mconv5_stage3" | |
| bottom: "pool_center_lower" | |
| top: "concat_stage4" | |
| concat_param { | |
| axis: 1 | |
| } | |
| } | |
| layer { | |
| name: "Mconv1_stage4" | |
| type: "Convolution" | |
| bottom: "concat_stage4" | |
| top: "Mconv1_stage4" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 5 | |
| kernel_size: 11 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "Mrelu1_stage4" | |
| type: "ReLU" | |
| bottom: "Mconv1_stage4" | |
| top: "Mconv1_stage4" | |
| } | |
| layer { | |
| name: "Mconv2_stage4" | |
| type: "Convolution" | |
| bottom: "Mconv1_stage4" | |
| top: "Mconv2_stage4" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 5 | |
| kernel_size: 11 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "Mrelu2_stage4" | |
| type: "ReLU" | |
| bottom: "Mconv2_stage4" | |
| top: "Mconv2_stage4" | |
| } | |
| layer { | |
| name: "Mconv3_stage4" | |
| type: "Convolution" | |
| bottom: "Mconv2_stage4" | |
| top: "Mconv3_stage4" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 5 | |
| kernel_size: 11 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "Mrelu3_stage4" | |
| type: "ReLU" | |
| bottom: "Mconv3_stage4" | |
| top: "Mconv3_stage4" | |
| } | |
| layer { | |
| name: "Mconv4_stage4" | |
| type: "Convolution" | |
| bottom: "Mconv3_stage4" | |
| top: "Mconv4_stage4" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| 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: "Mrelu4_stage4" | |
| type: "ReLU" | |
| bottom: "Mconv4_stage4" | |
| top: "Mconv4_stage4" | |
| } | |
| layer { | |
| name: "Mconv5_stage4" | |
| type: "Convolution" | |
| bottom: "Mconv4_stage4" | |
| top: "Mconv5_stage4" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 15 | |
| pad: 0 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv1_stage5" | |
| type: "Convolution" | |
| bottom: "pool3_stage2" | |
| top: "conv1_stage5" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 32 | |
| pad: 2 | |
| kernel_size: 5 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu1_stage5" | |
| type: "ReLU" | |
| bottom: "conv1_stage5" | |
| top: "conv1_stage5" | |
| } | |
| layer { | |
| name: "concat_stage5" | |
| type: "Concat" | |
| bottom: "conv1_stage5" | |
| bottom: "Mconv5_stage4" | |
| bottom: "pool_center_lower" | |
| top: "concat_stage5" | |
| concat_param { | |
| axis: 1 | |
| } | |
| } | |
| layer { | |
| name: "Mconv1_stage5" | |
| type: "Convolution" | |
| bottom: "concat_stage5" | |
| top: "Mconv1_stage5" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 5 | |
| kernel_size: 11 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "Mrelu1_stage5" | |
| type: "ReLU" | |
| bottom: "Mconv1_stage5" | |
| top: "Mconv1_stage5" | |
| } | |
| layer { | |
| name: "Mconv2_stage5" | |
| type: "Convolution" | |
| bottom: "Mconv1_stage5" | |
| top: "Mconv2_stage5" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 5 | |
| kernel_size: 11 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "Mrelu2_stage5" | |
| type: "ReLU" | |
| bottom: "Mconv2_stage5" | |
| top: "Mconv2_stage5" | |
| } | |
| layer { | |
| name: "Mconv3_stage5" | |
| type: "Convolution" | |
| bottom: "Mconv2_stage5" | |
| top: "Mconv3_stage5" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 5 | |
| kernel_size: 11 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "Mrelu3_stage5" | |
| type: "ReLU" | |
| bottom: "Mconv3_stage5" | |
| top: "Mconv3_stage5" | |
| } | |
| layer { | |
| name: "Mconv4_stage5" | |
| type: "Convolution" | |
| bottom: "Mconv3_stage5" | |
| top: "Mconv4_stage5" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| 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: "Mrelu4_stage5" | |
| type: "ReLU" | |
| bottom: "Mconv4_stage5" | |
| top: "Mconv4_stage5" | |
| } | |
| layer { | |
| name: "Mconv5_stage5" | |
| type: "Convolution" | |
| bottom: "Mconv4_stage5" | |
| top: "Mconv5_stage5" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 15 | |
| pad: 0 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv1_stage6" | |
| type: "Convolution" | |
| bottom: "pool3_stage2" | |
| top: "conv1_stage6" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 32 | |
| pad: 2 | |
| kernel_size: 5 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu1_stage6" | |
| type: "ReLU" | |
| bottom: "conv1_stage6" | |
| top: "conv1_stage6" | |
| } | |
| layer { | |
| name: "concat_stage6" | |
| type: "Concat" | |
| bottom: "conv1_stage6" | |
| bottom: "Mconv5_stage5" | |
| bottom: "pool_center_lower" | |
| top: "concat_stage6" | |
| concat_param { | |
| axis: 1 | |
| } | |
| } | |
| layer { | |
| name: "Mconv1_stage6" | |
| type: "Convolution" | |
| bottom: "concat_stage6" | |
| top: "Mconv1_stage6" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 5 | |
| kernel_size: 11 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "Mrelu1_stage6" | |
| type: "ReLU" | |
| bottom: "Mconv1_stage6" | |
| top: "Mconv1_stage6" | |
| } | |
| layer { | |
| name: "Mconv2_stage6" | |
| type: "Convolution" | |
| bottom: "Mconv1_stage6" | |
| top: "Mconv2_stage6" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 5 | |
| kernel_size: 11 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "Mrelu2_stage6" | |
| type: "ReLU" | |
| bottom: "Mconv2_stage6" | |
| top: "Mconv2_stage6" | |
| } | |
| layer { | |
| name: "Mconv3_stage6" | |
| type: "Convolution" | |
| bottom: "Mconv2_stage6" | |
| top: "Mconv3_stage6" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 5 | |
| kernel_size: 11 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "Mrelu3_stage6" | |
| type: "ReLU" | |
| bottom: "Mconv3_stage6" | |
| top: "Mconv3_stage6" | |
| } | |
| layer { | |
| name: "Mconv4_stage6" | |
| type: "Convolution" | |
| bottom: "Mconv3_stage6" | |
| top: "Mconv4_stage6" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| 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: "Mrelu4_stage6" | |
| type: "ReLU" | |
| bottom: "Mconv4_stage6" | |
| top: "Mconv4_stage6" | |
| } | |
| layer { | |
| name: "Mconv5_stage6" | |
| type: "Convolution" | |
| bottom: "Mconv4_stage6" | |
| top: "Mconv5_stage6" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 15 | |
| pad: 0 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } |
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