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| input: "image" | |
| input_dim: 1 | |
| input_dim: 3 | |
| input_dim: 1 | |
| input_dim: 1 | |
| layer { | |
| name: "conv1_1" | |
| type: "Convolution" | |
| bottom: "image" | |
| top: "conv1_1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv1_1_re" | |
| type: "ReLU" | |
| bottom: "conv1_1" | |
| top: "conv1_1" | |
| } | |
| layer { | |
| name: "conv1_2" | |
| type: "Convolution" | |
| bottom: "conv1_1" | |
| top: "conv1_2" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv1_2_re" | |
| type: "ReLU" | |
| bottom: "conv1_2" | |
| top: "conv1_2" | |
| } | |
| layer { | |
| name: "pool1" | |
| type: "Pooling" | |
| bottom: "conv1_2" | |
| top: "pool1" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "conv2_1" | |
| type: "Convolution" | |
| bottom: "pool1" | |
| top: "conv2_1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| 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: "conv2_1_re" | |
| type: "ReLU" | |
| bottom: "conv2_1" | |
| top: "conv2_1" | |
| } | |
| layer { | |
| name: "conv2_2" | |
| type: "Convolution" | |
| bottom: "conv2_1" | |
| top: "conv2_2" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| 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: "conv2_2_re" | |
| type: "ReLU" | |
| bottom: "conv2_2" | |
| top: "conv2_2" | |
| } | |
| layer { | |
| name: "pool2" | |
| type: "Pooling" | |
| bottom: "conv2_2" | |
| top: "pool2" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "conv3_1" | |
| type: "Convolution" | |
| bottom: "pool2" | |
| top: "conv3_1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv3_1_re" | |
| type: "ReLU" | |
| bottom: "conv3_1" | |
| top: "conv3_1" | |
| } | |
| layer { | |
| name: "conv3_2" | |
| type: "Convolution" | |
| bottom: "conv3_1" | |
| top: "conv3_2" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv3_2_re" | |
| type: "ReLU" | |
| bottom: "conv3_2" | |
| top: "conv3_2" | |
| } | |
| layer { | |
| name: "conv3_3" | |
| type: "Convolution" | |
| bottom: "conv3_2" | |
| top: "conv3_3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv3_3_re" | |
| type: "ReLU" | |
| bottom: "conv3_3" | |
| top: "conv3_3" | |
| } | |
| layer { | |
| name: "conv3_4" | |
| type: "Convolution" | |
| bottom: "conv3_3" | |
| top: "conv3_4" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv3_4_re" | |
| type: "ReLU" | |
| bottom: "conv3_4" | |
| top: "conv3_4" | |
| } | |
| layer { | |
| name: "pool3" | |
| type: "Pooling" | |
| bottom: "conv3_4" | |
| top: "pool3" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "conv4_1" | |
| type: "Convolution" | |
| bottom: "pool3" | |
| top: "conv4_1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv4_1_re" | |
| type: "ReLU" | |
| bottom: "conv4_1" | |
| top: "conv4_1" | |
| } | |
| layer { | |
| name: "conv4_2" | |
| type: "Convolution" | |
| bottom: "conv4_1" | |
| top: "conv4_2" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv4_2_re" | |
| type: "ReLU" | |
| bottom: "conv4_2" | |
| top: "conv4_2" | |
| } | |
| layer { | |
| name: "conv4_3" | |
| type: "Convolution" | |
| bottom: "conv4_2" | |
| top: "conv4_3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv4_3_re" | |
| type: "ReLU" | |
| bottom: "conv4_3" | |
| top: "conv4_3" | |
| } | |
| layer { | |
| name: "conv4_4" | |
| type: "Convolution" | |
| bottom: "conv4_3" | |
| top: "conv4_4" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv4_4_re" | |
| type: "ReLU" | |
| bottom: "conv4_4" | |
| top: "conv4_4" | |
| } | |
| layer { | |
| name: "conv5_1" | |
| type: "Convolution" | |
| bottom: "conv4_4" | |
| top: "conv5_1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv5_1_re" | |
| type: "ReLU" | |
| bottom: "conv5_1" | |
| top: "conv5_1" | |
| } | |
| layer { | |
| name: "conv5_2" | |
| type: "Convolution" | |
| bottom: "conv5_1" | |
| top: "conv5_2" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv5_2_re" | |
| type: "ReLU" | |
| bottom: "conv5_2" | |
| top: "conv5_2" | |
| } | |
| layer { | |
| name: "conv5_3_CPM" | |
| type: "Convolution" | |
| bottom: "conv5_2" | |
| top: "conv5_3_CPM" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| 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: "conv5_3_CPM_re" | |
| type: "ReLU" | |
| bottom: "conv5_3_CPM" | |
| top: "conv5_3_CPM" | |
| } | |
| layer { | |
| name: "conv6_1_CPM" | |
| type: "Convolution" | |
| bottom: "conv5_3_CPM" | |
| top: "conv6_1_CPM" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| 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: "conv6_1_CPM_re" | |
| type: "ReLU" | |
| bottom: "conv6_1_CPM" | |
| top: "conv6_1_CPM" | |
| } | |
| layer { | |
| name: "conv6_2_CPM" | |
| type: "Convolution" | |
| bottom: "conv6_1_CPM" | |
| top: "conv6_2_CPM" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 71 | |
| pad: 0 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "features_in_stage_2" | |
| type: "Concat" | |
| bottom: "conv6_2_CPM" | |
| bottom: "conv5_3_CPM" | |
| top: "features_in_stage_2" | |
| concat_param { | |
| axis: 1 | |
| } | |
| } | |
| layer { | |
| name: "Mconv1_stage2" | |
| type: "Convolution" | |
| bottom: "features_in_stage_2" | |
| top: "Mconv1_stage2" | |
| param { | |
| lr_mult: 4.0 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 8.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 3 | |
| kernel_size: 7 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "Mconv1_stage2_re" | |
| type: "ReLU" | |
| bottom: "Mconv1_stage2" | |
| top: "Mconv1_stage2" | |
| } | |
| layer { | |
| name: "Mconv2_stage2" | |
| type: "Convolution" | |
| bottom: "Mconv1_stage2" | |
| top: "Mconv2_stage2" | |
| param { | |
| lr_mult: 4.0 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 8.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 3 | |
| kernel_size: 7 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "Mconv2_stage2_re" | |
| type: "ReLU" | |
| bottom: "Mconv2_stage2" | |
| top: "Mconv2_stage2" | |
| } | |
| layer { | |
| name: "Mconv3_stage2" | |
| type: "Convolution" | |
| bottom: "Mconv2_stage2" | |
| top: "Mconv3_stage2" | |
| param { | |
| lr_mult: 4.0 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 8.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 3 | |
| kernel_size: 7 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "Mconv3_stage2_re" | |
| type: "ReLU" | |
| bottom: "Mconv3_stage2" | |
| top: "Mconv3_stage2" | |
| } | |
| layer { | |
| name: "Mconv4_stage2" | |
| type: "Convolution" | |
| bottom: "Mconv3_stage2" | |
| top: "Mconv4_stage2" | |
| param { | |
| lr_mult: 4.0 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 8.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 3 | |
| kernel_size: 7 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "Mconv4_stage2_re" | |
| type: "ReLU" | |
| bottom: "Mconv4_stage2" | |
| top: "Mconv4_stage2" | |
| } | |
| layer { | |
| name: "Mconv5_stage2" | |
| type: "Convolution" | |
| bottom: "Mconv4_stage2" | |
| top: "Mconv5_stage2" | |
| param { | |
| lr_mult: 4.0 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 8.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 3 | |
| kernel_size: 7 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "Mconv5_stage2_re" | |
| type: "ReLU" | |
| bottom: "Mconv5_stage2" | |
| top: "Mconv5_stage2" | |
| } | |
| layer { | |
| name: "Mconv6_stage2" | |
| type: "Convolution" | |
| bottom: "Mconv5_stage2" | |
| top: "Mconv6_stage2" | |
| param { | |
| lr_mult: 4.0 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 8.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 0 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "Mconv6_stage2_re" | |
| type: "ReLU" | |
| bottom: "Mconv6_stage2" | |
| top: "Mconv6_stage2" | |
| } | |
| layer { | |
| name: "Mconv7_stage2" | |
| type: "Convolution" | |
| bottom: "Mconv6_stage2" | |
| top: "Mconv7_stage2" | |
| param { | |
| lr_mult: 4.0 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 8.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 71 | |
| pad: 0 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "features_in_stage_3" | |
| type: "Concat" | |
| bottom: "Mconv7_stage2" | |
| bottom: "conv5_3_CPM" | |
| top: "features_in_stage_3" | |
| concat_param { | |
| axis: 1 | |
| } | |
| } | |
| layer { | |
| name: "Mconv1_stage3" | |
| type: "Convolution" | |
| bottom: "features_in_stage_3" | |
| top: "Mconv1_stage3" | |
| param { | |
| lr_mult: 4.0 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 8.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 3 | |
| kernel_size: 7 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "Mconv1_stage3_re" | |
| type: "ReLU" | |
| bottom: "Mconv1_stage3" | |
| top: "Mconv1_stage3" | |
| } | |
| layer { | |
| name: "Mconv2_stage3" | |
| type: "Convolution" | |
| bottom: "Mconv1_stage3" | |
| top: "Mconv2_stage3" | |
| param { | |
| lr_mult: 4.0 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 8.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 3 | |
| kernel_size: 7 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "Mconv2_stage3_re" | |
| type: "ReLU" | |
| bottom: "Mconv2_stage3" | |
| top: "Mconv2_stage3" | |
| } | |
| layer { | |
| name: "Mconv3_stage3" | |
| type: "Convolution" | |
| bottom: "Mconv2_stage3" | |
| top: "Mconv3_stage3" | |
| param { | |
| lr_mult: 4.0 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 8.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 3 | |
| kernel_size: 7 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "Mconv3_stage3_re" | |
| type: "ReLU" | |
| bottom: "Mconv3_stage3" | |
| top: "Mconv3_stage3" | |
| } | |
| layer { | |
| name: "Mconv4_stage3" | |
| type: "Convolution" | |
| bottom: "Mconv3_stage3" | |
| top: "Mconv4_stage3" | |
| param { | |
| lr_mult: 4.0 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 8.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 3 | |
| kernel_size: 7 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "Mconv4_stage3_re" | |
| type: "ReLU" | |
| bottom: "Mconv4_stage3" | |
| top: "Mconv4_stage3" | |
| } | |
| layer { | |
| name: "Mconv5_stage3" | |
| type: "Convolution" | |
| bottom: "Mconv4_stage3" | |
| top: "Mconv5_stage3" | |
| param { | |
| lr_mult: 4.0 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 8.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 3 | |
| kernel_size: 7 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "Mconv5_stage3_re" | |
| type: "ReLU" | |
| bottom: "Mconv5_stage3" | |
| top: "Mconv5_stage3" | |
| } | |
| layer { | |
| name: "Mconv6_stage3" | |
| type: "Convolution" | |
| bottom: "Mconv5_stage3" | |
| top: "Mconv6_stage3" | |
| param { | |
| lr_mult: 4.0 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 8.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 0 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "Mconv6_stage3_re" | |
| type: "ReLU" | |
| bottom: "Mconv6_stage3" | |
| top: "Mconv6_stage3" | |
| } | |
| layer { | |
| name: "Mconv7_stage3" | |
| type: "Convolution" | |
| bottom: "Mconv6_stage3" | |
| top: "Mconv7_stage3" | |
| param { | |
| lr_mult: 4.0 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 8.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 71 | |
| pad: 0 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "features_in_stage_4" | |
| type: "Concat" | |
| bottom: "Mconv7_stage3" | |
| bottom: "conv5_3_CPM" | |
| top: "features_in_stage_4" | |
| concat_param { | |
| axis: 1 | |
| } | |
| } | |
| layer { | |
| name: "Mconv1_stage4" | |
| type: "Convolution" | |
| bottom: "features_in_stage_4" | |
| top: "Mconv1_stage4" | |
| param { | |
| lr_mult: 4.0 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 8.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 3 | |
| kernel_size: 7 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "Mconv1_stage4_re" | |
| type: "ReLU" | |
| bottom: "Mconv1_stage4" | |
| top: "Mconv1_stage4" | |
| } | |
| layer { | |
| name: "Mconv2_stage4" | |
| type: "Convolution" | |
| bottom: "Mconv1_stage4" | |
| top: "Mconv2_stage4" | |
| param { | |
| lr_mult: 4.0 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 8.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 3 | |
| kernel_size: 7 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "Mconv2_stage4_re" | |
| type: "ReLU" | |
| bottom: "Mconv2_stage4" | |
| top: "Mconv2_stage4" | |
| } | |
| layer { | |
| name: "Mconv3_stage4" | |
| type: "Convolution" | |
| bottom: "Mconv2_stage4" | |
| top: "Mconv3_stage4" | |
| param { | |
| lr_mult: 4.0 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 8.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 3 | |
| kernel_size: 7 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "Mconv3_stage4_re" | |
| type: "ReLU" | |
| bottom: "Mconv3_stage4" | |
| top: "Mconv3_stage4" | |
| } | |
| layer { | |
| name: "Mconv4_stage4" | |
| type: "Convolution" | |
| bottom: "Mconv3_stage4" | |
| top: "Mconv4_stage4" | |
| param { | |
| lr_mult: 4.0 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 8.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 3 | |
| kernel_size: 7 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "Mconv4_stage4_re" | |
| type: "ReLU" | |
| bottom: "Mconv4_stage4" | |
| top: "Mconv4_stage4" | |
| } | |
| layer { | |
| name: "Mconv5_stage4" | |
| type: "Convolution" | |
| bottom: "Mconv4_stage4" | |
| top: "Mconv5_stage4" | |
| param { | |
| lr_mult: 4.0 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 8.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 3 | |
| kernel_size: 7 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "Mconv5_stage4_re" | |
| type: "ReLU" | |
| bottom: "Mconv5_stage4" | |
| top: "Mconv5_stage4" | |
| } | |
| layer { | |
| name: "Mconv6_stage4" | |
| type: "Convolution" | |
| bottom: "Mconv5_stage4" | |
| top: "Mconv6_stage4" | |
| param { | |
| lr_mult: 4.0 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 8.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 0 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "Mconv6_stage4_re" | |
| type: "ReLU" | |
| bottom: "Mconv6_stage4" | |
| top: "Mconv6_stage4" | |
| } | |
| layer { | |
| name: "Mconv7_stage4" | |
| type: "Convolution" | |
| bottom: "Mconv6_stage4" | |
| top: "Mconv7_stage4" | |
| param { | |
| lr_mult: 4.0 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 8.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 71 | |
| pad: 0 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "features_in_stage_5" | |
| type: "Concat" | |
| bottom: "Mconv7_stage4" | |
| bottom: "conv5_3_CPM" | |
| top: "features_in_stage_5" | |
| concat_param { | |
| axis: 1 | |
| } | |
| } | |
| layer { | |
| name: "Mconv1_stage5" | |
| type: "Convolution" | |
| bottom: "features_in_stage_5" | |
| top: "Mconv1_stage5" | |
| param { | |
| lr_mult: 4.0 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 8.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 3 | |
| kernel_size: 7 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "Mconv1_stage5_re" | |
| type: "ReLU" | |
| bottom: "Mconv1_stage5" | |
| top: "Mconv1_stage5" | |
| } | |
| layer { | |
| name: "Mconv2_stage5" | |
| type: "Convolution" | |
| bottom: "Mconv1_stage5" | |
| top: "Mconv2_stage5" | |
| param { | |
| lr_mult: 4.0 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 8.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 3 | |
| kernel_size: 7 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "Mconv2_stage5_re" | |
| type: "ReLU" | |
| bottom: "Mconv2_stage5" | |
| top: "Mconv2_stage5" | |
| } | |
| layer { | |
| name: "Mconv3_stage5" | |
| type: "Convolution" | |
| bottom: "Mconv2_stage5" | |
| top: "Mconv3_stage5" | |
| param { | |
| lr_mult: 4.0 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 8.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 3 | |
| kernel_size: 7 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "Mconv3_stage5_re" | |
| type: "ReLU" | |
| bottom: "Mconv3_stage5" | |
| top: "Mconv3_stage5" | |
| } | |
| layer { | |
| name: "Mconv4_stage5" | |
| type: "Convolution" | |
| bottom: "Mconv3_stage5" | |
| top: "Mconv4_stage5" | |
| param { | |
| lr_mult: 4.0 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 8.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 3 | |
| kernel_size: 7 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "Mconv4_stage5_re" | |
| type: "ReLU" | |
| bottom: "Mconv4_stage5" | |
| top: "Mconv4_stage5" | |
| } | |
| layer { | |
| name: "Mconv5_stage5" | |
| type: "Convolution" | |
| bottom: "Mconv4_stage5" | |
| top: "Mconv5_stage5" | |
| param { | |
| lr_mult: 4.0 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 8.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 3 | |
| kernel_size: 7 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "Mconv5_stage5_re" | |
| type: "ReLU" | |
| bottom: "Mconv5_stage5" | |
| top: "Mconv5_stage5" | |
| } | |
| layer { | |
| name: "Mconv6_stage5" | |
| type: "Convolution" | |
| bottom: "Mconv5_stage5" | |
| top: "Mconv6_stage5" | |
| param { | |
| lr_mult: 4.0 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 8.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 0 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "Mconv6_stage5_re" | |
| type: "ReLU" | |
| bottom: "Mconv6_stage5" | |
| top: "Mconv6_stage5" | |
| } | |
| layer { | |
| name: "Mconv7_stage5" | |
| type: "Convolution" | |
| bottom: "Mconv6_stage5" | |
| top: "Mconv7_stage5" | |
| param { | |
| lr_mult: 4.0 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 8.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 71 | |
| pad: 0 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "features_in_stage_6" | |
| type: "Concat" | |
| bottom: "Mconv7_stage5" | |
| bottom: "conv5_3_CPM" | |
| top: "features_in_stage_6" | |
| concat_param { | |
| axis: 1 | |
| } | |
| } | |
| layer { | |
| name: "Mconv1_stage6" | |
| type: "Convolution" | |
| bottom: "features_in_stage_6" | |
| top: "Mconv1_stage6" | |
| param { | |
| lr_mult: 4.0 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 8.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 3 | |
| kernel_size: 7 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "Mconv1_stage6_re" | |
| type: "ReLU" | |
| bottom: "Mconv1_stage6" | |
| top: "Mconv1_stage6" | |
| } | |
| layer { | |
| name: "Mconv2_stage6" | |
| type: "Convolution" | |
| bottom: "Mconv1_stage6" | |
| top: "Mconv2_stage6" | |
| param { | |
| lr_mult: 4.0 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 8.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 3 | |
| kernel_size: 7 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "Mconv2_stage6_re" | |
| type: "ReLU" | |
| bottom: "Mconv2_stage6" | |
| top: "Mconv2_stage6" | |
| } | |
| layer { | |
| name: "Mconv3_stage6" | |
| type: "Convolution" | |
| bottom: "Mconv2_stage6" | |
| top: "Mconv3_stage6" | |
| param { | |
| lr_mult: 4.0 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 8.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 3 | |
| kernel_size: 7 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "Mconv3_stage6_re" | |
| type: "ReLU" | |
| bottom: "Mconv3_stage6" | |
| top: "Mconv3_stage6" | |
| } | |
| layer { | |
| name: "Mconv4_stage6" | |
| type: "Convolution" | |
| bottom: "Mconv3_stage6" | |
| top: "Mconv4_stage6" | |
| param { | |
| lr_mult: 4.0 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 8.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 3 | |
| kernel_size: 7 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "Mconv4_stage6_re" | |
| type: "ReLU" | |
| bottom: "Mconv4_stage6" | |
| top: "Mconv4_stage6" | |
| } | |
| layer { | |
| name: "Mconv5_stage6" | |
| type: "Convolution" | |
| bottom: "Mconv4_stage6" | |
| top: "Mconv5_stage6" | |
| param { | |
| lr_mult: 4.0 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 8.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 3 | |
| kernel_size: 7 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "Mconv5_stage6_re" | |
| type: "ReLU" | |
| bottom: "Mconv5_stage6" | |
| top: "Mconv5_stage6" | |
| } | |
| layer { | |
| name: "Mconv6_stage6" | |
| type: "Convolution" | |
| bottom: "Mconv5_stage6" | |
| top: "Mconv6_stage6" | |
| param { | |
| lr_mult: 4.0 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 8.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 0 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "Mconv6_stage6_re" | |
| type: "ReLU" | |
| bottom: "Mconv6_stage6" | |
| top: "Mconv6_stage6" | |
| } | |
| layer { | |
| name: "Mconv7_stage6" | |
| type: "Convolution" | |
| bottom: "Mconv6_stage6" | |
| top: "net_output" | |
| param { | |
| lr_mult: 4.0 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 8.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 71 | |
| pad: 0 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |