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December 2, 2017 16:14
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| layer { | |
| name: "data" | |
| type: "CPMData" | |
| top: "data" | |
| top: "label" | |
| data_param { | |
| source: "/home/zhecao/COCO_kpt/lmdb_trainVal" | |
| batch_size: 10 | |
| backend: LMDB | |
| } | |
| cpm_transform_param { | |
| stride: 8 | |
| max_rotate_degree: 40 | |
| visualize: false | |
| crop_size_x: 368 | |
| crop_size_y: 368 | |
| scale_prob: 1 | |
| scale_min: 0.5 | |
| scale_max: 1.1 | |
| target_dist: 0.6 | |
| center_perterb_max: 40 | |
| do_clahe: false | |
| num_parts: 56 | |
| np_in_lmdb: 17 | |
| } | |
| } | |
| layer { | |
| name: "vec_weight" | |
| type: "Slice" | |
| bottom: "label" | |
| top: "vec_weight" | |
| top: "heat_weight" | |
| top: "vec_temp" | |
| top: "heat_temp" | |
| slice_param { | |
| slice_point: 38 | |
| slice_point: 57 | |
| slice_point: 95 | |
| axis: 1 | |
| } | |
| } | |
| layer { | |
| name: "label_vec" | |
| type: "Eltwise" | |
| bottom: "vec_weight" | |
| bottom: "vec_temp" | |
| top: "label_vec" | |
| eltwise_param { | |
| operation: PROD | |
| } | |
| } | |
| layer { | |
| name: "label_heat" | |
| type: "Eltwise" | |
| bottom: "heat_weight" | |
| bottom: "heat_temp" | |
| top: "label_heat" | |
| eltwise_param { | |
| operation: PROD | |
| } | |
| } | |
| layer { | |
| name: "image" | |
| type: "Slice" | |
| bottom: "data" | |
| top: "image" | |
| top: "center_map" | |
| slice_param { | |
| slice_point: 3 | |
| axis: 1 | |
| } | |
| } | |
| layer { | |
| name: "silence2" | |
| type: "Silence" | |
| bottom: "center_map" | |
| } | |
| layer { | |
| name: "conv1_1" | |
| type: "Convolution" | |
| bottom: "image" | |
| top: "conv1_1" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu1_1" | |
| type: "ReLU" | |
| bottom: "conv1_1" | |
| top: "conv1_1" | |
| } | |
| layer { | |
| name: "conv1_2" | |
| type: "Convolution" | |
| bottom: "conv1_1" | |
| top: "conv1_2" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu1_2" | |
| type: "ReLU" | |
| bottom: "conv1_2" | |
| top: "conv1_2" | |
| } | |
| layer { | |
| name: "pool1_stage1" | |
| type: "Pooling" | |
| bottom: "conv1_2" | |
| top: "pool1_stage1" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "conv2_1" | |
| type: "Convolution" | |
| bottom: "pool1_stage1" | |
| top: "conv2_1" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu2_1" | |
| type: "ReLU" | |
| bottom: "conv2_1" | |
| top: "conv2_1" | |
| } | |
| layer { | |
| name: "conv2_2" | |
| type: "Convolution" | |
| bottom: "conv2_1" | |
| top: "conv2_2" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu2_2" | |
| type: "ReLU" | |
| bottom: "conv2_2" | |
| top: "conv2_2" | |
| } | |
| layer { | |
| name: "pool2_stage1" | |
| type: "Pooling" | |
| bottom: "conv2_2" | |
| top: "pool2_stage1" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "conv3_1" | |
| type: "Convolution" | |
| bottom: "pool2_stage1" | |
| top: "conv3_1" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu3_1" | |
| type: "ReLU" | |
| bottom: "conv3_1" | |
| top: "conv3_1" | |
| } | |
| layer { | |
| name: "conv3_2" | |
| type: "Convolution" | |
| bottom: "conv3_1" | |
| top: "conv3_2" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu3_2" | |
| type: "ReLU" | |
| bottom: "conv3_2" | |
| top: "conv3_2" | |
| } | |
| layer { | |
| name: "conv3_3" | |
| type: "Convolution" | |
| bottom: "conv3_2" | |
| top: "conv3_3" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu3_3" | |
| type: "ReLU" | |
| bottom: "conv3_3" | |
| top: "conv3_3" | |
| } | |
| layer { | |
| name: "conv3_4" | |
| type: "Convolution" | |
| bottom: "conv3_3" | |
| top: "conv3_4" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu3_4" | |
| type: "ReLU" | |
| bottom: "conv3_4" | |
| top: "conv3_4" | |
| } | |
| layer { | |
| name: "pool3_stage1" | |
| type: "Pooling" | |
| bottom: "conv3_4" | |
| top: "pool3_stage1" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "conv4_1" | |
| type: "Convolution" | |
| bottom: "pool3_stage1" | |
| top: "conv4_1" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu4_1" | |
| type: "ReLU" | |
| bottom: "conv4_1" | |
| top: "conv4_1" | |
| } | |
| layer { | |
| name: "conv4_2" | |
| type: "Convolution" | |
| bottom: "conv4_1" | |
| top: "conv4_2" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu4_2" | |
| type: "ReLU" | |
| bottom: "conv4_2" | |
| top: "conv4_2" | |
| } | |
| layer { | |
| name: "conv4_3_CPM" | |
| type: "Convolution" | |
| bottom: "conv4_2" | |
| top: "conv4_3_CPM" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu4_3_CPM" | |
| type: "ReLU" | |
| bottom: "conv4_3_CPM" | |
| top: "conv4_3_CPM" | |
| } | |
| layer { | |
| name: "conv4_4_CPM" | |
| type: "Convolution" | |
| bottom: "conv4_3_CPM" | |
| top: "conv4_4_CPM" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu4_4_CPM" | |
| type: "ReLU" | |
| bottom: "conv4_4_CPM" | |
| top: "conv4_4_CPM" | |
| } | |
| layer { | |
| name: "conv5_1_CPM_L1" | |
| type: "Convolution" | |
| bottom: "conv4_4_CPM" | |
| top: "conv5_1_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 { | |
| name: "relu5_1_CPM_L1" | |
| type: "ReLU" | |
| bottom: "conv5_1_CPM_L1" | |
| top: "conv5_1_CPM_L1" | |
| } | |
| layer { | |
| name: "conv5_1_CPM_L2" | |
| type: "Convolution" | |
| bottom: "conv4_4_CPM" | |
| top: "conv5_1_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_1_CPM_L2" | |
| type: "ReLU" | |
| bottom: "conv5_1_CPM_L2" | |
| top: "conv5_1_CPM_L2" | |
| } | |
| layer { | |
| name: "conv5_2_CPM_L1" | |
| type: "Convolution" | |
| bottom: "conv5_1_CPM_L1" | |
| top: "conv5_2_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 { | |
| name: "relu5_2_CPM_L1" | |
| type: "ReLU" | |
| bottom: "conv5_2_CPM_L1" | |
| top: "conv5_2_CPM_L1" | |
| } | |
| layer { | |
| name: "conv5_2_CPM_L2" | |
| type: "Convolution" | |
| 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 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu5_2_CPM_L2" | |
| type: "ReLU" | |
| bottom: "conv5_2_CPM_L2" | |
| top: "conv5_2_CPM_L2" | |
| } | |
| layer { | |
| 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 { | |
| name: "relu5_3_CPM_L1" | |
| type: "ReLU" | |
| bottom: "conv5_3_CPM_L1" | |
| top: "conv5_3_CPM_L1" | |
| } | |
| layer { | |
| 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: "weight_stage1_L1" | |
| type: "Eltwise" | |
| bottom: "conv5_5_CPM_L1" | |
| bottom: "vec_weight" | |
| top: "weight_stage1_L1" | |
| eltwise_param { | |
| operation: PROD | |
| } | |
| } | |
| layer { | |
| name: "loss_stage1_L1" | |
| type: "EuclideanLoss" | |
| bottom: "weight_stage1_L1" | |
| bottom: "label_vec" | |
| top: "loss_stage1_L1" | |
| loss_weight: 1 | |
| } | |
| layer { | |
| name: "weight_stage1_L2" | |
| type: "Eltwise" | |
| bottom: "conv5_5_CPM_L2" | |
| bottom: "heat_weight" | |
| top: "weight_stage1_L2" | |
| eltwise_param { | |
| operation: PROD | |
| } | |
| } | |
| layer { | |
| name: "loss_stage1_L2" | |
| type: "EuclideanLoss" | |
| bottom: "weight_stage1_L2" | |
| bottom: "label_heat" | |
| top: "loss_stage1_L2" | |
| loss_weight: 1 | |
| } | |
| 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: "weight_stage2_L1" | |
| type: "Eltwise" | |
| bottom: "Mconv7_stage2_L1" | |
| bottom: "vec_weight" | |
| top: "weight_stage2_L1" | |
| eltwise_param { | |
| operation: PROD | |
| } | |
| } | |
| layer { | |
| name: "loss_stage2_L1" | |
| type: "EuclideanLoss" | |
| bottom: "weight_stage2_L1" | |
| bottom: "label_vec" | |
| top: "loss_stage2_L1" | |
| loss_weight: 1 | |
| } | |
| layer { | |
| name: "weight_stage2_L2" | |
| type: "Eltwise" | |
| bottom: "Mconv7_stage2_L2" | |
| bottom: "heat_weight" | |
| top: "weight_stage2_L2" | |
| eltwise_param { | |
| operation: PROD | |
| } | |
| } | |
| layer { | |
| name: "loss_stage2_L2" | |
| type: "EuclideanLoss" | |
| bottom: "weight_stage2_L2" | |
| bottom: "label_heat" | |
| top: "loss_stage2_L2" | |
| loss_weight: 1 | |
| } | |
| 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: "weight_stage3_L1" | |
| type: "Eltwise" | |
| bottom: "Mconv7_stage3_L1" | |
| bottom: "vec_weight" | |
| top: "weight_stage3_L1" | |
| eltwise_param { | |
| operation: PROD | |
| } | |
| } | |
| layer { | |
| name: "loss_stage3_L1" | |
| type: "EuclideanLoss" | |
| bottom: "weight_stage3_L1" | |
| bottom: "label_vec" | |
| top: "loss_stage3_L1" | |
| loss_weight: 1 | |
| } | |
| layer { | |
| name: "weight_stage3_L2" | |
| type: "Eltwise" | |
| bottom: "Mconv7_stage3_L2" | |
| bottom: "heat_weight" | |
| top: "weight_stage3_L2" | |
| eltwise_param { | |
| operation: PROD | |
| } | |
| } | |
| layer { | |
| name: "loss_stage3_L2" | |
| type: "EuclideanLoss" | |
| bottom: "weight_stage3_L2" | |
| bottom: "label_heat" | |
| top: "loss_stage3_L2" | |
| loss_weight: 1 | |
| } | |
| 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: "weight_stage4_L1" | |
| type: "Eltwise" | |
| bottom: "Mconv7_stage4_L1" | |
| bottom: "vec_weight" | |
| top: "weight_stage4_L1" | |
| eltwise_param { | |
| operation: PROD | |
| } | |
| } | |
| layer { | |
| name: "loss_stage4_L1" | |
| type: "EuclideanLoss" | |
| bottom: "weight_stage4_L1" | |
| bottom: "label_vec" | |
| top: "loss_stage4_L1" | |
| loss_weight: 1 | |
| } | |
| layer { | |
| name: "weight_stage4_L2" | |
| type: "Eltwise" | |
| bottom: "Mconv7_stage4_L2" | |
| bottom: "heat_weight" | |
| top: "weight_stage4_L2" | |
| eltwise_param { | |
| operation: PROD | |
| } | |
| } | |
| layer { | |
| name: "loss_stage4_L2" | |
| type: "EuclideanLoss" | |
| bottom: "weight_stage4_L2" | |
| bottom: "label_heat" | |
| top: "loss_stage4_L2" | |
| loss_weight: 1 | |
| } | |
| 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: "weight_stage5_L1" | |
| type: "Eltwise" | |
| bottom: "Mconv7_stage5_L1" | |
| bottom: "vec_weight" | |
| top: "weight_stage5_L1" | |
| eltwise_param { | |
| operation: PROD | |
| } | |
| } | |
| layer { | |
| name: "loss_stage5_L1" | |
| type: "EuclideanLoss" | |
| bottom: "weight_stage5_L1" | |
| bottom: "label_vec" | |
| top: "loss_stage5_L1" | |
| loss_weight: 1 | |
| } | |
| layer { | |
| name: "weight_stage5_L2" | |
| type: "Eltwise" | |
| bottom: "Mconv7_stage5_L2" | |
| bottom: "heat_weight" | |
| top: "weight_stage5_L2" | |
| eltwise_param { | |
| operation: PROD | |
| } | |
| } | |
| layer { | |
| name: "loss_stage5_L2" | |
| type: "EuclideanLoss" | |
| bottom: "weight_stage5_L2" | |
| bottom: "label_heat" | |
| top: "loss_stage5_L2" | |
| loss_weight: 1 | |
| } | |
| 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: "weight_stage6_L1" | |
| type: "Eltwise" | |
| bottom: "Mconv7_stage6_L1" | |
| bottom: "vec_weight" | |
| top: "weight_stage6_L1" | |
| eltwise_param { | |
| operation: PROD | |
| } | |
| } | |
| layer { | |
| name: "loss_stage6_L1" | |
| type: "EuclideanLoss" | |
| bottom: "weight_stage6_L1" | |
| bottom: "label_vec" | |
| top: "loss_stage6_L1" | |
| loss_weight: 1 | |
| } | |
| layer { | |
| name: "weight_stage6_L2" | |
| type: "Eltwise" | |
| bottom: "Mconv7_stage6_L2" | |
| bottom: "heat_weight" | |
| top: "weight_stage6_L2" | |
| eltwise_param { | |
| operation: PROD | |
| } | |
| } | |
| layer { | |
| name: "loss_stage6_L2" | |
| type: "EuclideanLoss" | |
| bottom: "weight_stage6_L2" | |
| bottom: "label_heat" | |
| top: "loss_stage6_L2" | |
| loss_weight: 1 | |
| } |
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