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November 13, 2016 13:48
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| name: "VGG_FACE_16_layers" | |
| layer { | |
| name: 'data' | |
| type: 'Python' | |
| top: 'data' | |
| top: 'labels' | |
| python_param { | |
| module: 'data_layer' | |
| layer: 'DataLayer' | |
| } | |
| } | |
| layer { | |
| name: "conv1_1" | |
| type: "Convolution" | |
| bottom: "data" | |
| top: "conv1_1" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type:"xavier" | |
| } | |
| } | |
| } | |
| 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: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type:"xavier" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu1_2" | |
| 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: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type:"xavier" | |
| } | |
| } | |
| } | |
| 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: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type:"xavier" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu2_2" | |
| 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: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type:"xavier" | |
| } | |
| } | |
| } | |
| 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: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type:"xavier" | |
| } | |
| } | |
| } | |
| 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: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type:"xavier" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu3_3" | |
| type: "ReLU" | |
| bottom: "conv3_3" | |
| top: "conv3_3" | |
| } | |
| layer { | |
| name: "pool3" | |
| type: "Pooling" | |
| bottom: "conv3_3" | |
| 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: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type:"xavier" | |
| } | |
| } | |
| } | |
| 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: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type:"xavier" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu4_2" | |
| type: "ReLU" | |
| bottom: "conv4_2" | |
| top: "conv4_2" | |
| } | |
| layer { | |
| name: "conv4_3" | |
| type: "Convolution" | |
| bottom: "conv4_2" | |
| top: "conv4_3" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type:"xavier" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu4_3" | |
| type: "ReLU" | |
| bottom: "conv4_3" | |
| top: "conv4_3" | |
| } | |
| layer { | |
| name: "pool4" | |
| type: "Pooling" | |
| bottom: "conv4_3" | |
| top: "pool4" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "conv5_1" | |
| type: "Convolution" | |
| bottom: "pool4" | |
| top: "conv5_1" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type:"xavier" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu5_1" | |
| type: "ReLU" | |
| bottom: "conv5_1" | |
| top: "conv5_1" | |
| } | |
| layer { | |
| name: "conv5_2" | |
| type: "Convolution" | |
| bottom: "conv5_1" | |
| top: "conv5_2" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type:"xavier" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu5_2" | |
| type: "ReLU" | |
| bottom: "conv5_2" | |
| top: "conv5_2" | |
| } | |
| layer { | |
| name: "conv5_3" | |
| type: "Convolution" | |
| bottom: "conv5_2" | |
| top: "conv5_3" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type:"xavier" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu5_3" | |
| type: "ReLU" | |
| bottom: "conv5_3" | |
| top: "conv5_3" | |
| } | |
| layer { | |
| name: "pool5" | |
| type: "Pooling" | |
| bottom: "conv5_3" | |
| top: "pool5" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "fc6" | |
| type: "InnerProduct" | |
| bottom: "pool5" | |
| top: "fc6" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 4096 | |
| weight_filler { | |
| type:"xavier" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu6" | |
| type: "ReLU" | |
| bottom: "fc6" | |
| top: "fc6" | |
| } | |
| layer { | |
| name: "drop6" | |
| type: "Dropout" | |
| bottom: "fc6" | |
| top: "fc6" | |
| dropout_param { | |
| dropout_ratio: 0.5 | |
| } | |
| } | |
| layer { | |
| name: "fc7" | |
| type: "InnerProduct" | |
| bottom: "fc6" | |
| top: "fc7" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 4096 | |
| weight_filler { | |
| type:"xavier" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu7" | |
| type: "ReLU" | |
| bottom: "fc7" | |
| top: "fc7" | |
| } | |
| layer { | |
| name: "drop7" | |
| type: "Dropout" | |
| bottom: "fc7" | |
| top: "fc7" | |
| dropout_param { | |
| dropout_ratio: 0.5 | |
| } | |
| } | |
| #======================softmax==================== | |
| # layer { | |
| # name: "fc9" | |
| # type: "InnerProduct" | |
| # bottom: "fc7" | |
| # top: "fc9" | |
| # param { | |
| # lr_mult: 1 | |
| # } | |
| # param { | |
| # lr_mult: 2 | |
| # } | |
| # inner_product_param { | |
| # num_output: 530 | |
| # weight_filler { | |
| # type: "xavier" | |
| # } | |
| # } | |
| # } | |
| # layer { | |
| # name: "accuracy" | |
| # type: "Accuracy" | |
| # bottom: "fc9" | |
| # bottom: "labels" | |
| # top: "accuracy" | |
| # include: { phase: TEST } | |
| # } | |
| # layer { | |
| # name: "loss_cls" | |
| # type: "SoftmaxWithLoss" | |
| # bottom: "fc9" | |
| # bottom: "labels" | |
| # top: "loss_cls" | |
| # loss_weight: 1 | |
| # } | |
| #====================tripletloss================== | |
| layer { | |
| name: "norm2" | |
| type: "Python" | |
| bottom: "fc7" | |
| top: "norm2" | |
| python_param { | |
| module: "l2norm_layer" | |
| layer: "L2NormLayer" | |
| } | |
| } | |
| layer { | |
| name: "fc9_1" | |
| type: "InnerProduct" | |
| bottom: "norm2" | |
| top: "fc9_1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 128 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler{ | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "tripletsample" | |
| type: "Python" | |
| bottom: "fc9_1" | |
| bottom: "labels" | |
| top: "anchor" | |
| top: "positive" | |
| top: "negative" | |
| python_param { | |
| module: "tripletsample_layer" | |
| layer: "TripletSampleLayer" | |
| } | |
| } | |
| layer { | |
| name: "tripletloss" | |
| type: "Python" | |
| bottom: "anchor" | |
| bottom: "positive" | |
| bottom: "negative" | |
| top: "loss" | |
| python_param { | |
| module: "tripletloss_layer" | |
| layer: "TripletLayer" | |
| param_str: "'margin': 0.2" | |
| } | |
| loss_weight: 1 | |
| } |
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