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March 24, 2017 06:24
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| name: "CaffeNet" | |
| input: "target" | |
| input: "image" | |
| input: "bbox" | |
| #target | |
| input_dim: 1 | |
| input_dim: 3 | |
| input_dim: 227 | |
| input_dim: 227 | |
| #image | |
| input_dim: 1 | |
| input_dim: 3 | |
| input_dim: 227 | |
| input_dim: 227 | |
| #bbox | |
| input_dim: 1 | |
| input_dim: 4 | |
| input_dim: 1 | |
| input_dim: 1 | |
| layer { | |
| name: "conv1" | |
| type: "Convolution" | |
| bottom: "target" | |
| top: "conv1" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 96 | |
| kernel_size: 11 | |
| stride: 4 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu1" | |
| type: "ReLU" | |
| bottom: "conv1" | |
| top: "conv1" | |
| } | |
| layer { | |
| name: "pool1" | |
| type: "Pooling" | |
| bottom: "conv1" | |
| top: "pool1" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 3 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "norm1" | |
| type: "LRN" | |
| bottom: "pool1" | |
| top: "norm1" | |
| lrn_param { | |
| local_size: 5 | |
| alpha: 0.0001 | |
| beta: 0.75 | |
| } | |
| } | |
| layer { | |
| name: "conv2" | |
| type: "Convolution" | |
| bottom: "norm1" | |
| top: "conv2" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| pad: 2 | |
| kernel_size: 5 | |
| group: 2 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu2" | |
| type: "ReLU" | |
| bottom: "conv2" | |
| top: "conv2" | |
| } | |
| layer { | |
| name: "pool2" | |
| type: "Pooling" | |
| bottom: "conv2" | |
| top: "pool2" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 3 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "norm2" | |
| type: "LRN" | |
| bottom: "pool2" | |
| top: "norm2" | |
| lrn_param { | |
| local_size: 5 | |
| alpha: 0.0001 | |
| beta: 0.75 | |
| } | |
| } | |
| layer { | |
| name: "conv3" | |
| type: "Convolution" | |
| bottom: "norm2" | |
| top: "conv3" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 384 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu3" | |
| type: "ReLU" | |
| bottom: "conv3" | |
| top: "conv3" | |
| } | |
| layer { | |
| name: "conv4" | |
| type: "Convolution" | |
| bottom: "conv3" | |
| top: "conv4" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 384 | |
| pad: 1 | |
| kernel_size: 3 | |
| group: 2 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu4" | |
| type: "ReLU" | |
| bottom: "conv4" | |
| top: "conv4" | |
| } | |
| layer { | |
| name: "conv5" | |
| type: "Convolution" | |
| bottom: "conv4" | |
| top: "conv5" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| pad: 1 | |
| kernel_size: 3 | |
| group: 2 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu5" | |
| type: "ReLU" | |
| bottom: "conv5" | |
| top: "conv5" | |
| } | |
| layer { | |
| name: "pool5" | |
| type: "Pooling" | |
| bottom: "conv5" | |
| top: "pool5" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 3 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "conv1_p" | |
| type: "Convolution" | |
| bottom: "image" | |
| top: "conv1_p" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 96 | |
| kernel_size: 11 | |
| stride: 4 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu1_p" | |
| type: "ReLU" | |
| bottom: "conv1_p" | |
| top: "conv1_p" | |
| } | |
| layer { | |
| name: "pool1_p" | |
| type: "Pooling" | |
| bottom: "conv1_p" | |
| top: "pool1_p" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 3 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "norm1_p" | |
| type: "LRN" | |
| bottom: "pool1_p" | |
| top: "norm1_p" | |
| lrn_param { | |
| local_size: 5 | |
| alpha: 0.0001 | |
| beta: 0.75 | |
| } | |
| } | |
| layer { | |
| name: "conv2_p" | |
| type: "Convolution" | |
| bottom: "norm1_p" | |
| top: "conv2_p" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| pad: 2 | |
| kernel_size: 5 | |
| group: 2 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu2_p" | |
| type: "ReLU" | |
| bottom: "conv2_p" | |
| top: "conv2_p" | |
| } | |
| layer { | |
| name: "pool2_p" | |
| type: "Pooling" | |
| bottom: "conv2_p" | |
| top: "pool2_p" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 3 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "norm2_p" | |
| type: "LRN" | |
| bottom: "pool2_p" | |
| top: "norm2_p" | |
| lrn_param { | |
| local_size: 5 | |
| alpha: 0.0001 | |
| beta: 0.75 | |
| } | |
| } | |
| layer { | |
| name: "conv3_p" | |
| type: "Convolution" | |
| bottom: "norm2_p" | |
| top: "conv3_p" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 384 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu3_p" | |
| type: "ReLU" | |
| bottom: "conv3_p" | |
| top: "conv3_p" | |
| } | |
| layer { | |
| name: "conv4_p" | |
| type: "Convolution" | |
| bottom: "conv3_p" | |
| top: "conv4_p" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 384 | |
| pad: 1 | |
| kernel_size: 3 | |
| group: 2 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu4_p" | |
| type: "ReLU" | |
| bottom: "conv4_p" | |
| top: "conv4_p" | |
| } | |
| layer { | |
| name: "conv5_p" | |
| type: "Convolution" | |
| bottom: "conv4_p" | |
| top: "conv5_p" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| pad: 1 | |
| kernel_size: 3 | |
| group: 2 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu5_p" | |
| type: "ReLU" | |
| bottom: "conv5_p" | |
| top: "conv5_p" | |
| } | |
| layer { | |
| name: "pool5_p" | |
| type: "Pooling" | |
| bottom: "conv5_p" | |
| top: "pool5_p" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 3 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "concat" | |
| type: "Concat" | |
| bottom: "pool5" | |
| bottom: "pool5_p" | |
| top: "pool5_concat" | |
| concat_param { | |
| axis: 1 | |
| } | |
| } | |
| layer { | |
| name: "fc6-new" | |
| type: "InnerProduct" | |
| bottom: "pool5_concat" | |
| top: "fc6" | |
| param { | |
| lr_mult: 10 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 20 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 4096 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.005 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| } | |
| } | |
| 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-new" | |
| type: "InnerProduct" | |
| bottom: "fc6" | |
| top: "fc7" | |
| param { | |
| lr_mult: 10 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 20 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 4096 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.005 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu7" | |
| type: "ReLU" | |
| bottom: "fc7" | |
| top: "fc7" | |
| } | |
| layer { | |
| name: "drop7" | |
| type: "Dropout" | |
| bottom: "fc7" | |
| top: "fc7" | |
| dropout_param { | |
| dropout_ratio: 0.5 | |
| } | |
| } | |
| layer { | |
| name: "fc7-newb" | |
| type: "InnerProduct" | |
| bottom: "fc7" | |
| top: "fc7b" | |
| param { | |
| lr_mult: 10 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 20 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 4096 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.005 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu7b" | |
| type: "ReLU" | |
| bottom: "fc7b" | |
| top: "fc7b" | |
| } | |
| layer { | |
| name: "drop7b" | |
| type: "Dropout" | |
| bottom: "fc7b" | |
| top: "fc7b" | |
| dropout_param { | |
| dropout_ratio: 0.5 | |
| } | |
| } | |
| layer { | |
| name: "fc8-shapes" | |
| type: "InnerProduct" | |
| bottom: "fc7b" | |
| top: "fc8" | |
| param { | |
| lr_mult: 10 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 20 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 4 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "neg" | |
| bottom: "bbox" | |
| top: "bbox_neg" | |
| type: "Power" | |
| power_param { | |
| power: 1 | |
| scale: -1 | |
| shift: 0 | |
| } | |
| } | |
| layer { | |
| name: "flatten" | |
| type: "Flatten" | |
| bottom: "bbox_neg" | |
| top: "bbox_neg_flat" | |
| } | |
| layer { | |
| name: "subtract" | |
| type: "Eltwise" | |
| bottom: "fc8" | |
| bottom: "bbox_neg_flat" | |
| top: "out_diff" | |
| } | |
| layer { | |
| name: "abssum" | |
| type: "Reduction" | |
| bottom: "out_diff" | |
| top: "loss" | |
| loss_weight: 1 | |
| reduction_param { | |
| operation: 2 | |
| } | |
| } |
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