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March 23, 2018 05:53
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| layer { | |
| name: "first_img" | |
| type: "Input" | |
| top: "first_img" | |
| top: "second_img" | |
| top: "first_label" | |
| top: "second_label" | |
| input_param { | |
| shape { | |
| dim: 1 | |
| dim: 3 | |
| dim: 224 | |
| dim: 224 | |
| } | |
| shape { | |
| dim: 1 | |
| dim: 3 | |
| dim: 500 | |
| dim: 500 | |
| } | |
| shape { | |
| dim: 1 | |
| dim: 1 | |
| dim: 224 | |
| dim: 224 | |
| } | |
| shape { | |
| dim: 1 | |
| dim: 1 | |
| dim: 500 | |
| dim: 500 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "tiled_first_label" | |
| type: "Tile" | |
| bottom: "first_label" | |
| top: "tiled_first_label" | |
| tile_param { | |
| axis: 1 | |
| tiles: 3 | |
| } | |
| } | |
| layer { | |
| name: "first_input" | |
| type: "Scale" | |
| bottom: "first_img" | |
| bottom: "tiled_first_label" | |
| top: "first_input" | |
| scale_param { | |
| axis: 0 | |
| } | |
| } | |
| layer { | |
| name: "conv1_1f" | |
| type: "Convolution" | |
| bottom: "first_input" | |
| top: "conv1_1f" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 0.2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| pad: 1 | |
| kernel_size: 3 | |
| engine: CUDNN | |
| } | |
| } | |
| layer { | |
| name: "relu1_1f" | |
| type: "ReLU" | |
| bottom: "conv1_1f" | |
| top: "conv1_1f" | |
| } | |
| layer { | |
| name: "conv1_2f" | |
| type: "Convolution" | |
| bottom: "conv1_1f" | |
| top: "conv1_2f" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 0.2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| pad: 1 | |
| kernel_size: 3 | |
| engine: CUDNN | |
| } | |
| } | |
| layer { | |
| name: "relu1_2f" | |
| type: "ReLU" | |
| bottom: "conv1_2f" | |
| top: "conv1_2f" | |
| } | |
| layer { | |
| name: "pool1f" | |
| type: "Pooling" | |
| bottom: "conv1_2f" | |
| top: "pool1f" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "conv2_1f" | |
| type: "Convolution" | |
| bottom: "pool1f" | |
| top: "conv2_1f" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 0.2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 1 | |
| kernel_size: 3 | |
| engine: CUDNN | |
| } | |
| } | |
| layer { | |
| name: "relu2_1f" | |
| type: "ReLU" | |
| bottom: "conv2_1f" | |
| top: "conv2_1f" | |
| } | |
| layer { | |
| name: "conv2_2f" | |
| type: "Convolution" | |
| bottom: "conv2_1f" | |
| top: "conv2_2f" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 0.2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 1 | |
| kernel_size: 3 | |
| engine: CUDNN | |
| } | |
| } | |
| layer { | |
| name: "relu2_2f" | |
| type: "ReLU" | |
| bottom: "conv2_2f" | |
| top: "conv2_2f" | |
| } | |
| layer { | |
| name: "pool2f" | |
| type: "Pooling" | |
| bottom: "conv2_2f" | |
| top: "pool2f" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "conv3_1f" | |
| type: "Convolution" | |
| bottom: "pool2f" | |
| top: "conv3_1f" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 0.2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| pad: 1 | |
| kernel_size: 3 | |
| engine: CUDNN | |
| } | |
| } | |
| layer { | |
| name: "relu3_1f" | |
| type: "ReLU" | |
| bottom: "conv3_1f" | |
| top: "conv3_1f" | |
| } | |
| layer { | |
| name: "conv3_2f" | |
| type: "Convolution" | |
| bottom: "conv3_1f" | |
| top: "conv3_2f" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 0.2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| pad: 1 | |
| kernel_size: 3 | |
| engine: CUDNN | |
| } | |
| } | |
| layer { | |
| name: "relu3_2f" | |
| type: "ReLU" | |
| bottom: "conv3_2f" | |
| top: "conv3_2f" | |
| } | |
| layer { | |
| name: "conv3_3f" | |
| type: "Convolution" | |
| bottom: "conv3_2f" | |
| top: "conv3_3f" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 0.2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| pad: 1 | |
| kernel_size: 3 | |
| engine: CUDNN | |
| } | |
| } | |
| layer { | |
| name: "relu3_3f" | |
| type: "ReLU" | |
| bottom: "conv3_3f" | |
| top: "conv3_3f" | |
| } | |
| layer { | |
| name: "pool3f" | |
| type: "Pooling" | |
| bottom: "conv3_3f" | |
| top: "pool3f" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "conv4_1f" | |
| type: "Convolution" | |
| bottom: "pool3f" | |
| top: "conv4_1f" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 0.2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| engine: CUDNN | |
| } | |
| } | |
| layer { | |
| name: "relu4_1f" | |
| type: "ReLU" | |
| bottom: "conv4_1f" | |
| top: "conv4_1f" | |
| } | |
| layer { | |
| name: "conv4_2f" | |
| type: "Convolution" | |
| bottom: "conv4_1f" | |
| top: "conv4_2f" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 0.2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| engine: CUDNN | |
| } | |
| } | |
| layer { | |
| name: "relu4_2f" | |
| type: "ReLU" | |
| bottom: "conv4_2f" | |
| top: "conv4_2f" | |
| } | |
| layer { | |
| name: "conv4_3f" | |
| type: "Convolution" | |
| bottom: "conv4_2f" | |
| top: "conv4_3f" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 0.2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| engine: CUDNN | |
| } | |
| } | |
| layer { | |
| name: "relu4_3f" | |
| type: "ReLU" | |
| bottom: "conv4_3f" | |
| top: "conv4_3f" | |
| } | |
| layer { | |
| name: "pool4f" | |
| type: "Pooling" | |
| bottom: "conv4_3f" | |
| top: "pool4f" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "conv5_1f" | |
| type: "Convolution" | |
| bottom: "pool4f" | |
| top: "conv5_1f" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 0.2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| engine: CUDNN | |
| } | |
| } | |
| layer { | |
| name: "relu5_1f" | |
| type: "ReLU" | |
| bottom: "conv5_1f" | |
| top: "conv5_1f" | |
| } | |
| layer { | |
| name: "conv5_2f" | |
| type: "Convolution" | |
| bottom: "conv5_1f" | |
| top: "conv5_2f" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 0.2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| engine: CUDNN | |
| } | |
| } | |
| layer { | |
| name: "relu5_2f" | |
| type: "ReLU" | |
| bottom: "conv5_2f" | |
| top: "conv5_2f" | |
| } | |
| layer { | |
| name: "conv5_3f" | |
| type: "Convolution" | |
| bottom: "conv5_2f" | |
| top: "conv5_3f" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 0.2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| engine: CUDNN | |
| } | |
| } | |
| layer { | |
| name: "relu5_3f" | |
| type: "ReLU" | |
| bottom: "conv5_3f" | |
| top: "conv5_3f" | |
| } | |
| layer { | |
| name: "pool5f" | |
| type: "Pooling" | |
| bottom: "conv5_3f" | |
| top: "pool5f" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "conv1_1s" | |
| type: "Convolution" | |
| bottom: "second_img" | |
| top: "conv1_1s" | |
| param { | |
| name: "conv1_1_w" | |
| lr_mult: 1.0 | |
| decay_mult: 1 | |
| } | |
| param { | |
| name: "conv1_1_b" | |
| lr_mult: 2.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| pad: 121 | |
| kernel_size: 3 | |
| engine: CUDNN | |
| } | |
| } | |
| layer { | |
| name: "relu1_1s" | |
| type: "ReLU" | |
| bottom: "conv1_1s" | |
| top: "conv1_1s" | |
| } | |
| layer { | |
| name: "conv1_2s" | |
| type: "Convolution" | |
| bottom: "conv1_1s" | |
| top: "conv1_2s" | |
| param { | |
| name: "conv1_2_w" | |
| lr_mult: 1.0 | |
| decay_mult: 1 | |
| } | |
| param { | |
| name: "conv1_2_b" | |
| lr_mult: 2.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| pad: 1 | |
| kernel_size: 3 | |
| engine: CUDNN | |
| } | |
| } | |
| layer { | |
| name: "relu1_2s" | |
| type: "ReLU" | |
| bottom: "conv1_2s" | |
| top: "conv1_2s" | |
| } | |
| layer { | |
| name: "pool1s" | |
| type: "Pooling" | |
| bottom: "conv1_2s" | |
| top: "pool1s" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "conv2_1s" | |
| type: "Convolution" | |
| bottom: "pool1s" | |
| top: "conv2_1s" | |
| param { | |
| name: "conv2_1_w" | |
| lr_mult: 1.0 | |
| decay_mult: 1 | |
| } | |
| param { | |
| name: "conv2_1_b" | |
| lr_mult: 2.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 1 | |
| kernel_size: 3 | |
| engine: CUDNN | |
| } | |
| } | |
| layer { | |
| name: "relu2_1s" | |
| type: "ReLU" | |
| bottom: "conv2_1s" | |
| top: "conv2_1s" | |
| } | |
| layer { | |
| name: "conv2_2s" | |
| type: "Convolution" | |
| bottom: "conv2_1s" | |
| top: "conv2_2s" | |
| param { | |
| name: "conv2_2_w" | |
| lr_mult: 1.0 | |
| decay_mult: 1 | |
| } | |
| param { | |
| name: "conv2_2_b" | |
| lr_mult: 2.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 1 | |
| kernel_size: 3 | |
| engine: CUDNN | |
| } | |
| } | |
| layer { | |
| name: "relu2_2s" | |
| type: "ReLU" | |
| bottom: "conv2_2s" | |
| top: "conv2_2s" | |
| } | |
| layer { | |
| name: "pool2s" | |
| type: "Pooling" | |
| bottom: "conv2_2s" | |
| top: "pool2s" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "conv3_1s" | |
| type: "Convolution" | |
| bottom: "pool2s" | |
| top: "conv3_1s" | |
| param { | |
| name: "conv3_1_w" | |
| lr_mult: 1.0 | |
| decay_mult: 1 | |
| } | |
| param { | |
| name: "conv3_1_b" | |
| lr_mult: 2.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| pad: 1 | |
| kernel_size: 3 | |
| engine: CUDNN | |
| } | |
| } | |
| layer { | |
| name: "relu3_1s" | |
| type: "ReLU" | |
| bottom: "conv3_1s" | |
| top: "conv3_1s" | |
| } | |
| layer { | |
| name: "conv3_2s" | |
| type: "Convolution" | |
| bottom: "conv3_1s" | |
| top: "conv3_2s" | |
| param { | |
| name: "conv3_2_w" | |
| lr_mult: 1.0 | |
| decay_mult: 1 | |
| } | |
| param { | |
| name: "conv3_2_b" | |
| lr_mult: 2.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| pad: 1 | |
| kernel_size: 3 | |
| engine: CUDNN | |
| } | |
| } | |
| layer { | |
| name: "relu3_2s" | |
| type: "ReLU" | |
| bottom: "conv3_2s" | |
| top: "conv3_2s" | |
| } | |
| layer { | |
| name: "conv3_3s" | |
| type: "Convolution" | |
| bottom: "conv3_2s" | |
| top: "conv3_3s" | |
| param { | |
| name: "conv3_3_w" | |
| lr_mult: 1.0 | |
| decay_mult: 1 | |
| } | |
| param { | |
| name: "conv3_3_b" | |
| lr_mult: 2.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| pad: 1 | |
| kernel_size: 3 | |
| engine: CUDNN | |
| } | |
| } | |
| layer { | |
| name: "relu3_3s" | |
| type: "ReLU" | |
| bottom: "conv3_3s" | |
| top: "conv3_3s" | |
| } | |
| layer { | |
| name: "pool3s" | |
| type: "Pooling" | |
| bottom: "conv3_3s" | |
| top: "pool3s" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "conv4_1s" | |
| type: "Convolution" | |
| bottom: "pool3s" | |
| top: "conv4_1s" | |
| param { | |
| name: "conv4_1_w" | |
| lr_mult: 1.0 | |
| decay_mult: 1 | |
| } | |
| param { | |
| name: "conv4_1_b" | |
| lr_mult: 2.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| engine: CUDNN | |
| } | |
| } | |
| layer { | |
| name: "relu4_1s" | |
| type: "ReLU" | |
| bottom: "conv4_1s" | |
| top: "conv4_1s" | |
| } | |
| layer { | |
| name: "conv4_2s" | |
| type: "Convolution" | |
| bottom: "conv4_1s" | |
| top: "conv4_2s" | |
| param { | |
| name: "conv4_2_w" | |
| lr_mult: 1.0 | |
| decay_mult: 1 | |
| } | |
| param { | |
| name: "conv4_2_b" | |
| lr_mult: 2.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| engine: CUDNN | |
| } | |
| } | |
| layer { | |
| name: "relu4_2s" | |
| type: "ReLU" | |
| bottom: "conv4_2s" | |
| top: "conv4_2s" | |
| } | |
| layer { | |
| name: "conv4_3s" | |
| type: "Convolution" | |
| bottom: "conv4_2s" | |
| top: "conv4_3s" | |
| param { | |
| name: "conv4_3_w" | |
| lr_mult: 1.0 | |
| decay_mult: 1 | |
| } | |
| param { | |
| name: "conv4_3_b" | |
| lr_mult: 2.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| engine: CUDNN | |
| } | |
| } | |
| layer { | |
| name: "relu4_3s" | |
| type: "ReLU" | |
| bottom: "conv4_3s" | |
| top: "conv4_3s" | |
| } | |
| layer { | |
| name: "pool4s" | |
| type: "Pooling" | |
| bottom: "conv4_3s" | |
| top: "pool4s" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "conv5_1s" | |
| type: "Convolution" | |
| bottom: "pool4s" | |
| top: "conv5_1s" | |
| param { | |
| name: "conv5_1_w" | |
| lr_mult: 1.0 | |
| decay_mult: 1 | |
| } | |
| param { | |
| name: "conv5_1_b" | |
| lr_mult: 2.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| engine: CUDNN | |
| } | |
| } | |
| layer { | |
| name: "relu5_1s" | |
| type: "ReLU" | |
| bottom: "conv5_1s" | |
| top: "conv5_1s" | |
| } | |
| layer { | |
| name: "conv5_2s" | |
| type: "Convolution" | |
| bottom: "conv5_1s" | |
| top: "conv5_2s" | |
| param { | |
| name: "conv5_2_w" | |
| lr_mult: 1.0 | |
| decay_mult: 1 | |
| } | |
| param { | |
| name: "conv5_2_b" | |
| lr_mult: 2.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| engine: CUDNN | |
| } | |
| } | |
| layer { | |
| name: "relu5_2s" | |
| type: "ReLU" | |
| bottom: "conv5_2s" | |
| top: "conv5_2s" | |
| } | |
| layer { | |
| name: "conv5_3s" | |
| type: "Convolution" | |
| bottom: "conv5_2s" | |
| top: "conv5_3s" | |
| param { | |
| name: "conv5_3_w" | |
| lr_mult: 1.0 | |
| decay_mult: 1 | |
| } | |
| param { | |
| name: "conv5_3_b" | |
| lr_mult: 2.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| engine: CUDNN | |
| } | |
| } | |
| layer { | |
| name: "relu5_3s" | |
| type: "ReLU" | |
| bottom: "conv5_3s" | |
| top: "conv5_3s" | |
| } | |
| layer { | |
| name: "pool5s" | |
| type: "Pooling" | |
| bottom: "conv5_3s" | |
| top: "pool5s" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "fc6s" | |
| type: "Convolution" | |
| bottom: "pool5s" | |
| top: "fc6s" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 4096 | |
| kernel_size: 7 | |
| engine: CUDNN | |
| } | |
| } | |
| layer { | |
| name: "relu6s" | |
| type: "ReLU" | |
| bottom: "fc6s" | |
| top: "fc6s" | |
| } | |
| layer { | |
| name: "fc6f" | |
| type: "InnerProduct" | |
| bottom: "pool5f" | |
| top: "fc6f" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 0.2 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 4096 | |
| } | |
| } | |
| layer { | |
| name: "relu6f" | |
| type: "ReLU" | |
| bottom: "fc6f" | |
| top: "fc6f" | |
| } | |
| layer { | |
| name: "fc7f" | |
| type: "InnerProduct" | |
| bottom: "fc6f" | |
| top: "fc7f" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 0.2 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 4096 | |
| } | |
| } | |
| layer { | |
| name: "relu7f" | |
| type: "ReLU" | |
| bottom: "fc7f" | |
| top: "fc7f" | |
| } | |
| layer { | |
| name: "fc7s" | |
| type: "Convolution" | |
| bottom: "fc6s" | |
| top: "fc7s" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 4096 | |
| kernel_size: 1 | |
| engine: CUDNN | |
| } | |
| } | |
| layer { | |
| name: "relu7s" | |
| type: "ReLU" | |
| bottom: "fc7s" | |
| top: "fc7s" | |
| } | |
| layer { | |
| name: "fc8f" | |
| type: "InnerProduct" | |
| bottom: "fc7f" | |
| top: "fc8f" | |
| param { | |
| lr_mult: 0.1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 0.2 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 1000 | |
| } | |
| } | |
| layer { | |
| name: "rw1s" | |
| type: "InnerProduct" | |
| bottom: "fc8f" | |
| top: "rw1s" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 4096 | |
| } | |
| } | |
| layer { | |
| name: "rb1s" | |
| type: "InnerProduct" | |
| bottom: "fc8f" | |
| top: "rb1s" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 1 | |
| } | |
| } | |
| layer { | |
| name: "w1s" | |
| type: "Reshape" | |
| bottom: "rw1s" | |
| top: "w1s" | |
| reshape_param { | |
| shape { | |
| dim: -1 | |
| dim: 1 | |
| dim: 4096 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "b1s" | |
| type: "Reshape" | |
| bottom: "rb1s" | |
| top: "b1s" | |
| reshape_param { | |
| shape { | |
| dim: -1 | |
| dim: 1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "Xs" | |
| type: "Reshape" | |
| bottom: "fc7s" | |
| top: "Xs" | |
| reshape_param { | |
| shape { | |
| dim: 0 | |
| dim: 0 | |
| dim: -1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "Xs_tiled" | |
| type: "Tile" | |
| bottom: "Xs" | |
| top: "Xs_tiled" | |
| tile_param { | |
| axis: 0 | |
| tiles: 1 | |
| } | |
| } | |
| layer { | |
| name: "w1sXs" | |
| type: "MatMult" | |
| bottom: "w1s" | |
| bottom: "Xs_tiled" | |
| top: "w1sXs" | |
| } | |
| layer { | |
| name: "rpred" | |
| type: "Bias" | |
| bottom: "w1sXs" | |
| bottom: "b1s" | |
| top: "rpred" | |
| bias_param { | |
| axis: 0 | |
| } | |
| } | |
| layer { | |
| name: "pred" | |
| type: "ReshapeLike" | |
| bottom: "rpred" | |
| bottom: "fc7s" | |
| top: "pred" | |
| reshape_param { | |
| shape { | |
| dim: -1 | |
| dim: 1 | |
| dim: 0 | |
| dim: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "uppred_offset" | |
| type: "Deconvolution" | |
| bottom: "pred" | |
| top: "uppred_offset" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 1 | |
| bias_term: false | |
| kernel_size: 64 | |
| stride: 32 | |
| } | |
| } | |
| layer { | |
| name: "pre_score" | |
| type: "Crop" | |
| bottom: "uppred_offset" | |
| bottom: "second_img" | |
| top: "pre_score" | |
| crop_param { | |
| axis: 2 | |
| offset: 40 | |
| } | |
| } | |
| layer { | |
| name: "rpre_score" | |
| type: "Reshape" | |
| bottom: "pre_score" | |
| top: "rpre_score" | |
| reshape_param { | |
| shape { | |
| dim: 1 | |
| dim: 1 | |
| dim: 1 | |
| dim: -1 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "rpre_score2" | |
| type: "Pooling" | |
| bottom: "rpre_score" | |
| top: "rpre_score2" | |
| pooling_param { | |
| pool: MAX | |
| kernel_h: 1 | |
| kernel_w: 1 | |
| } | |
| } | |
| layer { | |
| name: "pre_score2" | |
| type: "ReshapeLike" | |
| bottom: "rpre_score2" | |
| bottom: "second_img" | |
| top: "pre_score2" | |
| reshape_param { | |
| shape { | |
| dim: -1 | |
| dim: 1 | |
| dim: 0 | |
| dim: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "score" | |
| type: "Sigmoid" | |
| bottom: "pre_score2" | |
| top: "score" | |
| } | |
| layer { | |
| name: "loss" | |
| type: "SigmoidCrossEntropyLoss" | |
| bottom: "pre_score2" | |
| bottom: "second_label" | |
| top: "loss" | |
| } |
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