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Trainining and validation files for the RACNN Network.
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| name: "RA_CNN" | |
| layer { | |
| name: "data" | |
| type: "Data" | |
| top: "data" | |
| top: "label" | |
| include { | |
| phase: TRAIN | |
| } | |
| transform_param { | |
| mirror: true | |
| crop_size: 448 | |
| mean_value: 128 | |
| mean_value: 128 | |
| mean_value: 128 | |
| } | |
| data_param { | |
| source: "/media/data/lmdb/birds" | |
| batch_size: 2 | |
| backend: LMDB | |
| } | |
| } | |
| layer { | |
| name: "data" | |
| type: "Data" | |
| top: "data" | |
| top: "label" | |
| include { | |
| phase: TEST | |
| } | |
| transform_param { | |
| mirror: true | |
| crop_size: 448 | |
| mean_value: 128 | |
| mean_value: 128 | |
| mean_value: 128 | |
| } | |
| data_param { | |
| source: "/media/data/lmdb/birds" | |
| batch_size: 2 | |
| backend: LMDB | |
| } | |
| } | |
| #######Scale1####### | |
| layer { | |
| bottom: "data" | |
| top: "conv1_1" | |
| name: "conv1_1" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv1_1" | |
| top: "conv1_1" | |
| name: "relu1_1" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv1_1" | |
| top: "conv1_2" | |
| name: "conv1_2" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv1_2" | |
| top: "conv1_2" | |
| name: "relu1_2" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv1_2" | |
| top: "pool1" | |
| name: "pool1" | |
| type: "Pooling" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| bottom: "pool1" | |
| top: "conv2_1" | |
| name: "conv2_1" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv2_1" | |
| top: "conv2_1" | |
| name: "relu2_1" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv2_1" | |
| top: "conv2_2" | |
| name: "conv2_2" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv2_2" | |
| top: "conv2_2" | |
| name: "relu2_2" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv2_2" | |
| top: "pool2" | |
| name: "pool2" | |
| type: "Pooling" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| bottom: "pool2" | |
| top: "conv3_1" | |
| name: "conv3_1" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv3_1" | |
| top: "conv3_1" | |
| name: "relu3_1" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv3_1" | |
| top: "conv3_2" | |
| name: "conv3_2" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv3_2" | |
| top: "conv3_2" | |
| name: "relu3_2" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv3_2" | |
| top: "conv3_3" | |
| name: "conv3_3" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv3_3" | |
| top: "conv3_3" | |
| name: "relu3_3" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv3_3" | |
| top: "conv3_4" | |
| name: "conv3_4" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv3_4" | |
| top: "conv3_4" | |
| name: "relu3_4" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv3_4" | |
| top: "pool3" | |
| name: "pool3" | |
| type: "Pooling" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| bottom: "pool3" | |
| top: "conv4_1" | |
| name: "conv4_1" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv4_1" | |
| top: "conv4_1" | |
| name: "relu4_1" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv4_1" | |
| top: "conv4_2" | |
| name: "conv4_2" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv4_2" | |
| top: "conv4_2" | |
| name: "relu4_2" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv4_2" | |
| top: "conv4_3" | |
| name: "conv4_3" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv4_3" | |
| top: "conv4_3" | |
| name: "relu4_3" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv4_3" | |
| top: "conv4_4" | |
| name: "conv4_4" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv4_4" | |
| top: "conv4_4" | |
| name: "relu4_4" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv4_4" | |
| top: "pool4" | |
| name: "pool4" | |
| type: "Pooling" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| bottom: "pool4" | |
| top: "conv5_1" | |
| name: "conv5_1" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv5_1" | |
| top: "conv5_1" | |
| name: "relu5_1" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv5_1" | |
| top: "conv5_2" | |
| name: "conv5_2" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv5_2" | |
| top: "conv5_2" | |
| name: "relu5_2" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv5_2" | |
| top: "conv5_3" | |
| name: "conv5_3" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv5_3" | |
| top: "conv5_3" | |
| name: "relu5_3" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv5_3" | |
| top: "conv5_4" | |
| name: "conv5_4" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv5_4" | |
| top: "conv5_4" | |
| name: "relu5_4" | |
| type: "ReLU" | |
| } | |
| layer { | |
| name: "pool5" | |
| type: "Pooling" | |
| bottom: "conv5_4" | |
| top: "pool5" | |
| pooling_param { | |
| pool: AVE | |
| kernel_size: 28 | |
| stride: 28 | |
| } | |
| } | |
| #######APN1####### | |
| layer { | |
| bottom: "conv5_4" | |
| top: "anp_pool" | |
| name: "anp_pool" | |
| type: "Pooling" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "get_abc1" | |
| type: "InnerProduct" | |
| bottom: "anp_pool" | |
| top: "get_abc1" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 1024 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.001 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "tanh" | |
| bottom: "get_abc1" | |
| top: "tanh" | |
| type: "TanH" | |
| } | |
| layer { | |
| name: "get_abc2" | |
| type: "InnerProduct" | |
| bottom: "tanh" | |
| top: "get_abc2" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.001 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "sigmoid" | |
| bottom: "get_abc2" | |
| top: "sig_abc" | |
| type: "Sigmoid" | |
| } | |
| #######Scale2####### | |
| layer { | |
| name: "get448" | |
| bottom: "sig_abc" | |
| top: "get448" | |
| type: "Power" | |
| power_param { | |
| power: 1 | |
| scale: 448 | |
| shift: 0 | |
| } | |
| } | |
| layer{ | |
| name: "atten_crop" | |
| bottom: "data" | |
| bottom: "get448" | |
| top: "scale2_data" | |
| type: "AttentionCrop" | |
| } | |
| layer { | |
| bottom: "scale2_data" | |
| top: "conv1_1_A" | |
| name: "conv1_1_A" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv1_1_A" | |
| top: "conv1_1_A" | |
| name: "relu1_1_A" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv1_1_A" | |
| top: "conv1_2_A" | |
| name: "conv1_2_A" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv1_2_A" | |
| top: "conv1_2_A" | |
| name: "relu1_2_A" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv1_2_A" | |
| top: "pool1_A" | |
| name: "pool1_A" | |
| type: "Pooling" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| bottom: "pool1_A" | |
| top: "conv2_1_A" | |
| name: "conv2_1_A" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv2_1_A" | |
| top: "conv2_1_A" | |
| name: "relu2_1_A" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv2_1_A" | |
| top: "conv2_2_A" | |
| name: "conv2_2_A" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv2_2_A" | |
| top: "conv2_2_A" | |
| name: "relu2_2_A" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv2_2_A" | |
| top: "pool2_A" | |
| name: "pool2_A" | |
| type: "Pooling" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| bottom: "pool2_A" | |
| top: "conv3_1_A" | |
| name: "conv3_1_A" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv3_1_A" | |
| top: "conv3_1_A" | |
| name: "relu3_1_A" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv3_1_A" | |
| top: "conv3_2_A" | |
| name: "conv3_2_A" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv3_2_A" | |
| top: "conv3_2_A" | |
| name: "relu3_2_A" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv3_2_A" | |
| top: "conv3_3_A" | |
| name: "conv3_3_A" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv3_3_A" | |
| top: "conv3_3_A" | |
| name: "relu3_3_A" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv3_3_A" | |
| top: "conv3_4_A" | |
| name: "conv3_4_A" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv3_4_A" | |
| top: "conv3_4_A" | |
| name: "relu3_4_A" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv3_4_A" | |
| top: "pool3_A" | |
| name: "pool3_A" | |
| type: "Pooling" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| bottom: "pool3_A" | |
| top: "conv4_1_A" | |
| name: "conv4_1_A" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv4_1_A" | |
| top: "conv4_1_A" | |
| name: "relu4_1_A" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv4_1_A" | |
| top: "conv4_2_A" | |
| name: "conv4_2_A" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv4_2_A" | |
| top: "conv4_2_A" | |
| name: "relu4_2_A" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv4_2_A" | |
| top: "conv4_3_A" | |
| name: "conv4_3_A" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv4_3_A" | |
| top: "conv4_3_A" | |
| name: "relu4_3_A" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv4_3_A" | |
| top: "conv4_4_A" | |
| name: "conv4_4_A" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv4_4_A" | |
| top: "conv4_4_A" | |
| name: "relu4_4_A" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv4_4_A" | |
| top: "pool4_A" | |
| name: "pool4_A" | |
| type: "Pooling" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| bottom: "pool4_A" | |
| top: "conv5_1_A" | |
| name: "conv5_1_A" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv5_1_A" | |
| top: "conv5_1_A" | |
| name: "relu5_1_A" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv5_1_A" | |
| top: "conv5_2_A" | |
| name: "conv5_2_A" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv5_2_A" | |
| top: "conv5_2_A" | |
| name: "relu5_2_A" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv5_2_A" | |
| top: "conv5_3_A" | |
| name: "conv5_3_A" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv5_3_A" | |
| top: "conv5_3_A" | |
| name: "relu5_3_A" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv5_3_A" | |
| top: "conv5_4_A" | |
| name: "conv5_4_A" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv5_4_A" | |
| top: "conv5_4_A" | |
| name: "relu5_4_A" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv5_4_A" | |
| top: "pool5_A" | |
| name: "pool5_A" | |
| type: "Pooling" | |
| pooling_param { | |
| pool: AVE | |
| kernel_size: 14 | |
| stride: 14 | |
| } | |
| } | |
| #######APN2####### | |
| layer { | |
| name: "get_abc1_A" | |
| type: "InnerProduct" | |
| bottom: "conv5_4_A" | |
| top: "get_abc1_A" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 1024 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.001 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "tanh_A" | |
| bottom: "get_abc1_A" | |
| top: "tanh_A" | |
| type: "TanH" | |
| } | |
| layer { | |
| name: "get_abc2_A" | |
| type: "InnerProduct" | |
| bottom: "tanh_A" | |
| top: "get_abc2_A" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.001 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "sigmoid_A" | |
| bottom: "get_abc2_A" | |
| top: "sig_abc_A" | |
| type: "Sigmoid" | |
| } | |
| #######Scale3####### | |
| layer { | |
| name: "get224" | |
| bottom: "sig_abc_A" | |
| top: "get224" | |
| type: "Power" | |
| power_param { | |
| power: 1 | |
| scale: 224 | |
| shift: 0 | |
| } | |
| } | |
| layer{ | |
| name: "atten_crop_A" | |
| bottom: "scale2_data" | |
| bottom: "get224" | |
| top: "scale3_data" | |
| type: "AttentionCrop" | |
| } | |
| layer { | |
| bottom: "scale3_data" | |
| top: "conv1_1_A_A" | |
| name: "conv1_1_A_A" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv1_1_A_A" | |
| top: "conv1_1_A_A" | |
| name: "relu1_1_A_A" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv1_1_A_A" | |
| top: "conv1_2_A_A" | |
| name: "conv1_2_A_A" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv1_2_A_A" | |
| top: "conv1_2_A_A" | |
| name: "relu1_2_A_A" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv1_2_A_A" | |
| top: "pool1_A_A" | |
| name: "pool1_A_A" | |
| type: "Pooling" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| bottom: "pool1_A_A" | |
| top: "conv2_1_A_A" | |
| name: "conv2_1_A_A" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv2_1_A_A" | |
| top: "conv2_1_A_A" | |
| name: "relu2_1_A_A" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv2_1_A_A" | |
| top: "conv2_2_A_A" | |
| name: "conv2_2_A_A" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv2_2_A_A" | |
| top: "conv2_2_A_A" | |
| name: "relu2_2_A_A" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv2_2_A_A" | |
| top: "pool2_A_A" | |
| name: "pool2_A_A" | |
| type: "Pooling" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| bottom: "pool2_A_A" | |
| top: "conv3_1_A_A" | |
| name: "conv3_1_A_A" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv3_1_A_A" | |
| top: "conv3_1_A_A" | |
| name: "relu3_1_A_A" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv3_1_A_A" | |
| top: "conv3_2_A_A" | |
| name: "conv3_2_A_A" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv3_2_A_A" | |
| top: "conv3_2_A_A" | |
| name: "relu3_2_A_A" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv3_2_A_A" | |
| top: "conv3_3_A_A" | |
| name: "conv3_3_A_A" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv3_3_A_A" | |
| top: "conv3_3_A_A" | |
| name: "relu3_3_A_A" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv3_3_A_A" | |
| top: "conv3_4_A_A" | |
| name: "conv3_4_A_A" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv3_4_A_A" | |
| top: "conv3_4_A_A" | |
| name: "relu3_4_A_A" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv3_4_A_A" | |
| top: "pool3_A_A" | |
| name: "pool3_A_A" | |
| type: "Pooling" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| bottom: "pool3_A_A" | |
| top: "conv4_1_A_A" | |
| name: "conv4_1_A_A" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv4_1_A_A" | |
| top: "conv4_1_A_A" | |
| name: "relu4_1_A_A" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv4_1_A_A" | |
| top: "conv4_2_A_A" | |
| name: "conv4_2_A_A" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv4_2_A_A" | |
| top: "conv4_2_A_A" | |
| name: "relu4_2_A_A" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv4_2_A_A" | |
| top: "conv4_3_A_A" | |
| name: "conv4_3_A_A" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv4_3_A_A" | |
| top: "conv4_3_A_A" | |
| name: "relu4_3_A_A" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv4_3_A_A" | |
| top: "conv4_4_A_A" | |
| name: "conv4_4_A_A" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv4_4_A_A" | |
| top: "conv4_4_A_A" | |
| name: "relu4_4_A_A" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv4_4_A_A" | |
| top: "pool4_A_A" | |
| name: "pool4_A_A" | |
| type: "Pooling" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| bottom: "pool4_A_A" | |
| top: "conv5_1_A_A" | |
| name: "conv5_1_A_A" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv5_1_A_A" | |
| top: "conv5_1_A_A" | |
| name: "relu5_1_A_A" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv5_1_A_A" | |
| top: "conv5_2_A_A" | |
| name: "conv5_2_A_A" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv5_2_A_A" | |
| top: "conv5_2_A_A" | |
| name: "relu5_2_A_A" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv5_2_A_A" | |
| top: "conv5_3_A_A" | |
| name: "conv5_3_A_A" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv5_3_A_A" | |
| top: "conv5_3_A_A" | |
| name: "relu5_3_A_A" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv5_3_A_A" | |
| top: "conv5_4_A_A" | |
| name: "conv5_4_A_A" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv5_4_A_A" | |
| top: "conv5_4_A_A" | |
| name: "relu5_4_A_A" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv5_4_A_A" | |
| top: "pool5_A_A" | |
| name: "pool5_A_A" | |
| type: "Pooling" | |
| pooling_param { | |
| pool: AVE | |
| kernel_size: 14 | |
| stride: 14 | |
| } | |
| } | |
| #####feature_fusion##### | |
| layer { | |
| name: "reshape1" | |
| bottom: "pool5" | |
| top: "reshape1" | |
| type: "Reshape" | |
| reshape_param { | |
| shape { | |
| dim: -1 | |
| dim: 512 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "reshape2" | |
| bottom: "pool5_A" | |
| top: "reshape2" | |
| type: "Reshape" | |
| reshape_param { | |
| shape { | |
| dim: -1 | |
| dim: 512 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "reshape3" | |
| bottom: "pool5_A_A" | |
| top: "reshape3" | |
| type: "Reshape" | |
| reshape_param { | |
| shape { | |
| dim: -1 | |
| dim: 512 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "pow1" | |
| bottom: "reshape1" | |
| top: "pow1" | |
| type: "Power" | |
| power_param { | |
| power: 1 | |
| scale: 0.1 | |
| shift: 0 | |
| } | |
| } | |
| layer { | |
| name: "pow2" | |
| bottom: "reshape2" | |
| top: "pow2" | |
| type: "Power" | |
| power_param { | |
| power: 1 | |
| scale: 0.1 | |
| shift: 0 | |
| } | |
| } | |
| layer { | |
| name: "pow3" | |
| bottom: "reshape3" | |
| top: "pow3" | |
| type: "Power" | |
| power_param { | |
| power: 1 | |
| scale: 0.1 | |
| shift: 0 | |
| } | |
| } | |
| #layer { | |
| # name: "scale1+2+3" | |
| # bottom: "pow2" | |
| # bottom: "pow1" | |
| # bottom: "pow3" | |
| # top: "scale1+2+3" | |
| # type: "Concat" | |
| # concat_param { | |
| # axis: 1 | |
| # } | |
| #} | |
| #layer { | |
| # name: "scale1+2" | |
| # bottom: "pow2" | |
| # bottom: "pow1" | |
| # top: "scale1+2" | |
| # type: "Concat" | |
| # concat_param { | |
| # axis: 1 | |
| # } | |
| #} | |
| #layer { | |
| # name: "fc1_custom" | |
| # type: "InnerProduct" | |
| # bottom: "scale1+2+3" | |
| # top: "fc1_custom" | |
| # param { | |
| # lr_mult: 1.0 | |
| # decay_mult: 0 | |
| # } | |
| # param { | |
| # lr_mult: 1.0 | |
| # decay_mult: 0 | |
| # } | |
| # inner_product_param { | |
| # num_output: 100 | |
| # weight_filler { | |
| # type: "gaussian" | |
| # std: 0.01 | |
| # } | |
| # bias_filler { | |
| # type: "constant" | |
| # value: 0 | |
| # } | |
| # } | |
| #} | |
| #layer { | |
| # name: "accuracy1+2+3" | |
| # type: "Accuracy" | |
| # bottom: "fc1_custom" | |
| # bottom: "label" | |
| # top: "accuracy1+2+3" | |
| # include { | |
| # phase: TEST | |
| # } | |
| #} | |
| #layer { | |
| # name: "fc2_custom" | |
| # type: "InnerProduct" | |
| # bottom: "scale1+2" | |
| # top: "fc2_custom" | |
| # param { | |
| # lr_mult: 1.0 | |
| # decay_mult: 0 | |
| # } | |
| # param { | |
| # lr_mult: 1.0 | |
| # decay_mult: 0 | |
| # } | |
| # inner_product_param { | |
| # num_output: 100 | |
| # weight_filler { | |
| # type: "gaussian" | |
| # std: 0.01 | |
| # } | |
| # bias_filler { | |
| # type: "constant" | |
| # value: 0 | |
| # } | |
| # } | |
| #} | |
| #layer { | |
| # name: "accuracy1+2" | |
| # type: "Accuracy" | |
| # bottom: "fc2_custom" | |
| # bottom: "label" | |
| # top: "accuracy1+2" | |
| # include { | |
| # phase: TEST | |
| # } | |
| #} | |
| ###Evaluation### | |
| layer { | |
| name: "fc1_custom" | |
| type: "InnerProduct" | |
| bottom: "pow1" | |
| top: "fc1_custom" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 100 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "fc2_custom" | |
| type: "InnerProduct" | |
| bottom: "pow2" | |
| top: "fc2_custom" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 100 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "fc3_custom" | |
| type: "InnerProduct" | |
| bottom: "pow3" | |
| top: "fc3_custom" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 100 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "loss_1" | |
| type: "SoftmaxWithLoss" | |
| bottom: "fc1_custom" | |
| bottom: "label" | |
| top: "loss_1" | |
| loss_weight: 1.0 | |
| } | |
| layer { | |
| name: "loss_2" | |
| type: "SoftmaxWithLoss" | |
| bottom: "fc2_custom" | |
| bottom: "label" | |
| top: "loss_2" | |
| loss_weight: 1.0 | |
| } | |
| layer { | |
| name: "loss_3" | |
| type: "SoftmaxWithLoss" | |
| bottom: "fc3_custom" | |
| bottom: "label" | |
| top: "loss_3" | |
| loss_weight: 1.0 | |
| } | |
| ###ACC_Layers### | |
| ###Layer1### | |
| layer { | |
| name: "accuracy1_top-1" | |
| type: "Accuracy" | |
| bottom: "fc1_custom" | |
| bottom: "label" | |
| top: "accuracy1_top-1" | |
| include { | |
| phase: TEST | |
| } | |
| } | |
| layer { | |
| name: "accuracy1_top-5" | |
| type: "Accuracy" | |
| bottom: "fc1_custom" | |
| bottom: "label" | |
| top: "accuracy1_top-5" | |
| accuracy_param { | |
| top_k: 5 | |
| } | |
| include { | |
| phase: TEST | |
| } | |
| } | |
| ###Layer2### | |
| layer { | |
| name: "accura2_top-1" | |
| type: "Accuracy" | |
| bottom: "fc2_custom" | |
| bottom: "label" | |
| top: "accuracy2_top-1" | |
| include { | |
| phase: TEST | |
| } | |
| } | |
| layer { | |
| name: "accuracy2_top-5" | |
| type: "Accuracy" | |
| bottom: "fc2_custom" | |
| bottom: "label" | |
| top: "accuracy2_top-5" | |
| accuracy_param { | |
| top_k: 5 | |
| } | |
| include { | |
| phase: TEST | |
| } | |
| } | |
| ###Layer3### | |
| layer { | |
| name: "accuracy3_top-1" | |
| type: "Accuracy" | |
| bottom: "fc3_custom" | |
| bottom: "label" | |
| top: "accuracy3_top-1" | |
| include { | |
| phase: TEST | |
| } | |
| } | |
| layer { | |
| name: "accuracy3_top-5" | |
| type: "Accuracy" | |
| bottom: "fc3_custom" | |
| bottom: "label" | |
| top: "accuracy3_top-5" | |
| accuracy_param { | |
| top_k: 5 | |
| } | |
| include { | |
| phase: TEST | |
| } | |
| } |
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| name: "RA_CNN" | |
| layer { | |
| name: "data" | |
| type: "Data" | |
| top: "data" | |
| top: "label" | |
| include { | |
| phase: TRAIN | |
| } | |
| transform_param { | |
| mirror: true | |
| crop_size: 448 | |
| mean_value: 128 | |
| mean_value: 128 | |
| mean_value: 128 | |
| } | |
| data_param { | |
| source: "/media/data/lmdb/birds" | |
| batch_size: 2 | |
| backend: LMDB | |
| } | |
| } | |
| layer { | |
| name: "data" | |
| type: "Data" | |
| top: "data" | |
| top: "label" | |
| include { | |
| phase: TEST | |
| } | |
| transform_param { | |
| mirror: true | |
| crop_size: 448 | |
| mean_value: 128 | |
| mean_value: 128 | |
| mean_value: 128 | |
| } | |
| data_param { | |
| source: "/media/data/lmdb/birds" | |
| batch_size: 2 | |
| backend: LMDB | |
| } | |
| } | |
| #######Scale1####### | |
| layer { | |
| bottom: "data" | |
| top: "conv1_1" | |
| name: "conv1_1" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv1_1" | |
| top: "conv1_1" | |
| name: "relu1_1" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv1_1" | |
| top: "conv1_2" | |
| name: "conv1_2" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv1_2" | |
| top: "conv1_2" | |
| name: "relu1_2" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv1_2" | |
| top: "pool1" | |
| name: "pool1" | |
| type: "Pooling" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| bottom: "pool1" | |
| top: "conv2_1" | |
| name: "conv2_1" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv2_1" | |
| top: "conv2_1" | |
| name: "relu2_1" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv2_1" | |
| top: "conv2_2" | |
| name: "conv2_2" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv2_2" | |
| top: "conv2_2" | |
| name: "relu2_2" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv2_2" | |
| top: "pool2" | |
| name: "pool2" | |
| type: "Pooling" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| bottom: "pool2" | |
| top: "conv3_1" | |
| name: "conv3_1" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv3_1" | |
| top: "conv3_1" | |
| name: "relu3_1" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv3_1" | |
| top: "conv3_2" | |
| name: "conv3_2" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv3_2" | |
| top: "conv3_2" | |
| name: "relu3_2" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv3_2" | |
| top: "conv3_3" | |
| name: "conv3_3" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv3_3" | |
| top: "conv3_3" | |
| name: "relu3_3" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv3_3" | |
| top: "conv3_4" | |
| name: "conv3_4" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv3_4" | |
| top: "conv3_4" | |
| name: "relu3_4" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv3_4" | |
| top: "pool3" | |
| name: "pool3" | |
| type: "Pooling" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| bottom: "pool3" | |
| top: "conv4_1" | |
| name: "conv4_1" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv4_1" | |
| top: "conv4_1" | |
| name: "relu4_1" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv4_1" | |
| top: "conv4_2" | |
| name: "conv4_2" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv4_2" | |
| top: "conv4_2" | |
| name: "relu4_2" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv4_2" | |
| top: "conv4_3" | |
| name: "conv4_3" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv4_3" | |
| top: "conv4_3" | |
| name: "relu4_3" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv4_3" | |
| top: "conv4_4" | |
| name: "conv4_4" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv4_4" | |
| top: "conv4_4" | |
| name: "relu4_4" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv4_4" | |
| top: "pool4" | |
| name: "pool4" | |
| type: "Pooling" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| bottom: "pool4" | |
| top: "conv5_1" | |
| name: "conv5_1" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv5_1" | |
| top: "conv5_1" | |
| name: "relu5_1" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv5_1" | |
| top: "conv5_2" | |
| name: "conv5_2" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv5_2" | |
| top: "conv5_2" | |
| name: "relu5_2" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv5_2" | |
| top: "conv5_3" | |
| name: "conv5_3" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv5_3" | |
| top: "conv5_3" | |
| name: "relu5_3" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv5_3" | |
| top: "conv5_4" | |
| name: "conv5_4" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv5_4" | |
| top: "conv5_4" | |
| name: "relu5_4" | |
| type: "ReLU" | |
| } | |
| layer { | |
| name: "pool5" | |
| type: "Pooling" | |
| bottom: "conv5_4" | |
| top: "pool5" | |
| pooling_param { | |
| pool: AVE | |
| kernel_size: 28 | |
| stride: 28 | |
| } | |
| } | |
| #######APN1####### | |
| layer { | |
| bottom: "conv5_4" | |
| top: "anp_pool" | |
| name: "anp_pool" | |
| type: "Pooling" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "get_abc1" | |
| type: "InnerProduct" | |
| bottom: "anp_pool" | |
| top: "get_abc1" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 1024 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.001 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "tanh" | |
| bottom: "get_abc1" | |
| top: "tanh" | |
| type: "TanH" | |
| } | |
| layer { | |
| name: "get_abc2" | |
| type: "InnerProduct" | |
| bottom: "tanh" | |
| top: "get_abc2" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.001 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "sigmoid" | |
| bottom: "get_abc2" | |
| top: "sig_abc" | |
| type: "Sigmoid" | |
| } | |
| #######Scale2####### | |
| layer { | |
| name: "get448" | |
| bottom: "sig_abc" | |
| top: "get448" | |
| type: "Power" | |
| power_param { | |
| power: 1 | |
| scale: 448 | |
| shift: 0 | |
| } | |
| } | |
| layer{ | |
| name: "atten_crop" | |
| bottom: "data" | |
| bottom: "get448" | |
| top: "scale2_data" | |
| type: "AttentionCrop" | |
| } | |
| layer { | |
| bottom: "scale2_data" | |
| top: "conv1_1_A" | |
| name: "conv1_1_A" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv1_1_A" | |
| top: "conv1_1_A" | |
| name: "relu1_1_A" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv1_1_A" | |
| top: "conv1_2_A" | |
| name: "conv1_2_A" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv1_2_A" | |
| top: "conv1_2_A" | |
| name: "relu1_2_A" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv1_2_A" | |
| top: "pool1_A" | |
| name: "pool1_A" | |
| type: "Pooling" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| bottom: "pool1_A" | |
| top: "conv2_1_A" | |
| name: "conv2_1_A" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv2_1_A" | |
| top: "conv2_1_A" | |
| name: "relu2_1_A" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv2_1_A" | |
| top: "conv2_2_A" | |
| name: "conv2_2_A" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv2_2_A" | |
| top: "conv2_2_A" | |
| name: "relu2_2_A" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv2_2_A" | |
| top: "pool2_A" | |
| name: "pool2_A" | |
| type: "Pooling" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| bottom: "pool2_A" | |
| top: "conv3_1_A" | |
| name: "conv3_1_A" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv3_1_A" | |
| top: "conv3_1_A" | |
| name: "relu3_1_A" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv3_1_A" | |
| top: "conv3_2_A" | |
| name: "conv3_2_A" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv3_2_A" | |
| top: "conv3_2_A" | |
| name: "relu3_2_A" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv3_2_A" | |
| top: "conv3_3_A" | |
| name: "conv3_3_A" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv3_3_A" | |
| top: "conv3_3_A" | |
| name: "relu3_3_A" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv3_3_A" | |
| top: "conv3_4_A" | |
| name: "conv3_4_A" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv3_4_A" | |
| top: "conv3_4_A" | |
| name: "relu3_4_A" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv3_4_A" | |
| top: "pool3_A" | |
| name: "pool3_A" | |
| type: "Pooling" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| bottom: "pool3_A" | |
| top: "conv4_1_A" | |
| name: "conv4_1_A" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv4_1_A" | |
| top: "conv4_1_A" | |
| name: "relu4_1_A" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv4_1_A" | |
| top: "conv4_2_A" | |
| name: "conv4_2_A" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv4_2_A" | |
| top: "conv4_2_A" | |
| name: "relu4_2_A" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv4_2_A" | |
| top: "conv4_3_A" | |
| name: "conv4_3_A" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv4_3_A" | |
| top: "conv4_3_A" | |
| name: "relu4_3_A" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv4_3_A" | |
| top: "conv4_4_A" | |
| name: "conv4_4_A" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv4_4_A" | |
| top: "conv4_4_A" | |
| name: "relu4_4_A" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv4_4_A" | |
| top: "pool4_A" | |
| name: "pool4_A" | |
| type: "Pooling" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| bottom: "pool4_A" | |
| top: "conv5_1_A" | |
| name: "conv5_1_A" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv5_1_A" | |
| top: "conv5_1_A" | |
| name: "relu5_1_A" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv5_1_A" | |
| top: "conv5_2_A" | |
| name: "conv5_2_A" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv5_2_A" | |
| top: "conv5_2_A" | |
| name: "relu5_2_A" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv5_2_A" | |
| top: "conv5_3_A" | |
| name: "conv5_3_A" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv5_3_A" | |
| top: "conv5_3_A" | |
| name: "relu5_3_A" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv5_3_A" | |
| top: "conv5_4_A" | |
| name: "conv5_4_A" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv5_4_A" | |
| top: "conv5_4_A" | |
| name: "relu5_4_A" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv5_4_A" | |
| top: "pool5_A" | |
| name: "pool5_A" | |
| type: "Pooling" | |
| pooling_param { | |
| pool: AVE | |
| kernel_size: 14 | |
| stride: 14 | |
| } | |
| } | |
| #######APN2####### | |
| layer { | |
| name: "get_abc1_A" | |
| type: "InnerProduct" | |
| bottom: "conv5_4_A" | |
| top: "get_abc1_A" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 1024 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.001 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "tanh_A" | |
| bottom: "get_abc1_A" | |
| top: "tanh_A" | |
| type: "TanH" | |
| } | |
| layer { | |
| name: "get_abc2_A" | |
| type: "InnerProduct" | |
| bottom: "tanh_A" | |
| top: "get_abc2_A" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.001 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "sigmoid_A" | |
| bottom: "get_abc2_A" | |
| top: "sig_abc_A" | |
| type: "Sigmoid" | |
| } | |
| #######Scale3####### | |
| layer { | |
| name: "get224" | |
| bottom: "sig_abc_A" | |
| top: "get224" | |
| type: "Power" | |
| power_param { | |
| power: 1 | |
| scale: 224 | |
| shift: 0 | |
| } | |
| } | |
| layer{ | |
| name: "atten_crop_A" | |
| bottom: "scale2_data" | |
| bottom: "get224" | |
| top: "scale3_data" | |
| type: "AttentionCrop" | |
| } | |
| layer { | |
| bottom: "scale3_data" | |
| top: "conv1_1_A_A" | |
| name: "conv1_1_A_A" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv1_1_A_A" | |
| top: "conv1_1_A_A" | |
| name: "relu1_1_A_A" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv1_1_A_A" | |
| top: "conv1_2_A_A" | |
| name: "conv1_2_A_A" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv1_2_A_A" | |
| top: "conv1_2_A_A" | |
| name: "relu1_2_A_A" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv1_2_A_A" | |
| top: "pool1_A_A" | |
| name: "pool1_A_A" | |
| type: "Pooling" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| bottom: "pool1_A_A" | |
| top: "conv2_1_A_A" | |
| name: "conv2_1_A_A" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv2_1_A_A" | |
| top: "conv2_1_A_A" | |
| name: "relu2_1_A_A" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv2_1_A_A" | |
| top: "conv2_2_A_A" | |
| name: "conv2_2_A_A" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv2_2_A_A" | |
| top: "conv2_2_A_A" | |
| name: "relu2_2_A_A" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv2_2_A_A" | |
| top: "pool2_A_A" | |
| name: "pool2_A_A" | |
| type: "Pooling" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| bottom: "pool2_A_A" | |
| top: "conv3_1_A_A" | |
| name: "conv3_1_A_A" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv3_1_A_A" | |
| top: "conv3_1_A_A" | |
| name: "relu3_1_A_A" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv3_1_A_A" | |
| top: "conv3_2_A_A" | |
| name: "conv3_2_A_A" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv3_2_A_A" | |
| top: "conv3_2_A_A" | |
| name: "relu3_2_A_A" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv3_2_A_A" | |
| top: "conv3_3_A_A" | |
| name: "conv3_3_A_A" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv3_3_A_A" | |
| top: "conv3_3_A_A" | |
| name: "relu3_3_A_A" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv3_3_A_A" | |
| top: "conv3_4_A_A" | |
| name: "conv3_4_A_A" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv3_4_A_A" | |
| top: "conv3_4_A_A" | |
| name: "relu3_4_A_A" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv3_4_A_A" | |
| top: "pool3_A_A" | |
| name: "pool3_A_A" | |
| type: "Pooling" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| bottom: "pool3_A_A" | |
| top: "conv4_1_A_A" | |
| name: "conv4_1_A_A" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv4_1_A_A" | |
| top: "conv4_1_A_A" | |
| name: "relu4_1_A_A" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv4_1_A_A" | |
| top: "conv4_2_A_A" | |
| name: "conv4_2_A_A" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv4_2_A_A" | |
| top: "conv4_2_A_A" | |
| name: "relu4_2_A_A" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv4_2_A_A" | |
| top: "conv4_3_A_A" | |
| name: "conv4_3_A_A" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv4_3_A_A" | |
| top: "conv4_3_A_A" | |
| name: "relu4_3_A_A" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv4_3_A_A" | |
| top: "conv4_4_A_A" | |
| name: "conv4_4_A_A" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv4_4_A_A" | |
| top: "conv4_4_A_A" | |
| name: "relu4_4_A_A" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv4_4_A_A" | |
| top: "pool4_A_A" | |
| name: "pool4_A_A" | |
| type: "Pooling" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| bottom: "pool4_A_A" | |
| top: "conv5_1_A_A" | |
| name: "conv5_1_A_A" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv5_1_A_A" | |
| top: "conv5_1_A_A" | |
| name: "relu5_1_A_A" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv5_1_A_A" | |
| top: "conv5_2_A_A" | |
| name: "conv5_2_A_A" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv5_2_A_A" | |
| top: "conv5_2_A_A" | |
| name: "relu5_2_A_A" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv5_2_A_A" | |
| top: "conv5_3_A_A" | |
| name: "conv5_3_A_A" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv5_3_A_A" | |
| top: "conv5_3_A_A" | |
| name: "relu5_3_A_A" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv5_3_A_A" | |
| top: "conv5_4_A_A" | |
| name: "conv5_4_A_A" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv5_4_A_A" | |
| top: "conv5_4_A_A" | |
| name: "relu5_4_A_A" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv5_4_A_A" | |
| top: "pool5_A_A" | |
| name: "pool5_A_A" | |
| type: "Pooling" | |
| pooling_param { | |
| pool: AVE | |
| kernel_size: 14 | |
| stride: 14 | |
| } | |
| } | |
| #####feature_fusion##### | |
| layer { | |
| name: "reshape1" | |
| bottom: "pool5" | |
| top: "reshape1" | |
| type: "Reshape" | |
| reshape_param { | |
| shape { | |
| dim: -1 | |
| dim: 512 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "reshape2" | |
| bottom: "pool5_A" | |
| top: "reshape2" | |
| type: "Reshape" | |
| reshape_param { | |
| shape { | |
| dim: -1 | |
| dim: 512 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "reshape3" | |
| bottom: "pool5_A_A" | |
| top: "reshape3" | |
| type: "Reshape" | |
| reshape_param { | |
| shape { | |
| dim: -1 | |
| dim: 512 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "pow1" | |
| bottom: "reshape1" | |
| top: "pow1" | |
| type: "Power" | |
| power_param { | |
| power: 1 | |
| scale: 0.1 | |
| shift: 0 | |
| } | |
| } | |
| layer { | |
| name: "pow2" | |
| bottom: "reshape2" | |
| top: "pow2" | |
| type: "Power" | |
| power_param { | |
| power: 1 | |
| scale: 0.1 | |
| shift: 0 | |
| } | |
| } | |
| layer { | |
| name: "pow3" | |
| bottom: "reshape3" | |
| top: "pow3" | |
| type: "Power" | |
| power_param { | |
| power: 1 | |
| scale: 0.1 | |
| shift: 0 | |
| } | |
| } | |
| #layer { | |
| # name: "scale1+2+3" | |
| # bottom: "pow2" | |
| # bottom: "pow1" | |
| # bottom: "pow3" | |
| # top: "scale1+2+3" | |
| # type: "Concat" | |
| # concat_param { | |
| # axis: 1 | |
| # } | |
| #} | |
| #layer { | |
| # name: "scale1+2" | |
| # bottom: "pow2" | |
| # bottom: "pow1" | |
| # top: "scale1+2" | |
| # type: "Concat" | |
| # concat_param { | |
| # axis: 1 | |
| # } | |
| #} | |
| #layer { | |
| # name: "fc1_custom" | |
| # type: "InnerProduct" | |
| # bottom: "scale1+2+3" | |
| # top: "fc1_custom" | |
| # param { | |
| # lr_mult: 0.0 | |
| # decay_mult: 0 | |
| # } | |
| # param { | |
| # lr_mult: 0.0 | |
| # decay_mult: 0 | |
| # } | |
| # inner_product_param { | |
| # num_output: 100 | |
| # weight_filler { | |
| # type: "gaussian" | |
| # std: 0.01 | |
| # } | |
| # bias_filler { | |
| # type: "constant" | |
| # value: 0 | |
| # } | |
| # } | |
| #} | |
| #layer { | |
| # name: "accuracy1+2+3" | |
| # type: "Accuracy" | |
| # bottom: "fc1_custom" | |
| # bottom: "label" | |
| # top: "accuracy1+2+3" | |
| # include { | |
| # phase: TEST | |
| # } | |
| #} | |
| #layer { | |
| # name: "fc2_custom" | |
| # type: "InnerProduct" | |
| # bottom: "scale1+2" | |
| # top: "fc2_custom" | |
| # param { | |
| # lr_mult: 0.0 | |
| # decay_mult: 0 | |
| # } | |
| # param { | |
| # lr_mult: 0.0 | |
| # decay_mult: 0 | |
| # } | |
| # inner_product_param { | |
| # num_output: 100 | |
| # weight_filler { | |
| # type: "gaussian" | |
| # std: 0.01 | |
| # } | |
| # bias_filler { | |
| # type: "constant" | |
| # value: 0 | |
| # } | |
| # } | |
| #} | |
| #layer { | |
| # name: "accuracy1+2" | |
| # type: "Accuracy" | |
| # bottom: "fc2_custom" | |
| # bottom: "label" | |
| # top: "accuracy1+2" | |
| # include { | |
| # phase: TEST | |
| # } | |
| #} | |
| ###Evaluation### | |
| layer { | |
| name: "fc1_custom" | |
| type: "InnerProduct" | |
| bottom: "pow1" | |
| top: "fc1_custom" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 100 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "fc2_custom" | |
| type: "InnerProduct" | |
| bottom: "pow2" | |
| top: "fc2_custom" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 100 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "fc3_custom" | |
| type: "InnerProduct" | |
| bottom: "pow3" | |
| top: "fc3_custom" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 100 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "PRL_0" | |
| type: "PairwiseRankingLossLayer" | |
| bottom: "fc1_custom" | |
| bottom: "fc2_custom" | |
| top: "PRL_0" | |
| loss_weight: 1.0 | |
| } | |
| layer { | |
| name: "PRL_1" | |
| type: "PairwiseRankingLossLayer" | |
| bottom: "fc2_custom" | |
| bottom: "fc3_custom" | |
| top: "PRL_1" | |
| loss_weight: 1.0 | |
| } | |
| ###ACC_Layers### | |
| ###Layer1### | |
| layer { | |
| name: "accuracy1_top-1" | |
| type: "Accuracy" | |
| bottom: "fc1_custom" | |
| bottom: "label" | |
| top: "accuracy1_top-1" | |
| include { | |
| phase: TEST | |
| } | |
| } | |
| layer { | |
| name: "accuracy1_top-5" | |
| type: "Accuracy" | |
| bottom: "fc1_custom" | |
| bottom: "label" | |
| top: "accuracy1_top-5" | |
| accuracy_param { | |
| top_k: 5 | |
| } | |
| include { | |
| phase: TEST | |
| } | |
| } | |
| ###Layer2### | |
| layer { | |
| name: "accura2_top-1" | |
| type: "Accuracy" | |
| bottom: "fc2_custom" | |
| bottom: "label" | |
| top: "accuracy2_top-1" | |
| include { | |
| phase: TEST | |
| } | |
| } | |
| layer { | |
| name: "accuracy2_top-5" | |
| type: "Accuracy" | |
| bottom: "fc2_custom" | |
| bottom: "label" | |
| top: "accuracy2_top-5" | |
| accuracy_param { | |
| top_k: 5 | |
| } | |
| include { | |
| phase: TEST | |
| } | |
| } | |
| ###Layer3### | |
| layer { | |
| name: "accuracy3_top-1" | |
| type: "Accuracy" | |
| bottom: "fc3_custom" | |
| bottom: "label" | |
| top: "accuracy3_top-1" | |
| include { | |
| phase: TEST | |
| } | |
| } | |
| layer { | |
| name: "accuracy3_top-5" | |
| type: "Accuracy" | |
| bottom: "fc3_custom" | |
| bottom: "label" | |
| top: "accuracy3_top-5" | |
| accuracy_param { | |
| top_k: 5 | |
| } | |
| include { | |
| phase: TEST | |
| } | |
| } |
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| name: "RA_CNN" | |
| #######Scale1####### | |
| layer { | |
| name: "data" | |
| type: "Data" | |
| top: "data" | |
| top: "label" | |
| include { | |
| phase: TRAIN | |
| } | |
| transform_param { | |
| mirror: true | |
| crop_size: 448 | |
| mean_value: 128 | |
| mean_value: 128 | |
| mean_value: 128 | |
| } | |
| data_param { | |
| source: "/media/data/lmdb/birds" | |
| batch_size: 2 | |
| backend: LMDB | |
| } | |
| } | |
| layer { | |
| name: "data" | |
| type: "Data" | |
| top: "data" | |
| top: "label" | |
| include { | |
| phase: TEST | |
| } | |
| transform_param { | |
| mirror: true | |
| crop_size: 448 | |
| mean_value: 128 | |
| mean_value: 128 | |
| mean_value: 128 | |
| } | |
| data_param { | |
| source: "/media/data/lmdb/birds" | |
| batch_size: 2 | |
| backend: LMDB | |
| } | |
| } | |
| layer { | |
| bottom: "data" | |
| top: "conv1_1" | |
| name: "conv1_1" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv1_1" | |
| top: "conv1_1" | |
| name: "relu1_1" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv1_1" | |
| top: "conv1_2" | |
| name: "conv1_2" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv1_2" | |
| top: "conv1_2" | |
| name: "relu1_2" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv1_2" | |
| top: "pool1" | |
| name: "pool1" | |
| type: "Pooling" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| bottom: "pool1" | |
| top: "conv2_1" | |
| name: "conv2_1" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv2_1" | |
| top: "conv2_1" | |
| name: "relu2_1" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv2_1" | |
| top: "conv2_2" | |
| name: "conv2_2" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv2_2" | |
| top: "conv2_2" | |
| name: "relu2_2" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv2_2" | |
| top: "pool2" | |
| name: "pool2" | |
| type: "Pooling" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| bottom: "pool2" | |
| top: "conv3_1" | |
| name: "conv3_1" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv3_1" | |
| top: "conv3_1" | |
| name: "relu3_1" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv3_1" | |
| top: "conv3_2" | |
| name: "conv3_2" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv3_2" | |
| top: "conv3_2" | |
| name: "relu3_2" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv3_2" | |
| top: "conv3_3" | |
| name: "conv3_3" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv3_3" | |
| top: "conv3_3" | |
| name: "relu3_3" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv3_3" | |
| top: "conv3_4" | |
| name: "conv3_4" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv3_4" | |
| top: "conv3_4" | |
| name: "relu3_4" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv3_4" | |
| top: "pool3" | |
| name: "pool3" | |
| type: "Pooling" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| bottom: "pool3" | |
| top: "conv4_1" | |
| name: "conv4_1" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv4_1" | |
| top: "conv4_1" | |
| name: "relu4_1" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv4_1" | |
| top: "conv4_2" | |
| name: "conv4_2" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv4_2" | |
| top: "conv4_2" | |
| name: "relu4_2" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv4_2" | |
| top: "conv4_3" | |
| name: "conv4_3" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv4_3" | |
| top: "conv4_3" | |
| name: "relu4_3" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv4_3" | |
| top: "conv4_4" | |
| name: "conv4_4" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv4_4" | |
| top: "conv4_4" | |
| name: "relu4_4" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv4_4" | |
| top: "pool4" | |
| name: "pool4" | |
| type: "Pooling" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| bottom: "pool4" | |
| top: "conv5_1" | |
| name: "conv5_1" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv5_1" | |
| top: "conv5_1" | |
| name: "relu5_1" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv5_1" | |
| top: "conv5_2" | |
| name: "conv5_2" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv5_2" | |
| top: "conv5_2" | |
| name: "relu5_2" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv5_2" | |
| top: "conv5_3" | |
| name: "conv5_3" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv5_3" | |
| top: "conv5_3" | |
| name: "relu5_3" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv5_3" | |
| top: "conv5_4" | |
| name: "conv5_4" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv5_4" | |
| top: "conv5_4" | |
| name: "relu5_4" | |
| type: "ReLU" | |
| } | |
| layer { | |
| name: "pool5" | |
| type: "Pooling" | |
| bottom: "conv5_4" | |
| top: "pool5" | |
| pooling_param { | |
| pool: AVE | |
| kernel_size: 28 | |
| stride: 28 | |
| } | |
| } | |
| #######APN1####### | |
| layer { | |
| bottom: "conv5_4" | |
| top: "anp_pool" | |
| name: "anp_pool" | |
| type: "Pooling" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "get_abc1" | |
| type: "InnerProduct" | |
| bottom: "anp_pool" | |
| top: "get_abc1" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 1024 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.001 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "tanh" | |
| bottom: "get_abc1" | |
| top: "tanh" | |
| type: "TanH" | |
| } | |
| layer { | |
| name: "get_abc2" | |
| type: "InnerProduct" | |
| bottom: "tanh" | |
| top: "get_abc2" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.001 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "sigmoid" | |
| bottom: "get_abc2" | |
| top: "sig_abc" | |
| type: "Sigmoid" | |
| } | |
| #######Scale2####### | |
| layer { | |
| name: "get448" | |
| bottom: "sig_abc" | |
| top: "get448" | |
| type: "Power" | |
| power_param { | |
| power: 1 | |
| scale: 448 | |
| shift: 0 | |
| } | |
| } | |
| layer{ | |
| name: "atten_crop" | |
| bottom: "data" | |
| bottom: "get448" | |
| top: "scale2_data" | |
| type: "AttentionCrop" | |
| } | |
| layer { | |
| bottom: "scale2_data" | |
| top: "conv1_1_A" | |
| name: "conv1_1_A" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv1_1_A" | |
| top: "conv1_1_A" | |
| name: "relu1_1_A" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv1_1_A" | |
| top: "conv1_2_A" | |
| name: "conv1_2_A" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv1_2_A" | |
| top: "conv1_2_A" | |
| name: "relu1_2_A" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv1_2_A" | |
| top: "pool1_A" | |
| name: "pool1_A" | |
| type: "Pooling" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| bottom: "pool1_A" | |
| top: "conv2_1_A" | |
| name: "conv2_1_A" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv2_1_A" | |
| top: "conv2_1_A" | |
| name: "relu2_1_A" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv2_1_A" | |
| top: "conv2_2_A" | |
| name: "conv2_2_A" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv2_2_A" | |
| top: "conv2_2_A" | |
| name: "relu2_2_A" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv2_2_A" | |
| top: "pool2_A" | |
| name: "pool2_A" | |
| type: "Pooling" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| bottom: "pool2_A" | |
| top: "conv3_1_A" | |
| name: "conv3_1_A" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv3_1_A" | |
| top: "conv3_1_A" | |
| name: "relu3_1_A" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv3_1_A" | |
| top: "conv3_2_A" | |
| name: "conv3_2_A" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv3_2_A" | |
| top: "conv3_2_A" | |
| name: "relu3_2_A" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv3_2_A" | |
| top: "conv3_3_A" | |
| name: "conv3_3_A" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv3_3_A" | |
| top: "conv3_3_A" | |
| name: "relu3_3_A" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv3_3_A" | |
| top: "conv3_4_A" | |
| name: "conv3_4_A" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv3_4_A" | |
| top: "conv3_4_A" | |
| name: "relu3_4_A" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv3_4_A" | |
| top: "pool3_A" | |
| name: "pool3_A" | |
| type: "Pooling" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| bottom: "pool3_A" | |
| top: "conv4_1_A" | |
| name: "conv4_1_A" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv4_1_A" | |
| top: "conv4_1_A" | |
| name: "relu4_1_A" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv4_1_A" | |
| top: "conv4_2_A" | |
| name: "conv4_2_A" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv4_2_A" | |
| top: "conv4_2_A" | |
| name: "relu4_2_A" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv4_2_A" | |
| top: "conv4_3_A" | |
| name: "conv4_3_A" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv4_3_A" | |
| top: "conv4_3_A" | |
| name: "relu4_3_A" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv4_3_A" | |
| top: "conv4_4_A" | |
| name: "conv4_4_A" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv4_4_A" | |
| top: "conv4_4_A" | |
| name: "relu4_4_A" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv4_4_A" | |
| top: "pool4_A" | |
| name: "pool4_A" | |
| type: "Pooling" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| bottom: "pool4_A" | |
| top: "conv5_1_A" | |
| name: "conv5_1_A" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv5_1_A" | |
| top: "conv5_1_A" | |
| name: "relu5_1_A" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv5_1_A" | |
| top: "conv5_2_A" | |
| name: "conv5_2_A" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv5_2_A" | |
| top: "conv5_2_A" | |
| name: "relu5_2_A" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv5_2_A" | |
| top: "conv5_3_A" | |
| name: "conv5_3_A" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv5_3_A" | |
| top: "conv5_3_A" | |
| name: "relu5_3_A" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv5_3_A" | |
| top: "conv5_4_A" | |
| name: "conv5_4_A" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv5_4_A" | |
| top: "conv5_4_A" | |
| name: "relu5_4_A" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv5_4_A" | |
| top: "pool5_A" | |
| name: "pool5_A" | |
| type: "Pooling" | |
| pooling_param { | |
| pool: AVE | |
| kernel_size: 14 | |
| stride: 14 | |
| } | |
| } | |
| #######APN2####### | |
| layer { | |
| name: "get_abc1_A" | |
| type: "InnerProduct" | |
| bottom: "conv5_4_A" | |
| top: "get_abc1_A" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 1024 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.001 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "tanh_A" | |
| bottom: "get_abc1_A" | |
| top: "tanh_A" | |
| type: "TanH" | |
| } | |
| layer { | |
| name: "get_abc2_A" | |
| type: "InnerProduct" | |
| bottom: "tanh_A" | |
| top: "get_abc2_A" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.001 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "sigmoid_A" | |
| bottom: "get_abc2_A" | |
| top: "sig_abc_A" | |
| type: "Sigmoid" | |
| } | |
| #######Scale3####### | |
| layer { | |
| name: "get224" | |
| bottom: "sig_abc_A" | |
| top: "get224" | |
| type: "Power" | |
| power_param { | |
| power: 1 | |
| scale: 224 | |
| shift: 0 | |
| } | |
| } | |
| layer{ | |
| name: "atten_crop_A" | |
| bottom: "scale2_data" | |
| bottom: "get224" | |
| top: "scale3_data" | |
| type: "AttentionCrop" | |
| } | |
| layer { | |
| bottom: "scale3_data" | |
| top: "conv1_1_A_A" | |
| name: "conv1_1_A_A" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv1_1_A_A" | |
| top: "conv1_1_A_A" | |
| name: "relu1_1_A_A" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv1_1_A_A" | |
| top: "conv1_2_A_A" | |
| name: "conv1_2_A_A" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv1_2_A_A" | |
| top: "conv1_2_A_A" | |
| name: "relu1_2_A_A" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv1_2_A_A" | |
| top: "pool1_A_A" | |
| name: "pool1_A_A" | |
| type: "Pooling" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| bottom: "pool1_A_A" | |
| top: "conv2_1_A_A" | |
| name: "conv2_1_A_A" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv2_1_A_A" | |
| top: "conv2_1_A_A" | |
| name: "relu2_1_A_A" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv2_1_A_A" | |
| top: "conv2_2_A_A" | |
| name: "conv2_2_A_A" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv2_2_A_A" | |
| top: "conv2_2_A_A" | |
| name: "relu2_2_A_A" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv2_2_A_A" | |
| top: "pool2_A_A" | |
| name: "pool2_A_A" | |
| type: "Pooling" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| bottom: "pool2_A_A" | |
| top: "conv3_1_A_A" | |
| name: "conv3_1_A_A" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv3_1_A_A" | |
| top: "conv3_1_A_A" | |
| name: "relu3_1_A_A" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv3_1_A_A" | |
| top: "conv3_2_A_A" | |
| name: "conv3_2_A_A" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv3_2_A_A" | |
| top: "conv3_2_A_A" | |
| name: "relu3_2_A_A" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv3_2_A_A" | |
| top: "conv3_3_A_A" | |
| name: "conv3_3_A_A" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv3_3_A_A" | |
| top: "conv3_3_A_A" | |
| name: "relu3_3_A_A" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv3_3_A_A" | |
| top: "conv3_4_A_A" | |
| name: "conv3_4_A_A" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv3_4_A_A" | |
| top: "conv3_4_A_A" | |
| name: "relu3_4_A_A" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv3_4_A_A" | |
| top: "pool3_A_A" | |
| name: "pool3_A_A" | |
| type: "Pooling" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| bottom: "pool3_A_A" | |
| top: "conv4_1_A_A" | |
| name: "conv4_1_A_A" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv4_1_A_A" | |
| top: "conv4_1_A_A" | |
| name: "relu4_1_A_A" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv4_1_A_A" | |
| top: "conv4_2_A_A" | |
| name: "conv4_2_A_A" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv4_2_A_A" | |
| top: "conv4_2_A_A" | |
| name: "relu4_2_A_A" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv4_2_A_A" | |
| top: "conv4_3_A_A" | |
| name: "conv4_3_A_A" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv4_3_A_A" | |
| top: "conv4_3_A_A" | |
| name: "relu4_3_A_A" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv4_3_A_A" | |
| top: "conv4_4_A_A" | |
| name: "conv4_4_A_A" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv4_4_A_A" | |
| top: "conv4_4_A_A" | |
| name: "relu4_4_A_A" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv4_4_A_A" | |
| top: "pool4_A_A" | |
| name: "pool4_A_A" | |
| type: "Pooling" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| bottom: "pool4_A_A" | |
| top: "conv5_1_A_A" | |
| name: "conv5_1_A_A" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv5_1_A_A" | |
| top: "conv5_1_A_A" | |
| name: "relu5_1_A_A" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv5_1_A_A" | |
| top: "conv5_2_A_A" | |
| name: "conv5_2_A_A" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv5_2_A_A" | |
| top: "conv5_2_A_A" | |
| name: "relu5_2_A_A" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv5_2_A_A" | |
| top: "conv5_3_A_A" | |
| name: "conv5_3_A_A" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv5_3_A_A" | |
| top: "conv5_3_A_A" | |
| name: "relu5_3_A_A" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv5_3_A_A" | |
| top: "conv5_4_A_A" | |
| name: "conv5_4_A_A" | |
| type: "Convolution" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv5_4_A_A" | |
| top: "conv5_4_A_A" | |
| name: "relu5_4_A_A" | |
| type: "ReLU" | |
| } | |
| layer { | |
| bottom: "conv5_4_A_A" | |
| top: "pool5_A_A" | |
| name: "pool5_A_A" | |
| type: "Pooling" | |
| pooling_param { | |
| pool: AVE | |
| kernel_size: 14 | |
| stride: 14 | |
| } | |
| } | |
| #####feature_fusion##### | |
| layer { | |
| name: "reshape1" | |
| bottom: "pool5" | |
| top: "reshape1" | |
| type: "Reshape" | |
| reshape_param { | |
| shape { | |
| dim: -1 | |
| dim: 512 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "reshape2" | |
| bottom: "pool5_A" | |
| top: "reshape2" | |
| type: "Reshape" | |
| reshape_param { | |
| shape { | |
| dim: -1 | |
| dim: 512 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "reshape3" | |
| bottom: "pool5_A_A" | |
| top: "reshape3" | |
| type: "Reshape" | |
| reshape_param { | |
| shape { | |
| dim: -1 | |
| dim: 512 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "pow1" | |
| bottom: "reshape1" | |
| top: "pow1" | |
| type: "Power" | |
| power_param { | |
| power: 1 | |
| scale: 0.1 | |
| shift: 0 | |
| } | |
| } | |
| layer { | |
| name: "pow2" | |
| bottom: "reshape2" | |
| top: "pow2" | |
| type: "Power" | |
| power_param { | |
| power: 1 | |
| scale: 0.1 | |
| shift: 0 | |
| } | |
| } | |
| layer { | |
| name: "pow3" | |
| bottom: "reshape3" | |
| top: "pow3" | |
| type: "Power" | |
| power_param { | |
| power: 1 | |
| scale: 0.1 | |
| shift: 0 | |
| } | |
| } | |
| layer { | |
| name: "scale1+2+3" | |
| bottom: "pow2" | |
| bottom: "pow1" | |
| bottom: "pow3" | |
| top: "scale1+2+3" | |
| type: "Concat" | |
| concat_param { | |
| axis: 1 | |
| } | |
| } | |
| layer { | |
| name: "scale1+2" | |
| bottom: "pow2" | |
| bottom: "pow1" | |
| top: "scale1+2" | |
| type: "Concat" | |
| concat_param { | |
| axis: 1 | |
| } | |
| } | |
| layer { | |
| name: "fc1_custom" | |
| type: "InnerProduct" | |
| bottom: "scale1+2+3" | |
| top: "fc1_custom" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 100 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "accuracy1+2+3" | |
| type: "Accuracy" | |
| bottom: "fc1_custom" | |
| bottom: "label" | |
| top: "accuracy1+2+3" | |
| include { | |
| phase: TEST | |
| } | |
| } | |
| layer { | |
| name: "fc2_custom" | |
| type: "InnerProduct" | |
| bottom: "scale1+2" | |
| top: "fc2_custom" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 100 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "accuracy1+2" | |
| type: "Accuracy" | |
| bottom: "fc2_custom" | |
| bottom: "label" | |
| top: "accuracy1+2" | |
| include { | |
| phase: TEST | |
| } | |
| } | |
| layer { | |
| name: "loss_1" | |
| type: "SoftmaxWithLoss" | |
| bottom: "fc2_custom" | |
| bottom: "label" | |
| top: "loss_1" | |
| loss_weight: 1.0 | |
| } | |
| layer { | |
| name: "loss_0" | |
| type: "SoftmaxWithLoss" | |
| bottom: "fc1_custom" | |
| bottom: "label" | |
| top: "loss_0" | |
| loss_weight: 1.0 | |
| } |
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