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January 7, 2019 06:16
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| name: "SE_RESNET_50_V1" | |
| layer { | |
| name: "data" | |
| type: "Data" | |
| top: "data" | |
| top: "label" | |
| include { | |
| phase: TRAIN | |
| } | |
| transform_param { | |
| mirror: true | |
| scale: 0.017 | |
| crop_size: 224 | |
| mean_value: 103.94 | |
| mean_value: 116.78 | |
| mean_value: 123.68 | |
| } | |
| data_param { | |
| source: "/mnt/disk1/zhibin/experiment_data/imagenet/caffe_lmdb/ilsvrc12_encoded_train_lmdb" | |
| batch_size: 16 | |
| backend: LMDB | |
| } | |
| } | |
| layer { | |
| name: "data" | |
| type: "Data" | |
| top: "data" | |
| top: "label" | |
| include { | |
| phase: TEST | |
| } | |
| transform_param { | |
| mirror: false | |
| crop_size: 224 | |
| scale: 0.017 | |
| mean_value: 103.94 | |
| mean_value: 116.78 | |
| mean_value: 123.68 | |
| } | |
| data_param { | |
| source: "/mnt/disk1/zhibin/experiment_data/imagenet/caffe_lmdb/ilsvrc12_encoded_val_lmdb" | |
| batch_size: 16 | |
| backend: LMDB | |
| } | |
| } | |
| layer { | |
| name: "conv1" | |
| type: "Convolution" | |
| bottom: "data" | |
| top: "conv1" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| bias_term: false | |
| pad: 3 | |
| kernel_size: 7 | |
| stride: 2 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv1/bn" | |
| type: "BatchNorm" | |
| bottom: "conv1" | |
| top: "conv1" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| eps: 1e-4 | |
| } | |
| } | |
| layer { | |
| name: "conv1/scale" | |
| type: "Scale" | |
| bottom: "conv1" | |
| top: "conv1" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "relu1" | |
| type: "ReLU" | |
| bottom: "conv1" | |
| top: "conv1" | |
| } | |
| layer { | |
| name: "pool1" | |
| type: "Pooling" | |
| bottom: "conv1" | |
| top: "pool1" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 3 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "conv2_1/prj" | |
| type: "Convolution" | |
| bottom: "pool1" | |
| top: "conv2_1/prj" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv2_1/prj/bn" | |
| type: "BatchNorm" | |
| bottom: "conv2_1/prj" | |
| top: "conv2_1/prj" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| eps: 1e-4 | |
| } | |
| } | |
| layer { | |
| name: "conv2_1/prj/scale" | |
| type: "Scale" | |
| bottom: "conv2_1/prj" | |
| top: "conv2_1/prj" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv2_1/x1" | |
| type: "Convolution" | |
| bottom: "pool1" | |
| top: "conv2_1/x1" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| bias_term: false | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv2_1/x1/bn" | |
| type: "BatchNorm" | |
| bottom: "conv2_1/x1" | |
| top: "conv2_1/x1" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| eps: 1e-4 | |
| } | |
| } | |
| layer { | |
| name: "conv2_1/x1/scale" | |
| type: "Scale" | |
| bottom: "conv2_1/x1" | |
| top: "conv2_1/x1" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "relu2_1/x1" | |
| type: "ReLU" | |
| bottom: "conv2_1/x1" | |
| top: "conv2_1/x1" | |
| } | |
| layer { | |
| name: "conv2_1/x2" | |
| type: "Convolution" | |
| bottom: "conv2_1/x1" | |
| top: "conv2_1/x2" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv2_1/x2/bn" | |
| type: "BatchNorm" | |
| bottom: "conv2_1/x2" | |
| top: "conv2_1/x2" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| eps: 1e-4 | |
| } | |
| } | |
| layer { | |
| name: "conv2_1/x2/scale" | |
| type: "Scale" | |
| bottom: "conv2_1/x2" | |
| top: "conv2_1/x2" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "relu2_1/x2" | |
| type: "ReLU" | |
| bottom: "conv2_1/x2" | |
| top: "conv2_1/x2" | |
| } | |
| layer { | |
| name: "conv2_1/x3" | |
| type: "Convolution" | |
| bottom: "conv2_1/x2" | |
| top: "conv2_1/x3" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv2_1/x3/bn" | |
| type: "BatchNorm" | |
| bottom: "conv2_1/x3" | |
| top: "conv2_1/x3" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| eps: 1e-4 | |
| } | |
| } | |
| layer { | |
| name: "conv2_1/x3/scale" | |
| type: "Scale" | |
| bottom: "conv2_1/x3" | |
| top: "conv2_1/x3" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_1/gap" | |
| type: "Pooling" | |
| bottom: "conv2_1/x3" | |
| top: "pool2_1/gap" | |
| pooling_param { | |
| pool: AVE | |
| global_pooling: true | |
| } | |
| } | |
| layer { | |
| name: "fc2_1/sqz" | |
| type: "InnerProduct" | |
| bottom: "pool2_1/gap" | |
| top: "fc2_1/sqz" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| inner_product_param { | |
| num_output: 16 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu2_1/sqz" | |
| type: "ReLU" | |
| bottom: "fc2_1/sqz" | |
| top: "fc2_1/sqz" | |
| } | |
| layer { | |
| name: "fc2_1/exc" | |
| type: "InnerProduct" | |
| bottom: "fc2_1/sqz" | |
| top: "fc2_1/exc" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| inner_product_param { | |
| num_output: 256 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "sigm2_1/gate" | |
| type: "Sigmoid" | |
| bottom: "fc2_1/exc" | |
| top: "fc2_1/exc" | |
| } | |
| layer { | |
| name: "scale2_1" | |
| type: "Scale" | |
| bottom: "conv2_1/x3" | |
| bottom: "fc2_1/exc" | |
| top: "scale2_1" | |
| scale_param { | |
| axis: 0 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| name: "block_2_1" | |
| type: "Eltwise" | |
| bottom: "conv2_1/prj" | |
| bottom: "scale2_1" | |
| top: "block_2_1" | |
| } | |
| layer { | |
| name: "relu2_1" | |
| type: "ReLU" | |
| bottom: "block_2_1" | |
| top: "block_2_1" | |
| } | |
| layer { | |
| name: "conv2_2/x1" | |
| type: "Convolution" | |
| bottom: "block_2_1" | |
| top: "conv2_2/x1" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| bias_term: false | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv2_2/x1/bn" | |
| type: "BatchNorm" | |
| bottom: "conv2_2/x1" | |
| top: "conv2_2/x1" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| eps: 1e-4 | |
| } | |
| } | |
| layer { | |
| name: "conv2_2/x1/scale" | |
| type: "Scale" | |
| bottom: "conv2_2/x1" | |
| top: "conv2_2/x1" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "relu2_2/x1" | |
| type: "ReLU" | |
| bottom: "conv2_2/x1" | |
| top: "conv2_2/x1" | |
| } | |
| layer { | |
| name: "conv2_2/x2" | |
| type: "Convolution" | |
| bottom: "conv2_2/x1" | |
| top: "conv2_2/x2" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv2_2/x2/bn" | |
| type: "BatchNorm" | |
| bottom: "conv2_2/x2" | |
| top: "conv2_2/x2" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| eps: 1e-4 | |
| } | |
| } | |
| layer { | |
| name: "conv2_2/x2/scale" | |
| type: "Scale" | |
| bottom: "conv2_2/x2" | |
| top: "conv2_2/x2" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "relu2_2/x2" | |
| type: "ReLU" | |
| bottom: "conv2_2/x2" | |
| top: "conv2_2/x2" | |
| } | |
| layer { | |
| name: "conv2_2/x3" | |
| type: "Convolution" | |
| bottom: "conv2_2/x2" | |
| top: "conv2_2/x3" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv2_2/x3/bn" | |
| type: "BatchNorm" | |
| bottom: "conv2_2/x3" | |
| top: "conv2_2/x3" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| eps: 1e-4 | |
| } | |
| } | |
| layer { | |
| name: "conv2_2/x3/scale" | |
| type: "Scale" | |
| bottom: "conv2_2/x3" | |
| top: "conv2_2/x3" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_2/gap" | |
| type: "Pooling" | |
| bottom: "conv2_2/x3" | |
| top: "pool2_2/gap" | |
| pooling_param { | |
| pool: AVE | |
| global_pooling: true | |
| } | |
| } | |
| layer { | |
| name: "fc2_2/sqz" | |
| type: "InnerProduct" | |
| bottom: "pool2_2/gap" | |
| top: "fc2_2/sqz" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| inner_product_param { | |
| num_output: 16 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu2_2/sqz" | |
| type: "ReLU" | |
| bottom: "fc2_2/sqz" | |
| top: "fc2_2/sqz" | |
| } | |
| layer { | |
| name: "fc2_2/exc" | |
| type: "InnerProduct" | |
| bottom: "fc2_2/sqz" | |
| top: "fc2_2/exc" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| inner_product_param { | |
| num_output: 256 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "sigm2_2/gate" | |
| type: "Sigmoid" | |
| bottom: "fc2_2/exc" | |
| top: "fc2_2/exc" | |
| } | |
| layer { | |
| name: "scale2_2" | |
| type: "Scale" | |
| bottom: "conv2_2/x3" | |
| bottom: "fc2_2/exc" | |
| top: "scale2_2" | |
| scale_param { | |
| axis: 0 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| name: "block_2_2" | |
| type: "Eltwise" | |
| bottom: "block_2_1" | |
| bottom: "scale2_2" | |
| top: "block_2_2" | |
| } | |
| layer { | |
| name: "relu2_2" | |
| type: "ReLU" | |
| bottom: "block_2_2" | |
| top: "block_2_2" | |
| } | |
| layer { | |
| name: "conv2_3/x1" | |
| type: "Convolution" | |
| bottom: "block_2_2" | |
| top: "conv2_3/x1" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| bias_term: false | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv2_3/x1/bn" | |
| type: "BatchNorm" | |
| bottom: "conv2_3/x1" | |
| top: "conv2_3/x1" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| eps: 1e-4 | |
| } | |
| } | |
| layer { | |
| name: "conv2_3/x1/scale" | |
| type: "Scale" | |
| bottom: "conv2_3/x1" | |
| top: "conv2_3/x1" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "relu2_3/x1" | |
| type: "ReLU" | |
| bottom: "conv2_3/x1" | |
| top: "conv2_3/x1" | |
| } | |
| layer { | |
| name: "conv2_3/x2" | |
| type: "Convolution" | |
| bottom: "conv2_3/x1" | |
| top: "conv2_3/x2" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv2_3/x2/bn" | |
| type: "BatchNorm" | |
| bottom: "conv2_3/x2" | |
| top: "conv2_3/x2" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| eps: 1e-4 | |
| } | |
| } | |
| layer { | |
| name: "conv2_3/x2/scale" | |
| type: "Scale" | |
| bottom: "conv2_3/x2" | |
| top: "conv2_3/x2" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "relu2_3/x2" | |
| type: "ReLU" | |
| bottom: "conv2_3/x2" | |
| top: "conv2_3/x2" | |
| } | |
| layer { | |
| name: "conv2_3/x3" | |
| type: "Convolution" | |
| bottom: "conv2_3/x2" | |
| top: "conv2_3/x3" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv2_3/x3/bn" | |
| type: "BatchNorm" | |
| bottom: "conv2_3/x3" | |
| top: "conv2_3/x3" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| eps: 1e-4 | |
| } | |
| } | |
| layer { | |
| name: "conv2_3/x3/scale" | |
| type: "Scale" | |
| bottom: "conv2_3/x3" | |
| top: "conv2_3/x3" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "pool2_3/gap" | |
| type: "Pooling" | |
| bottom: "conv2_3/x3" | |
| top: "pool2_3/gap" | |
| pooling_param { | |
| pool: AVE | |
| global_pooling: true | |
| } | |
| } | |
| layer { | |
| name: "fc2_3/sqz" | |
| type: "InnerProduct" | |
| bottom: "pool2_3/gap" | |
| top: "fc2_3/sqz" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| inner_product_param { | |
| num_output: 16 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu2_3/sqz" | |
| type: "ReLU" | |
| bottom: "fc2_3/sqz" | |
| top: "fc2_3/sqz" | |
| } | |
| layer { | |
| name: "fc2_3/exc" | |
| type: "InnerProduct" | |
| bottom: "fc2_3/sqz" | |
| top: "fc2_3/exc" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| inner_product_param { | |
| num_output: 256 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "sigm2_3/gate" | |
| type: "Sigmoid" | |
| bottom: "fc2_3/exc" | |
| top: "fc2_3/exc" | |
| } | |
| layer { | |
| name: "scale2_3" | |
| type: "Scale" | |
| bottom: "conv2_3/x3" | |
| bottom: "fc2_3/exc" | |
| top: "scale2_3" | |
| scale_param { | |
| axis: 0 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| name: "block_2_3" | |
| type: "Eltwise" | |
| bottom: "block_2_2" | |
| bottom: "scale2_3" | |
| top: "block_2_3" | |
| } | |
| layer { | |
| name: "relu2_3" | |
| type: "ReLU" | |
| bottom: "block_2_3" | |
| top: "block_2_3" | |
| } | |
| layer { | |
| name: "conv3_1/prj" | |
| type: "Convolution" | |
| bottom: "block_2_3" | |
| top: "conv3_1/prj" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| bias_term: false | |
| kernel_size: 1 | |
| stride: 2 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv3_1/prj/bn" | |
| type: "BatchNorm" | |
| bottom: "conv3_1/prj" | |
| top: "conv3_1/prj" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| eps: 1e-4 | |
| } | |
| } | |
| layer { | |
| name: "conv3_1/prj/scale" | |
| type: "Scale" | |
| bottom: "conv3_1/prj" | |
| top: "conv3_1/prj" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv3_1/x1" | |
| type: "Convolution" | |
| bottom: "block_2_3" | |
| top: "conv3_1/x1" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| bias_term: false | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv3_1/x1/bn" | |
| type: "BatchNorm" | |
| bottom: "conv3_1/x1" | |
| top: "conv3_1/x1" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| eps: 1e-4 | |
| } | |
| } | |
| layer { | |
| name: "conv3_1/x1/scale" | |
| type: "Scale" | |
| bottom: "conv3_1/x1" | |
| top: "conv3_1/x1" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "relu3_1/x1" | |
| type: "ReLU" | |
| bottom: "conv3_1/x1" | |
| top: "conv3_1/x1" | |
| } | |
| layer { | |
| name: "conv3_1/x2" | |
| type: "Convolution" | |
| bottom: "conv3_1/x1" | |
| top: "conv3_1/x2" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 2 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv3_1/x2/bn" | |
| type: "BatchNorm" | |
| bottom: "conv3_1/x2" | |
| top: "conv3_1/x2" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| eps: 1e-4 | |
| } | |
| } | |
| layer { | |
| name: "conv3_1/x2/scale" | |
| type: "Scale" | |
| bottom: "conv3_1/x2" | |
| top: "conv3_1/x2" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "relu3_1/x2" | |
| type: "ReLU" | |
| bottom: "conv3_1/x2" | |
| top: "conv3_1/x2" | |
| } | |
| layer { | |
| name: "conv3_1/x3" | |
| type: "Convolution" | |
| bottom: "conv3_1/x2" | |
| top: "conv3_1/x3" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| bias_term: false | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv3_1/x3/bn" | |
| type: "BatchNorm" | |
| bottom: "conv3_1/x3" | |
| top: "conv3_1/x3" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| eps: 1e-4 | |
| } | |
| } | |
| layer { | |
| name: "conv3_1/x3/scale" | |
| type: "Scale" | |
| bottom: "conv3_1/x3" | |
| top: "conv3_1/x3" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "pool3_1/gap" | |
| type: "Pooling" | |
| bottom: "conv3_1/x3" | |
| top: "pool3_1/gap" | |
| pooling_param { | |
| pool: AVE | |
| global_pooling: true | |
| } | |
| } | |
| layer { | |
| name: "fc3_1/sqz" | |
| type: "InnerProduct" | |
| bottom: "pool3_1/gap" | |
| top: "fc3_1/sqz" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| inner_product_param { | |
| num_output: 32 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu3_1/sqz" | |
| type: "ReLU" | |
| bottom: "fc3_1/sqz" | |
| top: "fc3_1/sqz" | |
| } | |
| layer { | |
| name: "fc3_1/exc" | |
| type: "InnerProduct" | |
| bottom: "fc3_1/sqz" | |
| top: "fc3_1/exc" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| inner_product_param { | |
| num_output: 512 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "sigm3_1/gate" | |
| type: "Sigmoid" | |
| bottom: "fc3_1/exc" | |
| top: "fc3_1/exc" | |
| } | |
| layer { | |
| name: "scale3_1" | |
| type: "Scale" | |
| bottom: "conv3_1/x3" | |
| bottom: "fc3_1/exc" | |
| top: "scale3_1" | |
| scale_param { | |
| axis: 0 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| name: "block_3_1" | |
| type: "Eltwise" | |
| bottom: "conv3_1/prj" | |
| bottom: "scale3_1" | |
| top: "block_3_1" | |
| } | |
| layer { | |
| name: "relu3_1" | |
| type: "ReLU" | |
| bottom: "block_3_1" | |
| top: "block_3_1" | |
| } | |
| layer { | |
| name: "conv3_2/x1" | |
| type: "Convolution" | |
| bottom: "block_3_1" | |
| top: "conv3_2/x1" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| bias_term: false | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv3_2/x1/bn" | |
| type: "BatchNorm" | |
| bottom: "conv3_2/x1" | |
| top: "conv3_2/x1" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| eps: 1e-4 | |
| } | |
| } | |
| layer { | |
| name: "conv3_2/x1/scale" | |
| type: "Scale" | |
| bottom: "conv3_2/x1" | |
| top: "conv3_2/x1" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "relu3_2/x1" | |
| type: "ReLU" | |
| bottom: "conv3_2/x1" | |
| top: "conv3_2/x1" | |
| } | |
| layer { | |
| name: "conv3_2/x2" | |
| type: "Convolution" | |
| bottom: "conv3_2/x1" | |
| top: "conv3_2/x2" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv3_2/x2/bn" | |
| type: "BatchNorm" | |
| bottom: "conv3_2/x2" | |
| top: "conv3_2/x2" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| eps: 1e-4 | |
| } | |
| } | |
| layer { | |
| name: "conv3_2/x2/scale" | |
| type: "Scale" | |
| bottom: "conv3_2/x2" | |
| top: "conv3_2/x2" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "relu3_2/x2" | |
| type: "ReLU" | |
| bottom: "conv3_2/x2" | |
| top: "conv3_2/x2" | |
| } | |
| layer { | |
| name: "conv3_2/x3" | |
| type: "Convolution" | |
| bottom: "conv3_2/x2" | |
| top: "conv3_2/x3" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| bias_term: false | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv3_2/x3/bn" | |
| type: "BatchNorm" | |
| bottom: "conv3_2/x3" | |
| top: "conv3_2/x3" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| eps: 1e-4 | |
| } | |
| } | |
| layer { | |
| name: "conv3_2/x3/scale" | |
| type: "Scale" | |
| bottom: "conv3_2/x3" | |
| top: "conv3_2/x3" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "pool3_2/gap" | |
| type: "Pooling" | |
| bottom: "conv3_2/x3" | |
| top: "pool3_2/gap" | |
| pooling_param { | |
| pool: AVE | |
| global_pooling: true | |
| } | |
| } | |
| layer { | |
| name: "fc3_2/sqz" | |
| type: "InnerProduct" | |
| bottom: "pool3_2/gap" | |
| top: "fc3_2/sqz" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| inner_product_param { | |
| num_output: 32 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu3_2/sqz" | |
| type: "ReLU" | |
| bottom: "fc3_2/sqz" | |
| top: "fc3_2/sqz" | |
| } | |
| layer { | |
| name: "fc3_2/exc" | |
| type: "InnerProduct" | |
| bottom: "fc3_2/sqz" | |
| top: "fc3_2/exc" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| inner_product_param { | |
| num_output: 512 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "sigm3_2/gate" | |
| type: "Sigmoid" | |
| bottom: "fc3_2/exc" | |
| top: "fc3_2/exc" | |
| } | |
| layer { | |
| name: "scale3_2" | |
| type: "Scale" | |
| bottom: "conv3_2/x3" | |
| bottom: "fc3_2/exc" | |
| top: "scale3_2" | |
| scale_param { | |
| axis: 0 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| name: "block_3_2" | |
| type: "Eltwise" | |
| bottom: "block_3_1" | |
| bottom: "scale3_2" | |
| top: "block_3_2" | |
| } | |
| layer { | |
| name: "relu3_2" | |
| type: "ReLU" | |
| bottom: "block_3_2" | |
| top: "block_3_2" | |
| } | |
| layer { | |
| name: "conv3_3/x1" | |
| type: "Convolution" | |
| bottom: "block_3_2" | |
| top: "conv3_3/x1" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| bias_term: false | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv3_3/x1/bn" | |
| type: "BatchNorm" | |
| bottom: "conv3_3/x1" | |
| top: "conv3_3/x1" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| eps: 1e-4 | |
| } | |
| } | |
| layer { | |
| name: "conv3_3/x1/scale" | |
| type: "Scale" | |
| bottom: "conv3_3/x1" | |
| top: "conv3_3/x1" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "relu3_3/x1" | |
| type: "ReLU" | |
| bottom: "conv3_3/x1" | |
| top: "conv3_3/x1" | |
| } | |
| layer { | |
| name: "conv3_3/x2" | |
| type: "Convolution" | |
| bottom: "conv3_3/x1" | |
| top: "conv3_3/x2" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv3_3/x2/bn" | |
| type: "BatchNorm" | |
| bottom: "conv3_3/x2" | |
| top: "conv3_3/x2" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| eps: 1e-4 | |
| } | |
| } | |
| layer { | |
| name: "conv3_3/x2/scale" | |
| type: "Scale" | |
| bottom: "conv3_3/x2" | |
| top: "conv3_3/x2" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "relu3_3/x2" | |
| type: "ReLU" | |
| bottom: "conv3_3/x2" | |
| top: "conv3_3/x2" | |
| } | |
| layer { | |
| name: "conv3_3/x3" | |
| type: "Convolution" | |
| bottom: "conv3_3/x2" | |
| top: "conv3_3/x3" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| bias_term: false | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv3_3/x3/bn" | |
| type: "BatchNorm" | |
| bottom: "conv3_3/x3" | |
| top: "conv3_3/x3" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| eps: 1e-4 | |
| } | |
| } | |
| layer { | |
| name: "conv3_3/x3/scale" | |
| type: "Scale" | |
| bottom: "conv3_3/x3" | |
| top: "conv3_3/x3" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "pool3_3/gap" | |
| type: "Pooling" | |
| bottom: "conv3_3/x3" | |
| top: "pool3_3/gap" | |
| pooling_param { | |
| pool: AVE | |
| global_pooling: true | |
| } | |
| } | |
| layer { | |
| name: "fc3_3/sqz" | |
| type: "InnerProduct" | |
| bottom: "pool3_3/gap" | |
| top: "fc3_3/sqz" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| inner_product_param { | |
| num_output: 32 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu3_3/sqz" | |
| type: "ReLU" | |
| bottom: "fc3_3/sqz" | |
| top: "fc3_3/sqz" | |
| } | |
| layer { | |
| name: "fc3_3/exc" | |
| type: "InnerProduct" | |
| bottom: "fc3_3/sqz" | |
| top: "fc3_3/exc" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| inner_product_param { | |
| num_output: 512 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "sigm3_3/gate" | |
| type: "Sigmoid" | |
| bottom: "fc3_3/exc" | |
| top: "fc3_3/exc" | |
| } | |
| layer { | |
| name: "scale3_3" | |
| type: "Scale" | |
| bottom: "conv3_3/x3" | |
| bottom: "fc3_3/exc" | |
| top: "scale3_3" | |
| scale_param { | |
| axis: 0 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| name: "block_3_3" | |
| type: "Eltwise" | |
| bottom: "block_3_2" | |
| bottom: "scale3_3" | |
| top: "block_3_3" | |
| } | |
| layer { | |
| name: "relu3_3" | |
| type: "ReLU" | |
| bottom: "block_3_3" | |
| top: "block_3_3" | |
| } | |
| layer { | |
| name: "conv3_4/x1" | |
| type: "Convolution" | |
| bottom: "block_3_3" | |
| top: "conv3_4/x1" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| bias_term: false | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv3_4/x1/bn" | |
| type: "BatchNorm" | |
| bottom: "conv3_4/x1" | |
| top: "conv3_4/x1" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| eps: 1e-4 | |
| } | |
| } | |
| layer { | |
| name: "conv3_4/x1/scale" | |
| type: "Scale" | |
| bottom: "conv3_4/x1" | |
| top: "conv3_4/x1" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "relu3_4/x1" | |
| type: "ReLU" | |
| bottom: "conv3_4/x1" | |
| top: "conv3_4/x1" | |
| } | |
| layer { | |
| name: "conv3_4/x2" | |
| type: "Convolution" | |
| bottom: "conv3_4/x1" | |
| top: "conv3_4/x2" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv3_4/x2/bn" | |
| type: "BatchNorm" | |
| bottom: "conv3_4/x2" | |
| top: "conv3_4/x2" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| eps: 1e-4 | |
| } | |
| } | |
| layer { | |
| name: "conv3_4/x2/scale" | |
| type: "Scale" | |
| bottom: "conv3_4/x2" | |
| top: "conv3_4/x2" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "relu3_4/x2" | |
| type: "ReLU" | |
| bottom: "conv3_4/x2" | |
| top: "conv3_4/x2" | |
| } | |
| layer { | |
| name: "conv3_4/x3" | |
| type: "Convolution" | |
| bottom: "conv3_4/x2" | |
| top: "conv3_4/x3" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| bias_term: false | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv3_4/x3/bn" | |
| type: "BatchNorm" | |
| bottom: "conv3_4/x3" | |
| top: "conv3_4/x3" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| eps: 1e-4 | |
| } | |
| } | |
| layer { | |
| name: "conv3_4/x3/scale" | |
| type: "Scale" | |
| bottom: "conv3_4/x3" | |
| top: "conv3_4/x3" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "pool3_4/gap" | |
| type: "Pooling" | |
| bottom: "conv3_4/x3" | |
| top: "pool3_4/gap" | |
| pooling_param { | |
| pool: AVE | |
| global_pooling: true | |
| } | |
| } | |
| layer { | |
| name: "fc3_4/sqz" | |
| type: "InnerProduct" | |
| bottom: "pool3_4/gap" | |
| top: "fc3_4/sqz" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| inner_product_param { | |
| num_output: 32 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu3_4/sqz" | |
| type: "ReLU" | |
| bottom: "fc3_4/sqz" | |
| top: "fc3_4/sqz" | |
| } | |
| layer { | |
| name: "fc3_4/exc" | |
| type: "InnerProduct" | |
| bottom: "fc3_4/sqz" | |
| top: "fc3_4/exc" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| inner_product_param { | |
| num_output: 512 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "sigm3_4/gate" | |
| type: "Sigmoid" | |
| bottom: "fc3_4/exc" | |
| top: "fc3_4/exc" | |
| } | |
| layer { | |
| name: "scale3_4" | |
| type: "Scale" | |
| bottom: "conv3_4/x3" | |
| bottom: "fc3_4/exc" | |
| top: "scale3_4" | |
| scale_param { | |
| axis: 0 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| name: "block_3_4" | |
| type: "Eltwise" | |
| bottom: "block_3_3" | |
| bottom: "scale3_4" | |
| top: "block_3_4" | |
| } | |
| layer { | |
| name: "relu3_4" | |
| type: "ReLU" | |
| bottom: "block_3_4" | |
| top: "block_3_4" | |
| } | |
| layer { | |
| name: "conv4_1/prj" | |
| type: "Convolution" | |
| bottom: "block_3_4" | |
| top: "conv4_1/prj" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| convolution_param { | |
| num_output: 1024 | |
| bias_term: false | |
| kernel_size: 1 | |
| stride: 2 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv4_1/prj/bn" | |
| type: "BatchNorm" | |
| bottom: "conv4_1/prj" | |
| top: "conv4_1/prj" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| eps: 1e-4 | |
| } | |
| } | |
| layer { | |
| name: "conv4_1/prj/scale" | |
| type: "Scale" | |
| bottom: "conv4_1/prj" | |
| top: "conv4_1/prj" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv4_1/x1" | |
| type: "Convolution" | |
| bottom: "block_3_4" | |
| top: "conv4_1/x1" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv4_1/x1/bn" | |
| type: "BatchNorm" | |
| bottom: "conv4_1/x1" | |
| top: "conv4_1/x1" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| eps: 1e-4 | |
| } | |
| } | |
| layer { | |
| name: "conv4_1/x1/scale" | |
| type: "Scale" | |
| bottom: "conv4_1/x1" | |
| top: "conv4_1/x1" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "relu4_1/x1" | |
| type: "ReLU" | |
| bottom: "conv4_1/x1" | |
| top: "conv4_1/x1" | |
| } | |
| layer { | |
| name: "conv4_1/x2" | |
| type: "Convolution" | |
| bottom: "conv4_1/x1" | |
| top: "conv4_1/x2" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 2 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv4_1/x2/bn" | |
| type: "BatchNorm" | |
| bottom: "conv4_1/x2" | |
| top: "conv4_1/x2" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| eps: 1e-4 | |
| } | |
| } | |
| layer { | |
| name: "conv4_1/x2/scale" | |
| type: "Scale" | |
| bottom: "conv4_1/x2" | |
| top: "conv4_1/x2" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "relu4_1/x2" | |
| type: "ReLU" | |
| bottom: "conv4_1/x2" | |
| top: "conv4_1/x2" | |
| } | |
| layer { | |
| name: "conv4_1/x3" | |
| type: "Convolution" | |
| bottom: "conv4_1/x2" | |
| top: "conv4_1/x3" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| convolution_param { | |
| num_output: 1024 | |
| bias_term: false | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv4_1/x3/bn" | |
| type: "BatchNorm" | |
| bottom: "conv4_1/x3" | |
| top: "conv4_1/x3" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| eps: 1e-4 | |
| } | |
| } | |
| layer { | |
| name: "conv4_1/x3/scale" | |
| type: "Scale" | |
| bottom: "conv4_1/x3" | |
| top: "conv4_1/x3" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "pool4_1/gap" | |
| type: "Pooling" | |
| bottom: "conv4_1/x3" | |
| top: "pool4_1/gap" | |
| pooling_param { | |
| pool: AVE | |
| global_pooling: true | |
| } | |
| } | |
| layer { | |
| name: "fc4_1/sqz" | |
| type: "InnerProduct" | |
| bottom: "pool4_1/gap" | |
| top: "fc4_1/sqz" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| inner_product_param { | |
| num_output: 64 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu4_1/sqz" | |
| type: "ReLU" | |
| bottom: "fc4_1/sqz" | |
| top: "fc4_1/sqz" | |
| } | |
| layer { | |
| name: "fc4_1/exc" | |
| type: "InnerProduct" | |
| bottom: "fc4_1/sqz" | |
| top: "fc4_1/exc" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| inner_product_param { | |
| num_output: 1024 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "sigm4_1/gate" | |
| type: "Sigmoid" | |
| bottom: "fc4_1/exc" | |
| top: "fc4_1/exc" | |
| } | |
| layer { | |
| name: "scale4_1" | |
| type: "Scale" | |
| bottom: "conv4_1/x3" | |
| bottom: "fc4_1/exc" | |
| top: "scale4_1" | |
| scale_param { | |
| axis: 0 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| name: "block_4_1" | |
| type: "Eltwise" | |
| bottom: "conv4_1/prj" | |
| bottom: "scale4_1" | |
| top: "block_4_1" | |
| } | |
| layer { | |
| name: "relu4_1" | |
| type: "ReLU" | |
| bottom: "block_4_1" | |
| top: "block_4_1" | |
| } | |
| layer { | |
| name: "conv4_2/x1" | |
| type: "Convolution" | |
| bottom: "block_4_1" | |
| top: "conv4_2/x1" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv4_2/x1/bn" | |
| type: "BatchNorm" | |
| bottom: "conv4_2/x1" | |
| top: "conv4_2/x1" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| eps: 1e-4 | |
| } | |
| } | |
| layer { | |
| name: "conv4_2/x1/scale" | |
| type: "Scale" | |
| bottom: "conv4_2/x1" | |
| top: "conv4_2/x1" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "relu4_2/x1" | |
| type: "ReLU" | |
| bottom: "conv4_2/x1" | |
| top: "conv4_2/x1" | |
| } | |
| layer { | |
| name: "conv4_2/x2" | |
| type: "Convolution" | |
| bottom: "conv4_2/x1" | |
| top: "conv4_2/x2" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv4_2/x2/bn" | |
| type: "BatchNorm" | |
| bottom: "conv4_2/x2" | |
| top: "conv4_2/x2" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| eps: 1e-4 | |
| } | |
| } | |
| layer { | |
| name: "conv4_2/x2/scale" | |
| type: "Scale" | |
| bottom: "conv4_2/x2" | |
| top: "conv4_2/x2" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "relu4_2/x2" | |
| type: "ReLU" | |
| bottom: "conv4_2/x2" | |
| top: "conv4_2/x2" | |
| } | |
| layer { | |
| name: "conv4_2/x3" | |
| type: "Convolution" | |
| bottom: "conv4_2/x2" | |
| top: "conv4_2/x3" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| convolution_param { | |
| num_output: 1024 | |
| bias_term: false | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv4_2/x3/bn" | |
| type: "BatchNorm" | |
| bottom: "conv4_2/x3" | |
| top: "conv4_2/x3" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| eps: 1e-4 | |
| } | |
| } | |
| layer { | |
| name: "conv4_2/x3/scale" | |
| type: "Scale" | |
| bottom: "conv4_2/x3" | |
| top: "conv4_2/x3" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "pool4_2/gap" | |
| type: "Pooling" | |
| bottom: "conv4_2/x3" | |
| top: "pool4_2/gap" | |
| pooling_param { | |
| pool: AVE | |
| global_pooling: true | |
| } | |
| } | |
| layer { | |
| name: "fc4_2/sqz" | |
| type: "InnerProduct" | |
| bottom: "pool4_2/gap" | |
| top: "fc4_2/sqz" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| inner_product_param { | |
| num_output: 64 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu4_2/sqz" | |
| type: "ReLU" | |
| bottom: "fc4_2/sqz" | |
| top: "fc4_2/sqz" | |
| } | |
| layer { | |
| name: "fc4_2/exc" | |
| type: "InnerProduct" | |
| bottom: "fc4_2/sqz" | |
| top: "fc4_2/exc" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| inner_product_param { | |
| num_output: 1024 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "sigm4_2/gate" | |
| type: "Sigmoid" | |
| bottom: "fc4_2/exc" | |
| top: "fc4_2/exc" | |
| } | |
| layer { | |
| name: "scale4_2" | |
| type: "Scale" | |
| bottom: "conv4_2/x3" | |
| bottom: "fc4_2/exc" | |
| top: "scale4_2" | |
| scale_param { | |
| axis: 0 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| name: "block_4_2" | |
| type: "Eltwise" | |
| bottom: "block_4_1" | |
| bottom: "scale4_2" | |
| top: "block_4_2" | |
| } | |
| layer { | |
| name: "relu4_2" | |
| type: "ReLU" | |
| bottom: "block_4_2" | |
| top: "block_4_2" | |
| } | |
| layer { | |
| name: "conv4_3/x1" | |
| type: "Convolution" | |
| bottom: "block_4_2" | |
| top: "conv4_3/x1" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv4_3/x1/bn" | |
| type: "BatchNorm" | |
| bottom: "conv4_3/x1" | |
| top: "conv4_3/x1" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| eps: 1e-4 | |
| } | |
| } | |
| layer { | |
| name: "conv4_3/x1/scale" | |
| type: "Scale" | |
| bottom: "conv4_3/x1" | |
| top: "conv4_3/x1" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "relu4_3/x1" | |
| type: "ReLU" | |
| bottom: "conv4_3/x1" | |
| top: "conv4_3/x1" | |
| } | |
| layer { | |
| name: "conv4_3/x2" | |
| type: "Convolution" | |
| bottom: "conv4_3/x1" | |
| top: "conv4_3/x2" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv4_3/x2/bn" | |
| type: "BatchNorm" | |
| bottom: "conv4_3/x2" | |
| top: "conv4_3/x2" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| eps: 1e-4 | |
| } | |
| } | |
| layer { | |
| name: "conv4_3/x2/scale" | |
| type: "Scale" | |
| bottom: "conv4_3/x2" | |
| top: "conv4_3/x2" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "relu4_3/x2" | |
| type: "ReLU" | |
| bottom: "conv4_3/x2" | |
| top: "conv4_3/x2" | |
| } | |
| layer { | |
| name: "conv4_3/x3" | |
| type: "Convolution" | |
| bottom: "conv4_3/x2" | |
| top: "conv4_3/x3" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| convolution_param { | |
| num_output: 1024 | |
| bias_term: false | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv4_3/x3/bn" | |
| type: "BatchNorm" | |
| bottom: "conv4_3/x3" | |
| top: "conv4_3/x3" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| eps: 1e-4 | |
| } | |
| } | |
| layer { | |
| name: "conv4_3/x3/scale" | |
| type: "Scale" | |
| bottom: "conv4_3/x3" | |
| top: "conv4_3/x3" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "pool4_3/gap" | |
| type: "Pooling" | |
| bottom: "conv4_3/x3" | |
| top: "pool4_3/gap" | |
| pooling_param { | |
| pool: AVE | |
| global_pooling: true | |
| } | |
| } | |
| layer { | |
| name: "fc4_3/sqz" | |
| type: "InnerProduct" | |
| bottom: "pool4_3/gap" | |
| top: "fc4_3/sqz" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| inner_product_param { | |
| num_output: 64 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu4_3/sqz" | |
| type: "ReLU" | |
| bottom: "fc4_3/sqz" | |
| top: "fc4_3/sqz" | |
| } | |
| layer { | |
| name: "fc4_3/exc" | |
| type: "InnerProduct" | |
| bottom: "fc4_3/sqz" | |
| top: "fc4_3/exc" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| inner_product_param { | |
| num_output: 1024 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "sigm4_3/gate" | |
| type: "Sigmoid" | |
| bottom: "fc4_3/exc" | |
| top: "fc4_3/exc" | |
| } | |
| layer { | |
| name: "scale4_3" | |
| type: "Scale" | |
| bottom: "conv4_3/x3" | |
| bottom: "fc4_3/exc" | |
| top: "scale4_3" | |
| scale_param { | |
| axis: 0 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| name: "block_4_3" | |
| type: "Eltwise" | |
| bottom: "block_4_2" | |
| bottom: "scale4_3" | |
| top: "block_4_3" | |
| } | |
| layer { | |
| name: "relu4_3" | |
| type: "ReLU" | |
| bottom: "block_4_3" | |
| top: "block_4_3" | |
| } | |
| layer { | |
| name: "conv4_4/x1" | |
| type: "Convolution" | |
| bottom: "block_4_3" | |
| top: "conv4_4/x1" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv4_4/x1/bn" | |
| type: "BatchNorm" | |
| bottom: "conv4_4/x1" | |
| top: "conv4_4/x1" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| eps: 1e-4 | |
| } | |
| } | |
| layer { | |
| name: "conv4_4/x1/scale" | |
| type: "Scale" | |
| bottom: "conv4_4/x1" | |
| top: "conv4_4/x1" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "relu4_4/x1" | |
| type: "ReLU" | |
| bottom: "conv4_4/x1" | |
| top: "conv4_4/x1" | |
| } | |
| layer { | |
| name: "conv4_4/x2" | |
| type: "Convolution" | |
| bottom: "conv4_4/x1" | |
| top: "conv4_4/x2" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv4_4/x2/bn" | |
| type: "BatchNorm" | |
| bottom: "conv4_4/x2" | |
| top: "conv4_4/x2" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| eps: 1e-4 | |
| } | |
| } | |
| layer { | |
| name: "conv4_4/x2/scale" | |
| type: "Scale" | |
| bottom: "conv4_4/x2" | |
| top: "conv4_4/x2" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "relu4_4/x2" | |
| type: "ReLU" | |
| bottom: "conv4_4/x2" | |
| top: "conv4_4/x2" | |
| } | |
| layer { | |
| name: "conv4_4/x3" | |
| type: "Convolution" | |
| bottom: "conv4_4/x2" | |
| top: "conv4_4/x3" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| convolution_param { | |
| num_output: 1024 | |
| bias_term: false | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv4_4/x3/bn" | |
| type: "BatchNorm" | |
| bottom: "conv4_4/x3" | |
| top: "conv4_4/x3" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| eps: 1e-4 | |
| } | |
| } | |
| layer { | |
| name: "conv4_4/x3/scale" | |
| type: "Scale" | |
| bottom: "conv4_4/x3" | |
| top: "conv4_4/x3" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "pool4_4/gap" | |
| type: "Pooling" | |
| bottom: "conv4_4/x3" | |
| top: "pool4_4/gap" | |
| pooling_param { | |
| pool: AVE | |
| global_pooling: true | |
| } | |
| } | |
| layer { | |
| name: "fc4_4/sqz" | |
| type: "InnerProduct" | |
| bottom: "pool4_4/gap" | |
| top: "fc4_4/sqz" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| inner_product_param { | |
| num_output: 64 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu4_4/sqz" | |
| type: "ReLU" | |
| bottom: "fc4_4/sqz" | |
| top: "fc4_4/sqz" | |
| } | |
| layer { | |
| name: "fc4_4/exc" | |
| type: "InnerProduct" | |
| bottom: "fc4_4/sqz" | |
| top: "fc4_4/exc" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| inner_product_param { | |
| num_output: 1024 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "sigm4_4/gate" | |
| type: "Sigmoid" | |
| bottom: "fc4_4/exc" | |
| top: "fc4_4/exc" | |
| } | |
| layer { | |
| name: "scale4_4" | |
| type: "Scale" | |
| bottom: "conv4_4/x3" | |
| bottom: "fc4_4/exc" | |
| top: "scale4_4" | |
| scale_param { | |
| axis: 0 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| name: "block_4_4" | |
| type: "Eltwise" | |
| bottom: "block_4_3" | |
| bottom: "scale4_4" | |
| top: "block_4_4" | |
| } | |
| layer { | |
| name: "relu4_4" | |
| type: "ReLU" | |
| bottom: "block_4_4" | |
| top: "block_4_4" | |
| } | |
| layer { | |
| name: "conv4_5/x1" | |
| type: "Convolution" | |
| bottom: "block_4_4" | |
| top: "conv4_5/x1" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv4_5/x1/bn" | |
| type: "BatchNorm" | |
| bottom: "conv4_5/x1" | |
| top: "conv4_5/x1" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| eps: 1e-4 | |
| } | |
| } | |
| layer { | |
| name: "conv4_5/x1/scale" | |
| type: "Scale" | |
| bottom: "conv4_5/x1" | |
| top: "conv4_5/x1" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "relu4_5/x1" | |
| type: "ReLU" | |
| bottom: "conv4_5/x1" | |
| top: "conv4_5/x1" | |
| } | |
| layer { | |
| name: "conv4_5/x2" | |
| type: "Convolution" | |
| bottom: "conv4_5/x1" | |
| top: "conv4_5/x2" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv4_5/x2/bn" | |
| type: "BatchNorm" | |
| bottom: "conv4_5/x2" | |
| top: "conv4_5/x2" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| eps: 1e-4 | |
| } | |
| } | |
| layer { | |
| name: "conv4_5/x2/scale" | |
| type: "Scale" | |
| bottom: "conv4_5/x2" | |
| top: "conv4_5/x2" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "relu4_5/x2" | |
| type: "ReLU" | |
| bottom: "conv4_5/x2" | |
| top: "conv4_5/x2" | |
| } | |
| layer { | |
| name: "conv4_5/x3" | |
| type: "Convolution" | |
| bottom: "conv4_5/x2" | |
| top: "conv4_5/x3" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| convolution_param { | |
| num_output: 1024 | |
| bias_term: false | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv4_5/x3/bn" | |
| type: "BatchNorm" | |
| bottom: "conv4_5/x3" | |
| top: "conv4_5/x3" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| eps: 1e-4 | |
| } | |
| } | |
| layer { | |
| name: "conv4_5/x3/scale" | |
| type: "Scale" | |
| bottom: "conv4_5/x3" | |
| top: "conv4_5/x3" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "pool4_5/gap" | |
| type: "Pooling" | |
| bottom: "conv4_5/x3" | |
| top: "pool4_5/gap" | |
| pooling_param { | |
| pool: AVE | |
| global_pooling: true | |
| } | |
| } | |
| layer { | |
| name: "fc4_5/sqz" | |
| type: "InnerProduct" | |
| bottom: "pool4_5/gap" | |
| top: "fc4_5/sqz" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| inner_product_param { | |
| num_output: 64 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu4_5/sqz" | |
| type: "ReLU" | |
| bottom: "fc4_5/sqz" | |
| top: "fc4_5/sqz" | |
| } | |
| layer { | |
| name: "fc4_5/exc" | |
| type: "InnerProduct" | |
| bottom: "fc4_5/sqz" | |
| top: "fc4_5/exc" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| inner_product_param { | |
| num_output: 1024 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "sigm4_5/gate" | |
| type: "Sigmoid" | |
| bottom: "fc4_5/exc" | |
| top: "fc4_5/exc" | |
| } | |
| layer { | |
| name: "scale4_5" | |
| type: "Scale" | |
| bottom: "conv4_5/x3" | |
| bottom: "fc4_5/exc" | |
| top: "scale4_5" | |
| scale_param { | |
| axis: 0 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| name: "block_4_5" | |
| type: "Eltwise" | |
| bottom: "block_4_4" | |
| bottom: "scale4_5" | |
| top: "block_4_5" | |
| } | |
| layer { | |
| name: "relu4_5" | |
| type: "ReLU" | |
| bottom: "block_4_5" | |
| top: "block_4_5" | |
| } | |
| layer { | |
| name: "conv4_6/x1" | |
| type: "Convolution" | |
| bottom: "block_4_5" | |
| top: "conv4_6/x1" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv4_6/x1/bn" | |
| type: "BatchNorm" | |
| bottom: "conv4_6/x1" | |
| top: "conv4_6/x1" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| eps: 1e-4 | |
| } | |
| } | |
| layer { | |
| name: "conv4_6/x1/scale" | |
| type: "Scale" | |
| bottom: "conv4_6/x1" | |
| top: "conv4_6/x1" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "relu4_6/x1" | |
| type: "ReLU" | |
| bottom: "conv4_6/x1" | |
| top: "conv4_6/x1" | |
| } | |
| layer { | |
| name: "conv4_6/x2" | |
| type: "Convolution" | |
| bottom: "conv4_6/x1" | |
| top: "conv4_6/x2" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv4_6/x2/bn" | |
| type: "BatchNorm" | |
| bottom: "conv4_6/x2" | |
| top: "conv4_6/x2" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| eps: 1e-4 | |
| } | |
| } | |
| layer { | |
| name: "conv4_6/x2/scale" | |
| type: "Scale" | |
| bottom: "conv4_6/x2" | |
| top: "conv4_6/x2" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "relu4_6/x2" | |
| type: "ReLU" | |
| bottom: "conv4_6/x2" | |
| top: "conv4_6/x2" | |
| } | |
| layer { | |
| name: "conv4_6/x3" | |
| type: "Convolution" | |
| bottom: "conv4_6/x2" | |
| top: "conv4_6/x3" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| convolution_param { | |
| num_output: 1024 | |
| bias_term: false | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv4_6/x3/bn" | |
| type: "BatchNorm" | |
| bottom: "conv4_6/x3" | |
| top: "conv4_6/x3" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| eps: 1e-4 | |
| } | |
| } | |
| layer { | |
| name: "conv4_6/x3/scale" | |
| type: "Scale" | |
| bottom: "conv4_6/x3" | |
| top: "conv4_6/x3" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "pool4_6/gap" | |
| type: "Pooling" | |
| bottom: "conv4_6/x3" | |
| top: "pool4_6/gap" | |
| pooling_param { | |
| pool: AVE | |
| global_pooling: true | |
| } | |
| } | |
| layer { | |
| name: "fc4_6/sqz" | |
| type: "InnerProduct" | |
| bottom: "pool4_6/gap" | |
| top: "fc4_6/sqz" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| inner_product_param { | |
| num_output: 64 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu4_6/sqz" | |
| type: "ReLU" | |
| bottom: "fc4_6/sqz" | |
| top: "fc4_6/sqz" | |
| } | |
| layer { | |
| name: "fc4_6/exc" | |
| type: "InnerProduct" | |
| bottom: "fc4_6/sqz" | |
| top: "fc4_6/exc" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| inner_product_param { | |
| num_output: 1024 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "sigm4_6/gate" | |
| type: "Sigmoid" | |
| bottom: "fc4_6/exc" | |
| top: "fc4_6/exc" | |
| } | |
| layer { | |
| name: "scale4_6" | |
| type: "Scale" | |
| bottom: "conv4_6/x3" | |
| bottom: "fc4_6/exc" | |
| top: "scale4_6" | |
| scale_param { | |
| axis: 0 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| name: "block_4_6" | |
| type: "Eltwise" | |
| bottom: "block_4_5" | |
| bottom: "scale4_6" | |
| top: "block_4_6" | |
| } | |
| layer { | |
| name: "relu4_6" | |
| type: "ReLU" | |
| bottom: "block_4_6" | |
| top: "block_4_6" | |
| } | |
| layer { | |
| name: "conv5_1/prj" | |
| type: "Convolution" | |
| bottom: "block_4_6" | |
| top: "conv5_1/prj" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| convolution_param { | |
| num_output: 2048 | |
| bias_term: false | |
| kernel_size: 1 | |
| stride: 2 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv5_1/prj/bn" | |
| type: "BatchNorm" | |
| bottom: "conv5_1/prj" | |
| top: "conv5_1/prj" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| eps: 1e-4 | |
| } | |
| } | |
| layer { | |
| name: "conv5_1/prj/scale" | |
| type: "Scale" | |
| bottom: "conv5_1/prj" | |
| top: "conv5_1/prj" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "conv5_1/x1" | |
| type: "Convolution" | |
| bottom: "block_4_6" | |
| top: "conv5_1/x1" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| bias_term: false | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv5_1/x1/bn" | |
| type: "BatchNorm" | |
| bottom: "conv5_1/x1" | |
| top: "conv5_1/x1" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| eps: 1e-4 | |
| } | |
| } | |
| layer { | |
| name: "conv5_1/x1/scale" | |
| type: "Scale" | |
| bottom: "conv5_1/x1" | |
| top: "conv5_1/x1" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "relu5_1/x1" | |
| type: "ReLU" | |
| bottom: "conv5_1/x1" | |
| top: "conv5_1/x1" | |
| } | |
| layer { | |
| name: "conv5_1/x2" | |
| type: "Convolution" | |
| bottom: "conv5_1/x1" | |
| top: "conv5_1/x2" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 2 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv5_1/x2/bn" | |
| type: "BatchNorm" | |
| bottom: "conv5_1/x2" | |
| top: "conv5_1/x2" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| eps: 1e-4 | |
| } | |
| } | |
| layer { | |
| name: "conv5_1/x2/scale" | |
| type: "Scale" | |
| bottom: "conv5_1/x2" | |
| top: "conv5_1/x2" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "relu5_1/x2" | |
| type: "ReLU" | |
| bottom: "conv5_1/x2" | |
| top: "conv5_1/x2" | |
| } | |
| layer { | |
| name: "conv5_1/x3" | |
| type: "Convolution" | |
| bottom: "conv5_1/x2" | |
| top: "conv5_1/x3" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| convolution_param { | |
| num_output: 2048 | |
| bias_term: false | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv5_1/x3/bn" | |
| type: "BatchNorm" | |
| bottom: "conv5_1/x3" | |
| top: "conv5_1/x3" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| eps: 1e-4 | |
| } | |
| } | |
| layer { | |
| name: "conv5_1/x3/scale" | |
| type: "Scale" | |
| bottom: "conv5_1/x3" | |
| top: "conv5_1/x3" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "pool5_1/gap" | |
| type: "Pooling" | |
| bottom: "conv5_1/x3" | |
| top: "pool5_1/gap" | |
| pooling_param { | |
| pool: AVE | |
| global_pooling: true | |
| } | |
| } | |
| layer { | |
| name: "fc5_1/sqz" | |
| type: "InnerProduct" | |
| bottom: "pool5_1/gap" | |
| top: "fc5_1/sqz" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| inner_product_param { | |
| num_output: 128 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu5_1/sqz" | |
| type: "ReLU" | |
| bottom: "fc5_1/sqz" | |
| top: "fc5_1/sqz" | |
| } | |
| layer { | |
| name: "fc5_1/exc" | |
| type: "InnerProduct" | |
| bottom: "fc5_1/sqz" | |
| top: "fc5_1/exc" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| inner_product_param { | |
| num_output: 2048 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "sigm5_1/gate" | |
| type: "Sigmoid" | |
| bottom: "fc5_1/exc" | |
| top: "fc5_1/exc" | |
| } | |
| layer { | |
| name: "scale5_1" | |
| type: "Scale" | |
| bottom: "conv5_1/x3" | |
| bottom: "fc5_1/exc" | |
| top: "scale5_1" | |
| scale_param { | |
| axis: 0 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| name: "block_5_1" | |
| type: "Eltwise" | |
| bottom: "conv5_1/prj" | |
| bottom: "scale5_1" | |
| top: "block_5_1" | |
| } | |
| layer { | |
| name: "relu5_1" | |
| type: "ReLU" | |
| bottom: "block_5_1" | |
| top: "block_5_1" | |
| } | |
| layer { | |
| name: "conv5_2/x1" | |
| type: "Convolution" | |
| bottom: "block_5_1" | |
| top: "conv5_2/x1" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| bias_term: false | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv5_2/x1/bn" | |
| type: "BatchNorm" | |
| bottom: "conv5_2/x1" | |
| top: "conv5_2/x1" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| eps: 1e-4 | |
| } | |
| } | |
| layer { | |
| name: "conv5_2/x1/scale" | |
| type: "Scale" | |
| bottom: "conv5_2/x1" | |
| top: "conv5_2/x1" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "relu5_2/x1" | |
| type: "ReLU" | |
| bottom: "conv5_2/x1" | |
| top: "conv5_2/x1" | |
| } | |
| layer { | |
| name: "conv5_2/x2" | |
| type: "Convolution" | |
| bottom: "conv5_2/x1" | |
| top: "conv5_2/x2" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv5_2/x2/bn" | |
| type: "BatchNorm" | |
| bottom: "conv5_2/x2" | |
| top: "conv5_2/x2" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| eps: 1e-4 | |
| } | |
| } | |
| layer { | |
| name: "conv5_2/x2/scale" | |
| type: "Scale" | |
| bottom: "conv5_2/x2" | |
| top: "conv5_2/x2" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "relu5_2/x2" | |
| type: "ReLU" | |
| bottom: "conv5_2/x2" | |
| top: "conv5_2/x2" | |
| } | |
| layer { | |
| name: "conv5_2/x3" | |
| type: "Convolution" | |
| bottom: "conv5_2/x2" | |
| top: "conv5_2/x3" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| convolution_param { | |
| num_output: 2048 | |
| bias_term: false | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv5_2/x3/bn" | |
| type: "BatchNorm" | |
| bottom: "conv5_2/x3" | |
| top: "conv5_2/x3" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| eps: 1e-4 | |
| } | |
| } | |
| layer { | |
| name: "conv5_2/x3/scale" | |
| type: "Scale" | |
| bottom: "conv5_2/x3" | |
| top: "conv5_2/x3" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "pool5_2/gap" | |
| type: "Pooling" | |
| bottom: "conv5_2/x3" | |
| top: "pool5_2/gap" | |
| pooling_param { | |
| pool: AVE | |
| global_pooling: true | |
| } | |
| } | |
| layer { | |
| name: "fc5_2/sqz" | |
| type: "InnerProduct" | |
| bottom: "pool5_2/gap" | |
| top: "fc5_2/sqz" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| inner_product_param { | |
| num_output: 128 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu5_2/sqz" | |
| type: "ReLU" | |
| bottom: "fc5_2/sqz" | |
| top: "fc5_2/sqz" | |
| } | |
| layer { | |
| name: "fc5_2/exc" | |
| type: "InnerProduct" | |
| bottom: "fc5_2/sqz" | |
| top: "fc5_2/exc" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| inner_product_param { | |
| num_output: 2048 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "sigm5_2/gate" | |
| type: "Sigmoid" | |
| bottom: "fc5_2/exc" | |
| top: "fc5_2/exc" | |
| } | |
| layer { | |
| name: "scale5_2" | |
| type: "Scale" | |
| bottom: "conv5_2/x3" | |
| bottom: "fc5_2/exc" | |
| top: "scale5_2" | |
| scale_param { | |
| axis: 0 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| name: "block_5_2" | |
| type: "Eltwise" | |
| bottom: "block_5_1" | |
| bottom: "scale5_2" | |
| top: "block_5_2" | |
| } | |
| layer { | |
| name: "relu5_2" | |
| type: "ReLU" | |
| bottom: "block_5_2" | |
| top: "block_5_2" | |
| } | |
| layer { | |
| name: "conv5_3/x1" | |
| type: "Convolution" | |
| bottom: "block_5_2" | |
| top: "conv5_3/x1" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| bias_term: false | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv5_3/x1/bn" | |
| type: "BatchNorm" | |
| bottom: "conv5_3/x1" | |
| top: "conv5_3/x1" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| eps: 1e-4 | |
| } | |
| } | |
| layer { | |
| name: "conv5_3/x1/scale" | |
| type: "Scale" | |
| bottom: "conv5_3/x1" | |
| top: "conv5_3/x1" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "relu5_3/x1" | |
| type: "ReLU" | |
| bottom: "conv5_3/x1" | |
| top: "conv5_3/x1" | |
| } | |
| layer { | |
| name: "conv5_3/x2" | |
| type: "Convolution" | |
| bottom: "conv5_3/x1" | |
| top: "conv5_3/x2" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv5_3/x2/bn" | |
| type: "BatchNorm" | |
| bottom: "conv5_3/x2" | |
| top: "conv5_3/x2" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| eps: 1e-4 | |
| } | |
| } | |
| layer { | |
| name: "conv5_3/x2/scale" | |
| type: "Scale" | |
| bottom: "conv5_3/x2" | |
| top: "conv5_3/x2" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "relu5_3/x2" | |
| type: "ReLU" | |
| bottom: "conv5_3/x2" | |
| top: "conv5_3/x2" | |
| } | |
| layer { | |
| name: "conv5_3/x3" | |
| type: "Convolution" | |
| bottom: "conv5_3/x2" | |
| top: "conv5_3/x3" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| convolution_param { | |
| num_output: 2048 | |
| bias_term: false | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv5_3/x3/bn" | |
| type: "BatchNorm" | |
| bottom: "conv5_3/x3" | |
| top: "conv5_3/x3" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| eps: 1e-4 | |
| } | |
| } | |
| layer { | |
| name: "conv5_3/x3/scale" | |
| type: "Scale" | |
| bottom: "conv5_3/x3" | |
| top: "conv5_3/x3" | |
| scale_param { | |
| bias_term: true | |
| } | |
| } | |
| layer { | |
| name: "pool5_3/gap" | |
| type: "Pooling" | |
| bottom: "conv5_3/x3" | |
| top: "pool5_3/gap" | |
| pooling_param { | |
| pool: AVE | |
| global_pooling: true | |
| } | |
| } | |
| layer { | |
| name: "fc5_3/sqz" | |
| type: "InnerProduct" | |
| bottom: "pool5_3/gap" | |
| top: "fc5_3/sqz" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| inner_product_param { | |
| num_output: 128 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu5_3/sqz" | |
| type: "ReLU" | |
| bottom: "fc5_3/sqz" | |
| top: "fc5_3/sqz" | |
| } | |
| layer { | |
| name: "fc5_3/exc" | |
| type: "InnerProduct" | |
| bottom: "fc5_3/sqz" | |
| top: "fc5_3/exc" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| inner_product_param { | |
| num_output: 2048 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "sigm5_3/gate" | |
| type: "Sigmoid" | |
| bottom: "fc5_3/exc" | |
| top: "fc5_3/exc" | |
| } | |
| layer { | |
| name: "scale5_3" | |
| type: "Scale" | |
| bottom: "conv5_3/x3" | |
| bottom: "fc5_3/exc" | |
| top: "scale5_3" | |
| scale_param { | |
| axis: 0 | |
| bias_term: false | |
| } | |
| } | |
| layer { | |
| name: "block_5_3" | |
| type: "Eltwise" | |
| bottom: "block_5_2" | |
| bottom: "scale5_3" | |
| top: "block_5_3" | |
| } | |
| layer { | |
| name: "relu5_3" | |
| type: "ReLU" | |
| bottom: "block_5_3" | |
| top: "block_5_3" | |
| } | |
| layer { | |
| name: "pool5" | |
| type: "Pooling" | |
| bottom: "block_5_3" | |
| top: "pool5" | |
| pooling_param { | |
| pool: AVE | |
| global_pooling: true | |
| } | |
| } | |
| layer { | |
| name: "fc6" | |
| type: "Convolution" | |
| bottom: "pool5" | |
| top: "fc6" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 1000 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "loss" | |
| type: "SoftmaxWithLoss" | |
| bottom: "fc6" | |
| bottom: "label" | |
| top: "loss" | |
| } | |
| layer { | |
| name: "top1/acc" | |
| type: "Accuracy" | |
| bottom: "fc6" | |
| bottom: "label" | |
| top: "top1/acc" | |
| include { | |
| phase: TEST | |
| } | |
| } | |
| layer { | |
| name: "top5/acc" | |
| type: "Accuracy" | |
| bottom: "fc6" | |
| bottom: "label" | |
| top: "top5/acc" | |
| include { | |
| phase: TEST | |
| } | |
| accuracy_param { | |
| top_k: 5 | |
| } | |
| } |
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