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February 8, 2017 11:53
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| # data layers | |
| layer { | |
| name: "data" | |
| type: "Data" | |
| top: "data" | |
| include { | |
| phase: TRAIN | |
| } | |
| data_param { | |
| batch_size: 1 | |
| backend: LMDB | |
| } | |
| } | |
| layer { | |
| name: "label" | |
| type: "Data" | |
| top: "label" | |
| include { | |
| phase: TRAIN | |
| } | |
| data_param { | |
| batch_size: 1 | |
| backend: LMDB | |
| } | |
| } | |
| layer { | |
| name: "data" | |
| type: "Data" | |
| top: "data" | |
| include { | |
| phase: TEST | |
| } | |
| data_param { | |
| batch_size: 1 | |
| backend: LMDB | |
| } | |
| } | |
| layer { | |
| name: "label" | |
| type: "Data" | |
| top: "label" | |
| include { | |
| phase: TEST | |
| } | |
| data_param { | |
| batch_size: 1 | |
| backend: LMDB | |
| } | |
| } | |
| # data preprocessing | |
| layer { | |
| # Use Power layer in deploy phase for input scaling | |
| name: "shift" | |
| bottom: "data" | |
| top: "data_preprocessed" | |
| type: "Power" | |
| power_param { | |
| shift: -116.0 | |
| } | |
| } | |
| # main network description | |
| layer { | |
| name: "conv1_1" | |
| type: "Convolution" | |
| bottom: "data_preprocessed" | |
| top: "conv1_1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| pad: 100 | |
| kernel_size: 3 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "relu1_1" | |
| type: "ReLU" | |
| bottom: "conv1_1" | |
| top: "conv1_1" | |
| } | |
| layer { | |
| name: "conv1_2" | |
| type: "Convolution" | |
| bottom: "conv1_1" | |
| top: "conv1_2" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "relu1_2" | |
| type: "ReLU" | |
| bottom: "conv1_2" | |
| top: "conv1_2" | |
| } | |
| layer { | |
| name: "pool1" | |
| type: "Pooling" | |
| bottom: "conv1_2" | |
| top: "pool1" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "conv2_1" | |
| type: "Convolution" | |
| bottom: "pool1" | |
| top: "conv2_1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "relu2_1" | |
| type: "ReLU" | |
| bottom: "conv2_1" | |
| top: "conv2_1" | |
| } | |
| layer { | |
| name: "conv2_2" | |
| type: "Convolution" | |
| bottom: "conv2_1" | |
| top: "conv2_2" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "relu2_2" | |
| type: "ReLU" | |
| bottom: "conv2_2" | |
| top: "conv2_2" | |
| } | |
| layer { | |
| name: "pool2" | |
| type: "Pooling" | |
| bottom: "conv2_2" | |
| top: "pool2" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "conv3_1" | |
| type: "Convolution" | |
| bottom: "pool2" | |
| top: "conv3_1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "relu3_1" | |
| type: "ReLU" | |
| bottom: "conv3_1" | |
| top: "conv3_1" | |
| } | |
| layer { | |
| name: "conv3_2" | |
| type: "Convolution" | |
| bottom: "conv3_1" | |
| top: "conv3_2" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "relu3_2" | |
| type: "ReLU" | |
| bottom: "conv3_2" | |
| top: "conv3_2" | |
| } | |
| layer { | |
| name: "conv3_3" | |
| type: "Convolution" | |
| bottom: "conv3_2" | |
| top: "conv3_3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "relu3_3" | |
| type: "ReLU" | |
| bottom: "conv3_3" | |
| top: "conv3_3" | |
| } | |
| layer { | |
| name: "pool3" | |
| type: "Pooling" | |
| bottom: "conv3_3" | |
| top: "pool3" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "conv4_1" | |
| type: "Convolution" | |
| bottom: "pool3" | |
| top: "conv4_1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "relu4_1" | |
| type: "ReLU" | |
| bottom: "conv4_1" | |
| top: "conv4_1" | |
| } | |
| layer { | |
| name: "conv4_2" | |
| type: "Convolution" | |
| bottom: "conv4_1" | |
| top: "conv4_2" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "relu4_2" | |
| type: "ReLU" | |
| bottom: "conv4_2" | |
| top: "conv4_2" | |
| } | |
| layer { | |
| name: "conv4_3" | |
| type: "Convolution" | |
| bottom: "conv4_2" | |
| top: "conv4_3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "relu4_3" | |
| type: "ReLU" | |
| bottom: "conv4_3" | |
| top: "conv4_3" | |
| } | |
| layer { | |
| name: "pool4" | |
| type: "Pooling" | |
| bottom: "conv4_3" | |
| top: "pool4" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "conv5_1" | |
| type: "Convolution" | |
| bottom: "pool4" | |
| top: "conv5_1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "relu5_1" | |
| type: "ReLU" | |
| bottom: "conv5_1" | |
| top: "conv5_1" | |
| } | |
| layer { | |
| name: "conv5_2" | |
| type: "Convolution" | |
| bottom: "conv5_1" | |
| top: "conv5_2" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "relu5_2" | |
| type: "ReLU" | |
| bottom: "conv5_2" | |
| top: "conv5_2" | |
| } | |
| layer { | |
| name: "conv5_3" | |
| type: "Convolution" | |
| bottom: "conv5_2" | |
| top: "conv5_3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "relu5_3" | |
| type: "ReLU" | |
| bottom: "conv5_3" | |
| top: "conv5_3" | |
| } | |
| layer { | |
| name: "pool5" | |
| type: "Pooling" | |
| bottom: "conv5_3" | |
| top: "pool5" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "fc6" | |
| type: "Convolution" | |
| bottom: "pool5" | |
| top: "fc6" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 4096 | |
| pad: 0 | |
| kernel_size: 7 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "relu6" | |
| type: "ReLU" | |
| bottom: "fc6" | |
| top: "fc6" | |
| } | |
| layer { | |
| name: "drop6" | |
| type: "Dropout" | |
| bottom: "fc6" | |
| top: "fc6" | |
| dropout_param { | |
| dropout_ratio: 0.5 | |
| } | |
| } | |
| layer { | |
| name: "fc7" | |
| type: "Convolution" | |
| bottom: "fc6" | |
| top: "fc7" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 4096 | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| } | |
| } | |
| layer { | |
| name: "relu7" | |
| type: "ReLU" | |
| bottom: "fc7" | |
| top: "fc7" | |
| } | |
| layer { | |
| name: "drop7" | |
| type: "Dropout" | |
| bottom: "fc7" | |
| top: "fc7" | |
| dropout_param { | |
| dropout_ratio: 0.5 | |
| } | |
| } | |
| layer { | |
| name: "score_fr_2classes" | |
| type: "Convolution" | |
| bottom: "fc7" | |
| top: "score_fr_2classes" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 2 | |
| pad: 0 | |
| kernel_size: 1 | |
| } | |
| } | |
| layer { | |
| name: "upscore2_2classes" | |
| type: "Deconvolution" | |
| bottom: "score_fr_2classes" | |
| top: "upscore2_2classes" | |
| param { | |
| lr_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 2 | |
| bias_term: false | |
| kernel_size: 4 | |
| stride: 2 | |
| group: 2 | |
| weight_filler: { type: "bilinear" } | |
| } | |
| } | |
| layer { | |
| name: "score_pool4_2classes" | |
| type: "Convolution" | |
| bottom: "pool4" | |
| top: "score_pool4_2classes" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 2 | |
| pad: 0 | |
| kernel_size: 1 | |
| } | |
| } | |
| layer { | |
| name: "score_pool4c" | |
| type: "Crop" | |
| bottom: "score_pool4_2classes" | |
| bottom: "upscore2_2classes" | |
| top: "score_pool4c" | |
| crop_param { | |
| axis: 2 | |
| offset: 5 | |
| } | |
| } | |
| layer { | |
| name: "fuse_pool4" | |
| type: "Eltwise" | |
| bottom: "upscore2_2classes" | |
| bottom: "score_pool4c" | |
| top: "fuse_pool4" | |
| eltwise_param { | |
| operation: SUM | |
| } | |
| } | |
| layer { | |
| name: "upscore_pool4_2classes" | |
| type: "Deconvolution" | |
| bottom: "fuse_pool4" | |
| top: "upscore_pool4_2classes" | |
| param { | |
| lr_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 2 | |
| bias_term: false | |
| kernel_size: 4 | |
| stride: 2 | |
| group: 2 | |
| weight_filler: { type: "bilinear" } | |
| } | |
| } | |
| layer { | |
| name: "score_pool3_2classes" | |
| type: "Convolution" | |
| bottom: "pool3" | |
| top: "score_pool3_2classes" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 2 | |
| pad: 0 | |
| kernel_size: 1 | |
| } | |
| } | |
| layer { | |
| name: "score_pool3c" | |
| type: "Crop" | |
| bottom: "score_pool3_2classes" | |
| bottom: "upscore_pool4_2classes" | |
| top: "score_pool3c" | |
| crop_param { | |
| axis: 2 | |
| offset: 9 | |
| } | |
| } | |
| layer { | |
| name: "fuse_pool3" | |
| type: "Eltwise" | |
| bottom: "upscore_pool4_2classes" | |
| bottom: "score_pool3c" | |
| top: "fuse_pool3" | |
| eltwise_param { | |
| operation: SUM | |
| } | |
| } | |
| layer { | |
| name: "upscore8_2classes" | |
| type: "Deconvolution" | |
| bottom: "fuse_pool3" | |
| top: "upscore8_2classes" | |
| param { | |
| lr_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 2 | |
| bias_term: false | |
| kernel_size: 16 | |
| stride: 8 | |
| group: 2 | |
| weight_filler: { type: "bilinear" } | |
| } | |
| } | |
| layer { | |
| name: "score" | |
| type: "Crop" | |
| bottom: "upscore8_2classes" | |
| bottom: "data" | |
| top: "score" | |
| crop_param { | |
| axis: 2 | |
| offset: 31 | |
| } | |
| } | |
| layer { | |
| name: "loss" | |
| type: "SoftmaxWithLoss" | |
| bottom: "score" | |
| bottom: "label" | |
| top: "loss" | |
| loss_param { | |
| ignore_label: 255 | |
| normalize: true | |
| } | |
| exclude { | |
| stage: "deploy" | |
| } | |
| } | |
| layer { | |
| name: "accuracy" | |
| type: "Accuracy" | |
| bottom: "score" | |
| bottom: "label" | |
| top: "accuracy" | |
| include { stage: "val" } | |
| accuracy_param { ignore_label: 255 } | |
| } | |
| # Dice coefficient | |
| layer { | |
| type: 'Python' | |
| name: 'dice' | |
| bottom: 'score' | |
| bottom: 'label' | |
| top: 'dice' | |
| python_param { | |
| module: "digits_python_layers" | |
| layer: "Dice" | |
| } | |
| exclude { stage: "deploy" } | |
| } |
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