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| net: "train_val.prototxt" | |
| test_iter: 0 | |
| test_interval: 1000000 | |
| # lr for fine-tuning should be lower than when starting from scratch | |
| #debug_info: true | |
| base_lr: 1e-6 | |
| lr_policy: "step" | |
| gamma: 0.1 | |
| iter_size: 1 | |
| # stepsize should also be lower, as we're closer to being done | |
| stepsize: 5000 | |
| display: 10 | |
| max_iter: 15000 | |
| momentum: 0.9 | |
| weight_decay: 0.0002 | |
| snapshot: 1000 | |
| snapshot_prefix: "DS" | |
| solver_mode: GPU | |
| #solver_mode: CPU |
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| name: "HED" | |
| layer { | |
| name: "data" | |
| type: "ImageLabelmapData" | |
| top: "data" | |
| top: "label" | |
| include { | |
| phase: TRAIN | |
| } | |
| transform_param { | |
| mirror: false | |
| mean_value: 104.00699 | |
| mean_value: 116.66877 | |
| mean_value: 122.67892 | |
| } | |
| image_data_param { | |
| root_folder: "../../data/SK-LARGE/" | |
| source: "../../data/SK-LARGE/list_shuffled.txt" | |
| batch_size: 1 | |
| shuffle: false | |
| new_height: 0 | |
| new_width: 0 | |
| } | |
| } | |
| layer { | |
| name: "data" | |
| type: "ImageLabelmapData" | |
| top: "data" | |
| top: "label" | |
| include { | |
| phase: TEST | |
| } | |
| transform_param { | |
| mirror: false | |
| mean_value: 104.00699 | |
| mean_value: 116.66877 | |
| mean_value: 122.67892 | |
| } | |
| image_data_param { | |
| root_folder: "../../data/SK-LARGE/" | |
| source: "../../data/SK-LARGE/list_shuffled.txt" | |
| #Just setup the network. No real online testing | |
| batch_size: 1 | |
| shuffle: false | |
| new_height: 0 | |
| new_width: 0 | |
| } | |
| } | |
| layer { | |
| bottom: 'data' | |
| top: 'conv1_1' | |
| name: 'conv1_1' | |
| type: "Convolution" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| engine: CAFFE | |
| num_output: 64 | |
| pad: 35 | |
| kernel_size: 3 | |
| } | |
| } | |
| layer { | |
| bottom: 'conv1_1' | |
| top: 'conv1_1' | |
| name: 'relu1_1' | |
| type: "ReLU" | |
| } | |
| layer { bottom: 'conv1_1' top: 'conv1_2' name: 'conv1_2' type: "Convolution" | |
| param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0} | |
| convolution_param { engine: CAFFE num_output: 64 pad: 1 kernel_size: 3 } } | |
| layer { bottom: 'conv1_2' top: 'conv1_2' name: 'relu1_2' type: "ReLU" } | |
| layer { name: 'pool1' bottom: 'conv1_2' top: 'pool1' type: "Pooling" | |
| pooling_param { pool: MAX kernel_size: 2 stride: 2 } } | |
| layer { name: 'conv2_1' bottom: 'pool1' top: 'conv2_1' type: "Convolution" | |
| param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0} | |
| convolution_param { engine: CAFFE num_output: 128 pad: 1 kernel_size: 3 } } | |
| layer { bottom: 'conv2_1' top: 'conv2_1' name: 'relu2_1' type: "ReLU" } | |
| layer { bottom: 'conv2_1' top: 'conv2_2' name: 'conv2_2' type: "Convolution" | |
| param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0} | |
| convolution_param { engine: CAFFE num_output: 128 pad: 1 kernel_size: 3 } } | |
| layer { bottom: 'conv2_2' top: 'conv2_2' name: 'relu2_2' type: "ReLU" } | |
| layer { bottom: 'conv2_2' top: 'pool2' name: 'pool2' type: "Pooling" | |
| pooling_param { pool: MAX kernel_size: 2 stride: 2 } } | |
| layer { bottom: 'pool2' top: 'conv3_1' name: 'conv3_1' type: "Convolution" | |
| param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0} | |
| convolution_param { engine: CAFFE num_output: 256 pad: 1 kernel_size: 3 } } | |
| layer { bottom: 'conv3_1' top: 'conv3_1' name: 'relu3_1' type: "ReLU" } | |
| layer { bottom: 'conv3_1' top: 'conv3_2' name: 'conv3_2' type: "Convolution" | |
| param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0} | |
| convolution_param { engine: CAFFE num_output: 256 pad: 1 kernel_size: 3 } } | |
| layer { bottom: 'conv3_2' top: 'conv3_2' name: 'relu3_2' type: "ReLU" } | |
| layer { bottom: 'conv3_2' top: 'conv3_3' name: 'conv3_3' type: "Convolution" | |
| param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0} | |
| convolution_param { engine: CAFFE num_output: 256 pad: 1 kernel_size: 3 } } | |
| layer { bottom: 'conv3_3' top: 'conv3_3' name: 'relu3_3' type: "ReLU" } | |
| layer { bottom: 'conv3_3' top: 'pool3' name: 'pool3' type: "Pooling" | |
| pooling_param { pool: MAX kernel_size: 2 stride: 2 } } | |
| layer { bottom: 'pool3' top: 'conv4_1' name: 'conv4_1' type: "Convolution" | |
| param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0} | |
| convolution_param { engine: CAFFE num_output: 512 pad: 1 kernel_size: 3 } } | |
| layer { bottom: 'conv4_1' top: 'conv4_1' name: 'relu4_1' type: "ReLU" } | |
| layer { bottom: 'conv4_1' top: 'conv4_2' name: 'conv4_2' type: "Convolution" | |
| param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0} | |
| convolution_param { engine: CAFFE num_output: 512 pad: 1 kernel_size: 3 } } | |
| layer { bottom: 'conv4_2' top: 'conv4_2' name: 'relu4_2' type: "ReLU" } | |
| layer { bottom: 'conv4_2' top: 'conv4_3' name: 'conv4_3' type: "Convolution" | |
| param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0} | |
| convolution_param { engine: CAFFE num_output: 512 pad: 1 kernel_size: 3 } } | |
| layer { bottom: 'conv4_3' top: 'conv4_3' name: 'relu4_3' type: "ReLU" } | |
| layer { bottom: 'conv4_3' top: 'pool4' name: 'pool4' type: "Pooling" | |
| pooling_param { pool: MAX kernel_size: 2 stride: 2 } } | |
| layer { bottom: 'pool4' top: 'conv5_1' name: 'conv5_1' type: "Convolution" | |
| param { lr_mult: 100 decay_mult: 1 } param { lr_mult: 200 decay_mult: 0} | |
| convolution_param { engine: CAFFE num_output: 512 pad: 1 kernel_size: 3 } } | |
| layer { bottom: 'conv5_1' top: 'conv5_1' name: 'relu5_1' type: "ReLU" } | |
| layer { bottom: 'conv5_1' top: 'conv5_2' name: 'conv5_2' type: "Convolution" | |
| param { lr_mult: 100 decay_mult: 1 } param { lr_mult: 200 decay_mult: 0} | |
| convolution_param { engine: CAFFE num_output: 512 pad: 1 kernel_size: 3 } } | |
| layer { bottom: 'conv5_2' top: 'conv5_2' name: 'relu5_2' type: "ReLU" } | |
| layer { bottom: 'conv5_2' top: 'conv5_3' name: 'conv5_3' type: "Convolution" | |
| param { lr_mult: 100 decay_mult: 1 } param { lr_mult: 200 decay_mult: 0} | |
| convolution_param { engine: CAFFE num_output: 512 pad: 1 kernel_size: 3 } } | |
| layer { bottom: 'conv5_3' top: 'conv5_3' name: 'relu5_3' type: "ReLU" } | |
| ## DSN conv 1 ### | |
| #layer { name: 'score-dsn-1' type: "Convolution" bottom: 'conv1_2' top: 'score-dsn1-up' | |
| # param { lr_mult: 0.01 decay_mult: 1 } param { lr_mult: 0.02 decay_mult: 0} | |
| # convolution_param { engine: CAFFE num_output: 2 kernel_size: 1} | |
| #} | |
| #layer { type: "Crop" name: 'crop' bottom: 'score-dsn1-up' bottom: 'data' top: 'upscore-dsn1' } | |
| #layer { | |
| # type: "SoftmaxWithLoss" | |
| # name: "WeightedSoftmaxLoss1" | |
| # bottom: "upscore-dsn1" | |
| # bottom: "label" | |
| # top:"dsn1_loss" | |
| # loss_weight: 1 | |
| # loss_param { | |
| # normalize: false | |
| # } | |
| #} | |
| ### DSN conv 2 ### | |
| layer { | |
| name: 'score-dsn-2' type: "Convolution" bottom: 'conv2_2' top: 'score-dsn2' | |
| param { lr_mult: 0.01 decay_mult: 1 } | |
| param { lr_mult: 0.02 decay_mult: 0} | |
| convolution_param { engine: CAFFE num_output: 2 kernel_size: 1 | |
| } | |
| } | |
| layer { type: "Deconvolution" name: 'upsample-2' bottom: 'score-dsn2' top: 'score-dsn2-up' | |
| param { lr_mult: 0 decay_mult: 1 } param { lr_mult: 0 decay_mult: 0} | |
| convolution_param { kernel_size: 4 stride: 2 num_output: 2 } | |
| } | |
| layer { type: "Crop" name: 'crop' bottom: 'score-dsn2-up' bottom: 'data' top: 'upscore-dsn2' } | |
| layer { | |
| type: "SoftmaxWithLoss" | |
| name: "loss2" | |
| bottom: "upscore-dsn2" | |
| bottom: "label" | |
| top:"dsn2_loss" | |
| loss_weight: 1 | |
| loss_param { | |
| normalize: false | |
| } | |
| } | |
| ### DSN conv 3 ### | |
| layer { name: 'score-dsn-3' type: "Convolution" bottom: 'conv3_3' top: 'score-dsn3' | |
| param { lr_mult: 0.01 decay_mult: 1 } param { lr_mult: 0.02 decay_mult: 0} | |
| convolution_param { engine: CAFFE num_output: 3 kernel_size: 1 | |
| } | |
| } | |
| layer { type: "Deconvolution" name: 'upsample-4' bottom: 'score-dsn3' top: 'score-dsn3-up' | |
| param { lr_mult: 0 decay_mult: 1 } param { lr_mult: 0 decay_mult: 0} | |
| convolution_param { kernel_size: 8 stride: 4 num_output: 3 } | |
| } | |
| layer { type: "Crop" name: 'crop' bottom: 'score-dsn3-up' bottom: 'data' top: 'upscore-dsn3' } | |
| layer { | |
| type: "SoftmaxWithLoss" | |
| name: "loss3" | |
| bottom: "upscore-dsn3" | |
| bottom: "label" | |
| top:"dsn3_loss" | |
| loss_weight: 1 | |
| loss_param { | |
| normalize: false | |
| } | |
| } | |
| ###DSN conv 4### | |
| layer { name: 'score-dsn-4' type: "Convolution" bottom: 'conv4_3' top: 'score-dsn4' | |
| param { lr_mult: 0.01 decay_mult: 1 } param { lr_mult: 0.02 decay_mult: 0} | |
| convolution_param { engine: CAFFE num_output: 4 kernel_size: 1 } } | |
| layer { type: "Deconvolution" name: 'upsample-8' bottom: 'score-dsn4' top: 'score-dsn4-up' | |
| param { lr_mult: 0 decay_mult: 1 } param { lr_mult: 0 decay_mult: 0} | |
| convolution_param { kernel_size: 16 stride: 8 num_output: 4 } } | |
| layer { type: "Crop" name: 'crop' bottom: 'score-dsn4-up' bottom: 'data' top: 'upscore-dsn4' } | |
| layer { | |
| type: "SoftmaxWithLoss" | |
| name: "loss4" | |
| bottom: "upscore-dsn4" | |
| bottom: "label" | |
| top:"dsn4_loss" | |
| loss_weight: 1 | |
| loss_param { | |
| normalize: false | |
| } | |
| } | |
| ###DSN conv 5### | |
| layer { name: 'score-dsn-5' type: "Convolution" bottom: 'conv5_3' top: 'score-dsn5' | |
| param { lr_mult: 0.01 decay_mult: 1 } param { lr_mult: 0.02 decay_mult: 0} | |
| convolution_param { engine: CAFFE num_output: 5 kernel_size: 1 } } | |
| layer { | |
| type: "Deconvolution" | |
| name: 'upsample-16' | |
| bottom: 'score-dsn5' | |
| top: 'score-dsn5-up' | |
| param { lr_mult: 0 decay_mult: 1 } | |
| param { lr_mult: 0 decay_mult: 0 } | |
| convolution_param { kernel_size: 32 stride: 16 num_output: 5 } } | |
| layer { type: "Crop" name: 'crop' bottom: 'score-dsn5-up' bottom: 'data' top: 'upscore-dsn5' } | |
| layer { | |
| type: "SoftmaxWithLoss" | |
| name: "loss5" | |
| bottom: "upscore-dsn5" | |
| bottom: "label" | |
| top:"dsn5_loss" | |
| loss_weight: 1 | |
| loss_param { | |
| normalize: false | |
| } | |
| } | |
| ### Concat and multiscale weight layer ### | |
| #layer { name: "concat" bottom: "upscore-dsn1" bottom: "upscore-dsn2" bottom: "upscore-dsn3" | |
| # bottom: "upscore-dsn4" bottom: "upscore-dsn5" top: "concat-upscore" type: "Concat" | |
| # concat_param { concat_dim: 1} } | |
| #layer { name: 'new-score-weighting' type: "Convolution" bottom: 'concat-upscore' top: 'upscore-fuse' | |
| # param { lr_mult: 0.001 decay_mult: 1 } param { lr_mult: 0.002 decay_mult: 0} | |
| # convolution_param { engine: CAFFE num_output: 2 kernel_size: 1 weight_filler {type: "constant" value: 0.2} } } | |
| #layer { type: "WeightedSoftmaxLoss" bottom: "upscore-fuse" bottom: "label" top:"fuse_loss" loss_weight: 1 } | |
| ## slice | |
| layer{ | |
| type: "Slice" name: "slice2" | |
| bottom: "upscore-dsn2" | |
| top: "slice2-0" | |
| top: "slice2-1" | |
| slice_param { | |
| axis: 1 | |
| slice_point: 1 | |
| } | |
| } | |
| layer{ | |
| type: "Slice" name: "slice3" | |
| bottom: "upscore-dsn3" | |
| top: "slice3-0" | |
| top: "slice3-1" | |
| top: "slice3-2" | |
| slice_param { | |
| axis: 1 | |
| slice_point: 1 | |
| slice_point: 2 | |
| } | |
| } | |
| layer{ | |
| type: "Slice" name: "slice4" | |
| bottom: "upscore-dsn4" | |
| top: "slice4-0" | |
| top: "slice4-1" | |
| top: "slice4-2" | |
| top: "slice4-3" | |
| slice_param { | |
| axis: 1 | |
| slice_point: 1 | |
| slice_point: 2 | |
| slice_point: 3 | |
| } | |
| } | |
| layer{ | |
| type: "Slice" name: "slice5" | |
| bottom: "upscore-dsn5" | |
| top: "slice5-0" | |
| top: "slice5-1" | |
| top: "slice5-2" | |
| top: "slice5-3" | |
| top: "slice5-4" | |
| slice_param { | |
| axis: 1 | |
| slice_point: 1 | |
| slice_point: 2 | |
| slice_point: 3 | |
| slice_point: 4 | |
| } | |
| } | |
| ## concat | |
| layer { | |
| name: "concat0" type: "Concat" | |
| bottom: "slice2-0" bottom: "slice3-0" bottom: "slice4-0" bottom: "slice5-0" | |
| top: "concat0" | |
| concat_param { concat_dim: 1} | |
| } | |
| layer { | |
| name: "concat1" type: "Concat" | |
| bottom: "slice2-1" bottom: "slice3-1" bottom: "slice4-1" bottom: "slice5-1" | |
| top: "concat1" | |
| concat_param { concat_dim: 1} | |
| } | |
| layer { | |
| name: "concat2" type: "Concat" | |
| bottom: "slice3-2" bottom: "slice4-2" bottom: "slice5-2" | |
| top: "concat2" | |
| concat_param { concat_dim: 1} | |
| } | |
| layer { | |
| name: "concat3" type: "Concat" | |
| bottom: "slice4-3" bottom: "slice5-3" | |
| top: "concat3" | |
| concat_param { concat_dim: 1} | |
| } | |
| ## score concated | |
| layer { | |
| name: 'cat0-score' type: "Convolution" | |
| bottom: 'concat0' top: 'concat0-score' | |
| param { lr_mult: 0.05 decay_mult: 1 } param { lr_mult: 0.002 decay_mult: 0} | |
| convolution_param { engine: CAFFE num_output: 1 kernel_size: 1 weight_filler {type: "constant" value: 0.25 } } | |
| } | |
| layer { | |
| name: 'cat1-score' type: "Convolution" | |
| bottom: 'concat1' top: 'concat1-score' | |
| param { lr_mult: 0.05 decay_mult: 1 } param { lr_mult: 0.002 decay_mult: 0} | |
| convolution_param { engine: CAFFE num_output: 1 kernel_size: 1 weight_filler {type: "constant" value: 0.25 } } | |
| } | |
| layer { | |
| name: 'cat2-score' type: "Convolution" | |
| bottom: 'concat2' top: 'concat2-score' | |
| param { lr_mult: 0.01 decay_mult: 1 } param { lr_mult: 0.002 decay_mult: 0} | |
| convolution_param { engine: CAFFE num_output: 1 kernel_size: 1 weight_filler {type: "constant" value: 0.333 } } | |
| } | |
| layer { | |
| name: 'cat3-score' type: "Convolution" | |
| bottom: 'concat3' top: 'concat3-score' | |
| param { lr_mult: 0.05 decay_mult: 1 } param { lr_mult: 0.002 decay_mult: 0} | |
| convolution_param { engine: CAFFE num_output: 1 kernel_size: 1 weight_filler {type: "constant" value: 0.5 } } | |
| } | |
| layer { | |
| name: 'cat4-score' type: "Convolution" | |
| bottom: 'slice5-4' top: 'concat4-score' | |
| param { lr_mult: 0.05 decay_mult: 1 } param { lr_mult: 0.002 decay_mult: 0} | |
| convolution_param { engine: CAFFE num_output: 1 kernel_size: 1 weight_filler {type: "constant" value: 1 } } | |
| } | |
| ## concat | |
| layer { | |
| name: "concat-fuse" type: "Concat" | |
| bottom: "concat0-score" bottom: "concat1-score" bottom: "concat2-score" bottom: "concat3-score" bottom: "concat4-score" | |
| top: "concat-fuse" | |
| concat_param { concat_dim: 1} | |
| } | |
| layer { | |
| type: "SoftmaxWithLoss" | |
| name: "loss-fuse" | |
| bottom: "concat-fuse" | |
| bottom: "label" | |
| top:"fuse-loss" | |
| loss_weight: 1 | |
| loss_param { | |
| normalize: false | |
| } | |
| } |
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