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new experiments for FCN Alexnet 32 stride
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models/fcn_bvlc_alexnet/alexnet32s_no_gauss_2/deploy.prototxt
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| name: "FCN_AlexNet_32s" | ||
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| input: "data" | ||
| input_dim: 1 | ||
| input_dim: 3 | ||
| input_dim: 250 | ||
| input_dim: 250 | ||
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| layer { | ||
| name: "conv1" | ||
| type: "Convolution" | ||
| bottom: "data" | ||
| top: "conv1" | ||
| param { | ||
| lr_mult: 1 | ||
| decay_mult: 1 | ||
| } | ||
| param { | ||
| lr_mult: 2 | ||
| decay_mult: 0 | ||
| } | ||
| convolution_param { | ||
| num_output: 96 | ||
| kernel_size: 11 | ||
| stride: 4 | ||
| pad: 100 | ||
| } | ||
| } | ||
| 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: "norm1" | ||
| type: "LRN" | ||
| bottom: "pool1" | ||
| top: "norm1" | ||
| lrn_param { | ||
| local_size: 5 | ||
| alpha: 0.0001 | ||
| beta: 0.75 | ||
| } | ||
| } | ||
| layer { | ||
| name: "conv2" | ||
| type: "Convolution" | ||
| bottom: "norm1" | ||
| top: "conv2" | ||
| param { | ||
| lr_mult: 1 | ||
| decay_mult: 1 | ||
| } | ||
| param { | ||
| lr_mult: 2 | ||
| decay_mult: 0 | ||
| } | ||
| convolution_param { | ||
| num_output: 256 | ||
| pad: 2 | ||
| kernel_size: 5 | ||
| group: 2 | ||
| } | ||
| } | ||
| layer { | ||
| name: "relu2" | ||
| type: "ReLU" | ||
| bottom: "conv2" | ||
| top: "conv2" | ||
| } | ||
| layer { | ||
| name: "pool2" | ||
| type: "Pooling" | ||
| bottom: "conv2" | ||
| top: "pool2" | ||
| pooling_param { | ||
| pool: MAX | ||
| kernel_size: 3 | ||
| stride: 2 | ||
| } | ||
| } | ||
| layer { | ||
| name: "norm2" | ||
| type: "LRN" | ||
| bottom: "pool2" | ||
| top: "norm2" | ||
| lrn_param { | ||
| local_size: 5 | ||
| alpha: 0.0001 | ||
| beta: 0.75 | ||
| } | ||
| } | ||
| layer { | ||
| name: "conv3" | ||
| type: "Convolution" | ||
| bottom: "norm2" | ||
| top: "conv3" | ||
| param { | ||
| lr_mult: 1 | ||
| decay_mult: 1 | ||
| } | ||
| param { | ||
| lr_mult: 2 | ||
| decay_mult: 0 | ||
| } | ||
| convolution_param { | ||
| num_output: 384 | ||
| pad: 1 | ||
| kernel_size: 3 | ||
| } | ||
| } | ||
| layer { | ||
| name: "relu3" | ||
| type: "ReLU" | ||
| bottom: "conv3" | ||
| top: "conv3" | ||
| } | ||
| layer { | ||
| name: "conv4" | ||
| type: "Convolution" | ||
| bottom: "conv3" | ||
| top: "conv4" | ||
| param { | ||
| lr_mult: 1 | ||
| decay_mult: 1 | ||
| } | ||
| param { | ||
| lr_mult: 2 | ||
| decay_mult: 0 | ||
| } | ||
| convolution_param { | ||
| num_output: 384 | ||
| pad: 1 | ||
| kernel_size: 3 | ||
| group: 2 | ||
| } | ||
| } | ||
| layer { | ||
| name: "relu4" | ||
| type: "ReLU" | ||
| bottom: "conv4" | ||
| top: "conv4" | ||
| } | ||
| layer { | ||
| name: "conv5" | ||
| type: "Convolution" | ||
| bottom: "conv4" | ||
| top: "conv5" | ||
| param { | ||
| lr_mult: 1 | ||
| decay_mult: 1 | ||
| } | ||
| param { | ||
| lr_mult: 2 | ||
| decay_mult: 0 | ||
| } | ||
| convolution_param { | ||
| num_output: 256 | ||
| pad: 1 | ||
| kernel_size: 3 | ||
| group: 2 | ||
| } | ||
| } | ||
| layer { | ||
| name: "relu5" | ||
| type: "ReLU" | ||
| bottom: "conv5" | ||
| top: "conv5" | ||
| } | ||
| layer { | ||
| name: "pool5" | ||
| type: "Pooling" | ||
| bottom: "conv5" | ||
| top: "pool5" | ||
| pooling_param { | ||
| pool: MAX | ||
| kernel_size: 3 | ||
| 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 | ||
| kernel_size: 6 | ||
| } | ||
| } | ||
| 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 | ||
| kernel_size: 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 | ||
| } | ||
| } | ||
|
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| layer { | ||
| name: "score-fr-cs" | ||
| type: "Convolution" | ||
| bottom: "fc7" | ||
| top: "score-fc7" | ||
| param { | ||
| lr_mult: 1 | ||
| decay_mult: 1 | ||
| } | ||
| param { | ||
| lr_mult: 2 | ||
| decay_mult: 0 | ||
| } | ||
| convolution_param { | ||
| num_output: 11 | ||
| kernel_size: 1 | ||
| engine: CAFFE | ||
| } | ||
| } | ||
| layer { | ||
| name: "upsample-cs" | ||
| type: "Deconvolution" | ||
| bottom: "score-fc7" | ||
| top: "bigscore" | ||
| param { | ||
| lr_mult: 0 | ||
| } | ||
| param { | ||
| lr_mult: 0 | ||
| } | ||
| convolution_param { | ||
| num_output: 11 | ||
| kernel_size: 63 | ||
| # group: 32 | ||
| stride: 32 | ||
| } | ||
| } | ||
| layer { type: 'Crop' name: 'score' top: 'score' | ||
| bottom: 'bigscore' bottom: 'data' } |
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models/fcn_bvlc_alexnet/alexnet32s_no_gauss_2/solver.prototxt
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| net: "../models/alexnet32s_no_gaussian/train_val.prototxt" | ||
| test_iter: 20 | ||
| test_interval: 1000000 # py solving tests | ||
| display: 5 | ||
| average_loss: 20 | ||
| lr_policy: "fixed" | ||
| base_lr: 1e-4 | ||
| momentum: 0.9 | ||
| iter_size: 20 | ||
| # base_lr: 1e-9 | ||
| # momentum: 0.99 | ||
| # iter_size: 1 | ||
| max_iter: 20000 | ||
| weight_decay: 0.0005 | ||
| snapshot: 5000 | ||
| snapshot_prefix: "snapshots/train" | ||
| test_initialization: false | ||
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