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debug_fsds.ipynb
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| base_lr: 9.99999997475e-07 | |
| display: 10 | |
| max_iter: 15000 | |
| lr_policy: "step" | |
| gamma: 0.10000000149 | |
| momentum: 0.899999976158 | |
| weight_decay: 0.000199999994948 | |
| stepsize: 5000 | |
| snapshot: 1000 | |
| snapshot_prefix: "snapshot/fsds" | |
| random_seed: 831486 | |
| debug_info: false | |
| net: "model/fsds_train.pt" | |
| iter_size: 1 | |
| type: "SGD" |
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| layer { | |
| name: "data" | |
| type: "Python" | |
| top: "data" | |
| top: "label" | |
| python_param { | |
| module: "pylayer" | |
| layer: "FSDSDataLayer" | |
| param_str: "{\'shuffle\': False, \'source\': \'list_shuffled.txt\', \'phase\': \'train\', \'ignore_label\': -1, \'root\': \'data/SK-LARGE/\', \'mean\': (104.00699, 116.66877, 122.67892)}" | |
| } | |
| } | |
| layer { | |
| name: "conv1_1" | |
| type: "Convolution" | |
| bottom: "data" | |
| top: "conv1_1" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| pad: 35 | |
| kernel_size: 3 | |
| } | |
| } | |
| 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: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| } | |
| 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: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| } | |
| 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: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| } | |
| 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: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| } | |
| 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: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| } | |
| 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: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| } | |
| 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: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| } | |
| 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: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| } | |
| 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: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| } | |
| 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: 100.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 200.0 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| } | |
| 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: 100.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 200.0 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| } | |
| 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: 100.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 200.0 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 1 | |
| kernel_size: 3 | |
| } | |
| } | |
| layer { | |
| name: "relu5_3" | |
| type: "ReLU" | |
| bottom: "conv5_3" | |
| top: "conv5_3" | |
| } | |
| layer { | |
| name: "score_dsn2" | |
| type: "Convolution" | |
| bottom: "conv2_2" | |
| top: "score_dsn2" | |
| param { | |
| lr_mult: 0.00999999977648 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 0.019999999553 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 2 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "upsample_2" | |
| type: "Deconvolution" | |
| bottom: "score_dsn2" | |
| top: "upscore_dsn2" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 2 | |
| kernel_size: 4 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "crop_dsn2" | |
| type: "Crop" | |
| bottom: "upscore_dsn2" | |
| bottom: "data" | |
| top: "crop_dsn2" | |
| crop_param { | |
| axis: 2 | |
| offset: 35 | |
| } | |
| } | |
| layer { | |
| name: "loss2" | |
| type: "BalanceSoftmaxWithLoss" | |
| bottom: "crop_dsn2" | |
| bottom: "label" | |
| top: "dsn2_loss" | |
| loss_param { | |
| ignore_label: -1 | |
| normalize: false | |
| } | |
| } | |
| layer { | |
| name: "score_dsn3" | |
| type: "Convolution" | |
| bottom: "conv3_3" | |
| top: "score_dsn3" | |
| param { | |
| lr_mult: 0.00999999977648 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 0.019999999553 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 3 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "upsample_4" | |
| type: "Deconvolution" | |
| bottom: "score_dsn3" | |
| top: "upscore_dsn3" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 3 | |
| kernel_size: 8 | |
| stride: 4 | |
| } | |
| } | |
| layer { | |
| name: "crop_dsn3" | |
| type: "Crop" | |
| bottom: "upscore_dsn3" | |
| bottom: "data" | |
| top: "crop_dsn3" | |
| crop_param { | |
| axis: 2 | |
| offset: 36 | |
| } | |
| } | |
| layer { | |
| name: "loss3" | |
| type: "BalanceSoftmaxWithLoss" | |
| bottom: "crop_dsn3" | |
| bottom: "label" | |
| top: "dsn3_loss" | |
| loss_param { | |
| ignore_label: -1 | |
| normalize: false | |
| } | |
| } | |
| layer { | |
| name: "score_dsn4" | |
| type: "Convolution" | |
| bottom: "conv4_3" | |
| top: "score_dsn4" | |
| param { | |
| lr_mult: 0.00999999977648 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 0.019999999553 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 4 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "upsample_8" | |
| type: "Deconvolution" | |
| bottom: "score_dsn4" | |
| top: "upscore_dsn4" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 4 | |
| kernel_size: 16 | |
| stride: 8 | |
| } | |
| } | |
| layer { | |
| name: "crop_dsn4" | |
| type: "Crop" | |
| bottom: "upscore_dsn4" | |
| bottom: "data" | |
| top: "crop_dsn4" | |
| crop_param { | |
| axis: 2 | |
| offset: 38 | |
| } | |
| } | |
| layer { | |
| name: "loss4" | |
| type: "BalanceSoftmaxWithLoss" | |
| bottom: "crop_dsn4" | |
| bottom: "label" | |
| top: "dsn4_loss" | |
| loss_param { | |
| ignore_label: -1 | |
| normalize: false | |
| } | |
| } | |
| layer { | |
| name: "score_dsn5" | |
| type: "Convolution" | |
| bottom: "conv5_3" | |
| top: "score_dsn5" | |
| param { | |
| lr_mult: 0.00999999977648 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 0.019999999553 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 5 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "upsample_16" | |
| type: "Deconvolution" | |
| bottom: "score_dsn5" | |
| top: "upscore_dsn5" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 5 | |
| kernel_size: 32 | |
| stride: 16 | |
| } | |
| } | |
| layer { | |
| name: "crop_dsn5" | |
| type: "Crop" | |
| bottom: "upscore_dsn5" | |
| bottom: "data" | |
| top: "crop_dsn5" | |
| crop_param { | |
| axis: 2 | |
| offset: 42 | |
| } | |
| } | |
| layer { | |
| name: "loss5" | |
| type: "BalanceSoftmaxWithLoss" | |
| bottom: "crop_dsn5" | |
| bottom: "label" | |
| top: "dsn5_loss" | |
| loss_param { | |
| ignore_label: -1 | |
| normalize: false | |
| } | |
| } | |
| layer { | |
| name: "slice2" | |
| type: "Slice" | |
| bottom: "crop_dsn2" | |
| top: "slice2_0" | |
| top: "slice2_1" | |
| slice_param { | |
| slice_point: 1 | |
| axis: 1 | |
| } | |
| } | |
| layer { | |
| name: "slice3" | |
| type: "Slice" | |
| bottom: "crop_dsn3" | |
| top: "slice3_0" | |
| top: "slice3_1" | |
| top: "slice3_2" | |
| slice_param { | |
| slice_point: 1 | |
| slice_point: 2 | |
| axis: 1 | |
| } | |
| } | |
| layer { | |
| name: "slice4" | |
| type: "Slice" | |
| bottom: "crop_dsn4" | |
| top: "slice4_0" | |
| top: "slice4_1" | |
| top: "slice4_2" | |
| top: "slice4_3" | |
| slice_param { | |
| slice_point: 1 | |
| slice_point: 2 | |
| slice_point: 3 | |
| axis: 1 | |
| } | |
| } | |
| layer { | |
| name: "slice5" | |
| type: "Slice" | |
| bottom: "crop_dsn5" | |
| top: "slice5_0" | |
| top: "slice5_1" | |
| top: "slice5_2" | |
| top: "slice5_3" | |
| top: "slice5_4" | |
| slice_param { | |
| slice_point: 1 | |
| slice_point: 2 | |
| slice_point: 3 | |
| slice_point: 4 | |
| axis: 1 | |
| } | |
| } | |
| 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 | |
| } | |
| } | |
| layer { | |
| name: "cat0_score" | |
| type: "Convolution" | |
| bottom: "concat0" | |
| top: "concat0_score" | |
| param { | |
| lr_mult: 0.0500000007451 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 0.00200000009499 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| 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: 0.0500000007451 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 0.00200000009499 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| 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: 0.00999999977648 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 0.00200000009499 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 1 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "constant" | |
| value: 0.333000004292 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "cat3_score" | |
| type: "Convolution" | |
| bottom: "concat3" | |
| top: "concat3_score" | |
| param { | |
| lr_mult: 0.0500000007451 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 0.00200000009499 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| 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: 0.0500000007451 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 0.00200000009499 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 1 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "constant" | |
| value: 1.0 | |
| } | |
| } | |
| } | |
| 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 { | |
| name: "loss" | |
| type: "BalanceSoftmaxWithLoss" | |
| bottom: "concat_fuse" | |
| bottom: "label" | |
| top: "fuse_loss" | |
| loss_param { | |
| ignore_label: -1 | |
| normalize: false | |
| } | |
| } |
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