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|---|---|---|
| @@ -0,0 +1,359 @@ | ||
| name: "BrodyNet" | ||
| input: "data" | ||
| input_dim: 10 | ||
| input_dim: 3 | ||
| input_dim: 480 | ||
| input_dim: 640 | ||
|
|
||
| layers { | ||
| name: "L0" | ||
| type: CONVOLUTION | ||
| bottom: "data" | ||
| top: "L0" | ||
| blobs_lr: 1 | ||
| blobs_lr: 2 | ||
| weight_decay: 1 | ||
| weight_decay: 0 | ||
| convolution_param { | ||
| num_output: 96 | ||
| kernel_size: 11 | ||
| stride: 4 | ||
| weight_filler { | ||
| type: "gaussian" | ||
| std: 0.01 | ||
| } | ||
| bias_filler { | ||
| type: "constant" | ||
| value: 0.1 | ||
| } | ||
| } | ||
| } | ||
| layers { | ||
| name: "relu1" | ||
| type: RELU | ||
| bottom: "L0" | ||
| top: "L0" | ||
| } | ||
| layers { | ||
| name: "norm1" | ||
| type: LRN_FIXED | ||
| bottom: "L0" | ||
| top: "norm1" | ||
| lrn_param { | ||
| local_size: 5 | ||
| alpha: 0.0001 | ||
| beta: 0.75 | ||
| } | ||
| } | ||
| layers { | ||
| name: "pool1" | ||
| type: POOLING | ||
| bottom: "norm1" | ||
| top: "pool1" | ||
| pooling_param { | ||
| pool: MAX | ||
| kernel_size: 3 | ||
| stride: 2 | ||
| } | ||
| } | ||
| layers { | ||
| name: "L1" | ||
| type: CONVOLUTION | ||
| bottom: "pool1" | ||
| top: "L1" | ||
| blobs_lr: 1 | ||
| blobs_lr: 2 | ||
| weight_decay: 1 | ||
| weight_decay: 0 | ||
| convolution_param { | ||
| num_output: 256 | ||
| pad: 2 | ||
| kernel_size: 5 | ||
| group: 1 | ||
| weight_filler { | ||
| type: "gaussian" | ||
| std: 0.01 | ||
| } | ||
| bias_filler { | ||
| type: "constant" | ||
| value: 0.1 | ||
| } | ||
| } | ||
| } | ||
| layers { | ||
| name: "relu2" | ||
| type: RELU | ||
| bottom: "L1" | ||
| top: "L1" | ||
| } | ||
| layers { | ||
| name: "norm2" | ||
| type: LRN_FIXED | ||
| bottom: "L1" | ||
| top: "norm2" | ||
| lrn_param { | ||
| local_size: 5 | ||
| alpha: 0.0001 | ||
| beta: 0.75 | ||
| } | ||
| } | ||
| layers { | ||
| name: "pool2" | ||
| type: POOLING | ||
| bottom: "norm2" | ||
| top: "pool2" | ||
| pooling_param { | ||
| pool: MAX | ||
| kernel_size: 3 | ||
| stride: 2 | ||
| } | ||
| } | ||
| layers { | ||
| name: "L2" | ||
| type: CONVOLUTION | ||
| bottom: "pool2" | ||
| top: "L2" | ||
| blobs_lr: 1 | ||
| blobs_lr: 2 | ||
| weight_decay: 1 | ||
| weight_decay: 0 | ||
| convolution_param { | ||
| num_output: 384 | ||
| pad: 1 | ||
| kernel_size: 3 | ||
| weight_filler { | ||
| type: "gaussian" | ||
| std: 0.01 | ||
| } | ||
| bias_filler { | ||
| type: "constant" | ||
| value: 0.1 | ||
| } | ||
| } | ||
| } | ||
| layers { | ||
| name: "relu3" | ||
| type: RELU | ||
| bottom: "L2" | ||
| top: "L2" | ||
| } | ||
| layers { | ||
| name: "L3" | ||
| type: CONVOLUTION | ||
| bottom: "L2" | ||
| top: "L3" | ||
| blobs_lr: 1 | ||
| blobs_lr: 2 | ||
| weight_decay: 1 | ||
| weight_decay: 0 | ||
| convolution_param { | ||
| num_output: 384 | ||
| pad: 1 | ||
| kernel_size: 3 | ||
| group: 1 | ||
| weight_filler { | ||
| type: "gaussian" | ||
| std: 0.01 | ||
| } | ||
| bias_filler { | ||
| type: "constant" | ||
| value: 0.1 | ||
| } | ||
| } | ||
| } | ||
| layers { | ||
| name: "relu4" | ||
| type: RELU | ||
| bottom: "L3" | ||
| top: "L3" | ||
| } | ||
| layers { | ||
| name: "L4" | ||
| type: CONVOLUTION | ||
| bottom: "L3" | ||
| top: "L4" | ||
| blobs_lr: 1 | ||
| blobs_lr: 2 | ||
| weight_decay: 1 | ||
| weight_decay: 0 | ||
| convolution_param { | ||
| num_output: 384 | ||
| pad: 1 | ||
| kernel_size: 3 | ||
| group: 1 | ||
| weight_filler { | ||
| type: "gaussian" | ||
| std: 0.01 | ||
| } | ||
| bias_filler { | ||
| type: "constant" | ||
| value: 0.1 | ||
| } | ||
| } | ||
| } | ||
| layers { | ||
| name: "relu5" | ||
| type: RELU | ||
| bottom: "L4" | ||
| top: "L4" | ||
| } | ||
| layers { | ||
| name: "pool5" | ||
| type: POOLING | ||
| bottom: "L4" | ||
| top: "pool5" | ||
| pooling_param { | ||
| pool: MAX | ||
| kernel_size: 3 | ||
| stride: 2 | ||
| } | ||
| } | ||
| layers { | ||
| name: "L5" | ||
| type: CONVOLUTION | ||
| bottom: "pool5" | ||
| top: "L5" | ||
| blobs_lr: 1 | ||
| blobs_lr: 2 | ||
| weight_decay: 1 | ||
| weight_decay: 0 | ||
| convolution_param { | ||
| num_output: 4096 | ||
| kernel_size: 6 | ||
| pad: 3 | ||
| weight_filler { | ||
| type: "gaussian" | ||
| std: 0.01 | ||
| } | ||
| bias_filler { | ||
| type: "constant" | ||
| value: 1 | ||
| } | ||
| } | ||
| } | ||
| layers { | ||
| name: "relu6" | ||
| type: RELU | ||
| bottom: "L5" | ||
| top: "L5" | ||
| } | ||
| layers { | ||
| name: "drop6" | ||
| type: DROPOUT_FIXED | ||
| bottom: "L5" | ||
| top: "L5d" | ||
| dropout_param { | ||
| dropout_ratio: 0.5 | ||
| } | ||
| } | ||
| layers { | ||
| name: "L6" | ||
| type: CONVOLUTION | ||
| bottom: "L5d" | ||
| top: "L6" | ||
| blobs_lr: 1 | ||
| blobs_lr: 2 | ||
| weight_decay: 1 | ||
| weight_decay: 0 | ||
| convolution_param { | ||
| num_output: 4096 | ||
| kernel_size: 1 | ||
| weight_filler { | ||
| type: "gaussian" | ||
| std: 0.01 | ||
| } | ||
| bias_filler { | ||
| type: "constant" | ||
| value: 1 | ||
| } | ||
| } | ||
| } | ||
| layers { | ||
| name: "relu7" | ||
| type: RELU | ||
| bottom: "L6" | ||
| top: "L6" | ||
| } | ||
| layers { | ||
| name: "drop7" | ||
| type: DROPOUT_FIXED | ||
| bottom: "L6" | ||
| top: "L6d" | ||
| dropout_param { | ||
| dropout_ratio: 0.5 | ||
| } | ||
| } | ||
|
|
||
| layers { | ||
| name: "bb-output" | ||
| type: CONVOLUTION | ||
| bottom: "L6d" | ||
| top: "bb-output" | ||
| blobs_lr: 1 | ||
| blobs_lr: 2 | ||
| weight_decay: 1 | ||
| weight_decay: 0 | ||
| convolution_param { | ||
| num_output: 256 | ||
| kernel_size: 1 | ||
| weight_filler { | ||
| type: "gaussian" | ||
| std: 0.01 | ||
| } | ||
| bias_filler { | ||
| type: "constant" | ||
| value: 1.0 | ||
| } | ||
| } | ||
| } | ||
|
|
||
| layers { | ||
| name: "pixel-conv" | ||
| type: CONVOLUTION | ||
| bottom: "L6d" | ||
| top: "pixel-conv" | ||
| blobs_lr: 1 | ||
| blobs_lr: 2 | ||
| weight_decay: 1 | ||
| weight_decay: 0 | ||
| convolution_param { | ||
| num_output: 128 | ||
| kernel_size: 1 | ||
| weight_filler { | ||
| type: "gaussian" | ||
| std: 0.005 | ||
| } | ||
| bias_filler { | ||
| type: "constant" | ||
| value: 1.0 | ||
| } | ||
| } | ||
| } | ||
|
|
||
| layers { | ||
| name: "pixel-tile" | ||
| type: TILING | ||
| bottom: "pixel-conv" | ||
| top: "pixel-conv-tiled" | ||
| tiling_param { | ||
| tile_dim: 8 | ||
| } | ||
| } | ||
|
|
||
| layers { | ||
| name: "bb-tile" | ||
| type: TILING | ||
| bottom: "bb-output" | ||
| top: "bb-output-tiled" | ||
| tiling_param { | ||
| tile_dim: 8 | ||
| } | ||
| } | ||
|
|
||
| # Pixel level softmax prediction. | ||
| layers { | ||
| name: "pixel-prob" | ||
| type: SOFTMAX | ||
| bottom: "pixel-conv-tiled" | ||
| top: "pixel-prob" | ||
| } |