Last active
November 22, 2018 13:33
-
-
Save bpinaya/32fe4478ff0a3c47bdfb5b4d42be41f2 to your computer and use it in GitHub Desktop.
Taken from https://github.com/shelhamer/fcn.berkeleyvision.org, to see netscope check: https://ethereon.github.io/netscope/#/gist/32fe4478ff0a3c47bdfb5b4d42be41f2
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| name: "FCNVGG8" | |
| layer { | |
| name: "input" | |
| type: "Input" | |
| top: "data" | |
| input_param { | |
| # These dimensions are purely for sake of example; | |
| # see infer.py for how to reshape the net to the given input size. | |
| shape { dim: 1 dim: 3 dim: 500 dim: 500 } | |
| } | |
| } | |
| layer { | |
| name: "conv1_1" | |
| type: "Convolution" | |
| bottom: "data" | |
| 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: "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: "score_fr" | |
| type: "Convolution" | |
| bottom: "fc7" | |
| top: "score_fr" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 21 | |
| pad: 0 | |
| kernel_size: 1 | |
| } | |
| } | |
| layer { | |
| name: "upscore2" | |
| type: "Deconvolution" | |
| bottom: "score_fr" | |
| top: "upscore2" | |
| param { | |
| lr_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 21 | |
| bias_term: false | |
| kernel_size: 4 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "score_pool4" | |
| type: "Convolution" | |
| bottom: "pool4" | |
| top: "score_pool4" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 21 | |
| pad: 0 | |
| kernel_size: 1 | |
| } | |
| } | |
| layer { | |
| name: "score_pool4c" | |
| type: "Crop" | |
| bottom: "score_pool4" | |
| bottom: "upscore2" | |
| top: "score_pool4c" | |
| crop_param { | |
| axis: 2 | |
| offset: 5 | |
| } | |
| } | |
| layer { | |
| name: "fuse_pool4" | |
| type: "Eltwise" | |
| bottom: "upscore2" | |
| bottom: "score_pool4c" | |
| top: "fuse_pool4" | |
| eltwise_param { | |
| operation: SUM | |
| } | |
| } | |
| layer { | |
| name: "upscore_pool4" | |
| type: "Deconvolution" | |
| bottom: "fuse_pool4" | |
| top: "upscore_pool4" | |
| param { | |
| lr_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 21 | |
| bias_term: false | |
| kernel_size: 4 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "score_pool3" | |
| type: "Convolution" | |
| bottom: "pool3" | |
| top: "score_pool3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 21 | |
| pad: 0 | |
| kernel_size: 1 | |
| } | |
| } | |
| layer { | |
| name: "score_pool3c" | |
| type: "Crop" | |
| bottom: "score_pool3" | |
| bottom: "upscore_pool4" | |
| top: "score_pool3c" | |
| crop_param { | |
| axis: 2 | |
| offset: 9 | |
| } | |
| } | |
| layer { | |
| name: "fuse_pool3" | |
| type: "Eltwise" | |
| bottom: "upscore_pool4" | |
| bottom: "score_pool3c" | |
| top: "fuse_pool3" | |
| eltwise_param { | |
| operation: SUM | |
| } | |
| } | |
| layer { | |
| name: "upscore8" | |
| type: "Deconvolution" | |
| bottom: "fuse_pool3" | |
| top: "upscore8" | |
| param { | |
| lr_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 21 | |
| bias_term: false | |
| kernel_size: 16 | |
| stride: 8 | |
| } | |
| } | |
| layer { | |
| name: "score" | |
| type: "Crop" | |
| bottom: "upscore8" | |
| bottom: "data" | |
| top: "score" | |
| crop_param { | |
| axis: 2 | |
| offset: 31 | |
| } | |
| } |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment