Created
December 13, 2017 10:15
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name: "segnet" | |
layer { | |
name: "data" | |
type: "DenseImageData" | |
top: "data" | |
top: "label" | |
dense_image_data_param { | |
source: "/SegNet/CamVid/test.txt" # Change this to the absolute path to your data file | |
batch_size: 8 # Change this to be the number of Monte Carlo Dropout samples you wish to make | |
} | |
} | |
layer { | |
name: "norm" | |
type: "LRN" | |
bottom: "data" | |
top: "norm" | |
lrn_param { | |
local_size: 5 | |
alpha: 0.0001 | |
beta: 0.75 | |
} | |
} | |
layer { | |
name: "conv1" | |
type: "Convolution" | |
bottom: "norm" | |
top: "conv1" | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 64 | |
kernel_size: 7 | |
pad: 3 | |
weight_filler { | |
type: "msra" | |
} | |
bias_filler { | |
type: "constant" | |
} | |
} | |
} | |
layer { | |
bottom: "conv1" | |
top: "conv1" | |
name: "conv1_bn" | |
type: "BN" | |
bn_param { | |
bn_mode: INFERENCE | |
scale_filler { | |
type: "constant" | |
value: 1 | |
} | |
shift_filler { | |
type: "constant" | |
value: 0.001 | |
} | |
} | |
} | |
layer { | |
name: "relu1" | |
type: "ReLU" | |
bottom: "conv1" | |
top: "conv1" | |
} | |
layer { | |
name: "pool1" | |
type: "Pooling" | |
bottom: "conv1" | |
top: "pool1" | |
top: "pool1_mask" | |
pooling_param { | |
pool: MAX | |
kernel_size: 2 | |
stride: 2 | |
} | |
} | |
layer { | |
name: "conv2" | |
type: "Convolution" | |
bottom: "pool1" | |
top: "conv2" | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 64 | |
kernel_size: 7 | |
pad: 3 | |
weight_filler { | |
type: "msra" | |
} | |
bias_filler { | |
type: "constant" | |
} | |
} | |
} | |
layer { | |
bottom: "conv2" | |
top: "conv2" | |
name: "conv2_bn" | |
type: "BN" | |
bn_param { | |
bn_mode: INFERENCE | |
scale_filler { | |
type: "constant" | |
value: 1 | |
} | |
shift_filler { | |
type: "constant" | |
value: 0.001 | |
} | |
} | |
} | |
layer { | |
name: "relu2" | |
type: "ReLU" | |
bottom: "conv2" | |
top: "conv2" | |
} | |
layer { | |
name: "pool2" | |
type: "Pooling" | |
bottom: "conv2" | |
top: "pool2" | |
top: "pool2_mask" | |
pooling_param { | |
pool: MAX | |
kernel_size: 2 | |
stride: 2 | |
} | |
} | |
layer { | |
name: "conv3" | |
type: "Convolution" | |
bottom: "pool2" | |
top: "conv3" | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 64 | |
kernel_size: 7 | |
pad: 3 | |
weight_filler { | |
type: "msra" | |
} | |
bias_filler { | |
type: "constant" | |
} | |
} | |
} | |
layer { | |
bottom: "conv3" | |
top: "conv3" | |
name: "conv3_bn" | |
type: "BN" | |
bn_param { | |
bn_mode: INFERENCE | |
scale_filler { | |
type: "constant" | |
value: 1 | |
} | |
shift_filler { | |
type: "constant" | |
value: 0.001 | |
} | |
} | |
} | |
layer { | |
name: "relu3" | |
type: "ReLU" | |
bottom: "conv3" | |
top: "conv3" | |
} | |
layer { | |
name: "pool3" | |
type: "Pooling" | |
bottom: "conv3" | |
top: "pool3" | |
top: "pool3_mask" | |
pooling_param { | |
pool: MAX | |
kernel_size: 2 | |
stride: 2 | |
} | |
} | |
layer { | |
name: "encdrop3" | |
type: "Dropout" | |
bottom: "pool3" | |
top: "pool3" | |
dropout_param { | |
dropout_ratio: 0.5 | |
sample_weights_test: true | |
} | |
} | |
layer { | |
name: "conv4" | |
type: "Convolution" | |
bottom: "pool3" | |
top: "conv4" | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 64 | |
kernel_size: 7 | |
pad: 3 | |
weight_filler { | |
type: "msra" | |
} | |
bias_filler { | |
type: "constant" | |
} | |
} | |
} | |
layer { | |
bottom: "conv4" | |
top: "conv4" | |
name: "conv4_bn" | |
type: "BN" | |
bn_param { | |
bn_mode: INFERENCE | |
scale_filler { | |
type: "constant" | |
value: 1 | |
} | |
shift_filler { | |
type: "constant" | |
value: 0.001 | |
} | |
} | |
} | |
layer { | |
name: "relu4" | |
type: "ReLU" | |
bottom: "conv4" | |
top: "conv4" | |
} | |
layer { | |
name: "pool4" | |
type: "Pooling" | |
bottom: "conv4" | |
top: "pool4" | |
top: "pool4_mask" | |
pooling_param { | |
pool: MAX | |
kernel_size: 2 | |
stride: 2 | |
} | |
} | |
layer { | |
name: "encdrop4" | |
type: "Dropout" | |
bottom: "pool4" | |
top: "pool4" | |
dropout_param { | |
dropout_ratio: 0.5 | |
sample_weights_test: true | |
} | |
} | |
layer { | |
name: "upsample4" | |
type: "Upsample" | |
bottom: "pool4" | |
bottom: "pool4_mask" | |
top: "upsample4" | |
upsample_param { | |
scale: 2 | |
pad_out_h: true | |
} | |
} | |
layer { | |
name: "conv_decode4" | |
type: "Convolution" | |
bottom: "upsample4" | |
top: "conv_decode4" | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 64 | |
kernel_size: 7 | |
pad: 3 | |
weight_filler { | |
type: "msra" | |
} | |
bias_filler { | |
type: "constant" | |
} | |
} | |
} | |
layer { | |
bottom: "conv_decode4" | |
top: "conv_decode4" | |
name: "conv_decode4_bn" | |
type: "BN" | |
bn_param { | |
bn_mode: INFERENCE | |
scale_filler { | |
type: "constant" | |
value: 1 | |
} | |
shift_filler { | |
type: "constant" | |
value: 0.001 | |
} | |
} | |
} | |
layer { | |
name: "decdrop4" | |
type: "Dropout" | |
bottom: "conv_decode4" | |
top: "conv_decode4" | |
dropout_param { | |
dropout_ratio: 0.5 | |
sample_weights_test: true | |
} | |
} | |
layer { | |
name: "upsample3" | |
type: "Upsample" | |
bottom: "conv_decode4" | |
bottom: "pool3_mask" | |
top: "upsample3" | |
upsample_param { | |
scale: 2 | |
} | |
} | |
layer { | |
name: "conv_decode3" | |
type: "Convolution" | |
bottom: "upsample3" | |
top: "conv_decode3" | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 64 | |
kernel_size: 7 | |
pad: 3 | |
weight_filler { | |
type: "msra" | |
} | |
bias_filler { | |
type: "constant" | |
} | |
} | |
} | |
layer { | |
bottom: "conv_decode3" | |
top: "conv_decode3" | |
name: "conv_decode3_bn" | |
type: "BN" | |
bn_param { | |
bn_mode: INFERENCE | |
scale_filler { | |
type: "constant" | |
value: 1 | |
} | |
shift_filler { | |
type: "constant" | |
value: 0.001 | |
} | |
} | |
} | |
layer { | |
name: "decdrop3" | |
type: "Dropout" | |
bottom: "conv_decode3" | |
top: "conv_decode3" | |
dropout_param { | |
dropout_ratio: 0.5 | |
sample_weights_test: true | |
} | |
} | |
layer { | |
name: "upsample2" | |
type: "Upsample" | |
bottom: "conv_decode3" | |
bottom: "pool2_mask" | |
top: "upsample2" | |
upsample_param { | |
scale: 2 | |
} | |
} | |
layer { | |
name: "conv_decode2" | |
type: "Convolution" | |
bottom: "upsample2" | |
top: "conv_decode2" | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 64 | |
kernel_size: 7 | |
pad: 3 | |
weight_filler { | |
type: "msra" | |
} | |
bias_filler { | |
type: "constant" | |
} | |
} | |
} | |
layer { | |
bottom: "conv_decode2" | |
top: "conv_decode2" | |
name: "conv_decode2_bn" | |
type: "BN" | |
bn_param { | |
bn_mode: INFERENCE | |
scale_filler { | |
type: "constant" | |
value: 1 | |
} | |
shift_filler { | |
type: "constant" | |
value: 0.001 | |
} | |
} | |
} | |
layer { | |
name: "upsample1" | |
type: "Upsample" | |
bottom: "conv_decode2" | |
bottom: "pool1_mask" | |
top: "upsample1" | |
upsample_param { | |
scale: 2 | |
} | |
} | |
layer { | |
name: "conv_decode1" | |
type: "Convolution" | |
bottom: "upsample1" | |
top: "conv_decode1" | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 64 | |
kernel_size: 7 | |
pad: 3 | |
weight_filler { | |
type: "msra" | |
} | |
bias_filler { | |
type: "constant" | |
} | |
} | |
} | |
layer { | |
bottom: "conv_decode1" | |
top: "conv_decode1" | |
name: "conv_decode1_bn" | |
type: "BN" | |
bn_param { | |
bn_mode: INFERENCE | |
scale_filler { | |
type: "constant" | |
value: 1 | |
} | |
shift_filler { | |
type: "constant" | |
value: 0.001 | |
} | |
} | |
} | |
layer { | |
name: "dense_softmax_inner_prod" | |
type: "Convolution" | |
bottom: "conv_decode1" | |
top: "dense_softmax_inner_prod" | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 11 | |
kernel_size: 1 | |
weight_filler { | |
type: "msra" | |
} | |
bias_filler { | |
type: "constant" | |
} | |
} | |
} | |
layer { | |
name: "prob" | |
type: "Softmax" | |
bottom: "dense_softmax_inner_prod" | |
top: "prob" | |
softmax_param {engine: CAFFE} | |
} |
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