Last active
March 31, 2017 09:35
-
-
Save dongzhuoyao/abc90f78aca82752f2ad12fcb2e7a513 to your computer and use it in GitHub Desktop.
deeplabv2_vgg_blur
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
# VGG 16-layer network convolutional finetuning | |
# Network modified to have smaller receptive field (128 pixels) | |
# nand smaller stride (8 pixels) when run in convolutional mode. | |
# | |
# In this model we also change max pooling size in the first 4 layers | |
# from 2 to 3 while retaining stride = 2 | |
# which makes it easier to exactly align responses at different layers. | |
# | |
# For alignment to work, we set (we choose 32x so as to be able to evaluate | |
# the model for all different subsampling sizes): | |
# (1) input dimension equal to | |
# $n = 32 * k - 31$, e.g., 321 (for k = 11) | |
# Dimension after pooling w. subsampling: | |
# (16 * k - 15); (8 * k - 7); (4 * k - 3); (2 * k - 1); (k). | |
# For k = 11, these translate to | |
# 161; 81; 41; 21; 11 | |
# | |
name: "deeplabv2_vgg16_blur" | |
layer { | |
name: "data" | |
type: "ImageSegData" | |
top: "data" | |
top: "label" | |
top: "data_dim" | |
include { | |
phase: TRAIN | |
} | |
transform_param { | |
mirror: true | |
crop_size: 321 | |
mean_value: 104.008 | |
mean_value: 116.669 | |
mean_value: 122.675 | |
} | |
image_data_param { | |
root_folder: "" | |
source: "pascalvoc2012/list/train.txt" | |
batch_size: 4 | |
shuffle: true | |
label_type: PIXEL | |
} | |
} | |
layer { | |
name: "blur_data" | |
type: "Blur" | |
bottom: "data" | |
top: "blur_data" | |
blur_param { | |
kernel_h:1 | |
kernel_w:1 | |
} | |
} | |
layer { | |
name: "blur1_data" | |
type: "Blur" | |
bottom: "data" | |
top: "blur1_data" | |
blur_param { | |
kernel_h:3 | |
kernel_w:3 | |
} | |
} | |
layer { | |
name: "blur2_data" | |
type: "Blur" | |
bottom: "data" | |
top: "blur2_data" | |
blur_param { | |
kernel_h:5 | |
kernel_w:5 | |
} | |
} | |
###################### DeepLab Blur0 #################### | |
layer { | |
name: "conv1_1" | |
type: "Convolution" | |
bottom: "blur_data" | |
top: "conv1_1" | |
param { | |
name: "conv1_1_w" | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
name: "conv1_1_b" | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 64 | |
pad: 1 | |
kernel_size: 3 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
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 { | |
name: "conv1_2_w" | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
name: "conv1_2_b" | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 64 | |
pad: 1 | |
kernel_size: 3 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
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: 3 | |
stride: 2 | |
pad: 1 | |
} | |
} | |
layer { | |
name: "conv2_1" | |
type: "Convolution" | |
bottom: "pool1" | |
top: "conv2_1" | |
param { | |
name: "conv2_1_w" | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
name: "conv2_1_b" | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 128 | |
pad: 1 | |
kernel_size: 3 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
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 { | |
name: "conv2_2_w" | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
name: "conv2_2_b" | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 128 | |
pad: 1 | |
kernel_size: 3 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
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: 3 | |
stride: 2 | |
pad: 1 | |
} | |
} | |
layer { | |
name: "conv3_1" | |
type: "Convolution" | |
bottom: "pool2" | |
top: "conv3_1" | |
param { | |
name: "conv3_1_w" | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
name: "conv3_1_b" | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 256 | |
pad: 1 | |
kernel_size: 3 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
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 { | |
name: "conv3_2_w" | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
name: "conv3_2_b" | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 256 | |
pad: 1 | |
kernel_size: 3 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
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 { | |
name: "conv3_3_w" | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
name: "conv3_3_b" | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 256 | |
pad: 1 | |
kernel_size: 3 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
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: 3 | |
stride: 2 | |
pad: 1 | |
} | |
} | |
layer { | |
name: "conv4_1" | |
type: "Convolution" | |
bottom: "pool3" | |
top: "conv4_1" | |
param { | |
name: "conv4_1_w" | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
name: "conv4_1_b" | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 512 | |
pad: 1 | |
kernel_size: 3 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
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 { | |
name: "conv4_2_w" | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
name: "conv4_2_b" | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 512 | |
pad: 1 | |
kernel_size: 3 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
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 { | |
name: "conv4_3_w" | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
name: "conv4_3_b" | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 512 | |
pad: 1 | |
kernel_size: 3 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "relu4_3" | |
type: "ReLU" | |
bottom: "conv4_3" | |
top: "conv4_3" | |
} | |
layer { | |
bottom: "conv4_3" | |
top: "pool4" | |
name: "pool4" | |
type: "Pooling" | |
pooling_param { | |
pool: MAX | |
kernel_size: 3 | |
pad: 1 | |
stride: 1 | |
} | |
} | |
layer { | |
name: "conv5_1" | |
type: "Convolution" | |
bottom: "pool4" | |
top: "conv5_1" | |
param { | |
name: "conv5_1_w" | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
name: "conv5_1_b" | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 512 | |
pad: 2 | |
kernel_size: 3 | |
dilation: 2 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
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 { | |
name: "conv5_2_w" | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
name: "conv5_2_b" | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 512 | |
pad: 2 | |
kernel_size: 3 | |
dilation: 2 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
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 { | |
name: "conv5_3_w" | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
name: "conv5_3_b" | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 512 | |
pad: 2 | |
kernel_size: 3 | |
dilation: 2 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "relu5_3" | |
type: "ReLU" | |
bottom: "conv5_3" | |
top: "conv5_3" | |
} | |
layer { | |
bottom: "conv5_3" | |
top: "pool5" | |
name: "pool5" | |
type: "Pooling" | |
pooling_param { | |
pool: MAX | |
kernel_size: 3 | |
stride: 1 | |
pad: 1 | |
} | |
} | |
### hole = 6 | |
layer { | |
name: "fc6_1" | |
type: "Convolution" | |
bottom: "pool5" | |
top: "fc6_1" | |
param { | |
name: "fc6_1_w" | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
name: "fc6_1_b" | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 1024 | |
pad: 6 | |
kernel_size: 3 | |
dilation: 6 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "relu6_1" | |
type: "ReLU" | |
bottom: "fc6_1" | |
top: "fc6_1" | |
} | |
layer { | |
name: "drop6_1" | |
type: "Dropout" | |
bottom: "fc6_1" | |
top: "fc6_1" | |
dropout_param { | |
dropout_ratio: 0.5 | |
} | |
} | |
layer { | |
name: "fc7_1" | |
type: "Convolution" | |
bottom: "fc6_1" | |
top: "fc7_1" | |
param { | |
name: "fc7_1_w" | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
name: "fc7_1_b" | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 1024 | |
kernel_size: 1 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "relu7_1" | |
type: "ReLU" | |
bottom: "fc7_1" | |
top: "fc7_1" | |
} | |
layer { | |
name: "drop7_1" | |
type: "Dropout" | |
bottom: "fc7_1" | |
top: "fc7_1" | |
dropout_param { | |
dropout_ratio: 0.5 | |
} | |
} | |
layer { | |
name: "fc8_pascalvoc2012_1" | |
type: "Convolution" | |
bottom: "fc7_1" | |
top: "fc8_pascalvoc2012_1" | |
param { | |
name: "fc8_pascalvoc2012_1_w" | |
lr_mult: 10 | |
decay_mult: 1 | |
} | |
param { | |
name: "fc8_pascalvoc2012_1_b" | |
lr_mult: 20 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 21 | |
kernel_size: 1 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
### hole = 12 | |
layer { | |
name: "fc6_2" | |
type: "Convolution" | |
bottom: "pool5" | |
top: "fc6_2" | |
param { | |
name: "fc6_2_w" | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
name: "fc6_2_b" | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 1024 | |
pad: 12 | |
kernel_size: 3 | |
dilation: 12 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "relu6_2" | |
type: "ReLU" | |
bottom: "fc6_2" | |
top: "fc6_2" | |
} | |
layer { | |
name: "drop6_2" | |
type: "Dropout" | |
bottom: "fc6_2" | |
top: "fc6_2" | |
dropout_param { | |
dropout_ratio: 0.5 | |
} | |
} | |
layer { | |
name: "fc7_2" | |
type: "Convolution" | |
bottom: "fc6_2" | |
top: "fc7_2" | |
param { | |
name: "fc7_2_w" | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
name: "fc7_2_b" | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 1024 | |
kernel_size: 1 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "relu7_2" | |
type: "ReLU" | |
bottom: "fc7_2" | |
top: "fc7_2" | |
} | |
layer { | |
name: "drop7_2" | |
type: "Dropout" | |
bottom: "fc7_2" | |
top: "fc7_2" | |
dropout_param { | |
dropout_ratio: 0.5 | |
} | |
} | |
layer { | |
name: "fc8_pascalvoc2012_2" | |
type: "Convolution" | |
bottom: "fc7_2" | |
top: "fc8_pascalvoc2012_2" | |
param { | |
name: "fc8_pascalvoc2012_2_w" | |
lr_mult: 10 | |
decay_mult: 1 | |
} | |
param { | |
name: "fc8_pascalvoc2012_2_b" | |
lr_mult: 20 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 21 | |
kernel_size: 1 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
### hole = 18 | |
layer { | |
name: "fc6_3" | |
type: "Convolution" | |
bottom: "pool5" | |
top: "fc6_3" | |
param { | |
name: "fc6_3_w" | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
name: "fc6_3_b" | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 1024 | |
pad: 18 | |
kernel_size: 3 | |
dilation: 18 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "relu6_3" | |
type: "ReLU" | |
bottom: "fc6_3" | |
top: "fc6_3" | |
} | |
layer { | |
name: "drop6_3" | |
type: "Dropout" | |
bottom: "fc6_3" | |
top: "fc6_3" | |
dropout_param { | |
dropout_ratio: 0.5 | |
} | |
} | |
layer { | |
name: "fc7_3" | |
type: "Convolution" | |
bottom: "fc6_3" | |
top: "fc7_3" | |
param { | |
name: "fc7_3_w" | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
name: "fc7_3_b" | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 1024 | |
kernel_size: 1 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "relu7_3" | |
type: "ReLU" | |
bottom: "fc7_3" | |
top: "fc7_3" | |
} | |
layer { | |
name: "drop7_3" | |
type: "Dropout" | |
bottom: "fc7_3" | |
top: "fc7_3" | |
dropout_param { | |
dropout_ratio: 0.5 | |
} | |
} | |
layer { | |
name: "fc8_pascalvoc2012_3" | |
type: "Convolution" | |
bottom: "fc7_3" | |
top: "fc8_pascalvoc2012_3" | |
param { | |
name: "fc8_pascalvoc2012_3_w" | |
lr_mult: 10 | |
decay_mult: 1 | |
} | |
param { | |
name: "fc8_pascalvoc2012_3_b" | |
lr_mult: 20 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 21 | |
kernel_size: 1 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
### hole = 24 | |
layer { | |
name: "fc6_4" | |
type: "Convolution" | |
bottom: "pool5" | |
top: "fc6_4" | |
param { | |
name: "fc6_4_w" | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
name: "fc6_4_b" | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 1024 | |
pad: 24 | |
kernel_size: 3 | |
dilation: 24 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "relu6_4" | |
type: "ReLU" | |
bottom: "fc6_4" | |
top: "fc6_4" | |
} | |
layer { | |
name: "drop6_4" | |
type: "Dropout" | |
bottom: "fc6_4" | |
top: "fc6_4" | |
dropout_param { | |
dropout_ratio: 0.5 | |
} | |
} | |
layer { | |
name: "fc7_4" | |
type: "Convolution" | |
bottom: "fc6_4" | |
top: "fc7_4" | |
param { | |
name: "fc7_4_w" | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
name: "fc7_4_b" | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 1024 | |
kernel_size: 1 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "relu7_4" | |
type: "ReLU" | |
bottom: "fc7_4" | |
top: "fc7_4" | |
} | |
layer { | |
name: "drop7_4" | |
type: "Dropout" | |
bottom: "fc7_4" | |
top: "fc7_4" | |
dropout_param { | |
dropout_ratio: 0.5 | |
} | |
} | |
layer { | |
name: "fc8_pascalvoc2012_4" | |
type: "Convolution" | |
bottom: "fc7_4" | |
top: "fc8_pascalvoc2012_4" | |
param { | |
name: "fc8_pascalvoc2012_4_w" | |
lr_mult: 10 | |
decay_mult: 1 | |
} | |
param { | |
name: "fc8_pascalvoc2012_4_b" | |
lr_mult: 20 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 21 | |
kernel_size: 1 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
### SUM the four branches | |
layer { | |
bottom: "fc8_pascalvoc2012_1" | |
bottom: "fc8_pascalvoc2012_2" | |
bottom: "fc8_pascalvoc2012_3" | |
bottom: "fc8_pascalvoc2012_4" | |
top: "branch_sum" | |
name: "branch_sum" | |
type: "Eltwise" | |
eltwise_param { | |
operation: SUM | |
} | |
} | |
############### end Deeplab Blur0 ############ | |
###################### DeepLab Blur1 #################### | |
layer { | |
name: "blur1_conv1_1" | |
type: "Convolution" | |
bottom: "blur1_data" | |
top: "blur1_conv1_1" | |
param { | |
name: "conv1_1_w_blur1" | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
name: "conv1_1_b_blur1" | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 64 | |
pad: 1 | |
kernel_size: 3 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "blur1_relu1_1" | |
type: "ReLU" | |
bottom: "blur1_conv1_1" | |
top: "blur1_conv1_1" | |
} | |
layer { | |
name: "blur1_conv1_2" | |
type: "Convolution" | |
bottom: "blur1_conv1_1" | |
top: "blur1_conv1_2" | |
param { | |
name: "conv1_2_w_blur1" | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
name: "conv1_2_b_blur1" | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 64 | |
pad: 1 | |
kernel_size: 3 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "blur1_relu1_2" | |
type: "ReLU" | |
bottom: "blur1_conv1_2" | |
top: "blur1_conv1_2" | |
} | |
layer { | |
name: "blur1_pool1" | |
type: "Pooling" | |
bottom: "blur1_conv1_2" | |
top: "blur1_pool1" | |
pooling_param { | |
pool: MAX | |
kernel_size: 3 | |
stride: 2 | |
pad: 1 | |
} | |
} | |
layer { | |
name: "blur1_conv2_1" | |
type: "Convolution" | |
bottom: "blur1_pool1" | |
top: "blur1_conv2_1" | |
param { | |
name: "conv2_1_w_blur1" | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
name: "conv2_1_b_blur1" | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 128 | |
pad: 1 | |
kernel_size: 3 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "blur1_relu2_1" | |
type: "ReLU" | |
bottom: "blur1_conv2_1" | |
top: "blur1_conv2_1" | |
} | |
layer { | |
name: "blur1_conv2_2" | |
type: "Convolution" | |
bottom: "blur1_conv2_1" | |
top: "blur1_conv2_2" | |
param { | |
name: "conv2_2_w_blur1" | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
name: "conv2_2_b_blur1" | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 128 | |
pad: 1 | |
kernel_size: 3 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "blur1_relu2_2" | |
type: "ReLU" | |
bottom: "blur1_conv2_2" | |
top: "blur1_conv2_2" | |
} | |
layer { | |
name: "blur1_pool2" | |
type: "Pooling" | |
bottom: "blur1_conv2_2" | |
top: "blur1_pool2" | |
pooling_param { | |
pool: MAX | |
kernel_size: 3 | |
stride: 2 | |
pad: 1 | |
} | |
} | |
layer { | |
name: "blur1_conv3_1" | |
type: "Convolution" | |
bottom: "blur1_pool2" | |
top: "blur1_conv3_1" | |
param { | |
name: "conv3_1_w_blur1" | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
name: "conv3_1_b_blur1" | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 256 | |
pad: 1 | |
kernel_size: 3 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "blur1_relu3_1" | |
type: "ReLU" | |
bottom: "blur1_conv3_1" | |
top: "blur1_conv3_1" | |
} | |
layer { | |
name: "blur1_conv3_2" | |
type: "Convolution" | |
bottom: "blur1_conv3_1" | |
top: "blur1_conv3_2" | |
param { | |
name: "conv3_2_w_blur1" | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
name: "conv3_2_b_blur1" | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 256 | |
pad: 1 | |
kernel_size: 3 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "blur1_relu3_2" | |
type: "ReLU" | |
bottom: "blur1_conv3_2" | |
top: "blur1_conv3_2" | |
} | |
layer { | |
name: "blur1_conv3_3" | |
type: "Convolution" | |
bottom: "blur1_conv3_2" | |
top: "blur1_conv3_3" | |
param { | |
name: "conv3_3_w_blur1" | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
name: "conv3_3_b_blur1" | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 256 | |
pad: 1 | |
kernel_size: 3 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "blur1_relu3_3" | |
type: "ReLU" | |
bottom: "blur1_conv3_3" | |
top: "blur1_conv3_3" | |
} | |
layer { | |
name: "blur1_pool3" | |
type: "Pooling" | |
bottom: "blur1_conv3_3" | |
top: "blur1_pool3" | |
pooling_param { | |
pool: MAX | |
kernel_size: 3 | |
stride: 2 | |
pad: 1 | |
} | |
} | |
layer { | |
name: "blur1_conv4_1" | |
type: "Convolution" | |
bottom: "blur1_pool3" | |
top: "blur1_conv4_1" | |
param { | |
name: "conv4_1_w_blur1" | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
name: "conv4_1_b_blur1" | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 512 | |
pad: 1 | |
kernel_size: 3 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "blur1_relu4_1" | |
type: "ReLU" | |
bottom: "blur1_conv4_1" | |
top: "blur1_conv4_1" | |
} | |
layer { | |
name: "blur1_conv4_2" | |
type: "Convolution" | |
bottom: "blur1_conv4_1" | |
top: "blur1_conv4_2" | |
param { | |
name: "conv4_2_w_blur1" | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
name: "conv4_2_b_blur1" | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 512 | |
pad: 1 | |
kernel_size: 3 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "blur1_relu4_2" | |
type: "ReLU" | |
bottom: "blur1_conv4_2" | |
top: "blur1_conv4_2" | |
} | |
layer { | |
name: "blur1_conv4_3" | |
type: "Convolution" | |
bottom: "blur1_conv4_2" | |
top: "blur1_conv4_3" | |
param { | |
name: "conv4_3_w_blur1" | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
name: "conv4_3_b_blur1" | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 512 | |
pad: 1 | |
kernel_size: 3 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "blur1_relu4_3" | |
type: "ReLU" | |
bottom: "blur1_conv4_3" | |
top: "blur1_conv4_3" | |
} | |
layer { | |
bottom: "blur1_conv4_3" | |
top: "blur1_pool4" | |
name: "blur1_pool4" | |
type: "Pooling" | |
pooling_param { | |
pool: MAX | |
kernel_size: 3 | |
pad: 1 | |
stride: 1 | |
} | |
} | |
layer { | |
name: "blur1_conv5_1" | |
type: "Convolution" | |
bottom: "blur1_pool4" | |
top: "blur1_conv5_1" | |
param { | |
name: "conv5_1_w_blur1" | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
name: "conv5_1_b_blur1" | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 512 | |
pad: 2 | |
kernel_size: 3 | |
dilation: 2 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "blur1_relu5_1" | |
type: "ReLU" | |
bottom: "blur1_conv5_1" | |
top: "blur1_conv5_1" | |
} | |
layer { | |
name: "blur1_conv5_2" | |
type: "Convolution" | |
bottom: "blur1_conv5_1" | |
top: "blur1_conv5_2" | |
param { | |
name: "conv5_2_w_blur1" | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
name: "conv5_2_b_blur1" | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 512 | |
pad: 2 | |
kernel_size: 3 | |
dilation: 2 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "blur1_relu5_2" | |
type: "ReLU" | |
bottom: "blur1_conv5_2" | |
top: "blur1_conv5_2" | |
} | |
layer { | |
name: "blur1_conv5_3" | |
type: "Convolution" | |
bottom: "blur1_conv5_2" | |
top: "blur1_conv5_3" | |
param { | |
name: "conv5_3_w_blur1" | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
name: "conv5_3_b_blur1" | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 512 | |
pad: 2 | |
kernel_size: 3 | |
dilation: 2 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "blur1_relu5_3" | |
type: "ReLU" | |
bottom: "blur1_conv5_3" | |
top: "blur1_conv5_3" | |
} | |
layer { | |
bottom: "blur1_conv5_3" | |
top: "blur1_pool5" | |
name: "blur1_pool5" | |
type: "Pooling" | |
pooling_param { | |
pool: MAX | |
kernel_size: 3 | |
stride: 1 | |
pad: 1 | |
} | |
} | |
### hole = 6 | |
layer { | |
name: "blur1_fc6_1" | |
type: "Convolution" | |
bottom: "blur1_pool5" | |
top: "blur1_fc6_1" | |
param { | |
name: "fc6_1_w_blur1" | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
name: "fc6_1_b_blur1" | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 1024 | |
pad: 6 | |
kernel_size: 3 | |
dilation: 6 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "blur1_relu6_1" | |
type: "ReLU" | |
bottom: "blur1_fc6_1" | |
top: "blur1_fc6_1" | |
} | |
layer { | |
name: "blur1_drop6_1" | |
type: "Dropout" | |
bottom: "blur1_fc6_1" | |
top: "blur1_fc6_1" | |
dropout_param { | |
dropout_ratio: 0.5 | |
} | |
} | |
layer { | |
name: "blur1_fc7_1" | |
type: "Convolution" | |
bottom: "blur1_fc6_1" | |
top: "blur1_fc7_1" | |
param { | |
name: "fc7_1_w_blur1" | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
name: "fc7_1_b_blur1" | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 1024 | |
kernel_size: 1 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "blur1_relu7_1" | |
type: "ReLU" | |
bottom: "blur1_fc7_1" | |
top: "blur1_fc7_1" | |
} | |
layer { | |
name: "blur1_drop7_1" | |
type: "Dropout" | |
bottom: "blur1_fc7_1" | |
top: "blur1_fc7_1" | |
dropout_param { | |
dropout_ratio: 0.5 | |
} | |
} | |
layer { | |
name: "blur1_fc8_pascalvoc2012_1" | |
type: "Convolution" | |
bottom: "blur1_fc7_1" | |
top: "blur1_fc8_pascalvoc2012_1" | |
param { | |
name: "fc8_pascalvoc2012_1_w_blur1" | |
lr_mult: 10 | |
decay_mult: 1 | |
} | |
param { | |
name: "fc8_pascalvoc2012_1_b_blur1" | |
lr_mult: 20 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 21 | |
kernel_size: 1 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
### hole = 12 | |
layer { | |
name: "blur1_fc6_2" | |
type: "Convolution" | |
bottom: "blur1_pool5" | |
top: "blur1_fc6_2" | |
param { | |
name: "fc6_2_w_blur1" | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
name: "fc6_2_b_blur1" | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 1024 | |
pad: 12 | |
kernel_size: 3 | |
dilation: 12 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "blur1_relu6_2" | |
type: "ReLU" | |
bottom: "blur1_fc6_2" | |
top: "blur1_fc6_2" | |
} | |
layer { | |
name: "blur1_drop6_2" | |
type: "Dropout" | |
bottom: "blur1_fc6_2" | |
top: "blur1_fc6_2" | |
dropout_param { | |
dropout_ratio: 0.5 | |
} | |
} | |
layer { | |
name: "blur1_fc7_2" | |
type: "Convolution" | |
bottom: "blur1_fc6_2" | |
top: "blur1_fc7_2" | |
param { | |
name: "fc7_2_w_blur1" | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
name: "fc7_2_b_blur1" | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 1024 | |
kernel_size: 1 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "blur1_relu7_2" | |
type: "ReLU" | |
bottom: "blur1_fc7_2" | |
top: "blur1_fc7_2" | |
} | |
layer { | |
name: "blur1_drop7_2" | |
type: "Dropout" | |
bottom: "blur1_fc7_2" | |
top: "blur1_fc7_2" | |
dropout_param { | |
dropout_ratio: 0.5 | |
} | |
} | |
layer { | |
name: "blur1_fc8_pascalvoc2012_2" | |
type: "Convolution" | |
bottom: "blur1_fc7_2" | |
top: "blur1_fc8_pascalvoc2012_2" | |
param { | |
name: "fc8_pascalvoc2012_2_w_blur1" | |
lr_mult: 10 | |
decay_mult: 1 | |
} | |
param { | |
name: "fc8_pascalvoc2012_2_b_blur1" | |
lr_mult: 20 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 21 | |
kernel_size: 1 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
### hole = 18 | |
layer { | |
name: "blur1_fc6_3" | |
type: "Convolution" | |
bottom: "blur1_pool5" | |
top: "blur1_fc6_3" | |
param { | |
name: "fc6_3_w_blur1" | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
name: "fc6_3_b_blur1" | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 1024 | |
pad: 18 | |
kernel_size: 3 | |
dilation: 18 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "blur1_relu6_3" | |
type: "ReLU" | |
bottom: "blur1_fc6_3" | |
top: "blur1_fc6_3" | |
} | |
layer { | |
name: "blur1_drop6_3" | |
type: "Dropout" | |
bottom: "blur1_fc6_3" | |
top: "blur1_fc6_3" | |
dropout_param { | |
dropout_ratio: 0.5 | |
} | |
} | |
layer { | |
name: "blur1_fc7_3" | |
type: "Convolution" | |
bottom: "blur1_fc6_3" | |
top: "blur1_fc7_3" | |
param { | |
name: "fc7_3_w_blur1" | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
name: "fc7_3_b_blur1" | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 1024 | |
kernel_size: 1 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "blur1_relu7_3" | |
type: "ReLU" | |
bottom: "blur1_fc7_3" | |
top: "blur1_fc7_3" | |
} | |
layer { | |
name: "blur1_drop7_3" | |
type: "Dropout" | |
bottom: "blur1_fc7_3" | |
top: "blur1_fc7_3" | |
dropout_param { | |
dropout_ratio: 0.5 | |
} | |
} | |
layer { | |
name: "blur1_fc8_pascalvoc2012_3" | |
type: "Convolution" | |
bottom: "blur1_fc7_3" | |
top: "blur1_fc8_pascalvoc2012_3" | |
param { | |
name: "fc8_pascalvoc2012_3_w_blur1" | |
lr_mult: 10 | |
decay_mult: 1 | |
} | |
param { | |
name: "fc8_pascalvoc2012_3_b_blur1" | |
lr_mult: 20 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 21 | |
kernel_size: 1 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
### hole = 24 | |
layer { | |
name: "blur1_fc6_4" | |
type: "Convolution" | |
bottom: "blur1_pool5" | |
top: "blur1_fc6_4" | |
param { | |
name: "fc6_4_w_blur1" | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
name: "fc6_4_b_blur1" | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 1024 | |
pad: 24 | |
kernel_size: 3 | |
dilation: 24 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "blur1_relu6_4" | |
type: "ReLU" | |
bottom: "blur1_fc6_4" | |
top: "blur1_fc6_4" | |
} | |
layer { | |
name: "blur1_drop6_4" | |
type: "Dropout" | |
bottom: "blur1_fc6_4" | |
top: "blur1_fc6_4" | |
dropout_param { | |
dropout_ratio: 0.5 | |
} | |
} | |
layer { | |
name: "blur1_fc7_4" | |
type: "Convolution" | |
bottom: "blur1_fc6_4" | |
top: "blur1_fc7_4" | |
param { | |
name: "fc7_4_w_blur1" | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
name: "fc7_4_b_blur1" | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 1024 | |
kernel_size: 1 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "blur1_relu7_4" | |
type: "ReLU" | |
bottom: "blur1_fc7_4" | |
top: "blur1_fc7_4" | |
} | |
layer { | |
name: "blur1_drop7_4" | |
type: "Dropout" | |
bottom: "blur1_fc7_4" | |
top: "blur1_fc7_4" | |
dropout_param { | |
dropout_ratio: 0.5 | |
} | |
} | |
layer { | |
name: "blur1_fc8_pascalvoc2012_4" | |
type: "Convolution" | |
bottom: "blur1_fc7_4" | |
top: "blur1_fc8_pascalvoc2012_4" | |
param { | |
name: "fc8_pascalvoc2012_4_w_blur1" | |
lr_mult: 10 | |
decay_mult: 1 | |
} | |
param { | |
name: "fc8_pascalvoc2012_4_b_blur1" | |
lr_mult: 20 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 21 | |
kernel_size: 1 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
### SUM the four branches | |
layer { | |
bottom: "blur1_fc8_pascalvoc2012_1" | |
bottom: "blur1_fc8_pascalvoc2012_2" | |
bottom: "blur1_fc8_pascalvoc2012_3" | |
bottom: "blur1_fc8_pascalvoc2012_4" | |
top: "blur1_branch_sum" | |
name: "blur1_branch_sum" | |
type: "Eltwise" | |
eltwise_param { | |
operation: SUM | |
} | |
} | |
############### end Deeplab Blur1 ############ | |
###################### DeepLab Blur2 #################### | |
layer { | |
name: "blur2_conv1_1" | |
type: "Convolution" | |
bottom: "blur2_data" | |
top: "blur2_conv1_1" | |
param { | |
name: "conv1_1_w_blur2" | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
name: "conv1_1_b_blur2" | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 64 | |
pad: 1 | |
kernel_size: 3 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "blur2_relu1_1" | |
type: "ReLU" | |
bottom: "blur2_conv1_1" | |
top: "blur2_conv1_1" | |
} | |
layer { | |
name: "blur2_conv1_2" | |
type: "Convolution" | |
bottom: "blur2_conv1_1" | |
top: "blur2_conv1_2" | |
param { | |
name: "conv1_2_w_blur2" | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
name: "conv1_2_b_blur2" | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 64 | |
pad: 1 | |
kernel_size: 3 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "blur2_relu1_2" | |
type: "ReLU" | |
bottom: "blur2_conv1_2" | |
top: "blur2_conv1_2" | |
} | |
layer { | |
name: "blur2_pool1" | |
type: "Pooling" | |
bottom: "blur2_conv1_2" | |
top: "blur2_pool1" | |
pooling_param { | |
pool: MAX | |
kernel_size: 3 | |
stride: 2 | |
pad: 1 | |
} | |
} | |
layer { | |
name: "blur2_conv2_1" | |
type: "Convolution" | |
bottom: "blur2_pool1" | |
top: "blur2_conv2_1" | |
param { | |
name: "conv2_1_w_blur2" | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
name: "conv2_1_b_blur2" | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 128 | |
pad: 1 | |
kernel_size: 3 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "blur2_relu2_1" | |
type: "ReLU" | |
bottom: "blur2_conv2_1" | |
top: "blur2_conv2_1" | |
} | |
layer { | |
name: "blur2_conv2_2" | |
type: "Convolution" | |
bottom: "blur2_conv2_1" | |
top: "blur2_conv2_2" | |
param { | |
name: "conv2_2_w_blur2" | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
name: "conv2_2_b_blur2" | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 128 | |
pad: 1 | |
kernel_size: 3 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "blur2_relu2_2" | |
type: "ReLU" | |
bottom: "blur2_conv2_2" | |
top: "blur2_conv2_2" | |
} | |
layer { | |
name: "blur2_pool2" | |
type: "Pooling" | |
bottom: "blur2_conv2_2" | |
top: "blur2_pool2" | |
pooling_param { | |
pool: MAX | |
kernel_size: 3 | |
stride: 2 | |
pad: 1 | |
} | |
} | |
layer { | |
name: "blur2_conv3_1" | |
type: "Convolution" | |
bottom: "blur2_pool2" | |
top: "blur2_conv3_1" | |
param { | |
name: "conv3_1_w_blur2" | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
name: "conv3_1_b_blur2" | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 256 | |
pad: 1 | |
kernel_size: 3 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "blur2_relu3_1" | |
type: "ReLU" | |
bottom: "blur2_conv3_1" | |
top: "blur2_conv3_1" | |
} | |
layer { | |
name: "blur2_conv3_2" | |
type: "Convolution" | |
bottom: "blur2_conv3_1" | |
top: "blur2_conv3_2" | |
param { | |
name: "conv3_2_w_blur2" | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
name: "conv3_2_b_blur2" | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 256 | |
pad: 1 | |
kernel_size: 3 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "blur2_relu3_2" | |
type: "ReLU" | |
bottom: "blur2_conv3_2" | |
top: "blur2_conv3_2" | |
} | |
layer { | |
name: "blur2_conv3_3" | |
type: "Convolution" | |
bottom: "blur2_conv3_2" | |
top: "blur2_conv3_3" | |
param { | |
name: "conv3_3_w_blur2" | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
name: "conv3_3_b_blur2" | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 256 | |
pad: 1 | |
kernel_size: 3 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "blur2_relu3_3" | |
type: "ReLU" | |
bottom: "blur2_conv3_3" | |
top: "blur2_conv3_3" | |
} | |
layer { | |
name: "blur2_pool3" | |
type: "Pooling" | |
bottom: "blur2_conv3_3" | |
top: "blur2_pool3" | |
pooling_param { | |
pool: MAX | |
kernel_size: 3 | |
stride: 2 | |
pad: 1 | |
} | |
} | |
layer { | |
name: "blur2_conv4_1" | |
type: "Convolution" | |
bottom: "blur2_pool3" | |
top: "blur2_conv4_1" | |
param { | |
name: "conv4_1_w_blur2" | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
name: "conv4_1_b_blur2" | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 512 | |
pad: 1 | |
kernel_size: 3 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "blur2_relu4_1" | |
type: "ReLU" | |
bottom: "blur2_conv4_1" | |
top: "blur2_conv4_1" | |
} | |
layer { | |
name: "blur2_conv4_2" | |
type: "Convolution" | |
bottom: "blur2_conv4_1" | |
top: "blur2_conv4_2" | |
param { | |
name: "conv4_2_w_blur2" | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
name: "conv4_2_b_blur2" | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 512 | |
pad: 1 | |
kernel_size: 3 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "blur2_relu4_2" | |
type: "ReLU" | |
bottom: "blur2_conv4_2" | |
top: "blur2_conv4_2" | |
} | |
layer { | |
name: "blur2_conv4_3" | |
type: "Convolution" | |
bottom: "blur2_conv4_2" | |
top: "blur2_conv4_3" | |
param { | |
name: "conv4_3_w_blur2" | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
name: "conv4_3_b_blur2" | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 512 | |
pad: 1 | |
kernel_size: 3 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "blur2_relu4_3" | |
type: "ReLU" | |
bottom: "blur2_conv4_3" | |
top: "blur2_conv4_3" | |
} | |
layer { | |
bottom: "blur2_conv4_3" | |
top: "blur2_pool4" | |
name: "blur2_pool4" | |
type: "Pooling" | |
pooling_param { | |
pool: MAX | |
kernel_size: 3 | |
pad: 1 | |
stride: 1 | |
} | |
} | |
layer { | |
name: "blur2_conv5_1" | |
type: "Convolution" | |
bottom: "blur2_pool4" | |
top: "blur2_conv5_1" | |
param { | |
name: "conv5_1_w_blur2" | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
name: "conv5_1_b_blur2" | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 512 | |
pad: 2 | |
kernel_size: 3 | |
dilation: 2 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "blur2_relu5_1" | |
type: "ReLU" | |
bottom: "blur2_conv5_1" | |
top: "blur2_conv5_1" | |
} | |
layer { | |
name: "blur2_conv5_2" | |
type: "Convolution" | |
bottom: "blur2_conv5_1" | |
top: "blur2_conv5_2" | |
param { | |
name: "conv5_2_w_blur2" | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
name: "conv5_2_b_blur2" | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 512 | |
pad: 2 | |
kernel_size: 3 | |
dilation: 2 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "blur2_relu5_2" | |
type: "ReLU" | |
bottom: "blur2_conv5_2" | |
top: "blur2_conv5_2" | |
} | |
layer { | |
name: "blur2_conv5_3" | |
type: "Convolution" | |
bottom: "blur2_conv5_2" | |
top: "blur2_conv5_3" | |
param { | |
name: "conv5_3_w_blur2" | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
name: "conv5_3_b_blur2" | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 512 | |
pad: 2 | |
kernel_size: 3 | |
dilation: 2 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "blur2_relu5_3" | |
type: "ReLU" | |
bottom: "blur2_conv5_3" | |
top: "blur2_conv5_3" | |
} | |
layer { | |
bottom: "blur2_conv5_3" | |
top: "blur2_pool5" | |
name: "blur2_pool5" | |
type: "Pooling" | |
pooling_param { | |
pool: MAX | |
kernel_size: 3 | |
stride: 1 | |
pad: 1 | |
} | |
} | |
### hole = 6 | |
layer { | |
name: "blur2_fc6_1" | |
type: "Convolution" | |
bottom: "blur2_pool5" | |
top: "blur2_fc6_1" | |
param { | |
name: "fc6_1_w_blur2" | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
name: "fc6_1_b_blur2" | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 1024 | |
pad: 6 | |
kernel_size: 3 | |
dilation: 6 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "blur2_relu6_1" | |
type: "ReLU" | |
bottom: "blur2_fc6_1" | |
top: "blur2_fc6_1" | |
} | |
layer { | |
name: "blur2_drop6_1" | |
type: "Dropout" | |
bottom: "blur2_fc6_1" | |
top: "blur2_fc6_1" | |
dropout_param { | |
dropout_ratio: 0.5 | |
} | |
} | |
layer { | |
name: "blur2_fc7_1" | |
type: "Convolution" | |
bottom: "blur2_fc6_1" | |
top: "blur2_fc7_1" | |
param { | |
name: "fc7_1_w_blur2" | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
name: "fc7_1_b_blur2" | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 1024 | |
kernel_size: 1 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "blur2_relu7_1" | |
type: "ReLU" | |
bottom: "blur2_fc7_1" | |
top: "blur2_fc7_1" | |
} | |
layer { | |
name: "blur2_drop7_1" | |
type: "Dropout" | |
bottom: "blur2_fc7_1" | |
top: "blur2_fc7_1" | |
dropout_param { | |
dropout_ratio: 0.5 | |
} | |
} | |
layer { | |
name: "blur2_fc8_pascalvoc2012_1" | |
type: "Convolution" | |
bottom: "blur2_fc7_1" | |
top: "blur2_fc8_pascalvoc2012_1" | |
param { | |
name: "fc8_pascalvoc2012_1_w_blur2" | |
lr_mult: 10 | |
decay_mult: 1 | |
} | |
param { | |
name: "fc8_pascalvoc2012_1_b_blur2" | |
lr_mult: 20 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 21 | |
kernel_size: 1 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
### hole = 12 | |
layer { | |
name: "blur2_fc6_2" | |
type: "Convolution" | |
bottom: "blur2_pool5" | |
top: "blur2_fc6_2" | |
param { | |
name: "fc6_2_w_blur2" | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
name: "fc6_2_b_blur2" | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 1024 | |
pad: 12 | |
kernel_size: 3 | |
dilation: 12 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "blur2_relu6_2" | |
type: "ReLU" | |
bottom: "blur2_fc6_2" | |
top: "blur2_fc6_2" | |
} | |
layer { | |
name: "blur2_drop6_2" | |
type: "Dropout" | |
bottom: "blur2_fc6_2" | |
top: "blur2_fc6_2" | |
dropout_param { | |
dropout_ratio: 0.5 | |
} | |
} | |
layer { | |
name: "blur2_fc7_2" | |
type: "Convolution" | |
bottom: "blur2_fc6_2" | |
top: "blur2_fc7_2" | |
param { | |
name: "fc7_2_w_blur2" | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
name: "fc7_2_b_blur2" | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 1024 | |
kernel_size: 1 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "blur2_relu7_2" | |
type: "ReLU" | |
bottom: "blur2_fc7_2" | |
top: "blur2_fc7_2" | |
} | |
layer { | |
name: "blur2_drop7_2" | |
type: "Dropout" | |
bottom: "blur2_fc7_2" | |
top: "blur2_fc7_2" | |
dropout_param { | |
dropout_ratio: 0.5 | |
} | |
} | |
layer { | |
name: "blur2_fc8_pascalvoc2012_2" | |
type: "Convolution" | |
bottom: "blur2_fc7_2" | |
top: "blur2_fc8_pascalvoc2012_2" | |
param { | |
name: "fc8_pascalvoc2012_2_w_blur2" | |
lr_mult: 10 | |
decay_mult: 1 | |
} | |
param { | |
name: "fc8_pascalvoc2012_2_b_blur2" | |
lr_mult: 20 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 21 | |
kernel_size: 1 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
### hole = 18 | |
layer { | |
name: "blur2_fc6_3" | |
type: "Convolution" | |
bottom: "blur2_pool5" | |
top: "blur2_fc6_3" | |
param { | |
name: "fc6_3_w_blur2" | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
name: "fc6_3_b_blur2" | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 1024 | |
pad: 18 | |
kernel_size: 3 | |
dilation: 18 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "blur2_relu6_3" | |
type: "ReLU" | |
bottom: "blur2_fc6_3" | |
top: "blur2_fc6_3" | |
} | |
layer { | |
name: "blur2_drop6_3" | |
type: "Dropout" | |
bottom: "blur2_fc6_3" | |
top: "blur2_fc6_3" | |
dropout_param { | |
dropout_ratio: 0.5 | |
} | |
} | |
layer { | |
name: "blur2_fc7_3" | |
type: "Convolution" | |
bottom: "blur2_fc6_3" | |
top: "blur2_fc7_3" | |
param { | |
name: "fc7_3_w_blur2" | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
name: "fc7_3_b_blur2" | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 1024 | |
kernel_size: 1 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "blur2_relu7_3" | |
type: "ReLU" | |
bottom: "blur2_fc7_3" | |
top: "blur2_fc7_3" | |
} | |
layer { | |
name: "blur2_drop7_3" | |
type: "Dropout" | |
bottom: "blur2_fc7_3" | |
top: "blur2_fc7_3" | |
dropout_param { | |
dropout_ratio: 0.5 | |
} | |
} | |
layer { | |
name: "blur2_fc8_pascalvoc2012_3" | |
type: "Convolution" | |
bottom: "blur2_fc7_3" | |
top: "blur2_fc8_pascalvoc2012_3" | |
param { | |
name: "fc8_pascalvoc2012_3_w_blur2" | |
lr_mult: 10 | |
decay_mult: 1 | |
} | |
param { | |
name: "fc8_pascalvoc2012_3_b_blur2" | |
lr_mult: 20 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 21 | |
kernel_size: 1 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
### hole = 24 | |
layer { | |
name: "blur2_fc6_4" | |
type: "Convolution" | |
bottom: "blur2_pool5" | |
top: "blur2_fc6_4" | |
param { | |
name: "fc6_4_w_blur2" | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
name: "fc6_4_b_blur2" | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 1024 | |
pad: 24 | |
kernel_size: 3 | |
dilation: 24 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "blur2_relu6_4" | |
type: "ReLU" | |
bottom: "blur2_fc6_4" | |
top: "blur2_fc6_4" | |
} | |
layer { | |
name: "blur2_drop6_4" | |
type: "Dropout" | |
bottom: "blur2_fc6_4" | |
top: "blur2_fc6_4" | |
dropout_param { | |
dropout_ratio: 0.5 | |
} | |
} | |
layer { | |
name: "blur2_fc7_4" | |
type: "Convolution" | |
bottom: "blur2_fc6_4" | |
top: "blur2_fc7_4" | |
param { | |
name: "fc7_4_w_blur2" | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
name: "fc7_4_b_blur2" | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 1024 | |
kernel_size: 1 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "blur2_relu7_4" | |
type: "ReLU" | |
bottom: "blur2_fc7_4" | |
top: "blur2_fc7_4" | |
} | |
layer { | |
name: "blur2_drop7_4" | |
type: "Dropout" | |
bottom: "blur2_fc7_4" | |
top: "blur2_fc7_4" | |
dropout_param { | |
dropout_ratio: 0.5 | |
} | |
} | |
layer { | |
name: "blur2_fc8_pascalvoc2012_4" | |
type: "Convolution" | |
bottom: "blur2_fc7_4" | |
top: "blur2_fc8_pascalvoc2012_4" | |
param { | |
name: "fc8_pascalvoc2012_4_w_blur2" | |
lr_mult: 10 | |
decay_mult: 1 | |
} | |
param { | |
name: "fc8_pascalvoc2012_4_b_blur2" | |
lr_mult: 20 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 21 | |
kernel_size: 1 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
### SUM the four branches | |
layer { | |
bottom: "blur2_fc8_pascalvoc2012_1" | |
bottom: "blur2_fc8_pascalvoc2012_2" | |
bottom: "blur2_fc8_pascalvoc2012_3" | |
bottom: "blur2_fc8_pascalvoc2012_4" | |
top: "blur2_branch_sum" | |
name: "blur2_branch_sum" | |
type: "Eltwise" | |
eltwise_param { | |
operation: SUM | |
} | |
} | |
############### end Deeplab Blur2 ############ | |
##############sum############ | |
layer { | |
bottom: "branch_sum" | |
bottom: "blur1_branch_sum" | |
bottom: "blur2_branch_sum" | |
top: "allblur_sum_pascalvoc2012" | |
name: "allblur_sum_pascalvoc2012" | |
type: "Eltwise" | |
eltwise_param { | |
operation: MAX | |
} | |
} | |
layer { | |
bottom: "label" | |
top: "label_shrink" | |
name: "label_shrink" | |
type: "Interp" | |
interp_param { | |
shrink_factor: 8 | |
pad_beg: 0 | |
pad_end: 0 | |
} | |
} | |
layer { | |
name: "loss" | |
type: "SoftmaxWithLoss" | |
bottom: "allblur_sum_pascalvoc2012" | |
bottom: "label_shrink" | |
include { | |
phase: TRAIN | |
} | |
loss_param { | |
ignore_label: 255 | |
} | |
} | |
layer { | |
name: "accuracy" | |
type: "SegAccuracy" | |
bottom: "allblur_sum_pascalvoc2012" | |
bottom: "label_shrink" | |
top: "accuracy" | |
seg_accuracy_param { | |
ignore_label: 255 | |
} | |
} | |
layer { | |
name: "silence" | |
type: "Silence" | |
bottom: "data_dim" | |
} | |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment