Created
November 11, 2017 00:02
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sphereface train prototxt (64 layers); netscope: http://ethereon.github.io/netscope/#/gist/fa28659e989e9864df302bbd0678c5e3
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name: "SphereFaceNet-64" | |
layer { | |
name: "data" | |
type: "ImageData" | |
top: "data" | |
top: "label" | |
transform_param { | |
mean_value: 127.5 | |
mean_value: 127.5 | |
mean_value: 127.5 | |
scale: 0.0078125 | |
mirror: true | |
} | |
image_data_param { | |
source: "data/CASIA-WebFace-112X96.txt" | |
batch_size: 256 | |
shuffle: true | |
} | |
} | |
############## CNN Architecture ############### | |
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: 1 | |
kernel_size: 3 | |
stride: 2 | |
weight_filler { | |
type: "xavier" | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "prelu1_1" | |
type: "PReLU" | |
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: 0 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 64 | |
pad: 1 | |
kernel_size: 3 | |
stride: 1 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "prelu1_2" | |
type: "PReLU" | |
bottom: "conv1_2" | |
top: "conv1_2" | |
} | |
layer { | |
name: "conv1_3" | |
type: "Convolution" | |
bottom: "conv1_2" | |
top: "conv1_3" | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 0 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 64 | |
pad: 1 | |
kernel_size: 3 | |
stride: 1 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "prelu1_3" | |
type: "PReLU" | |
bottom: "conv1_3" | |
top: "conv1_3" | |
} | |
layer { | |
name: "res1_3" | |
type: "Eltwise" | |
bottom: "conv1_1" | |
bottom: "conv1_3" | |
top: "res1_3" | |
eltwise_param { | |
operation: SUM | |
} | |
} | |
layer { | |
name: "conv1_4" | |
type: "Convolution" | |
bottom: "res1_3" | |
top: "conv1_4" | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 0 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 64 | |
pad: 1 | |
kernel_size: 3 | |
stride: 1 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "prelu1_4" | |
type: "PReLU" | |
bottom: "conv1_4" | |
top: "conv1_4" | |
} | |
layer { | |
name: "conv1_5" | |
type: "Convolution" | |
bottom: "conv1_4" | |
top: "conv1_5" | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 0 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 64 | |
pad: 1 | |
kernel_size: 3 | |
stride: 1 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "prelu1_5" | |
type: "PReLU" | |
bottom: "conv1_5" | |
top: "conv1_5" | |
} | |
layer { | |
name: "res1_5" | |
type: "Eltwise" | |
bottom: "res1_3" | |
bottom: "conv1_5" | |
top: "res1_5" | |
eltwise_param { | |
operation: SUM | |
} | |
} | |
layer { | |
name: "conv1_6" | |
type: "Convolution" | |
bottom: "res1_5" | |
top: "conv1_6" | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 0 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 64 | |
pad: 1 | |
kernel_size: 3 | |
stride: 1 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "prelu1_6" | |
type: "PReLU" | |
bottom: "conv1_6" | |
top: "conv1_6" | |
} | |
layer { | |
name: "conv1_7" | |
type: "Convolution" | |
bottom: "conv1_6" | |
top: "conv1_7" | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 0 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 64 | |
pad: 1 | |
kernel_size: 3 | |
stride: 1 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "prelu1_7" | |
type: "PReLU" | |
bottom: "conv1_7" | |
top: "conv1_7" | |
} | |
layer { | |
name: "res1_7" | |
type: "Eltwise" | |
bottom: "res1_5" | |
bottom: "conv1_7" | |
top: "res1_7" | |
eltwise_param { | |
operation: SUM | |
} | |
} | |
layer { | |
name: "conv2_1" | |
type: "Convolution" | |
bottom: "res1_7" | |
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: 2 | |
weight_filler { | |
type: "xavier" | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "prelu2_1" | |
type: "PReLU" | |
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: 0 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 128 | |
pad: 1 | |
kernel_size: 3 | |
stride: 1 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "prelu2_2" | |
type: "PReLU" | |
bottom: "conv2_2" | |
top: "conv2_2" | |
} | |
layer { | |
name: "conv2_3" | |
type: "Convolution" | |
bottom: "conv2_2" | |
top: "conv2_3" | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 0 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 128 | |
pad: 1 | |
kernel_size: 3 | |
stride: 1 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "prelu2_3" | |
type: "PReLU" | |
bottom: "conv2_3" | |
top: "conv2_3" | |
} | |
layer { | |
name: "res2_3" | |
type: "Eltwise" | |
bottom: "conv2_1" | |
bottom: "conv2_3" | |
top: "res2_3" | |
eltwise_param { | |
operation: SUM | |
} | |
} | |
layer { | |
name: "conv2_4" | |
type: "Convolution" | |
bottom: "res2_3" | |
top: "conv2_4" | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 0 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 128 | |
pad: 1 | |
kernel_size: 3 | |
stride: 1 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "prelu2_4" | |
type: "PReLU" | |
bottom: "conv2_4" | |
top: "conv2_4" | |
} | |
layer { | |
name: "conv2_5" | |
type: "Convolution" | |
bottom: "conv2_4" | |
top: "conv2_5" | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 0 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 128 | |
pad: 1 | |
kernel_size: 3 | |
stride: 1 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "prelu2_5" | |
type: "PReLU" | |
bottom: "conv2_5" | |
top: "conv2_5" | |
} | |
layer { | |
name: "res2_5" | |
type: "Eltwise" | |
bottom: "res2_3" | |
bottom: "conv2_5" | |
top: "res2_5" | |
eltwise_param { | |
operation: SUM | |
} | |
} | |
layer { | |
name: "conv2_6" | |
type: "Convolution" | |
bottom: "res2_5" | |
top: "conv2_6" | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 0 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 128 | |
pad: 1 | |
kernel_size: 3 | |
stride: 1 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "prelu2_6" | |
type: "PReLU" | |
bottom: "conv2_6" | |
top: "conv2_6" | |
} | |
layer { | |
name: "conv2_7" | |
type: "Convolution" | |
bottom: "conv2_6" | |
top: "conv2_7" | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 0 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 128 | |
pad: 1 | |
kernel_size: 3 | |
stride: 1 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "prelu2_7" | |
type: "PReLU" | |
bottom: "conv2_7" | |
top: "conv2_7" | |
} | |
layer { | |
name: "res2_7" | |
type: "Eltwise" | |
bottom: "res2_5" | |
bottom: "conv2_7" | |
top: "res2_7" | |
eltwise_param { | |
operation: SUM | |
} | |
} | |
layer { | |
name: "conv2_8" | |
type: "Convolution" | |
bottom: "res2_7" | |
top: "conv2_8" | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 0 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 128 | |
pad: 1 | |
kernel_size: 3 | |
stride: 1 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "prelu2_8" | |
type: "PReLU" | |
bottom: "conv2_8" | |
top: "conv2_8" | |
} | |
layer { | |
name: "conv2_9" | |
type: "Convolution" | |
bottom: "conv2_8" | |
top: "conv2_9" | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 0 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 128 | |
pad: 1 | |
kernel_size: 3 | |
stride: 1 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "prelu2_9" | |
type: "PReLU" | |
bottom: "conv2_9" | |
top: "conv2_9" | |
} | |
layer { | |
name: "res2_9" | |
type: "Eltwise" | |
bottom: "res2_7" | |
bottom: "conv2_9" | |
top: "res2_9" | |
eltwise_param { | |
operation: SUM | |
} | |
} | |
layer { | |
name: "conv2_10" | |
type: "Convolution" | |
bottom: "res2_9" | |
top: "conv2_10" | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 0 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 128 | |
pad: 1 | |
kernel_size: 3 | |
stride: 1 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "prelu2_10" | |
type: "PReLU" | |
bottom: "conv2_10" | |
top: "conv2_10" | |
} | |
layer { | |
name: "conv2_11" | |
type: "Convolution" | |
bottom: "conv2_10" | |
top: "conv2_11" | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 0 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 128 | |
pad: 1 | |
kernel_size: 3 | |
stride: 1 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "prelu2_11" | |
type: "PReLU" | |
bottom: "conv2_11" | |
top: "conv2_11" | |
} | |
layer { | |
name: "res2_11" | |
type: "Eltwise" | |
bottom: "res2_9" | |
bottom: "conv2_11" | |
top: "res2_11" | |
eltwise_param { | |
operation: SUM | |
} | |
} | |
layer { | |
name: "conv2_12" | |
type: "Convolution" | |
bottom: "res2_11" | |
top: "conv2_12" | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 0 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 128 | |
pad: 1 | |
kernel_size: 3 | |
stride: 1 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "prelu2_12" | |
type: "PReLU" | |
bottom: "conv2_12" | |
top: "conv2_12" | |
} | |
layer { | |
name: "conv2_13" | |
type: "Convolution" | |
bottom: "conv2_12" | |
top: "conv2_13" | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 0 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 128 | |
pad: 1 | |
kernel_size: 3 | |
stride: 1 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "prelu2_13" | |
type: "PReLU" | |
bottom: "conv2_13" | |
top: "conv2_13" | |
} | |
layer { | |
name: "res2_13" | |
type: "Eltwise" | |
bottom: "res2_11" | |
bottom: "conv2_13" | |
top: "res2_13" | |
eltwise_param { | |
operation: SUM | |
} | |
} | |
layer { | |
name: "conv2_14" | |
type: "Convolution" | |
bottom: "res2_13" | |
top: "conv2_14" | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 0 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 128 | |
pad: 1 | |
kernel_size: 3 | |
stride: 1 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "prelu2_14" | |
type: "PReLU" | |
bottom: "conv2_14" | |
top: "conv2_14" | |
} | |
layer { | |
name: "conv2_15" | |
type: "Convolution" | |
bottom: "conv2_14" | |
top: "conv2_15" | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 0 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 128 | |
pad: 1 | |
kernel_size: 3 | |
stride: 1 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "prelu2_15" | |
type: "PReLU" | |
bottom: "conv2_15" | |
top: "conv2_15" | |
} | |
layer { | |
name: "res2_15" | |
type: "Eltwise" | |
bottom: "res2_13" | |
bottom: "conv2_15" | |
top: "res2_15" | |
eltwise_param { | |
operation: SUM | |
} | |
} | |
layer { | |
name: "conv2_16" | |
type: "Convolution" | |
bottom: "res2_15" | |
top: "conv2_16" | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 0 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 128 | |
pad: 1 | |
kernel_size: 3 | |
stride: 1 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "prelu2_16" | |
type: "PReLU" | |
bottom: "conv2_16" | |
top: "conv2_16" | |
} | |
layer { | |
name: "conv2_17" | |
type: "Convolution" | |
bottom: "conv2_16" | |
top: "conv2_17" | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 0 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 128 | |
pad: 1 | |
kernel_size: 3 | |
stride: 1 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "prelu2_17" | |
type: "PReLU" | |
bottom: "conv2_17" | |
top: "conv2_17" | |
} | |
layer { | |
name: "res2_17" | |
type: "Eltwise" | |
bottom: "res2_15" | |
bottom: "conv2_17" | |
top: "res2_17" | |
eltwise_param { | |
operation: SUM | |
} | |
} | |
layer { | |
name: "conv3_1" | |
type: "Convolution" | |
bottom: "res2_17" | |
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: 2 | |
weight_filler { | |
type: "xavier" | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "prelu3_1" | |
type: "PReLU" | |
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: 0 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 256 | |
pad: 1 | |
kernel_size: 3 | |
stride: 1 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "prelu3_2" | |
type: "PReLU" | |
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: 0 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 256 | |
pad: 1 | |
kernel_size: 3 | |
stride: 1 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "prelu3_3" | |
type: "PReLU" | |
bottom: "conv3_3" | |
top: "conv3_3" | |
} | |
layer { | |
name: "res3_3" | |
type: "Eltwise" | |
bottom: "conv3_1" | |
bottom: "conv3_3" | |
top: "res3_3" | |
eltwise_param { | |
operation: SUM | |
} | |
} | |
layer { | |
name: "conv3_4" | |
type: "Convolution" | |
bottom: "res3_3" | |
top: "conv3_4" | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 0 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 256 | |
pad: 1 | |
kernel_size: 3 | |
stride: 1 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "prelu3_4" | |
type: "PReLU" | |
bottom: "conv3_4" | |
top: "conv3_4" | |
} | |
layer { | |
name: "conv3_5" | |
type: "Convolution" | |
bottom: "conv3_4" | |
top: "conv3_5" | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 0 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 256 | |
pad: 1 | |
kernel_size: 3 | |
stride: 1 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "prelu3_5" | |
type: "PReLU" | |
bottom: "conv3_5" | |
top: "conv3_5" | |
} | |
layer { | |
name: "res3_5" | |
type: "Eltwise" | |
bottom: "res3_3" | |
bottom: "conv3_5" | |
top: "res3_5" | |
eltwise_param { | |
operation: SUM | |
} | |
} | |
layer { | |
name: "conv3_6" | |
type: "Convolution" | |
bottom: "res3_5" | |
top: "conv3_6" | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 0 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 256 | |
pad: 1 | |
kernel_size: 3 | |
stride: 1 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "prelu3_6" | |
type: "PReLU" | |
bottom: "conv3_6" | |
top: "conv3_6" | |
} | |
layer { | |
name: "conv3_7" | |
type: "Convolution" | |
bottom: "conv3_6" | |
top: "conv3_7" | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 0 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 256 | |
pad: 1 | |
kernel_size: 3 | |
stride: 1 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "prelu3_7" | |
type: "PReLU" | |
bottom: "conv3_7" | |
top: "conv3_7" | |
} | |
layer { | |
name: "res3_7" | |
type: "Eltwise" | |
bottom: "res3_5" | |
bottom: "conv3_7" | |
top: "res3_7" | |
eltwise_param { | |
operation: SUM | |
} | |
} | |
layer { | |
name: "conv3_8" | |
type: "Convolution" | |
bottom: "res3_7" | |
top: "conv3_8" | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 0 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 256 | |
pad: 1 | |
kernel_size: 3 | |
stride: 1 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "prelu3_8" | |
type: "PReLU" | |
bottom: "conv3_8" | |
top: "conv3_8" | |
} | |
layer { | |
name: "conv3_9" | |
type: "Convolution" | |
bottom: "conv3_8" | |
top: "conv3_9" | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 0 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 256 | |
pad: 1 | |
kernel_size: 3 | |
stride: 1 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "prelu3_9" | |
type: "PReLU" | |
bottom: "conv3_9" | |
top: "conv3_9" | |
} | |
layer { | |
name: "res3_9" | |
type: "Eltwise" | |
bottom: "res3_7" | |
bottom: "conv3_9" | |
top: "res3_9" | |
eltwise_param { | |
operation: SUM | |
} | |
} | |
layer { | |
name: "conv3_10" | |
type: "Convolution" | |
bottom: "res3_9" | |
top: "conv3_10" | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 0 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 256 | |
pad: 1 | |
kernel_size: 3 | |
stride: 1 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "prelu3_10" | |
type: "PReLU" | |
bottom: "conv3_10" | |
top: "conv3_10" | |
} | |
layer { | |
name: "conv3_11" | |
type: "Convolution" | |
bottom: "conv3_10" | |
top: "conv3_11" | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 0 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 256 | |
pad: 1 | |
kernel_size: 3 | |
stride: 1 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "prelu3_11" | |
type: "PReLU" | |
bottom: "conv3_11" | |
top: "conv3_11" | |
} | |
layer { | |
name: "res3_11" | |
type: "Eltwise" | |
bottom: "res3_9" | |
bottom: "conv3_11" | |
top: "res3_11" | |
eltwise_param { | |
operation: SUM | |
} | |
} | |
layer { | |
name: "conv3_12" | |
type: "Convolution" | |
bottom: "res3_11" | |
top: "conv3_12" | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 0 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 256 | |
pad: 1 | |
kernel_size: 3 | |
stride: 1 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "prelu3_12" | |
type: "PReLU" | |
bottom: "conv3_12" | |
top: "conv3_12" | |
} | |
layer { | |
name: "conv3_13" | |
type: "Convolution" | |
bottom: "conv3_12" | |
top: "conv3_13" | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 0 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 256 | |
pad: 1 | |
kernel_size: 3 | |
stride: 1 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "prelu3_13" | |
type: "PReLU" | |
bottom: "conv3_13" | |
top: "conv3_13" | |
} | |
layer { | |
name: "res3_13" | |
type: "Eltwise" | |
bottom: "res3_11" | |
bottom: "conv3_13" | |
top: "res3_13" | |
eltwise_param { | |
operation: SUM | |
} | |
} | |
layer { | |
name: "conv3_14" | |
type: "Convolution" | |
bottom: "res3_13" | |
top: "conv3_14" | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 0 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 256 | |
pad: 1 | |
kernel_size: 3 | |
stride: 1 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "prelu3_14" | |
type: "PReLU" | |
bottom: "conv3_14" | |
top: "conv3_14" | |
} | |
layer { | |
name: "conv3_15" | |
type: "Convolution" | |
bottom: "conv3_14" | |
top: "conv3_15" | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 0 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 256 | |
pad: 1 | |
kernel_size: 3 | |
stride: 1 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "prelu3_15" | |
type: "PReLU" | |
bottom: "conv3_15" | |
top: "conv3_15" | |
} | |
layer { | |
name: "res3_15" | |
type: "Eltwise" | |
bottom: "res3_13" | |
bottom: "conv3_15" | |
top: "res3_15" | |
eltwise_param { | |
operation: SUM | |
} | |
} | |
layer { | |
name: "conv3_16" | |
type: "Convolution" | |
bottom: "res3_15" | |
top: "conv3_16" | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 0 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 256 | |
pad: 1 | |
kernel_size: 3 | |
stride: 1 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "prelu3_16" | |
type: "PReLU" | |
bottom: "conv3_16" | |
top: "conv3_16" | |
} | |
layer { | |
name: "conv3_17" | |
type: "Convolution" | |
bottom: "conv3_16" | |
top: "conv3_17" | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 0 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 256 | |
pad: 1 | |
kernel_size: 3 | |
stride: 1 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "prelu3_17" | |
type: "PReLU" | |
bottom: "conv3_17" | |
top: "conv3_17" | |
} | |
layer { | |
name: "res3_17" | |
type: "Eltwise" | |
bottom: "res3_15" | |
bottom: "conv3_17" | |
top: "res3_17" | |
eltwise_param { | |
operation: SUM | |
} | |
} | |
layer { | |
name: "conv3_18" | |
type: "Convolution" | |
bottom: "res3_17" | |
top: "conv3_18" | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 0 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 256 | |
pad: 1 | |
kernel_size: 3 | |
stride: 1 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "prelu3_18" | |
type: "PReLU" | |
bottom: "conv3_18" | |
top: "conv3_18" | |
} | |
layer { | |
name: "conv3_19" | |
type: "Convolution" | |
bottom: "conv3_18" | |
top: "conv3_19" | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 0 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 256 | |
pad: 1 | |
kernel_size: 3 | |
stride: 1 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "prelu3_19" | |
type: "PReLU" | |
bottom: "conv3_19" | |
top: "conv3_19" | |
} | |
layer { | |
name: "res3_19" | |
type: "Eltwise" | |
bottom: "res3_17" | |
bottom: "conv3_19" | |
top: "res3_19" | |
eltwise_param { | |
operation: SUM | |
} | |
} | |
layer { | |
name: "conv3_20" | |
type: "Convolution" | |
bottom: "res3_19" | |
top: "conv3_20" | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 0 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 256 | |
pad: 1 | |
kernel_size: 3 | |
stride: 1 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "prelu3_20" | |
type: "PReLU" | |
bottom: "conv3_20" | |
top: "conv3_20" | |
} | |
layer { | |
name: "conv3_21" | |
type: "Convolution" | |
bottom: "conv3_20" | |
top: "conv3_21" | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 0 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 256 | |
pad: 1 | |
kernel_size: 3 | |
stride: 1 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "prelu3_21" | |
type: "PReLU" | |
bottom: "conv3_21" | |
top: "conv3_21" | |
} | |
layer { | |
name: "res3_21" | |
type: "Eltwise" | |
bottom: "res3_19" | |
bottom: "conv3_21" | |
top: "res3_21" | |
eltwise_param { | |
operation: SUM | |
} | |
} | |
layer { | |
name: "conv3_22" | |
type: "Convolution" | |
bottom: "res3_21" | |
top: "conv3_22" | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 0 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 256 | |
pad: 1 | |
kernel_size: 3 | |
stride: 1 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "prelu3_22" | |
type: "PReLU" | |
bottom: "conv3_22" | |
top: "conv3_22" | |
} | |
layer { | |
name: "conv3_23" | |
type: "Convolution" | |
bottom: "conv3_22" | |
top: "conv3_23" | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 0 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 256 | |
pad: 1 | |
kernel_size: 3 | |
stride: 1 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "prelu3_23" | |
type: "PReLU" | |
bottom: "conv3_23" | |
top: "conv3_23" | |
} | |
layer { | |
name: "res3_23" | |
type: "Eltwise" | |
bottom: "res3_21" | |
bottom: "conv3_23" | |
top: "res3_23" | |
eltwise_param { | |
operation: SUM | |
} | |
} | |
layer { | |
name: "conv3_24" | |
type: "Convolution" | |
bottom: "res3_23" | |
top: "conv3_24" | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 0 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 256 | |
pad: 1 | |
kernel_size: 3 | |
stride: 1 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "prelu3_24" | |
type: "PReLU" | |
bottom: "conv3_24" | |
top: "conv3_24" | |
} | |
layer { | |
name: "conv3_25" | |
type: "Convolution" | |
bottom: "conv3_24" | |
top: "conv3_25" | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 0 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 256 | |
pad: 1 | |
kernel_size: 3 | |
stride: 1 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "prelu3_25" | |
type: "PReLU" | |
bottom: "conv3_25" | |
top: "conv3_25" | |
} | |
layer { | |
name: "res3_25" | |
type: "Eltwise" | |
bottom: "res3_23" | |
bottom: "conv3_25" | |
top: "res3_25" | |
eltwise_param { | |
operation: SUM | |
} | |
} | |
layer { | |
name: "conv3_26" | |
type: "Convolution" | |
bottom: "res3_25" | |
top: "conv3_26" | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 0 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 256 | |
pad: 1 | |
kernel_size: 3 | |
stride: 1 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "prelu3_26" | |
type: "PReLU" | |
bottom: "conv3_26" | |
top: "conv3_26" | |
} | |
layer { | |
name: "conv3_27" | |
type: "Convolution" | |
bottom: "conv3_26" | |
top: "conv3_27" | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 0 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 256 | |
pad: 1 | |
kernel_size: 3 | |
stride: 1 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "prelu3_27" | |
type: "PReLU" | |
bottom: "conv3_27" | |
top: "conv3_27" | |
} | |
layer { | |
name: "res3_27" | |
type: "Eltwise" | |
bottom: "res3_25" | |
bottom: "conv3_27" | |
top: "res3_27" | |
eltwise_param { | |
operation: SUM | |
} | |
} | |
layer { | |
name: "conv3_28" | |
type: "Convolution" | |
bottom: "res3_27" | |
top: "conv3_28" | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 0 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 256 | |
pad: 1 | |
kernel_size: 3 | |
stride: 1 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "prelu3_28" | |
type: "PReLU" | |
bottom: "conv3_28" | |
top: "conv3_28" | |
} | |
layer { | |
name: "conv3_29" | |
type: "Convolution" | |
bottom: "conv3_28" | |
top: "conv3_29" | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 0 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 256 | |
pad: 1 | |
kernel_size: 3 | |
stride: 1 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "prelu3_29" | |
type: "PReLU" | |
bottom: "conv3_29" | |
top: "conv3_29" | |
} | |
layer { | |
name: "res3_29" | |
type: "Eltwise" | |
bottom: "res3_27" | |
bottom: "conv3_29" | |
top: "res3_29" | |
eltwise_param { | |
operation: SUM | |
} | |
} | |
layer { | |
name: "conv3_30" | |
type: "Convolution" | |
bottom: "res3_29" | |
top: "conv3_30" | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 0 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 256 | |
pad: 1 | |
kernel_size: 3 | |
stride: 1 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "prelu3_30" | |
type: "PReLU" | |
bottom: "conv3_30" | |
top: "conv3_30" | |
} | |
layer { | |
name: "conv3_31" | |
type: "Convolution" | |
bottom: "conv3_30" | |
top: "conv3_31" | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 0 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 256 | |
pad: 1 | |
kernel_size: 3 | |
stride: 1 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "prelu3_31" | |
type: "PReLU" | |
bottom: "conv3_31" | |
top: "conv3_31" | |
} | |
layer { | |
name: "res3_31" | |
type: "Eltwise" | |
bottom: "res3_29" | |
bottom: "conv3_31" | |
top: "res3_31" | |
eltwise_param { | |
operation: SUM | |
} | |
} | |
layer { | |
name: "conv3_32" | |
type: "Convolution" | |
bottom: "res3_31" | |
top: "conv3_32" | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 0 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 256 | |
pad: 1 | |
kernel_size: 3 | |
stride: 1 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "prelu3_32" | |
type: "PReLU" | |
bottom: "conv3_32" | |
top: "conv3_32" | |
} | |
layer { | |
name: "conv3_33" | |
type: "Convolution" | |
bottom: "conv3_32" | |
top: "conv3_33" | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 0 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 256 | |
pad: 1 | |
kernel_size: 3 | |
stride: 1 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "prelu3_33" | |
type: "PReLU" | |
bottom: "conv3_33" | |
top: "conv3_33" | |
} | |
layer { | |
name: "res3_33" | |
type: "Eltwise" | |
bottom: "res3_31" | |
bottom: "conv3_33" | |
top: "res3_33" | |
eltwise_param { | |
operation: SUM | |
} | |
} | |
layer { | |
name: "conv4_1" | |
type: "Convolution" | |
bottom: "res3_33" | |
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: 2 | |
weight_filler { | |
type: "xavier" | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "prelu4_1" | |
type: "PReLU" | |
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: 0 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 512 | |
pad: 1 | |
kernel_size: 3 | |
stride: 1 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "prelu4_2" | |
type: "PReLU" | |
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: 0 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 512 | |
pad: 1 | |
kernel_size: 3 | |
stride: 1 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "prelu4_3" | |
type: "PReLU" | |
bottom: "conv4_3" | |
top: "conv4_3" | |
} | |
layer { | |
name: "res4_3" | |
type: "Eltwise" | |
bottom: "conv4_1" | |
bottom: "conv4_3" | |
top: "res4_3" | |
eltwise_param { | |
operation: SUM | |
} | |
} | |
layer { | |
name: "conv4_4" | |
type: "Convolution" | |
bottom: "res4_3" | |
top: "conv4_4" | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 0 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 512 | |
pad: 1 | |
kernel_size: 3 | |
stride: 1 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "prelu4_4" | |
type: "PReLU" | |
bottom: "conv4_4" | |
top: "conv4_4" | |
} | |
layer { | |
name: "conv4_5" | |
type: "Convolution" | |
bottom: "conv4_4" | |
top: "conv4_5" | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 0 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 512 | |
pad: 1 | |
kernel_size: 3 | |
stride: 1 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "prelu4_5" | |
type: "PReLU" | |
bottom: "conv4_5" | |
top: "conv4_5" | |
} | |
layer { | |
name: "res4_5" | |
type: "Eltwise" | |
bottom: "res4_3" | |
bottom: "conv4_5" | |
top: "res4_5" | |
eltwise_param { | |
operation: SUM | |
} | |
} | |
layer { | |
name: "conv4_6" | |
type: "Convolution" | |
bottom: "res4_5" | |
top: "conv4_6" | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 0 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 512 | |
pad: 1 | |
kernel_size: 3 | |
stride: 1 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "prelu4_6" | |
type: "PReLU" | |
bottom: "conv4_6" | |
top: "conv4_6" | |
} | |
layer { | |
name: "conv4_7" | |
type: "Convolution" | |
bottom: "conv4_6" | |
top: "conv4_7" | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 0 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 512 | |
pad: 1 | |
kernel_size: 3 | |
stride: 1 | |
weight_filler { | |
type: "gaussian" | |
std: 0.01 | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
layer { | |
name: "prelu4_7" | |
type: "PReLU" | |
bottom: "conv4_7" | |
top: "conv4_7" | |
} | |
layer { | |
name: "res4_7" | |
type: "Eltwise" | |
bottom: "res4_5" | |
bottom: "conv4_7" | |
top: "res4_7" | |
eltwise_param { | |
operation: SUM | |
} | |
} | |
layer { | |
name: "fc5" | |
type: "InnerProduct" | |
bottom: "res4_7" | |
top: "fc5" | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
inner_product_param { | |
num_output: 512 | |
weight_filler { | |
type: "xavier" | |
} | |
bias_filler { | |
type: "constant" | |
value: 0 | |
} | |
} | |
} | |
############### A-Softmax Loss ############## | |
layer { | |
name: "fc6" | |
type: "MarginInnerProduct" | |
bottom: "fc5" | |
bottom: "label" | |
top: "fc6" | |
top: "lambda" | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
margin_inner_product_param { | |
num_output: 10572 | |
type: QUADRUPLE | |
weight_filler { | |
type: "xavier" | |
} | |
base: 1000 | |
gamma: 0.12 | |
power: 1 | |
lambda_min: 5 | |
iteration: 0 | |
} | |
} | |
layer { | |
name: "softmax_loss" | |
type: "SoftmaxWithLoss" | |
bottom: "fc6" | |
bottom: "label" | |
top: "softmax_loss" | |
} |
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