|
input: "image" |
|
input_dim: 1 |
|
input_dim: 3 |
|
input_dim: 1 # This value will be defined at runtime |
|
input_dim: 1 # This value will be defined at runtime |
|
layer { |
|
name: "conv1_1" |
|
type: "Convolution" |
|
bottom: "image" |
|
top: "conv1_1" |
|
param { |
|
lr_mult: 1.0 |
|
decay_mult: 1 |
|
} |
|
param { |
|
lr_mult: 2.0 |
|
decay_mult: 0 |
|
} |
|
convolution_param { |
|
num_output: 64 |
|
pad: 1 |
|
kernel_size: 3 |
|
weight_filler { |
|
type: "gaussian" |
|
std: 0.01 |
|
} |
|
bias_filler { |
|
type: "constant" |
|
} |
|
} |
|
} |
|
layer { |
|
name: "relu1_1" |
|
type: "ReLU" |
|
bottom: "conv1_1" |
|
top: "conv1_1" |
|
} |
|
layer { |
|
name: "conv1_2" |
|
type: "Convolution" |
|
bottom: "conv1_1" |
|
top: "conv1_2" |
|
param { |
|
lr_mult: 1.0 |
|
decay_mult: 1 |
|
} |
|
param { |
|
lr_mult: 2.0 |
|
decay_mult: 0 |
|
} |
|
convolution_param { |
|
num_output: 64 |
|
pad: 1 |
|
kernel_size: 3 |
|
weight_filler { |
|
type: "gaussian" |
|
std: 0.01 |
|
} |
|
bias_filler { |
|
type: "constant" |
|
} |
|
} |
|
} |
|
layer { |
|
name: "relu1_2" |
|
type: "ReLU" |
|
bottom: "conv1_2" |
|
top: "conv1_2" |
|
} |
|
layer { |
|
name: "pool1_stage1" |
|
type: "Pooling" |
|
bottom: "conv1_2" |
|
top: "pool1_stage1" |
|
pooling_param { |
|
pool: MAX |
|
kernel_size: 2 |
|
stride: 2 |
|
} |
|
} |
|
layer { |
|
name: "conv2_1" |
|
type: "Convolution" |
|
bottom: "pool1_stage1" |
|
top: "conv2_1" |
|
param { |
|
lr_mult: 1.0 |
|
decay_mult: 1 |
|
} |
|
param { |
|
lr_mult: 2.0 |
|
decay_mult: 0 |
|
} |
|
convolution_param { |
|
num_output: 128 |
|
pad: 1 |
|
kernel_size: 3 |
|
weight_filler { |
|
type: "gaussian" |
|
std: 0.01 |
|
} |
|
bias_filler { |
|
type: "constant" |
|
} |
|
} |
|
} |
|
layer { |
|
name: "relu2_1" |
|
type: "ReLU" |
|
bottom: "conv2_1" |
|
top: "conv2_1" |
|
} |
|
layer { |
|
name: "conv2_2" |
|
type: "Convolution" |
|
bottom: "conv2_1" |
|
top: "conv2_2" |
|
param { |
|
lr_mult: 1.0 |
|
decay_mult: 1 |
|
} |
|
param { |
|
lr_mult: 2.0 |
|
decay_mult: 0 |
|
} |
|
convolution_param { |
|
num_output: 128 |
|
pad: 1 |
|
kernel_size: 3 |
|
weight_filler { |
|
type: "gaussian" |
|
std: 0.01 |
|
} |
|
bias_filler { |
|
type: "constant" |
|
} |
|
} |
|
} |
|
layer { |
|
name: "relu2_2" |
|
type: "ReLU" |
|
bottom: "conv2_2" |
|
top: "conv2_2" |
|
} |
|
layer { |
|
name: "pool2_stage1" |
|
type: "Pooling" |
|
bottom: "conv2_2" |
|
top: "pool2_stage1" |
|
pooling_param { |
|
pool: MAX |
|
kernel_size: 2 |
|
stride: 2 |
|
} |
|
} |
|
layer { |
|
name: "conv3_1" |
|
type: "Convolution" |
|
bottom: "pool2_stage1" |
|
top: "conv3_1" |
|
param { |
|
lr_mult: 1.0 |
|
decay_mult: 1 |
|
} |
|
param { |
|
lr_mult: 2.0 |
|
decay_mult: 0 |
|
} |
|
convolution_param { |
|
num_output: 256 |
|
pad: 1 |
|
kernel_size: 3 |
|
weight_filler { |
|
type: "gaussian" |
|
std: 0.01 |
|
} |
|
bias_filler { |
|
type: "constant" |
|
} |
|
} |
|
} |
|
layer { |
|
name: "relu3_1" |
|
type: "ReLU" |
|
bottom: "conv3_1" |
|
top: "conv3_1" |
|
} |
|
layer { |
|
name: "conv3_2" |
|
type: "Convolution" |
|
bottom: "conv3_1" |
|
top: "conv3_2" |
|
param { |
|
lr_mult: 1.0 |
|
decay_mult: 1 |
|
} |
|
param { |
|
lr_mult: 2.0 |
|
decay_mult: 0 |
|
} |
|
convolution_param { |
|
num_output: 256 |
|
pad: 1 |
|
kernel_size: 3 |
|
weight_filler { |
|
type: "gaussian" |
|
std: 0.01 |
|
} |
|
bias_filler { |
|
type: "constant" |
|
} |
|
} |
|
} |
|
layer { |
|
name: "relu3_2" |
|
type: "ReLU" |
|
bottom: "conv3_2" |
|
top: "conv3_2" |
|
} |
|
layer { |
|
name: "conv3_3" |
|
type: "Convolution" |
|
bottom: "conv3_2" |
|
top: "conv3_3" |
|
param { |
|
lr_mult: 1.0 |
|
decay_mult: 1 |
|
} |
|
param { |
|
lr_mult: 2.0 |
|
decay_mult: 0 |
|
} |
|
convolution_param { |
|
num_output: 256 |
|
pad: 1 |
|
kernel_size: 3 |
|
weight_filler { |
|
type: "gaussian" |
|
std: 0.01 |
|
} |
|
bias_filler { |
|
type: "constant" |
|
} |
|
} |
|
} |
|
layer { |
|
name: "relu3_3" |
|
type: "ReLU" |
|
bottom: "conv3_3" |
|
top: "conv3_3" |
|
} |
|
layer { |
|
name: "conv3_4" |
|
type: "Convolution" |
|
bottom: "conv3_3" |
|
top: "conv3_4" |
|
param { |
|
lr_mult: 1.0 |
|
decay_mult: 1 |
|
} |
|
param { |
|
lr_mult: 2.0 |
|
decay_mult: 0 |
|
} |
|
convolution_param { |
|
num_output: 256 |
|
pad: 1 |
|
kernel_size: 3 |
|
weight_filler { |
|
type: "gaussian" |
|
std: 0.01 |
|
} |
|
bias_filler { |
|
type: "constant" |
|
} |
|
} |
|
} |
|
layer { |
|
name: "relu3_4" |
|
type: "ReLU" |
|
bottom: "conv3_4" |
|
top: "conv3_4" |
|
} |
|
layer { |
|
name: "pool3_stage1" |
|
type: "Pooling" |
|
bottom: "conv3_4" |
|
top: "pool3_stage1" |
|
pooling_param { |
|
pool: MAX |
|
kernel_size: 2 |
|
stride: 2 |
|
} |
|
} |
|
layer { |
|
name: "conv4_1" |
|
type: "Convolution" |
|
bottom: "pool3_stage1" |
|
top: "conv4_1" |
|
param { |
|
lr_mult: 1.0 |
|
decay_mult: 1 |
|
} |
|
param { |
|
lr_mult: 2.0 |
|
decay_mult: 0 |
|
} |
|
convolution_param { |
|
num_output: 512 |
|
pad: 1 |
|
kernel_size: 3 |
|
weight_filler { |
|
type: "gaussian" |
|
std: 0.01 |
|
} |
|
bias_filler { |
|
type: "constant" |
|
} |
|
} |
|
} |
|
layer { |
|
name: "relu4_1" |
|
type: "ReLU" |
|
bottom: "conv4_1" |
|
top: "conv4_1" |
|
} |
|
layer { |
|
name: "conv4_2" |
|
type: "Convolution" |
|
bottom: "conv4_1" |
|
top: "conv4_2" |
|
param { |
|
lr_mult: 1.0 |
|
decay_mult: 1 |
|
} |
|
param { |
|
lr_mult: 2.0 |
|
decay_mult: 0 |
|
} |
|
convolution_param { |
|
num_output: 512 |
|
pad: 1 |
|
kernel_size: 3 |
|
weight_filler { |
|
type: "gaussian" |
|
std: 0.01 |
|
} |
|
bias_filler { |
|
type: "constant" |
|
} |
|
} |
|
} |
|
layer { |
|
name: "relu4_2" |
|
type: "ReLU" |
|
bottom: "conv4_2" |
|
top: "conv4_2" |
|
} |
|
layer { |
|
name: "conv4_3_CPM" |
|
type: "Convolution" |
|
bottom: "conv4_2" |
|
top: "conv4_3_CPM" |
|
param { |
|
lr_mult: 1.0 |
|
decay_mult: 1 |
|
} |
|
param { |
|
lr_mult: 2.0 |
|
decay_mult: 0 |
|
} |
|
convolution_param { |
|
num_output: 256 |
|
pad: 1 |
|
kernel_size: 3 |
|
weight_filler { |
|
type: "gaussian" |
|
std: 0.01 |
|
} |
|
bias_filler { |
|
type: "constant" |
|
} |
|
} |
|
} |
|
layer { |
|
name: "relu4_3_CPM" |
|
type: "ReLU" |
|
bottom: "conv4_3_CPM" |
|
top: "conv4_3_CPM" |
|
} |
|
layer { |
|
name: "conv4_4_CPM" |
|
type: "Convolution" |
|
bottom: "conv4_3_CPM" |
|
top: "conv4_4_CPM" |
|
param { |
|
lr_mult: 1.0 |
|
decay_mult: 1 |
|
} |
|
param { |
|
lr_mult: 2.0 |
|
decay_mult: 0 |
|
} |
|
convolution_param { |
|
num_output: 128 |
|
pad: 1 |
|
kernel_size: 3 |
|
weight_filler { |
|
type: "gaussian" |
|
std: 0.01 |
|
} |
|
bias_filler { |
|
type: "constant" |
|
} |
|
} |
|
} |
|
layer { |
|
name: "relu4_4_CPM" |
|
type: "ReLU" |
|
bottom: "conv4_4_CPM" |
|
top: "conv4_4_CPM" |
|
} |
|
layer { |
|
name: "conv5_1_CPM_L1" |
|
type: "Convolution" |
|
bottom: "conv4_4_CPM" |
|
top: "conv5_1_CPM_L1" |
|
param { |
|
lr_mult: 1.0 |
|
decay_mult: 1 |
|
} |
|
param { |
|
lr_mult: 2.0 |
|
decay_mult: 0 |
|
} |
|
convolution_param { |
|
num_output: 128 |
|
pad: 1 |
|
kernel_size: 3 |
|
weight_filler { |
|
type: "gaussian" |
|
std: 0.01 |
|
} |
|
bias_filler { |
|
type: "constant" |
|
} |
|
} |
|
} |
|
layer { |
|
name: "relu5_1_CPM_L1" |
|
type: "ReLU" |
|
bottom: "conv5_1_CPM_L1" |
|
top: "conv5_1_CPM_L1" |
|
} |
|
layer { |
|
name: "conv5_1_CPM_L2" |
|
type: "Convolution" |
|
bottom: "conv4_4_CPM" |
|
top: "conv5_1_CPM_L2" |
|
param { |
|
lr_mult: 1.0 |
|
decay_mult: 1 |
|
} |
|
param { |
|
lr_mult: 2.0 |
|
decay_mult: 0 |
|
} |
|
convolution_param { |
|
num_output: 128 |
|
pad: 1 |
|
kernel_size: 3 |
|
weight_filler { |
|
type: "gaussian" |
|
std: 0.01 |
|
} |
|
bias_filler { |
|
type: "constant" |
|
} |
|
} |
|
} |
|
layer { |
|
name: "relu5_1_CPM_L2" |
|
type: "ReLU" |
|
bottom: "conv5_1_CPM_L2" |
|
top: "conv5_1_CPM_L2" |
|
} |
|
layer { |
|
name: "conv5_2_CPM_L1" |
|
type: "Convolution" |
|
bottom: "conv5_1_CPM_L1" |
|
top: "conv5_2_CPM_L1" |
|
param { |
|
lr_mult: 1.0 |
|
decay_mult: 1 |
|
} |
|
param { |
|
lr_mult: 2.0 |
|
decay_mult: 0 |
|
} |
|
convolution_param { |
|
num_output: 128 |
|
pad: 1 |
|
kernel_size: 3 |
|
weight_filler { |
|
type: "gaussian" |
|
std: 0.01 |
|
} |
|
bias_filler { |
|
type: "constant" |
|
} |
|
} |
|
} |
|
layer { |
|
name: "relu5_2_CPM_L1" |
|
type: "ReLU" |
|
bottom: "conv5_2_CPM_L1" |
|
top: "conv5_2_CPM_L1" |
|
} |
|
layer { |
|
name: "conv5_2_CPM_L2" |
|
type: "Convolution" |
|
bottom: "conv5_1_CPM_L2" |
|
top: "conv5_2_CPM_L2" |
|
param { |
|
lr_mult: 1.0 |
|
decay_mult: 1 |
|
} |
|
param { |
|
lr_mult: 2.0 |
|
decay_mult: 0 |
|
} |
|
convolution_param { |
|
num_output: 128 |
|
pad: 1 |
|
kernel_size: 3 |
|
weight_filler { |
|
type: "gaussian" |
|
std: 0.01 |
|
} |
|
bias_filler { |
|
type: "constant" |
|
} |
|
} |
|
} |
|
layer { |
|
name: "relu5_2_CPM_L2" |
|
type: "ReLU" |
|
bottom: "conv5_2_CPM_L2" |
|
top: "conv5_2_CPM_L2" |
|
} |
|
layer { |
|
name: "conv5_3_CPM_L1" |
|
type: "Convolution" |
|
bottom: "conv5_2_CPM_L1" |
|
top: "conv5_3_CPM_L1" |
|
param { |
|
lr_mult: 1.0 |
|
decay_mult: 1 |
|
} |
|
param { |
|
lr_mult: 2.0 |
|
decay_mult: 0 |
|
} |
|
convolution_param { |
|
num_output: 128 |
|
pad: 1 |
|
kernel_size: 3 |
|
weight_filler { |
|
type: "gaussian" |
|
std: 0.01 |
|
} |
|
bias_filler { |
|
type: "constant" |
|
} |
|
} |
|
} |
|
layer { |
|
name: "relu5_3_CPM_L1" |
|
type: "ReLU" |
|
bottom: "conv5_3_CPM_L1" |
|
top: "conv5_3_CPM_L1" |
|
} |
|
layer { |
|
name: "conv5_3_CPM_L2" |
|
type: "Convolution" |
|
bottom: "conv5_2_CPM_L2" |
|
top: "conv5_3_CPM_L2" |
|
param { |
|
lr_mult: 1.0 |
|
decay_mult: 1 |
|
} |
|
param { |
|
lr_mult: 2.0 |
|
decay_mult: 0 |
|
} |
|
convolution_param { |
|
num_output: 128 |
|
pad: 1 |
|
kernel_size: 3 |
|
weight_filler { |
|
type: "gaussian" |
|
std: 0.01 |
|
} |
|
bias_filler { |
|
type: "constant" |
|
} |
|
} |
|
} |
|
layer { |
|
name: "relu5_3_CPM_L2" |
|
type: "ReLU" |
|
bottom: "conv5_3_CPM_L2" |
|
top: "conv5_3_CPM_L2" |
|
} |
|
layer { |
|
name: "conv5_4_CPM_L1" |
|
type: "Convolution" |
|
bottom: "conv5_3_CPM_L1" |
|
top: "conv5_4_CPM_L1" |
|
param { |
|
lr_mult: 1.0 |
|
decay_mult: 1 |
|
} |
|
param { |
|
lr_mult: 2.0 |
|
decay_mult: 0 |
|
} |
|
convolution_param { |
|
num_output: 512 |
|
pad: 0 |
|
kernel_size: 1 |
|
weight_filler { |
|
type: "gaussian" |
|
std: 0.01 |
|
} |
|
bias_filler { |
|
type: "constant" |
|
} |
|
} |
|
} |
|
layer { |
|
name: "relu5_4_CPM_L1" |
|
type: "ReLU" |
|
bottom: "conv5_4_CPM_L1" |
|
top: "conv5_4_CPM_L1" |
|
} |
|
layer { |
|
name: "conv5_4_CPM_L2" |
|
type: "Convolution" |
|
bottom: "conv5_3_CPM_L2" |
|
top: "conv5_4_CPM_L2" |
|
param { |
|
lr_mult: 1.0 |
|
decay_mult: 1 |
|
} |
|
param { |
|
lr_mult: 2.0 |
|
decay_mult: 0 |
|
} |
|
convolution_param { |
|
num_output: 512 |
|
pad: 0 |
|
kernel_size: 1 |
|
weight_filler { |
|
type: "gaussian" |
|
std: 0.01 |
|
} |
|
bias_filler { |
|
type: "constant" |
|
} |
|
} |
|
} |
|
layer { |
|
name: "relu5_4_CPM_L2" |
|
type: "ReLU" |
|
bottom: "conv5_4_CPM_L2" |
|
top: "conv5_4_CPM_L2" |
|
} |
|
layer { |
|
name: "conv5_5_CPM_L1" |
|
type: "Convolution" |
|
bottom: "conv5_4_CPM_L1" |
|
top: "conv5_5_CPM_L1" |
|
param { |
|
lr_mult: 1.0 |
|
decay_mult: 1 |
|
} |
|
param { |
|
lr_mult: 2.0 |
|
decay_mult: 0 |
|
} |
|
convolution_param { |
|
num_output: 38 |
|
pad: 0 |
|
kernel_size: 1 |
|
weight_filler { |
|
type: "gaussian" |
|
std: 0.01 |
|
} |
|
bias_filler { |
|
type: "constant" |
|
} |
|
} |
|
} |
|
layer { |
|
name: "conv5_5_CPM_L2" |
|
type: "Convolution" |
|
bottom: "conv5_4_CPM_L2" |
|
top: "conv5_5_CPM_L2" |
|
param { |
|
lr_mult: 1.0 |
|
decay_mult: 1 |
|
} |
|
param { |
|
lr_mult: 2.0 |
|
decay_mult: 0 |
|
} |
|
convolution_param { |
|
num_output: 19 |
|
pad: 0 |
|
kernel_size: 1 |
|
weight_filler { |
|
type: "gaussian" |
|
std: 0.01 |
|
} |
|
bias_filler { |
|
type: "constant" |
|
} |
|
} |
|
} |
|
layer { |
|
name: "concat_stage2" |
|
type: "Concat" |
|
bottom: "conv5_5_CPM_L1" |
|
bottom: "conv5_5_CPM_L2" |
|
bottom: "conv4_4_CPM" |
|
top: "concat_stage2" |
|
concat_param { |
|
axis: 1 |
|
} |
|
} |
|
layer { |
|
name: "Mconv1_stage2_L1" |
|
type: "Convolution" |
|
bottom: "concat_stage2" |
|
top: "Mconv1_stage2_L1" |
|
param { |
|
lr_mult: 4.0 |
|
decay_mult: 1 |
|
} |
|
param { |
|
lr_mult: 8.0 |
|
decay_mult: 0 |
|
} |
|
convolution_param { |
|
num_output: 128 |
|
pad: 3 |
|
kernel_size: 7 |
|
weight_filler { |
|
type: "gaussian" |
|
std: 0.01 |
|
} |
|
bias_filler { |
|
type: "constant" |
|
} |
|
} |
|
} |
|
layer { |
|
name: "Mrelu1_stage2_L1" |
|
type: "ReLU" |
|
bottom: "Mconv1_stage2_L1" |
|
top: "Mconv1_stage2_L1" |
|
} |
|
layer { |
|
name: "Mconv1_stage2_L2" |
|
type: "Convolution" |
|
bottom: "concat_stage2" |
|
top: "Mconv1_stage2_L2" |
|
param { |
|
lr_mult: 4.0 |
|
decay_mult: 1 |
|
} |
|
param { |
|
lr_mult: 8.0 |
|
decay_mult: 0 |
|
} |
|
convolution_param { |
|
num_output: 128 |
|
pad: 3 |
|
kernel_size: 7 |
|
weight_filler { |
|
type: "gaussian" |
|
std: 0.01 |
|
} |
|
bias_filler { |
|
type: "constant" |
|
} |
|
} |
|
} |
|
layer { |
|
name: "Mrelu1_stage2_L2" |
|
type: "ReLU" |
|
bottom: "Mconv1_stage2_L2" |
|
top: "Mconv1_stage2_L2" |
|
} |
|
layer { |
|
name: "Mconv2_stage2_L1" |
|
type: "Convolution" |
|
bottom: "Mconv1_stage2_L1" |
|
top: "Mconv2_stage2_L1" |
|
param { |
|
lr_mult: 4.0 |
|
decay_mult: 1 |
|
} |
|
param { |
|
lr_mult: 8.0 |
|
decay_mult: 0 |
|
} |
|
convolution_param { |
|
num_output: 128 |
|
pad: 3 |
|
kernel_size: 7 |
|
weight_filler { |
|
type: "gaussian" |
|
std: 0.01 |
|
} |
|
bias_filler { |
|
type: "constant" |
|
} |
|
} |
|
} |
|
layer { |
|
name: "Mrelu2_stage2_L1" |
|
type: "ReLU" |
|
bottom: "Mconv2_stage2_L1" |
|
top: "Mconv2_stage2_L1" |
|
} |
|
layer { |
|
name: "Mconv2_stage2_L2" |
|
type: "Convolution" |
|
bottom: "Mconv1_stage2_L2" |
|
top: "Mconv2_stage2_L2" |
|
param { |
|
lr_mult: 4.0 |
|
decay_mult: 1 |
|
} |
|
param { |
|
lr_mult: 8.0 |
|
decay_mult: 0 |
|
} |
|
convolution_param { |
|
num_output: 128 |
|
pad: 3 |
|
kernel_size: 7 |
|
weight_filler { |
|
type: "gaussian" |
|
std: 0.01 |
|
} |
|
bias_filler { |
|
type: "constant" |
|
} |
|
} |
|
} |
|
layer { |
|
name: "Mrelu2_stage2_L2" |
|
type: "ReLU" |
|
bottom: "Mconv2_stage2_L2" |
|
top: "Mconv2_stage2_L2" |
|
} |
|
layer { |
|
name: "Mconv3_stage2_L1" |
|
type: "Convolution" |
|
bottom: "Mconv2_stage2_L1" |
|
top: "Mconv3_stage2_L1" |
|
param { |
|
lr_mult: 4.0 |
|
decay_mult: 1 |
|
} |
|
param { |
|
lr_mult: 8.0 |
|
decay_mult: 0 |
|
} |
|
convolution_param { |
|
num_output: 128 |
|
pad: 3 |
|
kernel_size: 7 |
|
weight_filler { |
|
type: "gaussian" |
|
std: 0.01 |
|
} |
|
bias_filler { |
|
type: "constant" |
|
} |
|
} |
|
} |
|
layer { |
|
name: "Mrelu3_stage2_L1" |
|
type: "ReLU" |
|
bottom: "Mconv3_stage2_L1" |
|
top: "Mconv3_stage2_L1" |
|
} |
|
layer { |
|
name: "Mconv3_stage2_L2" |
|
type: "Convolution" |
|
bottom: "Mconv2_stage2_L2" |
|
top: "Mconv3_stage2_L2" |
|
param { |
|
lr_mult: 4.0 |
|
decay_mult: 1 |
|
} |
|
param { |
|
lr_mult: 8.0 |
|
decay_mult: 0 |
|
} |
|
convolution_param { |
|
num_output: 128 |
|
pad: 3 |
|
kernel_size: 7 |
|
weight_filler { |
|
type: "gaussian" |
|
std: 0.01 |
|
} |
|
bias_filler { |
|
type: "constant" |
|
} |
|
} |
|
} |
|
layer { |
|
name: "Mrelu3_stage2_L2" |
|
type: "ReLU" |
|
bottom: "Mconv3_stage2_L2" |
|
top: "Mconv3_stage2_L2" |
|
} |
|
layer { |
|
name: "Mconv4_stage2_L1" |
|
type: "Convolution" |
|
bottom: "Mconv3_stage2_L1" |
|
top: "Mconv4_stage2_L1" |
|
param { |
|
lr_mult: 4.0 |
|
decay_mult: 1 |
|
} |
|
param { |
|
lr_mult: 8.0 |
|
decay_mult: 0 |
|
} |
|
convolution_param { |
|
num_output: 128 |
|
pad: 3 |
|
kernel_size: 7 |
|
weight_filler { |
|
type: "gaussian" |
|
std: 0.01 |
|
} |
|
bias_filler { |
|
type: "constant" |
|
} |
|
} |
|
} |
|
layer { |
|
name: "Mrelu4_stage2_L1" |
|
type: "ReLU" |
|
bottom: "Mconv4_stage2_L1" |
|
top: "Mconv4_stage2_L1" |
|
} |
|
layer { |
|
name: "Mconv4_stage2_L2" |
|
type: "Convolution" |
|
bottom: "Mconv3_stage2_L2" |
|
top: "Mconv4_stage2_L2" |
|
param { |
|
lr_mult: 4.0 |
|
decay_mult: 1 |
|
} |
|
param { |
|
lr_mult: 8.0 |
|
decay_mult: 0 |
|
} |
|
convolution_param { |
|
num_output: 128 |
|
pad: 3 |
|
kernel_size: 7 |
|
weight_filler { |
|
type: "gaussian" |
|
std: 0.01 |
|
} |
|
bias_filler { |
|
type: "constant" |
|
} |
|
} |
|
} |
|
layer { |
|
name: "Mrelu4_stage2_L2" |
|
type: "ReLU" |
|
bottom: "Mconv4_stage2_L2" |
|
top: "Mconv4_stage2_L2" |
|
} |
|
layer { |
|
name: "Mconv5_stage2_L1" |
|
type: "Convolution" |
|
bottom: "Mconv4_stage2_L1" |
|
top: "Mconv5_stage2_L1" |
|
param { |
|
lr_mult: 4.0 |
|
decay_mult: 1 |
|
} |
|
param { |
|
lr_mult: 8.0 |
|
decay_mult: 0 |
|
} |
|
convolution_param { |
|
num_output: 128 |
|
pad: 3 |
|
kernel_size: 7 |
|
weight_filler { |
|
type: "gaussian" |
|
std: 0.01 |
|
} |
|
bias_filler { |
|
type: "constant" |
|
} |
|
} |
|
} |
|
layer { |
|
name: "Mrelu5_stage2_L1" |
|
type: "ReLU" |
|
bottom: "Mconv5_stage2_L1" |
|
top: "Mconv5_stage2_L1" |
|
} |
|
layer { |
|
name: "Mconv5_stage2_L2" |
|
type: "Convolution" |
|
bottom: "Mconv4_stage2_L2" |
|
top: "Mconv5_stage2_L2" |
|
param { |
|
lr_mult: 4.0 |
|
decay_mult: 1 |
|
} |
|
param { |
|
lr_mult: 8.0 |
|
decay_mult: 0 |
|
} |
|
convolution_param { |
|
num_output: 128 |
|
pad: 3 |
|
kernel_size: 7 |
|
weight_filler { |
|
type: "gaussian" |
|
std: 0.01 |
|
} |
|
bias_filler { |
|
type: "constant" |
|
} |
|
} |
|
} |
|
layer { |
|
name: "Mrelu5_stage2_L2" |
|
type: "ReLU" |
|
bottom: "Mconv5_stage2_L2" |
|
top: "Mconv5_stage2_L2" |
|
} |
|
layer { |
|
name: "Mconv6_stage2_L1" |
|
type: "Convolution" |
|
bottom: "Mconv5_stage2_L1" |
|
top: "Mconv6_stage2_L1" |
|
param { |
|
lr_mult: 4.0 |
|
decay_mult: 1 |
|
} |
|
param { |
|
lr_mult: 8.0 |
|
decay_mult: 0 |
|
} |
|
convolution_param { |
|
num_output: 128 |
|
pad: 0 |
|
kernel_size: 1 |
|
weight_filler { |
|
type: "gaussian" |
|
std: 0.01 |
|
} |
|
bias_filler { |
|
type: "constant" |
|
} |
|
} |
|
} |
|
layer { |
|
name: "Mrelu6_stage2_L1" |
|
type: "ReLU" |
|
bottom: "Mconv6_stage2_L1" |
|
top: "Mconv6_stage2_L1" |
|
} |
|
layer { |
|
name: "Mconv6_stage2_L2" |
|
type: "Convolution" |
|
bottom: "Mconv5_stage2_L2" |
|
top: "Mconv6_stage2_L2" |
|
param { |
|
lr_mult: 4.0 |
|
decay_mult: 1 |
|
} |
|
param { |
|
lr_mult: 8.0 |
|
decay_mult: 0 |
|
} |
|
convolution_param { |
|
num_output: 128 |
|
pad: 0 |
|
kernel_size: 1 |
|
weight_filler { |
|
type: "gaussian" |
|
std: 0.01 |
|
} |
|
bias_filler { |
|
type: "constant" |
|
} |
|
} |
|
} |
|
layer { |
|
name: "Mrelu6_stage2_L2" |
|
type: "ReLU" |
|
bottom: "Mconv6_stage2_L2" |
|
top: "Mconv6_stage2_L2" |
|
} |
|
layer { |
|
name: "Mconv7_stage2_L1" |
|
type: "Convolution" |
|
bottom: "Mconv6_stage2_L1" |
|
top: "Mconv7_stage2_L1" |
|
param { |
|
lr_mult: 4.0 |
|
decay_mult: 1 |
|
} |
|
param { |
|
lr_mult: 8.0 |
|
decay_mult: 0 |
|
} |
|
convolution_param { |
|
num_output: 38 |
|
pad: 0 |
|
kernel_size: 1 |
|
weight_filler { |
|
type: "gaussian" |
|
std: 0.01 |
|
} |
|
bias_filler { |
|
type: "constant" |
|
} |
|
} |
|
} |
|
layer { |
|
name: "Mconv7_stage2_L2" |
|
type: "Convolution" |
|
bottom: "Mconv6_stage2_L2" |
|
top: "Mconv7_stage2_L2" |
|
param { |
|
lr_mult: 4.0 |
|
decay_mult: 1 |
|
} |
|
param { |
|
lr_mult: 8.0 |
|
decay_mult: 0 |
|
} |
|
convolution_param { |
|
num_output: 19 |
|
pad: 0 |
|
kernel_size: 1 |
|
weight_filler { |
|
type: "gaussian" |
|
std: 0.01 |
|
} |
|
bias_filler { |
|
type: "constant" |
|
} |
|
} |
|
} |
|
layer { |
|
name: "concat_stage3" |
|
type: "Concat" |
|
bottom: "Mconv7_stage2_L1" |
|
bottom: "Mconv7_stage2_L2" |
|
bottom: "conv4_4_CPM" |
|
top: "concat_stage3" |
|
concat_param { |
|
axis: 1 |
|
} |
|
} |
|
layer { |
|
name: "Mconv1_stage3_L1" |
|
type: "Convolution" |
|
bottom: "concat_stage3" |
|
top: "Mconv1_stage3_L1" |
|
param { |
|
lr_mult: 4.0 |
|
decay_mult: 1 |
|
} |
|
param { |
|
lr_mult: 8.0 |
|
decay_mult: 0 |
|
} |
|
convolution_param { |
|
num_output: 128 |
|
pad: 3 |
|
kernel_size: 7 |
|
weight_filler { |
|
type: "gaussian" |
|
std: 0.01 |
|
} |
|
bias_filler { |
|
type: "constant" |
|
} |
|
} |
|
} |
|
layer { |
|
name: "Mrelu1_stage3_L1" |
|
type: "ReLU" |
|
bottom: "Mconv1_stage3_L1" |
|
top: "Mconv1_stage3_L1" |
|
} |
|
layer { |
|
name: "Mconv1_stage3_L2" |
|
type: "Convolution" |
|
bottom: "concat_stage3" |
|
top: "Mconv1_stage3_L2" |
|
param { |
|
lr_mult: 4.0 |
|
decay_mult: 1 |
|
} |
|
param { |
|
lr_mult: 8.0 |
|
decay_mult: 0 |
|
} |
|
convolution_param { |
|
num_output: 128 |
|
pad: 3 |
|
kernel_size: 7 |
|
weight_filler { |
|
type: "gaussian" |
|
std: 0.01 |
|
} |
|
bias_filler { |
|
type: "constant" |
|
} |
|
} |
|
} |
|
layer { |
|
name: "Mrelu1_stage3_L2" |
|
type: "ReLU" |
|
bottom: "Mconv1_stage3_L2" |
|
top: "Mconv1_stage3_L2" |
|
} |
|
layer { |
|
name: "Mconv2_stage3_L1" |
|
type: "Convolution" |
|
bottom: "Mconv1_stage3_L1" |
|
top: "Mconv2_stage3_L1" |
|
param { |
|
lr_mult: 4.0 |
|
decay_mult: 1 |
|
} |
|
param { |
|
lr_mult: 8.0 |
|
decay_mult: 0 |
|
} |
|
convolution_param { |
|
num_output: 128 |
|
pad: 3 |
|
kernel_size: 7 |
|
weight_filler { |
|
type: "gaussian" |
|
std: 0.01 |
|
} |
|
bias_filler { |
|
type: "constant" |
|
} |
|
} |
|
} |
|
layer { |
|
name: "Mrelu2_stage3_L1" |
|
type: "ReLU" |
|
bottom: "Mconv2_stage3_L1" |
|
top: "Mconv2_stage3_L1" |
|
} |
|
layer { |
|
name: "Mconv2_stage3_L2" |
|
type: "Convolution" |
|
bottom: "Mconv1_stage3_L2" |
|
top: "Mconv2_stage3_L2" |
|
param { |
|
lr_mult: 4.0 |
|
decay_mult: 1 |
|
} |
|
param { |
|
lr_mult: 8.0 |
|
decay_mult: 0 |
|
} |
|
convolution_param { |
|
num_output: 128 |
|
pad: 3 |
|
kernel_size: 7 |
|
weight_filler { |
|
type: "gaussian" |
|
std: 0.01 |
|
} |
|
bias_filler { |
|
type: "constant" |
|
} |
|
} |
|
} |
|
layer { |
|
name: "Mrelu2_stage3_L2" |
|
type: "ReLU" |
|
bottom: "Mconv2_stage3_L2" |
|
top: "Mconv2_stage3_L2" |
|
} |
|
layer { |
|
name: "Mconv3_stage3_L1" |
|
type: "Convolution" |
|
bottom: "Mconv2_stage3_L1" |
|
top: "Mconv3_stage3_L1" |
|
param { |
|
lr_mult: 4.0 |
|
decay_mult: 1 |
|
} |
|
param { |
|
lr_mult: 8.0 |
|
decay_mult: 0 |
|
} |
|
convolution_param { |
|
num_output: 128 |
|
pad: 3 |
|
kernel_size: 7 |
|
weight_filler { |
|
type: "gaussian" |
|
std: 0.01 |
|
} |
|
bias_filler { |
|
type: "constant" |
|
} |
|
} |
|
} |
|
layer { |
|
name: "Mrelu3_stage3_L1" |
|
type: "ReLU" |
|
bottom: "Mconv3_stage3_L1" |
|
top: "Mconv3_stage3_L1" |
|
} |
|
layer { |
|
name: "Mconv3_stage3_L2" |
|
type: "Convolution" |
|
bottom: "Mconv2_stage3_L2" |
|
top: "Mconv3_stage3_L2" |
|
param { |
|
lr_mult: 4.0 |
|
decay_mult: 1 |
|
} |
|
param { |
|
lr_mult: 8.0 |
|
decay_mult: 0 |
|
} |
|
convolution_param { |
|
num_output: 128 |
|
pad: 3 |
|
kernel_size: 7 |
|
weight_filler { |
|
type: "gaussian" |
|
std: 0.01 |
|
} |
|
bias_filler { |
|
type: "constant" |
|
} |
|
} |
|
} |
|
layer { |
|
name: "Mrelu3_stage3_L2" |
|
type: "ReLU" |
|
bottom: "Mconv3_stage3_L2" |
|
top: "Mconv3_stage3_L2" |
|
} |
|
layer { |
|
name: "Mconv4_stage3_L1" |
|
type: "Convolution" |
|
bottom: "Mconv3_stage3_L1" |
|
top: "Mconv4_stage3_L1" |
|
param { |
|
lr_mult: 4.0 |
|
decay_mult: 1 |
|
} |
|
param { |
|
lr_mult: 8.0 |
|
decay_mult: 0 |
|
} |
|
convolution_param { |
|
num_output: 128 |
|
pad: 3 |
|
kernel_size: 7 |
|
weight_filler { |
|
type: "gaussian" |
|
std: 0.01 |
|
} |
|
bias_filler { |
|
type: "constant" |
|
} |
|
} |
|
} |
|
layer { |
|
name: "Mrelu4_stage3_L1" |
|
type: "ReLU" |
|
bottom: "Mconv4_stage3_L1" |
|
top: "Mconv4_stage3_L1" |
|
} |
|
layer { |
|
name: "Mconv4_stage3_L2" |
|
type: "Convolution" |
|
bottom: "Mconv3_stage3_L2" |
|
top: "Mconv4_stage3_L2" |
|
param { |
|
lr_mult: 4.0 |
|
decay_mult: 1 |
|
} |
|
param { |
|
lr_mult: 8.0 |
|
decay_mult: 0 |
|
} |
|
convolution_param { |
|
num_output: 128 |
|
pad: 3 |
|
kernel_size: 7 |
|
weight_filler { |
|
type: "gaussian" |
|
std: 0.01 |
|
} |
|
bias_filler { |
|
type: "constant" |
|
} |
|
} |
|
} |
|
layer { |
|
name: "Mrelu4_stage3_L2" |
|
type: "ReLU" |
|
bottom: "Mconv4_stage3_L2" |
|
top: "Mconv4_stage3_L2" |
|
} |
|
layer { |
|
name: "Mconv5_stage3_L1" |
|
type: "Convolution" |
|
bottom: "Mconv4_stage3_L1" |
|
top: "Mconv5_stage3_L1" |
|
param { |
|
lr_mult: 4.0 |
|
decay_mult: 1 |
|
} |
|
param { |
|
lr_mult: 8.0 |
|
decay_mult: 0 |
|
} |
|
convolution_param { |
|
num_output: 128 |
|
pad: 3 |
|
kernel_size: 7 |
|
weight_filler { |
|
type: "gaussian" |
|
std: 0.01 |
|
} |
|
bias_filler { |
|
type: "constant" |
|
} |
|
} |
|
} |
|
layer { |
|
name: "Mrelu5_stage3_L1" |
|
type: "ReLU" |
|
bottom: "Mconv5_stage3_L1" |
|
top: "Mconv5_stage3_L1" |
|
} |
|
layer { |
|
name: "Mconv5_stage3_L2" |
|
type: "Convolution" |
|
bottom: "Mconv4_stage3_L2" |
|
top: "Mconv5_stage3_L2" |
|
param { |
|
lr_mult: 4.0 |
|
decay_mult: 1 |
|
} |
|
param { |
|
lr_mult: 8.0 |
|
decay_mult: 0 |
|
} |
|
convolution_param { |
|
num_output: 128 |
|
pad: 3 |
|
kernel_size: 7 |
|
weight_filler { |
|
type: "gaussian" |
|
std: 0.01 |
|
} |
|
bias_filler { |
|
type: "constant" |
|
} |
|
} |
|
} |
|
layer { |
|
name: "Mrelu5_stage3_L2" |
|
type: "ReLU" |
|
bottom: "Mconv5_stage3_L2" |
|
top: "Mconv5_stage3_L2" |
|
} |
|
layer { |
|
name: "Mconv6_stage3_L1" |
|
type: "Convolution" |
|
bottom: "Mconv5_stage3_L1" |
|
top: "Mconv6_stage3_L1" |
|
param { |
|
lr_mult: 4.0 |
|
decay_mult: 1 |
|
} |
|
param { |
|
lr_mult: 8.0 |
|
decay_mult: 0 |
|
} |
|
convolution_param { |
|
num_output: 128 |
|
pad: 0 |
|
kernel_size: 1 |
|
weight_filler { |
|
type: "gaussian" |
|
std: 0.01 |
|
} |
|
bias_filler { |
|
type: "constant" |
|
} |
|
} |
|
} |
|
layer { |
|
name: "Mrelu6_stage3_L1" |
|
type: "ReLU" |
|
bottom: "Mconv6_stage3_L1" |
|
top: "Mconv6_stage3_L1" |
|
} |
|
layer { |
|
name: "Mconv6_stage3_L2" |
|
type: "Convolution" |
|
bottom: "Mconv5_stage3_L2" |
|
top: "Mconv6_stage3_L2" |
|
param { |
|
lr_mult: 4.0 |
|
decay_mult: 1 |
|
} |
|
param { |
|
lr_mult: 8.0 |
|
decay_mult: 0 |
|
} |
|
convolution_param { |
|
num_output: 128 |
|
pad: 0 |
|
kernel_size: 1 |
|
weight_filler { |
|
type: "gaussian" |
|
std: 0.01 |
|
} |
|
bias_filler { |
|
type: "constant" |
|
} |
|
} |
|
} |
|
layer { |
|
name: "Mrelu6_stage3_L2" |
|
type: "ReLU" |
|
bottom: "Mconv6_stage3_L2" |
|
top: "Mconv6_stage3_L2" |
|
} |
|
layer { |
|
name: "Mconv7_stage3_L1" |
|
type: "Convolution" |
|
bottom: "Mconv6_stage3_L1" |
|
top: "Mconv7_stage3_L1" |
|
param { |
|
lr_mult: 4.0 |
|
decay_mult: 1 |
|
} |
|
param { |
|
lr_mult: 8.0 |
|
decay_mult: 0 |
|
} |
|
convolution_param { |
|
num_output: 38 |
|
pad: 0 |
|
kernel_size: 1 |
|
weight_filler { |
|
type: "gaussian" |
|
std: 0.01 |
|
} |
|
bias_filler { |
|
type: "constant" |
|
} |
|
} |
|
} |
|
layer { |
|
name: "Mconv7_stage3_L2" |
|
type: "Convolution" |
|
bottom: "Mconv6_stage3_L2" |
|
top: "Mconv7_stage3_L2" |
|
param { |
|
lr_mult: 4.0 |
|
decay_mult: 1 |
|
} |
|
param { |
|
lr_mult: 8.0 |
|
decay_mult: 0 |
|
} |
|
convolution_param { |
|
num_output: 19 |
|
pad: 0 |
|
kernel_size: 1 |
|
weight_filler { |
|
type: "gaussian" |
|
std: 0.01 |
|
} |
|
bias_filler { |
|
type: "constant" |
|
} |
|
} |
|
} |
|
layer { |
|
name: "concat_stage4" |
|
type: "Concat" |
|
bottom: "Mconv7_stage3_L1" |
|
bottom: "Mconv7_stage3_L2" |
|
bottom: "conv4_4_CPM" |
|
top: "concat_stage4" |
|
concat_param { |
|
axis: 1 |
|
} |
|
} |
|
layer { |
|
name: "Mconv1_stage4_L1" |
|
type: "Convolution" |
|
bottom: "concat_stage4" |
|
top: "Mconv1_stage4_L1" |
|
param { |
|
lr_mult: 4.0 |
|
decay_mult: 1 |
|
} |
|
param { |
|
lr_mult: 8.0 |
|
decay_mult: 0 |
|
} |
|
convolution_param { |
|
num_output: 128 |
|
pad: 3 |
|
kernel_size: 7 |
|
weight_filler { |
|
type: "gaussian" |
|
std: 0.01 |
|
} |
|
bias_filler { |
|
type: "constant" |
|
} |
|
} |
|
} |
|
layer { |
|
name: "Mrelu1_stage4_L1" |
|
type: "ReLU" |
|
bottom: "Mconv1_stage4_L1" |
|
top: "Mconv1_stage4_L1" |
|
} |
|
layer { |
|
name: "Mconv1_stage4_L2" |
|
type: "Convolution" |
|
bottom: "concat_stage4" |
|
top: "Mconv1_stage4_L2" |
|
param { |
|
lr_mult: 4.0 |
|
decay_mult: 1 |
|
} |
|
param { |
|
lr_mult: 8.0 |
|
decay_mult: 0 |
|
} |
|
convolution_param { |
|
num_output: 128 |
|
pad: 3 |
|
kernel_size: 7 |
|
weight_filler { |
|
type: "gaussian" |
|
std: 0.01 |
|
} |
|
bias_filler { |
|
type: "constant" |
|
} |
|
} |
|
} |
|
layer { |
|
name: "Mrelu1_stage4_L2" |
|
type: "ReLU" |
|
bottom: "Mconv1_stage4_L2" |
|
top: "Mconv1_stage4_L2" |
|
} |
|
layer { |
|
name: "Mconv2_stage4_L1" |
|
type: "Convolution" |
|
bottom: "Mconv1_stage4_L1" |
|
top: "Mconv2_stage4_L1" |
|
param { |
|
lr_mult: 4.0 |
|
decay_mult: 1 |
|
} |
|
param { |
|
lr_mult: 8.0 |
|
decay_mult: 0 |
|
} |
|
convolution_param { |
|
num_output: 128 |
|
pad: 3 |
|
kernel_size: 7 |
|
weight_filler { |
|
type: "gaussian" |
|
std: 0.01 |
|
} |
|
bias_filler { |
|
type: "constant" |
|
} |
|
} |
|
} |
|
layer { |
|
name: "Mrelu2_stage4_L1" |
|
type: "ReLU" |
|
bottom: "Mconv2_stage4_L1" |
|
top: "Mconv2_stage4_L1" |
|
} |
|
layer { |
|
name: "Mconv2_stage4_L2" |
|
type: "Convolution" |
|
bottom: "Mconv1_stage4_L2" |
|
top: "Mconv2_stage4_L2" |
|
param { |
|
lr_mult: 4.0 |
|
decay_mult: 1 |
|
} |
|
param { |
|
lr_mult: 8.0 |
|
decay_mult: 0 |
|
} |
|
convolution_param { |
|
num_output: 128 |
|
pad: 3 |
|
kernel_size: 7 |
|
weight_filler { |
|
type: "gaussian" |
|
std: 0.01 |
|
} |
|
bias_filler { |
|
type: "constant" |
|
} |
|
} |
|
} |
|
layer { |
|
name: "Mrelu2_stage4_L2" |
|
type: "ReLU" |
|
bottom: "Mconv2_stage4_L2" |
|
top: "Mconv2_stage4_L2" |
|
} |
|
layer { |
|
name: "Mconv3_stage4_L1" |
|
type: "Convolution" |
|
bottom: "Mconv2_stage4_L1" |
|
top: "Mconv3_stage4_L1" |
|
param { |
|
lr_mult: 4.0 |
|
decay_mult: 1 |
|
} |
|
param { |
|
lr_mult: 8.0 |
|
decay_mult: 0 |
|
} |
|
convolution_param { |
|
num_output: 128 |
|
pad: 3 |
|
kernel_size: 7 |
|
weight_filler { |
|
type: "gaussian" |
|
std: 0.01 |
|
} |
|
bias_filler { |
|
type: "constant" |
|
} |
|
} |
|
} |
|
layer { |
|
name: "Mrelu3_stage4_L1" |
|
type: "ReLU" |
|
bottom: "Mconv3_stage4_L1" |
|
top: "Mconv3_stage4_L1" |
|
} |
|
layer { |
|
name: "Mconv3_stage4_L2" |
|
type: "Convolution" |
|
bottom: "Mconv2_stage4_L2" |
|
top: "Mconv3_stage4_L2" |
|
param { |
|
lr_mult: 4.0 |
|
decay_mult: 1 |
|
} |
|
param { |
|
lr_mult: 8.0 |
|
decay_mult: 0 |
|
} |
|
convolution_param { |
|
num_output: 128 |
|
pad: 3 |
|
kernel_size: 7 |
|
weight_filler { |
|
type: "gaussian" |
|
std: 0.01 |
|
} |
|
bias_filler { |
|
type: "constant" |
|
} |
|
} |
|
} |
|
layer { |
|
name: "Mrelu3_stage4_L2" |
|
type: "ReLU" |
|
bottom: "Mconv3_stage4_L2" |
|
top: "Mconv3_stage4_L2" |
|
} |
|
layer { |
|
name: "Mconv4_stage4_L1" |
|
type: "Convolution" |
|
bottom: "Mconv3_stage4_L1" |
|
top: "Mconv4_stage4_L1" |
|
param { |
|
lr_mult: 4.0 |
|
decay_mult: 1 |
|
} |
|
param { |
|
lr_mult: 8.0 |
|
decay_mult: 0 |
|
} |
|
convolution_param { |
|
num_output: 128 |
|
pad: 3 |
|
kernel_size: 7 |
|
weight_filler { |
|
type: "gaussian" |
|
std: 0.01 |
|
} |
|
bias_filler { |
|
type: "constant" |
|
} |
|
} |
|
} |
|
layer { |
|
name: "Mrelu4_stage4_L1" |
|
type: "ReLU" |
|
bottom: "Mconv4_stage4_L1" |
|
top: "Mconv4_stage4_L1" |
|
} |
|
layer { |
|
name: "Mconv4_stage4_L2" |
|
type: "Convolution" |
|
bottom: "Mconv3_stage4_L2" |
|
top: "Mconv4_stage4_L2" |
|
param { |
|
lr_mult: 4.0 |
|
decay_mult: 1 |
|
} |
|
param { |
|
lr_mult: 8.0 |
|
decay_mult: 0 |
|
} |
|
convolution_param { |
|
num_output: 128 |
|
pad: 3 |
|
kernel_size: 7 |
|
weight_filler { |
|
type: "gaussian" |
|
std: 0.01 |
|
} |
|
bias_filler { |
|
type: "constant" |
|
} |
|
} |
|
} |
|
layer { |
|
name: "Mrelu4_stage4_L2" |
|
type: "ReLU" |
|
bottom: "Mconv4_stage4_L2" |
|
top: "Mconv4_stage4_L2" |
|
} |
|
layer { |
|
name: "Mconv5_stage4_L1" |
|
type: "Convolution" |
|
bottom: "Mconv4_stage4_L1" |
|
top: "Mconv5_stage4_L1" |
|
param { |
|
lr_mult: 4.0 |
|
decay_mult: 1 |
|
} |
|
param { |
|
lr_mult: 8.0 |
|
decay_mult: 0 |
|
} |
|
convolution_param { |
|
num_output: 128 |
|
pad: 3 |
|
kernel_size: 7 |
|
weight_filler { |
|
type: "gaussian" |
|
std: 0.01 |
|
} |
|
bias_filler { |
|
type: "constant" |
|
} |
|
} |
|
} |
|
layer { |
|
name: "Mrelu5_stage4_L1" |
|
type: "ReLU" |
|
bottom: "Mconv5_stage4_L1" |
|
top: "Mconv5_stage4_L1" |
|
} |
|
layer { |
|
name: "Mconv5_stage4_L2" |
|
type: "Convolution" |
|
bottom: "Mconv4_stage4_L2" |
|
top: "Mconv5_stage4_L2" |
|
param { |
|
lr_mult: 4.0 |
|
decay_mult: 1 |
|
} |
|
param { |
|
lr_mult: 8.0 |
|
decay_mult: 0 |
|
} |
|
convolution_param { |
|
num_output: 128 |
|
pad: 3 |
|
kernel_size: 7 |
|
weight_filler { |
|
type: "gaussian" |
|
std: 0.01 |
|
} |
|
bias_filler { |
|
type: "constant" |
|
} |
|
} |
|
} |
|
layer { |
|
name: "Mrelu5_stage4_L2" |
|
type: "ReLU" |
|
bottom: "Mconv5_stage4_L2" |
|
top: "Mconv5_stage4_L2" |
|
} |
|
layer { |
|
name: "Mconv6_stage4_L1" |
|
type: "Convolution" |
|
bottom: "Mconv5_stage4_L1" |
|
top: "Mconv6_stage4_L1" |
|
param { |
|
lr_mult: 4.0 |
|
decay_mult: 1 |
|
} |
|
param { |
|
lr_mult: 8.0 |
|
decay_mult: 0 |
|
} |
|
convolution_param { |
|
num_output: 128 |
|
pad: 0 |
|
kernel_size: 1 |
|
weight_filler { |
|
type: "gaussian" |
|
std: 0.01 |
|
} |
|
bias_filler { |
|
type: "constant" |
|
} |
|
} |
|
} |
|
layer { |
|
name: "Mrelu6_stage4_L1" |
|
type: "ReLU" |
|
bottom: "Mconv6_stage4_L1" |
|
top: "Mconv6_stage4_L1" |
|
} |
|
layer { |
|
name: "Mconv6_stage4_L2" |
|
type: "Convolution" |
|
bottom: "Mconv5_stage4_L2" |
|
top: "Mconv6_stage4_L2" |
|
param { |
|
lr_mult: 4.0 |
|
decay_mult: 1 |
|
} |
|
param { |
|
lr_mult: 8.0 |
|
decay_mult: 0 |
|
} |
|
convolution_param { |
|
num_output: 128 |
|
pad: 0 |
|
kernel_size: 1 |
|
weight_filler { |
|
type: "gaussian" |
|
std: 0.01 |
|
} |
|
bias_filler { |
|
type: "constant" |
|
} |
|
} |
|
} |
|
layer { |
|
name: "Mrelu6_stage4_L2" |
|
type: "ReLU" |
|
bottom: "Mconv6_stage4_L2" |
|
top: "Mconv6_stage4_L2" |
|
} |
|
layer { |
|
name: "Mconv7_stage4_L1" |
|
type: "Convolution" |
|
bottom: "Mconv6_stage4_L1" |
|
top: "Mconv7_stage4_L1" |
|
param { |
|
lr_mult: 4.0 |
|
decay_mult: 1 |
|
} |
|
param { |
|
lr_mult: 8.0 |
|
decay_mult: 0 |
|
} |
|
convolution_param { |
|
num_output: 38 |
|
pad: 0 |
|
kernel_size: 1 |
|
weight_filler { |
|
type: "gaussian" |
|
std: 0.01 |
|
} |
|
bias_filler { |
|
type: "constant" |
|
} |
|
} |
|
} |
|
layer { |
|
name: "Mconv7_stage4_L2" |
|
type: "Convolution" |
|
bottom: "Mconv6_stage4_L2" |
|
top: "Mconv7_stage4_L2" |
|
param { |
|
lr_mult: 4.0 |
|
decay_mult: 1 |
|
} |
|
param { |
|
lr_mult: 8.0 |
|
decay_mult: 0 |
|
} |
|
convolution_param { |
|
num_output: 19 |
|
pad: 0 |
|
kernel_size: 1 |
|
weight_filler { |
|
type: "gaussian" |
|
std: 0.01 |
|
} |
|
bias_filler { |
|
type: "constant" |
|
} |
|
} |
|
} |
|
layer { |
|
name: "concat_stage5" |
|
type: "Concat" |
|
bottom: "Mconv7_stage4_L1" |
|
bottom: "Mconv7_stage4_L2" |
|
bottom: "conv4_4_CPM" |
|
top: "concat_stage5" |
|
concat_param { |
|
axis: 1 |
|
} |
|
} |
|
layer { |
|
name: "Mconv1_stage5_L1" |
|
type: "Convolution" |
|
bottom: "concat_stage5" |
|
top: "Mconv1_stage5_L1" |
|
param { |
|
lr_mult: 4.0 |
|
decay_mult: 1 |
|
} |
|
param { |
|
lr_mult: 8.0 |
|
decay_mult: 0 |
|
} |
|
convolution_param { |
|
num_output: 128 |
|
pad: 3 |
|
kernel_size: 7 |
|
weight_filler { |
|
type: "gaussian" |
|
std: 0.01 |
|
} |
|
bias_filler { |
|
type: "constant" |
|
} |
|
} |
|
} |
|
layer { |
|
name: "Mrelu1_stage5_L1" |
|
type: "ReLU" |
|
bottom: "Mconv1_stage5_L1" |
|
top: "Mconv1_stage5_L1" |
|
} |
|
layer { |
|
name: "Mconv1_stage5_L2" |
|
type: "Convolution" |
|
bottom: "concat_stage5" |
|
top: "Mconv1_stage5_L2" |
|
param { |
|
lr_mult: 4.0 |
|
decay_mult: 1 |
|
} |
|
param { |
|
lr_mult: 8.0 |
|
decay_mult: 0 |
|
} |
|
convolution_param { |
|
num_output: 128 |
|
pad: 3 |
|
kernel_size: 7 |
|
weight_filler { |
|
type: "gaussian" |
|
std: 0.01 |
|
} |
|
bias_filler { |
|
type: "constant" |
|
} |
|
} |
|
} |
|
layer { |
|
name: "Mrelu1_stage5_L2" |
|
type: "ReLU" |
|
bottom: "Mconv1_stage5_L2" |
|
top: "Mconv1_stage5_L2" |
|
} |
|
layer { |
|
name: "Mconv2_stage5_L1" |
|
type: "Convolution" |
|
bottom: "Mconv1_stage5_L1" |
|
top: "Mconv2_stage5_L1" |
|
param { |
|
lr_mult: 4.0 |
|
decay_mult: 1 |
|
} |
|
param { |
|
lr_mult: 8.0 |
|
decay_mult: 0 |
|
} |
|
convolution_param { |
|
num_output: 128 |
|
pad: 3 |
|
kernel_size: 7 |
|
weight_filler { |
|
type: "gaussian" |
|
std: 0.01 |
|
} |
|
bias_filler { |
|
type: "constant" |
|
} |
|
} |
|
} |
|
layer { |
|
name: "Mrelu2_stage5_L1" |
|
type: "ReLU" |
|
bottom: "Mconv2_stage5_L1" |
|
top: "Mconv2_stage5_L1" |
|
} |
|
layer { |
|
name: "Mconv2_stage5_L2" |
|
type: "Convolution" |
|
bottom: "Mconv1_stage5_L2" |
|
top: "Mconv2_stage5_L2" |
|
param { |
|
lr_mult: 4.0 |
|
decay_mult: 1 |
|
} |
|
param { |
|
lr_mult: 8.0 |
|
decay_mult: 0 |
|
} |
|
convolution_param { |
|
num_output: 128 |
|
pad: 3 |
|
kernel_size: 7 |
|
weight_filler { |
|
type: "gaussian" |
|
std: 0.01 |
|
} |
|
bias_filler { |
|
type: "constant" |
|
} |
|
} |
|
} |
|
layer { |
|
name: "Mrelu2_stage5_L2" |
|
type: "ReLU" |
|
bottom: "Mconv2_stage5_L2" |
|
top: "Mconv2_stage5_L2" |
|
} |
|
layer { |
|
name: "Mconv3_stage5_L1" |
|
type: "Convolution" |
|
bottom: "Mconv2_stage5_L1" |
|
top: "Mconv3_stage5_L1" |
|
param { |
|
lr_mult: 4.0 |
|
decay_mult: 1 |
|
} |
|
param { |
|
lr_mult: 8.0 |
|
decay_mult: 0 |
|
} |
|
convolution_param { |
|
num_output: 128 |
|
pad: 3 |
|
kernel_size: 7 |
|
weight_filler { |
|
type: "gaussian" |
|
std: 0.01 |
|
} |
|
bias_filler { |
|
type: "constant" |
|
} |
|
} |
|
} |
|
layer { |
|
name: "Mrelu3_stage5_L1" |
|
type: "ReLU" |
|
bottom: "Mconv3_stage5_L1" |
|
top: "Mconv3_stage5_L1" |
|
} |
|
layer { |
|
name: "Mconv3_stage5_L2" |
|
type: "Convolution" |
|
bottom: "Mconv2_stage5_L2" |
|
top: "Mconv3_stage5_L2" |
|
param { |
|
lr_mult: 4.0 |
|
decay_mult: 1 |
|
} |
|
param { |
|
lr_mult: 8.0 |
|
decay_mult: 0 |
|
} |
|
convolution_param { |
|
num_output: 128 |
|
pad: 3 |
|
kernel_size: 7 |
|
weight_filler { |
|
type: "gaussian" |
|
std: 0.01 |
|
} |
|
bias_filler { |
|
type: "constant" |
|
} |
|
} |
|
} |
|
layer { |
|
name: "Mrelu3_stage5_L2" |
|
type: "ReLU" |
|
bottom: "Mconv3_stage5_L2" |
|
top: "Mconv3_stage5_L2" |
|
} |
|
layer { |
|
name: "Mconv4_stage5_L1" |
|
type: "Convolution" |
|
bottom: "Mconv3_stage5_L1" |
|
top: "Mconv4_stage5_L1" |
|
param { |
|
lr_mult: 4.0 |
|
decay_mult: 1 |
|
} |
|
param { |
|
lr_mult: 8.0 |
|
decay_mult: 0 |
|
} |
|
convolution_param { |
|
num_output: 128 |
|
pad: 3 |
|
kernel_size: 7 |
|
weight_filler { |
|
type: "gaussian" |
|
std: 0.01 |
|
} |
|
bias_filler { |
|
type: "constant" |
|
} |
|
} |
|
} |
|
layer { |
|
name: "Mrelu4_stage5_L1" |
|
type: "ReLU" |
|
bottom: "Mconv4_stage5_L1" |
|
top: "Mconv4_stage5_L1" |
|
} |
|
layer { |
|
name: "Mconv4_stage5_L2" |
|
type: "Convolution" |
|
bottom: "Mconv3_stage5_L2" |
|
top: "Mconv4_stage5_L2" |
|
param { |
|
lr_mult: 4.0 |
|
decay_mult: 1 |
|
} |
|
param { |
|
lr_mult: 8.0 |
|
decay_mult: 0 |
|
} |
|
convolution_param { |
|
num_output: 128 |
|
pad: 3 |
|
kernel_size: 7 |
|
weight_filler { |
|
type: "gaussian" |
|
std: 0.01 |
|
} |
|
bias_filler { |
|
type: "constant" |
|
} |
|
} |
|
} |
|
layer { |
|
name: "Mrelu4_stage5_L2" |
|
type: "ReLU" |
|
bottom: "Mconv4_stage5_L2" |
|
top: "Mconv4_stage5_L2" |
|
} |
|
layer { |
|
name: "Mconv5_stage5_L1" |
|
type: "Convolution" |
|
bottom: "Mconv4_stage5_L1" |
|
top: "Mconv5_stage5_L1" |
|
param { |
|
lr_mult: 4.0 |
|
decay_mult: 1 |
|
} |
|
param { |
|
lr_mult: 8.0 |
|
decay_mult: 0 |
|
} |
|
convolution_param { |
|
num_output: 128 |
|
pad: 3 |
|
kernel_size: 7 |
|
weight_filler { |
|
type: "gaussian" |
|
std: 0.01 |
|
} |
|
bias_filler { |
|
type: "constant" |
|
} |
|
} |
|
} |
|
layer { |
|
name: "Mrelu5_stage5_L1" |
|
type: "ReLU" |
|
bottom: "Mconv5_stage5_L1" |
|
top: "Mconv5_stage5_L1" |
|
} |
|
layer { |
|
name: "Mconv5_stage5_L2" |
|
type: "Convolution" |
|
bottom: "Mconv4_stage5_L2" |
|
top: "Mconv5_stage5_L2" |
|
param { |
|
lr_mult: 4.0 |
|
decay_mult: 1 |
|
} |
|
param { |
|
lr_mult: 8.0 |
|
decay_mult: 0 |
|
} |
|
convolution_param { |
|
num_output: 128 |
|
pad: 3 |
|
kernel_size: 7 |
|
weight_filler { |
|
type: "gaussian" |
|
std: 0.01 |
|
} |
|
bias_filler { |
|
type: "constant" |
|
} |
|
} |
|
} |
|
layer { |
|
name: "Mrelu5_stage5_L2" |
|
type: "ReLU" |
|
bottom: "Mconv5_stage5_L2" |
|
top: "Mconv5_stage5_L2" |
|
} |
|
layer { |
|
name: "Mconv6_stage5_L1" |
|
type: "Convolution" |
|
bottom: "Mconv5_stage5_L1" |
|
top: "Mconv6_stage5_L1" |
|
param { |
|
lr_mult: 4.0 |
|
decay_mult: 1 |
|
} |
|
param { |
|
lr_mult: 8.0 |
|
decay_mult: 0 |
|
} |
|
convolution_param { |
|
num_output: 128 |
|
pad: 0 |
|
kernel_size: 1 |
|
weight_filler { |
|
type: "gaussian" |
|
std: 0.01 |
|
} |
|
bias_filler { |
|
type: "constant" |
|
} |
|
} |
|
} |
|
layer { |
|
name: "Mrelu6_stage5_L1" |
|
type: "ReLU" |
|
bottom: "Mconv6_stage5_L1" |
|
top: "Mconv6_stage5_L1" |
|
} |
|
layer { |
|
name: "Mconv6_stage5_L2" |
|
type: "Convolution" |
|
bottom: "Mconv5_stage5_L2" |
|
top: "Mconv6_stage5_L2" |
|
param { |
|
lr_mult: 4.0 |
|
decay_mult: 1 |
|
} |
|
param { |
|
lr_mult: 8.0 |
|
decay_mult: 0 |
|
} |
|
convolution_param { |
|
num_output: 128 |
|
pad: 0 |
|
kernel_size: 1 |
|
weight_filler { |
|
type: "gaussian" |
|
std: 0.01 |
|
} |
|
bias_filler { |
|
type: "constant" |
|
} |
|
} |
|
} |
|
layer { |
|
name: "Mrelu6_stage5_L2" |
|
type: "ReLU" |
|
bottom: "Mconv6_stage5_L2" |
|
top: "Mconv6_stage5_L2" |
|
} |
|
layer { |
|
name: "Mconv7_stage5_L1" |
|
type: "Convolution" |
|
bottom: "Mconv6_stage5_L1" |
|
top: "Mconv7_stage5_L1" |
|
param { |
|
lr_mult: 4.0 |
|
decay_mult: 1 |
|
} |
|
param { |
|
lr_mult: 8.0 |
|
decay_mult: 0 |
|
} |
|
convolution_param { |
|
num_output: 38 |
|
pad: 0 |
|
kernel_size: 1 |
|
weight_filler { |
|
type: "gaussian" |
|
std: 0.01 |
|
} |
|
bias_filler { |
|
type: "constant" |
|
} |
|
} |
|
} |
|
layer { |
|
name: "Mconv7_stage5_L2" |
|
type: "Convolution" |
|
bottom: "Mconv6_stage5_L2" |
|
top: "Mconv7_stage5_L2" |
|
param { |
|
lr_mult: 4.0 |
|
decay_mult: 1 |
|
} |
|
param { |
|
lr_mult: 8.0 |
|
decay_mult: 0 |
|
} |
|
convolution_param { |
|
num_output: 19 |
|
pad: 0 |
|
kernel_size: 1 |
|
weight_filler { |
|
type: "gaussian" |
|
std: 0.01 |
|
} |
|
bias_filler { |
|
type: "constant" |
|
} |
|
} |
|
} |
|
layer { |
|
name: "concat_stage6" |
|
type: "Concat" |
|
bottom: "Mconv7_stage5_L1" |
|
bottom: "Mconv7_stage5_L2" |
|
bottom: "conv4_4_CPM" |
|
top: "concat_stage6" |
|
concat_param { |
|
axis: 1 |
|
} |
|
} |
|
layer { |
|
name: "Mconv1_stage6_L1" |
|
type: "Convolution" |
|
bottom: "concat_stage6" |
|
top: "Mconv1_stage6_L1" |
|
param { |
|
lr_mult: 4.0 |
|
decay_mult: 1 |
|
} |
|
param { |
|
lr_mult: 8.0 |
|
decay_mult: 0 |
|
} |
|
convolution_param { |
|
num_output: 128 |
|
pad: 3 |
|
kernel_size: 7 |
|
weight_filler { |
|
type: "gaussian" |
|
std: 0.01 |
|
} |
|
bias_filler { |
|
type: "constant" |
|
} |
|
} |
|
} |
|
layer { |
|
name: "Mrelu1_stage6_L1" |
|
type: "ReLU" |
|
bottom: "Mconv1_stage6_L1" |
|
top: "Mconv1_stage6_L1" |
|
} |
|
layer { |
|
name: "Mconv1_stage6_L2" |
|
type: "Convolution" |
|
bottom: "concat_stage6" |
|
top: "Mconv1_stage6_L2" |
|
param { |
|
lr_mult: 4.0 |
|
decay_mult: 1 |
|
} |
|
param { |
|
lr_mult: 8.0 |
|
decay_mult: 0 |
|
} |
|
convolution_param { |
|
num_output: 128 |
|
pad: 3 |
|
kernel_size: 7 |
|
weight_filler { |
|
type: "gaussian" |
|
std: 0.01 |
|
} |
|
bias_filler { |
|
type: "constant" |
|
} |
|
} |
|
} |
|
layer { |
|
name: "Mrelu1_stage6_L2" |
|
type: "ReLU" |
|
bottom: "Mconv1_stage6_L2" |
|
top: "Mconv1_stage6_L2" |
|
} |
|
layer { |
|
name: "Mconv2_stage6_L1" |
|
type: "Convolution" |
|
bottom: "Mconv1_stage6_L1" |
|
top: "Mconv2_stage6_L1" |
|
param { |
|
lr_mult: 4.0 |
|
decay_mult: 1 |
|
} |
|
param { |
|
lr_mult: 8.0 |
|
decay_mult: 0 |
|
} |
|
convolution_param { |
|
num_output: 128 |
|
pad: 3 |
|
kernel_size: 7 |
|
weight_filler { |
|
type: "gaussian" |
|
std: 0.01 |
|
} |
|
bias_filler { |
|
type: "constant" |
|
} |
|
} |
|
} |
|
layer { |
|
name: "Mrelu2_stage6_L1" |
|
type: "ReLU" |
|
bottom: "Mconv2_stage6_L1" |
|
top: "Mconv2_stage6_L1" |
|
} |
|
layer { |
|
name: "Mconv2_stage6_L2" |
|
type: "Convolution" |
|
bottom: "Mconv1_stage6_L2" |
|
top: "Mconv2_stage6_L2" |
|
param { |
|
lr_mult: 4.0 |
|
decay_mult: 1 |
|
} |
|
param { |
|
lr_mult: 8.0 |
|
decay_mult: 0 |
|
} |
|
convolution_param { |
|
num_output: 128 |
|
pad: 3 |
|
kernel_size: 7 |
|
weight_filler { |
|
type: "gaussian" |
|
std: 0.01 |
|
} |
|
bias_filler { |
|
type: "constant" |
|
} |
|
} |
|
} |
|
layer { |
|
name: "Mrelu2_stage6_L2" |
|
type: "ReLU" |
|
bottom: "Mconv2_stage6_L2" |
|
top: "Mconv2_stage6_L2" |
|
} |
|
layer { |
|
name: "Mconv3_stage6_L1" |
|
type: "Convolution" |
|
bottom: "Mconv2_stage6_L1" |
|
top: "Mconv3_stage6_L1" |
|
param { |
|
lr_mult: 4.0 |
|
decay_mult: 1 |
|
} |
|
param { |
|
lr_mult: 8.0 |
|
decay_mult: 0 |
|
} |
|
convolution_param { |
|
num_output: 128 |
|
pad: 3 |
|
kernel_size: 7 |
|
weight_filler { |
|
type: "gaussian" |
|
std: 0.01 |
|
} |
|
bias_filler { |
|
type: "constant" |
|
} |
|
} |
|
} |
|
layer { |
|
name: "Mrelu3_stage6_L1" |
|
type: "ReLU" |
|
bottom: "Mconv3_stage6_L1" |
|
top: "Mconv3_stage6_L1" |
|
} |
|
layer { |
|
name: "Mconv3_stage6_L2" |
|
type: "Convolution" |
|
bottom: "Mconv2_stage6_L2" |
|
top: "Mconv3_stage6_L2" |
|
param { |
|
lr_mult: 4.0 |
|
decay_mult: 1 |
|
} |
|
param { |
|
lr_mult: 8.0 |
|
decay_mult: 0 |
|
} |
|
convolution_param { |
|
num_output: 128 |
|
pad: 3 |
|
kernel_size: 7 |
|
weight_filler { |
|
type: "gaussian" |
|
std: 0.01 |
|
} |
|
bias_filler { |
|
type: "constant" |
|
} |
|
} |
|
} |
|
layer { |
|
name: "Mrelu3_stage6_L2" |
|
type: "ReLU" |
|
bottom: "Mconv3_stage6_L2" |
|
top: "Mconv3_stage6_L2" |
|
} |
|
layer { |
|
name: "Mconv4_stage6_L1" |
|
type: "Convolution" |
|
bottom: "Mconv3_stage6_L1" |
|
top: "Mconv4_stage6_L1" |
|
param { |
|
lr_mult: 4.0 |
|
decay_mult: 1 |
|
} |
|
param { |
|
lr_mult: 8.0 |
|
decay_mult: 0 |
|
} |
|
convolution_param { |
|
num_output: 128 |
|
pad: 3 |
|
kernel_size: 7 |
|
weight_filler { |
|
type: "gaussian" |
|
std: 0.01 |
|
} |
|
bias_filler { |
|
type: "constant" |
|
} |
|
} |
|
} |
|
layer { |
|
name: "Mrelu4_stage6_L1" |
|
type: "ReLU" |
|
bottom: "Mconv4_stage6_L1" |
|
top: "Mconv4_stage6_L1" |
|
} |
|
layer { |
|
name: "Mconv4_stage6_L2" |
|
type: "Convolution" |
|
bottom: "Mconv3_stage6_L2" |
|
top: "Mconv4_stage6_L2" |
|
param { |
|
lr_mult: 4.0 |
|
decay_mult: 1 |
|
} |
|
param { |
|
lr_mult: 8.0 |
|
decay_mult: 0 |
|
} |
|
convolution_param { |
|
num_output: 128 |
|
pad: 3 |
|
kernel_size: 7 |
|
weight_filler { |
|
type: "gaussian" |
|
std: 0.01 |
|
} |
|
bias_filler { |
|
type: "constant" |
|
} |
|
} |
|
} |
|
layer { |
|
name: "Mrelu4_stage6_L2" |
|
type: "ReLU" |
|
bottom: "Mconv4_stage6_L2" |
|
top: "Mconv4_stage6_L2" |
|
} |
|
layer { |
|
name: "Mconv5_stage6_L1" |
|
type: "Convolution" |
|
bottom: "Mconv4_stage6_L1" |
|
top: "Mconv5_stage6_L1" |
|
param { |
|
lr_mult: 4.0 |
|
decay_mult: 1 |
|
} |
|
param { |
|
lr_mult: 8.0 |
|
decay_mult: 0 |
|
} |
|
convolution_param { |
|
num_output: 128 |
|
pad: 3 |
|
kernel_size: 7 |
|
weight_filler { |
|
type: "gaussian" |
|
std: 0.01 |
|
} |
|
bias_filler { |
|
type: "constant" |
|
} |
|
} |
|
} |
|
layer { |
|
name: "Mrelu5_stage6_L1" |
|
type: "ReLU" |
|
bottom: "Mconv5_stage6_L1" |
|
top: "Mconv5_stage6_L1" |
|
} |
|
layer { |
|
name: "Mconv5_stage6_L2" |
|
type: "Convolution" |
|
bottom: "Mconv4_stage6_L2" |
|
top: "Mconv5_stage6_L2" |
|
param { |
|
lr_mult: 4.0 |
|
decay_mult: 1 |
|
} |
|
param { |
|
lr_mult: 8.0 |
|
decay_mult: 0 |
|
} |
|
convolution_param { |
|
num_output: 128 |
|
pad: 3 |
|
kernel_size: 7 |
|
weight_filler { |
|
type: "gaussian" |
|
std: 0.01 |
|
} |
|
bias_filler { |
|
type: "constant" |
|
} |
|
} |
|
} |
|
layer { |
|
name: "Mrelu5_stage6_L2" |
|
type: "ReLU" |
|
bottom: "Mconv5_stage6_L2" |
|
top: "Mconv5_stage6_L2" |
|
} |
|
layer { |
|
name: "Mconv6_stage6_L1" |
|
type: "Convolution" |
|
bottom: "Mconv5_stage6_L1" |
|
top: "Mconv6_stage6_L1" |
|
param { |
|
lr_mult: 4.0 |
|
decay_mult: 1 |
|
} |
|
param { |
|
lr_mult: 8.0 |
|
decay_mult: 0 |
|
} |
|
convolution_param { |
|
num_output: 128 |
|
pad: 0 |
|
kernel_size: 1 |
|
weight_filler { |
|
type: "gaussian" |
|
std: 0.01 |
|
} |
|
bias_filler { |
|
type: "constant" |
|
} |
|
} |
|
} |
|
layer { |
|
name: "Mrelu6_stage6_L1" |
|
type: "ReLU" |
|
bottom: "Mconv6_stage6_L1" |
|
top: "Mconv6_stage6_L1" |
|
} |
|
layer { |
|
name: "Mconv6_stage6_L2" |
|
type: "Convolution" |
|
bottom: "Mconv5_stage6_L2" |
|
top: "Mconv6_stage6_L2" |
|
param { |
|
lr_mult: 4.0 |
|
decay_mult: 1 |
|
} |
|
param { |
|
lr_mult: 8.0 |
|
decay_mult: 0 |
|
} |
|
convolution_param { |
|
num_output: 128 |
|
pad: 0 |
|
kernel_size: 1 |
|
weight_filler { |
|
type: "gaussian" |
|
std: 0.01 |
|
} |
|
bias_filler { |
|
type: "constant" |
|
} |
|
} |
|
} |
|
layer { |
|
name: "Mrelu6_stage6_L2" |
|
type: "ReLU" |
|
bottom: "Mconv6_stage6_L2" |
|
top: "Mconv6_stage6_L2" |
|
} |
|
layer { |
|
name: "Mconv7_stage6_L1" |
|
type: "Convolution" |
|
bottom: "Mconv6_stage6_L1" |
|
top: "Mconv7_stage6_L1" |
|
param { |
|
lr_mult: 4.0 |
|
decay_mult: 1 |
|
} |
|
param { |
|
lr_mult: 8.0 |
|
decay_mult: 0 |
|
} |
|
convolution_param { |
|
num_output: 38 |
|
pad: 0 |
|
kernel_size: 1 |
|
weight_filler { |
|
type: "gaussian" |
|
std: 0.01 |
|
} |
|
bias_filler { |
|
type: "constant" |
|
} |
|
} |
|
} |
|
layer { |
|
name: "Mconv7_stage6_L2" |
|
type: "Convolution" |
|
bottom: "Mconv6_stage6_L2" |
|
top: "Mconv7_stage6_L2" |
|
param { |
|
lr_mult: 4.0 |
|
decay_mult: 1 |
|
} |
|
param { |
|
lr_mult: 8.0 |
|
decay_mult: 0 |
|
} |
|
convolution_param { |
|
num_output: 19 |
|
pad: 0 |
|
kernel_size: 1 |
|
weight_filler { |
|
type: "gaussian" |
|
std: 0.01 |
|
} |
|
bias_filler { |
|
type: "constant" |
|
} |
|
} |
|
} |
|
layer { |
|
name: "concat_stage7" |
|
type: "Concat" |
|
bottom: "Mconv7_stage6_L2" |
|
bottom: "Mconv7_stage6_L1" |
|
# top: "concat_stage7" |
|
top: "net_output" |
|
concat_param { |
|
axis: 1 |
|
} |
|
} |
|
|
