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openpose_pose_coco.prototxt
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openpose_pose_coco.prototxt
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# source: https://github.com/CMU-Perceptual-Computing-Lab/openpose/blob/master/models/pose/coco/pose_deploy_linevec.prototxt
input: "image"
input_dim: 1
input_dim: 3
input_dim: 368
input_dim: 368
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"