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pose_trainval.prototxt
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pose_trainval.prototxt
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layer {
name: "data"
type: "CPMData"
top: "data"
top: "label"
data_param {
source: "/data/xiaobing.wang/pose/COCO_kpt/lmdb"
batch_size: 8
backend: LMDB
}
cpm_transform_param {
stride: 8
max_rotate_degree: 40.0
visualize: false
crop_size_x: 368
crop_size_y: 368
scale_prob: 1.0
scale_min: 0.5
#scale_max: 2.10000002384
scale_max: 1.10000002384
target_dist: 0.600000023842
center_perterb_max: 40.0
do_clahe: false
#num_parts: 38
#np_in_lmdb: 11
upper_body: true
dataset: "COCO"
}
}
layer {
name: "vec_weight"
type: "Slice"
bottom: "label"
top: "vec_weight"
top: "heat_weight"
top: "vec_temp"
top: "heat_temp"
slice_param {
slice_point: 26
slice_point: 39
slice_point: 65
axis: 1
}
}
layer {
name: "label_vec"
type: "Eltwise"
bottom: "vec_weight"
bottom: "vec_temp"
top: "label_vec"
eltwise_param {
operation: PROD
}
}
layer {
name: "label_heat"
type: "Eltwise"
bottom: "heat_weight"
bottom: "heat_temp"
top: "label_heat"
eltwise_param {
operation: PROD
}
}
layer {
name: "image"
type: "Slice"
bottom: "data"
top: "image"
top: "center_map"
slice_param {
slice_point: 3
axis: 1
}
}
layer {
name: "silence2"
type: "Silence"
bottom: "center_map"
}
#======================= VGG 19 ==================
layer {
name: "conv1_1"
type: "Convolution"
bottom: "image"
top: "conv1_1"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 64
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
std: 0.00999999977648
}
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: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 64
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
std: 0.00999999977648
}
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: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 128
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
std: 0.00999999977648
}
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: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 128
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
std: 0.00999999977648
}
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: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
std: 0.00999999977648
}
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: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
std: 0.00999999977648
}
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: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
std: 0.00999999977648
}
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: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
std: 0.00999999977648
}
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: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 512
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
std: 0.00999999977648
}
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: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 512
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
std: 0.00999999977648
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "relu4_2"
type: "ReLU"
bottom: "conv4_2"
top: "conv4_2"
}
layer {
bottom: "conv4_2"
top: "conv4_3"
name: "conv4_3"
type: "Convolution"
convolution_param {
num_output: 512
pad: 1
kernel_size: 3
}
}
layer {
bottom: "conv4_3"
top: "conv4_3"
name: "relu4_3"
type: "ReLU"
}
layer {
bottom: "conv4_3"
top: "conv4_4"
name: "conv4_4"
type: "Convolution"
convolution_param {
num_output: 512
pad: 1
kernel_size: 3
}
}
layer {
bottom: "conv4_4"
top: "conv4_4"
name: "relu4_4"
type: "ReLU"
}
#========================================================================================
layer {
name: "conv4_3_CPM"
type: "Convolution"
bottom: "conv4_4"
top: "conv4_3_CPM"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
std: 0.00999999977648
}
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: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 128
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
std: 0.00999999977648
}
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: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 128
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
std: 0.00999999977648
}
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: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 128
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
std: 0.00999999977648
}
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: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 128
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
std: 0.00999999977648
}
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: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 128
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
std: 0.00999999977648
}
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: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 128
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
std: 0.00999999977648
}
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: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 128
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
std: 0.00999999977648
}
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: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 512
pad: 0
kernel_size: 1
weight_filler {
type: "gaussian"
std: 0.00999999977648
}
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: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 512
pad: 0
kernel_size: 1
weight_filler {
type: "gaussian"
std: 0.00999999977648
}
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: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 26
pad: 0
kernel_size: 1
weight_filler {
type: "gaussian"
std: 0.00999999977648
}
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: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 13
pad: 0
kernel_size: 1
weight_filler {
type: "gaussian"
std: 0.00999999977648
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "weight_stage1_L1"
type: "Eltwise"
bottom: "conv5_5_CPM_L1"
bottom: "vec_weight"
top: "weight_stage1_L1"
eltwise_param {
operation: PROD
}
}
layer {
name: "loss_stage1_L1"
type: "EuclideanLoss"
bottom: "weight_stage1_L1"
bottom: "label_vec"
top: "loss_stage1_L1"
loss_weight: 1.0
}
layer {
name: "weight_stage1_L2"
type: "Eltwise"
bottom: "conv5_5_CPM_L2"
bottom: "heat_weight"
top: "weight_stage1_L2"
eltwise_param {
operation: PROD
}
}
layer {
name: "loss_stage1_L2"
type: "EuclideanLoss"
bottom: "weight_stage1_L2"
bottom: "label_heat"
top: "loss_stage1_L2"
loss_weight: 1.0
}
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: 1.0
}
param {
lr_mult: 8.0
decay_mult: 0.0
}
convolution_param {
num_output: 128
pad: 3
kernel_size: 7
weight_filler {
type: "gaussian"
std: 0.00999999977648
}
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: 1.0
}
param {
lr_mult: 8.0
decay_mult: 0.0
}
convolution_param {
num_output: 128
pad: 3
kernel_size: 7
weight_filler {
type: "gaussian"
std: 0.00999999977648
}
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: 1.0
}
param {
lr_mult: 8.0
decay_mult: 0.0
}
convolution_param {
num_output: 128
pad: 3
kernel_size: 7
weight_filler {
type: "gaussian"
std: 0.00999999977648
}
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: 1.0
}
param {
lr_mult: 8.0
decay_mult: 0.0
}
convolution_param {