refinedet网络结构
refinedet只预测4个层,并且只有conv6_1、conv6_2,没有ssd中的conv7、8、9
refinedet的4个层都只有1个aspect ratio和1个min_size,所以每层每个点只有3个anchor,arm中做location的conv4_3_norm_mbox_loc等层都是3*4个channel,做confidence的conv4_3_norm_mbox_conf都是6个channel,因为这里变成了2分类,每个anchor必须要有negative和positive的概率
refinedet是两步都要回归bounding box的框,refinedet中的odm_loss就相当于ssd中的mbox_loss,mbox_loss获得了anchor的坐标后会加上回归再进行训练,odm_loss获得anchor的坐标后先要加上arm_loc的回归,再加odm_loc的回归,这样再去进行loss计算.
name: "vgg_1/8"
layer {
name: "data"
type: "AnnotatedData"
top: "data"
top: "label"
include {
phase: TRAIN
}
transform_param {
mirror: true
mean_value: 104.0
mean_value: 117.0
mean_value: 123.0
resize_param {
prob: 1.0
resize_mode: WARP
height:
width:
interp_mode: LINEAR
interp_mode: AREA
interp_mode: NEAREST
interp_mode: CUBIC
interp_mode: LANCZOS4
}
emit_constraint {
emit_type: CENTER
}
distort_param {
brightness_prob: 0.5
brightness_delta: 32.0
contrast_prob: 0.5
contrast_lower: 0.5
contrast_upper: 1.5
hue_prob: 0.5
hue_delta: 18.0
saturation_prob: 0.5
saturation_lower: 0.5
saturation_upper: 1.5
random_order_prob: 0.0
}
expand_param {
prob: 0.5
max_expand_ratio: 4.0
}
}
data_param {
source:"examples/cityscapes/cityscapes_train_lmdb"
batch_size:
backend: LMDB
}
annotated_data_param {
batch_sampler {
max_sample:
max_trials:
}
batch_sampler {
sampler {
min_scale: 0.300000011921
max_scale: 1.0
min_aspect_ratio: 0.5
max_aspect_ratio: 2.0
}
sample_constraint {
min_jaccard_overlap: 0.10000000149
}
max_sample:
max_trials:
}
batch_sampler {
sampler {
min_scale: 0.300000011921
max_scale: 1.0
min_aspect_ratio: 0.5
max_aspect_ratio: 2.0
}
sample_constraint {
min_jaccard_overlap: 0.300000011921
}
max_sample:
max_trials:
}
batch_sampler {
sampler {
min_scale: 0.300000011921
max_scale: 1.0
min_aspect_ratio: 0.5
max_aspect_ratio: 2.0
}
sample_constraint {
min_jaccard_overlap: 0.5
}
max_sample:
max_trials:
}
batch_sampler {
sampler {
min_scale: 0.300000011921
max_scale: 1.0
min_aspect_ratio: 0.5
max_aspect_ratio: 2.0
}
sample_constraint {
min_jaccard_overlap: 0.699999988079
}
max_sample:
max_trials:
}
batch_sampler {
sampler {
min_scale: 0.300000011921
max_scale: 1.0
min_aspect_ratio: 0.5
max_aspect_ratio: 2.0
}
sample_constraint {
min_jaccard_overlap: 0.899999976158
}
max_sample:
max_trials:
}
batch_sampler {
sampler {
min_scale: 0.300000011921
max_scale: 1.0
min_aspect_ratio: 0.5
max_aspect_ratio: 2.0
}
sample_constraint {
max_jaccard_overlap: 1.0
}
max_sample:
max_trials:
}
label_map_file: "data/cityscapes/labelmap_cityscapes.prototxt"
}
}
layer {
name: "conv1_1"
type: "Convolution"
bottom: "data"
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:
pad:
kernel_size:
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "relu1_1"
type: "ReLU"
bottom: "conv1_1"
top: "conv1_1"
}
layer {
name: "conv1_2"
type: "Convolution"
bottom: "conv1_1"
top: "conv1_2"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output:
pad:
kernel_size:
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "relu1_2"
type: "ReLU"
bottom: "conv1_2"
top: "conv1_2"
}
layer {
name: "pool1"
type: "Pooling"
bottom: "conv1_2"
top: "pool1"
pooling_param {
pool: MAX
kernel_size:
stride:
}
}
layer {
name: "conv2_1"
type: "Convolution"
bottom: "pool1"
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:
pad:
kernel_size:
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "relu2_1"
type: "ReLU"
bottom: "conv2_1"
top: "conv2_1"
}
layer {
name: "conv2_2"
type: "Convolution"
bottom: "conv2_1"
top: "conv2_2"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output:
pad:
kernel_size:
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "relu2_2"
type: "ReLU"
bottom: "conv2_2"
top: "conv2_2"
}
layer {
name: "pool2"
type: "Pooling"
bottom: "conv2_2"
top: "pool2"
pooling_param {
pool: MAX
kernel_size:
stride:
}
}
layer {
name: "conv3_1"
type: "Convolution"
bottom: "pool2"
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:
pad:
kernel_size:
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "relu3_1"
type: "ReLU"
bottom: "conv3_1"
top: "conv3_1"
}
layer {
name: "conv3_2"
type: "Convolution"
bottom: "conv3_1"
top: "conv3_2"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output:
pad:
kernel_size:
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "relu3_2"
type: "ReLU"
bottom: "conv3_2"
top: "conv3_2"
}
layer {
name: "conv3_3"
type: "Convolution"
bottom: "conv3_2"
top: "conv3_3"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output:
pad:
kernel_size:
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "relu3_3"
type: "ReLU"
bottom: "conv3_3"
top: "conv3_3"
}
layer {
name: "pool3"
type: "Pooling"
bottom: "conv3_3"
top: "pool3"
pooling_param {
pool: MAX
kernel_size:
stride:
}
}
layer {
name: "conv4_1"
type: "Convolution"
bottom: "pool3"
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:
pad:
kernel_size:
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "relu4_1"
type: "ReLU"
bottom: "conv4_1"
top: "conv4_1"
}
layer {
name: "conv4_2"
type: "Convolution"
bottom: "conv4_1"
top: "conv4_2"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output:
pad:
kernel_size:
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "relu4_2"
type: "ReLU"
bottom: "conv4_2"
top: "conv4_2"
}
layer {
name: "conv4_3"
type: "Convolution"
bottom: "conv4_2"
top: "conv4_3"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output:
pad:
kernel_size:
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "relu4_3"
type: "ReLU"
bottom: "conv4_3"
top: "conv4_3"
}
layer {
name: "pool4"
type: "Pooling"
bottom: "conv4_3"
top: "pool4"
pooling_param {
pool: MAX
kernel_size:
stride:
}
}
layer {
name: "conv5_1"
type: "Convolution"
bottom: "pool4"
top: "conv5_1"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output:
pad:
kernel_size:
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "relu5_1"
type: "ReLU"
bottom: "conv5_1"
top: "conv5_1"
}
layer {
name: "conv5_2"
type: "Convolution"
bottom: "conv5_1"
top: "conv5_2"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output:
pad:
kernel_size:
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "relu5_2"
type: "ReLU"
bottom: "conv5_2"
top: "conv5_2"
}
layer {
name: "conv5_3"
type: "Convolution"
bottom: "conv5_2"
top: "conv5_3"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output:
pad:
kernel_size:
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "relu5_3"
type: "ReLU"
bottom: "conv5_3"
top: "conv5_3"
}
layer {
name: "pool5"
type: "Pooling"
bottom: "conv5_3"
top: "pool5"
pooling_param {
pool: MAX
kernel_size:
stride:
}
}
layer {
name: "fc6"
type: "Convolution"
bottom: "pool5"
top: "fc6"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output:
pad:
kernel_size:
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "relu6"
type: "ReLU"
bottom: "fc6"
top: "fc6"
}
layer {
name: "fc7"
type: "Convolution"
bottom: "fc6"
top: "fc7"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output:
kernel_size:
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "relu7"
type: "ReLU"
bottom: "fc7"
top: "fc7"
}
layer {
name: "conv6_1"
type: "Convolution"
bottom: "fc7"
top: "conv6_1"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output:
pad:
kernel_size:
stride:
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "conv6_1_relu"
type: "ReLU"
bottom: "conv6_1"
top: "conv6_1"
}
layer {
name: "conv6_2"
type: "Convolution"
bottom: "conv6_1"
top: "conv6_2"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output:
pad:
kernel_size:
stride:
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "conv6_2_relu"
type: "ReLU"
bottom: "conv6_2"
top: "conv6_2"
}
layer {
name: "conv4_3_norm_mbox_loc"
type: "Convolution"
bottom: "conv4_3"
top: "conv4_3_norm_mbox_loc"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output:
pad:
kernel_size:
stride:
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "conv4_3_norm_mbox_loc_perm"
type: "Permute"
bottom: "conv4_3_norm_mbox_loc"
top: "conv4_3_norm_mbox_loc_perm"
permute_param {
order:
order:
order:
order:
}
}
layer {
name: "conv4_3_norm_mbox_loc_flat"
type: "Flatten"
bottom: "conv4_3_norm_mbox_loc_perm"
top: "conv4_3_norm_mbox_loc_flat"
flatten_param {
axis:
}
}
layer {
name: "conv4_3_norm_mbox_conf"
type: "Convolution"
bottom: "conv4_3"
top: "conv4_3_norm_mbox_conf"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output:
pad:
kernel_size:
stride:
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "conv4_3_norm_mbox_conf_perm"
type: "Permute"
bottom: "conv4_3_norm_mbox_conf"
top: "conv4_3_norm_mbox_conf_perm"
permute_param {
order:
order:
order:
order:
}
}
layer {
name: "conv4_3_norm_mbox_conf_flat"
type: "Flatten"
bottom: "conv4_3_norm_mbox_conf_perm"
top: "conv4_3_norm_mbox_conf_flat"
flatten_param {
axis:
}
}
layer {
name: "conv4_3_norm_mbox_priorbox"
type: "PriorBox"
bottom: "conv4_3"
bottom: "data"
top: "conv4_3_norm_mbox_priorbox"
prior_box_param {
min_size: 16.0
aspect_ratio: 2.0
flip: true
clip: false
variance: 0.10000000149
variance: 0.10000000149
variance: 0.20000000298
variance: 0.20000000298
step: 8.0
offset: 0.5
}
}
layer {
name: "conv5_3_norm_mbox_loc"
type: "Convolution"
bottom: "conv5_3"
top: "conv5_3_norm_mbox_loc"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output:
pad:
kernel_size:
stride:
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "conv5_3_norm_mbox_loc_perm"
type: "Permute"
bottom: "conv5_3_norm_mbox_loc"
top: "conv5_3_norm_mbox_loc_perm"
permute_param {
order:
order:
order:
order:
}
}
layer {
name: "conv5_3_norm_mbox_loc_flat"
type: "Flatten"
bottom: "conv5_3_norm_mbox_loc_perm"
top: "conv5_3_norm_mbox_loc_flat"
flatten_param {
axis:
}
}
layer {
name: "conv5_3_norm_mbox_conf"
type: "Convolution"
bottom: "conv5_3"
top: "conv5_3_norm_mbox_conf"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output:
pad:
kernel_size:
stride:
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "conv5_3_norm_mbox_conf_perm"
type: "Permute"
bottom: "conv5_3_norm_mbox_conf"
top: "conv5_3_norm_mbox_conf_perm"
permute_param {
order:
order:
order:
order:
}
}
layer {
name: "conv5_3_norm_mbox_conf_flat"
type: "Flatten"
bottom: "conv5_3_norm_mbox_conf_perm"
top: "conv5_3_norm_mbox_conf_flat"
flatten_param {
axis:
}
}
layer {
name: "conv5_3_norm_mbox_priorbox"
type: "PriorBox"
bottom: "conv5_3"
bottom: "data"
top: "conv5_3_norm_mbox_priorbox"
prior_box_param {
min_size: 32.0
aspect_ratio: 2.0
flip: true
clip: false
variance: 0.10000000149
variance: 0.10000000149
variance: 0.20000000298
variance: 0.20000000298
step: 16.0
offset: 0.5
}
}
layer {
name: "fc7_mbox_loc"
type: "Convolution"
bottom: "fc7"
top: "fc7_mbox_loc"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output:
pad:
kernel_size:
stride:
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "fc7_mbox_loc_perm"
type: "Permute"
bottom: "fc7_mbox_loc"
top: "fc7_mbox_loc_perm"
permute_param {
order:
order:
order:
order:
}
}
layer {
name: "fc7_mbox_loc_flat"
type: "Flatten"
bottom: "fc7_mbox_loc_perm"
top: "fc7_mbox_loc_flat"
flatten_param {
axis:
}
}
layer {
name: "fc7_mbox_conf"
type: "Convolution"
bottom: "fc7"
top: "fc7_mbox_conf"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output:
pad:
kernel_size:
stride:
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "fc7_mbox_conf_perm"
type: "Permute"
bottom: "fc7_mbox_conf"
top: "fc7_mbox_conf_perm"
permute_param {
order:
order:
order:
order:
}
}
layer {
name: "fc7_mbox_conf_flat"
type: "Flatten"
bottom: "fc7_mbox_conf_perm"
top: "fc7_mbox_conf_flat"
flatten_param {
axis:
}
}
layer {
name: "fc7_mbox_priorbox"
type: "PriorBox"
bottom: "fc7"
bottom: "data"
top: "fc7_mbox_priorbox"
prior_box_param {
min_size: 64.0
aspect_ratio: 2.0
flip: true
clip: false
variance: 0.10000000149
variance: 0.10000000149
variance: 0.20000000298
variance: 0.20000000298
step: 32.0
offset: 0.5
}
}
layer {
name: "conv6_2_mbox_loc"
type: "Convolution"
bottom: "conv6_2"
top: "conv6_2_mbox_loc"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output:
pad:
kernel_size:
stride:
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "conv6_2_mbox_loc_perm"
type: "Permute"
bottom: "conv6_2_mbox_loc"
top: "conv6_2_mbox_loc_perm"
permute_param {
order:
order:
order:
order:
}
}
layer {
name: "conv6_2_mbox_loc_flat"
type: "Flatten"
bottom: "conv6_2_mbox_loc_perm"
top: "conv6_2_mbox_loc_flat"
flatten_param {
axis:
}
}
layer {
name: "conv6_2_mbox_conf"
type: "Convolution"
bottom: "conv6_2"
top: "conv6_2_mbox_conf"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output:
pad:
kernel_size:
stride:
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "conv6_2_mbox_conf_perm"
type: "Permute"
bottom: "conv6_2_mbox_conf"
top: "conv6_2_mbox_conf_perm"
permute_param {
order:
order:
order:
order:
}
}
layer {
name: "conv6_2_mbox_conf_flat"
type: "Flatten"
bottom: "conv6_2_mbox_conf_perm"
top: "conv6_2_mbox_conf_flat"
flatten_param {
axis:
}
}
layer {
name: "conv6_2_mbox_priorbox"
type: "PriorBox"
bottom: "conv6_2"
bottom: "data"
top: "conv6_2_mbox_priorbox"
prior_box_param {
min_size: 128.0
aspect_ratio: 2.0
flip: true
clip: false
variance: 0.10000000149
variance: 0.10000000149
variance: 0.20000000298
variance: 0.20000000298
step: 64.0
offset: 0.5
}
}
layer {
name: "arm_loc"
type: "Concat"
bottom: "conv4_3_norm_mbox_loc_flat"
bottom: "conv5_3_norm_mbox_loc_flat"
bottom: "fc7_mbox_loc_flat"
bottom: "conv6_2_mbox_loc_flat"
top: "arm_loc"
concat_param {
axis:
}
}
layer {
name: "arm_conf"
type: "Concat"
bottom: "conv4_3_norm_mbox_conf_flat"
bottom: "conv5_3_norm_mbox_conf_flat"
bottom: "fc7_mbox_conf_flat"
bottom: "conv6_2_mbox_conf_flat"
top: "arm_conf"
concat_param {
axis:
}
}
layer {
name: "arm_priorbox"
type: "Concat"
bottom: "conv4_3_norm_mbox_priorbox"
bottom: "conv5_3_norm_mbox_priorbox"
bottom: "fc7_mbox_priorbox"
bottom: "conv6_2_mbox_priorbox"
top: "arm_priorbox"
concat_param {
axis:
}
}
layer {
name: "P3_mbox_loc_p"
type: "Convolution"
bottom: "conv4_3"
top: "P3_mbox_loc"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output:
pad:
kernel_size:
stride:
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "P3_mbox_loc_perm"
type: "Permute"
bottom: "P3_mbox_loc"
top: "P3_mbox_loc_perm"
permute_param {
order:
order:
order:
order:
}
}
layer {
name: "P3_mbox_loc_flat"
type: "Flatten"
bottom: "P3_mbox_loc_perm"
top: "P3_mbox_loc_flat"
flatten_param {
axis:
}
}
layer {
name: "P3_mbox_conf_p"
type: "Convolution"
bottom: "conv4_3"
top: "P3_mbox_conf"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output:
pad:
kernel_size:
stride:
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "P3_mbox_conf_perm"
type: "Permute"
bottom: "P3_mbox_conf"
top: "P3_mbox_conf_perm"
permute_param {
order:
order:
order:
order:
}
}
layer {
name: "P3_mbox_conf_flat"
type: "Flatten"
bottom: "P3_mbox_conf_perm"
top: "P3_mbox_conf_flat"
flatten_param {
axis:
}
}
layer {
name: "P4_mbox_loc_p"
type: "Convolution"
bottom: "conv5_3"
top: "P4_mbox_loc"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output:
pad:
kernel_size:
stride:
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "P4_mbox_loc_perm"
type: "Permute"
bottom: "P4_mbox_loc"
top: "P4_mbox_loc_perm"
permute_param {
order:
order:
order:
order:
}
}
layer {
name: "P4_mbox_loc_flat"
type: "Flatten"
bottom: "P4_mbox_loc_perm"
top: "P4_mbox_loc_flat"
flatten_param {
axis:
}
}
layer {
name: "P4_mbox_conf_p"
type: "Convolution"
bottom: "conv5_3"
top: "P4_mbox_conf"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output:
pad:
kernel_size:
stride:
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "P4_mbox_conf_perm"
type: "Permute"
bottom: "P4_mbox_conf"
top: "P4_mbox_conf_perm"
permute_param {
order:
order:
order:
order:
}
}
layer {
name: "P4_mbox_conf_flat"
type: "Flatten"
bottom: "P4_mbox_conf_perm"
top: "P4_mbox_conf_flat"
flatten_param {
axis:
}
}
layer {
name: "P5_mbox_loc_p"
type: "Convolution"
bottom: "fc7"
top: "P5_mbox_loc"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output:
pad:
kernel_size:
stride:
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "P5_mbox_loc_perm"
type: "Permute"
bottom: "P5_mbox_loc"
top: "P5_mbox_loc_perm"
permute_param {
order:
order:
order:
order:
}
}
layer {
name: "P5_mbox_loc_flat"
type: "Flatten"
bottom: "P5_mbox_loc_perm"
top: "P5_mbox_loc_flat"
flatten_param {
axis:
}
}
layer {
name: "P5_mbox_conf_p"
type: "Convolution"
bottom: "fc7"
top: "P5_mbox_conf"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output:
pad:
kernel_size:
stride:
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "P5_mbox_conf_perm"
type: "Permute"
bottom: "P5_mbox_conf"
top: "P5_mbox_conf_perm"
permute_param {
order:
order:
order:
order:
}
}
layer {
name: "P5_mbox_conf_flat"
type: "Flatten"
bottom: "P5_mbox_conf_perm"
top: "P5_mbox_conf_flat"
flatten_param {
axis:
}
}
layer {
name: "P6_mbox_loc_p"
type: "Convolution"
bottom: "conv6_2"
top: "P6_mbox_loc"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output:
pad:
kernel_size:
stride:
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "P6_mbox_loc_perm"
type: "Permute"
bottom: "P6_mbox_loc"
top: "P6_mbox_loc_perm"
permute_param {
order:
order:
order:
order:
}
}
layer {
name: "P6_mbox_loc_flat"
type: "Flatten"
bottom: "P6_mbox_loc_perm"
top: "P6_mbox_loc_flat"
flatten_param {
axis:
}
}
layer {
name: "P6_mbox_conf_p"
type: "Convolution"
bottom: "conv6_2"
top: "P6_mbox_conf"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output:
pad:
kernel_size:
stride:
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "P6_mbox_conf_perm"
type: "Permute"
bottom: "P6_mbox_conf"
top: "P6_mbox_conf_perm"
permute_param {
order:
order:
order:
order:
}
}
layer {
name: "P6_mbox_conf_flat"
type: "Flatten"
bottom: "P6_mbox_conf_perm"
top: "P6_mbox_conf_flat"
flatten_param {
axis:
}
}
layer {
name: "odm_loc"
type: "Concat"
bottom: "P3_mbox_loc_flat"
bottom: "P4_mbox_loc_flat"
bottom: "P5_mbox_loc_flat"
bottom: "P6_mbox_loc_flat"
top: "odm_loc"
concat_param {
axis:
}
}
layer {
name: "odm_conf"
type: "Concat"
bottom: "P3_mbox_conf_flat"
bottom: "P4_mbox_conf_flat"
bottom: "P5_mbox_conf_flat"
bottom: "P6_mbox_conf_flat"
top: "odm_conf"
concat_param {
axis:
}
}
layer {
name: "arm_loss"
type: "MultiBoxLoss"
bottom: "arm_loc"
bottom: "arm_conf"
bottom: "arm_priorbox"
bottom: "label"
top: "arm_loss"
include {
phase: TRAIN
}
propagate_down: true
propagate_down: true
propagate_down: false
propagate_down: false
loss_param {
normalization: VALID
}
multibox_loss_param {
loc_loss_type: SMOOTH_L1
conf_loss_type: SOFTMAX
loc_weight: 1.0
num_classes:
share_location: true
match_type: PER_PREDICTION
overlap_threshold: 0.5
use_prior_for_matching: true
background_label_id:
use_difficult_gt: true
neg_pos_ratio: 3.0
neg_overlap: 0.5
code_type: CENTER_SIZE
ignore_cross_boundary_bbox: false
mining_type: MAX_NEGATIVE
objectness_score: 0.00999999977648
}
}
layer {
name: "arm_conf_reshape"
type: "Reshape"
bottom: "arm_conf"
top: "arm_conf_reshape"
reshape_param {
shape {
dim:
dim: -
dim:
}
}
}
layer {
name: "arm_conf_softmax"
type: "Softmax"
bottom: "arm_conf_reshape"
top: "arm_conf_softmax"
softmax_param {
axis:
}
}
layer {
name: "arm_conf_flatten"
type: "Flatten"
bottom: "arm_conf_softmax"
top: "arm_conf_flatten"
flatten_param {
axis:
}
}
layer {
name: "odm_loss"
type: "MultiBoxLoss"
bottom: "odm_loc"
bottom: "odm_conf"
bottom: "arm_priorbox"
bottom: "label"
bottom: "arm_conf_flatten"
bottom: "arm_loc"
top: "odm_loss"
include {
phase: TRAIN
}
propagate_down: true
propagate_down: true
propagate_down: false
propagate_down: false
propagate_down: false
propagate_down: false
loss_param {
normalization: VALID
}
multibox_loss_param {
loc_loss_type: SMOOTH_L1
conf_loss_type: SOFTMAX
loc_weight: 1.0
num_classes:
share_location: true
match_type: PER_PREDICTION
overlap_threshold: 0.5
use_prior_for_matching: true
background_label_id:
use_difficult_gt: true
neg_pos_ratio: 3.0
neg_overlap: 0.5
code_type: CENTER_SIZE
ignore_cross_boundary_bbox: false
mining_type: MAX_NEGATIVE
objectness_score: 0.00999999977648
}
} layer {
name: "conv1_1_t"
type: "Convolution"
bottom: "data"
top: "conv1_1_t"
param {
lr_mult:
decay_mult:
}
param {
lr_mult:
decay_mult:
}
convolution_param {
num_output:
pad:
kernel_size:
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "relu1_1_t"
type: "ReLU"
bottom: "conv1_1_t"
top: "conv1_1_t"
}
layer {
name: "conv1_2_t"
type: "Convolution"
bottom: "conv1_1_t"
top: "conv1_2_t"
param {
lr_mult:
decay_mult:
}
param {
lr_mult:
decay_mult:
}
convolution_param {
num_output:
pad:
kernel_size:
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "relu1_2_t"
type: "ReLU"
bottom: "conv1_2_t"
top: "conv1_2_t"
}
layer {
name: "pool1_t"
type: "Pooling"
bottom: "conv1_2_t"
top: "pool1_t"
pooling_param {
pool: MAX
kernel_size:
stride:
}
}
layer {
name: "conv2_1_t"
type: "Convolution"
bottom: "pool1_t"
top: "conv2_1_t"
param {
lr_mult:
decay_mult:
}
param {
lr_mult:
decay_mult:
}
convolution_param {
num_output:
pad:
kernel_size:
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "relu2_1_t"
type: "ReLU"
bottom: "conv2_1_t"
top: "conv2_1_t"
}
layer {
name: "conv2_2_t"
type: "Convolution"
bottom: "conv2_1_t"
top: "conv2_2_t"
param {
lr_mult:
decay_mult:
}
param {
lr_mult:
decay_mult:
}
convolution_param {
num_output:
pad:
kernel_size:
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "relu2_2_t"
type: "ReLU"
bottom: "conv2_2_t"
top: "conv2_2_t"
}
layer {
name: "pool2_t"
type: "Pooling"
bottom: "conv2_2_t"
top: "pool2_t"
pooling_param {
pool: MAX
kernel_size:
stride:
}
}
layer {
name: "conv3_1_t"
type: "Convolution"
bottom: "pool2_t"
top: "conv3_1_t"
param {
lr_mult:
decay_mult:
}
param {
lr_mult:
decay_mult:
}
convolution_param {
num_output:
pad:
kernel_size:
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "relu3_1_t"
type: "ReLU"
bottom: "conv3_1_t"
top: "conv3_1_t"
}
layer {
name: "conv3_2_t"
type: "Convolution"
bottom: "conv3_1_t"
top: "conv3_2_t"
param {
lr_mult:
decay_mult:
}
param {
lr_mult:
decay_mult:
}
convolution_param {
num_output:
pad:
kernel_size:
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "relu3_2_t"
type: "ReLU"
bottom: "conv3_2_t"
top: "conv3_2_t"
}
layer {
name: "conv3_3_t"
type: "Convolution"
bottom: "conv3_2_t"
top: "conv3_3_t"
param {
lr_mult:
decay_mult:
}
param {
lr_mult:
decay_mult:
}
convolution_param {
num_output:
pad:
kernel_size:
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "relu3_3_t"
type: "ReLU"
bottom: "conv3_3_t"
top: "conv3_3_t"
}
layer {
name: "pool3_t"
type: "Pooling"
bottom: "conv3_3_t"
top: "pool3_t"
pooling_param {
pool: MAX
kernel_size:
stride:
}
}
layer {
name: "conv4_1_t"
type: "Convolution"
bottom: "pool3_t"
top: "conv4_1_t"
param {
lr_mult:
decay_mult:
}
param {
lr_mult:
decay_mult:
}
convolution_param {
num_output:
pad:
kernel_size:
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "relu4_1_t"
type: "ReLU"
bottom: "conv4_1_t"
top: "conv4_1_t"
}
layer {
name: "conv4_2_t"
type: "Convolution"
bottom: "conv4_1_t"
top: "conv4_2_t"
param {
lr_mult:
decay_mult:
}
param {
lr_mult:
decay_mult:
}
convolution_param {
num_output:
pad:
kernel_size:
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "relu4_2_t"
type: "ReLU"
bottom: "conv4_2_t"
top: "conv4_2_t"
}
layer {
name: "conv4_3_t"
type: "Convolution"
bottom: "conv4_2_t"
top: "conv4_3_t"
param {
lr_mult:
decay_mult:
}
param {
lr_mult:
decay_mult:
}
convolution_param {
num_output:
pad:
kernel_size:
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "relu4_3_t"
type: "ReLU"
bottom: "conv4_3_t"
top: "conv4_3_t"
}
layer {
name: "pool4_t"
type: "Pooling"
bottom: "conv4_3_t"
top: "pool4_t"
pooling_param {
pool: MAX
kernel_size:
stride:
}
}
layer {
name: "conv5_1_t"
type: "Convolution"
bottom: "pool4_t"
top: "conv5_1_t"
param {
lr_mult:
decay_mult:
}
param {
lr_mult:
decay_mult:
}
convolution_param {
num_output:
pad:
kernel_size:
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.0
}
dilation:
}
}
layer {
name: "relu5_1_t"
type: "ReLU"
bottom: "conv5_1_t"
top: "conv5_1_t"
}
layer {
name: "conv5_2_t"
type: "Convolution"
bottom: "conv5_1_t"
top: "conv5_2_t"
param {
lr_mult:
decay_mult:
}
param {
lr_mult:
decay_mult:
}
convolution_param {
num_output:
pad:
kernel_size:
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.0
}
dilation:
}
}
layer {
name: "relu5_2_t"
type: "ReLU"
bottom: "conv5_2_t"
top: "conv5_2_t"
}
layer {
name: "conv5_3_t"
type: "Convolution"
bottom: "conv5_2_t"
top: "conv5_3_t"
param {
lr_mult:
decay_mult:
}
param {
lr_mult:
decay_mult:
}
convolution_param {
num_output:
pad:
kernel_size:
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.0
}
dilation:
}
}
layer {
name: "relu5_3_t"
type: "ReLU"
bottom: "conv5_3_t"
top: "conv5_3_t"
} layer {
name: "conv5_3_m"
type: "Convolution"
bottom: "conv5_3"
top: "conv5_3_m"
propagate_down: true
param {
lr_mult:
decay_mult:
}
param {
lr_mult:
decay_mult:
}
convolution_param {
num_output:
kernel_size:
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value:
}
}
} layer {
name: "relu5_3_m"
type: "ReLU"
bottom: "conv5_3_m"
top: "conv5_3_m"
} layer {
name: "roi_pool_t"
type: "ROIPooling"
bottom: "conv5_3_t"
bottom: "label"
top: "pool_t"
roi_pooling_param {
pooled_w:
pooled_h:
}
propagate_down: false
propagate_down: false
}
layer {
name: "roi_pool_s"
type: "ROIPooling"
bottom: "conv5_3_m"
bottom: "label"
top: "pool_s"
roi_pooling_param {
pooled_w:
pooled_h:
}
propagate_down: true
propagate_down: false
} layer {
name: "mimic_loss"
type: "EuclideanLoss"
bottom: "pool_t"
bottom: "pool_s"
top: "mimic_loss"
propagate_down: false
propagate_down: true
loss_weight:
include {
phase: TRAIN
}
}
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