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init - 初始化项目

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Lee Nony
2022-05-06 02:00:56 +08:00
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# Wechat QRCode Model
微信二维码的模型包
## 参考
原地址： https://github.com/WeChatCV/opencv_3rdparty

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layer {
name: "data"
type: "Input"
top: "data"
input_param {
shape {
dim: 1
dim: 1
dim: 224
dim: 224
}
}
}
layer {
name: "conv0"
type: "Convolution"
bottom: "data"
top: "conv0"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 1.0
decay_mult: 0.0
}
convolution_param {
num_output: 32
bias_term: true
pad: 1
kernel_size: 3
group: 1
stride: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv0/lrelu"
type: "ReLU"
bottom: "conv0"
top: "conv0"
relu_param {
negative_slope: 0.05000000074505806
}
}
layer {
name: "db1/reduce"
type: "Convolution"
bottom: "conv0"
top: "db1/reduce"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 1.0
decay_mult: 0.0
}
convolution_param {
num_output: 8
bias_term: true
pad: 0
kernel_size: 1
group: 1
stride: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "db1/reduce/lrelu"
type: "ReLU"
bottom: "db1/reduce"
top: "db1/reduce"
relu_param {
negative_slope: 0.05000000074505806
}
}
layer {
name: "db1/3x3"
type: "Convolution"
bottom: "db1/reduce"
top: "db1/3x3"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 1.0
decay_mult: 0.0
}
convolution_param {
num_output: 8
bias_term: true
pad: 1
kernel_size: 3
group: 8
stride: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "db1/3x3/lrelu"
type: "ReLU"
bottom: "db1/3x3"
top: "db1/3x3"
relu_param {
negative_slope: 0.05000000074505806
}
}
layer {
name: "db1/1x1"
type: "Convolution"
bottom: "db1/3x3"
top: "db1/1x1"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 1.0
decay_mult: 0.0
}
convolution_param {
num_output: 32
bias_term: true
pad: 0
kernel_size: 1
group: 1
stride: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "db1/1x1/lrelu"
type: "ReLU"
bottom: "db1/1x1"
top: "db1/1x1"
relu_param {
negative_slope: 0.05000000074505806
}
}
layer {
name: "db1/concat"
type: "Concat"
bottom: "conv0"
bottom: "db1/1x1"
top: "db1/concat"
concat_param {
axis: 1
}
}
layer {
name: "db2/reduce"
type: "Convolution"
bottom: "db1/concat"
top: "db2/reduce"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 1.0
decay_mult: 0.0
}
convolution_param {
num_output: 8
bias_term: true
pad: 0
kernel_size: 1
group: 1
stride: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "db2/reduce/lrelu"
type: "ReLU"
bottom: "db2/reduce"
top: "db2/reduce"
relu_param {
negative_slope: 0.05000000074505806
}
}
layer {
name: "db2/3x3"
type: "Convolution"
bottom: "db2/reduce"
top: "db2/3x3"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 1.0
decay_mult: 0.0
}
convolution_param {
num_output: 8
bias_term: true
pad: 1
kernel_size: 3
group: 8
stride: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "db2/3x3/lrelu"
type: "ReLU"
bottom: "db2/3x3"
top: "db2/3x3"
relu_param {
negative_slope: 0.05000000074505806
}
}
layer {
name: "db2/1x1"
type: "Convolution"
bottom: "db2/3x3"
top: "db2/1x1"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 1.0
decay_mult: 0.0
}
convolution_param {
num_output: 32
bias_term: true
pad: 0
kernel_size: 1
group: 1
stride: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "db2/1x1/lrelu"
type: "ReLU"
bottom: "db2/1x1"
top: "db2/1x1"
relu_param {
negative_slope: 0.05000000074505806
}
}
layer {
name: "db2/concat"
type: "Concat"
bottom: "db1/concat"
bottom: "db2/1x1"
top: "db2/concat"
concat_param {
axis: 1
}
}
layer {
name: "upsample/reduce"
type: "Convolution"
bottom: "db2/concat"
top: "upsample/reduce"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 1.0
decay_mult: 0.0
}
convolution_param {
num_output: 32
bias_term: true
pad: 0
kernel_size: 1
group: 1
stride: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "upsample/reduce/lrelu"
type: "ReLU"
bottom: "upsample/reduce"
top: "upsample/reduce"
relu_param {
negative_slope: 0.05000000074505806
}
}
layer {
name: "upsample/deconv"
type: "Deconvolution"
bottom: "upsample/reduce"
top: "upsample/deconv"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 1.0
decay_mult: 0.0
}
convolution_param {
num_output: 32
bias_term: true
pad: 1
kernel_size: 3
group: 32
stride: 2
weight_filler {
type: "msra"
}
}
}
layer {
name: "upsample/lrelu"
type: "ReLU"
bottom: "upsample/deconv"
top: "upsample/deconv"
relu_param {
negative_slope: 0.05000000074505806
}
}
layer {
name: "upsample/rec"
type: "Convolution"
bottom: "upsample/deconv"
top: "upsample/rec"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 1.0
decay_mult: 0.0
}
convolution_param {
num_output: 1
bias_term: true
pad: 0
kernel_size: 1
group: 1
stride: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "nearest"
type: "Deconvolution"
bottom: "data"
top: "nearest"
param {
lr_mult: 0.0
decay_mult: 0.0
}
convolution_param {
num_output: 1
bias_term: false
pad: 0
kernel_size: 2
group: 1
stride: 2
weight_filler {
type: "constant"
value: 1.0
}
}
}
layer {
name: "Crop1"
type: "Crop"
bottom: "nearest"
bottom: "upsample/rec"
top: "Crop1"
}
layer {
name: "fc"
type: "Eltwise"
bottom: "Crop1"
bottom: "upsample/rec"
top: "fc"
eltwise_param {
operation: SUM
}
}