转载：https://zhuanlan.zhihu.com/p/33075914 MobileNet V2 论文初读
转载：https://blog.csdn.net/wfei101/article/details/79334659 网络模型压缩和优化:MobileNet V2网络结构理解
转载： https://zhuanlan.zhihu.com/p/50045821 mobilenetv1和mobilenetv2的区别
MobileNetV2: Inverted Residuals and Linear Bottlenecks：连接：https://128.84.21.199/pdf/1801.04381.pdf
MobileNet v1中使用的Depthwise Separable Convolution是模型压缩的一个最为经典的策略，它是通过将跨通道的 
卷积换成单通道的 卷积+跨通道的 
卷积来达到此目的的。
MobileNet V2主要的改进有两点：
1、Linear Bottlenecks。因为ReLU的在通道数较少的Feature Map上有非常严重信息损失问题，所以去掉了小维度输出层后面的非线性激活层ReLU，保留更多的特征信息，目的是为了保证模型的表达能力。
2、Inverted Residual block。该结构和传统residual block中维度先缩减再扩增正好相反，因此shotcut也就变成了连接的是维度缩减后的feature map。
相同点：
- 都采用 Depth-wise (DW) 卷积搭配 Point-wise (PW) 卷积的方式来提特征。这两个操作合起来也被称为 Depth-wise Separable Convolution，之前在 Xception 中被广泛使用。这么做的好处是理论上可以成倍的减少卷积层的时间复杂度和空间复杂度。由下式可知，因为卷积核的尺寸
通常远小于输出通道数 ，因此标准卷积的计算复杂度近似为 DW + PW 组合卷积的 倍。由于Depthwise卷积的每个通道Feature Map产生且仅产生一个与之对应的Feature Map，也就是说输出层的Feature Map的channel数量等于输入层的Feature map的数量。因此DepthwiseConv不需要控制输出层的Feature Map的数量，因此并没有num_filters 这个参数，这个参数是和输入特征的channels数相等。
通常远小于输出通道数 
，因此标准卷积的计算复杂度近似为 DW + PW 组合卷积的 
倍。由于Depthwise卷积的每个通道Feature Map产生且仅产生一个与之对应的Feature Map，也就是说输出层的Feature Map的channel数量等于输入层的Feature map的数量。因此standard Convolution运算量：3*3跨通道运算 C*（C*（K**2)*x），其中x为一个kernel核在一个一维的输入特征上运算需要滑动的次数，这里假设卷积核个数和输入通道数都是C；
Depth-wise Separable Convolution运算量：单通道运算（C*（K**2)*x）+ 跨通道1*1卷积 C*(C*(1**2)*x)，，其中x为一个kernel核在一个一维的输入特征上运算需要滑动的次数，这里假设卷积核个数和输入通道数都是C；
Depthwise卷积示意图（3个通道）
主要创新点：
1，Inverted residuals：V2 在 DW 卷积之前新加了一个 1*1 大小PW 卷积。这么做的原因，是因为 DW 卷积由于本身的计算特性决定它自己没有改变通道数的能力，上一层给它多少通道，它就只能输出多少通道。所以如果上一层给的通道数本身很少的话，DW 也只能很委屈的在低维空间提特征，因此效果不够好。现在 V2 为了改善这个问题，给每个 DW 之前都配备了一个 PW，专门用来升维，定义升维系数 t（而在v2中这个值一般是介于
之间的数，在作者的实验中， 
），这样不管输入通道数 
是多是少，经过第一个 PW 升维之后，DW 都是在相对的更高维 ( 
) 进行着辛勤工作的。主要也是为了提取更多的通道信息，得到更多的特征线信息。
2，Linear bottlenecks：V2 去掉了第二个 PW 的激活函数，意思就是bottleneck的输出不接非线性激活层。论文作者称其为 Linear Bottleneck。这么做的原因，是因为作者认为激活函数在高维空间能够有效的增加非线性，而在低维空间时则会破坏特征，不如线性的效果好。由于第二个 PW 的主要功能就是降维，因此按照上面的理论，降维之后就不宜再使用 ReLU6 了。
再看看MobileNetV2的block 与ResNet 的block：主要不同之处就在于，ResNet是：压缩”→“卷积提特征”→“扩张”，MobileNetV2则是Inverted residuals, 即：“扩张”→“卷积提特征”→ “压缩
具体mobilenetV2的宏观结构如下：t表示每个bottleneck的PW层的expand系数，也就是channels扩张系数，
c表示每个bottleneck的输出通道数，也就是每个bottleneck输出的PW的channels数，用于降维，
n表示有多少个bottleneck连接在一起，s表示第一个bottleneck的DW层的stride，表示下采样；
附上mobilenetv2的源码，可以通过netscope: https://ethereon.github.io/netscope/#/editor查看：
name: "MOBILENET_V2"
layer {
name: "data"
type: "ImageData"
top: "data"
top: "label"
include {
phase: TRAIN
}
transform_param {
mirror: true
crop_size: 224
}
image_data_param {
source: "./train.txt"
batch_size: 24
shuffle: false
}
}
layer {
name: "data"
type: "ImageData"
top: "data"
top: "label"
include {
phase: TEST
}
transform_param {
mirror: false
crop_size: 224
}
image_data_param {
source: "./valid.txt"
batch_size: 16
}
}
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 32
bias_term: false
pad: 1
kernel_size: 3
stride: 2
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv1/bn"
type: "BatchNorm"
bottom: "conv1"
top: "conv1/bn"
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
name: "conv1/scale"
type: "Scale"
bottom: "conv1/bn"
top: "conv1/bn"
param {
lr_mult: 1.0
decay_mult: 0.0
}
param {
lr_mult: 1.0
decay_mult: 0.0
}
scale_param {
filler {
value: 0.5
}
bias_term: true
bias_filler {
value: 0
}
l1_lambda: 0.001
}
}
layer {
name: "relu1"
type: "ReLU"
bottom: "conv1/bn"
top: "conv1/bn"
}
layer {
name: "conv2_1/expand"
type: "Convolution"
bottom: "conv1/bn"
top: "conv2_1/expand"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 32
bias_term: false
kernel_size: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv2_1/expand/bn"
type: "BatchNorm"
bottom: "conv2_1/expand"
top: "conv2_1/expand/bn"
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
name: "conv2_1/expand/scale"
type: "Scale"
bottom: "conv2_1/expand/bn"
top: "conv2_1/expand/bn"
param {
lr_mult: 1.0
decay_mult: 0.0
}
param {
lr_mult: 1.0
decay_mult: 0.0
}
scale_param {
filler {
value: 0.5
}
bias_term: true
bias_filler {
value: 0
}
l1_lambda: 0.001
}
}
layer {
name: "relu2_1/expand"
type: "ReLU"
bottom: "conv2_1/expand/bn"
top: "conv2_1/expand/bn"
}
layer {
name: "conv2_1/dwise"
type: "Convolution"
bottom: "conv2_1/expand/bn"
top: "conv2_1/dwise"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 32
bias_term: false
pad: 1
kernel_size: 3
group: 32
weight_filler {
type: "msra"
}
engine: CAFFE
}
}
layer {
name: "conv2_1/dwise/bn"
type: "BatchNorm"
bottom: "conv2_1/dwise"
top: "conv2_1/dwise/bn"
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
name: "conv2_1/dwise/scale"
type: "Scale"
bottom: "conv2_1/dwise/bn"
top: "conv2_1/dwise/bn"
param {
lr_mult: 1.0
decay_mult: 0.0
}
param {
lr_mult: 1.0
decay_mult: 0.0
}
scale_param {
filler {
value: 0.5
}
bias_term: true
bias_filler {
value: 0
}
l1_lambda: 0.000000001
}
}
layer {
name: "relu2_1/dwise"
type: "ReLU"
bottom: "conv2_1/dwise/bn"
top: "conv2_1/dwise/bn"
}
layer {
name: "conv2_1/linear"
type: "Convolution"
bottom: "conv2_1/dwise/bn"
top: "conv2_1/linear"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 16
bias_term: false
kernel_size: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv2_1/linear/bn"
type: "BatchNorm"
bottom: "conv2_1/linear"
top: "conv2_1/linear/bn"
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
name: "conv2_1/linear/scale"
type: "Scale"
bottom: "conv2_1/linear/bn"
top: "conv2_1/linear/bn"
param {
lr_mult: 1.0
decay_mult: 0.0
}
param {
lr_mult: 1.0
decay_mult: 0.0
}
scale_param {
filler {
value: 0.5
}
bias_term: true
bias_filler {
value: 0
}
l1_lambda: 0.000000001
}
}
layer {
name: "conv2_2/expand"
type: "Convolution"
bottom: "conv2_1/linear/bn"
top: "conv2_2/expand"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 96
bias_term: false
kernel_size: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv2_2/expand/bn"
type: "BatchNorm"
bottom: "conv2_2/expand"
top: "conv2_2/expand/bn"
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
name: "conv2_2/expand/scale"
type: "Scale"
bottom: "conv2_2/expand/bn"
top: "conv2_2/expand/bn"
param {
lr_mult: 1.0
decay_mult: 0.0
}
param {
lr_mult: 1.0
decay_mult: 0.0
}
scale_param {
filler {
value: 0.5
}
bias_term: true
bias_filler {
value: 0
}
l1_lambda: 0.001
}
}
layer {
name: "relu2_2/expand"
type: "ReLU"
bottom: "conv2_2/expand/bn"
top: "conv2_2/expand/bn"
}
layer {
name: "conv2_2/dwise"
type: "Convolution"
bottom: "conv2_2/expand/bn"
top: "conv2_2/dwise"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 96
bias_term: false
pad: 1
kernel_size: 3
group: 96
stride: 2
weight_filler {
type: "msra"
}
engine: CAFFE
}
}
layer {
name: "conv2_2/dwise/bn"
type: "BatchNorm"
bottom: "conv2_2/dwise"
top: "conv2_2/dwise/bn"
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
name: "conv2_2/dwise/scale"
type: "Scale"
bottom: "conv2_2/dwise/bn"
top: "conv2_2/dwise/bn"
param {
lr_mult: 1.0
decay_mult: 0.0
}
param {
lr_mult: 1.0
decay_mult: 0.0
}
scale_param {
filler {
value: 0.5
}
bias_term: true
bias_filler {
value: 0
}
l1_lambda: 0.000000001
}
}
layer {
name: "relu2_2/dwise"
type: "ReLU"
bottom: "conv2_2/dwise/bn"
top: "conv2_2/dwise/bn"
}
layer {
name: "conv2_2/linear"
type: "Convolution"
bottom: "conv2_2/dwise/bn"
top: "conv2_2/linear"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 24
bias_term: false
kernel_size: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv2_2/linear/bn"
type: "BatchNorm"
bottom: "conv2_2/linear"
top: "conv2_2/linear/bn"
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
name: "conv2_2/linear/scale"
type: "Scale"
bottom: "conv2_2/linear/bn"
top: "conv2_2/linear/bn"
param {
lr_mult: 1.0
decay_mult: 0.0
}
param {
lr_mult: 1.0
decay_mult: 0.0
}
scale_param {
filler {
value: 0.5
}
bias_term: true
bias_filler {
value: 0
}
l1_lambda: 0.000000001
}
}
layer {
name: "conv3_1/expand"
type: "Convolution"
bottom: "conv2_2/linear/bn"
top: "conv3_1/expand"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 144
bias_term: false
kernel_size: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv3_1/expand/bn"
type: "BatchNorm"
bottom: "conv3_1/expand"
top: "conv3_1/expand/bn"
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
name: "conv3_1/expand/scale"
type: "Scale"
bottom: "conv3_1/expand/bn"
top: "conv3_1/expand/bn"
param {
lr_mult: 1.0
decay_mult: 0.0
}
param {
lr_mult: 1.0
decay_mult: 0.0
}
scale_param {
filler {
value: 0.5
}
bias_term: true
bias_filler {
value: 0
}
l1_lambda: 0.001
}
}
layer {
name: "relu3_1/expand"
type: "ReLU"
bottom: "conv3_1/expand/bn"
top: "conv3_1/expand/bn"
}
layer {
name: "conv3_1/dwise"
type: "Convolution"
bottom: "conv3_1/expand/bn"
top: "conv3_1/dwise"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 144
bias_term: false
pad: 1
kernel_size: 3
group: 144
weight_filler {
type: "msra"
}
engine: CAFFE
}
}
layer {
name: "conv3_1/dwise/bn"
type: "BatchNorm"
bottom: "conv3_1/dwise"
top: "conv3_1/dwise/bn"
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
name: "conv3_1/dwise/scale"
type: "Scale"
bottom: "conv3_1/dwise/bn"
top: "conv3_1/dwise/bn"
param {
lr_mult: 1.0
decay_mult: 0.0
}
param {
lr_mult: 1.0
decay_mult: 0.0
}
scale_param {
filler {
value: 0.5
}
bias_term: true
bias_filler {
value: 0
}
l1_lambda: 0.000000001
}
}
layer {
name: "relu3_1/dwise"
type: "ReLU"
bottom: "conv3_1/dwise/bn"
top: "conv3_1/dwise/bn"
}
layer {
name: "conv3_1/linear"
type: "Convolution"
bottom: "conv3_1/dwise/bn"
top: "conv3_1/linear"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 24
bias_term: false
kernel_size: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv3_1/linear/bn"
type: "BatchNorm"
bottom: "conv3_1/linear"
top: "conv3_1/linear/bn"
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
name: "conv3_1/linear/scale"
type: "Scale"
bottom: "conv3_1/linear/bn"
top: "conv3_1/linear/bn"
param {
lr_mult: 1.0
decay_mult: 0.0
}
param {
lr_mult: 1.0
decay_mult: 0.0
}
scale_param {
filler {
value: 0.5
}
bias_term: true
bias_filler {
value: 0
}
l1_lambda: 0.000000001
}
}
layer {
name: "block_3_1"
type: "Eltwise"
bottom: "conv2_2/linear/bn"
bottom: "conv3_1/linear/bn"
top: "block_3_1"
}
layer {
name: "conv3_2/expand"
type: "Convolution"
bottom: "block_3_1"
top: "conv3_2/expand"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 144
bias_term: false
kernel_size: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv3_2/expand/bn"
type: "BatchNorm"
bottom: "conv3_2/expand"
top: "conv3_2/expand/bn"
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
name: "conv3_2/expand/scale"
type: "Scale"
bottom: "conv3_2/expand/bn"
top: "conv3_2/expand/bn"
param {
lr_mult: 1.0
decay_mult: 0.0
}
param {
lr_mult: 1.0
decay_mult: 0.0
}
scale_param {
filler {
value: 0.5
}
bias_term: true
bias_filler {
value: 0
}
l1_lambda: 0.001
}
}
layer {
name: "relu3_2/expand"
type: "ReLU"
bottom: "conv3_2/expand/bn"
top: "conv3_2/expand/bn"
}
layer {
name: "conv3_2/dwise"
type: "Convolution"
bottom: "conv3_2/expand/bn"
top: "conv3_2/dwise"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 144
bias_term: false
pad: 1
kernel_size: 3
group: 144
stride: 2
weight_filler {
type: "msra"
}
engine: CAFFE
}
}
layer {
name: "conv3_2/dwise/bn"
type: "BatchNorm"
bottom: "conv3_2/dwise"
top: "conv3_2/dwise/bn"
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
name: "conv3_2/dwise/scale"
type: "Scale"
bottom: "conv3_2/dwise/bn"
top: "conv3_2/dwise/bn"
param {
lr_mult: 1.0
decay_mult: 0.0
}
param {
lr_mult: 1.0
decay_mult: 0.0
}
scale_param {
filler {
value: 0.5
}
bias_term: true
bias_filler {
value: 0
}
l1_lambda: 0.000000001
}
}
layer {
name: "relu3_2/dwise"
type: "ReLU"
bottom: "conv3_2/dwise/bn"
top: "conv3_2/dwise/bn"
}
layer {
name: "conv3_2/linear"
type: "Convolution"
bottom: "conv3_2/dwise/bn"
top: "conv3_2/linear"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 32
bias_term: false
kernel_size: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv3_2/linear/bn"
type: "BatchNorm"
bottom: "conv3_2/linear"
top: "conv3_2/linear/bn"
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
name: "conv3_2/linear/scale"
type: "Scale"
bottom: "conv3_2/linear/bn"
top: "conv3_2/linear/bn"
param {
lr_mult: 1.0
decay_mult: 0.0
}
param {
lr_mult: 1.0
decay_mult: 0.0
}
scale_param {
filler {
value: 0.5
}
bias_term: true
bias_filler {
value: 0
}
l1_lambda: 0.000000001
}
}
layer {
name: "conv4_1/expand"
type: "Convolution"
bottom: "conv3_2/linear/bn"
top: "conv4_1/expand"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 192
bias_term: false
kernel_size: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv4_1/expand/bn"
type: "BatchNorm"
bottom: "conv4_1/expand"
top: "conv4_1/expand/bn"
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
name: "conv4_1/expand/scale"
type: "Scale"
bottom: "conv4_1/expand/bn"
top: "conv4_1/expand/bn"
param {
lr_mult: 1.0
decay_mult: 0.0
}
param {
lr_mult: 1.0
decay_mult: 0.0
}
scale_param {
filler {
value: 0.5
}
bias_term: true
bias_filler {
value: 0
}
l1_lambda: 0.001
}
}
layer {
name: "relu4_1/expand"
type: "ReLU"
bottom: "conv4_1/expand/bn"
top: "conv4_1/expand/bn"
}
layer {
name: "conv4_1/dwise"
type: "Convolution"
bottom: "conv4_1/expand/bn"
top: "conv4_1/dwise"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 192
bias_term: false
pad: 1
kernel_size: 3
group: 192
weight_filler {
type: "msra"
}
engine: CAFFE
}
}
layer {
name: "conv4_1/dwise/bn"
type: "BatchNorm"
bottom: "conv4_1/dwise"
top: "conv4_1/dwise/bn"
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
name: "conv4_1/dwise/scale"
type: "Scale"
bottom: "conv4_1/dwise/bn"
top: "conv4_1/dwise/bn"
param {
lr_mult: 1.0
decay_mult: 0.0
}
param {
lr_mult: 1.0
decay_mult: 0.0
}
scale_param {
filler {
value: 0.5
}
bias_term: true
bias_filler {
value: 0
}
l1_lambda: 0.000000001
}
}
layer {
name: "relu4_1/dwise"
type: "ReLU"
bottom: "conv4_1/dwise/bn"
top: "conv4_1/dwise/bn"
}
layer {
name: "conv4_1/linear"
type: "Convolution"
bottom: "conv4_1/dwise/bn"
top: "conv4_1/linear"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 32
bias_term: false
kernel_size: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv4_1/linear/bn"
type: "BatchNorm"
bottom: "conv4_1/linear"
top: "conv4_1/linear/bn"
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
name: "conv4_1/linear/scale"
type: "Scale"
bottom: "conv4_1/linear/bn"
top: "conv4_1/linear/bn"
param {
lr_mult: 1.0
decay_mult: 0.0
}
param {
lr_mult: 1.0
decay_mult: 0.0
}
scale_param {
filler {
value: 0.5
}
bias_term: true
bias_filler {
value: 0
}
l1_lambda: 0.000000001
}
}
layer {
name: "block_4_1"
type: "Eltwise"
bottom: "conv3_2/linear/bn"
bottom: "conv4_1/linear/bn"
top: "block_4_1"
}
layer {
name: "conv4_2/expand"
type: "Convolution"
bottom: "block_4_1"
top: "conv4_2/expand"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 192
bias_term: false
kernel_size: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv4_2/expand/bn"
type: "BatchNorm"
bottom: "conv4_2/expand"
top: "conv4_2/expand/bn"
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
name: "conv4_2/expand/scale"
type: "Scale"
bottom: "conv4_2/expand/bn"
top: "conv4_2/expand/bn"
param {
lr_mult: 1.0
decay_mult: 0.0
}
param {
lr_mult: 1.0
decay_mult: 0.0
}
scale_param {
filler {
value: 0.5
}
bias_term: true
bias_filler {
value: 0
}
l1_lambda: 0.001
}
}
layer {
name: "relu4_2/expand"
type: "ReLU"
bottom: "conv4_2/expand/bn"
top: "conv4_2/expand/bn"
}
layer {
name: "conv4_2/dwise"
type: "Convolution"
bottom: "conv4_2/expand/bn"
top: "conv4_2/dwise"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 192
bias_term: false
pad: 1
kernel_size: 3
group: 192
weight_filler {
type: "msra"
}
engine: CAFFE
}
}
layer {
name: "conv4_2/dwise/bn"
type: "BatchNorm"
bottom: "conv4_2/dwise"
top: "conv4_2/dwise/bn"
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
name: "conv4_2/dwise/scale"
type: "Scale"
bottom: "conv4_2/dwise/bn"
top: "conv4_2/dwise/bn"
param {
lr_mult: 1.0
decay_mult: 0.0
}
param {
lr_mult: 1.0
decay_mult: 0.0
}
scale_param {
filler {
value: 0.5
}
bias_term: true
bias_filler {
value: 0
}
l1_lambda: 0.000000001
}
}
layer {
name: "relu4_2/dwise"
type: "ReLU"
bottom: "conv4_2/dwise/bn"
top: "conv4_2/dwise/bn"
}
layer {
name: "conv4_2/linear"
type: "Convolution"
bottom: "conv4_2/dwise/bn"
top: "conv4_2/linear"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 32
bias_term: false
kernel_size: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv4_2/linear/bn"
type: "BatchNorm"
bottom: "conv4_2/linear"
top: "conv4_2/linear/bn"
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
name: "conv4_2/linear/scale"
type: "Scale"
bottom: "conv4_2/linear/bn"
top: "conv4_2/linear/bn"
param {
lr_mult: 1.0
decay_mult: 0.0
}
param {
lr_mult: 1.0
decay_mult: 0.0
}
scale_param {
filler {
value: 0.5
}
bias_term: true
bias_filler {
value: 0
}
l1_lambda: 0.000000001
}
}
layer {
name: "block_4_2"
type: "Eltwise"
bottom: "block_4_1"
bottom: "conv4_2/linear/bn"
top: "block_4_2"
}
layer {
name: "conv4_3/expand"
type: "Convolution"
bottom: "block_4_2"
top: "conv4_3/expand"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 192
bias_term: false
kernel_size: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv4_3/expand/bn"
type: "BatchNorm"
bottom: "conv4_3/expand"
top: "conv4_3/expand/bn"
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
name: "conv4_3/expand/scale"
type: "Scale"
bottom: "conv4_3/expand/bn"
top: "conv4_3/expand/bn"
param {
lr_mult: 1.0
decay_mult: 0.0
}
param {
lr_mult: 1.0
decay_mult: 0.0
}
scale_param {
filler {
value: 0.5
}
bias_term: true
bias_filler {
value: 0
}
l1_lambda: 0.001
}
}
layer {
name: "relu4_3/expand"
type: "ReLU"
bottom: "conv4_3/expand/bn"
top: "conv4_3/expand/bn"
}
layer {
name: "conv4_3/dwise"
type: "Convolution"
bottom: "conv4_3/expand/bn"
top: "conv4_3/dwise"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 192
bias_term: false
pad: 1
kernel_size: 3
group: 192
weight_filler {
type: "msra"
}
engine: CAFFE
}
}
layer {
name: "conv4_3/dwise/bn"
type: "BatchNorm"
bottom: "conv4_3/dwise"
top: "conv4_3/dwise/bn"
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
name: "conv4_3/dwise/scale"
type: "Scale"
bottom: "conv4_3/dwise/bn"
top: "conv4_3/dwise/bn"
param {
lr_mult: 1.0
decay_mult: 0.0
}
param {
lr_mult: 1.0
decay_mult: 0.0
}
scale_param {
filler {
value: 0.5
}
bias_term: true
bias_filler {
value: 0
}
l1_lambda: 0.000000001
}
}
layer {
name: "relu4_3/dwise"
type: "ReLU"
bottom: "conv4_3/dwise/bn"
top: "conv4_3/dwise/bn"
}
layer {
name: "conv4_3/linear"
type: "Convolution"
bottom: "conv4_3/dwise/bn"
top: "conv4_3/linear"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 64
bias_term: false
kernel_size: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv4_3/linear/bn"
type: "BatchNorm"
bottom: "conv4_3/linear"
top: "conv4_3/linear/bn"
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
name: "conv4_3/linear/scale"
type: "Scale"
bottom: "conv4_3/linear/bn"
top: "conv4_3/linear/bn"
param {
lr_mult: 1.0
decay_mult: 0.0
}
param {
lr_mult: 1.0
decay_mult: 0.0
}
scale_param {
filler {
value: 0.5
}
bias_term: true
bias_filler {
value: 0
}
l1_lambda: 0.000000001
}
}
layer {
name: "conv4_4/expand"
type: "Convolution"
bottom: "conv4_3/linear/bn"
top: "conv4_4/expand"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 384
bias_term: false
kernel_size: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv4_4/expand/bn"
type: "BatchNorm"
bottom: "conv4_4/expand"
top: "conv4_4/expand/bn"
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
name: "conv4_4/expand/scale"
type: "Scale"
bottom: "conv4_4/expand/bn"
top: "conv4_4/expand/bn"
param {
lr_mult: 1.0
decay_mult: 0.0
}
param {
lr_mult: 1.0
decay_mult: 0.0
}
scale_param {
filler {
value: 0.5
}
bias_term: true
bias_filler {
value: 0
}
l1_lambda: 0.001
}
}
layer {
name: "relu4_4/expand"
type: "ReLU"
bottom: "conv4_4/expand/bn"
top: "conv4_4/expand/bn"
}
layer {
name: "conv4_4/dwise"
type: "Convolution"
bottom: "conv4_4/expand/bn"
top: "conv4_4/dwise"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 384
bias_term: false
pad: 1
kernel_size: 3
group: 384
weight_filler {
type: "msra"
}
engine: CAFFE
}
}
layer {
name: "conv4_4/dwise/bn"
type: "BatchNorm"
bottom: "conv4_4/dwise"
top: "conv4_4/dwise/bn"
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
name: "conv4_4/dwise/scale"
type: "Scale"
bottom: "conv4_4/dwise/bn"
top: "conv4_4/dwise/bn"
param {
lr_mult: 1.0
decay_mult: 0.0
}
param {
lr_mult: 1.0
decay_mult: 0.0
}
scale_param {
filler {
value: 0.5
}
bias_term: true
bias_filler {
value: 0
}
l1_lambda: 0.000000001
}
}
layer {
name: "relu4_4/dwise"
type: "ReLU"
bottom: "conv4_4/dwise/bn"
top: "conv4_4/dwise/bn"
}
layer {
name: "conv4_4/linear"
type: "Convolution"
bottom: "conv4_4/dwise/bn"
top: "conv4_4/linear"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 64
bias_term: false
kernel_size: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv4_4/linear/bn"
type: "BatchNorm"
bottom: "conv4_4/linear"
top: "conv4_4/linear/bn"
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
name: "conv4_4/linear/scale"
type: "Scale"
bottom: "conv4_4/linear/bn"
top: "conv4_4/linear/bn"
param {
lr_mult: 1.0
decay_mult: 0.0
}
param {
lr_mult: 1.0
decay_mult: 0.0
}
scale_param {
filler {
value: 0.5
}
bias_term: true
bias_filler {
value: 0
}
l1_lambda: 0.000000001
}
}
layer {
name: "block_4_4"
type: "Eltwise"
bottom: "conv4_3/linear/bn"
bottom: "conv4_4/linear/bn"
top: "block_4_4"
}
layer {
name: "conv4_5/expand"
type: "Convolution"
bottom: "block_4_4"
top: "conv4_5/expand"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 384
bias_term: false
kernel_size: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv4_5/expand/bn"
type: "BatchNorm"
bottom: "conv4_5/expand"
top: "conv4_5/expand/bn"
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
name: "conv4_5/expand/scale"
type: "Scale"
bottom: "conv4_5/expand/bn"
top: "conv4_5/expand/bn"
param {
lr_mult: 1.0
decay_mult: 0.0
}
param {
lr_mult: 1.0
decay_mult: 0.0
}
scale_param {
filler {
value: 0.5
}
bias_term: true
bias_filler {
value: 0
}
l1_lambda: 0.001
}
}
layer {
name: "relu4_5/expand"
type: "ReLU"
bottom: "conv4_5/expand/bn"
top: "conv4_5/expand/bn"
}
layer {
name: "conv4_5/dwise"
type: "Convolution"
bottom: "conv4_5/expand/bn"
top: "conv4_5/dwise"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 384
bias_term: false
pad: 1
kernel_size: 3
group: 384
weight_filler {
type: "msra"
}
engine: CAFFE
}
}
layer {
name: "conv4_5/dwise/bn"
type: "BatchNorm"
bottom: "conv4_5/dwise"
top: "conv4_5/dwise/bn"
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
name: "conv4_5/dwise/scale"
type: "Scale"
bottom: "conv4_5/dwise/bn"
top: "conv4_5/dwise/bn"
param {
lr_mult: 1.0
decay_mult: 0.0
}
param {
lr_mult: 1.0
decay_mult: 0.0
}
scale_param {
filler {
value: 0.5
}
bias_term: true
bias_filler {
value: 0
}
l1_lambda: 0.000000001
}
}
layer {
name: "relu4_5/dwise"
type: "ReLU"
bottom: "conv4_5/dwise/bn"
top: "conv4_5/dwise/bn"
}
layer {
name: "conv4_5/linear"
type: "Convolution"
bottom: "conv4_5/dwise/bn"
top: "conv4_5/linear"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 64
bias_term: false
kernel_size: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv4_5/linear/bn"
type: "BatchNorm"
bottom: "conv4_5/linear"
top: "conv4_5/linear/bn"
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
name: "conv4_5/linear/scale"
type: "Scale"
bottom: "conv4_5/linear/bn"
top: "conv4_5/linear/bn"
param {
lr_mult: 1.0
decay_mult: 0.0
}
param {
lr_mult: 1.0
decay_mult: 0.0
}
scale_param {
filler {
value: 0.5
}
bias_term: true
bias_filler {
value: 0
}
l1_lambda: 0.000000001
}
}
layer {
name: "block_4_5"
type: "Eltwise"
bottom: "block_4_4"
bottom: "conv4_5/linear/bn"
top: "block_4_5"
}
layer {
name: "conv4_6/expand"
type: "Convolution"
bottom: "block_4_5"
top: "conv4_6/expand"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 384
bias_term: false
kernel_size: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv4_6/expand/bn"
type: "BatchNorm"
bottom: "conv4_6/expand"
top: "conv4_6/expand/bn"
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
name: "conv4_6/expand/scale"
type: "Scale"
bottom: "conv4_6/expand/bn"
top: "conv4_6/expand/bn"
param {
lr_mult: 1.0
decay_mult: 0.0
}
param {
lr_mult: 1.0
decay_mult: 0.0
}
scale_param {
filler {
value: 0.5
}
bias_term: true
bias_filler {
value: 0
}
l1_lambda: 0.001
}
}
layer {
name: "relu4_6/expand"
type: "ReLU"
bottom: "conv4_6/expand/bn"
top: "conv4_6/expand/bn"
}
layer {
name: "conv4_6/dwise"
type: "Convolution"
bottom: "conv4_6/expand/bn"
top: "conv4_6/dwise"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 384
bias_term: false
pad: 1
kernel_size: 3
group: 384
weight_filler {
type: "msra"
}
engine: CAFFE
}
}
layer {
name: "conv4_6/dwise/bn"
type: "BatchNorm"
bottom: "conv4_6/dwise"
top: "conv4_6/dwise/bn"
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
name: "conv4_6/dwise/scale"
type: "Scale"
bottom: "conv4_6/dwise/bn"
top: "conv4_6/dwise/bn"
param {
lr_mult: 1.0
decay_mult: 0.0
}
param {
lr_mult: 1.0
decay_mult: 0.0
}
scale_param {
filler {
value: 0.5
}
bias_term: true
bias_filler {
value: 0
}
l1_lambda: 0.000000001
}
}
layer {
name: "relu4_6/dwise"
type: "ReLU"
bottom: "conv4_6/dwise/bn"
top: "conv4_6/dwise/bn"
}
layer {
name: "conv4_6/linear"
type: "Convolution"
bottom: "conv4_6/dwise/bn"
top: "conv4_6/linear"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 64
bias_term: false
kernel_size: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv4_6/linear/bn"
type: "BatchNorm"
bottom: "conv4_6/linear"
top: "conv4_6/linear/bn"
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
name: "conv4_6/linear/scale"
type: "Scale"
bottom: "conv4_6/linear/bn"
top: "conv4_6/linear/bn"
param {
lr_mult: 1.0
decay_mult: 0.0
}
param {
lr_mult: 1.0
decay_mult: 0.0
}
scale_param {
filler {
value: 0.5
}
bias_term: true
bias_filler {
value: 0
}
l1_lambda: 0.000000001
}
}
layer {
name: "block_4_6"
type: "Eltwise"
bottom: "block_4_5"
bottom: "conv4_6/linear/bn"
top: "block_4_6"
}
layer {
name: "conv4_7/expand"
type: "Convolution"
bottom: "block_4_6"
top: "conv4_7/expand"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 384
bias_term: false
kernel_size: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv4_7/expand/bn"
type: "BatchNorm"
bottom: "conv4_7/expand"
top: "conv4_7/expand/bn"
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
name: "conv4_7/expand/scale"
type: "Scale"
bottom: "conv4_7/expand/bn"
top: "conv4_7/expand/bn"
param {
lr_mult: 1.0
decay_mult: 0.0
}
param {
lr_mult: 1.0
decay_mult: 0.0
}
scale_param {
filler {
value: 0.5
}
bias_term: true
bias_filler {
value: 0
}
l1_lambda: 0.001
}
}
layer {
name: "relu4_7/expand"
type: "ReLU"
bottom: "conv4_7/expand/bn"
top: "conv4_7/expand/bn"
}
layer {
name: "conv4_7/dwise"
type: "Convolution"
bottom: "conv4_7/expand/bn"
top: "conv4_7/dwise"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 384
bias_term: false
pad: 1
kernel_size: 3
group: 384
stride: 2
weight_filler {
type: "msra"
}
engine: CAFFE
}
}
layer {
name: "conv4_7/dwise/bn"
type: "BatchNorm"
bottom: "conv4_7/dwise"
top: "conv4_7/dwise/bn"
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
name: "conv4_7/dwise/scale"
type: "Scale"
bottom: "conv4_7/dwise/bn"
top: "conv4_7/dwise/bn"
param {
lr_mult: 1.0
decay_mult: 0.0
}
param {
lr_mult: 1.0
decay_mult: 0.0
}
scale_param {
filler {
value: 0.5
}
bias_term: true
bias_filler {
value: 0
}
l1_lambda: 0.000000001
}
}
layer {
name: "relu4_7/dwise"
type: "ReLU"
bottom: "conv4_7/dwise/bn"
top: "conv4_7/dwise/bn"
}
layer {
name: "conv4_7/linear"
type: "Convolution"
bottom: "conv4_7/dwise/bn"
top: "conv4_7/linear"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 96
bias_term: false
kernel_size: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv4_7/linear/bn"
type: "BatchNorm"
bottom: "conv4_7/linear"
top: "conv4_7/linear/bn"
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
name: "conv4_7/linear/scale"
type: "Scale"
bottom: "conv4_7/linear/bn"
top: "conv4_7/linear/bn"
param {
lr_mult: 1.0
decay_mult: 0.0
}
param {
lr_mult: 1.0
decay_mult: 0.0
}
scale_param {
filler {
value: 0.5
}
bias_term: true
bias_filler {
value: 0
}
l1_lambda: 0.000000001
}
}
layer {
name: "conv5_1/expand"
type: "Convolution"
bottom: "conv4_7/linear/bn"
top: "conv5_1/expand"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 576
bias_term: false
kernel_size: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv5_1/expand/bn"
type: "BatchNorm"
bottom: "conv5_1/expand"
top: "conv5_1/expand/bn"
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
name: "conv5_1/expand/scale"
type: "Scale"
bottom: "conv5_1/expand/bn"
top: "conv5_1/expand/bn"
param {
lr_mult: 1.0
decay_mult: 0.0
}
param {
lr_mult: 1.0
decay_mult: 0.0
}
scale_param {
filler {
value: 0.5
}
bias_term: true
bias_filler {
value: 0
}
l1_lambda: 0.001
}
}
layer {
name: "relu5_1/expand"
type: "ReLU"
bottom: "conv5_1/expand/bn"
top: "conv5_1/expand/bn"
}
layer {
name: "conv5_1/dwise"
type: "Convolution"
bottom: "conv5_1/expand/bn"
top: "conv5_1/dwise"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 576
bias_term: false
pad: 1
kernel_size: 3
group: 576
weight_filler {
type: "msra"
}
engine: CAFFE
}
}
layer {
name: "conv5_1/dwise/bn"
type: "BatchNorm"
bottom: "conv5_1/dwise"
top: "conv5_1/dwise/bn"
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
name: "conv5_1/dwise/scale"
type: "Scale"
bottom: "conv5_1/dwise/bn"
top: "conv5_1/dwise/bn"
param {
lr_mult: 1.0
decay_mult: 0.0
}
param {
lr_mult: 1.0
decay_mult: 0.0
}
scale_param {
filler {
value: 0.5
}
bias_term: true
bias_filler {
value: 0
}
l1_lambda: 0.000000001
}
}
layer {
name: "relu5_1/dwise"
type: "ReLU"
bottom: "conv5_1/dwise/bn"
top: "conv5_1/dwise/bn"
}
layer {
name: "conv5_1/linear"
type: "Convolution"
bottom: "conv5_1/dwise/bn"
top: "conv5_1/linear"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 96
bias_term: false
kernel_size: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv5_1/linear/bn"
type: "BatchNorm"
bottom: "conv5_1/linear"
top: "conv5_1/linear/bn"
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
name: "conv5_1/linear/scale"
type: "Scale"
bottom: "conv5_1/linear/bn"
top: "conv5_1/linear/bn"
param {
lr_mult: 1.0
decay_mult: 0.0
}
param {
lr_mult: 1.0
decay_mult: 0.0
}
scale_param {
filler {
value: 0.5
}
bias_term: true
bias_filler {
value: 0
}
l1_lambda: 0.000000001
}
}
layer {
name: "block_5_1"
type: "Eltwise"
bottom: "conv4_7/linear/bn"
bottom: "conv5_1/linear/bn"
top: "block_5_1"
}
layer {
name: "conv5_2/expand"
type: "Convolution"
bottom: "block_5_1"
top: "conv5_2/expand"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 576
bias_term: false
kernel_size: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv5_2/expand/bn"
type: "BatchNorm"
bottom: "conv5_2/expand"
top: "conv5_2/expand/bn"
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
name: "conv5_2/expand/scale"
type: "Scale"
bottom: "conv5_2/expand/bn"
top: "conv5_2/expand/bn"
param {
lr_mult: 1.0
decay_mult: 0.0
}
param {
lr_mult: 1.0
decay_mult: 0.0
}
scale_param {
filler {
value: 0.5
}
bias_term: true
bias_filler {
value: 0
}
l1_lambda: 0.001
}
}
layer {
name: "relu5_2/expand"
type: "ReLU"
bottom: "conv5_2/expand/bn"
top: "conv5_2/expand/bn"
}
layer {
name: "conv5_2/dwise"
type: "Convolution"
bottom: "conv5_2/expand/bn"
top: "conv5_2/dwise"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 576
bias_term: false
pad: 1
kernel_size: 3
group: 576
weight_filler {
type: "msra"
}
engine: CAFFE
}
}
layer {
name: "conv5_2/dwise/bn"
type: "BatchNorm"
bottom: "conv5_2/dwise"
top: "conv5_2/dwise/bn"
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
name: "conv5_2/dwise/scale"
type: "Scale"
bottom: "conv5_2/dwise/bn"
top: "conv5_2/dwise/bn"
param {
lr_mult: 1.0
decay_mult: 0.0
}
param {
lr_mult: 1.0
decay_mult: 0.0
}
scale_param {
filler {
value: 0.5
}
bias_term: true
bias_filler {
value: 0
}
l1_lambda: 0.000000001
}
}
layer {
name: "relu5_2/dwise"
type: "ReLU"
bottom: "conv5_2/dwise/bn"
top: "conv5_2/dwise/bn"
}
layer {
name: "conv5_2/linear"
type: "Convolution"
bottom: "conv5_2/dwise/bn"
top: "conv5_2/linear"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 96
bias_term: false
kernel_size: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv5_2/linear/bn"
type: "BatchNorm"
bottom: "conv5_2/linear"
top: "conv5_2/linear/bn"
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
name: "conv5_2/linear/scale"
type: "Scale"
bottom: "conv5_2/linear/bn"
top: "conv5_2/linear/bn"
param {
lr_mult: 1.0
decay_mult: 0.0
}
param {
lr_mult: 1.0
decay_mult: 0.0
}
scale_param {
filler {
value: 0.5
}
bias_term: true
bias_filler {
value: 0
}
l1_lambda: 0.000000001
}
}
layer {
name: "block_5_2"
type: "Eltwise"
bottom: "block_5_1"
bottom: "conv5_2/linear/bn"
top: "block_5_2"
}
layer {
name: "conv5_3/expand"
type: "Convolution"
bottom: "block_5_2"
top: "conv5_3/expand"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 576
bias_term: false
kernel_size: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv5_3/expand/bn"
type: "BatchNorm"
bottom: "conv5_3/expand"
top: "conv5_3/expand/bn"
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
name: "conv5_3/expand/scale"
type: "Scale"
bottom: "conv5_3/expand/bn"
top: "conv5_3/expand/bn"
param {
lr_mult: 1.0
decay_mult: 0.0
}
param {
lr_mult: 1.0
decay_mult: 0.0
}
scale_param {
filler {
value: 0.5
}
bias_term: true
bias_filler {
value: 0
}
l1_lambda: 0.001
}
}
layer {
name: "relu5_3/expand"
type: "ReLU"
bottom: "conv5_3/expand/bn"
top: "conv5_3/expand/bn"
}
layer {
name: "conv5_3/dwise"
type: "Convolution"
bottom: "conv5_3/expand/bn"
top: "conv5_3/dwise"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 576
bias_term: false
pad: 1
kernel_size: 3
group: 576
stride: 2
weight_filler {
type: "msra"
}
engine: CAFFE
}
}
layer {
name: "conv5_3/dwise/bn"
type: "BatchNorm"
bottom: "conv5_3/dwise"
top: "conv5_3/dwise/bn"
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
name: "conv5_3/dwise/scale"
type: "Scale"
bottom: "conv5_3/dwise/bn"
top: "conv5_3/dwise/bn"
param {
lr_mult: 1.0
decay_mult: 0.0
}
param {
lr_mult: 1.0
decay_mult: 0.0
}
scale_param {
filler {
value: 0.5
}
bias_term: true
bias_filler {
value: 0
}
l1_lambda: 0.000000001
}
}
layer {
name: "relu5_3/dwise"
type: "ReLU"
bottom: "conv5_3/dwise/bn"
top: "conv5_3/dwise/bn"
}
layer {
name: "conv5_3/linear"
type: "Convolution"
bottom: "conv5_3/dwise/bn"
top: "conv5_3/linear"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 160
bias_term: false
kernel_size: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv5_3/linear/bn"
type: "BatchNorm"
bottom: "conv5_3/linear"
top: "conv5_3/linear/bn"
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
name: "conv5_3/linear/scale"
type: "Scale"
bottom: "conv5_3/linear/bn"
top: "conv5_3/linear/bn"
param {
lr_mult: 1.0
decay_mult: 0.0
}
param {
lr_mult: 1.0
decay_mult: 0.0
}
scale_param {
filler {
value: 0.5
}
bias_term: true
bias_filler {
value: 0
}
l1_lambda: 0.000000001
}
}
layer {
name: "conv6_1/expand"
type: "Convolution"
bottom: "conv5_3/linear/bn"
top: "conv6_1/expand"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 960
bias_term: false
kernel_size: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv6_1/expand/bn"
type: "BatchNorm"
bottom: "conv6_1/expand"
top: "conv6_1/expand/bn"
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
name: "conv6_1/expand/scale"
type: "Scale"
bottom: "conv6_1/expand/bn"
top: "conv6_1/expand/bn"
param {
lr_mult: 1.0
decay_mult: 0.0
}
param {
lr_mult: 1.0
decay_mult: 0.0
}
scale_param {
filler {
value: 0.5
}
bias_term: true
bias_filler {
value: 0
}
l1_lambda: 0.001
}
}
layer {
name: "relu6_1/expand"
type: "ReLU"
bottom: "conv6_1/expand/bn"
top: "conv6_1/expand/bn"
}
layer {
name: "conv6_1/dwise"
type: "Convolution"
bottom: "conv6_1/expand/bn"
top: "conv6_1/dwise"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 960
bias_term: false
pad: 1
kernel_size: 3
group: 960
weight_filler {
type: "msra"
}
engine: CAFFE
}
}
layer {
name: "conv6_1/dwise/bn"
type: "BatchNorm"
bottom: "conv6_1/dwise"
top: "conv6_1/dwise/bn"
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
name: "conv6_1/dwise/scale"
type: "Scale"
bottom: "conv6_1/dwise/bn"
top: "conv6_1/dwise/bn"
param {
lr_mult: 1.0
decay_mult: 0.0
}
param {
lr_mult: 1.0
decay_mult: 0.0
}
scale_param {
filler {
value: 0.5
}
bias_term: true
bias_filler {
value: 0
}
l1_lambda: 0.000000001
}
}
layer {
name: "relu6_1/dwise"
type: "ReLU"
bottom: "conv6_1/dwise/bn"
top: "conv6_1/dwise/bn"
}
layer {
name: "conv6_1/linear"
type: "Convolution"
bottom: "conv6_1/dwise/bn"
top: "conv6_1/linear"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 160
bias_term: false
kernel_size: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv6_1/linear/bn"
type: "BatchNorm"
bottom: "conv6_1/linear"
top: "conv6_1/linear/bn"
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
name: "conv6_1/linear/scale"
type: "Scale"
bottom: "conv6_1/linear/bn"
top: "conv6_1/linear/bn"
param {
lr_mult: 1.0
decay_mult: 0.0
}
param {
lr_mult: 1.0
decay_mult: 0.0
}
scale_param {
filler {
value: 0.5
}
bias_term: true
bias_filler {
value: 0
}
l1_lambda: 0.000000001
}
}
layer {
name: "block_6_1"
type: "Eltwise"
bottom: "conv5_3/linear/bn"
bottom: "conv6_1/linear/bn"
top: "block_6_1"
}
layer {
name: "conv6_2/expand"
type: "Convolution"
bottom: "block_6_1"
top: "conv6_2/expand"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 960
bias_term: false
kernel_size: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv6_2/expand/bn"
type: "BatchNorm"
bottom: "conv6_2/expand"
top: "conv6_2/expand/bn"
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
name: "conv6_2/expand/scale"
type: "Scale"
bottom: "conv6_2/expand/bn"
top: "conv6_2/expand/bn"
param {
lr_mult: 1.0
decay_mult: 0.0
}
param {
lr_mult: 1.0
decay_mult: 0.0
}
scale_param {
filler {
value: 0.5
}
bias_term: true
bias_filler {
value: 0
}
l1_lambda: 0.001
}
}
layer {
name: "relu6_2/expand"
type: "ReLU"
bottom: "conv6_2/expand/bn"
top: "conv6_2/expand/bn"
}
layer {
name: "conv6_2/dwise"
type: "Convolution"
bottom: "conv6_2/expand/bn"
top: "conv6_2/dwise"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 960
bias_term: false
pad: 1
kernel_size: 3
group: 960
weight_filler {
type: "msra"
}
engine: CAFFE
}
}
layer {
name: "conv6_2/dwise/bn"
type: "BatchNorm"
bottom: "conv6_2/dwise"
top: "conv6_2/dwise/bn"
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
name: "conv6_2/dwise/scale"
type: "Scale"
bottom: "conv6_2/dwise/bn"
top: "conv6_2/dwise/bn"
param {
lr_mult: 1.0
decay_mult: 0.0
}
param {
lr_mult: 1.0
decay_mult: 0.0
}
scale_param {
filler {
value: 0.5
}
bias_term: true
bias_filler {
value: 0
}
l1_lambda: 0.000000001
}
}
layer {
name: "relu6_2/dwise"
type: "ReLU"
bottom: "conv6_2/dwise/bn"
top: "conv6_2/dwise/bn"
}
layer {
name: "conv6_2/linear"
type: "Convolution"
bottom: "conv6_2/dwise/bn"
top: "conv6_2/linear"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 160
bias_term: false
kernel_size: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv6_2/linear/bn"
type: "BatchNorm"
bottom: "conv6_2/linear"
top: "conv6_2/linear/bn"
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
name: "conv6_2/linear/scale"
type: "Scale"
bottom: "conv6_2/linear/bn"
top: "conv6_2/linear/bn"
param {
lr_mult: 1.0
decay_mult: 0.0
}
param {
lr_mult: 1.0
decay_mult: 0.0
}
scale_param {
filler {
value: 0.5
}
bias_term: true
bias_filler {
value: 0
}
l1_lambda: 0.000000001
}
}
layer {
name: "block_6_2"
type: "Eltwise"
bottom: "block_6_1"
bottom: "conv6_2/linear/bn"
top: "block_6_2"
}
layer {
name: "conv6_3/expand"
type: "Convolution"
bottom: "block_6_2"
top: "conv6_3/expand"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 960
bias_term: false
kernel_size: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv6_3/expand/bn"
type: "BatchNorm"
bottom: "conv6_3/expand"
top: "conv6_3/expand/bn"
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
name: "conv6_3/expand/scale"
type: "Scale"
bottom: "conv6_3/expand/bn"
top: "conv6_3/expand/bn"
param {
lr_mult: 1.0
decay_mult: 0.0
}
param {
lr_mult: 1.0
decay_mult: 0.0
}
scale_param {
filler {
value: 0.5
}
bias_term: true
bias_filler {
value: 0
}
l1_lambda: 0.001
}
}
layer {
name: "relu6_3/expand"
type: "ReLU"
bottom: "conv6_3/expand/bn"
top: "conv6_3/expand/bn"
}
layer {
name: "conv6_3/dwise"
type: "Convolution"
bottom: "conv6_3/expand/bn"
top: "conv6_3/dwise"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 960
bias_term: false
pad: 1
kernel_size: 3
group: 960
weight_filler {
type: "msra"
}
engine: CAFFE
}
}
layer {
name: "conv6_3/dwise/bn"
type: "BatchNorm"
bottom: "conv6_3/dwise"
top: "conv6_3/dwise/bn"
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
name: "conv6_3/dwise/scale"
type: "Scale"
bottom: "conv6_3/dwise/bn"
top: "conv6_3/dwise/bn"
param {
lr_mult: 1.0
decay_mult: 0.0
}
param {
lr_mult: 1.0
decay_mult: 0.0
}
scale_param {
filler {
value: 0.5
}
bias_term: true
bias_filler {
value: 0
}
l1_lambda: 0.000000001
}
}
layer {
name: "relu6_3/dwise"
type: "ReLU"
bottom: "conv6_3/dwise/bn"
top: "conv6_3/dwise/bn"
}
layer {
name: "conv6_3/linear"
type: "Convolution"
bottom: "conv6_3/dwise/bn"
top: "conv6_3/linear"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 320
bias_term: false
kernel_size: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv6_3/linear/bn"
type: "BatchNorm"
bottom: "conv6_3/linear"
top: "conv6_3/linear/bn"
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
name: "conv6_3/linear/scale"
type: "Scale"
bottom: "conv6_3/linear/bn"
top: "conv6_3/linear/bn"
param {
lr_mult: 1.0
decay_mult: 0.0
}
param {
lr_mult: 1.0
decay_mult: 0.0
}
scale_param {
filler {
value: 0.5
}
bias_term: true
bias_filler {
value: 0
}
l1_lambda: 0.000000001
}
}
layer {
name: "conv6_4"
type: "Convolution"
bottom: "conv6_3/linear/bn"
top: "conv6_4"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 1280
bias_term: false
kernel_size: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "conv6_4/bn"
type: "BatchNorm"
bottom: "conv6_4"
top: "conv6_4/bn"
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
name: "conv6_4/scale"
type: "Scale"
bottom: "conv6_4/bn"
top: "conv6_4/bn"
param {
lr_mult: 1.0
decay_mult: 0.0
}
param {
lr_mult: 1.0
decay_mult: 0.0
}
scale_param {
filler {
value: 0.5
}
bias_term: true
bias_filler {
value: 0
}
l1_lambda: 0.001
}
}
layer {
name: "relu6_4"
type: "ReLU"
bottom: "conv6_4/bn"
top: "conv6_4/bn"
}
layer {
name: "pool6"
type: "Pooling"
bottom: "conv6_4/bn"
top: "pool6"
pooling_param {
pool: AVE
global_pooling: true
}
}
layer {
name: "food_fc7"
type: "Convolution"
bottom: "pool6"
top: "fc7"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
#num_output: 143
num_output: 43
kernel_size: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "loss"
type: "SoftmaxWithLoss"
bottom: "fc7"
bottom: "label"
top: "loss"
}
layer {
name: "top1/acc"
type: "Accuracy"
bottom: "fc7"
bottom: "label"
top: "top1/acc"
include {
phase: TEST
}
}
layer {
name: "top5/acc"
type: "Accuracy"
bottom: "fc7"
bottom: "label"
top: "top5/acc"
include {
phase: TEST
}
accuracy_param {
top_k: 5
}
}