MobileNet V2深入理解
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。
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:
}
image_data_param {
source: "./train.txt"
batch_size:
shuffle: false
}
}
layer {
name: "data"
type: "ImageData"
top: "data"
top: "label"
include {
phase: TEST
}
transform_param {
mirror: false
crop_size:
}
image_data_param {
source: "./valid.txt"
batch_size:
}
}
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output:
bias_term: false
pad:
kernel_size:
stride:
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:
}
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:
bias_term: false
kernel_size:
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:
}
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:
bias_term: false
pad:
kernel_size:
group:
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:
}
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:
bias_term: false
kernel_size:
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:
}
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:
bias_term: false
kernel_size:
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:
}
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:
bias_term: false
pad:
kernel_size:
group:
stride:
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:
}
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:
bias_term: false
kernel_size:
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:
}
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:
bias_term: false
kernel_size:
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:
}
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:
bias_term: false
pad:
kernel_size:
group:
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:
}
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:
bias_term: false
kernel_size:
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:
}
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:
bias_term: false
kernel_size:
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:
}
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:
bias_term: false
pad:
kernel_size:
group:
stride:
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:
}
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:
bias_term: false
kernel_size:
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:
}
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:
bias_term: false
kernel_size:
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:
}
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:
bias_term: false
pad:
kernel_size:
group:
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:
}
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:
bias_term: false
kernel_size:
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:
}
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:
bias_term: false
kernel_size:
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:
}
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:
bias_term: false
pad:
kernel_size:
group:
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:
}
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:
bias_term: false
kernel_size:
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:
}
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:
bias_term: false
kernel_size:
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:
}
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:
bias_term: false
pad:
kernel_size:
group:
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:
}
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:
bias_term: false
kernel_size:
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:
}
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:
bias_term: false
kernel_size:
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:
}
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:
bias_term: false
pad:
kernel_size:
group:
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:
}
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:
bias_term: false
kernel_size:
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:
}
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:
bias_term: false
kernel_size:
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:
}
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:
bias_term: false
pad:
kernel_size:
group:
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:
}
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:
bias_term: false
kernel_size:
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:
}
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:
bias_term: false
kernel_size:
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:
}
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:
bias_term: false
pad:
kernel_size:
group:
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:
}
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:
bias_term: false
kernel_size:
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:
}
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:
bias_term: false
kernel_size:
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:
}
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:
bias_term: false
pad:
kernel_size:
group:
stride:
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:
}
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:
bias_term: false
kernel_size:
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:
}
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:
bias_term: false
kernel_size:
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:
}
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:
bias_term: false
pad:
kernel_size:
group:
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:
}
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:
bias_term: false
kernel_size:
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:
}
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:
bias_term: false
kernel_size:
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:
}
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:
bias_term: false
pad:
kernel_size:
group:
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:
}
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:
bias_term: false
kernel_size:
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:
}
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:
bias_term: false
kernel_size:
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:
}
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:
bias_term: false
pad:
kernel_size:
group:
stride:
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:
}
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:
bias_term: false
kernel_size:
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:
}
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:
bias_term: false
kernel_size:
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:
}
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:
bias_term: false
pad:
kernel_size:
group:
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:
}
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:
bias_term: false
kernel_size:
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:
}
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:
bias_term: false
kernel_size:
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:
}
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:
bias_term: false
pad:
kernel_size:
group:
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:
}
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:
bias_term: false
kernel_size:
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:
}
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:
bias_term: false
kernel_size:
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:
}
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:
bias_term: false
pad:
kernel_size:
group:
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:
}
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:
bias_term: false
kernel_size:
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:
}
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:
bias_term: false
kernel_size:
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:
}
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:
num_output:
kernel_size:
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:
}
}
MobileNet V2深入理解的更多相关文章
- 轻量化模型：MobileNet v2
MobileNet v2 论文链接:https://arxiv.org/abs/1801.04381 MobileNet v2是对MobileNet v1的改进,也是一个轻量化模型. 关于Mobile ...
- mobile-net v2 学习记录。我是菜鸡！
声明:只是自己写博客总结下,不保证正确性,我的理解很可能是错的.. 首先,mobile net V1的主要特点是: 1.深度可分离卷积.用depth-wise convolution来分层过滤特征,再 ...
- YOLO V2论文理解
概述 YOLO(You Only Look Once: Unified, Real-Time Object Detection)从v1版本进化到了v2版本,作者在darknet主页先行一步放出源代码, ...
- MobileNet V2
https://zhuanlan.zhihu.com/p/33075914 http://blog.csdn.net/u011995719/article/details/79135818 https ...
- 卷积神经网络学习笔记——轻量化网络MobileNet系列（V1，V2，V3）
完整代码及其数据,请移步小编的GitHub地址 传送门:请点击我 如果点击有误:https://github.com/LeBron-Jian/DeepLearningNote 这里结合网络的资料和Mo ...
- 从Inception v1,v2,v3,v4,RexNeXt到Xception再到MobileNets,ShuffleNet,MobileNetV2
from:https://blog.csdn.net/qq_14845119/article/details/73648100 Inception v1的网络,主要提出了Inceptionmodule ...
- 深度学习笔记（十一）网络 Inception, Xception, MobileNet, ShuffeNet, ResNeXt, SqueezeNet, EfficientNet, MixConv
1. Abstract 本文旨在简单介绍下各种轻量级网络,纳尼?!好吧,不限于轻量级 2. Introduction 2.1 Inception 在最初的版本 Inception/GoogleNet, ...
- MobileNet系列
最近一段时间,重新研读了谷歌的mobilenet系列,对该系列有新的认识. 1.MobileNet V1 这篇论文是谷歌在2017年提出了,专注于移动端或者嵌入式设备中的轻量级CNN网络.该论文最大的 ...
- 轻量级卷积神经网络——MobileNet
谷歌论文题目: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications 其他参考: CNN ...
随机推荐
- Go语言——值方法 & 指针方法
1 package main import ( "fmt" "sort" ) type SortableStrings []string type Sortab ...
- aiops常用算法
1.数据聚合/关联技术 概念聚类算法AOI分类算法K近邻/贝叶斯分类器/logistic回归(LR)/支持向量机(SVM)/随机森林(RF) 2.数据异常点检测技术独立森林算法 3.故障诊断和分析策略 ...
- linux mint安装mysql-8.0.16
1.使用通用二进制文件在Unix / Linux上安装MySQL 下载的文件:mysql-8.0.16-linux-glibc2.12-x86_64.tar.xz 注意: 如果您以前使用操作系统本机程 ...
- ckeditor粘贴word文档图片的思路
由于工作需要必须将word文档内容粘贴到编辑器中使用 但发现word中的图片粘贴后变成了file:///xxxx.jpg这种内容,如果上传到服务器后其他人也访问不了,网上找了很多编辑器发现没有一个能直 ...
- learning armbian steps(4) ----- armbian 技术内幕
在学习新的框架之前,肯定有一个原型机,通过最普通的指令来实现其功能. 做到这一点之后,所有的东西都不在是秘密,缺的时间进行系统深入的学习. 其实可以自已先来手动构建一个原生的arm 文件系统,基于qe ...
- F. Make Them Similar （ 暴力折半枚举 + 小技巧 ）
传送门 题意: 给你 n 个数 a[ 1 ] ~ a[ n ], n <= 100: 让你找一个 x , 使得 a[ 1 ] = a[ 1 ] ^ x ~ a[ n ] = a[ n ] ^ ...
- Codevs 2492 上帝造题的七分钟 2(线段树)
时间限制: 1 s 空间限制: 64000 KB 题目等级 : 大师 Master 题目描述 Description XLk觉得<上帝造题的七分钟>不太过瘾,于是有了第二部. " ...
- pause函数
pause函数 调用该函数可以造成进程主动挂起,等待信号唤醒.调用该系统调用的进程将处于阻塞状态(主动放弃cpu) 直到有信号递达将其唤醒. int pause(void); 返回值:-1 并 ...
- C# 窗体 类似framest 左侧点击右侧显示 左侧菜单右侧显示
首先托一个splitContainer调节大小位置 然后进行再新创建一个窗体名为add 在左侧拖入button按钮 进入代码阶段 更改属性 public Main() { InitializeComp ...
- Docker安装redis3.2
1.拉取redis3.2镜像 2.使用docker images查看拉去下来的镜像 3.运行容器,命令如下 docker run -p : -v $PWD/data:/data -d redis:3. ...