“Silence”层的作用:
如果没有这一层,则如果某个变量在后续没有被用到,就会被打印出来。silcence层将使得这个变量不被打印
layer {
name: "SilenceUnused1"
type: "Silence"
bottom: "unused1"
}
下面一层 type: "Eltwise" layer的作用是,将图像的取值从0~255变为0~1
layer {
name: "Eltwise1"
type: "Eltwise"
bottom: "blob0"
top: "blob3"
eltwise_param {
operation: SUM
coeff: 0.00392156862745098
}
}
接下来, type: "DataAugmentation" , type: "GenerateAugmentationParameters", type: "FlowAugmentation" :
type: "DataAugmentation" 为对输入的数据进行扩增,有两种输入扩增要求的方式,一是在layer中指明:
layer {
name: "img0s_aug"
type: "DataAugmentation"
bottom: "blob3"
top: "img0_aug"
top: "blob6"
augmentation_param {
max_multiplier: 1
augment_during_test: false
recompute_mean: 1000
mean_per_pixel: false
translate {
rand_type: "uniform_bernoulli"
exp: false
mean: 0
spread: 0.4
prob: 1.0
}
rotate {
rand_type: "uniform_bernoulli"
exp: false
mean: 0
spread: 0.4
prob: 1.0
}
zoom {
rand_type: "uniform_bernoulli"
exp: true
mean: 0.2
spread: 0.4
prob: 1.0
}
squeeze {
rand_type: "uniform_bernoulli"
exp: true
mean: 0
spread: 0.3
prob: 1.0
}
lmult_pow {
rand_type: "uniform_bernoulli"
exp: true
mean: -0.2
spread: 0.4
prob: 1.0
}
lmult_mult {
rand_type: "uniform_bernoulli"
exp: true
mean: 0.0
spread: 0.4
prob: 1.0
}
lmult_add {
rand_type: "uniform_bernoulli"
exp: false
mean: 0
spread: 0.03
prob: 1.0
}
sat_pow {
rand_type: "uniform_bernoulli"
exp: true
mean: 0
spread: 0.4
prob: 1.0
}
sat_mult {
rand_type: "uniform_bernoulli"
exp: true
mean: -0.3
spread: 0.5
prob: 1.0
}
sat_add {
rand_type: "uniform_bernoulli"
exp: false
mean: 0
spread: 0.03
prob: 1.0
}
col_pow {
rand_type: "gaussian_bernoulli"
exp: true
mean: 0
spread: 0.4
prob: 1.0
}
col_mult {
rand_type: "gaussian_bernoulli"
exp: true
mean: 0
spread: 0.2
prob: 1.0
}
col_add {
rand_type: "gaussian_bernoulli"
exp: false
mean: 0
spread: 0.02
prob: 1.0
}
ladd_pow {
rand_type: "gaussian_bernoulli"
exp: true
mean: 0
spread: 0.4
prob: 1.0
}
ladd_mult {
rand_type: "gaussian_bernoulli"
exp: true
mean: 0.0
spread: 0.4
prob: 1.0
}
ladd_add {
rand_type: "gaussian_bernoulli"
exp: false
mean: 0
spread: 0.04
prob: 1.0
}
col_rotate {
rand_type: "uniform_bernoulli"
exp: false
mean: 0
spread: 1
prob: 1.0
}
crop_width: 448
crop_height: 320
chromatic_eigvec: 0.51
chromatic_eigvec: 0.56
chromatic_eigvec: 0.65
chromatic_eigvec: 0.79
chromatic_eigvec: 0.01
chromatic_eigvec: -0.62
chromatic_eigvec: 0.35
chromatic_eigvec: -0.83
chromatic_eigvec: 0.44
noise {
rand_type: "uniform_bernoulli"
exp: false
mean: 0.03
spread: 0.03
prob: 1.0
}
}
}
二是,借助另一层的参数:
layer {
name: "img1s_aug"
type: "DataAugmentation"
bottom: "blob4"
bottom: "blob7" #通过 Image 0的扩张函数来获得同样的参数
top: "img1_aug"
augmentation_param {
max_multiplier: 1
augment_during_test: false
recompute_mean: 1000
mean_per_pixel: false
crop_width: 448
crop_height: 320
chromatic_eigvec: 0.51
chromatic_eigvec: 0.56
chromatic_eigvec: 0.65
chromatic_eigvec: 0.79
chromatic_eigvec: 0.01
chromatic_eigvec: -0.62
chromatic_eigvec: 0.35
chromatic_eigvec: -0.83
chromatic_eigvec: 0.44
}
}
type: "GenerateAugmentationParameters":生成适合于当前图片的变换参数
type: "FlowAugmentation" :按照图像的变换,对flow也进行相应的变换