About the GenerateAugmentationParameters layer

[thbupt]

I have some questions about the GenerateAugmentationParameters layer. In the following FlowNet2.prototxt, it seems that this layer can generate more augmentation parameters than DataAugmentation layer if the aug_.mode() == "add", such as the brightness, gamma, constract and color, however, I think they can also be generated in the DataAugmentation layer, so why not use the DataAugmentation layer to generate all the parameters?
Besides in the FlowNetC.prototxt, I found the spatial transforming paramters (rotate, translate, zoom )are both generated by the two layers. If the aug_.mode() == "add", it seems there are two different groups of spatial transforming paramters in the output blob (that is blob8 in the FlowNetC.prototxt).

FlowNet2.prototxt
layer {
name: "img0s_aug"
type: "DataAugmentation"
bottom: "blob13"
top: "img0_aug"
top: "blob16"
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: 0.2
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
}
}
}
layer {
name: "aug_params1"
type: "GenerateAugmentationParameters"
bottom: "blob16"
bottom: "blob13"
bottom: "img0_aug"
top: "blob17"
augmentation_param {
augment_during_test: false
gamma {
rand_type: "gaussian_bernoulli"
exp: true
mean: 0
spread: 0.02
prob: 1.0
}
brightness {
rand_type: "gaussian_bernoulli"
exp: false
mean: 0
spread: 0.02
prob: 1.0
}
contrast {
rand_type: "gaussian_bernoulli"
exp: true
mean: 0
spread: 0.02
prob: 1.0
}
color {
rand_type: "gaussian_bernoulli"
exp: true
mean: 0
spread: 0.02
prob: 1.0
}
}
coeff_schedule_param {
half_life: 50000
initial_coeff: 0.5
final_coeff: 1
}
}

FlowNetC.prototxt
layer {
name: "img0s_aug"
type: "DataAugmentation"
bottom: "blob4"
top: "img0_aug"
top: "blob7"
propagate_down: false
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
}
}
}
layer {
name: "aug_params1"
type: "GenerateAugmentationParameters"
bottom: "blob7"
bottom: "blob4"
bottom: "img0_aug"
top: "blob8"
augmentation_param {
augment_during_test: false
translate {
rand_type: "gaussian_bernoulli"
exp: false
mean: 0
spread: 0.03
prob: 1.0
}
rotate {
rand_type: "gaussian_bernoulli"
exp: false
mean: 0
spread: 0.03
prob: 1.0
}
zoom {
rand_type: "gaussian_bernoulli"
exp: true
mean: 0
spread: 0.03
prob: 1.0
}
gamma {
rand_type: "gaussian_bernoulli"
exp: true
mean: 0
spread: 0.02
prob: 1.0
}
brightness {
rand_type: "gaussian_bernoulli"
exp: false
mean: 0
spread: 0.02
prob: 1.0
}
contrast {
rand_type: "gaussian_bernoulli"
exp: true
mean: 0
spread: 0.02
prob: 1.0
}
color {
rand_type: "gaussian_bernoulli"
exp: true
mean: 0
spread: 0.02
prob: 1.0
}
}
coeff_schedule_param {
half_life: 50000
initial_coeff: 0.5
final_coeff: 1
}
}

[eddy-ilg]

Hi, the DataAugmentation layer generates parameters from scratch and directly augments data. The GenerateAugmentationParameters layer only generates parameters. It can additionally take existing parameters and modify them by adding transformations.