data augmentation horizontal flip

[shimhyeonwoo]

I found there is a parameter 'mirror' in data augmentation layer.
I guess it's about horizontal flip of image and flow data.
( I hope to add horizontal flip augmenting input data, during training)
However, I can't use it properly.
I tried to understand the source code several days, but i couldn't.

If I want add horizontal flip augmentation during training, how can i edit the augmentation layer parameter?

Thank you.

[Nikolaus Mayer]

Hi,

to use the horizontal flip augmentation, add this to your augmentation layer's prototxt, in the augmentation_param section:

mirror {
  rand_type: "uniform_bernoulli"
  exp: false
  mean: XXX
  spread: XXX
  prob: 1.0
}

You might also need to add it to the GenerateAugmentationParameters layer. Note that you need to specify the "XXX" parameters which determine when this augmentation triggers.

[shimhyeonwoo]

I really appreciate for your help!

[shimhyeonwoo]

About adjusting "XXX" parameters...

refering to "message AugmentationCoeff" in "caffe.proto", the default value of "float mirror" is zero:

message AugmentationCoeff {
  // Spatial
  optional float mirror = 1 [default = 0];
  optional float dx = 2 [default = 0];
  optional float dy = 3 [default = 0];
  optional float angle = 4 [default = 0];
  optional float zoom_x = 5 [default = 1];
  optional float zoom_y = 6 [default = 1];

And line 138, in "augmentation_layer_base.cpp", it seems I can use mirror augmentation when "float mirror" is not zero. maybe it means, when the "mirror" is not zero it always flip data :

            // move the origin and mirror
            if (coeff.mirror()) {
                x1 = - static_cast<Dtype>(x) + .5 * static_cast<Dtype>(cropped_width);
                y1 =   static_cast<Dtype>(y) - .5 * static_cast<Dtype>(cropped_height);
            }  else {
                x1 =   static_cast<Dtype>(x) - .5 * static_cast<Dtype>(cropped_width);
                y1 =   static_cast<Dtype>(y) - .5 * static_cast<Dtype>(cropped_height);
            }

If my opinion were right, and I want use horizontal flip with only, for example, 30%, how about use the parameter like this?

mirror {
  rand_type: "uniform_bernoulli"
  exp: false
  mean: 1
  spread: 0
  prob: 0.3
}

or

mirror {
  rand_type: "uniform_bernoulli"
  exp: false
  mean: 0
  spread: 0
  prob: 0.7
}

[Nikolaus Mayer]

I want use horizontal flip with only, for example, 30%

You can choose from different distributions; check rng.cpp for a list and the implementations. If you want a fixed 30% probability for a binary decision, you might want to use the bernoulli variant with the prob parameter set to 0.3.

[shimhyeonwoo]

Oh, there is more appropriate distribution.

mirror {
  rand_type: "bernoulli"
  prob: 0.3
}

Thank you.