Non-Beginner Discussion

Part 1 2022
45

I’m trying to use RandomErasing() and apply it only on the input images, while it applies it on both input and target.

Like this guy tried to do this here:

I found out that this augmentation is being called twice:

  • once for all of the input images in a batch;
  • and another time for all of the target images of the batch.

I need to make it be called only once, for the input.

Here’s the source code:

# Cell
def cutout_gaussian(x, areas):
    "Replace all `areas` in `x` with N(0,1) noise"
    chan,img_h,img_w = x.shape[-3:]
    for rl,rh,cl,ch in areas: x[...,rl:rh, cl:ch].normal_()
    return x

# Cell
def _slice(area, sz):
    bound = int(round(math.sqrt(area)))
    loc = random.randint(0, max(sz-bound, 0))
    return loc,loc+bound

# Cell
class RandomErasing(RandTransform):
    "Randomly selects a rectangle region in an image and randomizes its pixels."
    order = 100 # After Normalize
    def __init__(self, p=0.5, sl=0., sh=0.3, min_aspect=0.3, max_count=1):
        store_attr()
        super().__init__(p=p)
        self.log_ratio = (math.log(min_aspect), math.log(1/min_aspect))

    def _bounds(self, area, img_h, img_w):
        r_area = random.uniform(self.sl,self.sh) * area
        aspect = math.exp(random.uniform(*self.log_ratio))
        return _slice(r_area*aspect, img_h) + _slice(r_area/aspect, img_w)

    def encodes(self,x:TensorRawImage):
        count = random.randint(1, self.max_count)
        _,img_h,img_w = x.shape[-3:]
        area = img_h*img_w/count
        areas = [self._bounds(area, img_h, img_w) for _ in range(count)]
        return cutout_gaussian(x, areas)

Maybe the solution is to change somewhere the source code to let it apply only on the input. Any idea?