Class: TorchVision::Transforms::RandomResizedCrop

Inherits:
Torch::NN::Module
  • Object
show all
Defined in:
lib/torchvision/transforms/random_resized_crop.rb

Instance Method Summary collapse

Constructor Details

#initialize(size, scale: [0.08, 1.0], ratio: [3.0 / 4.0, 4.0 / 3.0]) ⇒ RandomResizedCrop

Returns a new instance of RandomResizedCrop.



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# File 'lib/torchvision/transforms/random_resized_crop.rb', line 4

def initialize(size, scale: [0.08, 1.0], ratio: [3.0 / 4.0, 4.0 / 3.0])
  super()
  @size = setup_size(size, "Please provide only two dimensions (h, w) for size.")
  # @interpolation = interpolation
  @scale = scale
  @ratio = ratio
end

Instance Method Details

#forward(img) ⇒ Object 



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# File 'lib/torchvision/transforms/random_resized_crop.rb', line 50

def forward(img)
  i, j, h, w = params(img, @scale, @ratio)
  F.resized_crop(img, i, j, h, w, @size) #, @interpolation)
end

#params(img, scale, ratio) ⇒ Object 



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# File 'lib/torchvision/transforms/random_resized_crop.rb', line 12

def params(img, scale, ratio)
  width, height = F.send(:image_size, img)
  area = height * width

  log_ratio = Torch.log(Torch.tensor(ratio))
  10.times do
    target_area = area * Torch.empty(1).uniform!(scale[0], scale[1]).item
    aspect_ratio = Torch.exp(
      Torch.empty(1).uniform!(log_ratio[0], log_ratio[1])
    ).item

    w = Math.sqrt(target_area * aspect_ratio).round
    h = Math.sqrt(target_area / aspect_ratio).round

    if 0 < w && w <= width && 0 < h && h <= height
      i = Torch.randint(0, height - h + 1, size: [1]).item
      j = Torch.randint(0, width - w + 1, size: [1]).item
      return i, j, h, w
    end
  end

  # Fallback to central crop
  in_ratio = width.to_f / height.to_f
  if in_ratio < ratio.min
    w = width
    h = (w / ratio.min).round
  elsif in_ratio > ratio.max
    h = height
    w = (h * ratio.max).round
  else # whole image
    w = width
    h = height
  end
  i = (height - h).div(2)
  j = (width - w).div(2)
  [i, j, h, w]
end