Class: TorchVision::Transforms::Functional

Inherits:
Object
  • Object
show all
Defined in:
lib/torchvision/transforms/functional.rb

Class Method Summary collapse

Class Method Details

.center_crop(img, output_size) ⇒ Object 



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# File 'lib/torchvision/transforms/functional.rb', line 121

def center_crop(img, output_size)
  if output_size.is_a?(Integer)
    output_size = [output_size.to_i, output_size.to_i]
  elsif output_size.is_a?(Array) && output_size.length == 1
    output_size = [output_size[0], output_size[0]]
  end

  image_width, image_height = image_size(img)
  crop_height, crop_width = output_size

  if crop_width > image_width || crop_height > image_height
    padding_ltrb = [
      crop_width > image_width ? (crop_width - image_width).div(2) : 0,
      crop_height > image_height ? (crop_height - image_height).div(2) : 0,
      crop_width > image_width ? (crop_width - image_width + 1).div(2) : 0,
      crop_height > image_height ? (crop_height - image_height + 1).div(2) : 0
    ]
    # TODO
    img = pad(img, padding_ltrb, fill: 0)
    image_width, image_height = image_size(img)
    if crop_width == image_width && crop_height == image_height
      return img
    end
  end

  crop_top = ((image_height - crop_height) / 2.0).round
  crop_left = ((image_width - crop_width) / 2.0).round
  crop(img, crop_top, crop_left, crop_height, crop_width)
end

.crop(img, top, left, height, width) ⇒ Object 



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# File 'lib/torchvision/transforms/functional.rb', line 111

def crop(img, top, left, height, width)
  if img.is_a?(Torch::Tensor)
    assert_image_tensor(img)
    indexes = [true] * (img.dim - 2)
    img[*indexes, top...(top + height), left...(left + width)]
  else
    img.crop(left, top, width, height)
  end
end

.hflip(img) ⇒ Object 



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# File 'lib/torchvision/transforms/functional.rb', line 93

def hflip(img)
  if img.is_a?(Torch::Tensor)
    assert_image_tensor(img)
    img.flip(-1)
  else
    img.flip(:horizontal)
  end
end

.normalize(tensor, mean, std, inplace: false) ⇒ Object 



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# File 'lib/torchvision/transforms/functional.rb', line 5

def normalize(tensor, mean, std, inplace: false)
  unless Torch.tensor?(tensor)
    raise ArgumentError, "tensor should be a torch tensor. Got #{tensor.class.name}"
  end

  if tensor.ndimension != 3
    raise ArgumentError, "Expected tensor to be a tensor image of size (C, H, W). Got tensor.size() = #{tensor.size}"
  end

  tensor = tensor.clone unless inplace

  dtype = tensor.dtype
  # TODO Torch.as_tensor
  mean = Torch.tensor(mean, dtype: dtype, device: tensor.device)
  std = Torch.tensor(std, dtype: dtype, device: tensor.device)

  # TODO
  if std.to_a.any? { |v| v == 0 }
    raise ArgumentError, "std evaluated to zero after conversion to #{dtype}, leading to division by zero."
  end
  if mean.ndim == 1
    mean = mean[0...mean.size(0), nil, nil]
  end
  if std.ndim == 1
    std = std[0...std.size(0), nil, nil]
  end
  tensor.sub!(mean).div!(std)
  tensor
end

.resize(img, size) ⇒ Object 



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# File 'lib/torchvision/transforms/functional.rb', line 35

def resize(img, size)
  raise "img should be Vips::Image. Got #{img.class.name}" unless img.is_a?(Vips::Image)

  if size.is_a?(Integer)
    w, h = img.size
    if (w <= h && w == size) || (h <= w && h == size)
      return img
    end
    if w < h
      ow = size
      oh = (size * h / w).to_i
      img.thumbnail_image(ow, height: oh)
    else
      oh = size
      ow = (size * w / h).to_i
      img.thumbnail_image(ow, height: oh)
    end
  else
    img.thumbnail_image(size[0], height: size[1], size: :force)
  end
end

.resized_crop(img, top, left, height, width, size) ⇒ Object

TODO interpolation



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# File 'lib/torchvision/transforms/functional.rb', line 152

def resized_crop(img, top, left, height, width, size)
  img = crop(img, top, left, height, width)
  img = resize(img, size) #, interpolation)
  img
end

.to_tensor(pic) ⇒ Object

TODO improve



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# File 'lib/torchvision/transforms/functional.rb', line 58

def to_tensor(pic)
  if !pic.is_a?(Numo::NArray) && !pic.is_a?(Vips::Image)
    raise ArgumentError, "pic should be Vips::Image or Numo::NArray. Got #{pic.class.name}"
  end

  if pic.is_a?(Numo::NArray) && ![2, 3].include?(pic.ndim)
    raise ArgumentError, "pic should be 2/3 dimensional. Got #{pic.dim} dimensions."
  end

  if pic.is_a?(Numo::NArray)
    if pic.ndim == 2
      pic = pic.reshape(*pic.shape, 1)
    end

    img = Torch.from_numo(pic.transpose(2, 0, 1))
    if img.dtype == :uint8
      return img.float.div(255)
    else
      return img
    end
  end

  case pic.format
  when :uchar
    img = Torch::ByteTensor.new(Torch::ByteStorage.from_buffer(pic.write_to_memory))
  else
    raise Error, "Format not supported yet: #{pic.format}"
  end

  img = img.view(pic.height, pic.width, pic.bands)
  # put it from HWC to CHW format
  img = img.permute([2, 0, 1]).contiguous
  img.float.div(255)
end

.vflip(img) ⇒ Object 



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# File 'lib/torchvision/transforms/functional.rb', line 102

def vflip(img)
  if img.is_a?(Torch::Tensor)
    assert_image_tensor(img)
    img.flip(-2)
  else
    img.flip(:vertical)
  end
end