Class: Transformers::ImageProcessingMixin

Inherits:
Object
  • Object
show all
Defined in:
lib/transformers/image_processing_base.rb

Direct Known Subclasses

BaseImageProcessor

Class Method Summary collapse

Class Method Details

.from_dict(image_processor_dict, **kwargs) ⇒ Object 



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# File 'lib/transformers/image_processing_base.rb', line 133

def self.from_dict(image_processor_dict, **kwargs)
  image_processor_dict = image_processor_dict.dup
  return_unused_kwargs = kwargs.delete(:return_unused_kwargs) { false }

  # The `size` parameter is a dict and was previously an int or tuple in feature extractors.
  # We set `size` here directly to the `image_processor_dict` so that it is converted to the appropriate
  # dict within the image processor and isn't overwritten if `size` is passed in as a kwarg.
  if kwargs.include?(:size) && image_processor_dict.include?(:size)
    image_processor_dict[:size] = kwargs.delete(:size)
  end
  if kwargs.include?(:crop_size) && image_processor_dict.include?(:crop_size)
    image_processor_dict[:crop_size] = kwargs.delete(:crop_size)
  end

  image_processor = new(**image_processor_dict)

  # Update image_processor with kwargs if needed
  to_remove = []
  kwargs.each do |key, value|
    if image_processor.instance_variable_defined?("@#{key}")
      image_processor.instance_variable_set("@#{key}", value)
      to_remove << key
    end
  end
  to_remove.each do |key|
    kwargs.delete(key)
  end

  Transformers.logger.info("Image processor #{image_processor}")
  if return_unused_kwargs
    [image_processor, kwargs]
  else
    image_processor
  end
end

.from_pretrained(pretrained_model_name_or_path, cache_dir: nil, force_download: false, local_files_only: false, token: nil, revision: "main", **kwargs) ⇒ Object 



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# File 'lib/transformers/image_processing_base.rb', line 17

def self.from_pretrained(
  pretrained_model_name_or_path,
  cache_dir: nil,
  force_download: false,
  local_files_only: false,
  token: nil,
  revision: "main",
  **kwargs
)
  kwargs[:cache_dir] = cache_dir
  kwargs[:force_download] = force_download
  kwargs[:local_files_only] = local_files_only
  kwargs[:revision] = revision

  if !token.nil?
    kwargs[:token] = token
  end

  image_processor_dict, kwargs = get_image_processor_dict(pretrained_model_name_or_path, **kwargs)

  from_dict(image_processor_dict, **kwargs)
end

.get_image_processor_dict(pretrained_model_name_or_path, **kwargs) ⇒ Object 



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# File 'lib/transformers/image_processing_base.rb', line 40

def self.get_image_processor_dict(
  pretrained_model_name_or_path, **kwargs
)
  cache_dir = kwargs.delete(:cache_dir)
  force_download = kwargs.delete(:force_download) { false }
  resume_download = kwargs.delete(:resume_download)
  proxies = kwargs.delete(:proxies)
  token = kwargs.delete(:token)
  _use_auth_token = kwargs.delete(:use_auth_token)
  local_files_only = kwargs.delete(:local_files_only) { false }
  revision = kwargs.delete(:revision)
  subfolder = kwargs.delete(:subfolder) { "" }

  from_pipeline = kwargs.delete(:_from_pipeline)
  from_auto_class = kwargs.delete(:_from_auto) { false }

  user_agent = {file_type: "image processor", from_auto_class: from_auto_class}
  if !from_pipeline.nil?
    user_agent[:using_pipeline] = from_pipeline
  end

  if Utils::Hub.is_offline_mode && !local_files_only
    Transformers.logger.info("Offline mode: forcing local_files_only: true")
    local_files_only = true
  end

  pretrained_model_name_or_path = pretrained_model_name_or_path.to_s
  is_local = Dir.exist?(pretrained_model_name_or_path)
  if Dir.exist?(pretrained_model_name_or_path)
    image_processor_file = File.join(pretrained_model_name_or_path, IMAGE_PROCESSOR_NAME)
  end
  if File.exist?(pretrained_model_name_or_path)
    resolved_image_processor_file = pretrained_model_name_or_path
    is_local = true
  elsif Utils::Hub.is_remote_url(pretrained_model_name_or_path)
    raise Todo
  else
    image_processor_file = IMAGE_PROCESSOR_NAME
    begin
      # Load from local folder or from cache or download from model Hub and cache
      resolved_image_processor_file = Utils::Hub.cached_file(
        pretrained_model_name_or_path,
        image_processor_file,
        cache_dir: cache_dir,
        force_download: force_download,
        proxies: proxies,
        resume_download: resume_download,
        local_files_only: local_files_only,
        token: token,
        user_agent: user_agent,
        revision: revision,
        subfolder: subfolder
      )
    rescue EnvironmentError
      # Raise any environment error raise by `cached_file`. It will have a helpful error message adapted to
      # the original exception.
      raise
    rescue
      # For any other exception, we throw a generic error.
      raise EnvironmentError,
        "Can't load image processor for '#{pretrained_model_name_or_path}'. If you were trying to load" +
        " it from 'https://huggingface.co/models', make sure you don't have a local directory with the" +
        " same name. Otherwise, make sure '#{pretrained_model_name_or_path}' is the correct path to a" +
        " directory containing a #{IMAGE_PROCESSOR_NAME} file"
    end
  end

  begin
    image_processor_dict = JSON.load_file(resolved_image_processor_file).transform_keys(&:to_sym)
  rescue JSON::ParserError
    raise EnvironmentError,
      "It looks like the config file at '#{resolved_image_processor_file}' is not a valid JSON file."
  end

  if is_local
    Transformers.logger.info("loading configuration file #{resolved_image_processor_file}")
  else
    Transformers.logger.info(
      "loading configuration file #{image_processor_file} from cache at #{resolved_image_processor_file}"
    )
  end

  if !is_local
    if image_processor_dict.include?("auto_map")
      raise Todo
    end
    if image_processor_dict.include?("custom_pipelines")
      raise Todo
    end
  end
  [image_processor_dict, kwargs]
end