Class: Transformers::ImageFeatureExtractionPipeline

Inherits:
Pipeline
  • Object
show all
Defined in:
lib/transformers/pipelines/image_feature_extraction.rb

Instance Method Summary collapse

Methods inherited from Pipeline

#call, #check_model_type, #get_iterator, #initialize, #torch_dtype

Constructor Details

This class inherits a constructor from Transformers::Pipeline

Instance Method Details

#_forward(model_inputs) ⇒ Object 



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# File 'lib/transformers/pipelines/image_feature_extraction.rb', line 31

def _forward(model_inputs)
  model_outputs = @model.(**model_inputs)
  model_outputs
end

#_sanitize_parameters(image_processor_kwargs: nil, return_tensors: nil, pool: nil, **kwargs) ⇒ Object 



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# File 'lib/transformers/pipelines/image_feature_extraction.rb', line 3

def _sanitize_parameters(image_processor_kwargs: nil, return_tensors: nil, pool: nil, **kwargs)
  preprocess_params = image_processor_kwargs.nil? ? {} : image_processor_kwargs

  postprocess_params = {}
  if !pool.nil?
    postprocess_params[:pool] = pool
  end
  if !return_tensors.nil?
    postprocess_params[:return_tensors] = return_tensors
  end

  if kwargs.include?(:timeout)
    preprocess_params[:timeout] = kwargs[:timeout]
  end

  [preprocess_params, {}, postprocess_params]
end

#postprocess(model_outputs, pool: nil, return_tensors: false) ⇒ Object 



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# File 'lib/transformers/pipelines/image_feature_extraction.rb', line 36

def postprocess(model_outputs, pool: nil, return_tensors: false)
  pool = !pool.nil? ? pool : false

  if pool
    raise Todo
  else
    # [0] is the first available tensor, logits or last_hidden_state.
    outputs = model_outputs[0]
  end

  if return_tensors
    return outputs
  end
  if @framework == "pt"
    outputs.to_a
  else
    raise Todo
  end
end

#preprocess(image, timeout: nil, **image_processor_kwargs) ⇒ Object 



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# File 'lib/transformers/pipelines/image_feature_extraction.rb', line 21

def preprocess(image, timeout: nil, **image_processor_kwargs)
  image = ImageUtils.load_image(image, timeout: timeout)
  model_inputs = @image_processor.(image, return_tensors: @framework, **image_processor_kwargs)
  if @framework == "pt"
    # TODO
    # model_inputs = model_inputs.to(torch_dtype)
  end
  model_inputs
end