pyiqa.archs.q_align.modeling_mplug_owl2
=======================================

.. py:module:: pyiqa.archs.q_align.modeling_mplug_owl2








Module Contents
---------------

.. py:data:: IGNORE_INDEX
   :value: -100


.. py:data:: IMAGE_TOKEN_INDEX
   :value: -200


.. py:data:: DEFAULT_IMAGE_TOKEN
   :value: '<|image|>'


.. py:function:: tokenizer_image_token(prompt, tokenizer, image_token_index=IMAGE_TOKEN_INDEX, return_tensors=None)

.. py:function:: expand2square(pil_img, background_color)

.. py:class:: MPLUGOwl2MetaModel(config)

   .. py:method:: get_vision_tower()


   .. py:method:: get_visual_abstractor()


.. py:class:: MPLUGOwl2MetaForCausalLM

   Bases: :py:obj:`abc.ABC`


   Helper class that provides a standard way to create an ABC using
   inheritance.


   .. py:method:: get_model()
      :abstractmethod:



   .. py:method:: encode_images(images)


   .. py:method:: prepare_inputs_labels_for_multimodal(input_ids, attention_mask, past_key_values, labels, images)


.. py:class:: MPLUGOwl2LlamaModel(config: pyiqa.archs.q_align.configuration_mplug_owl2.MPLUGOwl2Config)

   Bases: :py:obj:`MPLUGOwl2MetaModel`, :py:obj:`pyiqa.archs.q_align.modeling_llama2.LlamaModel`


.. py:class:: MPLUGOwl2LlamaForCausalLM(config)

   Bases: :py:obj:`pyiqa.archs.q_align.modeling_llama2.LlamaForCausalLM`, :py:obj:`transformers.generation.GenerationMixin`, :py:obj:`MPLUGOwl2MetaForCausalLM`


   Helper class that provides a standard way to create an ABC using
   inheritance.


   .. py:method:: get_model()


   .. py:method:: score(images, task_: str = 'quality', input_: str = 'image', return_dict=False, image_tensor=None)


   .. py:method:: forward(input_ids: torch.LongTensor = None, attention_mask: Optional[torch.Tensor] = None, past_key_values: Optional[List[torch.FloatTensor]] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, images: Optional[torch.FloatTensor] = None, return_dict: Optional[bool] = None) -> Union[Tuple, transformers.modeling_outputs.CausalLMOutputWithPast]


   .. py:method:: prepare_inputs_for_generation(input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs)