182 lines
7.0 KiB
Python
182 lines
7.0 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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from pathlib import Path
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from typing import Callable, List, Optional, Union
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import numpy as np
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from mmcv.image import imread
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from mmengine.config import Config
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from mmengine.dataset import Compose, default_collate
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from mmpretrain.registry import TRANSFORMS
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from mmpretrain.structures import DataSample
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from .base import BaseInferencer
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from .model import list_models
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class VisualQuestionAnsweringInferencer(BaseInferencer):
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"""The inferencer for visual question answering.
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Args:
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model (BaseModel | str | Config): A model name or a path to the config
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file, or a :obj:`BaseModel` object. The model name can be found
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by ``VisualQuestionAnsweringInferencer.list_models()`` and you can
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also query it in :doc:`/modelzoo_statistics`.
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pretrained (str, optional): Path to the checkpoint. If None, it will
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try to find a pre-defined weight from the model you specified
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(only work if the ``model`` is a model name). Defaults to None.
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device (str, optional): Device to run inference. If None, the available
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device will be automatically used. Defaults to None.
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**kwargs: Other keyword arguments to initialize the model (only work if
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the ``model`` is a model name).
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Example:
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>>> from mmpretrain import VisualQuestionAnsweringInferencer
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>>> inferencer = VisualQuestionAnsweringInferencer('ofa-base_3rdparty-zeroshot_vqa')
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>>> inferencer('demo/cat-dog.png', "What's the animal next to the dog?")[0]
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{'question': "What's the animal next to the dog?", 'pred_answer': 'cat'}
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""" # noqa: E501
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visualize_kwargs: set = {'resize', 'show', 'show_dir', 'wait_time'}
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def __call__(self,
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images: Union[str, np.ndarray, list],
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questions: Union[str, list],
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return_datasamples: bool = False,
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batch_size: int = 1,
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objects: Optional[List[str]] = None,
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**kwargs) -> dict:
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"""Call the inferencer.
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Args:
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images (str | array | list): The image path or array, or a list of
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images.
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questions (str | list): The question to the correspondding image.
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return_datasamples (bool): Whether to return results as
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:obj:`DataSample`. Defaults to False.
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batch_size (int): Batch size. Defaults to 1.
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objects (List[List[str]], optional): Some algorithms like OFA
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fine-tuned VQA models requires extra object description list
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for every image. Defaults to None.
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resize (int, optional): Resize the short edge of the image to the
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specified length before visualization. Defaults to None.
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show (bool): Whether to display the visualization result in a
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window. Defaults to False.
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wait_time (float): The display time (s). Defaults to 0, which means
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"forever".
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show_dir (str, optional): If not None, save the visualization
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results in the specified directory. Defaults to None.
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Returns:
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list: The inference results.
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"""
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if not isinstance(images, (list, tuple)):
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assert isinstance(questions, str)
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inputs = [{'img': images, 'question': questions}]
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if objects is not None:
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assert isinstance(objects[0], str)
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inputs[0]['objects'] = objects
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else:
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inputs = []
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for i in range(len(images)):
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input_ = {'img': images[i], 'question': questions[i]}
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if objects is not None:
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input_['objects'] = objects[i]
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inputs.append(input_)
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return super().__call__(inputs, return_datasamples, batch_size,
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**kwargs)
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def _init_pipeline(self, cfg: Config) -> Callable:
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test_pipeline_cfg = cfg.test_dataloader.dataset.pipeline
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if test_pipeline_cfg[0]['type'] == 'LoadImageFromFile':
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# Image loading is finished in `self.preprocess`.
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test_pipeline_cfg = test_pipeline_cfg[1:]
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test_pipeline = Compose(
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[TRANSFORMS.build(t) for t in test_pipeline_cfg])
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return test_pipeline
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def preprocess(self, inputs: List[dict], batch_size: int = 1):
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def load_image(input_: dict):
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img = imread(input_['img'])
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if img is None:
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raise ValueError(f'Failed to read image {input_}.')
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return {**input_, 'img': img}
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pipeline = Compose([load_image, self.pipeline])
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chunked_data = self._get_chunk_data(map(pipeline, inputs), batch_size)
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yield from map(default_collate, chunked_data)
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def visualize(self,
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ori_inputs: List[dict],
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preds: List[DataSample],
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show: bool = False,
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wait_time: int = 0,
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resize: Optional[int] = None,
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show_dir=None):
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if not show and show_dir is None:
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return None
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if self.visualizer is None:
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from mmpretrain.visualization import UniversalVisualizer
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self.visualizer = UniversalVisualizer()
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visualization = []
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for i, (input_, data_sample) in enumerate(zip(ori_inputs, preds)):
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image = imread(input_['img'])
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if isinstance(input_['img'], str):
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# The image loaded from path is BGR format.
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image = image[..., ::-1]
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name = Path(input_['img']).stem
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else:
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name = str(i)
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if show_dir is not None:
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show_dir = Path(show_dir)
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show_dir.mkdir(exist_ok=True)
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out_file = str((show_dir / name).with_suffix('.png'))
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else:
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out_file = None
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self.visualizer.visualize_vqa(
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image,
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data_sample,
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resize=resize,
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show=show,
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wait_time=wait_time,
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name=name,
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out_file=out_file)
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visualization.append(self.visualizer.get_image())
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if show:
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self.visualizer.close()
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return visualization
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def postprocess(self,
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preds: List[DataSample],
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visualization: List[np.ndarray],
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return_datasamples=False) -> dict:
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if return_datasamples:
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return preds
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results = []
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for data_sample in preds:
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results.append({
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'question': data_sample.get('question'),
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'pred_answer': data_sample.get('pred_answer'),
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})
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return results
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@staticmethod
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def list_models(pattern: Optional[str] = None):
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"""List all available model names.
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Args:
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pattern (str | None): A wildcard pattern to match model names.
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Returns:
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List[str]: a list of model names.
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"""
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return list_models(pattern=pattern, task='Visual Question Answering')
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