343 lines
12 KiB
Python
343 lines
12 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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import torch
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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 BaseDataset, Compose, default_collate
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from mmengine.device import get_device
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from mmengine.infer import BaseInferencer
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from mmengine.model import BaseModel
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from mmengine.runner import load_checkpoint
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from mmpretrain.registry import TRANSFORMS
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from mmpretrain.structures import DataSample
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from .model import get_model, list_models
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ModelType = Union[BaseModel, str, Config]
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InputType = Union[str, np.ndarray, list]
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class ImageRetrievalInferencer(BaseInferencer):
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"""The inferencer for image to image retrieval.
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Args:
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model (BaseModel | str | Config): A model name or a path to the confi
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file, or a :obj:`BaseModel` object. The model name can be found
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by ``ImageClassificationInferencer.list_models()`` and you can also
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query it in :doc:`/modelzoo_statistics`.
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weights (str, optional): Path to the checkpoint. If None, it will try
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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, use CPU or
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the device of the input model. Defaults to None.
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Example:
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1. Use a pre-trained model in MMPreTrain to inference an image.
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>>> from mmpretrain import ImageClassificationInferencer
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>>> inferencer = ImageClassificationInferencer('resnet50_8xb32_in1k')
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>>> inferencer('demo/demo.JPEG')
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[{'pred_score': array([...]),
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'pred_label': 65,
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'pred_score': 0.6649367809295654,
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'pred_class': 'sea snake'}]
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2. Use a config file and checkpoint to inference multiple images on GPU,
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and save the visualization results in a folder.
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>>> from mmpretrain import ImageClassificationInferencer
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>>> inferencer = ImageClassificationInferencer(
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model='configs/resnet/resnet50_8xb32_in1k.py',
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weights='https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth',
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device='cuda')
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>>> inferencer(['demo/dog.jpg', 'demo/bird.JPEG'], show_dir="./visualize/")
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""" # noqa: E501
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visualize_kwargs: set = {
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'draw_score', 'resize', 'show_dir', 'show', 'wait_time'
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}
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postprocess_kwargs: set = {'topk'}
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def __init__(
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self,
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model: ModelType,
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prototype,
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prototype_vecs=None,
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prepare_batch_size=8,
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pretrained: Union[bool, str] = True,
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device: Union[str, torch.device, None] = None,
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) -> None:
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device = device or get_device()
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if isinstance(model, BaseModel):
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if isinstance(pretrained, str):
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load_checkpoint(model, pretrained, map_location='cpu')
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model = model.to(device)
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else:
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model = get_model(model, pretrained, device)
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model.eval()
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self.config = model.config
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self.model = model
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self.pipeline = self._init_pipeline(self.config)
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self.collate_fn = default_collate
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self.visualizer = None
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self.prototype_dataset = self._prepare_prototype(
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prototype, prototype_vecs, prepare_batch_size)
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# An ugly hack to escape from the duplicated arguments check in the
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# base class
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self.visualize_kwargs.add('topk')
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def _prepare_prototype(self, prototype, prototype_vecs=None, batch_size=8):
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from mmengine.dataset import DefaultSampler
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from torch.utils.data import DataLoader
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def build_dataloader(dataset):
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return DataLoader(
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dataset,
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batch_size=batch_size,
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collate_fn=self.collate_fn,
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sampler=DefaultSampler(dataset, shuffle=False),
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persistent_workers=False,
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)
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test_pipeline = self.config.test_dataloader.dataset.pipeline
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if isinstance(prototype, str):
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# A directory path of images
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from mmpretrain.datasets import CustomDataset
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dataset = CustomDataset(
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data_root=prototype, pipeline=test_pipeline, with_label=False)
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dataloader = build_dataloader(dataset)
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elif isinstance(prototype, dict):
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# A config of dataset
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from mmpretrain.registry import DATASETS
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prototype.setdefault('pipeline', test_pipeline)
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dataset = DATASETS.build(prototype)
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dataloader = build_dataloader(dataset)
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elif isinstance(prototype, DataLoader):
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dataset = prototype.dataset
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dataloader = prototype
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elif isinstance(prototype, BaseDataset):
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dataset = prototype
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dataloader = build_dataloader(dataset)
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else:
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raise TypeError(f'Unsupported prototype type {type(prototype)}.')
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if prototype_vecs is not None and Path(prototype_vecs).exists():
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self.model.prototype = prototype_vecs
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else:
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self.model.prototype = dataloader
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self.model.prepare_prototype()
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from mmengine.logging import MMLogger
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logger = MMLogger.get_current_instance()
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if prototype_vecs is None:
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logger.info('The prototype has been prepared, you can use '
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'`save_prototype_vecs` to dump it into a pickle '
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'file for the future usage.')
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elif not Path(prototype_vecs).exists():
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self.save_prototype_vecs(prototype_vecs)
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logger.info(f'The prototype has been saved at {prototype_vecs}.')
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return dataset
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def save_prototype_vecs(self, path):
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self.model.dump_prototype(path)
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def __call__(self,
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inputs: InputType,
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return_datasamples: bool = False,
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batch_size: int = 1,
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**kwargs) -> dict:
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"""Call the inferencer.
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Args:
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inputs (str | array | list): The image path or array, or a list of
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images.
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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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resize (int, optional): Resize the long edge of the image to the
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specified length before visualization. Defaults to None.
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draw_score (bool): Whether to draw the match scores.
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Defaults to True.
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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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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[InputType], batch_size: int = 1):
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def load_image(input_):
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img = imread(input_)
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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 dict(
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img=img,
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img_shape=img.shape[:2],
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ori_shape=img.shape[:2],
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)
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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(self.collate_fn, chunked_data)
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def visualize(self,
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ori_inputs: List[InputType],
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preds: List[DataSample],
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topk: int = 3,
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resize: Optional[int] = 224,
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show: bool = False,
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wait_time: int = 0,
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draw_score=True,
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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_)
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if isinstance(input_, 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_).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_image_retrieval(
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image,
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data_sample,
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self.prototype_dataset,
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topk=topk,
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resize=resize,
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draw_score=draw_score,
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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(
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self,
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preds: List[DataSample],
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visualization: List[np.ndarray],
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return_datasamples=False,
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topk=1,
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) -> 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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match_scores, indices = torch.topk(data_sample.pred_score, k=topk)
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matches = []
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for match_score, sample_idx in zip(match_scores, indices):
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sample = self.prototype_dataset.get_data_info(
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sample_idx.item())
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sample_idx = sample.pop('sample_idx')
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matches.append({
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'match_score': match_score,
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'sample_idx': sample_idx,
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'sample': sample
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})
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results.append(matches)
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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='Image Retrieval')
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def _dispatch_kwargs(self, **kwargs):
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"""Dispatch kwargs to preprocess(), forward(), visualize() and
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postprocess() according to the actual demands.
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Override this method to allow same argument for different methods.
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Returns:
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Tuple[Dict, Dict, Dict, Dict]: kwargs passed to preprocess,
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forward, visualize and postprocess respectively.
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"""
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method_kwargs = set.union(
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self.preprocess_kwargs,
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self.forward_kwargs,
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self.visualize_kwargs,
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self.postprocess_kwargs,
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)
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union_kwargs = method_kwargs | set(kwargs.keys())
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if union_kwargs != method_kwargs:
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unknown_kwargs = union_kwargs - method_kwargs
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raise ValueError(
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f'unknown argument {unknown_kwargs} for `preprocess`, '
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'`forward`, `visualize` and `postprocess`')
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preprocess_kwargs = {}
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forward_kwargs = {}
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visualize_kwargs = {}
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postprocess_kwargs = {}
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for key, value in kwargs.items():
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if key in self.preprocess_kwargs:
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preprocess_kwargs[key] = value
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if key in self.forward_kwargs:
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forward_kwargs[key] = value
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if key in self.visualize_kwargs:
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visualize_kwargs[key] = value
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if key in self.postprocess_kwargs:
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postprocess_kwargs[key] = value
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return (
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preprocess_kwargs,
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forward_kwargs,
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visualize_kwargs,
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postprocess_kwargs,
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)
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