286 lines
11 KiB
Python
286 lines
11 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 mmpretrain.registry import TRANSFORMS
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from mmpretrain.structures import DataSample
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from .base import BaseInferencer, InputType, ModelType
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from .model import list_models
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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 config
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file, or a :obj:`BaseModel` object. The model name can be found
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by ``ImageRetrievalInferencer.list_models()`` and you can also
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query it in :doc:`/modelzoo_statistics`.
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prototype (str | list | dict | DataLoader, BaseDataset): The images to
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be retrieved. It can be the following types:
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- str: The directory of the the images.
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- list: A list of path of the images.
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- dict: A config dict of the a prototype dataset.
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- BaseDataset: A prototype dataset.
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- DataLoader: A data loader to load the prototype data.
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prototype_cache (str, optional): The path of the generated prototype
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features. If exists, directly load the cache instead of re-generate
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the prototype features. If not exists, save the generated features
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to the path. Defaults to None.
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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 ImageRetrievalInferencer
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>>> inferencer = ImageRetrievalInferencer(
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... 'resnet50-arcface_inshop',
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... prototype='./demo/',
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... prototype_cache='img_retri.pth')
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>>> inferencer('demo/cat-dog.png', topk=2)[0][1]
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{'match_score': tensor(0.4088, device='cuda:0'),
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'sample_idx': 3,
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'sample': {'img_path': './demo/dog.jpg'}}
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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', 'topk'
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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_cache=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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**kwargs,
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) -> None:
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super().__init__(
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model=model, pretrained=pretrained, device=device, **kwargs)
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self.prototype_dataset = self._prepare_prototype(
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prototype, prototype_cache, prepare_batch_size)
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def _prepare_prototype(self, prototype, cache=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=default_collate,
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sampler=DefaultSampler(dataset, shuffle=False),
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persistent_workers=False,
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)
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if isinstance(prototype, str):
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# A directory path of images
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prototype = dict(
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type='CustomDataset', with_label=False, data_root=prototype)
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if isinstance(prototype, list):
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test_pipeline = [dict(type='LoadImageFromFile'), self.pipeline]
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dataset = BaseDataset(
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lazy_init=True, serialize_data=False, pipeline=test_pipeline)
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dataset.data_list = [{
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'sample_idx': i,
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'img_path': file
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} for i, file in enumerate(prototype)]
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dataset._fully_initialized = True
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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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test_pipeline = [dict(type='LoadImageFromFile'), self.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 cache is not None and Path(cache).exists():
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self.model.prototype = cache
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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 cache is None:
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logger.info('The prototype has been prepared, you can use '
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'`save_prototype` to dump it into a pickle '
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'file for the future usage.')
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elif not Path(cache).exists():
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self.save_prototype(cache)
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logger.info(f'The prototype has been saved at {cache}.')
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return dataset
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def save_prototype(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(default_collate, 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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