mmpretrain.apis.image_retrieval 源代码

# Copyright (c) OpenMMLab. All rights reserved.
from pathlib import Path
from typing import Callable, List, Optional, Union

import numpy as np
import torch
from mmcv.image import imread
from mmengine.config import Config
from mmengine.dataset import BaseDataset, Compose, default_collate

from mmpretrain.registry import TRANSFORMS
from mmpretrain.structures import DataSample
from .base import BaseInferencer, InputType, ModelType
from .model import list_models

[文档]class ImageRetrievalInferencer(BaseInferencer): """The inferencer for image to image retrieval. Args: model (BaseModel | str | Config): A model name or a path to the config file, or a :obj:`BaseModel` object. The model name can be found by ``ImageRetrievalInferencer.list_models()`` and you can also query it in :doc:`/modelzoo_statistics`. prototype (str | list | dict | DataLoader, BaseDataset): The images to be retrieved. It can be the following types: - str: The directory of the the images. - list: A list of path of the images. - dict: A config dict of the a prototype dataset. - BaseDataset: A prototype dataset. - DataLoader: A data loader to load the prototype data. prototype_cache (str, optional): The path of the generated prototype features. If exists, directly load the cache instead of re-generate the prototype features. If not exists, save the generated features to the path. Defaults to None. pretrained (str, optional): Path to the checkpoint. If None, it will try to find a pre-defined weight from the model you specified (only work if the ``model`` is a model name). Defaults to None. device (str, optional): Device to run inference. If None, the available device will be automatically used. Defaults to None. **kwargs: Other keyword arguments to initialize the model (only work if the ``model`` is a model name). Example: >>> from mmpretrain import ImageRetrievalInferencer >>> inferencer = ImageRetrievalInferencer( ... 'resnet50-arcface_inshop', ... prototype='./demo/', ... prototype_cache='img_retri.pth') >>> inferencer('demo/cat-dog.png', topk=2)[0][1] {'match_score': tensor(0.4088, device='cuda:0'), 'sample_idx': 3, 'sample': {'img_path': './demo/dog.jpg'}} """ # noqa: E501 visualize_kwargs: set = { 'draw_score', 'resize', 'show_dir', 'show', 'wait_time', 'topk' } postprocess_kwargs: set = {'topk'} def __init__( self, model: ModelType, prototype, prototype_cache=None, prepare_batch_size=8, pretrained: Union[bool, str] = True, device: Union[str, torch.device, None] = None, **kwargs, ) -> None: super().__init__( model=model, pretrained=pretrained, device=device, **kwargs) self.prototype_dataset = self._prepare_prototype( prototype, prototype_cache, prepare_batch_size) def _prepare_prototype(self, prototype, cache=None, batch_size=8): from mmengine.dataset import DefaultSampler from import DataLoader def build_dataloader(dataset): return DataLoader( dataset, batch_size=batch_size, collate_fn=default_collate, sampler=DefaultSampler(dataset, shuffle=False), persistent_workers=False, ) if isinstance(prototype, str): # A directory path of images prototype = dict( type='CustomDataset', with_label=False, data_root=prototype) if isinstance(prototype, list): test_pipeline = [dict(type='LoadImageFromFile'), self.pipeline] dataset = BaseDataset( lazy_init=True, serialize_data=False, pipeline=test_pipeline) dataset.data_list = [{ 'sample_idx': i, 'img_path': file } for i, file in enumerate(prototype)] dataset._fully_initialized = True dataloader = build_dataloader(dataset) elif isinstance(prototype, dict): # A config of dataset from mmpretrain.registry import DATASETS test_pipeline = [dict(type='LoadImageFromFile'), self.pipeline] prototype.setdefault('pipeline', test_pipeline) dataset = dataloader = build_dataloader(dataset) elif isinstance(prototype, DataLoader): dataset = prototype.dataset dataloader = prototype elif isinstance(prototype, BaseDataset): dataset = prototype dataloader = build_dataloader(dataset) else: raise TypeError(f'Unsupported prototype type {type(prototype)}.') if cache is not None and Path(cache).exists(): self.model.prototype = cache else: self.model.prototype = dataloader self.model.prepare_prototype() from mmengine.logging import MMLogger logger = MMLogger.get_current_instance() if cache is None:'The prototype has been prepared, you can use ' '`save_prototype` to dump it into a pickle ' 'file for the future usage.') elif not Path(cache).exists(): self.save_prototype(cache)'The prototype has been saved at {cache}.') return dataset def save_prototype(self, path): self.model.dump_prototype(path)
[文档] def __call__(self, inputs: InputType, return_datasamples: bool = False, batch_size: int = 1, **kwargs) -> dict: """Call the inferencer. Args: inputs (str | array | list): The image path or array, or a list of images. return_datasamples (bool): Whether to return results as :obj:`DataSample`. Defaults to False. batch_size (int): Batch size. Defaults to 1. resize (int, optional): Resize the long edge of the image to the specified length before visualization. Defaults to None. draw_score (bool): Whether to draw the match scores. Defaults to True. show (bool): Whether to display the visualization result in a window. Defaults to False. wait_time (float): The display time (s). Defaults to 0, which means "forever". show_dir (str, optional): If not None, save the visualization results in the specified directory. Defaults to None. Returns: list: The inference results. """ return super().__call__(inputs, return_datasamples, batch_size, **kwargs)
def _init_pipeline(self, cfg: Config) -> Callable: test_pipeline_cfg = cfg.test_dataloader.dataset.pipeline from mmpretrain.datasets import remove_transform # Image loading is finished in `self.preprocess`. test_pipeline_cfg = remove_transform(test_pipeline_cfg, 'LoadImageFromFile') test_pipeline = Compose( [ for t in test_pipeline_cfg]) return test_pipeline def preprocess(self, inputs: List[InputType], batch_size: int = 1): def load_image(input_): img = imread(input_) if img is None: raise ValueError(f'Failed to read image {input_}.') return dict( img=img, img_shape=img.shape[:2], ori_shape=img.shape[:2], ) pipeline = Compose([load_image, self.pipeline]) chunked_data = self._get_chunk_data(map(pipeline, inputs), batch_size) yield from map(default_collate, chunked_data) def visualize(self, ori_inputs: List[InputType], preds: List[DataSample], topk: int = 3, resize: Optional[int] = 224, show: bool = False, wait_time: int = 0, draw_score=True, show_dir=None): if not show and show_dir is None: return None if self.visualizer is None: from mmpretrain.visualization import UniversalVisualizer self.visualizer = UniversalVisualizer() visualization = [] for i, (input_, data_sample) in enumerate(zip(ori_inputs, preds)): image = imread(input_) if isinstance(input_, str): # The image loaded from path is BGR format. image = image[..., ::-1] name = Path(input_).stem else: name = str(i) if show_dir is not None: show_dir = Path(show_dir) show_dir.mkdir(exist_ok=True) out_file = str((show_dir / name).with_suffix('.png')) else: out_file = None self.visualizer.visualize_image_retrieval( image, data_sample, self.prototype_dataset, topk=topk, resize=resize, draw_score=draw_score, show=show, wait_time=wait_time, name=name, out_file=out_file) visualization.append(self.visualizer.get_image()) if show: self.visualizer.close() return visualization def postprocess( self, preds: List[DataSample], visualization: List[np.ndarray], return_datasamples=False, topk=1, ) -> dict: if return_datasamples: return preds results = [] for data_sample in preds: match_scores, indices = torch.topk(data_sample.pred_score, k=topk) matches = [] for match_score, sample_idx in zip(match_scores, indices): sample = self.prototype_dataset.get_data_info( sample_idx.item()) sample_idx = sample.pop('sample_idx') matches.append({ 'match_score': match_score, 'sample_idx': sample_idx, 'sample': sample }) results.append(matches) return results
[文档] @staticmethod def list_models(pattern: Optional[str] = None): """List all available model names. Args: pattern (str | None): A wildcard pattern to match model names. Returns: List[str]: a list of model names. """ return list_models(pattern=pattern, task='Image Retrieval')
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