Source code for mmpretrain.apis.image_retrieval
# 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
[docs]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 torch.utils.data 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 = DATASETS.build(prototype)
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:
logger.info('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)
logger.info(f'The prototype has been saved at {cache}.')
return dataset
def save_prototype(self, path):
self.model.dump_prototype(path)
[docs] 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(
[TRANSFORMS.build(t) 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
[docs] @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')