Shortcuts

Source code for mmpretrain.apis.multimodal_retrieval

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

import mmengine
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 mmpretrain.utils import track
from .base import BaseInferencer
from .base import InputType as ImageType
from .base import ModelType
from .model import list_models


def filter_transforms(transforms: list, data_info: dict):
    """Filter pipeline to avoid KeyError with partial data info."""
    data_info = deepcopy(data_info)
    filtered_transforms = []
    for t in transforms:
        try:
            data_info = t(data_info)
            filtered_transforms.append(t)
        except KeyError:
            pass
    return filtered_transforms


[docs]class TextToImageRetrievalInferencer(BaseInferencer): """The inferencer for text 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 ``TextToImageRetrievalInferencer.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. fast_match (bool): Some algorithms will record extra image features for further matching, which may consume large memory, set True to avoid this behavior. Defaults to True. 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 TextToImageRetrievalInferencer >>> inferencer = TextToImageRetrievalInferencer( ... 'blip-base_3rdparty_retrieval', ... prototype='./demo/', ... prototype_cache='t2i_retri.pth') >>> inferencer('A cat and a dog.')[0] {'match_score': tensor(0.3855, device='cuda:0'), 'sample_idx': 1, 'sample': {'img_path': './demo/cat-dog.png'}} """ # noqa: E501 visualize_kwargs: set = { 'draw_score', 'show_dir', 'show', 'wait_time', 'figsize', 'topk' } postprocess_kwargs: set = {'topk'} def __init__(self, model: ModelType, prototype, prototype_cache=None, fast_match=True, 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.img_pipeline, self.text_pipeline = self.pipeline if hasattr(self.model, 'fast_match'): self.model.fast_match = fast_match self.prototype_dataset = self._prepare_prototype( prototype, prototype_cache, batch_size=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.img_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.img_pipeline] prototype.setdefault('pipeline', test_pipeline) dataset = DATASETS.build(prototype) dataloader = build_dataloader(dataset) elif isinstance(prototype, list): test_pipeline = [dict(type='LoadImageFromFile'), self.img_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, 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.prototype = torch.load(cache) else: prototype = [] for data_batch in track(dataloader, 'Prepare prototype...'): with torch.no_grad(): data_batch = self.model.data_preprocessor( data_batch, False) feats = self.model._run_forward(data_batch, mode='tensor') prototype.append(feats) prototype = { k: torch.cat([d[k] for d in prototype]) for k in prototype[0] } self.prototype = 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): torch.save(self.prototype, path)
[docs] def __call__(self, inputs: ImageType, 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)
[docs] @torch.no_grad() def forward(self, data: dict, **kwargs): """Feed the inputs to the model.""" data = self.model.data_preprocessor(data, False) data_samples = data['data_samples'] feats = self.prototype.copy() feats.update(self.model.extract_feat(data_samples=data_samples)) return self.model.predict_all(feats, data_samples, cal_i2t=False)[0]
def _init_pipeline(self, cfg: Config) -> Callable: test_pipeline_cfg = cfg.test_dataloader.dataset.pipeline test_transfroms = [TRANSFORMS.build(t) for t in test_pipeline_cfg] img_info = {'img': np.zeros((224, 224, 3), dtype=np.uint8)} text_info = {'text': 'example'} img_pipeline = Compose(filter_transforms(test_transfroms, img_info)) text_pipeline = Compose(filter_transforms(test_transfroms, text_info)) return img_pipeline, text_pipeline def preprocess(self, inputs: List[str], batch_size: int = 1): def process_text(input_: str): return self.text_pipeline({'text': input_}) chunked_data = self._get_chunk_data( map(process_text, inputs), batch_size) yield from map(default_collate, chunked_data) def visualize(self, ori_inputs: List[str], preds: List[DataSample], topk: int = 3, figsize: Tuple[int, int] = (16, 9), 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, (text, data_sample) in enumerate(zip(ori_inputs, preds)): 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_t2i_retrieval( text, data_sample, self.prototype_dataset, topk=topk, fig_cfg=dict(figsize=figsize), 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='Text-To-Image Retrieval')
[docs]class ImageToTextRetrievalInferencer(BaseInferencer): """The inferencer for image to text 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 ``ImageToTextRetrievalInferencer.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 file path to load the string list. - list: A list of string. 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. fast_match (bool): Some algorithms will record extra image features for further matching, which may consume large memory, set True to avoid this behavior. Defaults to True. 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 ImageToTextRetrievalInferencer >>> inferencer = ImageToTextRetrievalInferencer( ... 'blip-base_3rdparty_retrieval', ... prototype=['cat', 'dog', 'snake', 'bird'], ... prototype_cache='i2t_retri.pth') >>> inferencer('demo/bird.JPEG')[0] {'match_score': tensor(0.3855, device='cuda:0'), 'sample_idx': 1, 'sample': {'img_path': './demo/cat-dog.png'}} """ # 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, fast_match=True, 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.img_pipeline, self.text_pipeline = self.pipeline if hasattr(self.model, 'fast_match'): self.model.fast_match = fast_match self.prototype_dataset = self._prepare_prototype( prototype, cache=prototype_cache, batch_size=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( [ self.text_pipeline({ 'sample_idx': i, 'text': text }) for i, text in enumerate(dataset) ], batch_size=batch_size, collate_fn=default_collate, sampler=DefaultSampler(dataset, shuffle=False), persistent_workers=False, ) if isinstance(prototype, str): # A file path of a list of string dataset = mmengine.list_from_file(prototype) elif mmengine.utils.is_seq_of(prototype, str): dataset = prototype else: raise TypeError(f'Unsupported prototype type {type(prototype)}.') dataloader = build_dataloader(dataset) if cache is not None and Path(cache).exists(): self.prototype = torch.load(cache) else: prototype = [] for data_batch in track(dataloader, 'Prepare prototype...'): with torch.no_grad(): data_batch = self.model.data_preprocessor( data_batch, False) feats = self.model._run_forward(data_batch, mode='tensor') prototype.append(feats) prototype = { k: torch.cat([d[k] for d in prototype]) for k in prototype[0] } self.prototype = 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): torch.save(self.prototype, path)
[docs] def __call__(self, inputs: ImageType, 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)
[docs] @torch.no_grad() def forward(self, data: dict, **kwargs): """Feed the inputs to the model.""" data = self.model.data_preprocessor(data, False) feats = self.prototype.copy() feats.update(self.model.extract_feat(images=data['images'])) return self.model.predict_all( feats, data['data_samples'], cal_t2i=False)[0]
def _init_pipeline(self, cfg: Config) -> Callable: test_pipeline_cfg = cfg.test_dataloader.dataset.pipeline test_transfroms = [TRANSFORMS.build(t) for t in test_pipeline_cfg] img_info = {'img': np.zeros((224, 224, 3), dtype=np.uint8)} text_info = {'text': 'example'} img_pipeline = Compose(filter_transforms(test_transfroms, img_info)) text_pipeline = Compose(filter_transforms(test_transfroms, text_info)) return img_pipeline, text_pipeline def preprocess(self, inputs: List[ImageType], 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.img_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[ImageType], 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_i2t_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): text = self.prototype_dataset[sample_idx.item()] matches.append({ 'match_score': match_score, 'sample_idx': sample_idx, 'text': text }) 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-To-Text Retrieval')
Read the Docs v: dev
Versions
latest
stable
mmcls-1.x
mmcls-0.x
dev
Downloads
epub
On Read the Docs
Project Home
Builds

Free document hosting provided by Read the Docs.