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')