222 lines
8.4 KiB
Python
222 lines
8.4 KiB
Python
# 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 Compose, default_collate
|
|
|
|
from mmpretrain.registry import TRANSFORMS
|
|
from mmpretrain.structures import DataSample
|
|
from .base import BaseInferencer, InputType, ModelType
|
|
from .model import list_models
|
|
|
|
|
|
class ImageClassificationInferencer(BaseInferencer):
|
|
"""The inferencer for image classification.
|
|
|
|
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 ``ImageClassificationInferencer.list_models()`` and you can also
|
|
query it in :doc:`/modelzoo_statistics`.
|
|
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:
|
|
1. Use a pre-trained model in MMPreTrain to inference an image.
|
|
|
|
>>> from mmpretrain import ImageClassificationInferencer
|
|
>>> inferencer = ImageClassificationInferencer('resnet50_8xb32_in1k')
|
|
>>> inferencer('demo/demo.JPEG')
|
|
[{'pred_score': array([...]),
|
|
'pred_label': 65,
|
|
'pred_score': 0.6649367809295654,
|
|
'pred_class': 'sea snake'}]
|
|
|
|
2. Use a config file and checkpoint to inference multiple images on GPU,
|
|
and save the visualization results in a folder.
|
|
|
|
>>> from mmpretrain import ImageClassificationInferencer
|
|
>>> inferencer = ImageClassificationInferencer(
|
|
model='configs/resnet/resnet50_8xb32_in1k.py',
|
|
pretrained='https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth',
|
|
device='cuda')
|
|
>>> inferencer(['demo/dog.jpg', 'demo/bird.JPEG'], show_dir="./visualize/")
|
|
""" # noqa: E501
|
|
|
|
visualize_kwargs: set = {
|
|
'resize', 'rescale_factor', 'draw_score', 'show', 'show_dir',
|
|
'wait_time'
|
|
}
|
|
|
|
def __init__(self,
|
|
model: ModelType,
|
|
pretrained: Union[bool, str] = True,
|
|
device: Union[str, torch.device, None] = None,
|
|
classes=None,
|
|
**kwargs) -> None:
|
|
super().__init__(
|
|
model=model, pretrained=pretrained, device=device, **kwargs)
|
|
|
|
if classes is not None:
|
|
self.classes = classes
|
|
else:
|
|
self.classes = getattr(self.model, '_dataset_meta',
|
|
{}).get('classes')
|
|
|
|
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 short edge of the image to the
|
|
specified length before visualization. Defaults to None.
|
|
rescale_factor (float, optional): Rescale the image by the rescale
|
|
factor for visualization. This is helpful when the image is too
|
|
large or too small for visualization. Defaults to None.
|
|
draw_score (bool): Whether to draw the prediction scores
|
|
of prediction categories. 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=return_datasamples,
|
|
batch_size=batch_size,
|
|
**kwargs)
|
|
|
|
def _init_pipeline(self, cfg: Config) -> Callable:
|
|
test_pipeline_cfg = cfg.test_dataloader.dataset.pipeline
|
|
if test_pipeline_cfg[0]['type'] == 'LoadImageFromFile':
|
|
# Image loading is finished in `self.preprocess`.
|
|
test_pipeline_cfg = test_pipeline_cfg[1:]
|
|
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],
|
|
show: bool = False,
|
|
wait_time: int = 0,
|
|
resize: Optional[int] = None,
|
|
rescale_factor: Optional[float] = None,
|
|
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_cls(
|
|
image,
|
|
data_sample,
|
|
classes=self.classes,
|
|
resize=resize,
|
|
show=show,
|
|
wait_time=wait_time,
|
|
rescale_factor=rescale_factor,
|
|
draw_gt=False,
|
|
draw_pred=True,
|
|
draw_score=draw_score,
|
|
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) -> dict:
|
|
if return_datasamples:
|
|
return preds
|
|
|
|
results = []
|
|
for data_sample in preds:
|
|
pred_scores = data_sample.pred_score
|
|
pred_score = float(torch.max(pred_scores).item())
|
|
pred_label = torch.argmax(pred_scores).item()
|
|
result = {
|
|
'pred_scores': pred_scores.detach().cpu().numpy(),
|
|
'pred_label': pred_label,
|
|
'pred_score': pred_score,
|
|
}
|
|
if self.classes is not None:
|
|
result['pred_class'] = self.classes[pred_label]
|
|
results.append(result)
|
|
|
|
return results
|
|
|
|
@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 Classification')
|