mmpretrain.apis.image_classification 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 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 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/', pretrained='', 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')
[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 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 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], 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
[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 Classification')
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