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You are reading the documentation for MMClassification 0.x, which will soon be deprecated at the end of 2022. We recommend you upgrade to MMClassification 1.0 to enjoy fruitful new features and better performance brought by OpenMMLab 2.0. Check the installation tutorial, migration tutorial and changelog for more details.

Source code for mmcls.apis.inference

# Copyright (c) OpenMMLab. All rights reserved.
import warnings

import mmcv
import numpy as np
import torch
from mmcv.parallel import collate, scatter
from mmcv.runner import load_checkpoint

from mmcls.datasets.pipelines import Compose
from mmcls.models import build_classifier


[docs]def init_model(config, checkpoint=None, device='cuda:0', options=None): """Initialize a classifier from config file. Args: config (str or :obj:`mmcv.Config`): Config file path or the config object. checkpoint (str, optional): Checkpoint path. If left as None, the model will not load any weights. options (dict): Options to override some settings in the used config. Returns: nn.Module: The constructed classifier. """ if isinstance(config, str): config = mmcv.Config.fromfile(config) elif not isinstance(config, mmcv.Config): raise TypeError('config must be a filename or Config object, ' f'but got {type(config)}') if options is not None: config.merge_from_dict(options) config.model.pretrained = None model = build_classifier(config.model) if checkpoint is not None: # Mapping the weights to GPU may cause unexpected video memory leak # which refers to https://github.com/open-mmlab/mmdetection/pull/6405 checkpoint = load_checkpoint(model, checkpoint, map_location='cpu') if 'CLASSES' in checkpoint.get('meta', {}): model.CLASSES = checkpoint['meta']['CLASSES'] else: from mmcls.datasets import ImageNet warnings.simplefilter('once') warnings.warn('Class names are not saved in the checkpoint\'s ' 'meta data, use imagenet by default.') model.CLASSES = ImageNet.CLASSES model.cfg = config # save the config in the model for convenience model.to(device) model.eval() return model
[docs]def inference_model(model, img): """Inference image(s) with the classifier. Args: model (nn.Module): The loaded classifier. img (str/ndarray): The image filename or loaded image. Returns: result (dict): The classification results that contains `class_name`, `pred_label` and `pred_score`. """ cfg = model.cfg device = next(model.parameters()).device # model device # build the data pipeline if isinstance(img, str): if cfg.data.test.pipeline[0]['type'] != 'LoadImageFromFile': cfg.data.test.pipeline.insert(0, dict(type='LoadImageFromFile')) data = dict(img_info=dict(filename=img), img_prefix=None) else: if cfg.data.test.pipeline[0]['type'] == 'LoadImageFromFile': cfg.data.test.pipeline.pop(0) data = dict(img=img) test_pipeline = Compose(cfg.data.test.pipeline) data = test_pipeline(data) data = collate([data], samples_per_gpu=1) if next(model.parameters()).is_cuda: # scatter to specified GPU data = scatter(data, [device])[0] # forward the model with torch.no_grad(): scores = model(return_loss=False, **data) pred_score = np.max(scores, axis=1)[0] pred_label = np.argmax(scores, axis=1)[0] result = {'pred_label': pred_label, 'pred_score': float(pred_score)} result['pred_class'] = model.CLASSES[result['pred_label']] return result
[docs]def show_result_pyplot(model, img, result, fig_size=(15, 10), title='result', wait_time=0): """Visualize the classification results on the image. Args: model (nn.Module): The loaded classifier. img (str or np.ndarray): Image filename or loaded image. result (list): The classification result. fig_size (tuple): Figure size of the pyplot figure. Defaults to (15, 10). title (str): Title of the pyplot figure. Defaults to 'result'. wait_time (int): How many seconds to display the image. Defaults to 0. """ if hasattr(model, 'module'): model = model.module model.show_result( img, result, show=True, fig_size=fig_size, win_name=title, wait_time=wait_time)
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