209 lines
7.4 KiB
Python
209 lines
7.4 KiB
Python
import logging
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import re
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from argparse import ArgumentParser
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from pathlib import Path
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from time import time
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from typing import OrderedDict
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import numpy as np
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import torch
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from mmcv import Config
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from mmcv.parallel import collate, scatter
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from rich.console import Console
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from rich.table import Table
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from mmcls.apis import init_model
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from mmcls.core.visualization.image import imshow_infos
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from mmcls.datasets.imagenet import ImageNet
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from mmcls.datasets.pipelines import Compose
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from mmcls.utils import get_root_logger
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console = Console()
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MMCLS_ROOT = Path(__file__).absolute().parents[2]
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CIFAR10_CLASSES = [
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'airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse',
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'ship', 'truck'
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]
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CIFAR100_CLASSES = [
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'apple', 'aquarium_fish', 'baby', 'bear', 'beaver', 'bed', 'bee', 'beetle',
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'bicycle', 'bottle', 'bowl', 'boy', 'bridge', 'bus', 'butterfly', 'camel',
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'can', 'castle', 'caterpillar', 'cattle', 'chair', 'chimpanzee', 'clock',
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'cloud', 'cockroach', 'couch', 'crab', 'crocodile', 'cup', 'dinosaur',
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'dolphin', 'elephant', 'flatfish', 'forest', 'fox', 'girl', 'hamster',
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'house', 'kangaroo', 'keyboard', 'lamp', 'lawn_mower', 'leopard', 'lion',
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'lizard', 'lobster', 'man', 'maple_tree', 'motorcycle', 'mountain',
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'mouse', 'mushroom', 'oak_tree', 'orange', 'orchid', 'otter', 'palm_tree',
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'pear', 'pickup_truck', 'pine_tree', 'plain', 'plate', 'poppy',
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'porcupine', 'possum', 'rabbit', 'raccoon', 'ray', 'road', 'rocket',
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'rose', 'sea', 'seal', 'shark', 'shrew', 'skunk', 'skyscraper', 'snail',
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'snake', 'spider', 'squirrel', 'streetcar', 'sunflower', 'sweet_pepper',
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'table', 'tank', 'telephone', 'television', 'tiger', 'tractor', 'train',
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'trout', 'tulip', 'turtle', 'wardrobe', 'whale', 'willow_tree', 'wolf',
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'woman', 'worm'
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]
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classes_map = {
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'ImageNet-1k': ImageNet.CLASSES,
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'CIFAR-10': CIFAR10_CLASSES,
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'CIFAR-100': CIFAR100_CLASSES
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}
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def parse_args():
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parser = ArgumentParser(description='Valid all models in model-index.yml')
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parser.add_argument(
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'--checkpoint-root',
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help='Checkpoint file root path. If set, load checkpoint before test.')
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parser.add_argument('--img', default='demo/demo.JPEG', help='Image file')
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parser.add_argument('--models', nargs='+', help='models name to inference')
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parser.add_argument('--show', action='store_true', help='show results')
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parser.add_argument(
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'--wait-time',
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type=float,
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default=1,
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help='the interval of show (s), 0 is block')
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parser.add_argument(
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'--inference-time',
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action='store_true',
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help='Test inference time by run 10 times for each model.')
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parser.add_argument(
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'--device', default='cuda:0', help='Device used for inference')
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args = parser.parse_args()
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return args
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def inference(config_file, checkpoint, classes, args):
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cfg = Config.fromfile(config_file)
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model = init_model(cfg, checkpoint, device=args.device)
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model.CLASSES = classes
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# build the data pipeline
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if cfg.data.test.pipeline[0]['type'] != 'LoadImageFromFile':
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cfg.data.test.pipeline.insert(0, dict(type='LoadImageFromFile'))
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if cfg.data.test.type in ['CIFAR10', 'CIFAR100']:
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# The image shape of CIFAR is (32, 32, 3)
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cfg.data.test.pipeline.insert(1, dict(type='Resize', size=32))
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data = dict(img_info=dict(filename=args.img), img_prefix=None)
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test_pipeline = Compose(cfg.data.test.pipeline)
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data = test_pipeline(data)
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resolution = tuple(data['img'].shape[1:])
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data = collate([data], samples_per_gpu=1)
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if next(model.parameters()).is_cuda:
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# scatter to specified GPU
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data = scatter(data, [args.device])[0]
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# forward the model
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result = {'resolution': resolution}
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with torch.no_grad():
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if args.inference_time:
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time_record = []
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for _ in range(10):
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start = time()
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scores = model(return_loss=False, **data)
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time_record.append((time() - start) * 1000)
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result['time_mean'] = np.mean(time_record[1:-1])
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result['time_std'] = np.std(time_record[1:-1])
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else:
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scores = model(return_loss=False, **data)
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pred_score = np.max(scores, axis=1)[0]
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pred_label = np.argmax(scores, axis=1)[0]
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result['pred_label'] = pred_label
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result['pred_score'] = float(pred_score)
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result['pred_class'] = model.CLASSES[result['pred_label']]
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result['model'] = config_file.stem
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return result
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def show_summary(summary_data):
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table = Table(title='Validation Benchmark Regression Summary')
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table.add_column('Model')
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table.add_column('Validation')
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table.add_column('Resolution (h, w)')
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table.add_column('Inference Time (std) (ms/im)')
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for model_name, summary in summary_data.items():
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row = [model_name]
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valid = summary['valid']
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color = 'green' if valid == 'PASS' else 'red'
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row.append(f'[{color}]{valid}[/{color}]')
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if valid == 'PASS':
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row.append(str(summary['resolution']))
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if 'time_mean' in summary:
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time_mean = f"{summary['time_mean']:.2f}"
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time_std = f"{summary['time_std']:.2f}"
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row.append(f'{time_mean}\t({time_std})'.expandtabs(8))
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table.add_row(*row)
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console.print(table)
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# Sample test whether the inference code is correct
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def main(args):
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model_index_file = MMCLS_ROOT / 'model-index.yml'
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model_index = Config.fromfile(model_index_file)
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models = OrderedDict()
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for file in model_index.Import:
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metafile = Config.fromfile(MMCLS_ROOT / file)
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models.update({model.Name: model for model in metafile.Models})
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logger = get_root_logger(
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log_file='benchmark_test_image.log', log_level=logging.INFO)
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if args.models:
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patterns = [re.compile(pattern) for pattern in args.models]
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filter_models = {}
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for k, v in models.items():
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if any([re.match(pattern, k) for pattern in patterns]):
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filter_models[k] = v
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if len(filter_models) == 0:
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print('No model found, please specify models in:')
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print('\n'.join(models.keys()))
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return
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models = filter_models
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summary_data = {}
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for model_name, model_info in models.items():
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config = Path(model_info.Config)
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assert config.exists(), f'{model_name}: {config} not found.'
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logger.info(f'Processing: {model_name}')
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http_prefix = 'https://download.openmmlab.com/mmclassification/'
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dataset = model_info.Results[0]['Dataset']
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if args.checkpoint_root is not None:
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root = Path(args.checkpoint_root)
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checkpoint = root / model_info.Weights[len(http_prefix):]
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checkpoint = str(checkpoint)
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else:
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checkpoint = None
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try:
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# build the model from a config file and a checkpoint file
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result = inference(MMCLS_ROOT / config, checkpoint,
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classes_map[dataset], args)
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result['valid'] = 'PASS'
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except Exception as e:
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logger.error(f'"{config}" : {repr(e)}')
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result = {'valid': 'FAIL'}
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summary_data[model_name] = result
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# show the results
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if args.show:
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imshow_infos(args.img, result, wait_time=args.wait_time)
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show_summary(summary_data)
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if __name__ == '__main__':
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args = parse_args()
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main(args)
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