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- # Copyright (c) OpenMMLab. All rights reserved.
- import hashlib
- import logging
- import os
- import os.path as osp
- import warnings
- from argparse import ArgumentParser
-
- import requests
- from mmcv import Config
-
- from mmseg.apis import inference_segmentor, init_segmentor, show_result_pyplot
- from mmseg.utils import get_root_logger
-
- # ignore warnings when segmentors inference
- warnings.filterwarnings('ignore')
-
-
- def download_checkpoint(checkpoint_name, model_name, config_name, collect_dir):
- """Download checkpoint and check if hash code is true."""
- url = f'https://download.openmmlab.com/mmsegmentation/v0.5/{model_name}/{config_name}/{checkpoint_name}' # noqa
-
- r = requests.get(url)
- assert r.status_code != 403, f'{url} Access denied.'
-
- with open(osp.join(collect_dir, checkpoint_name), 'wb') as code:
- code.write(r.content)
-
- true_hash_code = osp.splitext(checkpoint_name)[0].split('-')[1]
-
- # check hash code
- with open(osp.join(collect_dir, checkpoint_name), 'rb') as fp:
- sha256_cal = hashlib.sha256()
- sha256_cal.update(fp.read())
- cur_hash_code = sha256_cal.hexdigest()[:8]
-
- assert true_hash_code == cur_hash_code, f'{url} download failed, '
- 'incomplete downloaded file or url invalid.'
-
- if cur_hash_code != true_hash_code:
- os.remove(osp.join(collect_dir, checkpoint_name))
-
-
- def parse_args():
- parser = ArgumentParser()
- parser.add_argument('config', help='test config file path')
- parser.add_argument('checkpoint_root', help='Checkpoint file root path')
- parser.add_argument(
- '-i', '--img', default='demo/demo.png', help='Image file')
- parser.add_argument('-a', '--aug', action='store_true', help='aug test')
- parser.add_argument('-m', '--model-name', help='model name to inference')
- parser.add_argument(
- '-s', '--show', action='store_true', help='show results')
- parser.add_argument(
- '-d', '--device', default='cuda:0', help='Device used for inference')
- return parser.parse_args()
-
-
- def inference_model(config_name, checkpoint, args, logger=None):
- cfg = Config.fromfile(config_name)
- if args.aug:
- if 'flip' in cfg.data.test.pipeline[
- 1] and 'img_scale' in cfg.data.test.pipeline[1]:
- cfg.data.test.pipeline[1].img_ratios = [
- 0.5, 0.75, 1.0, 1.25, 1.5, 1.75
- ]
- cfg.data.test.pipeline[1].flip = True
- elif logger is None:
- print(f'{config_name}: unable to start aug test', flush=True)
- else:
- logger.error(f'{config_name}: unable to start aug test')
-
- model = init_segmentor(cfg, checkpoint, device=args.device)
- # test a single image
- result = inference_segmentor(model, args.img)
-
- # show the results
- if args.show:
- show_result_pyplot(model, args.img, result)
- return result
-
-
- # Sample test whether the inference code is correct
- def main(args):
- config = Config.fromfile(args.config)
-
- if not os.path.exists(args.checkpoint_root):
- os.makedirs(args.checkpoint_root, 0o775)
-
- # test single model
- if args.model_name:
- if args.model_name in config:
- model_infos = config[args.model_name]
- if not isinstance(model_infos, list):
- model_infos = [model_infos]
- for model_info in model_infos:
- config_name = model_info['config'].strip()
- print(f'processing: {config_name}', flush=True)
- checkpoint = osp.join(args.checkpoint_root,
- model_info['checkpoint'].strip())
- try:
- # build the model from a config file and a checkpoint file
- inference_model(config_name, checkpoint, args)
- except Exception:
- print(f'{config_name} test failed!')
- continue
- return
- else:
- raise RuntimeError('model name input error.')
-
- # test all model
- logger = get_root_logger(
- log_file='benchmark_inference_image.log', log_level=logging.ERROR)
-
- for model_name in config:
- model_infos = config[model_name]
-
- if not isinstance(model_infos, list):
- model_infos = [model_infos]
- for model_info in model_infos:
- print('processing: ', model_info['config'], flush=True)
- config_path = model_info['config'].strip()
- config_name = osp.splitext(osp.basename(config_path))[0]
- checkpoint_name = model_info['checkpoint'].strip()
- checkpoint = osp.join(args.checkpoint_root, checkpoint_name)
-
- # ensure checkpoint exists
- try:
- if not osp.exists(checkpoint):
- download_checkpoint(checkpoint_name, model_name,
- config_name.rstrip('.py'),
- args.checkpoint_root)
- except Exception:
- logger.error(f'{checkpoint_name} download error')
- continue
-
- # test model inference with checkpoint
- try:
- # build the model from a config file and a checkpoint file
- inference_model(config_path, checkpoint, args, logger)
- except Exception as e:
- logger.error(f'{config_path} " : {repr(e)}')
-
-
- if __name__ == '__main__':
- args = parse_args()
- main(args)
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