mmpose-0.29.0/tests/test_config.py

# Copyright (c) OpenMMLab. All rights reserved.
from os.path import dirname, exists, join, relpath

import torch
from mmcv.runner import build_optimizer


def _get_config_directory():
 """Find the predefined detector config directory."""
 try:
 # Assume we are running in the source mmdetection repo
 repo_dpath = dirname(dirname(__file__))
 except NameError:
 # For IPython development when this __file__ is not defined
 import mmpose
 repo_dpath = dirname(dirname(mmpose.__file__))
 config_dpath = join(repo_dpath, 'configs')
 if not exists(config_dpath):
 raise Exception('Cannot find config path')
 return config_dpath


def test_config_build_detector():
 """Test that all detection models defined in the configs can be
 initialized."""
 from mmcv import Config

 from mmpose.models import build_posenet

 config_dpath = _get_config_directory()
 print(f'Found config_dpath = {config_dpath}')

 import glob
 config_fpaths = list(glob.glob(join(config_dpath, '**', '*.py')))
 config_fpaths = [p for p in config_fpaths if p.find('_base_') == -1]
 config_names = [relpath(p, config_dpath) for p in config_fpaths]

 print(f'Using {len(config_names)} config files')

 for config_fname in config_names:
 config_fpath = join(config_dpath, config_fname)
 config_mod = Config.fromfile(config_fpath)

 print(f'Building detector, config_fpath = {config_fpath}')

 # Remove pretrained keys to allow for testing in an offline environment
 if 'pretrained' in config_mod.model:
 config_mod.model['pretrained'] = None

 detector = build_posenet(config_mod.model)
 assert detector is not None

 optimizer = build_optimizer(detector, config_mod.optimizer)
 assert isinstance(optimizer, torch.optim.Optimizer)