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# 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)
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