|
|
|
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: |
|
|
|
repo_dpath = dirname(dirname(__file__)) |
|
except NameError: |
|
|
|
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}') |
|
|
|
|
|
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) |
|
|