def load(fpath):
"""
Load model from path
"""
import pie
with tarfile.open(utils.ensure_ext(fpath, 'tar'), 'r') as tar:
# check commit
try:
commit = utils.get_gzip_from_tar(tar, 'pie-commit.zip')
except Exception:
commit = None
if (pie.__commit__ and commit) and pie.__commit__ != commit:
logging.warn(
("Model {} was serialized with a previous "
"version of `pie`. This might result in issues. "
"Model commit is {}, whereas current `pie` commit is {}."
).format(fpath, commit, pie.__commit__))
# load label encoder
le = MultiLabelEncoder.load_from_string(
utils.get_gzip_from_tar(tar, 'label_encoder.zip'))
# load tasks
tasks = json.loads(utils.get_gzip_from_tar(tar, 'tasks.zip'))
# load model parameters
params = json.loads(utils.get_gzip_from_tar(tar, 'parameters.zip'))
# instantiate model
model_type = getattr(pie.models,
utils.get_gzip_from_tar(tar, 'class.zip'))
with utils.shutup():
model = model_type(le, tasks, *params['args'],
**params['kwargs'])
# load settings
try:
settings = Settings(
json.loads(utils.get_gzip_from_tar(tar, 'settings.zip')))
model._settings = settings
except Exception:
logging.warn(
"Couldn't load settings for model {}!".format(fpath))
# load state_dict
with utils.tmpfile() as tmppath:
tar.extract('state_dict.pt', path=tmppath)
dictpath = os.path.join(tmppath, 'state_dict.pt')
model.load_state_dict(torch.load(dictpath, map_location='cpu'))
model.eval()
return model
def load(fpath):
"""
Load model from path
"""
import pie
with tarfile.open(utils.ensure_ext(fpath, 'tar'), 'r') as tar:
# check commit
try:
commit = get_gzip_from_tar(tar, 'pie-commit.zip')
except Exception:
# no commit in file
commit = None
if pie.__commit__ is not None and commit is not None \
and pie.__commit__ != commit:
logging.warn(
("Model {} was serialized with a previous "
"version of `pie`. This might result in issues. "
"Model commit is {}, whereas current `pie` commit is {}."
).format(fpath, commit, pie.__commit__))
# load label encoder
le = MultiLabelEncoder.load_from_string(
get_gzip_from_tar(tar, 'label_encoder.zip'))
# load model parameters
params = json.loads(get_gzip_from_tar(tar, 'parameters.zip'))
# instantiate model
model_type = getattr(pie.models,
get_gzip_from_tar(tar, 'class.zip'))
with utils.shutup():
model = model_type(le, *params['args'], **params['kwargs'])
# (optional) load settings
try:
settings = Settings(
json.loads(get_gzip_from_tar(tar, 'settings.zip')))
model._settings = settings
except:
pass
# load state_dict
tmppath = '/tmp/{}'.format(str(uuid.uuid1()))
tar.extract('state_dict.pt', path=tmppath)
model.load_state_dict(
torch.load(os.path.join(tmppath, 'state_dict.pt')))
shutil.rmtree(tmppath)
model.eval()
return model