def load_from_metrics(cls,
weights_path,
tags_csv,
on_gpu,
map_location=None):
"""
Primary way of loading model from csv weights path
:param weights_path:
:param tags_csv:
:param on_gpu:
:param map_location: dic for mapping storage {'cuda:1':'cuda:0'}
:return:
"""
hparams = load_hparams_from_tags_csv(tags_csv)
hparams.__setattr__('on_gpu', on_gpu)
if on_gpu:
if map_location is not None:
checkpoint = torch.load(weights_path,
map_location=map_location)
else:
checkpoint = torch.load(weights_path)
else:
checkpoint = torch.load(weights_path,
map_location=lambda storage, loc: storage)
model = cls(hparams)
# allow model to load
model.load_model_specific(checkpoint)
model.load_state_dict(checkpoint['state_dict'], strict=False)
return model
def load_from_metrics(cls,
weights_path,
tags_csv,
on_gpu,
map_location=None):
"""
Primary way of loading model from csv weights path
:param weights_path:
:param tags_csv:
:param on_gpu:
:param map_location: dic for mapping storage {'cuda:1':'cuda:0'}
:return:
"""
hparams = load_hparams_from_tags_csv(tags_csv)
hparams.__setattr__('on_gpu', on_gpu)
if on_gpu:
if map_location is not None:
checkpoint = torch.load(weights_path,
map_location=map_location)
else:
checkpoint = torch.load(weights_path)
else:
checkpoint = torch.load(weights_path,
map_location=lambda storage, loc: storage)
# load the state_dict on the model automatically
model = cls(hparams)
model.load_state_dict(checkpoint['state_dict'])
# give model a chance to load something
model.on_load_checkpoint(checkpoint)
return model
def test_loading_meta_tags():
hparams = get_hparams()
# save tags
exp = get_exp(False)
exp.tag({'some_str': 'a_str', 'an_int': 1, 'a_float': 2.0})
exp.argparse(hparams)
exp.save()
# load tags
tags_path = exp.get_data_path(exp.name, exp.version) + '/meta_tags.csv'
tags = model_saving.load_hparams_from_tags_csv(tags_path)
assert tags.batch_size == 32 and tags.hidden_dim == 1000
clear_save_dir()
