Skip to content

Set weights_only to false for pytorch convert #3997

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
Wovchena merged 1 commit into from
Feb 12, 2025
Merged

Set weights_only to false for pytorch convert #3997

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
11 changes: 9 additions & 2 deletions models/public/regnetx-3.2gf/model.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -12,11 +12,18 @@
# See the License for the specific language governing permissions and
# limitations under the License.

import pycls.core.checkpoint
import torch
import pycls.core.config
import pycls.models.model_zoo
from pycls.core.checkpoint import unwrap_model

def regnet(config_path, weights_path):
pycls.core.config.cfg.merge_from_file(config_path)
model = pycls.models.model_zoo.RegNet()
pycls.core.checkpoint.load_checkpoint(weights_path, model)
checkpoint = torch.load(weights_path, map_location="cpu", weights_only=False)
test_err = checkpoint.get("test_err", 100)
ema_err = checkpoint.get("ema_err", 100)
ema_state = "ema_state" if "ema_state" in checkpoint else "model_state"
best_state = "model_state" if test_err <= ema_err else ema_state
unwrap_model(model).load_state_dict(checkpoint[best_state])
return model
2 changes: 1 addition & 1 deletion tools/model_tools/src/omz_tools/internal_scripts/pytorch_to_onnx.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -144,7 +144,7 @@ def load_model(model_name, weights, model_paths, module_name, model_params):

try:
if weights:
model.load_state_dict(torch.load(weights, map_location='cpu'))
model.load_state_dict(torch.load(weights, map_location='cpu', weights_only=False))
except RuntimeError as err:
print('ERROR: Weights from {} cannot be loaded for model {}! Check matching between model and weights'.format(
weights, model_name))
Expand Down
Loading