/home/chaeyun/.conda/envs/cris/lib/python3.9/site-packages/timm/models/layers/__init__.py:48: FutureWarning: Importing from timm.models.layers is deprecated, please import via timm.layers warnings.warn(f"Importing from {__name__} is deprecated, please import via timm.layers", FutureWarning) /home/chaeyun/.conda/envs/cris/lib/python3.9/site-packages/torch/functional.py:478: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:2894.) return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] Image size: 480 Easy & Hard Example Experiments - dataset : refcocog, split : motion loading dataset refcocog into memory... creating index... index created. DONE (t=6.33s) lavt_one Window size 12! Randomly initialize Multi-modal Swin Transformer weights. /home/chaeyun/.conda/envs/cris/lib/python3.9/site-packages/torchvision/transforms/functional.py:417: UserWarning: Argument 'interpolation' of type int is deprecated since 0.13 and will be removed in 0.15. Please use InterpolationMode enum. warnings.warn( /home/chaeyun/.conda/envs/cris/lib/python3.9/site-packages/torchvision/transforms/functional.py:417: UserWarning: Argument 'interpolation' of type int is deprecated since 0.13 and will be removed in 0.15. Please use InterpolationMode enum. warnings.warn( /home/chaeyun/.conda/envs/cris/lib/python3.9/site-packages/torchvision/transforms/functional.py:417: UserWarning: Argument 'interpolation' of type int is deprecated since 0.13 and will be removed in 0.15. Please use InterpolationMode enum. warnings.warn( /home/chaeyun/.conda/envs/cris/lib/python3.9/site-packages/torchvision/transforms/functional.py:417: UserWarning: Argument 'interpolation' of type int is deprecated since 0.13 and will be removed in 0.15. Please use InterpolationMode enum. warnings.warn( Test: [ 0/151] eta: 0:08:05 time: 3.2140 data: 0.9841 max mem: 1021 Test: [100/151] eta: 0:00:05 time: 0.0848 data: 0.0016 max mem: 1021 Test: Total time: 0:00:16 Final results: Mean IoU is 54.43 precision@0.5 = 60.93 precision@0.6 = 58.94 precision@0.7 = 53.64 precision@0.8 = 49.01 precision@0.9 = 29.14 overall IoU = 48.27 /home/chaeyun/.conda/envs/cris/lib/python3.9/site-packages/timm/models/layers/__init__.py:48: FutureWarning: Importing from timm.models.layers is deprecated, please import via timm.layers warnings.warn(f"Importing from {__name__} is deprecated, please import via timm.layers", FutureWarning) /home/chaeyun/.conda/envs/cris/lib/python3.9/site-packages/torch/functional.py:478: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:2894.) return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] Image size: 480 Easy & Hard Example Experiments - dataset : refcocog, split : static loading dataset refcocog into memory... creating index... index created. DONE (t=6.38s) lavt_one Window size 12! Randomly initialize Multi-modal Swin Transformer weights. /home/chaeyun/.conda/envs/cris/lib/python3.9/site-packages/torchvision/transforms/functional.py:417: UserWarning: Argument 'interpolation' of type int is deprecated since 0.13 and will be removed in 0.15. Please use InterpolationMode enum. warnings.warn( /home/chaeyun/.conda/envs/cris/lib/python3.9/site-packages/torchvision/transforms/functional.py:417: UserWarning: Argument 'interpolation' of type int is deprecated since 0.13 and will be removed in 0.15. Please use InterpolationMode enum. warnings.warn( /home/chaeyun/.conda/envs/cris/lib/python3.9/site-packages/torchvision/transforms/functional.py:417: UserWarning: Argument 'interpolation' of type int is deprecated since 0.13 and will be removed in 0.15. Please use InterpolationMode enum. warnings.warn( /home/chaeyun/.conda/envs/cris/lib/python3.9/site-packages/torchvision/transforms/functional.py:417: UserWarning: Argument 'interpolation' of type int is deprecated since 0.13 and will be removed in 0.15. Please use InterpolationMode enum. warnings.warn( Test: [ 0/151] eta: 0:07:47 time: 3.0938 data: 0.8532 max mem: 1021 Test: [100/151] eta: 0:00:05 time: 0.0853 data: 0.0016 max mem: 1021 Test: Total time: 0:00:15 Final results: Mean IoU is 73.06 precision@0.5 = 82.12 precision@0.6 = 80.79 precision@0.7 = 75.50 precision@0.8 = 70.20 precision@0.9 = 39.07 overall IoU = 67.73