maskrcnn_resnet50_fpn use _save_for_lite_interpreter error

🐛 Bug

To Reproduce

Steps to reproduce the behavior:

model = torchvision.models.detection.maskrcnn_resnet50_fpn(num_classes=24,pretrained=False).to(device)
model.load_state_dict(checkpoint['model'])
model.eval()
2.
script_model = torch.jit.script(model)
opt_model = optimize_for_mobile(script_model)
opt_model._save_for_lite_interpreter("mask_rcnn_1.pt")
3.
RuntimeError: torch types other than torchbind (torch.torch.classes)are not supported in lite interpreter. Workaround: instead of using arbitrary class type (class Foo()), define a pytorch class (class Foo(torch.nn.Module)).

Expected behavior

Environment

Please copy and paste the output from our
environment collection script
(or fill out the checklist below manually).

You can get the script and run it with:

wget https://raw.githubusercontent.com/pytorch/pytorch/master/torch/utils/collect_env.py
# For security purposes, please check the contents of collect_env.py before running it.
python collect_env.py

PyTorch version: 1.9.0+cpu
Is debug build: False
CUDA used to build PyTorch: None
ROCM used to build PyTorch: N/A

OS: Microsoft Windows 10 专业版
GCC version: Could not collect
Clang version: Could not collect
CMake version: version 3.18.1
Libc version: N/A

Python version: 3.6.8 (tags/v3.6.8:3c6b436a57, Dec 24 2018, 00:16:47) [MSC v.1916 64 bit (AMD64)] (64-bit runtime)
Python platform: Windows-10-10.0.19041-SP0
Is CUDA available: False
CUDA runtime version: No CUDA
GPU models and configuration: No CUDA
Nvidia driver version: No CUDA
cuDNN version: No CUDA
HIP runtime version: N/A
MIOpen runtime version: N/A

Versions of relevant libraries:
[pip3] numpy==1.18.5
[pip3] torch==1.9.0
[pip3] torchaudio==0.9.0
[pip3] torchvision==0.10.0
[conda] Could not collect

Additional context

cc @datumbox @fmassa @vfdev-5 @pmeier

Reproduce the failure with maskrcnn_resnet50_fpn, torch.jit.script, optimize_for_mobile, and _save_for_lite_interpreter using the reported PyTorch 1.9.0 and torchvision 0.10.0 versions. Trace which model component produces the unsupported type and check related TorchScript or lite-interpreter tests. Done means the model can be saved for the lite interpreter without this RuntimeError.