Unexpected behaviour when replicating FPN and ResNet using DataParallel

## Instructions To Reproduce the Issue:

Running any training script in DataParallel mode on more than 2 devices will eventually trigger self.to(device) method to be called on FPN and ResNet. Unfortunately those classes store some modules using List instead of ModuleList (see for instance FPN.lateral_convs, FPN.output_convs or ResNet.stages) and those modules are not properly moved when self.to(device) method is called (which then triggers an error when the self.forward method is called on tensor on another device...)

## Expected behavior:

The expected behavior would be that all submodules involved in the forward methods of FPN and ResNet should be properly moved when .to() method is called. Using ModuleList instead of List should a good start to solve this issue.

## Your environment:

# platform: win-64
abseil-cpp=20210324.1=h0e60522_0
absl-py=1.0.0=pyhd8ed1ab_0
aiohttp=3.7.0=py37h4ab8f01_0
alembic=1.7.6=pyhd8ed1ab_0
antlr4-python3-runtime=4.8=pypi_0
appdirs=1.4.4=pypi_0
arrow-cpp=4.0.0=py37hb1a8454_3_cpu
async-timeout=3.0.1=py_1000
attrs=21.4.0=pyhd8ed1ab_0
autopage=0.5.0=pyhd8ed1ab_0
aws-c-cal=0.5.11=he19cf47_0
aws-c-common=0.6.2=h8ffe710_0
aws-c-event-stream=0.2.7=h70e1b0c_13
aws-c-io=0.10.5=h2fe331c_0
aws-checksums=0.1.11=h1e232aa_7
aws-sdk-cpp=1.8.186=hb0612c5_3
backports=1.0=py_2
backports.functools_lru_cache=1.6.4=pyhd8ed1ab_0
black=21.4b2=pypi_0
blas=1.0=mkl
blinker=1.4=py_1
brotli=1.0.9=h8ffe710_6
brotli-bin=1.0.9=h8ffe710_6
brotli-python=1.0.9=py37hf2a7229_6
brotlicffi=1.0.9.2=py37hf2a7229_1
brotlipy=0.7.0=py37hcc03f2d_1003
bzip2=1.0.8=h8ffe710_4
c-ares=1.18.1=h8ffe710_0
ca-certificates=2020.10.14=0
cachetools=5.0.0=pyhd8ed1ab_0
certifi=2020.6.20=py37_0
cffi=1.15.0=py37hd8e9650_0
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cliff=3.10.1=pyhd8ed1ab_0
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conllu=4.4.1=pyhd8ed1ab_0
cryptography=36.0.1=py37h65266a2_0
cudatoolkit=11.3.1=h59b6b97_2
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dataclasses=0.8=pyhc8e2a94_3
datasets=1.11.0=pyhd8ed1ab_0
detectron2=0.6=dev_0
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iopath=0.1.9=pypi_0
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m2w64-gcc-libs-core=5.3.0=7
m2w64-gmp=6.1.0=2
m2w64-libwinpthread-git=5.0.0.4634.697f757=2
mako=1.1.6=pyhd8ed1ab_0
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matplotlib-base=3.5.1=py37h4a79c79_0
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mkl_random=1.2.2=py37hf11a4ad_0
msys2-conda-epoch=20160418=1
multidict=6.0.2=py37hcc03f2d_0
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olefile=0.46=py37_0
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openssl=1.1.1m=h2bbff1b_0
optuna=2.10.0=pyhd8ed1ab_0
packaging=21.3=pyhd8ed1ab_0
pandas=1.1.3=py37ha925a31_0
parquet-cpp=1.5.1=2
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pbr=5.8.1=pyhd8ed1ab_0
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pip=21.2.4=py37haa95532_0
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prettytable=3.1.1=pyhd8ed1ab_0
protobuf=3.16.0=py37hf2a7229_0
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pyasn1=0.4.8=py_0
pyasn1-modules=0.2.7=py_0
pybcj=0.5.0=py37hcc03f2d_2
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pydot=1.4.2=pypi_0
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pysocks=1.7.1=py37h03978a9_4
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python-dateutil=2.8.1=py_0
python-xxhash=3.0.0=py37hcc03f2d_0
python_abi=3.7=2_cp37m
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pytorch-mutex=1.0=cuda
pytz=2020.1=py_0
pyu2f=0.1.5=pyhd8ed1ab_0
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pyyaml=6.0=py37hcc03f2d_3
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requests=2.27.1=pyhd8ed1ab_0
requests-oauthlib=1.3.1=pyhd8ed1ab_0
rsa=4.8=pyhd8ed1ab_0
sacremoses=0.0.46=pyhd8ed1ab_0
scikit-learn=1.0.2=py37hcabfae0_0
scipy=1.7.3=py37hb6553fb_0
seaborn=0.11.0=py_0
sentencepiece=0.1.96=py37h8c56517_0
seqeval=1.2.2=pyhd3deb0d_0
setuptools=58.0.4=py37haa95532_0
six=1.16.0=pyhd3eb1b0_1
snappy=1.1.8=ha925a31_3
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sqlite=3.37.2=h2bbff1b_0
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tabulate=0.8.9=pypi_0
tenacity=8.0.1=py37haa95532_0
tensorboard=2.8.0=pyhd8ed1ab_1
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tensorboard-plugin-wit=1.8.1=pyhd8ed1ab_0
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torchvision=0.11.3=py37_cu113
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tqdm=4.49.0=pyh9f0ad1d_0
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typing-extensions=3.10.0.2=hd3eb1b0_0
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vc=14.2=h21ff451_1
vs2015_runtime=14.27.29016=h5e58377_2
wcwidth=0.2.5=pyh9f0ad1d_2
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wheel=0.37.1=pyhd3eb1b0_0
win_inet_pton=1.1.0=py37h03978a9_3
wincertstore=0.2=py37haa95532_2
xxhash=0.8.0=h8ffe710_3
xz=5.2.5=h62dcd97_0
yacs=0.1.8=pypi_0
yaml=0.2.5=h8ffe710_2
yarl=1.6.0=py37h4ab8f01_0
zipp=3.7.0=pyhd8ed1ab_1
zlib=1.2.11=h8cc25b3_4
zstd=1.4.9=h19a0ad4_0

Locate FPN.lateral_convs, FPN.output_convs, and ResNet.stages, then reproduce the failure by running a training script with DataParallel on more than two devices. Check how .to(device) handles these stored submodules and add or update coverage for the forward path. Done means all submodules used by FPN and ResNet move correctly and the multi-device forward no longer raises a device mismatch error.

Written by the indexing model from the issue text.