Depreciation warning when running inference w/Pytorch 1.10.1

## Depreciation warning when running inference with Detectron2 v0.6 + pytorch 1.10.1, cuda 10.2

## Details

detectron2/detectron2/structures/image_list.py:88: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').
max_size = (max_size + (stride - 1)) // stride * stride

## Environment:

---------------------- ----------------------------------------------------------------
sys.platform linux
Python 3.9.6 (default, Feb 18 2022, 13:15:09) [GCC 7.5.0]
numpy 1.22.1
detectron2 0.6 @/opt/thirdparty/detectron2/detectron2
Compiler GCC 7.5
CUDA compiler CUDA 10.2
detectron2 arch flags 5.0, 6.0, 6.1, 7.0, 7.5
DETECTRON2_ENV_MODULE
PyTorch 1.10.1+cu102 @/usr/local/lib/python3.9/site-packages/torch
PyTorch debug build False
GPU available Yes
GPU 0,1,2,3 NVIDIA GeForce GTX 1080 (arch=6.1)
Driver version 470.103.01
CUDA_HOME /usr/local/cuda
TORCH_CUDA_ARCH_LIST 5.0;6.0;6.1;7.0;7.5
Pillow 9.0.1
torchvision 0.11.2+cu102 @/usr/local/lib/python3.9/site-packages/torchvision
torchvision arch flags 3.5, 5.0, 6.0, 7.0, 7.5
fvcore 0.1.5.post20220212
iopath 0.1.9
cv2 4.4.0
---------------------- ----------------------------------------------------------------
PyTorch built with:
- GCC 7.3
- C++ Version: 201402
- Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications
- Intel(R) MKL-DNN v2.2.3 (Git Hash 7336ca9f055cf1bfa13efb658fe15dc9b41f0740)
- OpenMP 201511 (a.k.a. OpenMP 4.5)
- LAPACK is enabled (usually provided by MKL)
- NNPACK is enabled
- CPU capability usage: AVX2
- CUDA Runtime 10.2
- NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70
- CuDNN 7.6.5
- Magma 2.5.2
- Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=10.2, CUDNN_VERSION=7.6.5, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.10.1, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON,

Testing NCCL connectivity ... this should not hang.
NCCL succeeded.

The warning points to detectron2/detectron2/structures/image_list.py:88; start by tracing the max_size calculation and checking its behavior under PyTorch 1.10.1. Use an inference path that exercises ImageList sizing to confirm the warning is gone and the rounding behavior remains correct.

Written by the indexing model from the issue text.