Custom Transformer overwritten in FasterRCNN

🐛 Describe the bug

In faster_rcnn.py during initialization a transform is created and passed along

transform = GeneralizedRCNNTransform(min_size, max_size, image_mean, image_std, **kwargs)
super().__init__(backbone, rpn, roi_heads, transform)

If the user wants to use a custom transform whatever reason and passes it in during initialization it is not used, shouldn't the code instead look something like

        if kwargs.get('transform', None) is None:
            transform = GeneralizedRCNNTransform(min_size, max_size, image_mean, image_std, **kwargs)
        else:
            transform = kwargs['transform']
        super().__init__(backbone, rpn, roi_heads, transform)

Versions

Collecting environment information...
PyTorch version: 2.3.0+cu121
Is debug build: False
CUDA used to build PyTorch: 12.1
ROCM used to build PyTorch: N/A

OS: Ubuntu 20.04.6 LTS (x86_64)
GCC version: (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0
Clang version: Could not collect
CMake version: version 3.16.3
Libc version: glibc-2.31

Python version: 3.8.10 (default, Nov  7 2024, 13:10:47)  [GCC 9.4.0] (64-bit runtime)
Python platform: Linux-5.15.0-125-generic-x86_64-with-glibc2.29
Is CUDA available: True
CUDA runtime version: Could not collect
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: GPU 0: NVIDIA TITAN X (Pascal)
Nvidia driver version: 535.183.01
cuDNN version: Could not collect
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

CPU:
Architecture:                         x86_64
CPU op-mode(s):                       32-bit, 64-bit
Byte Order:                           Little Endian
Address sizes:                        39 bits physical, 48 bits virtual
CPU(s):                               8
On-line CPU(s) list:                  0-7
Thread(s) per core:                   2
Core(s) per socket:                   4
Socket(s):                            1
NUMA node(s):                         1
Vendor ID:                            GenuineIntel
CPU family:                           6
Model:                                60
Model name:                           Intel(R) Core(TM) i7-4790K CPU @ 4.00GHz
Stepping:                             3
CPU MHz:                              4315.243
CPU max MHz:                          4400.0000
CPU min MHz:                          800.0000
BogoMIPS:                             7997.66
Virtualization:                       VT-x
L1d cache:                            128 KiB
L1i cache:                            128 KiB
L2 cache:                             1 MiB
L3 cache:                             8 MiB
NUMA node0 CPU(s):                    0-7
Vulnerability Gather data sampling:   Not affected
Vulnerability Itlb multihit:          KVM: Mitigation: VMX disabled
Vulnerability L1tf:                   Mitigation; PTE Inversion; VMX conditional cache flushes, SMT vulnerable
Vulnerability Mds:                    Mitigation; Clear CPU buffers; SMT vulnerable
Vulnerability Meltdown:               Mitigation; PTI
Vulnerability Mmio stale data:        Unknown: No mitigations
Vulnerability Reg file data sampling: Not affected
Vulnerability Retbleed:               Not affected
Vulnerability Spec rstack overflow:   Not affected
Vulnerability Spec store bypass:      Mitigation; Speculative Store Bypass disabled via prctl and seccomp
Vulnerability Spectre v1:             Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:             Mitigation; Retpolines; IBPB conditional; IBRS_FW; STIBP conditional; RSB filling; PBRSB-eIBRS Not affected; BHI Not affected
Vulnerability Srbds:                  Mitigation; Microcode
Vulnerability Tsx async abort:        Not affected
Flags:                                fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 monitor ds_cpl vmx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm cpuid_fault invpcid_single pti ssbd ibrs ibpb stibp tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid xsaveopt dtherm ida arat pln pts md_clear flush_l1d

Versions of relevant libraries:
[pip3] mypy-extensions==1.0.0
[pip3] numpy==1.24.4
[pip3] nvidia-cublas-cu12==12.1.3.1
[pip3] nvidia-cuda-cupti-cu12==12.1.105
[pip3] nvidia-cuda-nvrtc-cu12==12.1.105
[pip3] nvidia-cuda-runtime-cu12==12.1.105
[pip3] nvidia-cudnn-cu12==8.9.2.26
[pip3] nvidia-cufft-cu12==11.0.2.54
[pip3] nvidia-curand-cu12==10.3.2.106
[pip3] nvidia-cusolver-cu12==11.4.5.107
[pip3] nvidia-cusparse-cu12==12.1.0.106
[pip3] nvidia-nccl-cu12==2.20.5
[pip3] nvidia-nvjitlink-cu12==12.2.140
[pip3] nvidia-nvtx-cu12==12.1.105
[pip3] torch==2.3.0
[pip3] torchaudio==2.3.0
[pip3] torchmetrics==1.4.0
[pip3] torchserve==0.10.0
[pip3] torchvision==0.18.0
[pip3] triton==2.3.0
[conda] Could not collect

Start in faster_rcnn.py at the initialization code shown in the issue and trace how the transform is passed to the parent class. Confirm that a caller-provided custom transform is preserved while the default is still created when none is supplied; done means both initialization paths behave as intended.