CocoDetection wrapped with wrap_dataset_for_transforms_v2 returns image_id even if not in target_keys

Describe the bug

The COCO dataset contains examples without any object. For these samples, the target list returned by CocoDetection is empty. After wrapping the dataset with wrap_dataset_for_transforms_v2, it always returns {'image_id': image_id} for these empty samples, even if "image_id" is not in the target_keys wrapper argument. This can cause bugs downstream, for example when batching, or in custom target transformers relying on the absence of the image_id entry.

See below for a minimal example using a target transformer to encode class ids to multi-label binary vectors:

import pathlib

import torch
from torchvision import datasets
from torchvision.transforms import v2

DATA_ROOT = pathlib.Path("data") / "COCO"
ANNOTATIONS_DIR = DATA_ROOT / "annotations"

val_dataset_notransform = datasets.CocoDetection(
    str(DATA_ROOT / "val2017"),
    ANNOTATIONS_DIR / "instances_val2017.json",
)
cat_ids = torch.tensor(sorted(val_dataset_notransform.coco.getCatIds()))
multi_label_binarizer = v2.Lambda(
    lambda y: torch.zeros(
        len(cat_ids), dtype=torch.float
    ).scatter_(
        dim=0, index=torch.bucketize(y, cat_ids), value=1
    )
)

val_dataset = datasets.CocoDetection(
    str(DATA_ROOT / "val2017"),
    ANNOTATIONS_DIR / "instances_val2017.json",
    target_transform=multi_label_binarizer,
)
val_dataset = datasets.wrap_dataset_for_transforms_v2(val_dataset, target_keys=['labels'])

for i in range(len(val_dataset)):
    try:
        val_dataset[i]
    except:
        print(f"Error at {i=}")
        raise

Versions

Collecting environment information... PyTorch version: 2.7.1+cu126 Is debug build: False CUDA used to build PyTorch: 12.6 ROCM used to build PyTorch: N/A

OS: Ubuntu 24.04.1 LTS (x86_64) GCC version: (Ubuntu 13.3.0-6ubuntu2~24.04) 13.3.0 Clang version: Could not collect CMake version: version 3.28.3 Libc version: glibc-2.39

Python version: 3.12.9 | packaged by Anaconda, Inc. | (main, Feb 6 2025, 18:56:27) [GCC 11.2.0] (64-bit runtime) Python platform: Linux-5.15.0-122-generic-x86_64-with-glibc2.39 Is CUDA available: False CUDA runtime version: 12.8.61 CUDA_MODULE_LOADING set to: N/A GPU models and configuration: Could not collect Nvidia driver version: Could not collect cuDNN version: Probably one of the following: /usr/lib/x86_64-linux-gnu/libcudnn.so.9.7.0 /usr/lib/x86_64-linux-gnu/libcudnn_adv.so.9.7.0 /usr/lib/x86_64-linux-gnu/libcudnn_cnn.so.9.7.0 /usr/lib/x86_64-linux-gnu/libcudnn_engines_precompiled.so.9.7.0 /usr/lib/x86_64-linux-gnu/libcudnn_engines_runtime_compiled.so.9.7.0 /usr/lib/x86_64-linux-gnu/libcudnn_graph.so.9.7.0 /usr/lib/x86_64-linux-gnu/libcudnn_heuristic.so.9.7.0 /usr/lib/x86_64-linux-gnu/libcudnn_ops.so.9.7.0 Is XPU available: False 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 Address sizes: 46 bits physical, 48 bits virtual Byte Order: Little Endian CPU(s): 60 On-line CPU(s) list: 0-59 Vendor ID: GenuineIntel Model name: Intel(R) Xeon(R) Gold 6226R CPU @ 2.90GHz CPU family: 6 Model: 85 Thread(s) per core: 1 Core(s) per socket: 1 Socket(s): 60 Stepping: 7 BogoMIPS: 5786.40 Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss syscall nx pdpe1gb rdtscp lm constant_tsc arch_perfmon rep_good nopl xtopology cpuid tsc_known_freq pni pclmulqdq vmx ssse3 fma cx16 pdcm pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch cpuid_fault invpcid_single ssbd ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid mpx avx512f avx512dq rdseed adx smap clflushopt clwb avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves arat umip pku ospke avx512_vnni md_clear arch_capabilities Virtualization: VT-x Hypervisor vendor: KVM Virtualization type: full L1d cache: 1.9 MiB (60 instances) L1i cache: 1.9 MiB (60 instances) L2 cache: 240 MiB (60 instances) L3 cache: 960 MiB (60 instances) NUMA node(s): 1 NUMA node0 CPU(s): 0-59 Vulnerability Gather data sampling: Not affected Vulnerability Itlb multihit: Not affected Vulnerability L1tf: Not affected Vulnerability Mds: Not affected Vulnerability Meltdown: Not affected Vulnerability Mmio stale data: Mitigation; Clear CPU buffers; SMT Host state unknown Vulnerability Reg file data sampling: Not affected Vulnerability Retbleed: Mitigation; Enhanced IBRS 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; Enhanced / Automatic IBRS; IBPB conditional; RSB filling; PBRSB-eIBRS SW sequence; BHI SW loop, KVM SW loop Vulnerability Srbds: Not affected Vulnerability Tsx async abort: Mitigation; TSX disabled

Versions of relevant libraries: [pip3] numpy==2.3.0 [pip3] nvidia-cublas-cu12==12.6.4.1 [pip3] nvidia-cuda-cupti-cu12==12.6.80 [pip3] nvidia-cuda-nvrtc-cu12==12.6.77 [pip3] nvidia-cuda-runtime-cu12==12.6.77 [pip3] nvidia-cudnn-cu12==9.5.1.17 [pip3] nvidia-cufft-cu12==11.3.0.4 [pip3] nvidia-curand-cu12==10.3.7.77 [pip3] nvidia-cusolver-cu12==11.7.1.2 [pip3] nvidia-cusparse-cu12==12.5.4.2 [pip3] nvidia-cusparselt-cu12==0.6.3 [pip3] nvidia-nccl-cu12==2.26.2 [pip3] nvidia-nvjitlink-cu12==12.6.85 [pip3] nvidia-nvtx-cu12==12.6.77 [pip3] torch==2.7.1 [pip3] torchaudio==2.7.1 [pip3] torchvision==0.22.1 [pip3] triton==3.3.1 [conda] numpy 2.3.0 pypi_0 pypi [conda] nvidia-cublas-cu12 12.6.4.1 pypi_0 pypi [conda] nvidia-cuda-cupti-cu12 12.6.80 pypi_0 pypi [conda] nvidia-cuda-nvrtc-cu12 12.6.77 pypi_0 pypi [conda] nvidia-cuda-runtime-cu12 12.6.77 pypi_0 pypi [conda] nvidia-cudnn-cu12 9.5.1.17 pypi_0 pypi [conda] nvidia-cufft-cu12 11.3.0.4 pypi_0 pypi [conda] nvidia-curand-cu12 10.3.7.77 pypi_0 pypi [conda] nvidia-cusolver-cu12 11.7.1.2 pypi_0 pypi [conda] nvidia-cusparse-cu12 12.5.4.2 pypi_0 pypi [conda] nvidia-cusparselt-cu12 0.6.3 pypi_0 pypi [conda] nvidia-nccl-cu12 2.26.2 pypi_0 pypi [conda] nvidia-nvjitlink-cu12 12.6.85 pypi_0 pypi [conda] nvidia-nvtx-cu12 12.6.77 pypi_0 pypi [conda] torch 2.7.1 pypi_0 pypi [conda] torchaudio 2.7.1 pypi_0 pypi [conda] torchvision 0.22.1 pypi_0 pypi [conda] triton 3.3.1 pypi_0 pypi