Bug on version 3.6: "TypeError: 'ImageDetectionPrediction' object is not iterable" Cycling through prediction results. Version 3.5 works ok.

🐛 Describe the bug

This just happens in 3.6, same code in 3.5 works fine. Do a simple prediction on any custom trainded model and cycle through prediction to process each prediction image:

from super_gradients.training import models

device = torch.device("cuda:0") if torch.cuda.is_available() else torch.device("cpu")
model = models.get(
    model_name='yolo_nas_s',
    checkpoint_path='models/MyCustomTrainedModel.pth',
    num_classes=len(CLASSES),
    pretrained_weights="coco"
).to(device)
image_path = 'test/T_01_1920.jpg'
prediction = model.predict(image_path, iou=0.05, conf=0.3, skip_image_resizing=False)

for image_prediction in prediction:
        # Obtener los datos de la predicción        
        class_names = image_prediction.class_names
        labels           = image_prediction.prediction.labels
        bboxes_list  = image_prediction.prediction.bboxes_xyxy

You'll get:

Traceback (most recent call last):
  File "/ddata/4_predict.py", line 13, in XXXXX
    for image_prediction in predictions:
TypeError: 'ImageDetectionPrediction' object is not iterable

Versions

Collecting environment information... PyTorch version: 2.2.0+cu121 Is debug build: False CUDA used to build PyTorch: 12.1 ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.3 LTS (x86_64) GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 Clang version: Could not collect CMake version: Could not collect Libc version: glibc-2.35

Python version: 3.9.18 (main, Sep 11 2023, 13:41:44) [GCC 11.2.0] (64-bit runtime) Python platform: Linux-5.15.133.1-microsoft-standard-WSL2-x86_64-with-glibc2.35 Is CUDA available: True CUDA runtime version: Could not collect CUDA_MODULE_LOADING set to: LAZY GPU models and configuration: GPU 0: NVIDIA GeForce GTX 1050 Ti Nvidia driver version: 546.01 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 Address sizes: 46 bits physical, 48 bits virtual Byte Order: Little Endian CPU(s): 20 On-line CPU(s) list: 0-19 Vendor ID: GenuineIntel Model name: 12th Gen Intel(R) Core(TM) i7-12700K CPU family: 6 Model: 151 Thread(s) per core: 2 Core(s) per socket: 10 Socket(s): 1 Stepping: 2 BogoMIPS: 7219.19 Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology tsc_reliable nonstop_tsc cpuid pni pclmulqdq vmx ssse3 fma cx16 sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch ssbd ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves avx_vnni umip waitpkg gfni vaes vpclmulqdq rdpid movdiri movdir64b fsrm serialize flush_l1d arch_capabilities Virtualization: VT-x Hypervisor vendor: Microsoft Virtualization type: full L1d cache: 480 KiB (10 instances) L1i cache: 320 KiB (10 instances) L2 cache: 12.5 MiB (10 instances) L3 cache: 25 MiB (1 instance) 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: 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 IBRS, IBPB conditional, RSB filling, PBRSB-eIBRS SW sequence Vulnerability Srbds: Not affected Vulnerability Tsx async abort: Not affected

Versions of relevant libraries: [pip3] numpy==1.23.0 [pip3] onnx==1.13.0 [pip3] onnx-simplifier==0.4.35 [pip3] onnxruntime==1.13.1 [pip3] onnxsim==0.4.35 [pip3] torch==2.2.0 [pip3] torchaudio==2.2.0+cu121 [pip3] torchmetrics==0.8.0 [pip3] torchvision==0.17.0 [pip3] triton==2.2.0 [conda] numpy 1.23.0 pypi_0 pypi [conda] torch 2.2.0 pypi_0 pypi [conda] torchaudio 2.2.0+cu121 pypi_0 pypi [conda] torchmetrics 0.8.0 pypi_0 pypi [conda] torchvision 0.17.0 pypi_0 pypi [conda] triton 2.2.0 pypi_0 pypi

Deci-AI / super-gradients

Bug on version 3.6: "TypeError: 'ImageDetectionPrediction' object is not iterable" Cycling through prediction results. Version 3.5 works ok. #1837

🐛 Describe the bug

Versions