Fixing SGG-Benchmark Demo_video.py 'ValueError: Not In List'

Hey there, fellow AI enthusiasts and developers! Ever been excited to run a cool demo, only to be hit with a cryptic error that leaves you scratching your head? Well, you're definitely not alone. Today, we're diving deep into a very specific, yet surprisingly common, issue encountered when trying to run the demo_video.py script within the SGG-Benchmark project, especially when you're leveraging the react_PSG/best_model_epoch_11.pth pre-trained weights. If you've seen that infamous ValueError: '12.0_person' is not in list message pop up, then buckle up, because we're going to break it down, understand why it happens, and most importantly, get you back on track to visualizing those awesome scene graphs!

This article is designed to be your friendly guide, a conversational walk-through that makes sense of the technical jargon and provides actionable steps. We'll go beyond just fixing the error; we'll explore the underlying reasons, making sure you gain a deeper understanding of how these powerful models handle object classifications. Our goal is to ensure you not only resolve this immediate problem but also equip you with the debugging mindset to tackle similar challenges in your AI journey. So, let's roll up our sleeves and get this SGG-Benchmark demo running smoothly, shall we?

The Heart of the Problem: ValueError: '12.0_person' is not in list

Alright, guys, let's zoom in on the main culprit that halted your SGG-Benchmark demo_video.py script: the ValueError: '12.0_person' is not in list. This error message might seem a bit vague at first glance, but it's actually incredibly specific and points directly to a mismatch in how your program is trying to identify an object. In simple terms, your code is trying to find something – in this case, the string '12.0_person' – within a predefined list of valid options, and it's just not there. Imagine trying to find a purple unicorn on a shopping list that only contains apples, bananas, and oranges; the unicorn simply doesn't exist on that list, and the program is telling you exactly that.

The traceback you provided gives us the exact location of this showdown: color = self.obj_class_colors[self.stats['obj_classes'].index(label)]. This single line of code is where all the magic (and the error) happens. Let's dissect it. We have self.obj_class_colors, which is likely a mapping from class names to their corresponding display colors. To get the correct color, the code needs the index of the label within self.stats['obj_classes']. The .index() method is super useful for finding an item's position, but it throws a ValueError if the item isn't found. And boom! That's precisely what's happening to '12.0_person'.

So, what exactly are label and self.stats['obj_classes']? Let's break it down further. The label variable, at the moment of the crash, holds the value '12.0_person'. This is the specific object class name that the model has detected or is attempting to draw for a given bounding box. On the other hand, self.stats['obj_classes'] is the master list of all object class names that your SGG-Benchmark model, particularly the one loaded from react_PSG/best_model_epoch_11.pth, expects to encounter and knows how to handle. This list usually comes directly from the dataset the model was trained on or is explicitly defined in its configuration. The core issue is that the label string, '12.0_person', is not present in that self.stats['obj_classes'] master list. This disparity is the root cause of your demo hitting a snag. Understanding this fundamental conflict between the label being generated and the list of expected labels is the first crucial step towards a successful fix, making sure our program knows exactly which fruit we're looking for on the list. Without this understanding, we'd just be guessing, but now we know precisely what we need to align.

Unpacking the SGG-Benchmark Context

Now that we've pinpointed the exact error, let's widen our lens and look at the broader context of the SGG-Benchmark project. You're running demo_video.py and utilizing the react_PSG/best_model_epoch_11.pth checkpoint. This is crucial information, guys, because it tells us a lot about the expectations of your model. The PSG in react_PSG likely refers to the Predicate Scene Graphs dataset or a similar variant, which means the model was trained on a specific set of object and predicate classes. When you load a pre-trained model like best_model_epoch_11.pth, it inherently carries with it the knowledge of the classes it was taught during its training phase. This knowledge includes an ordered list of object names, which is exactly what self.stats['obj_classes'] represents. The model expects to see these exact names when performing inference or visualization.

It's highly probable that the obj_classes list within this particular react_PSG model was trained using simpler, plain object names, something like ['person', 'car', 'tree', ...], without any numeric prefixes. The demo_video.py script, in its attempt to process and display detections, is somehow generating a label like '12.0_person'. This suggests that somewhere in the pipeline, an object ID (like 12.0) is being concatenated with the actual class name (person) before the lookup in self.stats['obj_classes'] occurs. This mismatch between the format of the generated label (e.g., ID_ClassName) and the expected format in the model's internal class list (e.g., ClassName only) is the core of our problem. This isn't a flaw in the model itself, but rather a slight disagreement in how the inference script prepares the labels for display.

Before we dive into the solution, let's briefly address those other warnings you might have noticed in your traceback. First, there's the OpenCV: FFMPEG: tag 0x44495658/'XVID' is not supported... fallback to use tag 0x7634706d/'mp4v' message. This is merely a warning from OpenCV, the powerful computer vision library, about video codec compatibility. It means that the video output stream initially tried to use a specific codec (XVID) which wasn't supported for the MP4 format you're trying to use, so it gracefully fell back to another one (mp4v). This is not the cause of your ValueError and won't prevent your demo from running once the main issue is fixed. It simply might result in a slightly different video compression than originally intended. Think of it as your car using regular unleaded instead of premium – it still runs, just maybe not exactly as specified. Second, you saw a torch.searchsorted(): input value tensor is non-contiguous... warning. This is a performance-related heads-up from PyTorch, suggesting that an input tensor isn't optimally structured for maximum speed. Again, this is not the reason for your program crashing with a ValueError; it's a minor performance note. While it's good practice to make tensors contiguous for optimal performance, it's not a critical error preventing execution. Focusing on these warnings would be a distraction from our primary mission of fixing the ValueError. Our main focus remains on aligning those object labels to get your SGG-Benchmark demo running flawlessly.

Why This Mismatch Happens: Common Scenarios

Okay, so we know what the error is and where it's happening, but the million-dollar question is why? Why would a pre-trained model and a demo script have such a fundamental disagreement about how to name a