inference_on_dataset get Killed

Hi everyone,
I'm currently working on evaluating my model using the validation dataset. However, I'm encountering an error after running the inference_on_dataset method.

## Instructions To Reproduce the Issue:

```
from detectron2.engine import DefaultPredictor
from detectron2.evaluation import COCOEvaluator, inference_on_dataset
from detectron2.data import build_detection_test_loader
from detectron2.config import get_cfg
from detectron2 import model_zoo
import os
import torch
from register_dataset import load_dataset

# Paths to JSON annotations and image roots
train_json_path = ""
train_image_root = ""
val_json_path = ""
val_image_root = ""

# Load test dataset
_, _, val_dataset_dicts, val_metadata = load_dataset(train_json_path, train_image_root, val_json_path, val_image_root)

# Initialize configuration
cfg = get_cfg()
cfg.OUTPUT_DIR = ""
cfg.DATASETS.TEST = ("validation_set",)
cfg.merge_from_file(model_zoo.get_config_file("COCO-Detection/faster_rcnn_R_50_FPN_1x.yaml"))
cfg.MODEL.WEIGHTS = os.path.join(cfg.OUTPUT_DIR, "one_epoch.pth")
cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.01
number_of_classes = len(val_metadata.thing_classes)
cfg.MODEL.ROI_HEADS.NUM_CLASSES = number_of_classes
cfg.MODEL.DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
print("Model device, ", cfg.MODEL.DEVICE)

# Create a predictor
predictor = DefaultPredictor(cfg)
print("Predictor created")

# Set up evaluator
evaluator = COCOEvaluator("validation_set", cfg, False, output_dir=cfg.OUTPUT_DIR)
print("Evaluator created")

# Create validation data loader
validation_loader = build_detection_test_loader(cfg, "validation_set", batch_size=1, num_workers=2)
print("Validation loader created")

# Perform inference on the validation dataset
results = inference_on_dataset(predictor.model, validation_loader, evaluator)
print("Evaluation results:", results)

```
Where `load_dataset` is:

```
def load_dataset(train_json_path, train_image_root, val_json_path, val_image_root):
# Register COCO instances for training and validation sets
register_coco_instances("training_set", {}, train_json_path, train_image_root)
register_coco_instances("validation_set", {}, val_json_path, val_image_root)

# Retrieve dataset dictionaries and metadata for training set
train_dataset_dicts = DatasetCatalog.get("training_set")
train_metadata = MetadataCatalog.get("training_set")

# Retrieve dataset dictionaries and metadata for validation set
val_dataset_dicts = DatasetCatalog.get("validation_set")
val_metadata = MetadataCatalog.get("validation_set")
return train_dataset_dicts, train_metadata, val_dataset_dicts, val_metadata

```

I'm expecting to obtain the predictions on the validation set, but I when I compile it, this error occurs:

```
Category ids in annotations are not in [1, #categories]! We'll apply a mapping for you.
Model device, cuda
Skip loading parameter 'roi_heads.box_predictor.cls_score.weight' to the model due to incompatible shapes: (2861, 1024) in the checkpoint but (1180, 1024) in the model! You might want to double check if this is expected.
Skip loading parameter 'roi_heads.box_predictor.cls_score.bias' to the model due to incompatible shapes: (2861,) in the checkpoint but (1180,) in the model! You might want to double check if this is expected.
Skip loading parameter 'roi_heads.box_predictor.bbox_pred.weight' to the model due to incompatible shapes: (11440, 1024) in the checkpoint but (4716, 1024) in the model! You might want to double check if this is expected.
Skip loading parameter 'roi_heads.box_predictor.bbox_pred.bias' to the model due to incompatible shapes: (11440,) in the checkpoint but (4716,) in the model! You might want to double check if this is expected.
Some model parameters or buffers are not found in the checkpoint:
roi_heads.box_predictor.bbox_pred.{bias, weight}
roi_heads.box_predictor.cls_score.{bias, weight}
Predictor created
COCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.
Evaluator created

Category ids in annotations are not in [1, #categories]! We'll apply a mapping for you.

Validation loader created
Starting inference on the validation dataset...
Loading and preparing results...
DONE (t=11.53s)
creating index...
index created!
Killed
```
I don't get why the process is killed. Have anyone had the same issue? Thanks in advance!

Start with the reproduction script and the `inference_on_dataset` call; inspect resource usage and process termination after the COCO evaluator indexes results. Compare the checkpoint and model class-count warnings in the logs, and establish whether evaluation completes without the process being killed.

Written by the indexing model from the issue text.