AttributeError: `EasyDict` object has no attribute `moment_layers` when loading pre-trained CG-DETR checkpoints.

Loading a pre-trained CG-DETR checkpoint from the [uploaded pre-trained weights](https://drive.google.com/file/d/1jxs_bvwttXTF9Lk3aKLohkqfYOonLyrO/view?usp=sharing) fails with the following error:

```
AttributeError: 'EasyDict' object has no attribute 'moment_layers'
```

**To reproduce**
Download pre-trained model weights from the [google drive URL](https://drive.google.com/file/d/1jxs_bvwttXTF9Lk3aKLohkqfYOonLyrO/view?usp=sharing) mentioned in the Readme and then load a CG-DETR checkpoint using `lighthouse.models.CGDETRPredictor`.

```python
from lighthouse.models import CGDETRPredictor
model = CGDETRPredictor('results/cg_detr/qvhighlight/clip/best.ckpt', device='cpu', feature_name='clip')
```

**Full traceback**
```
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
Cell In[19], line 1
----> 1 model = CGDETRPredictor('results/cg_detr/qvhighlight/clip/best.ckpt', device='cpu',
2 feature_name='clip')

File .../.venv/lib/python3.9/site-packages/lighthouse/models.py:363, in CGDETRPredictor.__init__(self, ckpt_path, device, feature_name, slowfast_path, pann_path)
355 def __init__(
356 self,
357 ckpt_path: str,
(...)
361 pann_path: Optional[str] = None
362 ) -> None:
--> 363 super().__init__('cg_detr', ckpt_path, device,
364 feature_name, slowfast_path, pann_path)

File .../.venv/lib/python3.9/site-packages/lighthouse/models.py:85, in BasePredictor.__init__(self, model_name, ckpt_path, device, feature_name, slowfast_path, pann_path)
81 self._audio_encoder = self._initialize_audio_encoder(feature_name, pann_path)
83 self._text_encoder: TextEncoder = self._initialize_text_encoder(feature_name)
---> 85 self._model: torch.nn.Module = self._initialize_model(args, model_name)
86 self._load_weights(ckpt['model'])
88 self._feature_name: str = feature_name

File .../.venv/lib/python3.9/site-packages/lighthouse/models.py:107, in BasePredictor._initialize_model(self, args, model_name)
96 model_builders = {
97 'moment_detr': build_model_moment_detr,
98 'qd_detr': build_model_qd_detr,
(...)
103 'taskweave': build_model_task_weave
104 }
106 if model_name in model_builders:
--> 107 model, _ = model_builders[model_name](args)
108 return model
109 else:

File .../.venv/lib/python3.9/site-packages/lighthouse/common/cg_detr.py:998, in build_model(args)
995 def build_model(args):
996 device = torch.device(args.device)
--> 998 transformer = build_transformer(args)
999 position_embedding, txt_position_embedding = build_position_encoding(args)
1001 model = CGDETR(
1002 transformer,
1003 position_embedding,
(...)
1013 args=args
1014 )

File .../.venv/lib/python3.9/site-packages/lighthouse/common/cg_detr_transformer.py:883, in build_transformer(args)
882 def build_transformer(args):
--> 883 return Transformer(
884 d_model=args.hidden_dim,
885 dropout=args.dropout,
886 nhead=args.nheads,
887 dim_feedforward=args.dim_feedforward,
888 num_encoder_layers=args.enc_layers,
889 num_decoder_layers=args.dec_layers,
890 normalize_before=False,
891 return_intermediate_dec=True,
892 activation='prelu',
893 args=args
894 )

File .../.venv/lib/python3.9/site-packages/lighthouse/common/cg_detr_transformer.py:150, in Transformer.__init__(self, d_model, nhead, num_queries, num_encoder_layers, num_decoder_layers, dim_feedforward, dropout, activation, normalize_before, return_intermediate_dec, query_dim, keep_query_pos, query_scale_type, num_patterns, modulate_t_attn, bbox_embed_diff_each_layer, args)
147 mcls_encoder_layer = TransformerEncoderLayer(d_model, nhead, dim_feedforward,
148 dropout, activation, normalize_before)
149 mcls_encoder_norm = nn.LayerNorm(d_model) if normalize_before else None
--> 150 self.mcls_encoder = TransformerEncoder(mcls_encoder_layer, args.moment_layers, mcls_encoder_norm)
152 t2v_encoder_layer = T2V_TransformerEncoderLayer(d_model, nhead, dim_feedforward,
153 dropout, activation, normalize_before, self.args.num_dummies)
154 encoder_norm = nn.LayerNorm(d_model) if normalize_before else None

AttributeError: 'EasyDict' object has no attribute 'moment_layers'
```

The checkpoint doesn't contain the `moment_layers` key although the code expects it to. Maybe the checkpoint was generated on an older version where this attribute wasn't saved?

I attempted to work around this by patching the checkpoint to include `moment_layers`:
```
if not hasattr(ckpt['opt'], 'moment_layers'):
ckpt['opt']['moment_layers'] = 1 # Default value for moment_layers

torch.save(ckpt, 'results/cg_detr/qvhighlight/clip_slowfast/best_patched.ckpt')
```

Loading the model with the patched checkpoint gave a new error: `AttributeError: 'EasyDict' object has no attribute 'num_dummies`.

It seems like these checkpoints are outdated/incompatible with the current Lighthouse version and should either be replaced by new ones or the code made compatible with these checkpoints. I might've just misunderstood the inference instructions though and I'm doing something wrong on my end, in which case sorry for the issue!

Start by tracing checkpoint loading in lighthouse/models.py, then compare the model construction paths in lighthouse/common/cg_detr.py and lighthouse/common/cg_detr_transformer.py with the saved checkpoint options. Check which configuration keys are required by the current code and absent from the provided weights. Done means the README-linked CG-DETR checkpoint loads through CGDETRPredictor without missing-attribute errors.

Written by the indexing model from the issue text.