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# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
"""
YOLO-NAS model interface.
Example:
```python
from ultralytics import NAS
model = NAS("yolo_nas_s")
results = model.predict("ultralytics/assets/bus.jpg")
```
"""
from pathlib import Path
import torch
from ultralytics.engine.model import Model
from ultralytics.utils.downloads import attempt_download_asset
from ultralytics.utils.torch_utils import model_info
from .predict import NASPredictor
from .val import NASValidator
class NAS(Model):
"""
YOLO NAS model for object detection.
This class provides an interface for the YOLO-NAS models and extends the `Model` class from Ultralytics engine.
It is designed to facilitate the task of object detection using pre-trained or custom-trained YOLO-NAS models.
Example:
```python
from ultralytics import NAS
model = NAS("yolo_nas_s")
results = model.predict("ultralytics/assets/bus.jpg")
```
Attributes:
model (str): Path to the pre-trained model or model name. Defaults to 'yolo_nas_s.pt'.
Note:
YOLO-NAS models only support pre-trained models. Do not provide YAML configuration files.
"""
def __init__(self, model="yolo_nas_s.pt") -> None:
"""Initializes the NAS model with the provided or default 'yolo_nas_s.pt' model."""
assert Path(model).suffix not in {".yaml", ".yml"}, "YOLO-NAS models only support pre-trained models."
super().__init__(model, task="detect")
def _load(self, weights: str, task=None) -> None:
"""Loads an existing NAS model weights or creates a new NAS model with pretrained weights if not provided."""
import super_gradients
suffix = Path(weights).suffix
if suffix == ".pt":
self.model = torch.load(attempt_download_asset(weights))
elif suffix == "":
self.model = super_gradients.training.models.get(weights, pretrained_weights="coco")
# Override the forward method to ignore additional arguments
def new_forward(x, *args, **kwargs):
"""Ignore additional __call__ arguments."""
return self.model._original_forward(x)
self.model._original_forward = self.model.forward
self.model.forward = new_forward
# Standardize model
self.model.fuse = lambda verbose=True: self.model
self.model.stride = torch.tensor([32])
self.model.names = dict(enumerate(self.model._class_names))
self.model.is_fused = lambda: False # for info()
self.model.yaml = {} # for info()
self.model.pt_path = weights # for export()
self.model.task = "detect" # for export()
def info(self, detailed=False, verbose=True):
"""
Logs model info.
Args:
detailed (bool): Show detailed information about model.
verbose (bool): Controls verbosity.
"""
return model_info(self.model, detailed=detailed, verbose=verbose, imgsz=640)
@property
def task_map(self):
"""Returns a dictionary mapping tasks to respective predictor and validator classes."""
return {"detect": {"predictor": NASPredictor, "validator": NASValidator}}
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