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# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
import torch
from ultralytics.engine.predictor import BasePredictor
from ultralytics.engine.results import Results
from ultralytics.utils import ops
class NASPredictor(BasePredictor):
"""
Ultralytics YOLO NAS Predictor for object detection.
This class extends the `BasePredictor` from Ultralytics engine and is responsible for post-processing the
raw predictions generated by the YOLO NAS models. It applies operations like non-maximum suppression and
scaling the bounding boxes to fit the original image dimensions.
Attributes:
args (Namespace): Namespace containing various configurations for post-processing.
Example:
```python
from ultralytics import NAS
model = NAS("yolo_nas_s")
predictor = model.predictor
# Assumes that raw_preds, img, orig_imgs are available
results = predictor.postprocess(raw_preds, img, orig_imgs)
```
Note:
Typically, this class is not instantiated directly. It is used internally within the `NAS` class.
"""
def postprocess(self, preds_in, img, orig_imgs):
"""Postprocess predictions and returns a list of Results objects."""
# Cat boxes and class scores
boxes = ops.xyxy2xywh(preds_in[0][0])
preds = torch.cat((boxes, preds_in[0][1]), -1).permute(0, 2, 1)
preds = ops.non_max_suppression(
preds,
self.args.conf,
self.args.iou,
agnostic=self.args.agnostic_nms,
max_det=self.args.max_det,
classes=self.args.classes,
)
if not isinstance(orig_imgs, list): # input images are a torch.Tensor, not a list
orig_imgs = ops.convert_torch2numpy_batch(orig_imgs)
results = []
for pred, orig_img, img_path in zip(preds, orig_imgs, self.batch[0]):
pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], orig_img.shape)
results.append(Results(orig_img, path=img_path, names=self.model.names, boxes=pred))
return results
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