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# Ultralytics YOLO 🚀, AGPL-3.0 license
import torch
from ultralytics.engine.predictor import BasePredictor
from ultralytics.engine.results import Results
from ultralytics.utils import ops
from ultralytics.utils.ops import xyxy2xywh
class NASPredictor(BasePredictor):
def postprocess(self, preds_in, img, orig_imgs):
"""Postprocesses predictions and returns a list of Results objects."""
# Cat boxes and class scores
boxes = 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)
results = []
for i, pred in enumerate(preds):
orig_img = orig_imgs[i] if isinstance(orig_imgs, list) else orig_imgs
if not isinstance(orig_imgs, torch.Tensor):
pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], orig_img.shape)
path = self.batch[0]
img_path = path[i] if isinstance(path, list) else path
results.append(Results(orig_img=orig_img, path=img_path, names=self.model.names, boxes=pred))
return results
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