|
|
|
|
# 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):
|
|
|
|
|
"""Postprocess 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
|
