remi-or

    def aug_test(self, 
                 imgs: List[Tensor], 
                 img_metas: List[dict],
                 rescale: bool = False) -> Tensor:
        acc_boxes = np.zeros((0, 5))
        acc_score = np.zeros((0, self.roi_head.bbox_head.num_classes))
        for img, img_meta in zip(imgs, img_metas):
            for label, dets in enumerate(self.simple_test(img, img_meta, None, rescale)[0]):
                boxes, scores = dets[:, :-1], dets[:, -1]
                acc_boxes = np.vstack((acc_boxes, boxes))

remi-or

import torch
from torch import nn
from torch.nn import functional as F
from torchvision.transforms.v2 import functional as F
import matplotlib.pyplot as plt


INPUT_SIZE = (3, 41, 70)

remi-or

from typing import Tuple
import torch
from torch import Tensor
from torchvision.transforms.v2 import functional as F


def reference_resize(image: Tensor, new_size: Tuple[int, int]) -> Tensor:
    image = F.resize(image, new_size, interpolation=F.InterpolationMode.BICUBIC, antialias=True)
    return image