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# Copyright (c) OpenMMLab. All rights reserved.
from typing import Optional
import torch
from torch import Tensor
def yolov5_bbox_decoder(priors: Tensor, bbox_preds: Tensor,
stride: Tensor) -> Tensor:
bbox_preds = bbox_preds.sigmoid()
x_center = (priors[..., 0] + priors[..., 2]) * 0.5
y_center = (priors[..., 1] + priors[..., 3]) * 0.5
w = priors[..., 2] - priors[..., 0]
h = priors[..., 3] - priors[..., 1]
x_center_pred = (bbox_preds[..., 0] - 0.5) * 2 * stride + x_center
y_center_pred = (bbox_preds[..., 1] - 0.5) * 2 * stride + y_center
w_pred = (bbox_preds[..., 2] * 2)**2 * w
h_pred = (bbox_preds[..., 3] * 2)**2 * h
decoded_bboxes = torch.stack(
[x_center_pred, y_center_pred, w_pred, h_pred], dim=-1)
return decoded_bboxes
def rtmdet_bbox_decoder(priors: Tensor, bbox_preds: Tensor,
stride: Optional[Tensor]) -> Tensor:
stride = stride[None, :, None]
bbox_preds *= stride
tl_x = (priors[..., 0] - bbox_preds[..., 0])
tl_y = (priors[..., 1] - bbox_preds[..., 1])
br_x = (priors[..., 0] + bbox_preds[..., 2])
br_y = (priors[..., 1] + bbox_preds[..., 3])
decoded_bboxes = torch.stack([tl_x, tl_y, br_x, br_y], -1)
return decoded_bboxes
def yolox_bbox_decoder(priors: Tensor, bbox_preds: Tensor,
stride: Optional[Tensor]) -> Tensor:
stride = stride[None, :, None]
xys = (bbox_preds[..., :2] * stride) + priors
whs = bbox_preds[..., 2:].exp() * stride
decoded_bboxes = torch.cat([xys, whs], -1)
return decoded_bboxes
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