79 lines
2.7 KiB
Python
79 lines
2.7 KiB
Python
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import torch
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from ..utils import _log_api_usage_once
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from ._utils import _upcast_non_float
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from .diou_loss import _diou_iou_loss
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def complete_box_iou_loss(
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boxes1: torch.Tensor,
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boxes2: torch.Tensor,
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reduction: str = "none",
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eps: float = 1e-7,
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) -> torch.Tensor:
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"""
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Gradient-friendly IoU loss with an additional penalty that is non-zero when the
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boxes do not overlap. This loss function considers important geometrical
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factors such as overlap area, normalized central point distance and aspect ratio.
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This loss is symmetric, so the boxes1 and boxes2 arguments are interchangeable.
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Both sets of boxes are expected to be in ``(x1, y1, x2, y2)`` format with
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``0 <= x1 < x2`` and ``0 <= y1 < y2``, and The two boxes should have the
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same dimensions.
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Args:
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boxes1 : (Tensor[N, 4] or Tensor[4]) first set of boxes
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boxes2 : (Tensor[N, 4] or Tensor[4]) second set of boxes
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reduction : (string, optional) Specifies the reduction to apply to the output:
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``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: No reduction will be
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applied to the output. ``'mean'``: The output will be averaged.
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``'sum'``: The output will be summed. Default: ``'none'``
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eps : (float): small number to prevent division by zero. Default: 1e-7
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Returns:
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Tensor: Loss tensor with the reduction option applied.
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Reference:
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Zhaohui Zheng et al.: Complete Intersection over Union Loss:
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https://arxiv.org/abs/1911.08287
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"""
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# Original Implementation from https://github.com/facebookresearch/detectron2/blob/main/detectron2/layers/losses.py
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if not torch.jit.is_scripting() and not torch.jit.is_tracing():
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_log_api_usage_once(complete_box_iou_loss)
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boxes1 = _upcast_non_float(boxes1)
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boxes2 = _upcast_non_float(boxes2)
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diou_loss, iou = _diou_iou_loss(boxes1, boxes2)
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x1, y1, x2, y2 = boxes1.unbind(dim=-1)
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x1g, y1g, x2g, y2g = boxes2.unbind(dim=-1)
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# width and height of boxes
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w_pred = x2 - x1
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h_pred = y2 - y1
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w_gt = x2g - x1g
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h_gt = y2g - y1g
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v = (4 / (torch.pi**2)) * torch.pow((torch.atan(w_gt / h_gt) - torch.atan(w_pred / h_pred)), 2)
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with torch.no_grad():
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alpha = v / (1 - iou + v + eps)
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loss = diou_loss + alpha * v
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# Check reduction option and return loss accordingly
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if reduction == "none":
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pass
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elif reduction == "mean":
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loss = loss.mean() if loss.numel() > 0 else 0.0 * loss.sum()
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elif reduction == "sum":
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loss = loss.sum()
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else:
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raise ValueError(
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f"Invalid Value for arg 'reduction': '{reduction} \n Supported reduction modes: 'none', 'mean', 'sum'"
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)
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return loss
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