import math import torch def diou_loss( boxes1: torch.Tensor, boxes2: torch.Tensor, reduction: str = "none", eps: float = 1e-7, ) -> torch.Tensor: """ Distance Intersection over Union Loss (Zhaohui Zheng et. al) https://arxiv.org/abs/1911.08287 Args: boxes1, boxes2 (Tensor): box locations in XYXY format, shape (N, 4) or (4,). reduction: 'none' | 'mean' | 'sum' 'none': No reduction will be applied to the output. 'mean': The output will be averaged. 'sum': The output will be summed. eps (float): small number to prevent division by zero """ x1, y1, x2, y2 = boxes1.unbind(dim=-1) x1g, y1g, x2g, y2g = boxes2.unbind(dim=-1) # TODO: use torch._assert_async() when pytorch 1.8 support is dropped assert (x2 >= x1).all(), "bad box: x1 larger than x2" assert (y2 >= y1).all(), "bad box: y1 larger than y2" # Intersection keypoints xkis1 = torch.max(x1, x1g) ykis1 = torch.max(y1, y1g) xkis2 = torch.min(x2, x2g) ykis2 = torch.min(y2, y2g) intsct = torch.zeros_like(x1) mask = (ykis2 > ykis1) & (xkis2 > xkis1) intsct[mask] = (xkis2[mask] - xkis1[mask]) * (ykis2[mask] - ykis1[mask]) union = (x2 - x1) * (y2 - y1) + (x2g - x1g) * (y2g - y1g) - intsct + eps iou = intsct / union # smallest enclosing box xc1 = torch.min(x1, x1g) yc1 = torch.min(y1, y1g) xc2 = torch.max(x2, x2g) yc2 = torch.max(y2, y2g) diag_len = ((xc2 - xc1) ** 2) + ((yc2 - yc1) ** 2) + eps # centers of boxes x_p = (x2 + x1) / 2 y_p = (y2 + y1) / 2 x_g = (x1g + x2g) / 2 y_g = (y1g + y2g) / 2 distance = ((x_p - x_g) ** 2) + ((y_p - y_g) ** 2) # Eqn. (7) loss = 1 - iou + (distance / diag_len) if reduction == "mean": loss = loss.mean() if loss.numel() > 0 else 0.0 * loss.sum() elif reduction == "sum": loss = loss.sum() return loss def ciou_loss( boxes1: torch.Tensor, boxes2: torch.Tensor, reduction: str = "none", eps: float = 1e-7, ) -> torch.Tensor: """ Complete Intersection over Union Loss (Zhaohui Zheng et. al) https://arxiv.org/abs/1911.08287 Args: boxes1, boxes2 (Tensor): box locations in XYXY format, shape (N, 4) or (4,). reduction: 'none' | 'mean' | 'sum' 'none': No reduction will be applied to the output. 'mean': The output will be averaged. 'sum': The output will be summed. eps (float): small number to prevent division by zero """ x1, y1, x2, y2 = boxes1.unbind(dim=-1) x1g, y1g, x2g, y2g = boxes2.unbind(dim=-1) # TODO: use torch._assert_async() when pytorch 1.8 support is dropped assert (x2 >= x1).all(), "bad box: x1 larger than x2" assert (y2 >= y1).all(), "bad box: y1 larger than y2" # Intersection keypoints xkis1 = torch.max(x1, x1g) ykis1 = torch.max(y1, y1g) xkis2 = torch.min(x2, x2g) ykis2 = torch.min(y2, y2g) intsct = torch.zeros_like(x1) mask = (ykis2 > ykis1) & (xkis2 > xkis1) intsct[mask] = (xkis2[mask] - xkis1[mask]) * (ykis2[mask] - ykis1[mask]) union = (x2 - x1) * (y2 - y1) + (x2g - x1g) * (y2g - y1g) - intsct + eps iou = intsct / union # smallest enclosing box xc1 = torch.min(x1, x1g) yc1 = torch.min(y1, y1g) xc2 = torch.max(x2, x2g) yc2 = torch.max(y2, y2g) diag_len = ((xc2 - xc1) ** 2) + ((yc2 - yc1) ** 2) + eps # centers of boxes x_p = (x2 + x1) / 2 y_p = (y2 + y1) / 2 x_g = (x1g + x2g) / 2 y_g = (y1g + y2g) / 2 distance = ((x_p - x_g) ** 2) + ((y_p - y_g) ** 2) # width and height of boxes w_pred = x2 - x1 h_pred = y2 - y1 w_gt = x2g - x1g h_gt = y2g - y1g v = (4 / (math.pi**2)) * torch.pow((torch.atan(w_gt / h_gt) - torch.atan(w_pred / h_pred)), 2) with torch.no_grad(): alpha = v / (1 - iou + v + eps) # Eqn. (10) loss = 1 - iou + (distance / diag_len) + alpha * v if reduction == "mean": loss = loss.mean() if loss.numel() > 0 else 0.0 * loss.sum() elif reduction == "sum": loss = loss.sum() return loss