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Running
on
Zero
| 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 | |