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import torch |
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import torch.nn as nn |
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class IOUloss(nn.Module): |
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def __init__(self, reduction="none", loss_type="iou"): |
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super(IOUloss, self).__init__() |
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self.reduction = reduction |
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self.loss_type = loss_type |
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def forward(self, pred, target): |
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assert pred.shape[0] == target.shape[0] |
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pred = pred.view(-1, 4) |
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target = target.view(-1, 4) |
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tl = torch.max( |
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(pred[:, :2] - pred[:, 2:] / 2), (target[:, :2] - target[:, 2:] / 2) |
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) |
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br = torch.min( |
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(pred[:, :2] + pred[:, 2:] / 2), (target[:, :2] + target[:, 2:] / 2) |
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) |
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area_p = torch.prod(pred[:, 2:], 1) |
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area_g = torch.prod(target[:, 2:], 1) |
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en = (tl < br).type(tl.type()).prod(dim=1) |
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area_i = torch.prod(br - tl, 1) * en |
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area_u = area_p + area_g - area_i |
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iou = (area_i) / (area_u + 1e-16) |
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if self.loss_type == "iou": |
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loss = 1 - iou ** 2 |
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elif self.loss_type == "giou": |
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c_tl = torch.min( |
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(pred[:, :2] - pred[:, 2:] / 2), (target[:, :2] - target[:, 2:] / 2) |
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) |
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c_br = torch.max( |
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(pred[:, :2] + pred[:, 2:] / 2), (target[:, :2] + target[:, 2:] / 2) |
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) |
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area_c = torch.prod(c_br - c_tl, 1) |
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giou = iou - (area_c - area_u) / area_c.clamp(1e-16) |
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loss = 1 - giou.clamp(min=-1.0, max=1.0) |
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if self.reduction == "mean": |
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loss = loss.mean() |
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elif self.reduction == "sum": |
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loss = loss.sum() |
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return loss |
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