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import numpy as np |
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import torch |
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from torchvision.ops.boxes import box_area |
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def get_ious(bboxes1, |
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bboxes2, |
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box_mode="xyxy", |
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iou_type="iou"): |
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""" |
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Compute iou loss of type ['iou', 'giou', 'linear_iou'] |
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Args: |
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inputs (tensor): pred values |
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targets (tensor): target values |
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weight (tensor): loss weight |
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box_mode (str): 'xyxy' or 'ltrb', 'ltrb' is currently supported. |
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loss_type (str): 'giou' or 'iou' or 'linear_iou' |
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reduction (str): reduction manner |
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Returns: |
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loss (tensor): computed iou loss. |
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""" |
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if box_mode == "ltrb": |
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bboxes1 = torch.cat((-bboxes1[..., :2], bboxes1[..., 2:]), dim=-1) |
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bboxes2 = torch.cat((-bboxes2[..., :2], bboxes2[..., 2:]), dim=-1) |
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elif box_mode != "xyxy": |
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raise NotImplementedError |
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eps = torch.finfo(torch.float32).eps |
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bboxes1_area = (bboxes1[..., 2] - bboxes1[..., 0]).clamp_(min=0) \ |
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* (bboxes1[..., 3] - bboxes1[..., 1]).clamp_(min=0) |
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bboxes2_area = (bboxes2[..., 2] - bboxes2[..., 0]).clamp_(min=0) \ |
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* (bboxes2[..., 3] - bboxes2[..., 1]).clamp_(min=0) |
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w_intersect = (torch.min(bboxes1[..., 2], bboxes2[..., 2]) |
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- torch.max(bboxes1[..., 0], bboxes2[..., 0])).clamp_(min=0) |
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h_intersect = (torch.min(bboxes1[..., 3], bboxes2[..., 3]) |
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- torch.max(bboxes1[..., 1], bboxes2[..., 1])).clamp_(min=0) |
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area_intersect = w_intersect * h_intersect |
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area_union = bboxes2_area + bboxes1_area - area_intersect |
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ious = area_intersect / area_union.clamp(min=eps) |
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if iou_type == "iou": |
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return ious |
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elif iou_type == "giou": |
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g_w_intersect = torch.max(bboxes1[..., 2], bboxes2[..., 2]) \ |
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- torch.min(bboxes1[..., 0], bboxes2[..., 0]) |
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g_h_intersect = torch.max(bboxes1[..., 3], bboxes2[..., 3]) \ |
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- torch.min(bboxes1[..., 1], bboxes2[..., 1]) |
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ac_uion = g_w_intersect * g_h_intersect |
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gious = ious - (ac_uion - area_union) / ac_uion.clamp(min=eps) |
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return gious |
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else: |
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raise NotImplementedError |
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def box_iou(boxes1, boxes2): |
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area1 = box_area(boxes1) |
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area2 = box_area(boxes2) |
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lt = torch.max(boxes1[:, None, :2], boxes2[:, :2]) |
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rb = torch.min(boxes1[:, None, 2:], boxes2[:, 2:]) |
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wh = (rb - lt).clamp(min=0) |
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inter = wh[:, :, 0] * wh[:, :, 1] |
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union = area1[:, None] + area2 - inter |
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iou = inter / union |
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return iou, union |
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def rescale_bboxes(bboxes, orig_size): |
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orig_w, orig_h = orig_size[0], orig_size[1] |
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bboxes[..., [0, 2]] = np.clip( |
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bboxes[..., [0, 2]] * orig_w, a_min=0., a_max=orig_w |
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) |
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bboxes[..., [1, 3]] = np.clip( |
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bboxes[..., [1, 3]] * orig_h, a_min=0., a_max=orig_h |
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) |
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return bboxes |
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if __name__ == '__main__': |
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box1 = torch.tensor([[10, 10, 20, 20]]) |
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box2 = torch.tensor([[15, 15, 25, 25]]) |
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