SipMask-benchmark/fcos_core/modeling/rpn/sipmask/loss.py
def bbox_overlaps(bboxes1, bboxes2, mode='iou', is_aligned=False):
    """Calculate overlap between two set of bboxes.

    If ``is_aligned`` is ``False``, then calculate the ious between each bbox
    of bboxes1 and bboxes2, otherwise the ious between each aligned pair of
    bboxes1 and bboxes2.

    Args:
        bboxes1 (Tensor): shape (m, 4)
        bboxes2 (Tensor): shape (n, 4), if is_aligned is ``True``, then m and n
            must be equal.
        mode (str): "iou" (intersection over union) or iof (intersection over
            foreground).

    Returns:
        ious(Tensor): shape (m, n) if is_aligned == False else shape (m, 1)
    """

    assert mode in ['iou', 'iof']

    rows = bboxes1.size(0)
    cols = bboxes2.size(0)
    if is_aligned:
        assert rows == cols

    if rows * cols == 0:
        return bboxes1.new(rows, 1) if is_aligned else bboxes1.new(rows, cols)

    if is_aligned:
        lt = torch.max(bboxes1[:, :2], bboxes2[:, :2])  # [rows, 2]
        rb = torch.min(bboxes1[:, 2:], bboxes2[:, 2:])  # [rows, 2]

        wh = (rb - lt + 1).clamp(min=0)  # [rows, 2]
        overlap = wh[:, 0] * wh[:, 1]
        area1 = (bboxes1[:, 2] - bboxes1[:, 0] + 1) * (
            bboxes1[:, 3] - bboxes1[:, 1] + 1)

        if mode == 'iou':
            area2 = (bboxes2[:, 2] - bboxes2[:, 0] + 1) * (
                bboxes2[:, 3] - bboxes2[:, 1] + 1)
            ious = overlap / (area1 + area2 - overlap)
        else:
            ious = overlap / area1
    else:
        lt = torch.max(bboxes1[:, None, :2], bboxes2[:, :2])  # [rows, cols, 2]
        rb = torch.min(bboxes1[:, None, 2:], bboxes2[:, 2:])  # [rows, cols, 2]

        wh = (rb - lt + 1).clamp(min=0)  # [rows, cols, 2]
        overlap = wh[:, :, 0] * wh[:, :, 1]
        area1 = (bboxes1[:, 2] - bboxes1[:, 0] + 1) * (
            bboxes1[:, 3] - bboxes1[:, 1] + 1)

        if mode == 'iou':
            area2 = (bboxes2[:, 2] - bboxes2[:, 0] + 1) * (
                bboxes2[:, 3] - bboxes2[:, 1] + 1)
            ious = overlap / (area1[:, None] + area2 - overlap)
        else:
            ious = overlap / (area1[:, None])

    return ious
Calculate overlap between two set of bboxes.

    If ``is_aligned`` is ``False``, then calculate the ious between each bbox
    of bboxes1 and bboxes2, otherwise the ious between each aligned pair of
    bboxes1 and bboxes2.

    Args:
        bboxes1 (Tensor): shape (m, 4)
        bboxes2 (Tensor): shape (n, 4), if is_aligned is ``True``, then m and n
            must be equal.
        mode (str): "iou" (intersection over union) or iof (intersection over
            foreground).

    Returns:
        ious(Tensor): shape (m, n) if is_aligned == False else shape (m, 1)
    
Calculate overlap between two set of bboxes.
Calculate overlap between two set of bboxes .
def bbox_overlaps(bboxes1, bboxes2, mode='iou', is_aligned=False):
    assert mode in ['iou', 'iof']
    rows = bboxes1.size(0)
    cols = bboxes2.size(0)
    if is_aligned:
        assert rows == cols
    if rows * cols == 0:
        return bboxes1.new(rows, 1) if is_aligned else bboxes1.new(rows, cols)
    if is_aligned:
        lt = torch.max(bboxes1[:, :2], bboxes2[:, :2])  
        rb = torch.min(bboxes1[:, 2:], bboxes2[:, 2:])  
        wh = (rb - lt + 1).clamp(min=0)  
        overlap = wh[:, 0] * wh[:, 1]
        area1 = (bboxes1[:, 2] - bboxes1[:, 0] + 1) * (
            bboxes1[:, 3] - bboxes1[:, 1] + 1)
        if mode == 'iou':
            area2 = (bboxes2[:, 2] - bboxes2[:, 0] + 1) * (
                bboxes2[:, 3] - bboxes2[:, 1] + 1)
            ious = overlap / (area1 + area2 - overlap)
        else:
            ious = overlap / area1
    else:
        lt = torch.max(bboxes1[:, None, :2], bboxes2[:, :2])  
        rb = torch.min(bboxes1[:, None, 2:], bboxes2[:, 2:])  
        wh = (rb - lt + 1).clamp(min=0)  
        overlap = wh[:, :, 0] * wh[:, :, 1]
        area1 = (bboxes1[:, 2] - bboxes1[:, 0] + 1) * (
            bboxes1[:, 3] - bboxes1[:, 1] + 1)
        if mode == 'iou':
            area2 = (bboxes2[:, 2] - bboxes2[:, 0] + 1) * (
                bboxes2[:, 3] - bboxes2[:, 1] + 1)
            ious = overlap / (area1[:, None] + area2 - overlap)
        else:
            ious = overlap / (area1[:, None])
    return ious
def bbox_overlaps ( bboxes1 , bboxes2 , mode = 'iou' , is_aligned = False ) : assert mode in [ 'iou' , 'iof' ] rows = bboxes1 . size ( 0 ) cols = bboxes2 . size ( 0 ) if is_aligned : assert rows == cols if rows * cols == 0 : return bboxes1 . new ( rows , 1 ) if is_aligned else bboxes1 . new ( rows , cols ) if is_aligned : lt = torch . max ( bboxes1 [ : , : 2 ] , bboxes2 [ : , : 2 ] ) rb = torch . min ( bboxes1 [ : , 2 : ] , bboxes2 [ : , 2 : ] ) wh = ( rb - lt + 1 ) . clamp ( min = 0 ) overlap = wh [ : , 0 ] * wh [ : , 1 ] area1 = ( bboxes1 [ : , 2 ] - bboxes1 [ : , 0 ] + 1 ) * ( bboxes1 [ : , 3 ] - bboxes1 [ : , 1 ] + 1 ) if mode == 'iou' : area2 = ( bboxes2 [ : , 2 ] - bboxes2 [ : , 0 ] + 1 ) * ( bboxes2 [ : , 3 ] - bboxes2 [ : , 1 ] + 1 ) ious = overlap / ( area1 + area2 - overlap ) else : ious = overlap / area1 else : lt = torch . max ( bboxes1 [ : , None , : 2 ] , bboxes2 [ : , : 2 ] ) rb = torch . min ( bboxes1 [ : , None , 2 : ] , bboxes2 [ : , 2 : ] ) wh = ( rb - lt + 1 ) . clamp ( min = 0 ) overlap = wh [ : , : , 0 ] * wh [ : , : , 1 ] area1 = ( bboxes1 [ : , 2 ] - bboxes1 [ : , 0 ] + 1 ) * ( bboxes1 [ : , 3 ] - bboxes1 [ : , 1 ] + 1 ) if mode == 'iou' : area2 = ( bboxes2 [ : , 2 ] - bboxes2 [ : , 0 ] + 1 ) * ( bboxes2 [ : , 3 ] - bboxes2 [ : , 1 ] + 1 ) ious = overlap / ( area1 [ : , None ] + area2 - overlap ) else : ious = overlap / ( area1 [ : , None ] ) return ious
Calculate overlap between two set of bboxes.
Calculate overlap between two set of bboxes .
"""Calculate overlap between two set of bboxes.

    If ``is_aligned`` is ``False``, then calculate the ious between each bbox
    of bboxes1 and bboxes2, otherwise the ious between each aligned pair of
    bboxes1 and bboxes2.

    Args:
        bboxes1 (Tensor): shape (m, 4)
        bboxes2 (Tensor): shape (n, 4), if is_aligned is ``True``, then m and n
            must be equal.
        mode (str): "iou" (intersection over union) or iof (intersection over
            foreground).

    Returns:
        ious(Tensor): shape (m, n) if is_aligned == False else shape (m, 1)
    """ # [rows, 2] # [rows, 2] # [rows, 2] # [rows, cols, 2] # [rows, cols, 2] # [rows, cols, 2]
shape (m, n) if is_aligned == False else shape (m, 1)
shape ( m n ) if is_aligned == False else shape ( m 1 )
ious(Tensor)
bboxes1
None
bboxes2
shape (n, 4), if is_aligned is ``True``, then m and n
must be equal.
shape ( n 4 ) if is_aligned is ` ` True ` ` then m and n must be equal .
mode
"iou" (intersection over union) or iof (intersection over
foreground).
" iou " ( intersection over union ) or iof ( intersection over foreground ) .
is_aligned