util/box_loss.py

# borrow from https://github.com/Zzh-tju/CIoU/blob/master/layers/modules/multibox_loss.py

import torch, math



def ciou(bboxes1, bboxes2):
    bboxes1 = torch.sigmoid(bboxes1)
    bboxes2 = torch.sigmoid(bboxes2)
    rows = bboxes1.shape[0]
    cols = bboxes2.shape[0]
    cious = torch.zeros((rows, cols))
    if rows * cols == 0:
        return cious
    exchange = False
    if bboxes1.shape[0] > bboxes2.shape[0]:
        bboxes1, bboxes2 = bboxes2, bboxes1
        cious = torch.zeros((cols, rows))
        exchange = True
    w1 = torch.exp(bboxes1[:, 2])
    h1 = torch.exp(bboxes1[:, 3])
    w2 = torch.exp(bboxes2[:, 2])
    h2 = torch.exp(bboxes2[:, 3])
    area1 = w1 * h1
    area2 = w2 * h2
    center_x1 = bboxes1[:, 0]
    center_y1 = bboxes1[:, 1]
    center_x2 = bboxes2[:, 0]
    center_y2 = bboxes2[:, 1]

    inter_l = torch.max(center_x1 - w1 / 2,center_x2 - w2 / 2)
    inter_r = torch.min(center_x1 + w1 / 2,center_x2 + w2 / 2)
    inter_t = torch.max(center_y1 - h1 / 2,center_y2 - h2 / 2)
    inter_b = torch.min(center_y1 + h1 / 2,center_y2 + h2 / 2)
    inter_area = torch.clamp((inter_r - inter_l),min=0) * torch.clamp((inter_b - inter_t),min=0)

    c_l = torch.min(center_x1 - w1 / 2,center_x2 - w2 / 2)
    c_r = torch.max(center_x1 + w1 / 2,center_x2 + w2 / 2)
    c_t = torch.min(center_y1 - h1 / 2,center_y2 - h2 / 2)
    c_b = torch.max(center_y1 + h1 / 2,center_y2 + h2 / 2)

    inter_diag = (center_x2 - center_x1)**2 + (center_y2 - center_y1)**2
    c_diag = torch.clamp((c_r - c_l),min=0)**2 + torch.clamp((c_b - c_t),min=0)**2

    union = area1+area2-inter_area
    u = (inter_diag) / c_diag
    iou = inter_area / union
    v = (4 / (math.pi ** 2)) * torch.pow((torch.atan(w2 / h2) - torch.atan(w1 / h1)), 2)
    with torch.no_grad():
        S = (iou>0.5).float()
        alpha= S*v/(1-iou+v)
    cious = iou - u - alpha * v
    cious = torch.clamp(cious,min=-1.0,max = 1.0)
    if exchange:
        cious = cious.T
    return 1-cious

def diou(bboxes1, bboxes2):
    bboxes1 = torch.sigmoid(bboxes1)
    bboxes2 = torch.sigmoid(bboxes2)
    rows = bboxes1.shape[0]
    cols = bboxes2.shape[0]
    cious = torch.zeros((rows, cols))
    if rows * cols == 0:
        return cious
    exchange = False
    if bboxes1.shape[0] > bboxes2.shape[0]:
        bboxes1, bboxes2 = bboxes2, bboxes1
        cious = torch.zeros((cols, rows))
        exchange = True
    w1 = torch.exp(bboxes1[:, 2])
    h1 = torch.exp(bboxes1[:, 3])
    w2 = torch.exp(bboxes2[:, 2])
    h2 = torch.exp(bboxes2[:, 3])
    area1 = w1 * h1
    area2 = w2 * h2
    center_x1 = bboxes1[:, 0]
    center_y1 = bboxes1[:, 1]
    center_x2 = bboxes2[:, 0]
    center_y2 = bboxes2[:, 1]

    inter_l = torch.max(center_x1 - w1 / 2,center_x2 - w2 / 2)
    inter_r = torch.min(center_x1 + w1 / 2,center_x2 + w2 / 2)
    inter_t = torch.max(center_y1 - h1 / 2,center_y2 - h2 / 2)
    inter_b = torch.min(center_y1 + h1 / 2,center_y2 + h2 / 2)
    inter_area = torch.clamp((inter_r - inter_l),min=0) * torch.clamp((inter_b - inter_t),min=0)

    c_l = torch.min(center_x1 - w1 / 2,center_x2 - w2 / 2)
    c_r = torch.max(center_x1 + w1 / 2,center_x2 + w2 / 2)
    c_t = torch.min(center_y1 - h1 / 2,center_y2 - h2 / 2)
    c_b = torch.max(center_y1 + h1 / 2,center_y2 + h2 / 2)

    inter_diag = (center_x2 - center_x1)**2 + (center_y2 - center_y1)**2
    c_diag = torch.clamp((c_r - c_l),min=0)**2 + torch.clamp((c_b - c_t),min=0)**2

    union = area1+area2-inter_area
    u = (inter_diag) / c_diag
    iou = inter_area / union
    dious = iou - u
    dious = torch.clamp(dious,min=-1.0,max = 1.0)
    if exchange:
        dious = dious.T
    return 1-dious


if __name__ == "__main__":
    x = torch.rand(10, 4)
    y = torch.rand(10,4)
    import ipdb;ipdb.set_trace()
    cxy = ciou(x, y)
    dxy = diou(x, y)
    print(cxy.shape, dxy.shape)