How to apply distance IoU loss?
I'm currently training custom dataset using this repository: https://github.com/zylo117/Yet-Another-EfficientDet-Pytorch.
The result of training is not satisfactory for me, so I'm gonna change the regression loss, which is L1-smooth loss, into distance IoU loss.
The code for regresssion loss for this repo is below:
                anchor_widths_pi = anchor_widths[positive_indices]
                anchor_heights_pi = anchor_heights[positive_indices]
                anchor_ctr_x_pi = anchor_ctr_x[positive_indices]
                anchor_ctr_y_pi = anchor_ctr_y[positive_indices]

                gt_widths = assigned_annotations[:, 2] - assigned_annotations[:, 0]
                gt_heights = assigned_annotations[:, 3] - assigned_annotations[:, 1]
                gt_ctr_x = assigned_annotations[:, 0] + 0.5 * gt_widths
                gt_ctr_y = assigned_annotations[:, 1] + 0.5 * gt_heights

                # efficientdet style
                gt_widths = torch.clamp(gt_widths, min=1)
                gt_heights = torch.clamp(gt_heights, min=1)

                targets_dx = (gt_ctr_x - anchor_ctr_x_pi) / anchor_widths_pi
                targets_dy = (gt_ctr_y - anchor_ctr_y_pi) / anchor_heights_pi
                targets_dw = torch.log(gt_widths / anchor_widths_pi)
                targets_dh = torch.log(gt_heights / anchor_heights_pi)

                targets = torch.stack((targets_dy, targets_dx, targets_dh, targets_dw))
                targets = targets.t()
                
                
                # L1 loss
                regression_diff = torch.abs(targets - regression[positive_indices, :])

                regression_loss = torch.where(
                    torch.le(regression_diff, 1.0 / 9.0),
                    0.5 * 9.0 * torch.pow(regression_diff, 2),
                    regression_diff - 0.5 / 9.0

The code that i'm using as distance IoU is below:
    rows = bboxes1.shape[0]
    cols = bboxes2.shape[0]
    dious = torch.zeros((rows, cols))
    if rows * cols == 0:
        return dious
    exchange = False
    
    bboxes1 = bboxes1.index_select(1, torch.LongTensor([1, 0, 3, 2]).to('cuda'))
    
    if bboxes1.shape[0] &gt; bboxes2.shape[0]:
        bboxes1, bboxes2 = bboxes2, bboxes1
        dious = torch.zeros((cols, rows))
        exchange = True

    w1 = bboxes1[:, 2] - bboxes1[:, 0]
    h1 = bboxes1[:, 3] - bboxes1[:, 1] 
    w2 = bboxes2[:, 2] - bboxes2[:, 0]
    h2 = bboxes2[:, 3] - bboxes2[:, 1]
    
    area1 = w1 * h1
    area2 = w2 * h2
 
    center_x1 = (bboxes1[:, 2] + bboxes1[:, 0]) / 2 
    center_y1 = (bboxes1[:, 3] + bboxes1[:, 1]) / 2 
    center_x2 = (bboxes2[:, 2] + bboxes2[:, 0]) / 2
    center_y2 = (bboxes2[:, 3] + bboxes2[:, 1]) / 2
 
    inter_max_xy = torch.min(bboxes1[:, 2:],bboxes2[:, 2:]) 
    inter_min_xy = torch.max(bboxes1[:, :2],bboxes2[:, :2]) 
    out_max_xy = torch.max(bboxes1[:, 2:],bboxes2[:, 2:]) 
    out_min_xy = torch.min(bboxes1[:, :2],bboxes2[:, :2])
 
    inter = torch.clamp((inter_max_xy - inter_min_xy), min=0)
    inter_area = inter[:, 0] * inter[:, 1]
    inter_diag = (center_x2 - center_x1)**2 + (center_y2 - center_y1)**2
    outer = torch.clamp((out_max_xy - out_min_xy), min=0)
    outer_diag = (outer[:, 0] ** 2) + (outer[:, 1] ** 2)
    union = area1+area2-inter_area
    dious = inter_area / union - (inter_diag) / outer_diag
    dious = torch.clamp(dious,min=-1.0,max = 1.0)
    if exchange:
        dious = dious.T
        
    loss = 1 - dious
    return loss

The question is here:

Should I apply the distance IoU loss for one target bbox to all pred bbox?
For example, there is 2 annotated bboxes, and 1000 predicted bboxes.
Should I calculate losses for twice like each annotated bbox vs 1000 predicted bboxes?

Should I change the predicted bbox into real coordinates for calculation?




I've seen this done a couple ways, but typically the methods work by assigning the boxes. Calculating 1000x2 array of IOU, you can assign each prediction box a ground truth target and threshold the IOU for good/bad predictions as seen in RetinaNet or assign each ground truth target the best prediction box as seen in older YOLO. Either way, the loss is applied only to the assigned box pairs, not each combination so each assigned prediction focuses on a single target.

DIOU is invariant to scale


