How to get values for number of true positives, false positives and false negatives for object detection evaluation in pascal.ipynb notebook

[Monk5088]

Hey Author,
Is there a way on how we can print the number of total positive detections,FPs, FNs in the Object detection pascal.ipynb notebook.
Here is the code the you have used for calculation of AP for object detection evaluation:

def compute_class_AP(model, dl, n_classes, iou_thresh=0.5, detect_thresh=0.35, num_keep=100):
    tps, clas, p_scores = [], [], []
    classes, n_gts = LongTensor(range(n_classes)),torch.zeros(n_classes).long()
    with torch.no_grad():
        for input,target in progress_bar(dl):
            output = model(input)
            for i in range(target[0].size(0)):
                bbox_pred, preds, scores = get_predictions(output, i, detect_thresh)
                tgt_bbox, tgt_clas = unpad(target[0][i], target[1][i])
                if len(bbox_pred) != 0 and len(tgt_bbox) != 0:
                    ious = IoU_values(bbox_pred, tgt_bbox)
                    max_iou, matches = ious.max(1)
                    detected = []
                    for i in range_of(preds):
                        if max_iou[i] >= iou_thresh and matches[i] not in detected and tgt_clas[matches[i]] == preds[i]:
                            detected.append(matches[i])
                            tps.append(1)
                        else: tps.append(0)
                    clas.append(preds.cpu())
                    p_scores.append(scores.cpu())
                n_gts += (tgt_clas.cpu()[:,None] == classes[None,:]).sum(0)
    tps, p_scores, clas = torch.tensor(tps), torch.cat(p_scores,0), torch.cat(clas,0)
    fps = 1-tps
    idx = p_scores.argsort(descending=True)
    tps, fps, clas = tps[idx], fps[idx], clas[idx]
    aps = []
    #return tps, clas
    for cls in range(n_classes):
        tps_cls, fps_cls = tps[clas==cls].float().cumsum(0), fps[clas==cls].float().cumsum(0)
        if tps_cls.numel() != 0 and tps_cls[-1] != 0:
            precision = tps_cls / (tps_cls + fps_cls + 1e-8)
            recall = tps_cls / (n_gts[cls] + 1e-8)
            aps.append(compute_ap(precision, recall))
        else: aps.append(0.)
    return aps

From this code how can we get the total positive , false detection by the model.
Thanks and regards,
Harshit