960a107ad419d2690e352972950e76495deb1ad6,scripts/detection/ssd/eval_ssd.py,,validate,#Any#Any#Any#Any#Any#Any#,63

Before Change

    net.set_nms(nms_thresh=0.45, nms_topk=400)
    net.hybridize()
    with tqdm(total=size) as pbar:
        for ib, batch in enumerate(val_data):
            data = gluon.utils.split_and_load(batch[0], ctx_list=ctx, batch_axis=0, even_split=False)
            label = gluon.utils.split_and_load(batch[1], ctx_list=ctx, batch_axis=0, even_split=False)
            det_bboxes = []
            det_ids = []
            det_scores = []
            gt_bboxes = []
            gt_ids = []
            gt_difficults = []
            for x, y in zip(data, label):
                ids, scores, bboxes = net(x)
                det_ids.append(ids)
                det_scores.append(scores)
                // clip to image size
                det_bboxes.append(bboxes.clip(0, batch[0].shape[2]))
                // split ground truths
                gt_ids.append(y.slice_axis(axis=-1, begin=4, end=5))
                gt_bboxes.append(y.slice_axis(axis=-1, begin=0, end=4))
                gt_difficults.append(y.slice_axis(axis=-1, begin=5, end=6) if y.shape[-1] > 5 else None)

            metric.update(det_bboxes, det_ids, det_scores, gt_bboxes, gt_ids, gt_difficults)
            pbar.update(batch[0].shape[0])
    return metric.get()

if __name__ == "__main__":
    args = parse_args()

After Change

        net.set_nms(nms_thresh=0.45, nms_topk=400)
    net.hybridize()
    with tqdm(total=size) as pbar:
        start = time.time()
        for ib, batch in enumerate(val_data):
            data = gluon.utils.split_and_load(batch[0], ctx_list=ctx, batch_axis=0, even_split=False)
            label = gluon.utils.split_and_load(batch[1], ctx_list=ctx, batch_axis=0, even_split=False)
            det_bboxes = []
            det_ids = []
            det_scores = []
            gt_bboxes = []
            gt_ids = []
            gt_difficults = []
            for x, y in zip(data, label):
                ids, scores, bboxes = net(x)
                det_ids.append(ids)
                det_scores.append(scores)
                // clip to image size
                det_bboxes.append(bboxes.clip(0, batch[0].shape[2]))
                // split ground truths
                gt_ids.append(y.slice_axis(axis=-1, begin=4, end=5))
                gt_bboxes.append(y.slice_axis(axis=-1, begin=0, end=4))
                gt_difficults.append(y.slice_axis(axis=-1, begin=5, end=6) if y.shape[-1] > 5 else None)

            metric.update(det_bboxes, det_ids, det_scores, gt_bboxes, gt_ids, gt_difficults)
            pbar.update(batch[0].shape[0])
        end = time.time()
        speed = size / (end - start)
        print("Throughput is %f img/sec."% speed)
    return metric.get()

if __name__ == "__main__":

Instances