def test_net(net, imdb):
    num_images = len(imdb.image_index)
    # heuristic: keep an average of 40 detections per class per images prior
    # to NMS
    max_per_set = 40 * num_images
    # heuristic: keep at most 100 detection per class per image prior to NMS
    max_per_image = 100
    # detection thresold for each class (this is adaptively set based on the
    # max_per_set constraint)
    thresh = -np.inf * np.ones(imdb.num_classes)
    # top_scores will hold one minheap of scores per class (used to enforce
    # the max_per_set constraint)
    top_scores = [[] for _ in xrange(imdb.num_classes)]
    # all detections are collected into:
    #    all_boxes[cls][image] = N x 5 array of detections in
    #    (x1, y1, x2, y2, score)
    all_boxes = [[[] for _ in xrange(num_images)]
                 for _ in xrange(imdb.num_classes)]

    output_dir = get_output_dir(imdb, net)
    if not os.path.exists(output_dir):
        os.makedirs(output_dir)

    # timers
    _t = {'im_detect' : Timer(), 'misc' : Timer()}

    roidb = imdb.roidb
    for i in xrange(num_images):
        im = cv2.imread(imdb.image_path_at(i))
        _t['im_detect'].tic()
        scores, boxes = im_detect(net, im, roidb[i]['boxes'])
        _t['im_detect'].toc()

        _t['misc'].tic()
        for j in xrange(1, imdb.num_classes):
            inds = np.where((scores[:, j] > thresh[j]) &
                            (roidb[i]['gt_classes'] == 0))[0]
            cls_scores = scores[inds, j]
            cls_boxes = boxes[inds, j*4:(j+1)*4]
            top_inds = np.argsort(-cls_scores)[:max_per_image]
            cls_scores = cls_scores[top_inds]
            cls_boxes = cls_boxes[top_inds, :]
            # push new scores onto the minheap
            for val in cls_scores:
                heapq.heappush(top_scores[j], val)
            # if we've collected more than the max number of detection,
            # then pop items off the minheap and update the class threshold
            if len(top_scores[j]) > max_per_set:
                while len(top_scores[j]) > max_per_set:
                    heapq.heappop(top_scores[j])
                thresh[j] = top_scores[j][0]

            all_boxes[j][i] = \
                    np.hstack((cls_boxes, cls_scores[:, np.newaxis])) \
                    .astype(np.float32, copy=False)

            if 0:
                keep = nms(all_boxes[j][i], 0.3)
                vis_detections(im, imdb.classes[j], all_boxes[j][i][keep, :])
        _t['misc'].toc()

        print 'im_detect: {:d}/{:d} {:.3f}s {:.3f}s' \
              .format(i + 1, num_images, _t['im_detect'].average_time,
                      _t['misc'].average_time)

    for j in xrange(1, imdb.num_classes):
        for i in xrange(num_images):
            inds = np.where(all_boxes[j][i][:, -1] > thresh[j])[0]
            all_boxes[j][i] = all_boxes[j][i][inds, :]

    det_file = os.path.join(output_dir, 'detections.pkl')
    with open(det_file, 'wb') as f:
        cPickle.dump(all_boxes, f, cPickle.HIGHEST_PROTOCOL)

    print 'Applying NMS to all detections'
    nms_dets = apply_nms(all_boxes, cfg.TEST.NMS)

    print 'Evaluating detections'
    imdb.evaluate_detections(nms_dets, output_dir)
:return: True if handle is closing or closed, False otherwise
dataset/ETHPy150Open rbgirshick/fast-rcnn/lib/fast_rcnn/test.py/test_net--crossover with--dataset/ETHPy150Open veegee/guv/pyuv_cffi/__init__.py/Handle.closing