Python torch.kthvalue() Examples

The following are 30 code examples of torch.kthvalue(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module torch , or try the search function .
Example #1
Source Project: mmdetection   Author: open-mmlab   File: dynamic_roi_head.py    License: Apache License 2.0 6 votes vote down vote up
def _bbox_forward_train(self, x, sampling_results, gt_bboxes, gt_labels,
                            img_metas):
        num_imgs = len(img_metas)
        rois = bbox2roi([res.bboxes for res in sampling_results])
        bbox_results = self._bbox_forward(x, rois)

        bbox_targets = self.bbox_head.get_targets(sampling_results, gt_bboxes,
                                                  gt_labels, self.train_cfg)
        # record the `beta_topk`-th smallest target
        # `bbox_targets[2]` and `bbox_targets[3]` stand for bbox_targets
        # and bbox_weights, respectively
        pos_inds = bbox_targets[3][:, 0].nonzero().squeeze(1)
        num_pos = len(pos_inds)
        cur_target = bbox_targets[2][pos_inds, :2].abs().mean(dim=1)
        beta_topk = min(self.train_cfg.dynamic_rcnn.beta_topk * num_imgs,
                        num_pos)
        cur_target = torch.kthvalue(cur_target, beta_topk)[0].item()
        self.beta_history.append(cur_target)
        loss_bbox = self.bbox_head.loss(bbox_results['cls_score'],
                                        bbox_results['bbox_pred'], rois,
                                        *bbox_targets)

        bbox_results.update(loss_bbox=loss_bbox)
        return bbox_results 
Example #2
Source Project: EmbedMask   Author: yinghdb   File: inference.py    License: MIT License 6 votes vote down vote up
def select_over_all_levels(self, boxlists):
        num_images = len(boxlists)
        results = []
        for i in range(num_images):
            # multiclass nms
            result = boxlist_ml_nms(boxlists[i], self.nms_thresh)
            number_of_detections = len(result)

            # Limit to max_per_image detections **over all classes**
            if number_of_detections > self.fpn_post_nms_top_n > 0:
                cls_scores = result.get_field("scores")
                image_thresh, _ = torch.kthvalue(
                    cls_scores.cpu(),
                    number_of_detections - self.fpn_post_nms_top_n + 1
                )
                keep = cls_scores >= image_thresh.item()
                keep = torch.nonzero(keep).squeeze(1)
                result = result[keep]
            results.append(result)
        return results 
Example #3
Source Project: EmbedMask   Author: yinghdb   File: inference.py    License: MIT License 6 votes vote down vote up
def select_over_all_levels(self, boxlists):
        num_images = len(boxlists)
        results = []
        for i in range(num_images):
            # multiclass nms
            result = boxlist_ml_nms(boxlists[i], self.nms_thresh)
            number_of_detections = len(result)

            # Limit to max_per_image detections **over all classes**
            if number_of_detections > self.fpn_post_nms_top_n > 0:
                cls_scores = result.get_field("scores")
                image_thresh, _ = torch.kthvalue(
                    cls_scores.cpu(),
                    number_of_detections - self.fpn_post_nms_top_n + 1
                )
                keep = cls_scores >= image_thresh.item()
                keep = torch.nonzero(keep).squeeze(1)
                result = result[keep]
            results.append(result)
        return results 
Example #4
Source Project: Torchelie   Author: Vermeille   File: callbacks.py    License: MIT License 5 votes vote down vote up
def compute_image(self, x):
        grad_img = x.grad.abs().sum(1, keepdim=True)
        b, c, h, w = grad_img.shape
        gi_flat = grad_img.view(b, c, -1)
        cl = torch.kthvalue(gi_flat, int(grad_img[0].numel() * 0.99),
                            dim=-1)[0]
        cl = cl.unsqueeze(-1).unsqueeze(-1)
        grad_img = torch.min(grad_img, cl) / cl
        x = x.detach()
        xm = x.min()
        xM = x.max()
        x = (x - xm) / (xM - xm)
        img = x * grad_img + 0.5 * (1 - grad_img)
        return img 
Example #5
Source Project: pytorch-dp   Author: facebookresearch   File: utils.py    License: Apache License 2.0 5 votes vote down vote up
def _pvalue(data: torch.Tensor, ratio: float = 0.25, **kwargs):
    """
    Finds the P-(ratio* 100)'s value in the tensor, equivalent
    to the kth largest element where k = ratio * len(data)
    """
    cut = max(1, int(data.numel() * (1 - ratio)))
    return torch.kthvalue(data, cut)[0].item() 
Example #6
Source Project: casme   Author: kondiz   File: model_basics.py    License: BSD 3-Clause "New" or "Revised" License 5 votes vote down vote up
def binarize_mask(mask):
    with torch.no_grad():
        avg = F.avg_pool2d(mask, 224, stride=1).squeeze()
        flat_mask = mask.cpu().view(mask.size(0), -1)
        binarized_mask = torch.zeros_like(flat_mask)
        for i in range(mask.size(0)):
            kth = 1 + int((flat_mask[i].size(0) - 1) * (1 - avg[i].item()) + 0.5)
            th, _ = torch.kthvalue(flat_mask[i], kth)
            th.clamp_(1e-6, 1 - 1e-6)
            binarized_mask[i] = flat_mask[i].gt(th).float()
        binarized_mask = binarized_mask.view(mask.size())

        return binarized_mask 
Example #7
Source Project: TinyBenchmark   Author: ucas-vg   File: locnet.py    License: MIT License 5 votes vote down vote up
def __call__(self, images, *targets_list):
        import matplotlib.pyplot as plt
        import seaborn as sbn
        if (self.counter + 1) % self.show_iter != 0:
            self.counter += 1
            return
        self.counter += 1
        colors = sbn.color_palette(n_colors=len(targets_list))
        img = images.tensors[0].permute((1, 2, 0)).cpu().numpy() + self.image_mean
        img = img[:, :, [2, 1, 0]]
        plt.imshow(img/255)
        title = "boxes:"
        for ci, targets in enumerate(targets_list):
            if targets is not None:
                bboxes = targets[0].bbox.detach().cpu().numpy().tolist()
                scores = targets[0].extra_fields['scores'].detach().cpu() if 'scores' in targets[0].extra_fields else None
                locations = targets[0].extra_fields['det_locations'].detach().cpu() if 'det_locations' in targets[0].extra_fields else None
                labels = targets[0].extra_fields['labels'].cpu()
                if scores is None or len(scores) == 0:
                    self.plot1(bboxes, scores, locations, labels, None, (1, 0, 0))  # ground-truth
                else:
                    score_th = -torch.kthvalue(-scores, self.show_score_topk)[0]\
                        if self.score_th is None else self.score_th
                    self.plot(bboxes, scores, locations, labels, score_th, colors[ci])
                count = len(targets[0].bbox) if scores is None else (scores > score_th).sum()
                title += "{}({}) ".format(count, len(targets[0].bbox))
        plt.title(title)
        plt.show()
        input() 
Example #8
Source Project: TinyBenchmark   Author: ucas-vg   File: cascade_fcos.py    License: MIT License 5 votes vote down vote up
def __call__(self, images, *targets_list):
        import matplotlib.pyplot as plt
        import seaborn as sbn
        if (self.counter + 1) % self.show_iter != 0:
            self.counter += 1
            return
        self.counter += 1
        colors = sbn.color_palette(n_colors=len(targets_list))
        img = images.tensors[0].permute((1, 2, 0)).cpu().numpy() + self.image_mean
        img = img[:, :, [2, 1, 0]]
        plt.imshow(img/255)
        title = "boxes:"
        for ci, targets in enumerate(targets_list):
            if targets is not None:
                bboxes = targets[0].bbox.cpu().numpy().tolist()
                scores = targets[0].extra_fields['scores'].cpu() if 'scores' in targets[0].extra_fields else None
                locations = targets[0].extra_fields['det_locations'].cpu() if 'det_locations' in targets[0].extra_fields else None
                labels = targets[0].extra_fields['labels'].cpu()
                if scores is None:
                    self.plot1(bboxes, scores, locations, labels, None, (1, 0, 0))  # ground-truth
                else:
                    score_th = -torch.kthvalue(-scores, self.show_score_topk)[0]\
                        if self.score_th is None else self.score_th
                    self.plot(bboxes, scores, locations, labels, score_th, colors[ci])
                count = len(targets[0].bbox) if scores is None else (scores > score_th).sum()
                title += "{}({}) ".format(count, len(targets[0].bbox))
        plt.title(title)
        plt.show()
        input() 
Example #9
Source Project: Res2Net-maskrcnn   Author: Res2Net   File: inference.py    License: MIT License 4 votes vote down vote up
def filter_results(self, boxlist, num_classes):
        """Returns bounding-box detection results by thresholding on scores and
        applying non-maximum suppression (NMS).
        """
        # unwrap the boxlist to avoid additional overhead.
        # if we had multi-class NMS, we could perform this directly on the boxlist
        boxes = boxlist.bbox.reshape(-1, num_classes * 4)
        scores = boxlist.get_field("scores").reshape(-1, num_classes)

        device = scores.device
        result = []
        # Apply threshold on detection probabilities and apply NMS
        # Skip j = 0, because it's the background class
        inds_all = scores > self.score_thresh
        for j in range(1, num_classes):
            inds = inds_all[:, j].nonzero().squeeze(1)
            scores_j = scores[inds, j]
            boxes_j = boxes[inds, j * 4 : (j + 1) * 4]
            boxlist_for_class = BoxList(boxes_j, boxlist.size, mode="xyxy")
            boxlist_for_class.add_field("scores", scores_j)
            boxlist_for_class = boxlist_nms(
                boxlist_for_class, self.nms
            )
            num_labels = len(boxlist_for_class)
            boxlist_for_class.add_field(
                "labels", torch.full((num_labels,), j, dtype=torch.int64, device=device)
            )
            result.append(boxlist_for_class)

        result = cat_boxlist(result)
        number_of_detections = len(result)

        # Limit to max_per_image detections **over all classes**
        if number_of_detections > self.detections_per_img > 0:
            cls_scores = result.get_field("scores")
            image_thresh, _ = torch.kthvalue(
                cls_scores.cpu(), number_of_detections - self.detections_per_img + 1
            )
            keep = cls_scores >= image_thresh.item()
            keep = torch.nonzero(keep).squeeze(1)
            result = result[keep]
        return result 
Example #10
Source Project: Res2Net-maskrcnn   Author: Res2Net   File: inference.py    License: MIT License 4 votes vote down vote up
def select_over_all_levels(self, boxlists):
        num_images = len(boxlists)
        results = []
        for i in range(num_images):
            scores = boxlists[i].get_field("scores")
            labels = boxlists[i].get_field("labels")
            boxes = boxlists[i].bbox
            boxlist = boxlists[i]
            result = []
            # skip the background
            for j in range(1, self.num_classes):
                inds = (labels == j).nonzero().view(-1)

                scores_j = scores[inds]
                boxes_j = boxes[inds, :].view(-1, 4)
                boxlist_for_class = BoxList(boxes_j, boxlist.size, mode="xyxy")
                boxlist_for_class.add_field("scores", scores_j)
                boxlist_for_class = boxlist_nms(
                    boxlist_for_class, self.nms_thresh,
                    score_field="scores"
                )
                num_labels = len(boxlist_for_class)
                boxlist_for_class.add_field(
                    "labels", torch.full((num_labels,), j,
                                         dtype=torch.int64,
                                         device=scores.device)
                )
                result.append(boxlist_for_class)

            result = cat_boxlist(result)
            number_of_detections = len(result)

            # Limit to max_per_image detections **over all classes**
            if number_of_detections > self.fpn_post_nms_top_n > 0:
                cls_scores = result.get_field("scores")
                image_thresh, _ = torch.kthvalue(
                    cls_scores.cpu(),
                    number_of_detections - self.fpn_post_nms_top_n + 1
                )
                keep = cls_scores >= image_thresh.item()
                keep = torch.nonzero(keep).squeeze(1)
                result = result[keep]
            results.append(result)
        return results 
Example #11
Source Project: Parsing-R-CNN   Author: soeaver   File: inference.py    License: MIT License 4 votes vote down vote up
def filter_results(self, boxlist, num_classes):
        """Returns bounding-box detection results by thresholding on scores and
        applying non-maximum suppression (NMS).
        """
        # unwrap the boxlist to avoid additional overhead.
        # if we had multi-class NMS, we could perform this directly on the boxlist
        boxes = boxlist.bbox.reshape(-1, num_classes * 4)
        scores = boxlist.get_field("scores").reshape(-1, num_classes)

        device = scores.device
        result = []
        # Apply threshold on detection probabilities and apply NMS
        # Skip j = 0, because it's the background class
        inds_all = scores > self.score_thresh
        for j in range(1, num_classes):
            inds = inds_all[:, j].nonzero().squeeze(1)
            scores_j = scores[inds, j]
            boxes_j = boxes[inds, j * 4: (j + 1) * 4]
            boxlist_for_class = BoxList(boxes_j, boxlist.size, mode="xyxy")
            boxlist_for_class.add_field("scores", scores_j)
            boxlist_for_class_old = boxlist_for_class
            if cfg.TEST.SOFT_NMS.ENABLED:
                boxlist_for_class = boxlist_soft_nms(
                    boxlist_for_class,
                    sigma=cfg.TEST.SOFT_NMS.SIGMA,
                    overlap_thresh=self.nms,
                    score_thresh=0.0001,
                    method=cfg.TEST.SOFT_NMS.METHOD
                )
            else:
                boxlist_for_class = boxlist_nms(
                    boxlist_for_class, self.nms
                )
            # Refine the post-NMS boxes using bounding-box voting
            if cfg.TEST.BBOX_VOTE.ENABLED and boxes_j.shape[0] > 0:
                boxlist_for_class = boxlist_box_voting(
                    boxlist_for_class,
                    boxlist_for_class_old,
                    cfg.TEST.BBOX_VOTE.VOTE_TH,
                    scoring_method=cfg.TEST.BBOX_VOTE.SCORING_METHOD
                )
            num_labels = len(boxlist_for_class)
            boxlist_for_class.add_field(
                "labels", torch.full((num_labels,), j, dtype=torch.int64, device=device)
            )
            result.append(boxlist_for_class)

        result = cat_boxlist(result)
        number_of_detections = len(result)

        # Limit to max_per_image detections **over all classes**
        if number_of_detections > self.detections_per_img > 0:
            cls_scores = result.get_field("scores")
            image_thresh, _ = torch.kthvalue(
                cls_scores.cpu(), number_of_detections - self.detections_per_img + 1
            )
            keep = cls_scores >= image_thresh.item()
            keep = torch.nonzero(keep).squeeze(1)
            result = result[keep]
        return result 
Example #12
Source Project: R2CNN.pytorch   Author: Xiangyu-CAS   File: inference.py    License: MIT License 4 votes vote down vote up
def filter_results(self, boxlist, num_classes):
        """Returns bounding-box detection results by thresholding on scores and
        applying non-maximum suppression (NMS).
        """
        # unwrap the boxlist to avoid additional overhead.
        # if we had multi-class NMS, we could perform this directly on the boxlist
        boxes = boxlist.bbox.reshape(-1, num_classes * 4)
        quad_boxes = boxlist.quad_bbox.reshape(-1, num_classes * 8)
        scores = boxlist.get_field("scores").reshape(-1, num_classes)

        device = scores.device
        result = []
        # Apply threshold on detection probabilities and apply NMS
        # Skip j = 0, because it's the background class
        inds_all = scores > self.score_thresh
        for j in range(1, num_classes):
            inds = inds_all[:, j].nonzero().squeeze(1)
            scores_j = scores[inds, j]
            boxes_j = boxes[inds, j * 4 : (j + 1) * 4]
            quad_boxes_j = quad_boxes[inds, j * 8 : (j + 1) * 8]
            boxlist_for_class = QuadBoxList(quad_boxes_j, boxlist.size, mode="xyxy")
            boxlist_for_class.bbox = boxes_j
            boxlist_for_class.add_field("scores", scores_j)
            boxlist_for_class = boxlist_nms(
                boxlist_for_class, self.nms
            )
            num_labels = len(boxlist_for_class)
            boxlist_for_class.add_field(
                "labels", torch.full((num_labels,), j, dtype=torch.int64, device=device)
            )
            result.append(boxlist_for_class)

        result = cat_boxlist(result)
        number_of_detections = len(result)

        # Limit to max_per_image detections **over all classes**
        if number_of_detections > self.detections_per_img > 0:
            cls_scores = result.get_field("scores")
            image_thresh, _ = torch.kthvalue(
                cls_scores.cpu(), number_of_detections - self.detections_per_img + 1
            )
            keep = cls_scores >= image_thresh.item()
            keep = torch.nonzero(keep).squeeze(1)
            result = result[keep]
        return result 
Example #13
Source Project: R2CNN.pytorch   Author: Xiangyu-CAS   File: inference.py    License: MIT License 4 votes vote down vote up
def select_over_all_levels(self, boxlists):
        num_images = len(boxlists)
        results = []
        for i in range(num_images):
            scores = boxlists[i].get_field("scores")
            labels = boxlists[i].get_field("labels")
            boxes = boxlists[i].bbox
            boxlist = boxlists[i]
            result = []
            # skip the background
            for j in range(1, self.num_classes):
                inds = (labels == j).nonzero().view(-1)

                scores_j = scores[inds]
                boxes_j = boxes[inds, :].view(-1, 4)
                boxlist_for_class = BoxList(boxes_j, boxlist.size, mode="xyxy")
                boxlist_for_class.add_field("scores", scores_j)
                boxlist_for_class = boxlist_nms(
                    boxlist_for_class, self.nms_thresh,
                    score_field="scores"
                )
                num_labels = len(boxlist_for_class)
                boxlist_for_class.add_field(
                    "labels", torch.full((num_labels,), j,
                                         dtype=torch.int64,
                                         device=scores.device)
                )
                result.append(boxlist_for_class)

            result = cat_boxlist(result)
            number_of_detections = len(result)

            # Limit to max_per_image detections **over all classes**
            if number_of_detections > self.fpn_post_nms_top_n > 0:
                cls_scores = result.get_field("scores")
                image_thresh, _ = torch.kthvalue(
                    cls_scores.cpu(),
                    number_of_detections - self.fpn_post_nms_top_n + 1
                )
                keep = cls_scores >= image_thresh.item()
                keep = torch.nonzero(keep).squeeze(1)
                result = result[keep]
            results.append(result)
        return results 
Example #14
Source Project: Clothing-Detection   Author: simaiden   File: inference.py    License: GNU General Public License v3.0 4 votes vote down vote up
def filter_results(self, boxlist, num_classes):
        """Returns bounding-box detection results by thresholding on scores and
        applying non-maximum suppression (NMS).
        """
        # unwrap the boxlist to avoid additional overhead.
        # if we had multi-class NMS, we could perform this directly on the boxlist
        boxes = boxlist.bbox.reshape(-1, num_classes * 4)
        scores = boxlist.get_field("scores").reshape(-1, num_classes)

        device = scores.device
        result = []
        # Apply threshold on detection probabilities and apply NMS
        # Skip j = 0, because it's the background class
        inds_all = scores > self.score_thresh
        for j in range(1, num_classes):
            inds = inds_all[:, j].nonzero().squeeze(1)
            scores_j = scores[inds, j]
            boxes_j = boxes[inds, j * 4 : (j + 1) * 4]
            boxlist_for_class = BoxList(boxes_j, boxlist.size, mode="xyxy")
            boxlist_for_class.add_field("scores", scores_j)
            boxlist_for_class = boxlist_nms(
                boxlist_for_class, self.nms
            )
            num_labels = len(boxlist_for_class)
            boxlist_for_class.add_field(
                "labels", torch.full((num_labels,), j, dtype=torch.int64, device=device)
            )
            result.append(boxlist_for_class)

        result = cat_boxlist(result)
        number_of_detections = len(result)

        # Limit to max_per_image detections **over all classes**
        if number_of_detections > self.detections_per_img > 0:
            cls_scores = result.get_field("scores")
            image_thresh, _ = torch.kthvalue(
                cls_scores.cpu(), number_of_detections - self.detections_per_img + 1
            )
            keep = cls_scores >= image_thresh.item()
            keep = torch.nonzero(keep).squeeze(1)
            result = result[keep]
        return result 
Example #15
Source Project: Clothing-Detection   Author: simaiden   File: inference.py    License: GNU General Public License v3.0 4 votes vote down vote up
def select_over_all_levels(self, boxlists):
        num_images = len(boxlists)
        results = []
        for i in range(num_images):
            scores = boxlists[i].get_field("scores")
            labels = boxlists[i].get_field("labels")
            boxes = boxlists[i].bbox
            boxlist = boxlists[i]
            result = []
            # skip the background
            for j in range(1, self.num_classes):
                inds = (labels == j).nonzero().view(-1)

                scores_j = scores[inds]
                boxes_j = boxes[inds, :].view(-1, 4)
                boxlist_for_class = BoxList(boxes_j, boxlist.size, mode="xyxy")
                boxlist_for_class.add_field("scores", scores_j)
                boxlist_for_class = boxlist_nms(
                    boxlist_for_class, self.nms_thresh,
                    score_field="scores"
                )
                num_labels = len(boxlist_for_class)
                boxlist_for_class.add_field(
                    "labels", torch.full((num_labels,), j,
                                         dtype=torch.int64,
                                         device=scores.device)
                )
                result.append(boxlist_for_class)

            result = cat_boxlist(result)
            number_of_detections = len(result)

            # Limit to max_per_image detections **over all classes**
            if number_of_detections > self.fpn_post_nms_top_n > 0:
                cls_scores = result.get_field("scores")
                image_thresh, _ = torch.kthvalue(
                    cls_scores.cpu(),
                    number_of_detections - self.fpn_post_nms_top_n + 1
                )
                keep = cls_scores >= image_thresh.item()
                keep = torch.nonzero(keep).squeeze(1)
                result = result[keep]
            results.append(result)
        return results 
Example #16
Source Project: DetNAS   Author: megvii-model   File: inference.py    License: MIT License 4 votes vote down vote up
def filter_results(self, boxlist, num_classes):
        """Returns bounding-box detection results by thresholding on scores and
        applying non-maximum suppression (NMS).
        """
        # unwrap the boxlist to avoid additional overhead.
        # if we had multi-class NMS, we could perform this directly on the boxlist
        boxes = boxlist.bbox.reshape(-1, num_classes * 4)
        scores = boxlist.get_field("scores").reshape(-1, num_classes)

        device = scores.device
        result = []
        # Apply threshold on detection probabilities and apply NMS
        # Skip j = 0, because it's the background class
        inds_all = scores > self.score_thresh
        for j in range(1, num_classes):
            inds = inds_all[:, j].nonzero().squeeze(1)
            scores_j = scores[inds, j]
            boxes_j = boxes[inds, j * 4 : (j + 1) * 4]
            boxlist_for_class = BoxList(boxes_j, boxlist.size, mode="xyxy")
            boxlist_for_class.add_field("scores", scores_j)
            boxlist_for_class = boxlist_nms(
                boxlist_for_class, self.nms
            )
            num_labels = len(boxlist_for_class)
            boxlist_for_class.add_field(
                "labels", torch.full((num_labels,), j, dtype=torch.int64, device=device)
            )
            result.append(boxlist_for_class)

        result = cat_boxlist(result)
        number_of_detections = len(result)

        # Limit to max_per_image detections **over all classes**
        if number_of_detections > self.detections_per_img > 0:
            cls_scores = result.get_field("scores")
            image_thresh, _ = torch.kthvalue(
                cls_scores.cpu(), number_of_detections - self.detections_per_img + 1
            )
            keep = cls_scores >= image_thresh.item()
            keep = torch.nonzero(keep).squeeze(1)
            result = result[keep]
        return result 
Example #17
Source Project: DetNAS   Author: megvii-model   File: inference.py    License: MIT License 4 votes vote down vote up
def select_over_all_levels(self, boxlists):
        num_images = len(boxlists)
        results = []
        for i in range(num_images):
            scores = boxlists[i].get_field("scores")
            labels = boxlists[i].get_field("labels")
            boxes = boxlists[i].bbox
            boxlist = boxlists[i]
            result = []
            # skip the background
            for j in range(1, self.num_classes):
                inds = (labels == j).nonzero().view(-1)

                scores_j = scores[inds]
                boxes_j = boxes[inds, :].view(-1, 4)
                boxlist_for_class = BoxList(boxes_j, boxlist.size, mode="xyxy")
                boxlist_for_class.add_field("scores", scores_j)
                boxlist_for_class = boxlist_nms(
                    boxlist_for_class, self.nms_thresh,
                    score_field="scores"
                )
                num_labels = len(boxlist_for_class)
                boxlist_for_class.add_field(
                    "labels", torch.full((num_labels,), j,
                                         dtype=torch.int64,
                                         device=scores.device)
                )
                result.append(boxlist_for_class)

            result = cat_boxlist(result)
            number_of_detections = len(result)

            # Limit to max_per_image detections **over all classes**
            if number_of_detections > self.fpn_post_nms_top_n > 0:
                cls_scores = result.get_field("scores")
                image_thresh, _ = torch.kthvalue(
                    cls_scores.cpu(),
                    number_of_detections - self.fpn_post_nms_top_n + 1
                )
                keep = cls_scores >= image_thresh.item()
                keep = torch.nonzero(keep).squeeze(1)
                result = result[keep]
            results.append(result)
        return results 
Example #18
Source Project: remote_sensing_object_detection_2019   Author: clw5180   File: inference.py    License: MIT License 4 votes vote down vote up
def filter_results(self, boxlist, num_classes):
        """Returns bounding-box detection results by thresholding on scores and
        applying non-maximum suppression (NMS).
        """
        # unwrap the boxlist to avoid additional overhead.
        # if we had multi-class NMS, we could perform this directly on the boxlist
        boxes = boxlist.bbox.reshape(-1, num_classes * 5)
        scores = boxlist.get_field("scores").reshape(-1, num_classes)

        device = scores.device
        result = []
        # Apply threshold on detection probabilities and apply NMS
        # Skip j = 0, because it's the background class
        inds_all = scores > self.score_thresh
        for j in range(1, num_classes):
            inds = inds_all[:, j].nonzero().squeeze(1)
            scores_j = scores[inds, j]
            boxes_j = boxes[inds, j * 5 : (j + 1) * 5]
            boxlist_for_class = RBoxList(boxes_j, boxlist.size, mode="xywha")
            boxlist_for_class.add_field("scores", scores_j)
            boxlist_for_class = boxlist_nms(
                boxlist_for_class, self.nms, score_field="scores"
            )
            num_labels = len(boxlist_for_class)
            boxlist_for_class.add_field(
                "labels", torch.full((num_labels,), j, dtype=torch.int64, device=device)
            )
            result.append(boxlist_for_class)

        result = cat_boxlist(result)
        number_of_detections = len(result)

        # Limit to max_per_image detections **over all classes**
        if number_of_detections > self.detections_per_img > 0:
            cls_scores = result.get_field("scores")
            image_thresh, _ = torch.kthvalue(
                cls_scores.cpu(), number_of_detections - self.detections_per_img + 1
            )
            keep = cls_scores >= image_thresh.item()
            keep = torch.nonzero(keep).squeeze(1)
            result = result[keep]
        return result 
Example #19
Source Project: remote_sensing_object_detection_2019   Author: clw5180   File: inference.py    License: MIT License 4 votes vote down vote up
def filter_results(self, boxlist, num_classes):
        """Returns bounding-box detection results by thresholding on scores and
        applying non-maximum suppression (NMS).
        """
        # unwrap the boxlist to avoid additional overhead.
        # if we had multi-class NMS, we could perform this directly on the boxlist
        boxes = boxlist.bbox.reshape(-1, num_classes * 4)
        scores = boxlist.get_field("scores").reshape(-1, num_classes)

        device = scores.device
        result = []
        # Apply threshold on detection probabilities and apply NMS
        # Skip j = 0, because it's the background class
        inds_all = scores > self.score_thresh
        for j in range(1, num_classes):
            inds = inds_all[:, j].nonzero().squeeze(1)
            scores_j = scores[inds, j]
            boxes_j = boxes[inds, j * 4 : (j + 1) * 4]
            boxlist_for_class = BoxList(boxes_j, boxlist.size, mode="xyxy")
            boxlist_for_class.add_field("scores", scores_j)
            boxlist_for_class = boxlist_nms(
                boxlist_for_class, self.nms, score_field="scores"
            )
            num_labels = len(boxlist_for_class)
            boxlist_for_class.add_field(
                "labels", torch.full((num_labels,), j, dtype=torch.int64, device=device)
            )
            result.append(boxlist_for_class)

        result = cat_boxlist(result)
        number_of_detections = len(result)

        # Limit to max_per_image detections **over all classes**
        if number_of_detections > self.detections_per_img > 0:
            cls_scores = result.get_field("scores")
            image_thresh, _ = torch.kthvalue(
                cls_scores.cpu(), number_of_detections - self.detections_per_img + 1
            )
            keep = cls_scores >= image_thresh.item()
            keep = torch.nonzero(keep).squeeze(1)
            result = result[keep]
        return result 
Example #20
Source Project: training_results_v0.5   Author: mlperf   File: fast_rcnn.py    License: Apache License 2.0 4 votes vote down vote up
def box_results_with_nms_and_limit(
    scores, boxes, score_thresh=0.05, nms=0.5, detections_per_img=100
):
    """Returns bounding-box detection results by thresholding on scores and
    applying non-maximum suppression (NMS).
    `boxes` has shape (#detections, 4 * #classes), where each row represents
    a list of predicted bounding boxes for each of the object classes in the
    dataset (including the background class). The detections in each row
    originate from the same object proposal.
    `scores` has shape (#detection, #classes), where each row represents a list
    of object detection confidence scores for each of the object classes in the
    dataset (including the background class). `scores[i, j]`` corresponds to the
    box at `boxes[i, j * 4:(j + 1) * 4]`.
    """
    num_classes = scores.shape[1]
    cls_boxes = []
    cls_scores = []
    labels = []
    device = scores.device
    # Apply threshold on detection probabilities and apply NMS
    # Skip j = 0, because it's the background class
    for j in range(1, num_classes):
        inds = scores[:, j] > score_thresh
        scores_j = scores[inds, j]
        boxes_j = boxes[inds, j * 4 : (j + 1) * 4]
        keep = box_nms(boxes_j, scores_j, nms)
        cls_boxes.append(boxes_j[keep])
        cls_scores.append(scores_j[keep])
        # TODO see why we need the device argument
        labels.append(torch.full_like(keep, j, device=device))

    cls_scores = torch.cat(cls_scores, dim=0)
    cls_boxes = torch.cat(cls_boxes, dim=0)
    labels = torch.cat(labels, dim=0)
    number_of_detections = len(cls_scores)

    # Limit to max_per_image detections **over all classes**
    if number_of_detections > detections_per_img > 0:
        image_thresh, _ = torch.kthvalue(
            cls_scores.cpu(), number_of_detections - detections_per_img + 1
        )
        keep = cls_scores >= image_thresh.item()
        keep = torch.nonzero(keep)
        keep = keep.squeeze(1) if keep.numel() else keep
        cls_boxes = cls_boxes[keep]
        cls_scores = cls_scores[keep]
        labels = labels[keep]
    return cls_scores, cls_boxes, labels 
Example #21
Source Project: sampling-free   Author: ChenJoya   File: inference.py    License: MIT License 4 votes vote down vote up
def filter_results(self, boxlist, num_classes):
        """Returns bounding-box detection results by thresholding on scores and
        applying non-maximum suppression (NMS).
        """
        # unwrap the boxlist to avoid additional overhead.
        # if we had multi-class NMS, we could perform this directly on the boxlist
        boxes = boxlist.bbox.reshape(-1, num_classes * 4)
        scores = boxlist.get_field("scores").reshape(-1, num_classes)

        device = scores.device
        result = []
        # Apply threshold on detection probabilities and apply NMS
        # Skip j = 0, because it's the background class
        if self.imbalanced_decider is None:
            inds_all = scores > self.score_thresh
        else:
            inds_all = self.imbalanced_decider(scores)
        for j in range(1, num_classes):
            inds = inds_all[:, j].nonzero().squeeze(1)
            scores_j = scores[inds, j]
            boxes_j = boxes[inds, j * 4 : (j + 1) * 4]
            boxlist_for_class = BoxList(boxes_j, boxlist.size, mode="xyxy")
            boxlist_for_class.add_field("scores", scores_j)
            boxlist_for_class = boxlist_nms(
                boxlist_for_class, self.nms
            )
            num_labels = len(boxlist_for_class)
            boxlist_for_class.add_field(
                "labels", torch.full((num_labels,), j, dtype=torch.int64, device=device)
            )
            result.append(boxlist_for_class)

        result = cat_boxlist(result)
        number_of_detections = len(result)

        # Limit to max_per_image detections **over all classes**
        if number_of_detections > self.detections_per_img > 0:
            cls_scores = result.get_field("scores")
            image_thresh, _ = torch.kthvalue(
                cls_scores.cpu(), number_of_detections - self.detections_per_img + 1
            )
            keep = cls_scores >= image_thresh.item()
            keep = torch.nonzero(keep).squeeze(1)
            result = result[keep]
        return result 
Example #22
Source Project: sampling-free   Author: ChenJoya   File: inference.py    License: MIT License 4 votes vote down vote up
def select_over_all_levels(self, boxlists):
        num_images = len(boxlists)
        results = []
        for i in range(num_images):
            scores = boxlists[i].get_field("scores")
            labels = boxlists[i].get_field("labels")
            boxes = boxlists[i].bbox
            boxlist = boxlists[i]
            result = []
            # skip the background
            for j in range(1, self.num_classes):
                inds = (labels == j).nonzero().view(-1)

                scores_j = scores[inds]
                boxes_j = boxes[inds, :].view(-1, 4)
                boxlist_for_class = BoxList(boxes_j, boxlist.size, mode="xyxy")
                boxlist_for_class.add_field("scores", scores_j)
                boxlist_for_class = boxlist_nms(
                    boxlist_for_class, self.nms_thresh,
                    score_field="scores"
                )
                num_labels = len(boxlist_for_class)
                boxlist_for_class.add_field(
                    "labels", torch.full((num_labels,), j,
                                         dtype=torch.int64,
                                         device=scores.device)
                )
                result.append(boxlist_for_class)

            result = cat_boxlist(result)
            number_of_detections = len(result)

            # Limit to max_per_image detections **over all classes**
            if number_of_detections > self.fpn_post_nms_top_n > 0:
                cls_scores = result.get_field("scores")
                image_thresh, _ = torch.kthvalue(
                    cls_scores.cpu(),
                    number_of_detections - self.fpn_post_nms_top_n + 1
                )
                keep = cls_scores >= image_thresh.item()
                keep = torch.nonzero(keep).squeeze(1)
                result = result[keep]
            results.append(result)
        return results 
Example #23
Source Project: HRNet-MaskRCNN-Benchmark   Author: HRNet   File: inference.py    License: MIT License 4 votes vote down vote up
def filter_results(self, boxlist, num_classes):
        """Returns bounding-box detection results by thresholding on scores and
        applying non-maximum suppression (NMS).
        """
        # unwrap the boxlist to avoid additional overhead.
        # if we had multi-class NMS, we could perform this directly on the boxlist
        boxes = boxlist.bbox.reshape(-1, num_classes * 4)
        scores = boxlist.get_field("scores").reshape(-1, num_classes)

        device = scores.device
        result = []
        # Apply threshold on detection probabilities and apply NMS
        # Skip j = 0, because it's the background class
        inds_all = scores > self.score_thresh
        for j in range(1, num_classes):
            inds = inds_all[:, j].nonzero().squeeze(1)
            scores_j = scores[inds, j]
            boxes_j = boxes[inds, j * 4 : (j + 1) * 4]
            boxlist_for_class = BoxList(boxes_j, boxlist.size, mode="xyxy")
            boxlist_for_class.add_field("scores", scores_j)
            boxlist_for_class = boxlist_nms(
                boxlist_for_class, self.nms, score_field="scores"
            )
            num_labels = len(boxlist_for_class)
            boxlist_for_class.add_field(
                "labels", torch.full((num_labels,), j, dtype=torch.int64, device=device)
            )
            result.append(boxlist_for_class)

        result = cat_boxlist(result)
        number_of_detections = len(result)

        # Limit to max_per_image detections **over all classes**
        if number_of_detections > self.detections_per_img > 0:
            cls_scores = result.get_field("scores")
            image_thresh, _ = torch.kthvalue(
                cls_scores.cpu(), number_of_detections - self.detections_per_img + 1
            )
            keep = cls_scores >= image_thresh.item()
            keep = torch.nonzero(keep).squeeze(1)
            result = result[keep]
        return result 
Example #24
Source Project: maskrcnn-benchmark   Author: facebookresearch   File: inference.py    License: MIT License 4 votes vote down vote up
def filter_results(self, boxlist, num_classes):
        """Returns bounding-box detection results by thresholding on scores and
        applying non-maximum suppression (NMS).
        """
        # unwrap the boxlist to avoid additional overhead.
        # if we had multi-class NMS, we could perform this directly on the boxlist
        boxes = boxlist.bbox.reshape(-1, num_classes * 4)
        scores = boxlist.get_field("scores").reshape(-1, num_classes)

        device = scores.device
        result = []
        # Apply threshold on detection probabilities and apply NMS
        # Skip j = 0, because it's the background class
        inds_all = scores > self.score_thresh
        for j in range(1, num_classes):
            inds = inds_all[:, j].nonzero().squeeze(1)
            scores_j = scores[inds, j]
            boxes_j = boxes[inds, j * 4 : (j + 1) * 4]
            boxlist_for_class = BoxList(boxes_j, boxlist.size, mode="xyxy")
            boxlist_for_class.add_field("scores", scores_j)
            boxlist_for_class = boxlist_nms(
                boxlist_for_class, self.nms
            )
            num_labels = len(boxlist_for_class)
            boxlist_for_class.add_field(
                "labels", torch.full((num_labels,), j, dtype=torch.int64, device=device)
            )
            result.append(boxlist_for_class)

        result = cat_boxlist(result)
        number_of_detections = len(result)

        # Limit to max_per_image detections **over all classes**
        if number_of_detections > self.detections_per_img > 0:
            cls_scores = result.get_field("scores")
            image_thresh, _ = torch.kthvalue(
                cls_scores.cpu(), number_of_detections - self.detections_per_img + 1
            )
            keep = cls_scores >= image_thresh.item()
            keep = torch.nonzero(keep).squeeze(1)
            result = result[keep]
        return result 
Example #25
Source Project: maskrcnn-benchmark   Author: facebookresearch   File: inference.py    License: MIT License 4 votes vote down vote up
def select_over_all_levels(self, boxlists):
        num_images = len(boxlists)
        results = []
        for i in range(num_images):
            scores = boxlists[i].get_field("scores")
            labels = boxlists[i].get_field("labels")
            boxes = boxlists[i].bbox
            boxlist = boxlists[i]
            result = []
            # skip the background
            for j in range(1, self.num_classes):
                inds = (labels == j).nonzero().view(-1)

                scores_j = scores[inds]
                boxes_j = boxes[inds, :].view(-1, 4)
                boxlist_for_class = BoxList(boxes_j, boxlist.size, mode="xyxy")
                boxlist_for_class.add_field("scores", scores_j)
                boxlist_for_class = boxlist_nms(
                    boxlist_for_class, self.nms_thresh,
                    score_field="scores"
                )
                num_labels = len(boxlist_for_class)
                boxlist_for_class.add_field(
                    "labels", torch.full((num_labels,), j,
                                         dtype=torch.int64,
                                         device=scores.device)
                )
                result.append(boxlist_for_class)

            result = cat_boxlist(result)
            number_of_detections = len(result)

            # Limit to max_per_image detections **over all classes**
            if number_of_detections > self.fpn_post_nms_top_n > 0:
                cls_scores = result.get_field("scores")
                image_thresh, _ = torch.kthvalue(
                    cls_scores.cpu(),
                    number_of_detections - self.fpn_post_nms_top_n + 1
                )
                keep = cls_scores >= image_thresh.item()
                keep = torch.nonzero(keep).squeeze(1)
                result = result[keep]
            results.append(result)
        return results 
Example #26
Source Project: EmbedMask   Author: yinghdb   File: inference.py    License: MIT License 4 votes vote down vote up
def filter_results(self, boxlist, num_classes):
        """Returns bounding-box detection results by thresholding on scores and
        applying non-maximum suppression (NMS).
        """
        # unwrap the boxlist to avoid additional overhead.
        # if we had multi-class NMS, we could perform this directly on the boxlist
        boxes = boxlist.bbox.reshape(-1, num_classes * 4)
        scores = boxlist.get_field("scores").reshape(-1, num_classes)

        device = scores.device
        result = []
        # Apply threshold on detection probabilities and apply NMS
        # Skip j = 0, because it's the background class
        inds_all = scores > self.score_thresh
        for j in range(1, num_classes):
            inds = inds_all[:, j].nonzero().squeeze(1)
            scores_j = scores[inds, j]
            boxes_j = boxes[inds, j * 4 : (j + 1) * 4]
            boxlist_for_class = BoxList(boxes_j, boxlist.size, mode="xyxy")
            boxlist_for_class.add_field("scores", scores_j)
            boxlist_for_class = boxlist_nms(
                boxlist_for_class, self.nms
            )
            num_labels = len(boxlist_for_class)
            boxlist_for_class.add_field(
                "labels", torch.full((num_labels,), j, dtype=torch.int64, device=device)
            )
            result.append(boxlist_for_class)

        result = cat_boxlist(result)
        number_of_detections = len(result)

        # Limit to max_per_image detections **over all classes**
        if number_of_detections > self.detections_per_img > 0:
            cls_scores = result.get_field("scores")
            image_thresh, _ = torch.kthvalue(
                cls_scores.cpu(), number_of_detections - self.detections_per_img + 1
            )
            keep = cls_scores >= image_thresh.item()
            keep = torch.nonzero(keep).squeeze(1)
            result = result[keep]
        return result 
Example #27
Source Project: EmbedMask   Author: yinghdb   File: inference.py    License: MIT License 4 votes vote down vote up
def select_over_all_levels(self, boxlists):
        num_images = len(boxlists)
        results = []
        for i in range(num_images):
            scores = boxlists[i].get_field("scores")
            labels = boxlists[i].get_field("labels")
            boxes = boxlists[i].bbox
            boxlist = boxlists[i]
            result = []
            # skip the background
            for j in range(1, self.num_classes):
                inds = (labels == j).nonzero().view(-1)

                scores_j = scores[inds]
                boxes_j = boxes[inds, :].view(-1, 4)
                boxlist_for_class = BoxList(boxes_j, boxlist.size, mode="xyxy")
                boxlist_for_class.add_field("scores", scores_j)
                boxlist_for_class = boxlist_nms(
                    boxlist_for_class, self.nms_thresh,
                    score_field="scores"
                )
                num_labels = len(boxlist_for_class)
                boxlist_for_class.add_field(
                    "labels", torch.full((num_labels,), j,
                                         dtype=torch.int64,
                                         device=scores.device)
                )
                result.append(boxlist_for_class)

            result = cat_boxlist(result)
            number_of_detections = len(result)

            # Limit to max_per_image detections **over all classes**
            if number_of_detections > self.fpn_post_nms_top_n > 0:
                cls_scores = result.get_field("scores")
                image_thresh, _ = torch.kthvalue(
                    cls_scores.cpu(),
                    number_of_detections - self.fpn_post_nms_top_n + 1
                )
                keep = cls_scores >= image_thresh.item()
                keep = torch.nonzero(keep).squeeze(1)
                result = result[keep]
            results.append(result)
        return results 
Example #28
Source Project: FreeAnchor   Author: zhangxiaosong18   File: inference.py    License: MIT License 4 votes vote down vote up
def filter_results(self, boxlist, num_classes):
        """Returns bounding-box detection results by thresholding on scores and
        applying non-maximum suppression (NMS).
        """
        # unwrap the boxlist to avoid additional overhead.
        # if we had multi-class NMS, we could perform this directly on the boxlist
        boxes = boxlist.bbox.reshape(-1, num_classes * 4)
        scores = boxlist.get_field("scores").reshape(-1, num_classes)

        device = scores.device
        result = []
        # Apply threshold on detection probabilities and apply NMS
        # Skip j = 0, because it's the background class
        inds_all = scores > self.score_thresh
        no_background = int(self.free_anchor)
        for j in range(1 - no_background, num_classes - no_background):
            inds = inds_all[:, j].nonzero().squeeze(1)
            scores_j = scores[inds, j]
            boxes_j = boxes[inds, j * 4 : (j + 1) * 4]
            boxlist_for_class = BoxList(boxes_j, boxlist.size, mode="xyxy")
            boxlist_for_class.add_field("scores", scores_j)
            boxlist_for_class = boxlist_nms(
                boxlist_for_class, self.nms, score_field="scores"
            )
            num_labels = len(boxlist_for_class)
            boxlist_for_class.add_field(
                "labels", torch.full((num_labels,), j + no_background, dtype=torch.int64, device=device)
            )
            result.append(boxlist_for_class)

        result = cat_boxlist(result)
        number_of_detections = len(result)

        # Limit to max_per_image detections **over all classes**
        if number_of_detections > self.detections_per_img > 0:
            cls_scores = result.get_field("scores")
            image_thresh, _ = torch.kthvalue(
                cls_scores.cpu(), number_of_detections - self.detections_per_img + 1
            )
            keep = cls_scores >= image_thresh.item()
            keep = torch.nonzero(keep).squeeze(1)
            result = result[keep]
        return result 
Example #29
Source Project: FreeAnchor   Author: zhangxiaosong18   File: retinanet_infer.py    License: MIT License 4 votes vote down vote up
def select_over_all_levels(self, boxlists):
        num_images = len(boxlists)
        results = []
        for i in range(num_images):
            scores = boxlists[i].get_field("scores")
            labels = boxlists[i].get_field("labels")
            boxes = boxlists[i].bbox
            boxlist = boxlists[i]
            result = []
            # skip the background
            for j in range(1, 81):
                inds = (labels == j).nonzero().view(-1)
                if len(inds) == 0:
                    continue

                scores_j = scores[inds]
                boxes_j = boxes[inds, :].view(-1, 4)
                boxlist_for_class = BoxList(boxes_j, boxlist.size, mode="xyxy")
                boxlist_for_class.add_field("scores", scores_j)
                boxlist_for_class = boxlist_nms(
                    boxlist_for_class, self.nms_thresh,
                    score_field="scores"
                )
                num_labels = len(boxlist_for_class)
                boxlist_for_class.add_field(
                    "labels", torch.full((num_labels,), j,
                                         dtype=torch.int64,
                                         device=scores.device)
                )
                result.append(boxlist_for_class)

            if len(result) > 0:
                result = cat_boxlist(result)
                number_of_detections = len(result)

                # Limit to max_per_image detections **over all classes**
                if number_of_detections > self.fpn_post_nms_top_n > 0:
                    cls_scores = result.get_field("scores")
                    image_thresh, _ = torch.kthvalue(
                        cls_scores.cpu(),
                        number_of_detections - self.fpn_post_nms_top_n + 1
                    )
                    keep = cls_scores >= image_thresh.item()
                    keep = torch.nonzero(keep).squeeze(1)
                    result = result[keep]
                results.append(result)
            else:
                empty_boxlist = BoxList(torch.zeros(1, 4).to('cuda'), boxlist.size)
                empty_boxlist.add_field(
                    "labels", torch.LongTensor([1]).to('cuda'))
                empty_boxlist.add_field(
                    "scores", torch.Tensor([0.01]).to('cuda'))
                results.append(empty_boxlist)
        return results 
Example #30
Source Project: FreeAnchor   Author: zhangxiaosong18   File: without_nms_postprocessor.py    License: MIT License 4 votes vote down vote up
def select_over_all_levels_with(self, boxlists):
        num_images = len(boxlists)
        results = []
        for i in range(num_images):
            scores = boxlists[i].get_field("scores")
            labels = boxlists[i].get_field("labels")
            boxes = boxlists[i].bbox
            boxlist = boxlists[i]
            result = []
            # skip the background
            for j in range(1, 81):
                inds = (labels == j).nonzero().view(-1)
                if len(inds) == 0:
                    continue

                scores_j = scores[inds]
                boxes_j = boxes[inds, :].view(-1, 4)
                boxlist_for_class = BoxList(boxes_j, boxlist.size, mode="xyxy")
                boxlist_for_class.add_field("scores", scores_j)
                boxlist_for_class = boxlist_nms(
                    boxlist_for_class, self.nms_thresh,
                    score_field="scores"
                )
                num_labels = len(boxlist_for_class)
                boxlist_for_class.add_field(
                    "labels", torch.full((num_labels,), j,
                                         dtype=torch.int64,
                                         device=scores.device)
                )
                result.append(boxlist_for_class)

            if len(result) > 0:
                result = cat_boxlist(result)
                number_of_detections = len(result)

                # Limit to max_per_image detections **over all classes**
                if number_of_detections > self.fpn_post_nms_top_n > 0:
                    cls_scores = result.get_field("scores")
                    image_thresh, _ = torch.kthvalue(
                        cls_scores.cpu(),
                        number_of_detections - self.fpn_post_nms_top_n + 1
                    )
                    keep = cls_scores >= image_thresh.item()
                    keep = torch.nonzero(keep).squeeze(1)
                    result = result[keep]
                results.append(result)
            else:
                empty_boxlist = BoxList(torch.zeros(1, 4).to('cuda'), boxlist.size)
                empty_boxlist.add_field(
                    "labels", torch.LongTensor([1]).to('cuda'))
                empty_boxlist.add_field(
                    "scores", torch.Tensor([0.01]).to('cuda'))
                results.append(empty_boxlist)
        return results