Python core.bbox2roi方法代码示例

# 需要导入模块: from mmdet import core [as 别名]
# 或者: from mmdet.core import bbox2roi [as 别名]
def _mask_point_forward_train(self, x, sampling_results, mask_pred,
                                  gt_masks, img_metas):
        """Run forward function and calculate loss for point head in
        training."""
        pos_labels = torch.cat([res.pos_gt_labels for res in sampling_results])
        rel_roi_points = self.point_head.get_roi_rel_points_train(
            mask_pred, pos_labels, cfg=self.train_cfg)
        rois = bbox2roi([res.pos_bboxes for res in sampling_results])

        fine_grained_point_feats = self._get_fine_grained_point_feats(
            x, rois, rel_roi_points, img_metas)
        coarse_point_feats = point_sample(mask_pred, rel_roi_points)
        mask_point_pred = self.point_head(fine_grained_point_feats,
                                          coarse_point_feats)
        mask_point_target = self.point_head.get_targets(
            rois, rel_roi_points, sampling_results, gt_masks, self.train_cfg)
        loss_mask_point = self.point_head.loss(mask_point_pred,
                                               mask_point_target, pos_labels)

        return loss_mask_point 

# 需要导入模块: from mmdet import core [as 别名]
# 或者: from mmdet.core import bbox2roi [as 别名]
def forward_dummy(self, x, proposals):
        """Dummy forward function."""
        # bbox head
        outs = ()
        rois = bbox2roi([proposals])
        if self.with_bbox:
            bbox_results = self._bbox_forward(x, rois)
            outs = outs + (bbox_results['cls_score'],
                           bbox_results['bbox_pred'])

        # grid head
        grid_rois = rois[:100]
        grid_feats = self.grid_roi_extractor(
            x[:self.grid_roi_extractor.num_inputs], grid_rois)
        if self.with_shared_head:
            grid_feats = self.shared_head(grid_feats)
        grid_pred = self.grid_head(grid_feats)
        outs = outs + (grid_pred, )

        # mask head
        if self.with_mask:
            mask_rois = rois[:100]
            mask_results = self._mask_forward(x, mask_rois)
            outs = outs + (mask_results['mask_pred'], )
        return outs 

# 需要导入模块: from mmdet import core [as 别名]
# 或者: from mmdet.core import bbox2roi [as 别名]
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 

# 需要导入模块: from mmdet import core [as 别名]
# 或者: from mmdet.core import bbox2roi [as 别名]
def forward_dummy(self, img):
        outs = ()
        # backbone
        x = self.extract_feat(img)
        # rpn
        if self.with_rpn:
            rpn_outs = self.rpn_head(x)
            outs = outs + (rpn_outs, )
        proposals = torch.randn(1000, 4).cuda()
        # bbox head
        rois = bbox2roi([proposals])
        bbox_feats = self.bbox_roi_extractor(
            x[:self.bbox_roi_extractor.num_inputs], rois)
        if self.with_shared_head:
            bbox_feats = self.shared_head(bbox_feats)
        cls_score, bbox_pred = self.bbox_head(bbox_feats)
        # grid head
        grid_rois = rois[:100]
        grid_feats = self.grid_roi_extractor(
            x[:self.grid_roi_extractor.num_inputs], grid_rois)
        if self.with_shared_head:
            grid_feats = self.shared_head(grid_feats)
        grid_pred = self.grid_head(grid_feats)
        return rpn_outs, cls_score, bbox_pred, grid_pred 

# 需要导入模块: from mmdet import core [as 别名]
# 或者: from mmdet.core import bbox2roi [as 别名]
def forward_dummy(self, img):
        outs = ()
        # backbone
        x = self.extract_feat(img)
        # rpn
        if self.with_rpn:
            rpn_outs = self.rpn_head(x)
            outs = outs + (rpn_outs, )
        proposals = torch.randn(1000, 4).cuda()
        # bbox head
        rois = bbox2roi([proposals])
        bbox_cls_feats = self.bbox_roi_extractor(
            x[:self.bbox_roi_extractor.num_inputs], rois)
        bbox_reg_feats = self.bbox_roi_extractor(
            x[:self.bbox_roi_extractor.num_inputs],
            rois,
            roi_scale_factor=self.reg_roi_scale_factor)
        if self.with_shared_head:
            bbox_cls_feats = self.shared_head(bbox_cls_feats)
            bbox_reg_feats = self.shared_head(bbox_reg_feats)
        cls_score, bbox_pred = self.bbox_head(bbox_cls_feats, bbox_reg_feats)
        outs += (cls_score, bbox_pred)
        return outs 

# 需要导入模块: from mmdet import core [as 别名]
# 或者: from mmdet.core import bbox2roi [as 别名]
def simple_test_mask(self,
                         x,
                         img_metas,
                         det_bboxes,
                         det_labels,
                         rescale=False):
        """Obtain mask prediction without augmentation."""
        # image shape of the first image in the batch (only one)
        ori_shape = img_metas[0]['ori_shape']
        scale_factor = img_metas[0]['scale_factor']
        if det_bboxes.shape[0] == 0:
            segm_result = [[] for _ in range(self.mask_head.num_classes)]
        else:
            # if det_bboxes is rescaled to the original image size, we need to
            # rescale it back to the testing scale to obtain RoIs.
            if rescale and not isinstance(scale_factor, float):
                scale_factor = det_bboxes.new_tensor(scale_factor)
            _bboxes = (
                det_bboxes[:, :4] * scale_factor if rescale else det_bboxes)
            mask_rois = bbox2roi([_bboxes])
            mask_results = self._mask_forward(x, mask_rois)
            mask_results['mask_pred'] = self._mask_point_forward_test(
                x, mask_rois, det_labels, mask_results['mask_pred'], img_metas)
            segm_result = self.mask_head.get_seg_masks(
                mask_results['mask_pred'], _bboxes, det_labels, self.test_cfg,
                ori_shape, scale_factor, rescale)
        return segm_result 

# 需要导入模块: from mmdet import core [as 别名]
# 或者: from mmdet.core import bbox2roi [as 别名]
def aug_test_mask(self, feats, img_metas, det_bboxes, det_labels):
        """Test for mask head with test time augmentation."""
        if det_bboxes.shape[0] == 0:
            segm_result = [[] for _ in range(self.mask_head.num_classes)]
        else:
            aug_masks = []
            for x, img_meta in zip(feats, img_metas):
                img_shape = img_meta[0]['img_shape']
                scale_factor = img_meta[0]['scale_factor']
                flip = img_meta[0]['flip']
                _bboxes = bbox_mapping(det_bboxes[:, :4], img_shape,
                                       scale_factor, flip)
                mask_rois = bbox2roi([_bboxes])
                mask_results = self._mask_forward(x, mask_rois)
                mask_results['mask_pred'] = self._mask_point_forward_test(
                    x, mask_rois, det_labels, mask_results['mask_pred'],
                    img_metas)
                # convert to numpy array to save memory
                aug_masks.append(
                    mask_results['mask_pred'].sigmoid().cpu().numpy())
            merged_masks = merge_aug_masks(aug_masks, img_metas, self.test_cfg)

            ori_shape = img_metas[0][0]['ori_shape']
            segm_result = self.mask_head.get_seg_masks(
                merged_masks,
                det_bboxes,
                det_labels,
                self.test_cfg,
                ori_shape,
                scale_factor=1.0,
                rescale=False)
        return segm_result 

# 需要导入模块: from mmdet import core [as 别名]
# 或者: from mmdet.core import bbox2roi [as 别名]
def _bbox_forward_train(self, x, sampling_results, gt_bboxes, gt_labels,
                            img_metas):
        """Run forward function and calculate loss for box head in training."""
        bbox_results = super(GridRoIHead,
                             self)._bbox_forward_train(x, sampling_results,
                                                       gt_bboxes, gt_labels,
                                                       img_metas)

        # Grid head forward and loss
        sampling_results = self._random_jitter(sampling_results, img_metas)
        pos_rois = bbox2roi([res.pos_bboxes for res in sampling_results])

        # GN in head does not support zero shape input
        if pos_rois.shape[0] == 0:
            return bbox_results

        grid_feats = self.grid_roi_extractor(
            x[:self.grid_roi_extractor.num_inputs], pos_rois)
        if self.with_shared_head:
            grid_feats = self.shared_head(grid_feats)
        # Accelerate training
        max_sample_num_grid = self.train_cfg.get('max_num_grid', 192)
        sample_idx = torch.randperm(
            grid_feats.shape[0])[:min(grid_feats.shape[0], max_sample_num_grid
                                      )]
        grid_feats = grid_feats[sample_idx]

        grid_pred = self.grid_head(grid_feats)

        grid_targets = self.grid_head.get_targets(sampling_results,
                                                  self.train_cfg)
        grid_targets = grid_targets[sample_idx]

        loss_grid = self.grid_head.loss(grid_pred, grid_targets)

        bbox_results['loss_bbox'].update(loss_grid)
        return bbox_results 

# 需要导入模块: from mmdet import core [as 别名]
# 或者: from mmdet.core import bbox2roi [as 别名]
def forward_dummy(self, x, proposals):
        """Dummy forward function."""
        # bbox head
        outs = ()
        rois = bbox2roi([proposals])
        if self.with_bbox:
            bbox_results = self._bbox_forward(x, rois)
            outs = outs + (bbox_results['cls_score'],
                           bbox_results['bbox_pred'])
        # mask head
        if self.with_mask:
            mask_rois = rois[:100]
            mask_results = self._mask_forward(x, mask_rois)
            outs = outs + (mask_results['mask_pred'], )
        return outs 

# 需要导入模块: from mmdet import core [as 别名]
# 或者: from mmdet.core import bbox2roi [as 别名]
def _bbox_forward_train(self, x, sampling_results, gt_bboxes, gt_labels,
                            img_metas):
        """Run forward function and calculate loss for box head in training."""
        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)
        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 

# 需要导入模块: from mmdet import core [as 别名]
# 或者: from mmdet.core import bbox2roi [as 别名]
def _mask_forward_train(self, x, sampling_results, bbox_feats, gt_masks,
                            img_metas):
        """Run forward function and calculate loss for mask head in
        training."""
        if not self.share_roi_extractor:
            pos_rois = bbox2roi([res.pos_bboxes for res in sampling_results])
            if pos_rois.shape[0] == 0:
                return dict(loss_mask=None)
            mask_results = self._mask_forward(x, pos_rois)
        else:
            pos_inds = []
            device = bbox_feats.device
            for res in sampling_results:
                pos_inds.append(
                    torch.ones(
                        res.pos_bboxes.shape[0],
                        device=device,
                        dtype=torch.uint8))
                pos_inds.append(
                    torch.zeros(
                        res.neg_bboxes.shape[0],
                        device=device,
                        dtype=torch.uint8))
            pos_inds = torch.cat(pos_inds)
            if pos_inds.shape[0] == 0:
                return dict(loss_mask=None)
            mask_results = self._mask_forward(
                x, pos_inds=pos_inds, bbox_feats=bbox_feats)

        mask_targets = self.mask_head.get_targets(sampling_results, gt_masks,
                                                  self.train_cfg)
        pos_labels = torch.cat([res.pos_gt_labels for res in sampling_results])
        loss_mask = self.mask_head.loss(mask_results['mask_pred'],
                                        mask_targets, pos_labels)

        mask_results.update(loss_mask=loss_mask, mask_targets=mask_targets)
        return mask_results 

# 需要导入模块: from mmdet import core [as 别名]
# 或者: from mmdet.core import bbox2roi [as 别名]
def simple_test_mask(self,
                         x,
                         img_metas,
                         det_bboxes,
                         det_labels,
                         rescale=False):
        """Obtain mask prediction without augmentation."""
        # image shape of the first image in the batch (only one)
        ori_shape = img_metas[0]['ori_shape']
        scale_factor = img_metas[0]['scale_factor']

        if det_bboxes.shape[0] == 0:
            segm_result = [[] for _ in range(self.mask_head.num_classes)]
            mask_scores = [[] for _ in range(self.mask_head.num_classes)]
        else:
            # if det_bboxes is rescaled to the original image size, we need to
            # rescale it back to the testing scale to obtain RoIs.
            _bboxes = (
                det_bboxes[:, :4] *
                det_bboxes.new_tensor(scale_factor) if rescale else det_bboxes)
            mask_rois = bbox2roi([_bboxes])
            mask_results = self._mask_forward(x, mask_rois)
            segm_result = self.mask_head.get_seg_masks(
                mask_results['mask_pred'], _bboxes, det_labels, self.test_cfg,
                ori_shape, scale_factor, rescale)
            # get mask scores with mask iou head
            mask_iou_pred = self.mask_iou_head(
                mask_results['mask_feats'],
                mask_results['mask_pred'][range(det_labels.size(0)),
                                          det_labels])
            mask_scores = self.mask_iou_head.get_mask_scores(
                mask_iou_pred, det_bboxes, det_labels)
        return segm_result, mask_scores 

# 需要导入模块: from mmdet import core [as 别名]
# 或者: from mmdet.core import bbox2roi [as 别名]
def forward_dummy(self, img):
        outs = ()
        # backbone
        x = self.extract_feat(img)
        # rpn
        if self.with_rpn:
            rpn_outs = self.rpn_head(x)
            outs = outs + (rpn_outs, )
        proposals = torch.randn(1000, 4).cuda()
        # bbox head
        rois = bbox2roi([proposals])
        if self.with_bbox:
            bbox_feats = self.bbox_roi_extractor(
                x[:self.bbox_roi_extractor.num_inputs], rois)
            if self.with_shared_head:
                bbox_feats = self.shared_head(bbox_feats)
            cls_score, bbox_pred = self.bbox_head(bbox_feats)
            outs = outs + (cls_score, bbox_pred)
        # mask head
        if self.with_mask:
            mask_rois = rois[:100]
            mask_feats = self.mask_roi_extractor(
                x[:self.mask_roi_extractor.num_inputs], mask_rois)
            if self.with_shared_head:
                mask_feats = self.shared_head(mask_feats)
            mask_pred = self.mask_head(mask_feats)
            outs = outs + (mask_pred, )
        return outs 

# 需要导入模块: from mmdet import core [as 别名]
# 或者: from mmdet.core import bbox2roi [as 别名]
def simple_test(self, img, img_meta, proposals=None, rescale=False):
        """Test without augmentation."""
        assert self.with_bbox, "Bbox head must be implemented."

        x = self.extract_feat(img)

        proposal_list = self.simple_test_rpn(
            x, img_meta, self.test_cfg.rpn) if proposals is None else proposals

        det_bboxes, det_labels = self.simple_test_bboxes(
            x, img_meta, proposal_list, self.test_cfg.rcnn, rescale=False)

        # pack rois into bboxes
        grid_rois = bbox2roi([det_bboxes[:, :4]])
        grid_feats = self.grid_roi_extractor(
            x[:len(self.grid_roi_extractor.featmap_strides)], grid_rois)
        if grid_rois.shape[0] != 0:
            self.grid_head.test_mode = True
            grid_pred = self.grid_head(grid_feats)
            det_bboxes = self.grid_head.get_bboxes(det_bboxes,
                                                   grid_pred['fused'],
                                                   img_meta)
            if rescale:
                det_bboxes[:, :4] /= img_meta[0]['scale_factor']
        else:
            det_bboxes = torch.Tensor([])

        bbox_results = bbox2result(det_bboxes, det_labels,
                                   self.bbox_head.num_classes)

        return bbox_results 

# 需要导入模块: from mmdet import core [as 别名]
# 或者: from mmdet.core import bbox2roi [as 别名]
def simple_test_mask(self,
                         x,
                         img_meta,
                         det_bboxes,
                         det_labels,
                         rescale=False):
        # image shape of the first image in the batch (only one)
        ori_shape = img_meta[0]['ori_shape']
        scale_factor = img_meta[0]['scale_factor']

        if det_bboxes.shape[0] == 0:
            segm_result = [[] for _ in range(self.mask_head.num_classes - 1)]
            mask_scores = [[] for _ in range(self.mask_head.num_classes - 1)]
        else:
            # if det_bboxes is rescaled to the original image size, we need to
            # rescale it back to the testing scale to obtain RoIs.
            _bboxes = (
                det_bboxes[:, :4] * scale_factor if rescale else det_bboxes)
            mask_rois = bbox2roi([_bboxes])
            mask_feats = self.mask_roi_extractor(
                x[:len(self.mask_roi_extractor.featmap_strides)], mask_rois)
            if self.with_shared_head:
                mask_feats = self.shared_head(mask_feats)
            mask_pred = self.mask_head(mask_feats)
            segm_result = self.mask_head.get_seg_masks(mask_pred, _bboxes,
                                                       det_labels,
                                                       self.test_cfg.rcnn,
                                                       ori_shape, scale_factor,
                                                       rescale)
            # get mask scores with mask iou head
            mask_iou_pred = self.mask_iou_head(
                mask_feats,
                mask_pred[range(det_labels.size(0)), det_labels + 1])
            mask_scores = self.mask_iou_head.get_mask_scores(
                mask_iou_pred, det_bboxes, det_labels)
        return segm_result, mask_scores