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| import torch | |
| import torch.nn as nn | |
| # from mmdet.core import bbox2result, bbox2roi, build_assigner, build_sampler | |
| from ..builder import DETECTORS, build_backbone, build_head, build_neck | |
| from .base import BaseDetector | |
| class TwoStageDetector(BaseDetector): | |
| """Base class for two-stage detectors. | |
| Two-stage detectors typically consisting of a region proposal network and a | |
| task-specific regression head. | |
| """ | |
| def __init__(self, | |
| backbone, | |
| neck=None, | |
| rpn_head=None, | |
| roi_head=None, | |
| train_cfg=None, | |
| test_cfg=None, | |
| pretrained=None): | |
| super(TwoStageDetector, self).__init__() | |
| self.backbone = build_backbone(backbone) | |
| if neck is not None: | |
| self.neck = build_neck(neck) | |
| if rpn_head is not None: | |
| rpn_train_cfg = train_cfg.rpn if train_cfg is not None else None | |
| rpn_head_ = rpn_head.copy() | |
| rpn_head_.update(train_cfg=rpn_train_cfg, test_cfg=test_cfg.rpn) | |
| self.rpn_head = build_head(rpn_head_) | |
| if roi_head is not None: | |
| # update train and test cfg here for now | |
| # TODO: refactor assigner & sampler | |
| rcnn_train_cfg = train_cfg.rcnn if train_cfg is not None else None | |
| roi_head.update(train_cfg=rcnn_train_cfg) | |
| roi_head.update(test_cfg=test_cfg.rcnn) | |
| self.roi_head = build_head(roi_head) | |
| self.train_cfg = train_cfg | |
| self.test_cfg = test_cfg | |
| self.init_weights(pretrained=pretrained) | |
| def with_rpn(self): | |
| """bool: whether the detector has RPN""" | |
| return hasattr(self, 'rpn_head') and self.rpn_head is not None | |
| def with_roi_head(self): | |
| """bool: whether the detector has a RoI head""" | |
| return hasattr(self, 'roi_head') and self.roi_head is not None | |
| def init_weights(self, pretrained=None): | |
| """Initialize the weights in detector. | |
| Args: | |
| pretrained (str, optional): Path to pre-trained weights. | |
| Defaults to None. | |
| """ | |
| super(TwoStageDetector, self).init_weights(pretrained) | |
| self.backbone.init_weights(pretrained=pretrained) | |
| if self.with_neck: | |
| if isinstance(self.neck, nn.Sequential): | |
| for m in self.neck: | |
| m.init_weights() | |
| else: | |
| self.neck.init_weights() | |
| if self.with_rpn: | |
| self.rpn_head.init_weights() | |
| if self.with_roi_head: | |
| self.roi_head.init_weights(pretrained) | |
| def extract_feat(self, img): | |
| """Directly extract features from the backbone+neck.""" | |
| x = self.backbone(img) | |
| if self.with_neck: | |
| x = self.neck(x) | |
| return x | |
| def forward_dummy(self, img): | |
| """Used for computing network flops. | |
| See `mmdetection/tools/analysis_tools/get_flops.py` | |
| """ | |
| 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).to(img.device) | |
| # roi_head | |
| roi_outs = self.roi_head.forward_dummy(x, proposals) | |
| outs = outs + (roi_outs, ) | |
| return outs | |
| def forward_train(self, | |
| img, | |
| img_metas, | |
| gt_bboxes, | |
| gt_labels, | |
| gt_bboxes_ignore=None, | |
| gt_masks=None, | |
| proposals=None, | |
| **kwargs): | |
| """ | |
| Args: | |
| img (Tensor): of shape (N, C, H, W) encoding input images. | |
| Typically these should be mean centered and std scaled. | |
| img_metas (list[dict]): list of image info dict where each dict | |
| has: 'img_shape', 'scale_factor', 'flip', and may also contain | |
| 'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'. | |
| For details on the values of these keys see | |
| `mmdet/datasets/pipelines/formatting.py:Collect`. | |
| gt_bboxes (list[Tensor]): Ground truth bboxes for each image with | |
| shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format. | |
| gt_labels (list[Tensor]): class indices corresponding to each box | |
| gt_bboxes_ignore (None | list[Tensor]): specify which bounding | |
| boxes can be ignored when computing the loss. | |
| gt_masks (None | Tensor) : true segmentation masks for each box | |
| used if the architecture supports a segmentation task. | |
| proposals : override rpn proposals with custom proposals. Use when | |
| `with_rpn` is False. | |
| Returns: | |
| dict[str, Tensor]: a dictionary of loss components | |
| """ | |
| x = self.extract_feat(img) | |
| losses = dict() | |
| # RPN forward and loss | |
| if self.with_rpn: | |
| proposal_cfg = self.train_cfg.get('rpn_proposal', | |
| self.test_cfg.rpn) | |
| rpn_losses, proposal_list = self.rpn_head.forward_train( | |
| x, | |
| img_metas, | |
| gt_bboxes, | |
| gt_labels=None, | |
| gt_bboxes_ignore=gt_bboxes_ignore, | |
| proposal_cfg=proposal_cfg) | |
| losses.update(rpn_losses) | |
| else: | |
| proposal_list = proposals | |
| roi_losses = self.roi_head.forward_train(x, img_metas, proposal_list, | |
| gt_bboxes, gt_labels, | |
| gt_bboxes_ignore, gt_masks, | |
| **kwargs) | |
| losses.update(roi_losses) | |
| return losses | |
| async def async_simple_test(self, | |
| img, | |
| img_meta, | |
| proposals=None, | |
| rescale=False): | |
| """Async test without augmentation.""" | |
| assert self.with_bbox, 'Bbox head must be implemented.' | |
| x = self.extract_feat(img) | |
| if proposals is None: | |
| proposal_list = await self.rpn_head.async_simple_test_rpn( | |
| x, img_meta) | |
| else: | |
| proposal_list = proposals | |
| return await self.roi_head.async_simple_test( | |
| x, proposal_list, img_meta, rescale=rescale) | |
| def simple_test(self, img, img_metas, proposals=None, rescale=False): | |
| """Test without augmentation.""" | |
| assert self.with_bbox, 'Bbox head must be implemented.' | |
| x = self.extract_feat(img) | |
| # get origin input shape to onnx dynamic input shape | |
| if torch.onnx.is_in_onnx_export(): | |
| img_shape = torch._shape_as_tensor(img)[2:] | |
| img_metas[0]['img_shape_for_onnx'] = img_shape | |
| if proposals is None: | |
| proposal_list = self.rpn_head.simple_test_rpn(x, img_metas) | |
| else: | |
| proposal_list = proposals | |
| return self.roi_head.simple_test( | |
| x, proposal_list, img_metas, rescale=rescale) | |
| def aug_test(self, imgs, img_metas, rescale=False): | |
| """Test with augmentations. | |
| If rescale is False, then returned bboxes and masks will fit the scale | |
| of imgs[0]. | |
| """ | |
| x = self.extract_feats(imgs) | |
| proposal_list = self.rpn_head.aug_test_rpn(x, img_metas) | |
| return self.roi_head.aug_test( | |
| x, proposal_list, img_metas, rescale=rescale) | |