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| import torch | |
| import torch.nn as nn | |
| from mmdet.core import bbox2result | |
| from ..builder import DETECTORS, build_backbone, build_head, build_neck | |
| from .base import BaseDetector | |
| class SingleStageDetector(BaseDetector): | |
| """Base class for single-stage detectors. | |
| Single-stage detectors directly and densely predict bounding boxes on the | |
| output features of the backbone+neck. | |
| """ | |
| def __init__(self, | |
| backbone, | |
| neck=None, | |
| bbox_head=None, | |
| train_cfg=None, | |
| test_cfg=None, | |
| pretrained=None): | |
| super(SingleStageDetector, self).__init__() | |
| self.backbone = build_backbone(backbone) | |
| if neck is not None: | |
| self.neck = build_neck(neck) | |
| bbox_head.update(train_cfg=train_cfg) | |
| bbox_head.update(test_cfg=test_cfg) | |
| self.bbox_head = build_head(bbox_head) | |
| self.train_cfg = train_cfg | |
| self.test_cfg = test_cfg | |
| self.init_weights(pretrained=pretrained) | |
| def init_weights(self, pretrained=None): | |
| """Initialize the weights in detector. | |
| Args: | |
| pretrained (str, optional): Path to pre-trained weights. | |
| Defaults to None. | |
| """ | |
| super(SingleStageDetector, 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() | |
| self.bbox_head.init_weights() | |
| 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` | |
| """ | |
| x = self.extract_feat(img) | |
| outs = self.bbox_head(x) | |
| return outs | |
| def forward_train(self, | |
| img, | |
| img_metas, | |
| gt_bboxes, | |
| gt_labels, | |
| gt_bboxes_ignore=None): | |
| """ | |
| Args: | |
| img (Tensor): Input images of shape (N, C, H, W). | |
| Typically these should be mean centered and std scaled. | |
| img_metas (list[dict]): A 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 | |
| :class:`mmdet.datasets.pipelines.Collect`. | |
| gt_bboxes (list[Tensor]): Each item are the truth boxes for each | |
| image 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. | |
| Returns: | |
| dict[str, Tensor]: A dictionary of loss components. | |
| """ | |
| super(SingleStageDetector, self).forward_train(img, img_metas) | |
| x = self.extract_feat(img) | |
| losses = self.bbox_head.forward_train(x, img_metas, gt_bboxes, | |
| gt_labels, gt_bboxes_ignore) | |
| return losses | |
| def simple_test(self, img, img_metas, rescale=False): | |
| """Test function without test time augmentation. | |
| Args: | |
| imgs (list[torch.Tensor]): List of multiple images | |
| img_metas (list[dict]): List of image information. | |
| rescale (bool, optional): Whether to rescale the results. | |
| Defaults to False. | |
| Returns: | |
| list[list[np.ndarray]]: BBox results of each image and classes. | |
| The outer list corresponds to each image. The inner list | |
| corresponds to each class. | |
| """ | |
| x = self.extract_feat(img) | |
| outs = self.bbox_head(x) | |
| # get origin input shape to support onnx dynamic shape | |
| if torch.onnx.is_in_onnx_export(): | |
| # get shape as tensor | |
| img_shape = torch._shape_as_tensor(img)[2:] | |
| img_metas[0]['img_shape_for_onnx'] = img_shape | |
| bbox_list = self.bbox_head.get_bboxes( | |
| *outs, img_metas, rescale=rescale) | |
| # skip post-processing when exporting to ONNX | |
| if torch.onnx.is_in_onnx_export(): | |
| return bbox_list | |
| bbox_results = [ | |
| bbox2result(det_bboxes, det_labels, self.bbox_head.num_classes) | |
| for det_bboxes, det_labels in bbox_list | |
| ] | |
| return bbox_results | |
| def aug_test(self, imgs, img_metas, rescale=False): | |
| """Test function with test time augmentation. | |
| Args: | |
| imgs (list[Tensor]): the outer list indicates test-time | |
| augmentations and inner Tensor should have a shape NxCxHxW, | |
| which contains all images in the batch. | |
| img_metas (list[list[dict]]): the outer list indicates test-time | |
| augs (multiscale, flip, etc.) and the inner list indicates | |
| images in a batch. each dict has image information. | |
| rescale (bool, optional): Whether to rescale the results. | |
| Defaults to False. | |
| Returns: | |
| list[list[np.ndarray]]: BBox results of each image and classes. | |
| The outer list corresponds to each image. The inner list | |
| corresponds to each class. | |
| """ | |
| assert hasattr(self.bbox_head, 'aug_test'), \ | |
| f'{self.bbox_head.__class__.__name__}' \ | |
| ' does not support test-time augmentation' | |
| feats = self.extract_feats(imgs) | |
| return [self.bbox_head.aug_test(feats, img_metas, rescale=rescale)] | |