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- # Copyright (c) OpenMMLab. All rights reserved.
- import warnings
-
- import torch
- import torch.nn as nn
- from mmcv.runner import force_fp32
-
- from mmdet.core import (anchor_inside_flags, build_assigner, build_bbox_coder,
- build_prior_generator, build_sampler, images_to_levels,
- multi_apply, unmap)
- from ..builder import HEADS, build_loss
- from .base_dense_head import BaseDenseHead
- from .dense_test_mixins import BBoxTestMixin
-
-
- @HEADS.register_module()
- class AnchorHead(BaseDenseHead, BBoxTestMixin):
- """Anchor-based head (RPN, RetinaNet, SSD, etc.).
-
- Args:
- num_classes (int): Number of categories excluding the background
- category.
- in_channels (int): Number of channels in the input feature map.
- feat_channels (int): Number of hidden channels. Used in child classes.
- anchor_generator (dict): Config dict for anchor generator
- bbox_coder (dict): Config of bounding box coder.
- reg_decoded_bbox (bool): If true, the regression loss would be
- applied directly on decoded bounding boxes, converting both
- the predicted boxes and regression targets to absolute
- coordinates format. Default False. It should be `True` when
- using `IoULoss`, `GIoULoss`, or `DIoULoss` in the bbox head.
- loss_cls (dict): Config of classification loss.
- loss_bbox (dict): Config of localization loss.
- train_cfg (dict): Training config of anchor head.
- test_cfg (dict): Testing config of anchor head.
- init_cfg (dict or list[dict], optional): Initialization config dict.
- """ # noqa: W605
-
- def __init__(self,
- num_classes,
- in_channels,
- feat_channels=256,
- anchor_generator=dict(
- type='AnchorGenerator',
- scales=[8, 16, 32],
- ratios=[0.5, 1.0, 2.0],
- strides=[4, 8, 16, 32, 64]),
- bbox_coder=dict(
- type='DeltaXYWHBBoxCoder',
- clip_border=True,
- target_means=(.0, .0, .0, .0),
- target_stds=(1.0, 1.0, 1.0, 1.0)),
- reg_decoded_bbox=False,
- loss_cls=dict(
- type='CrossEntropyLoss',
- use_sigmoid=True,
- loss_weight=1.0),
- loss_bbox=dict(
- type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0),
- train_cfg=None,
- test_cfg=None,
- init_cfg=dict(type='Normal', layer='Conv2d', std=0.01)):
- super(AnchorHead, self).__init__(init_cfg)
- self.in_channels = in_channels
- self.num_classes = num_classes
- self.feat_channels = feat_channels
- self.use_sigmoid_cls = loss_cls.get('use_sigmoid', False)
- if self.use_sigmoid_cls:
- self.cls_out_channels = num_classes
- else:
- self.cls_out_channels = num_classes + 1
-
- if self.cls_out_channels <= 0:
- raise ValueError(f'num_classes={num_classes} is too small')
- self.reg_decoded_bbox = reg_decoded_bbox
-
- self.bbox_coder = build_bbox_coder(bbox_coder)
- self.loss_cls = build_loss(loss_cls)
- self.loss_bbox = build_loss(loss_bbox)
- self.train_cfg = train_cfg
- self.test_cfg = test_cfg
- if self.train_cfg:
- self.assigner = build_assigner(self.train_cfg.assigner)
- if hasattr(self.train_cfg,
- 'sampler') and self.train_cfg.sampler.type.split(
- '.')[-1] != 'PseudoSampler':
- self.sampling = True
- sampler_cfg = self.train_cfg.sampler
- # avoid BC-breaking
- if loss_cls['type'] in [
- 'FocalLoss', 'GHMC', 'QualityFocalLoss'
- ]:
- warnings.warn(
- 'DeprecationWarning: Determining whether to sampling'
- 'by loss type is deprecated, please delete sampler in'
- 'your config when using `FocalLoss`, `GHMC`, '
- '`QualityFocalLoss` or other FocalLoss variant.')
- self.sampling = False
- sampler_cfg = dict(type='PseudoSampler')
- else:
- self.sampling = False
- sampler_cfg = dict(type='PseudoSampler')
- self.sampler = build_sampler(sampler_cfg, context=self)
- self.fp16_enabled = False
-
- self.prior_generator = build_prior_generator(anchor_generator)
-
- # Usually the numbers of anchors for each level are the same
- # except SSD detectors. So it is an int in the most dense
- # heads but a list of int in SSDHead
- self.num_base_priors = self.prior_generator.num_base_priors[0]
- self._init_layers()
-
- @property
- def num_anchors(self):
- warnings.warn('DeprecationWarning: `num_anchors` is deprecated, '
- 'for consistency or also use '
- '`num_base_priors` instead')
- return self.prior_generator.num_base_priors[0]
-
- @property
- def anchor_generator(self):
- warnings.warn('DeprecationWarning: anchor_generator is deprecated, '
- 'please use "prior_generator" instead')
- return self.prior_generator
-
- def _init_layers(self):
- """Initialize layers of the head."""
- self.conv_cls = nn.Conv2d(self.in_channels,
- self.num_base_priors * self.cls_out_channels,
- 1)
- self.conv_reg = nn.Conv2d(self.in_channels, self.num_base_priors * 4,
- 1)
-
- def forward_single(self, x):
- """Forward feature of a single scale level.
-
- Args:
- x (Tensor): Features of a single scale level.
-
- Returns:
- tuple:
- cls_score (Tensor): Cls scores for a single scale level \
- the channels number is num_base_priors * num_classes.
- bbox_pred (Tensor): Box energies / deltas for a single scale \
- level, the channels number is num_base_priors * 4.
- """
- cls_score = self.conv_cls(x)
- bbox_pred = self.conv_reg(x)
- return cls_score, bbox_pred
-
- def forward(self, feats):
- """Forward features from the upstream network.
-
- Args:
- feats (tuple[Tensor]): Features from the upstream network, each is
- a 4D-tensor.
-
- Returns:
- tuple: A tuple of classification scores and bbox prediction.
-
- - cls_scores (list[Tensor]): Classification scores for all \
- scale levels, each is a 4D-tensor, the channels number \
- is num_base_priors * num_classes.
- - bbox_preds (list[Tensor]): Box energies / deltas for all \
- scale levels, each is a 4D-tensor, the channels number \
- is num_base_priors * 4.
- """
- return multi_apply(self.forward_single, feats)
-
- def get_anchors(self, featmap_sizes, img_metas, device='cuda'):
- """Get anchors according to feature map sizes.
-
- Args:
- featmap_sizes (list[tuple]): Multi-level feature map sizes.
- img_metas (list[dict]): Image meta info.
- device (torch.device | str): Device for returned tensors
-
- Returns:
- tuple:
- anchor_list (list[Tensor]): Anchors of each image.
- valid_flag_list (list[Tensor]): Valid flags of each image.
- """
- num_imgs = len(img_metas)
-
- # since feature map sizes of all images are the same, we only compute
- # anchors for one time
- multi_level_anchors = self.prior_generator.grid_priors(
- featmap_sizes, device=device)
- anchor_list = [multi_level_anchors for _ in range(num_imgs)]
-
- # for each image, we compute valid flags of multi level anchors
- valid_flag_list = []
- for img_id, img_meta in enumerate(img_metas):
- multi_level_flags = self.prior_generator.valid_flags(
- featmap_sizes, img_meta['pad_shape'], device)
- valid_flag_list.append(multi_level_flags)
-
- return anchor_list, valid_flag_list
-
- def _get_targets_single(self,
- flat_anchors,
- valid_flags,
- gt_bboxes,
- gt_bboxes_ignore,
- gt_labels,
- img_meta,
- label_channels=1,
- unmap_outputs=True):
- """Compute regression and classification targets for anchors in a
- single image.
-
- Args:
- flat_anchors (Tensor): Multi-level anchors of the image, which are
- concatenated into a single tensor of shape (num_anchors ,4)
- valid_flags (Tensor): Multi level valid flags of the image,
- which are concatenated into a single tensor of
- shape (num_anchors,).
- gt_bboxes (Tensor): Ground truth bboxes of the image,
- shape (num_gts, 4).
- gt_bboxes_ignore (Tensor): Ground truth bboxes to be
- ignored, shape (num_ignored_gts, 4).
- img_meta (dict): Meta info of the image.
- gt_labels (Tensor): Ground truth labels of each box,
- shape (num_gts,).
- label_channels (int): Channel of label.
- unmap_outputs (bool): Whether to map outputs back to the original
- set of anchors.
-
- Returns:
- tuple:
- labels_list (list[Tensor]): Labels of each level
- label_weights_list (list[Tensor]): Label weights of each level
- bbox_targets_list (list[Tensor]): BBox targets of each level
- bbox_weights_list (list[Tensor]): BBox weights of each level
- num_total_pos (int): Number of positive samples in all images
- num_total_neg (int): Number of negative samples in all images
- """
- inside_flags = anchor_inside_flags(flat_anchors, valid_flags,
- img_meta['img_shape'][:2],
- self.train_cfg.allowed_border)
- if not inside_flags.any():
- return (None, ) * 7
- # assign gt and sample anchors
- anchors = flat_anchors[inside_flags, :]
-
- assign_result = self.assigner.assign(
- anchors, gt_bboxes, gt_bboxes_ignore,
- None if self.sampling else gt_labels)
- sampling_result = self.sampler.sample(assign_result, anchors,
- gt_bboxes)
-
- num_valid_anchors = anchors.shape[0]
- bbox_targets = torch.zeros_like(anchors)
- bbox_weights = torch.zeros_like(anchors)
- labels = anchors.new_full((num_valid_anchors, ),
- self.num_classes,
- dtype=torch.long)
- label_weights = anchors.new_zeros(num_valid_anchors, dtype=torch.float)
-
- pos_inds = sampling_result.pos_inds
- neg_inds = sampling_result.neg_inds
- if len(pos_inds) > 0:
- if not self.reg_decoded_bbox:
- pos_bbox_targets = self.bbox_coder.encode(
- sampling_result.pos_bboxes, sampling_result.pos_gt_bboxes)
- else:
- pos_bbox_targets = sampling_result.pos_gt_bboxes
- bbox_targets[pos_inds, :] = pos_bbox_targets
- bbox_weights[pos_inds, :] = 1.0
- if gt_labels is None:
- # Only rpn gives gt_labels as None
- # Foreground is the first class since v2.5.0
- labels[pos_inds] = 0
- else:
- labels[pos_inds] = gt_labels[
- sampling_result.pos_assigned_gt_inds]
- if self.train_cfg.pos_weight <= 0:
- label_weights[pos_inds] = 1.0
- else:
- label_weights[pos_inds] = self.train_cfg.pos_weight
- if len(neg_inds) > 0:
- label_weights[neg_inds] = 1.0
-
- # map up to original set of anchors
- if unmap_outputs:
- num_total_anchors = flat_anchors.size(0)
- labels = unmap(
- labels, num_total_anchors, inside_flags,
- fill=self.num_classes) # fill bg label
- label_weights = unmap(label_weights, num_total_anchors,
- inside_flags)
- bbox_targets = unmap(bbox_targets, num_total_anchors, inside_flags)
- bbox_weights = unmap(bbox_weights, num_total_anchors, inside_flags)
-
- return (labels, label_weights, bbox_targets, bbox_weights, pos_inds,
- neg_inds, sampling_result)
-
- def get_targets(self,
- anchor_list,
- valid_flag_list,
- gt_bboxes_list,
- img_metas,
- gt_bboxes_ignore_list=None,
- gt_labels_list=None,
- label_channels=1,
- unmap_outputs=True,
- return_sampling_results=False):
- """Compute regression and classification targets for anchors in
- multiple images.
-
- Args:
- anchor_list (list[list[Tensor]]): Multi level anchors of each
- image. The outer list indicates images, and the inner list
- corresponds to feature levels of the image. Each element of
- the inner list is a tensor of shape (num_anchors, 4).
- valid_flag_list (list[list[Tensor]]): Multi level valid flags of
- each image. The outer list indicates images, and the inner list
- corresponds to feature levels of the image. Each element of
- the inner list is a tensor of shape (num_anchors, )
- gt_bboxes_list (list[Tensor]): Ground truth bboxes of each image.
- img_metas (list[dict]): Meta info of each image.
- gt_bboxes_ignore_list (list[Tensor]): Ground truth bboxes to be
- ignored.
- gt_labels_list (list[Tensor]): Ground truth labels of each box.
- label_channels (int): Channel of label.
- unmap_outputs (bool): Whether to map outputs back to the original
- set of anchors.
-
- Returns:
- tuple: Usually returns a tuple containing learning targets.
-
- - labels_list (list[Tensor]): Labels of each level.
- - label_weights_list (list[Tensor]): Label weights of each
- level.
- - bbox_targets_list (list[Tensor]): BBox targets of each level.
- - bbox_weights_list (list[Tensor]): BBox weights of each level.
- - num_total_pos (int): Number of positive samples in all
- images.
- - num_total_neg (int): Number of negative samples in all
- images.
-
- additional_returns: This function enables user-defined returns from
- `self._get_targets_single`. These returns are currently refined
- to properties at each feature map (i.e. having HxW dimension).
- The results will be concatenated after the end
- """
- num_imgs = len(img_metas)
- assert len(anchor_list) == len(valid_flag_list) == num_imgs
-
- # anchor number of multi levels
- num_level_anchors = [anchors.size(0) for anchors in anchor_list[0]]
- # concat all level anchors to a single tensor
- concat_anchor_list = []
- concat_valid_flag_list = []
- for i in range(num_imgs):
- assert len(anchor_list[i]) == len(valid_flag_list[i])
- concat_anchor_list.append(torch.cat(anchor_list[i]))
- concat_valid_flag_list.append(torch.cat(valid_flag_list[i]))
-
- # compute targets for each image
- if gt_bboxes_ignore_list is None:
- gt_bboxes_ignore_list = [None for _ in range(num_imgs)]
- if gt_labels_list is None:
- gt_labels_list = [None for _ in range(num_imgs)]
- results = multi_apply(
- self._get_targets_single,
- concat_anchor_list,
- concat_valid_flag_list,
- gt_bboxes_list,
- gt_bboxes_ignore_list,
- gt_labels_list,
- img_metas,
- label_channels=label_channels,
- unmap_outputs=unmap_outputs)
- (all_labels, all_label_weights, all_bbox_targets, all_bbox_weights,
- pos_inds_list, neg_inds_list, sampling_results_list) = results[:7]
- rest_results = list(results[7:]) # user-added return values
- # no valid anchors
- if any([labels is None for labels in all_labels]):
- return None
- # sampled anchors of all images
- num_total_pos = sum([max(inds.numel(), 1) for inds in pos_inds_list])
- num_total_neg = sum([max(inds.numel(), 1) for inds in neg_inds_list])
- # split targets to a list w.r.t. multiple levels
- labels_list = images_to_levels(all_labels, num_level_anchors)
- label_weights_list = images_to_levels(all_label_weights,
- num_level_anchors)
- bbox_targets_list = images_to_levels(all_bbox_targets,
- num_level_anchors)
- bbox_weights_list = images_to_levels(all_bbox_weights,
- num_level_anchors)
- res = (labels_list, label_weights_list, bbox_targets_list,
- bbox_weights_list, num_total_pos, num_total_neg)
- if return_sampling_results:
- res = res + (sampling_results_list, )
- for i, r in enumerate(rest_results): # user-added return values
- rest_results[i] = images_to_levels(r, num_level_anchors)
-
- return res + tuple(rest_results)
-
- def loss_single(self, cls_score, bbox_pred, anchors, labels, label_weights,
- bbox_targets, bbox_weights, num_total_samples):
- """Compute loss of a single scale level.
-
- Args:
- cls_score (Tensor): Box scores for each scale level
- Has shape (N, num_anchors * num_classes, H, W).
- bbox_pred (Tensor): Box energies / deltas for each scale
- level with shape (N, num_anchors * 4, H, W).
- anchors (Tensor): Box reference for each scale level with shape
- (N, num_total_anchors, 4).
- labels (Tensor): Labels of each anchors with shape
- (N, num_total_anchors).
- label_weights (Tensor): Label weights of each anchor with shape
- (N, num_total_anchors)
- bbox_targets (Tensor): BBox regression targets of each anchor
- weight shape (N, num_total_anchors, 4).
- bbox_weights (Tensor): BBox regression loss weights of each anchor
- with shape (N, num_total_anchors, 4).
- num_total_samples (int): If sampling, num total samples equal to
- the number of total anchors; Otherwise, it is the number of
- positive anchors.
-
- Returns:
- dict[str, Tensor]: A dictionary of loss components.
- """
- # classification loss
- labels = labels.reshape(-1)
- label_weights = label_weights.reshape(-1)
- cls_score = cls_score.permute(0, 2, 3,
- 1).reshape(-1, self.cls_out_channels)
- loss_cls = self.loss_cls(
- cls_score, labels, label_weights, avg_factor=num_total_samples)
- # regression loss
- bbox_targets = bbox_targets.reshape(-1, 4)
- bbox_weights = bbox_weights.reshape(-1, 4)
- bbox_pred = bbox_pred.permute(0, 2, 3, 1).reshape(-1, 4)
- if self.reg_decoded_bbox:
- # When the regression loss (e.g. `IouLoss`, `GIouLoss`)
- # is applied directly on the decoded bounding boxes, it
- # decodes the already encoded coordinates to absolute format.
- anchors = anchors.reshape(-1, 4)
- bbox_pred = self.bbox_coder.decode(anchors, bbox_pred)
- loss_bbox = self.loss_bbox(
- bbox_pred,
- bbox_targets,
- bbox_weights,
- avg_factor=num_total_samples)
- return loss_cls, loss_bbox
-
- @force_fp32(apply_to=('cls_scores', 'bbox_preds'))
- def loss(self,
- cls_scores,
- bbox_preds,
- gt_bboxes,
- gt_labels,
- img_metas,
- gt_bboxes_ignore=None):
- """Compute losses of the head.
-
- Args:
- cls_scores (list[Tensor]): Box scores for each scale level
- Has shape (N, num_anchors * num_classes, H, W)
- bbox_preds (list[Tensor]): Box energies / deltas for each scale
- level with shape (N, num_anchors * 4, H, W)
- 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
- img_metas (list[dict]): Meta information of each image, e.g.,
- image size, scaling factor, etc.
- gt_bboxes_ignore (None | list[Tensor]): specify which bounding
- boxes can be ignored when computing the loss. Default: None
-
- Returns:
- dict[str, Tensor]: A dictionary of loss components.
- """
- featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
- assert len(featmap_sizes) == self.prior_generator.num_levels
-
- device = cls_scores[0].device
-
- anchor_list, valid_flag_list = self.get_anchors(
- featmap_sizes, img_metas, device=device)
- label_channels = self.cls_out_channels if self.use_sigmoid_cls else 1
- cls_reg_targets = self.get_targets(
- anchor_list,
- valid_flag_list,
- gt_bboxes,
- img_metas,
- gt_bboxes_ignore_list=gt_bboxes_ignore,
- gt_labels_list=gt_labels,
- label_channels=label_channels)
- if cls_reg_targets is None:
- return None
- (labels_list, label_weights_list, bbox_targets_list, bbox_weights_list,
- num_total_pos, num_total_neg) = cls_reg_targets
- num_total_samples = (
- num_total_pos + num_total_neg if self.sampling else num_total_pos)
-
- # anchor number of multi levels
- num_level_anchors = [anchors.size(0) for anchors in anchor_list[0]]
- # concat all level anchors and flags to a single tensor
- concat_anchor_list = []
- for i in range(len(anchor_list)):
- concat_anchor_list.append(torch.cat(anchor_list[i]))
- all_anchor_list = images_to_levels(concat_anchor_list,
- num_level_anchors)
-
- losses_cls, losses_bbox = multi_apply(
- self.loss_single,
- cls_scores,
- bbox_preds,
- all_anchor_list,
- labels_list,
- label_weights_list,
- bbox_targets_list,
- bbox_weights_list,
- num_total_samples=num_total_samples)
- return dict(loss_cls=losses_cls, loss_bbox=losses_bbox)
-
- def aug_test(self, feats, img_metas, rescale=False):
- """Test function with test time augmentation.
-
- Args:
- feats (list[Tensor]): the outer list indicates test-time
- augmentations and inner Tensor should have a shape NxCxHxW,
- which contains features for 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[tuple[Tensor, Tensor]]: Each item in result_list is 2-tuple.
- The first item is ``bboxes`` with shape (n, 5), where
- 5 represent (tl_x, tl_y, br_x, br_y, score).
- The shape of the second tensor in the tuple is ``labels``
- with shape (n,), The length of list should always be 1.
- """
- return self.aug_test_bboxes(feats, img_metas, rescale=rescale)
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