Source code for mmdet.models.dense_heads.paa_head

import numpy as np
import torch
from mmcv.runner import force_fp32

from mmdet.core import multi_apply, multiclass_nms
from mmdet.core.bbox.iou_calculators import bbox_overlaps
from mmdet.models import HEADS
from mmdet.models.dense_heads import ATSSHead

EPS = 1e-12
    import sklearn.mixture as skm
except ImportError:
    skm = None

def levels_to_images(mlvl_tensor):
    """Concat multi-level feature maps by image.

    [feature_level0, feature_level1...] -> [feature_image0, feature_image1...]
    Convert the shape of each element in mlvl_tensor from (N, C, H, W) to
    (N, H*W , C), then split the element to N elements with shape (H*W, C), and
    concat elements in same image of all level along first dimension.

        mlvl_tensor (list[torch.Tensor]): list of Tensor which collect from
            corresponding level. Each element is of shape (N, C, H, W)

        list[torch.Tensor]: A list that contains N tensors and each tensor is
            of shape (num_elements, C)
    batch_size = mlvl_tensor[0].size(0)
    batch_list = [[] for _ in range(batch_size)]
    channels = mlvl_tensor[0].size(1)
    for t in mlvl_tensor:
        t = t.permute(0, 2, 3, 1)
        t = t.view(batch_size, -1, channels).contiguous()
        for img in range(batch_size):
    return [, 0) for item in batch_list]

[docs]@HEADS.register_module() class PAAHead(ATSSHead): """Head of PAAAssignment: Probabilistic Anchor Assignment with IoU Prediction for Object Detection. Code is modified from the `official github repo < /modeling/rpn/paa/>`_. More details can be found in the `paper <>`_ . Args: topk (int): Select topk samples with smallest loss in each level. score_voting (bool): Whether to use score voting in post-process. covariance_type : String describing the type of covariance parameters to be used in :class:`sklearn.mixture.GaussianMixture`. It must be one of: - 'full': each component has its own general covariance matrix - 'tied': all components share the same general covariance matrix - 'diag': each component has its own diagonal covariance matrix - 'spherical': each component has its own single variance Default: 'diag'. From 'full' to 'spherical', the gmm fitting process is faster yet the performance could be influenced. For most cases, 'diag' should be a good choice. """ def __init__(self, *args, topk=9, score_voting=True, covariance_type='diag', **kwargs): # topk used in paa reassign process self.topk = topk self.with_score_voting = score_voting self.covariance_type = covariance_type super(PAAHead, self).__init__(*args, **kwargs)
[docs] @force_fp32(apply_to=('cls_scores', 'bbox_preds', 'iou_preds')) def loss(self, cls_scores, bbox_preds, iou_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) iou_preds (list[Tensor]): iou_preds for each scale level with shape (N, num_anchors * 1, 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 (list[Tensor] | None): Specify which bounding boxes can be ignored when are computing the loss. Returns: dict[str, Tensor]: A dictionary of loss gmm_assignment. """ featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores] assert len(featmap_sizes) == self.anchor_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, ) (labels, labels_weight, bboxes_target, bboxes_weight, pos_inds, pos_gt_index) = cls_reg_targets cls_scores = levels_to_images(cls_scores) cls_scores = [ item.reshape(-1, self.cls_out_channels) for item in cls_scores ] bbox_preds = levels_to_images(bbox_preds) bbox_preds = [item.reshape(-1, 4) for item in bbox_preds] iou_preds = levels_to_images(iou_preds) iou_preds = [item.reshape(-1, 1) for item in iou_preds] pos_losses_list, = multi_apply(self.get_pos_loss, anchor_list, cls_scores, bbox_preds, labels, labels_weight, bboxes_target, bboxes_weight, pos_inds) with torch.no_grad(): reassign_labels, reassign_label_weight, \ reassign_bbox_weights, num_pos = multi_apply( self.paa_reassign, pos_losses_list, labels, labels_weight, bboxes_weight, pos_inds, pos_gt_index, anchor_list) num_pos = sum(num_pos) # convert all tensor list to a flatten tensor cls_scores =, 0).view(-1, cls_scores[0].size(-1)) bbox_preds =, 0).view(-1, bbox_preds[0].size(-1)) iou_preds =, 0).view(-1, iou_preds[0].size(-1)) labels =, 0).view(-1) flatten_anchors = [, 0) for item in anchor_list]) labels_weight =, 0).view(-1) bboxes_target =, 0).view(-1, bboxes_target[0].size(-1)) pos_inds_flatten = ((labels >= 0) & (labels < self.num_classes)).nonzero().reshape(-1) losses_cls = self.loss_cls( cls_scores, labels, labels_weight, avg_factor=max(num_pos, len(img_metas))) # avoid num_pos=0 if num_pos: pos_bbox_pred = self.bbox_coder.decode( flatten_anchors[pos_inds_flatten], bbox_preds[pos_inds_flatten]) pos_bbox_target = bboxes_target[pos_inds_flatten] iou_target = bbox_overlaps( pos_bbox_pred.detach(), pos_bbox_target, is_aligned=True) losses_iou = self.loss_centerness( iou_preds[pos_inds_flatten], iou_target.unsqueeze(-1), avg_factor=num_pos) losses_bbox = self.loss_bbox( pos_bbox_pred, pos_bbox_target, iou_target.clamp(min=EPS), avg_factor=iou_target.sum()) else: losses_iou = iou_preds.sum() * 0 losses_bbox = bbox_preds.sum() * 0 return dict( loss_cls=losses_cls, loss_bbox=losses_bbox, loss_iou=losses_iou)
[docs] def get_pos_loss(self, anchors, cls_score, bbox_pred, label, label_weight, bbox_target, bbox_weight, pos_inds): """Calculate loss of all potential positive samples obtained from first match process. Args: anchors (list[Tensor]): Anchors of each scale. cls_score (Tensor): Box scores of single image with shape (num_anchors, num_classes) bbox_pred (Tensor): Box energies / deltas of single image with shape (num_anchors, 4) label (Tensor): classification target of each anchor with shape (num_anchors,) label_weight (Tensor): Classification loss weight of each anchor with shape (num_anchors). bbox_target (dict): Regression target of each anchor with shape (num_anchors, 4). bbox_weight (Tensor): Bbox weight of each anchor with shape (num_anchors, 4). pos_inds (Tensor): Index of all positive samples got from first assign process. Returns: Tensor: Losses of all positive samples in single image. """ if not len(pos_inds): return[]), anchors_all_level =, 0) pos_scores = cls_score[pos_inds] pos_bbox_pred = bbox_pred[pos_inds] pos_label = label[pos_inds] pos_label_weight = label_weight[pos_inds] pos_bbox_target = bbox_target[pos_inds] pos_bbox_weight = bbox_weight[pos_inds] pos_anchors = anchors_all_level[pos_inds] pos_bbox_pred = self.bbox_coder.decode(pos_anchors, pos_bbox_pred) # to keep loss dimension loss_cls = self.loss_cls( pos_scores, pos_label, pos_label_weight, avg_factor=self.loss_cls.loss_weight, reduction_override='none') loss_bbox = self.loss_bbox( pos_bbox_pred, pos_bbox_target, pos_bbox_weight, avg_factor=self.loss_cls.loss_weight, reduction_override='none') loss_cls = loss_cls.sum(-1) pos_loss = loss_bbox + loss_cls return pos_loss,
[docs] def paa_reassign(self, pos_losses, label, label_weight, bbox_weight, pos_inds, pos_gt_inds, anchors): """Fit loss to GMM distribution and separate positive, ignore, negative samples again with GMM model. Args: pos_losses (Tensor): Losses of all positive samples in single image. label (Tensor): classification target of each anchor with shape (num_anchors,) label_weight (Tensor): Classification loss weight of each anchor with shape (num_anchors). bbox_weight (Tensor): Bbox weight of each anchor with shape (num_anchors, 4). pos_inds (Tensor): Index of all positive samples got from first assign process. pos_gt_inds (Tensor): Gt_index of all positive samples got from first assign process. anchors (list[Tensor]): Anchors of each scale. Returns: tuple: Usually returns a tuple containing learning targets. - label (Tensor): classification target of each anchor after paa assign, with shape (num_anchors,) - label_weight (Tensor): Classification loss weight of each anchor after paa assign, with shape (num_anchors). - bbox_weight (Tensor): Bbox weight of each anchor with shape (num_anchors, 4). - num_pos (int): The number of positive samples after paa assign. """ if not len(pos_inds): return label, label_weight, bbox_weight, 0 label = label.clone() label_weight = label_weight.clone() bbox_weight = bbox_weight.clone() num_gt = pos_gt_inds.max() + 1 num_level = len(anchors) num_anchors_each_level = [item.size(0) for item in anchors] num_anchors_each_level.insert(0, 0) inds_level_interval = np.cumsum(num_anchors_each_level) pos_level_mask = [] for i in range(num_level): mask = (pos_inds >= inds_level_interval[i]) & ( pos_inds < inds_level_interval[i + 1]) pos_level_mask.append(mask) pos_inds_after_paa = [label.new_tensor([])] ignore_inds_after_paa = [label.new_tensor([])] for gt_ind in range(num_gt): pos_inds_gmm = [] pos_loss_gmm = [] gt_mask = pos_gt_inds == gt_ind for level in range(num_level): level_mask = pos_level_mask[level] level_gt_mask = level_mask & gt_mask value, topk_inds = pos_losses[level_gt_mask].topk( min(level_gt_mask.sum(), self.topk), largest=False) pos_inds_gmm.append(pos_inds[level_gt_mask][topk_inds]) pos_loss_gmm.append(value) pos_inds_gmm = pos_loss_gmm = # fix gmm need at least two sample if len(pos_inds_gmm) < 2: continue device = pos_inds_gmm.device pos_loss_gmm, sort_inds = pos_loss_gmm.sort() pos_inds_gmm = pos_inds_gmm[sort_inds] pos_loss_gmm = pos_loss_gmm.view(-1, 1).cpu().numpy() min_loss, max_loss = pos_loss_gmm.min(), pos_loss_gmm.max() means_init = np.array([min_loss, max_loss]).reshape(2, 1) weights_init = np.array([0.5, 0.5]) precisions_init = np.array([1.0, 1.0]).reshape(2, 1, 1) # full if self.covariance_type == 'spherical': precisions_init = precisions_init.reshape(2) elif self.covariance_type == 'diag': precisions_init = precisions_init.reshape(2, 1) elif self.covariance_type == 'tied': precisions_init = np.array([[1.0]]) if skm is None: raise ImportError('Please run "pip install sklearn" ' 'to install sklearn first.') gmm = skm.GaussianMixture( 2, weights_init=weights_init, means_init=means_init, precisions_init=precisions_init, covariance_type=self.covariance_type) gmm_assignment = gmm.predict(pos_loss_gmm) scores = gmm.score_samples(pos_loss_gmm) gmm_assignment = torch.from_numpy(gmm_assignment).to(device) scores = torch.from_numpy(scores).to(device) pos_inds_temp, ignore_inds_temp = self.gmm_separation_scheme( gmm_assignment, scores, pos_inds_gmm) pos_inds_after_paa.append(pos_inds_temp) ignore_inds_after_paa.append(ignore_inds_temp) pos_inds_after_paa = ignore_inds_after_paa = reassign_mask = (pos_inds.unsqueeze(1) != pos_inds_after_paa).all(1) reassign_ids = pos_inds[reassign_mask] label[reassign_ids] = self.num_classes label_weight[ignore_inds_after_paa] = 0 bbox_weight[reassign_ids] = 0 num_pos = len(pos_inds_after_paa) return label, label_weight, bbox_weight, num_pos
[docs] def gmm_separation_scheme(self, gmm_assignment, scores, pos_inds_gmm): """A general separation scheme for gmm model. It separates a GMM distribution of candidate samples into three parts, 0 1 and uncertain areas, and you can implement other separation schemes by rewriting this function. Args: gmm_assignment (Tensor): The prediction of GMM which is of shape (num_samples,). The 0/1 value indicates the distribution that each sample comes from. scores (Tensor): The probability of sample coming from the fit GMM distribution. The tensor is of shape (num_samples,). pos_inds_gmm (Tensor): All the indexes of samples which are used to fit GMM model. The tensor is of shape (num_samples,) Returns: tuple[Tensor]: The indices of positive and ignored samples. - pos_inds_temp (Tensor): Indices of positive samples. - ignore_inds_temp (Tensor): Indices of ignore samples. """ # The implementation is (c) in Fig.3 in origin paper instead of (b). # You can refer to issues such as # and # fgs = gmm_assignment == 0 pos_inds_temp = fgs.new_tensor([], dtype=torch.long) ignore_inds_temp = fgs.new_tensor([], dtype=torch.long) if fgs.nonzero().numel(): _, pos_thr_ind = scores[fgs].topk(1) pos_inds_temp = pos_inds_gmm[fgs][:pos_thr_ind + 1] ignore_inds_temp = pos_inds_gmm.new_tensor([]) return pos_inds_temp, ignore_inds_temp
[docs] 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, ): """Get targets for PAA head. This method is almost the same as `AnchorHead.get_targets()`. We direct return the results from _get_targets_single instead map it to levels by images_to_levels function. 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[Tensor]): Labels of all anchors, each with shape (num_anchors,). - label_weights (list[Tensor]): Label weights of all anchor. each with shape (num_anchors,). - bbox_targets (list[Tensor]): BBox targets of all anchors. each with shape (num_anchors, 4). - bbox_weights (list[Tensor]): BBox weights of all anchors. each with shape (num_anchors, 4). - pos_inds (list[Tensor]): Contains all index of positive sample in all anchor. - gt_inds (list[Tensor]): Contains all gt_index of positive sample in all anchor. """ num_imgs = len(img_metas) assert len(anchor_list) == len(valid_flag_list) == num_imgs 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([i])) concat_valid_flag_list.append([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) (labels, label_weights, bbox_targets, bbox_weights, valid_pos_inds, valid_neg_inds, sampling_result) = results # Due to valid flag of anchors, we have to calculate the real pos_inds # in origin anchor set. pos_inds = [] for i, single_labels in enumerate(labels): pos_mask = (0 <= single_labels) & ( single_labels < self.num_classes) pos_inds.append(pos_mask.nonzero().view(-1)) gt_inds = [item.pos_assigned_gt_inds for item in sampling_result] return (labels, label_weights, bbox_targets, bbox_weights, pos_inds, gt_inds)
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. This method is same as `AnchorHead._get_targets_single()`. """ assert unmap_outputs, 'We must map outputs back to the original' \ 'set of anchors in PAAhead' return super(ATSSHead, self)._get_targets_single( flat_anchors, valid_flags, gt_bboxes, gt_bboxes_ignore, gt_labels, img_meta, label_channels=1, unmap_outputs=True) def _get_bboxes(self, cls_scores, bbox_preds, iou_preds, mlvl_anchors, img_shapes, scale_factors, cfg, rescale=False, with_nms=True): """Transform outputs for a single batch item into labeled boxes. This method is almost same as `ATSSHead._get_bboxes()`. We use sqrt(iou_preds * cls_scores) in NMS process instead of just cls_scores. Besides, score voting is used when `` score_voting`` is set to True. """ assert with_nms, 'PAA only supports "with_nms=True" now and it ' \ 'means PAAHead does not support ' \ 'test-time augmentation' assert len(cls_scores) == len(bbox_preds) == len(mlvl_anchors) batch_size = cls_scores[0].shape[0] mlvl_bboxes = [] mlvl_scores = [] mlvl_iou_preds = [] for cls_score, bbox_pred, iou_preds, anchors in zip( cls_scores, bbox_preds, iou_preds, mlvl_anchors): assert cls_score.size()[-2:] == bbox_pred.size()[-2:] scores = cls_score.permute(0, 2, 3, 1).reshape( batch_size, -1, self.cls_out_channels).sigmoid() bbox_pred = bbox_pred.permute(0, 2, 3, 1).reshape(batch_size, -1, 4) iou_preds = iou_preds.permute(0, 2, 3, 1).reshape(batch_size, -1).sigmoid() nms_pre = cfg.get('nms_pre', -1) if nms_pre > 0 and scores.shape[1] > nms_pre: max_scores, _ = (scores * iou_preds[..., None]).sqrt().max(-1) _, topk_inds = max_scores.topk(nms_pre) batch_inds = torch.arange(batch_size).view( -1, 1).expand_as(topk_inds).long() anchors = anchors[topk_inds, :] bbox_pred = bbox_pred[batch_inds, topk_inds, :] scores = scores[batch_inds, topk_inds, :] iou_preds = iou_preds[batch_inds, topk_inds] else: anchors = anchors.expand_as(bbox_pred) bboxes = self.bbox_coder.decode( anchors, bbox_pred, max_shape=img_shapes) mlvl_bboxes.append(bboxes) mlvl_scores.append(scores) mlvl_iou_preds.append(iou_preds) batch_mlvl_bboxes =, dim=1) if rescale: batch_mlvl_bboxes /= batch_mlvl_bboxes.new_tensor( scale_factors).unsqueeze(1) batch_mlvl_scores =, dim=1) # Add a dummy background class to the backend when using sigmoid # remind that we set FG labels to [0, num_class-1] since mmdet v2.0 # BG cat_id: num_class padding = batch_mlvl_scores.new_zeros(batch_size, batch_mlvl_scores.shape[1], 1) batch_mlvl_scores =[batch_mlvl_scores, padding], dim=-1) batch_mlvl_iou_preds =, dim=1) batch_mlvl_nms_scores = (batch_mlvl_scores * batch_mlvl_iou_preds[..., None]).sqrt() det_results = [] for (mlvl_bboxes, mlvl_scores) in zip(batch_mlvl_bboxes, batch_mlvl_nms_scores): det_bbox, det_label = multiclass_nms( mlvl_bboxes, mlvl_scores, cfg.score_thr, cfg.nms, cfg.max_per_img, score_factors=None) if self.with_score_voting and len(det_bbox) > 0: det_bbox, det_label = self.score_voting( det_bbox, det_label, mlvl_bboxes, mlvl_scores, cfg.score_thr) det_results.append(tuple([det_bbox, det_label])) return det_results
[docs] def score_voting(self, det_bboxes, det_labels, mlvl_bboxes, mlvl_nms_scores, score_thr): """Implementation of score voting method works on each remaining boxes after NMS procedure. Args: det_bboxes (Tensor): Remaining boxes after NMS procedure, with shape (k, 5), each dimension means (x1, y1, x2, y2, score). det_labels (Tensor): The label of remaining boxes, with shape (k, 1),Labels are 0-based. mlvl_bboxes (Tensor): All boxes before the NMS procedure, with shape (num_anchors,4). mlvl_nms_scores (Tensor): The scores of all boxes which is used in the NMS procedure, with shape (num_anchors, num_class) mlvl_iou_preds (Tensor): The predictions of IOU of all boxes before the NMS procedure, with shape (num_anchors, 1) score_thr (float): The score threshold of bboxes. Returns: tuple: Usually returns a tuple containing voting results. - det_bboxes_voted (Tensor): Remaining boxes after score voting procedure, with shape (k, 5), each dimension means (x1, y1, x2, y2, score). - det_labels_voted (Tensor): Label of remaining bboxes after voting, with shape (num_anchors,). """ candidate_mask = mlvl_nms_scores > score_thr candidate_mask_nonzeros = candidate_mask.nonzero() candidate_inds = candidate_mask_nonzeros[:, 0] candidate_labels = candidate_mask_nonzeros[:, 1] candidate_bboxes = mlvl_bboxes[candidate_inds] candidate_scores = mlvl_nms_scores[candidate_mask] det_bboxes_voted = [] det_labels_voted = [] for cls in range(self.cls_out_channels): candidate_cls_mask = candidate_labels == cls if not candidate_cls_mask.any(): continue candidate_cls_scores = candidate_scores[candidate_cls_mask] candidate_cls_bboxes = candidate_bboxes[candidate_cls_mask] det_cls_mask = det_labels == cls det_cls_bboxes = det_bboxes[det_cls_mask].view( -1, det_bboxes.size(-1)) det_candidate_ious = bbox_overlaps(det_cls_bboxes[:, :4], candidate_cls_bboxes) for det_ind in range(len(det_cls_bboxes)): single_det_ious = det_candidate_ious[det_ind] pos_ious_mask = single_det_ious > 0.01 pos_ious = single_det_ious[pos_ious_mask] pos_bboxes = candidate_cls_bboxes[pos_ious_mask] pos_scores = candidate_cls_scores[pos_ious_mask] pis = (torch.exp(-(1 - pos_ious)**2 / 0.025) * pos_scores)[:, None] voted_box = torch.sum( pis * pos_bboxes, dim=0) / torch.sum( pis, dim=0) voted_score = det_cls_bboxes[det_ind][-1:][None, :] det_bboxes_voted.append([None, :], voted_score), dim=1)) det_labels_voted.append(cls) det_bboxes_voted =, dim=0) det_labels_voted = det_labels.new_tensor(det_labels_voted) return det_bboxes_voted, det_labels_voted