# --------------------------------------------------------------------- # Copyright (c) Megvii Inc. All rights reserved. # --------------------------------------------------------------------- import torch import torch.nn.functional as F from utils.box_ops import * class YoloxMatcher(object): """ This code referenced to https://github.com/Megvii-BaseDetection/YOLOX/blob/main/yolox/models/yolo_head.py """ def __init__(self, num_classes, center_sampling_radius, topk_candidate ): self.num_classes = num_classes self.center_sampling_radius = center_sampling_radius self.topk_candidate = topk_candidate @torch.no_grad() def __call__(self, fpn_strides, anchors, pred_obj, pred_cls, pred_box, tgt_labels, tgt_bboxes): # [M,] strides_tensor = torch.cat([torch.ones_like(anchor_i[:, 0]) * stride_i for stride_i, anchor_i in zip(fpn_strides, anchors)], dim=-1) # List[F, M, 2] -> [M, 2] anchors = torch.cat(anchors, dim=0) num_anchor = anchors.shape[0] num_gt = len(tgt_labels) # ----------------------- Find inside points ----------------------- fg_mask, is_in_boxes_and_center = self.get_in_boxes_info( tgt_bboxes, anchors, strides_tensor, num_anchor, num_gt) obj_preds = pred_obj[fg_mask].float() # [Mp, 1] cls_preds = pred_cls[fg_mask].float() # [Mp, C] box_preds = pred_box[fg_mask].float() # [Mp, 4] # ----------------------- Reg cost ----------------------- pair_wise_ious, _ = box_iou(tgt_bboxes, box_preds) # [N, Mp] reg_cost = -torch.log(pair_wise_ious + 1e-8) # [N, Mp] # ----------------------- Cls cost ----------------------- with torch.cuda.amp.autocast(enabled=False): # [Mp, C] score_preds = torch.sqrt(obj_preds.sigmoid_()* cls_preds.sigmoid_()) # [N, Mp, C] score_preds = score_preds.unsqueeze(0).repeat(num_gt, 1, 1) # prepare cls_target cls_targets = F.one_hot(tgt_labels.long(), self.num_classes).float() cls_targets = cls_targets.unsqueeze(1).repeat(1, score_preds.size(1), 1) # [N, Mp] cls_cost = F.binary_cross_entropy(score_preds, cls_targets, reduction="none").sum(-1) del score_preds #----------------------- Dynamic K-Matching ----------------------- cost_matrix = ( cls_cost + 3.0 * reg_cost + 100000.0 * (~is_in_boxes_and_center) ) # [N, Mp] ( assigned_labels, # [num_fg,] assigned_ious, # [num_fg,] assigned_indexs, # [num_fg,] ) = self.dynamic_k_matching( cost_matrix, pair_wise_ious, tgt_labels, num_gt, fg_mask ) del cls_cost, cost_matrix, pair_wise_ious, reg_cost return fg_mask, assigned_labels, assigned_ious, assigned_indexs def get_in_boxes_info( self, gt_bboxes, # [N, 4] anchors, # [M, 2] strides, # [M,] num_anchors, # M num_gt, # N ): # anchor center x_centers = anchors[:, 0] y_centers = anchors[:, 1] # [M,] -> [1, M] -> [N, M] x_centers = x_centers.unsqueeze(0).repeat(num_gt, 1) y_centers = y_centers.unsqueeze(0).repeat(num_gt, 1) # [N,] -> [N, 1] -> [N, M] gt_bboxes_l = gt_bboxes[:, 0].unsqueeze(1).repeat(1, num_anchors) # x1 gt_bboxes_t = gt_bboxes[:, 1].unsqueeze(1).repeat(1, num_anchors) # y1 gt_bboxes_r = gt_bboxes[:, 2].unsqueeze(1).repeat(1, num_anchors) # x2 gt_bboxes_b = gt_bboxes[:, 3].unsqueeze(1).repeat(1, num_anchors) # y2 b_l = x_centers - gt_bboxes_l b_r = gt_bboxes_r - x_centers b_t = y_centers - gt_bboxes_t b_b = gt_bboxes_b - y_centers bbox_deltas = torch.stack([b_l, b_t, b_r, b_b], 2) is_in_boxes = bbox_deltas.min(dim=-1).values > 0.0 is_in_boxes_all = is_in_boxes.sum(dim=0) > 0 # in fixed center center_radius = self.center_sampling_radius # [N, 2] gt_centers = (gt_bboxes[:, :2] + gt_bboxes[:, 2:]) * 0.5 # [1, M] center_radius_ = center_radius * strides.unsqueeze(0) gt_bboxes_l = gt_centers[:, 0].unsqueeze(1).repeat(1, num_anchors) - center_radius_ # x1 gt_bboxes_t = gt_centers[:, 1].unsqueeze(1).repeat(1, num_anchors) - center_radius_ # y1 gt_bboxes_r = gt_centers[:, 0].unsqueeze(1).repeat(1, num_anchors) + center_radius_ # x2 gt_bboxes_b = gt_centers[:, 1].unsqueeze(1).repeat(1, num_anchors) + center_radius_ # y2 c_l = x_centers - gt_bboxes_l c_r = gt_bboxes_r - x_centers c_t = y_centers - gt_bboxes_t c_b = gt_bboxes_b - y_centers center_deltas = torch.stack([c_l, c_t, c_r, c_b], 2) is_in_centers = center_deltas.min(dim=-1).values > 0.0 is_in_centers_all = is_in_centers.sum(dim=0) > 0 # in boxes and in centers is_in_boxes_anchor = is_in_boxes_all | is_in_centers_all is_in_boxes_and_center = ( is_in_boxes[:, is_in_boxes_anchor] & is_in_centers[:, is_in_boxes_anchor] ) return is_in_boxes_anchor, is_in_boxes_and_center def dynamic_k_matching( self, cost, pair_wise_ious, gt_classes, num_gt, fg_mask ): # Dynamic K # --------------------------------------------------------------- matching_matrix = torch.zeros_like(cost, dtype=torch.uint8) ious_in_boxes_matrix = pair_wise_ious n_candidate_k = min(self.topk_candidate, ious_in_boxes_matrix.size(1)) topk_ious, _ = torch.topk(ious_in_boxes_matrix, n_candidate_k, dim=1) dynamic_ks = torch.clamp(topk_ious.sum(1).int(), min=1) dynamic_ks = dynamic_ks.tolist() for gt_idx in range(num_gt): _, pos_idx = torch.topk( cost[gt_idx], k=dynamic_ks[gt_idx], largest=False ) matching_matrix[gt_idx][pos_idx] = 1 del topk_ious, dynamic_ks, pos_idx anchor_matching_gt = matching_matrix.sum(0) if (anchor_matching_gt > 1).sum() > 0: _, cost_argmin = torch.min(cost[:, anchor_matching_gt > 1], dim=0) matching_matrix[:, anchor_matching_gt > 1] *= 0 matching_matrix[cost_argmin, anchor_matching_gt > 1] = 1 fg_mask_inboxes = matching_matrix.sum(0) > 0 fg_mask[fg_mask.clone()] = fg_mask_inboxes assigned_indexs = matching_matrix[:, fg_mask_inboxes].argmax(0) assigned_labels = gt_classes[assigned_indexs] assigned_ious = (matching_matrix * pair_wise_ious).sum(0)[ fg_mask_inboxes ] return assigned_labels, assigned_ious, assigned_indexs