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412 lines
16 KiB
412 lines
16 KiB
#!/usr/bin/env python3
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# -*- coding:utf-8 -*-
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# The code is based on
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# https://github.com/Megvii-BaseDetection/YOLOX/blob/main/yolox/models/yolo_head.py
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# Copyright (c) Megvii, Inc. and its affiliates.
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import torch
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import torch.nn as nn
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import numpy as np
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import torch.nn.functional as F
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from yolov6.utils.figure_iou import IOUloss, pairwise_bbox_iou
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class ComputeLoss:
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'''Loss computation func.
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This func contains SimOTA and siou loss.
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'''
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def __init__(self,
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reg_weight=5.0,
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iou_weight=3.0,
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cls_weight=1.0,
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center_radius=2.5,
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eps=1e-7,
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in_channels=[256, 512, 1024],
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strides=[8, 16, 32],
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n_anchors=1,
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iou_type='ciou'
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):
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self.reg_weight = reg_weight
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self.iou_weight = iou_weight
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self.cls_weight = cls_weight
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self.center_radius = center_radius
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self.eps = eps
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self.n_anchors = n_anchors
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self.strides = strides
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self.grids = [torch.zeros(1)] * len(in_channels)
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# Define criteria
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self.l1_loss = nn.L1Loss(reduction="none")
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self.bcewithlog_loss = nn.BCEWithLogitsLoss(reduction="none")
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self.iou_loss = IOUloss(iou_type=iou_type, reduction="none")
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def __call__(
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self,
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outputs,
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targets
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):
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dtype = outputs[0].type()
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device = targets.device
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loss_cls, loss_obj, loss_iou, loss_l1 = torch.zeros(1, device=device), torch.zeros(1, device=device), \
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torch.zeros(1, device=device), torch.zeros(1, device=device)
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num_classes = outputs[0].shape[-1] - 5
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outputs, outputs_origin, gt_bboxes_scale, xy_shifts, expanded_strides = self.get_outputs_and_grids(
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outputs, self.strides, dtype, device)
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total_num_anchors = outputs.shape[1]
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bbox_preds = outputs[:, :, :4] # [batch, n_anchors_all, 4]
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bbox_preds_org = outputs_origin[:, :, :4] # [batch, n_anchors_all, 4]
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obj_preds = outputs[:, :, 4].unsqueeze(-1) # [batch, n_anchors_all, 1]
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cls_preds = outputs[:, :, 5:] # [batch, n_anchors_all, n_cls]
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# targets
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batch_size = bbox_preds.shape[0]
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targets_list = np.zeros((batch_size, 1, 5)).tolist()
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for i, item in enumerate(targets.cpu().numpy().tolist()):
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targets_list[int(item[0])].append(item[1:])
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max_len = max((len(l) for l in targets_list))
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targets = torch.from_numpy(np.array(list(map(lambda l:l + [[-1,0,0,0,0]]*(max_len - len(l)), targets_list)))[:,1:,:]).to(targets.device)
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num_targets_list = (targets.sum(dim=2) > 0).sum(dim=1) # number of objects
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num_fg, num_gts = 0, 0
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cls_targets, reg_targets, l1_targets, obj_targets, fg_masks = [], [], [], [], []
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for batch_idx in range(batch_size):
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num_gt = int(num_targets_list[batch_idx])
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num_gts += num_gt
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if num_gt == 0:
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cls_target = outputs.new_zeros((0, num_classes))
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reg_target = outputs.new_zeros((0, 4))
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l1_target = outputs.new_zeros((0, 4))
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obj_target = outputs.new_zeros((total_num_anchors, 1))
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fg_mask = outputs.new_zeros(total_num_anchors).bool()
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else:
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gt_bboxes_per_image = targets[batch_idx, :num_gt, 1:5].mul_(gt_bboxes_scale)
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gt_classes = targets[batch_idx, :num_gt, 0]
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bboxes_preds_per_image = bbox_preds[batch_idx]
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cls_preds_per_image = cls_preds[batch_idx]
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obj_preds_per_image = obj_preds[batch_idx]
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try:
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(
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gt_matched_classes,
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fg_mask,
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pred_ious_this_matching,
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matched_gt_inds,
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num_fg_img,
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) = self.get_assignments(
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batch_idx,
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num_gt,
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total_num_anchors,
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gt_bboxes_per_image,
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gt_classes,
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bboxes_preds_per_image,
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cls_preds_per_image,
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obj_preds_per_image,
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expanded_strides,
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xy_shifts,
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num_classes
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)
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except RuntimeError:
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print(
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"OOM RuntimeError is raised due to the huge memory cost during label assignment. \
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CPU mode is applied in this batch. If you want to avoid this issue, \
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try to reduce the batch size or image size."
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)
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torch.cuda.empty_cache()
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print("------------CPU Mode for This Batch-------------")
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_gt_bboxes_per_image = gt_bboxes_per_image.cpu().float()
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_gt_classes = gt_classes.cpu().float()
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_bboxes_preds_per_image = bboxes_preds_per_image.cpu().float()
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_cls_preds_per_image = cls_preds_per_image.cpu().float()
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_obj_preds_per_image = obj_preds_per_image.cpu().float()
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_expanded_strides = expanded_strides.cpu().float()
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_xy_shifts = xy_shifts.cpu()
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(
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gt_matched_classes,
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fg_mask,
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pred_ious_this_matching,
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matched_gt_inds,
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num_fg_img,
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) = self.get_assignments(
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batch_idx,
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num_gt,
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total_num_anchors,
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_gt_bboxes_per_image,
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_gt_classes,
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_bboxes_preds_per_image,
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_cls_preds_per_image,
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_obj_preds_per_image,
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_expanded_strides,
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_xy_shifts,
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num_classes
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)
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gt_matched_classes = gt_matched_classes.cuda()
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fg_mask = fg_mask.cuda()
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pred_ious_this_matching = pred_ious_this_matching.cuda()
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matched_gt_inds = matched_gt_inds.cuda()
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torch.cuda.empty_cache()
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num_fg += num_fg_img
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if num_fg_img > 0:
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cls_target = F.one_hot(
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gt_matched_classes.to(torch.int64), num_classes
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) * pred_ious_this_matching.unsqueeze(-1)
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obj_target = fg_mask.unsqueeze(-1)
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reg_target = gt_bboxes_per_image[matched_gt_inds]
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l1_target = self.get_l1_target(
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outputs.new_zeros((num_fg_img, 4)),
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gt_bboxes_per_image[matched_gt_inds],
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expanded_strides[0][fg_mask],
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xy_shifts=xy_shifts[0][fg_mask],
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)
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cls_targets.append(cls_target)
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reg_targets.append(reg_target)
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obj_targets.append(obj_target)
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l1_targets.append(l1_target)
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fg_masks.append(fg_mask)
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cls_targets = torch.cat(cls_targets, 0)
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reg_targets = torch.cat(reg_targets, 0)
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obj_targets = torch.cat(obj_targets, 0)
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l1_targets = torch.cat(l1_targets, 0)
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fg_masks = torch.cat(fg_masks, 0)
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num_fg = max(num_fg, 1)
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# loss
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loss_iou += (self.iou_loss(bbox_preds.view(-1, 4)[fg_masks].T, reg_targets)).sum() / num_fg
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loss_l1 += (self.l1_loss(bbox_preds_org.view(-1, 4)[fg_masks], l1_targets)).sum() / num_fg
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loss_obj += (self.bcewithlog_loss(obj_preds.view(-1, 1), obj_targets*1.0)).sum() / num_fg
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loss_cls += (self.bcewithlog_loss(cls_preds.view(-1, num_classes)[fg_masks], cls_targets)).sum() / num_fg
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total_losses = self.reg_weight * loss_iou + loss_l1 + loss_obj + loss_cls
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return total_losses, torch.cat((self.reg_weight * loss_iou, loss_l1, loss_obj, loss_cls)).detach()
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def decode_output(self, output, k, stride, dtype, device):
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grid = self.grids[k].to(device)
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batch_size = output.shape[0]
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hsize, wsize = output.shape[2:4]
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if grid.shape[2:4] != output.shape[2:4]:
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yv, xv = torch.meshgrid([torch.arange(hsize), torch.arange(wsize)])
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grid = torch.stack((xv, yv), 2).view(1, 1, hsize, wsize, 2).type(dtype).to(device)
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self.grids[k] = grid
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output = output.reshape(batch_size, self.n_anchors * hsize * wsize, -1)
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output_origin = output.clone()
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grid = grid.view(1, -1, 2)
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output[..., :2] = (output[..., :2] + grid) * stride
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output[..., 2:4] = torch.exp(output[..., 2:4]) * stride
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return output, output_origin, grid, hsize, wsize
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def get_outputs_and_grids(self, outputs, strides, dtype, device):
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xy_shifts = []
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expanded_strides = []
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outputs_new = []
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outputs_origin = []
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for k, output in enumerate(outputs):
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output, output_origin, grid, feat_h, feat_w = self.decode_output(
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output, k, strides[k], dtype, device)
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xy_shift = grid
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expanded_stride = torch.full((1, grid.shape[1], 1), strides[k], dtype=grid.dtype, device=grid.device)
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xy_shifts.append(xy_shift)
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expanded_strides.append(expanded_stride)
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outputs_new.append(output)
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outputs_origin.append(output_origin)
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xy_shifts = torch.cat(xy_shifts, 1) # [1, n_anchors_all, 2]
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expanded_strides = torch.cat(expanded_strides, 1) # [1, n_anchors_all, 1]
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outputs_origin = torch.cat(outputs_origin, 1)
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outputs = torch.cat(outputs_new, 1)
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feat_h *= strides[-1]
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feat_w *= strides[-1]
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gt_bboxes_scale = torch.Tensor([[feat_w, feat_h, feat_w, feat_h]]).type_as(outputs)
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return outputs, outputs_origin, gt_bboxes_scale, xy_shifts, expanded_strides
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def get_l1_target(self, l1_target, gt, stride, xy_shifts, eps=1e-8):
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l1_target[:, 0:2] = gt[:, 0:2] / stride - xy_shifts
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l1_target[:, 2:4] = torch.log(gt[:, 2:4] / stride + eps)
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return l1_target
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@torch.no_grad()
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def get_assignments(
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self,
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batch_idx,
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num_gt,
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total_num_anchors,
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gt_bboxes_per_image,
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gt_classes,
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bboxes_preds_per_image,
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cls_preds_per_image,
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obj_preds_per_image,
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expanded_strides,
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xy_shifts,
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num_classes
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):
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fg_mask, is_in_boxes_and_center = self.get_in_boxes_info(
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gt_bboxes_per_image,
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expanded_strides,
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xy_shifts,
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total_num_anchors,
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num_gt,
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)
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bboxes_preds_per_image = bboxes_preds_per_image[fg_mask]
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cls_preds_ = cls_preds_per_image[fg_mask]
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obj_preds_ = obj_preds_per_image[fg_mask]
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num_in_boxes_anchor = bboxes_preds_per_image.shape[0]
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# cost
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pair_wise_ious = pairwise_bbox_iou(gt_bboxes_per_image, bboxes_preds_per_image, box_format='xywh')
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pair_wise_ious_loss = -torch.log(pair_wise_ious + 1e-8)
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gt_cls_per_image = (
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F.one_hot(gt_classes.to(torch.int64), num_classes)
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.float()
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.unsqueeze(1)
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.repeat(1, num_in_boxes_anchor, 1)
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)
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with torch.cuda.amp.autocast(enabled=False):
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cls_preds_ = (
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cls_preds_.float().sigmoid_().unsqueeze(0).repeat(num_gt, 1, 1)
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* obj_preds_.float().sigmoid_().unsqueeze(0).repeat(num_gt, 1, 1)
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)
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pair_wise_cls_loss = F.binary_cross_entropy(
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cls_preds_.sqrt_(), gt_cls_per_image, reduction="none"
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).sum(-1)
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del cls_preds_, obj_preds_
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cost = (
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self.cls_weight * pair_wise_cls_loss
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+ self.iou_weight * pair_wise_ious_loss
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+ 100000.0 * (~is_in_boxes_and_center)
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)
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(
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num_fg,
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gt_matched_classes,
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pred_ious_this_matching,
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matched_gt_inds,
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) = self.dynamic_k_matching(cost, pair_wise_ious, gt_classes, num_gt, fg_mask)
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del pair_wise_cls_loss, cost, pair_wise_ious, pair_wise_ious_loss
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return (
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gt_matched_classes,
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fg_mask,
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pred_ious_this_matching,
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matched_gt_inds,
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num_fg,
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)
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def get_in_boxes_info(
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self,
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gt_bboxes_per_image,
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expanded_strides,
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xy_shifts,
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total_num_anchors,
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num_gt,
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):
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expanded_strides_per_image = expanded_strides[0]
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xy_shifts_per_image = xy_shifts[0] * expanded_strides_per_image
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xy_centers_per_image = (
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(xy_shifts_per_image + 0.5 * expanded_strides_per_image)
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.unsqueeze(0)
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.repeat(num_gt, 1, 1)
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) # [n_anchor, 2] -> [n_gt, n_anchor, 2]
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gt_bboxes_per_image_lt = (
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(gt_bboxes_per_image[:, 0:2] - 0.5 * gt_bboxes_per_image[:, 2:4])
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.unsqueeze(1)
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.repeat(1, total_num_anchors, 1)
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)
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gt_bboxes_per_image_rb = (
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(gt_bboxes_per_image[:, 0:2] + 0.5 * gt_bboxes_per_image[:, 2:4])
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.unsqueeze(1)
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.repeat(1, total_num_anchors, 1)
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) # [n_gt, 2] -> [n_gt, n_anchor, 2]
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b_lt = xy_centers_per_image - gt_bboxes_per_image_lt
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b_rb = gt_bboxes_per_image_rb - xy_centers_per_image
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bbox_deltas = torch.cat([b_lt, b_rb], 2)
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is_in_boxes = bbox_deltas.min(dim=-1).values > 0.0
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is_in_boxes_all = is_in_boxes.sum(dim=0) > 0
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# in fixed center
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gt_bboxes_per_image_lt = (gt_bboxes_per_image[:, 0:2]).unsqueeze(1).repeat(
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1, total_num_anchors, 1
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) - self.center_radius * expanded_strides_per_image.unsqueeze(0)
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gt_bboxes_per_image_rb = (gt_bboxes_per_image[:, 0:2]).unsqueeze(1).repeat(
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1, total_num_anchors, 1
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) + self.center_radius * expanded_strides_per_image.unsqueeze(0)
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c_lt = xy_centers_per_image - gt_bboxes_per_image_lt
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c_rb = gt_bboxes_per_image_rb - xy_centers_per_image
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center_deltas = torch.cat([c_lt, c_rb], 2)
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is_in_centers = center_deltas.min(dim=-1).values > 0.0
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is_in_centers_all = is_in_centers.sum(dim=0) > 0
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# in boxes and in centers
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is_in_boxes_anchor = is_in_boxes_all | is_in_centers_all
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is_in_boxes_and_center = (
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is_in_boxes[:, is_in_boxes_anchor] & is_in_centers[:, is_in_boxes_anchor]
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)
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return is_in_boxes_anchor, is_in_boxes_and_center
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def dynamic_k_matching(self, cost, pair_wise_ious, gt_classes, num_gt, fg_mask):
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matching_matrix = torch.zeros_like(cost, dtype=torch.uint8)
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ious_in_boxes_matrix = pair_wise_ious
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n_candidate_k = min(10, ious_in_boxes_matrix.size(1))
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topk_ious, _ = torch.topk(ious_in_boxes_matrix, n_candidate_k, dim=1)
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dynamic_ks = torch.clamp(topk_ious.sum(1).int(), min=1)
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dynamic_ks = dynamic_ks.tolist()
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for gt_idx in range(num_gt):
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_, pos_idx = torch.topk(
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cost[gt_idx], k=dynamic_ks[gt_idx], largest=False
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)
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matching_matrix[gt_idx][pos_idx] = 1
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del topk_ious, dynamic_ks, pos_idx
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anchor_matching_gt = matching_matrix.sum(0)
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if (anchor_matching_gt > 1).sum() > 0:
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_, cost_argmin = torch.min(cost[:, anchor_matching_gt > 1], dim=0)
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matching_matrix[:, anchor_matching_gt > 1] *= 0
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matching_matrix[cost_argmin, anchor_matching_gt > 1] = 1
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fg_mask_inboxes = matching_matrix.sum(0) > 0
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num_fg = fg_mask_inboxes.sum().item()
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fg_mask[fg_mask.clone()] = fg_mask_inboxes
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matched_gt_inds = matching_matrix[:, fg_mask_inboxes].argmax(0)
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gt_matched_classes = gt_classes[matched_gt_inds]
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pred_ious_this_matching = (matching_matrix * pair_wise_ious).sum(0)[
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fg_mask_inboxes
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]
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return num_fg, gt_matched_classes, pred_ious_this_matching, matched_gt_inds
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