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- #!/usr/bin/env python3
- # -*- coding:utf-8 -*-
- import torch
- import torch.nn as nn
- from .loss import build_criterion
- from .yolov3 import YOLOv3
- # build object detector
- def build_yolov3(args, cfg, device, num_classes=80, trainable=False, deploy=False):
- print('==============================')
- print('Build {} ...'.format(args.model.upper()))
-
- print('==============================')
- print('Model Configuration: \n', cfg)
-
- # -------------- Build YOLO --------------
- model = YOLOv3(
- cfg=cfg,
- device=device,
- num_classes=num_classes,
- trainable=trainable,
- conf_thresh=args.conf_thresh,
- nms_thresh=args.nms_thresh,
- topk=args.topk,
- deploy = deploy,
- nms_class_agnostic = args.nms_class_agnostic
- )
- # -------------- Initialize YOLO --------------
- for m in model.modules():
- if isinstance(m, nn.BatchNorm2d):
- m.eps = 1e-3
- m.momentum = 0.03
- # Init bias
- init_prob = 0.01
- bias_value = -torch.log(torch.tensor((1. - init_prob) / init_prob))
- # obj pred
- for obj_pred in model.obj_preds:
- b = obj_pred.bias.view(1, -1)
- b.data.fill_(bias_value.item())
- obj_pred.bias = torch.nn.Parameter(b.view(-1), requires_grad=True)
- # cls pred
- for cls_pred in model.cls_preds:
- b = cls_pred.bias.view(1, -1)
- b.data.fill_(bias_value.item())
- cls_pred.bias = torch.nn.Parameter(b.view(-1), requires_grad=True)
- # reg pred
- for reg_pred in model.reg_preds:
- b = reg_pred.bias.view(-1, )
- b.data.fill_(1.0)
- reg_pred.bias = torch.nn.Parameter(b.view(-1), requires_grad=True)
- w = reg_pred.weight
- w.data.fill_(0.)
- reg_pred.weight = torch.nn.Parameter(w, requires_grad=True)
- # -------------- Build criterion --------------
- criterion = None
- if trainable:
- # build criterion for training
- criterion = build_criterion(cfg, device, num_classes)
- return model, criterion
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