RT-ODLab/models/detectors/artdet/build.py

#!/usr/bin/env python3
# -*- coding:utf-8 -*-

import torch
import torch.nn as nn

from .loss import build_criterion
from .artdet import ARTDet


# build object detector
def build_artdet(args, cfg, device, num_classes=80, trainable=False, deploy=False):
    print('==============================')
    print('Build {} ...'.format(args.model.upper()))
        
    # -------------- Build ARTDet --------------
    model = ARTDet(
        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
        )

    # -------------- Initialize ARTDet --------------
    for m in model.modules():
        if isinstance(m, nn.BatchNorm2d):
            m.eps = 1e-3
            m.momentum = 0.03    
    # Init head
    init_prob = 0.01
    bias_value = -torch.log(torch.tensor((1. - init_prob) / init_prob))
    for det_head in model.det_heads:
        # cls pred
        b = det_head.cls_pred.bias.view(1, -1)
        b.data.fill_(bias_value.item())
        det_head.cls_pred.bias = torch.nn.Parameter(b.view(-1), requires_grad=True)
        # reg pred
        b = det_head.reg_pred.bias.view(-1, )
        b.data.fill_(1.0)
        det_head.reg_pred.bias = torch.nn.Parameter(b.view(-1), requires_grad=True)
        w = det_head.reg_pred.weight
        w.data.fill_(0.)
        det_head.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