yolov5-7.0更改resnet主干网络

参考链接

ClearML教程:https://blog.csdn.net/qq_40243750/article/details/126445671

b站教学视频：https://www.bilibili.com/video/BV1Mx4y1A7jy/spm_id_from=333.788&vd_source=b52b79abfe565901e6969da2a1191407

开始

github地址:https://github.com/z1069614715/objectdetection_script/tree/master

首先安装timm库：pip install -i https://pypi.tuna.tsinghua.edu.cn/simple timm

可以查看timm中包含的所有库：timm.list_models()

报错1:

解决:直接去这个路径下，删掉__init__.py就可以了，正常来说这是一个空文件，建议删除前检查一下

步骤1:

更改models中的yolov5.py文件

导入timm库

步骤2:

更改models中的yolov5.py文件

先改309行的parse_model函数

def parse_model(d, ch):  model_dict, input_channels(3)
    # Parse a YOLOv5 model.yaml dictionary
    LOGGER.info(f"\n{'':3}{'from':18}{'n':3}{'params':10}  {'module':<40}{'arguments':<30}")
    anchors, nc, gd, gw, act = d['anchors'], d['nc'], d['depth_multiple'], d['width_multiple'], d.get('activation')
    if act:
        Conv.default_act = eval(act)  redefine default activation, i.e. Conv.default_act = nn.SiLU()
        LOGGER.info(f"{colorstr('activation:')} {act}")  print
    na = (len(anchors[0]) // 2) if isinstance(anchors, list) else anchors  number of anchors
    no = na * (nc + 5)  number of outputs = anchors * (classes + 5)

    is_backbone = False
    layers, save, c2 = [], [], ch[-1]  layers, savelist, ch out
    for i, (f, n, m, args) in enumerate(d['backbone'] d['head']):  from, number, module, args
        try:
            t = m
            m = eval(m) if isinstance(m, str) else m  eval strings
        except:
            pass
        for j, a in enumerate(args):
            with contextlib.suppress(NameError):
                try:
                    args[j] = eval(a) if isinstance(a, str) else a  eval strings
                except:
                    args[j] = a

        n = n_ = max(round(n * gd), 1) if n > 1 else n  depth gain
        if m in {
                Conv, GhostConv, Bottleneck, GhostBottleneck, SPP, SPPF, DWConv, MixConv2d, Focus, CrossConv,
                BottleneckCSP, C3, C3TR, C3SPP, C3Ghost, nn.ConvTranspose2d, DWConvTranspose2d, C3x}:
            c1, c2 = ch[f], args[0]
            if c2 != no:  if not output
                c2 = make_divisible(c2 * gw, 8)

            args = [c1, c2, *args[1:]]
            if m in {BottleneckCSP, C3, C3TR, C3Ghost, C3x}:
                args.insert(2, n)  number of repeats
                n = 1
        elif m is nn.BatchNorm2d:
            args = [ch[f]]
        elif m is Concat:
            c2 = sum(ch[x] for x in f)
        TODO: channel, gw, gd
        elif m in {Detect, Segment}:
            args.append([ch[x] for x in f])
            if isinstance(args[1], int):  number of anchors
                args[1] = [list(range(args[1] 2))] len(f)
            if m is Segment:
                args[3] = make_divisible(args[3] gw, 8)
        elif m is Contract:
            c2 = ch[f] args[0] ** 2
        elif m is Expand:
            c2 = ch[f] // args[0] ** 2
        elif isinstance(m, str):
            t = m
            m = timm.create_model(m, pretrained=args[0], features_only=True)
            c2 = m.feature_info.channels()
        elif m in {}:
        #     m = m(*args)
        #     c2 = m.channel
        else:
            c2 = ch[f]
        if isinstance(c2, list):
            is_backbone = True
            m_ = m
            m_.backbone = True
        else:
            m_ = nn.Sequential(*(m(*args) for _ in range(n))) if n > 1 else m(*args)  module
            t = str(m)[8:-2].replace('__main__.', '')  module type
        np = sum(x.numel() for x in m_.parameters())  number params
        m_.i, m_.f, m_.type, m_.np = i + 4 if is_backbone else i, f, t, np  attach index, 'from' index, type, number params
        LOGGER.info(f'{i:3}{str(f):18}{n_:3}{np:10.0f}  {t:<40}{str(args):<30}')  print
        save.extend(x % (i + 4 if is_backbone else i) for x in ([f] if isinstance(f, int) else f) if x != -1)  append to savelist
        layers.append(m_)
        if i == 0:
            ch = []
        if isinstance(c2, list):
            ch.extend(c2)
            for _ in range(5 len(ch)):
                ch.insert(0, 0)
        else:
            ch.append(c2)
    return nn.Sequential(*layers), sorted(save)

步骤3:

更改108行BaseModel类中的_forward_once函数

def _forward_once(self, x, profile=False, visualize=False):
    y, dt = [], []  # outputs
    for m in self.model:
        if m.f != -1:  # if not from previous layer
            x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f]  # from earlier layers
        if profile:
            self._profile_one_layer(m, x, dt)
        if hasattr(m, 'backbone'):
            x = m(x)
            for _ in range(5 - len(x)):
                x.insert(0, None)
            for i_idx, i in enumerate(x):
                if i_idx in self.save:
                    y.append(i)
                else:
                    y.append(None)
            x = x[-1]
        else:
            x = m(x)  # run
            y.append(x if m.i in self.save else None)  # save output
        if visualize:
            feature_visualization(x, m.type, m.i, save_dir=visualize)
    return x

步骤4:

在model文件下创建yovov5-custom.yaml文件

# YOLOv5 

标签：yolov5,ch,args,resnet,backbone,else,7.0,c2,model	

From： https://blog.csdn.net/2301_79573948/article/details/139601254