【YOLOV5-6.x讲解】模型搭建模块 models/yolo.py

  主干目录:

【YOLOV5-6.x 版本讲解】整体项目代码注释导航现在YOLOV5已经更新到6.X版本,现在网上很多还停留在5.X的源码注释上,因此特开一贴传承开源精神!5.X版本的可以看其他大佬的帖子本文章主要从6.X版本出发,主要解决6.X版本的项目注释与代码分析!......https://blog.csdn.net/qq_39237205/article/details/125729662

以下内容为本栏目的一部分,更多关注以上链接目录,查找YOLOV5的更多信息

祝福你朋友早日发表sci!


# YOLOv5  by Ultralytics, GPL-3.0 license

# https://blog.csdn.net/qq_39237205/category_11911202.html

"""
YOLO-specific modules

        这个模块是yolov5的模型搭建模块,非常的重要,不过代码量并不大,不是很难,
        只是yolov5的作者把封装的太好了,模型扩展了很多的额外的功能,导致看起来很难,其实真正有用的代码不多的。
        重点是抓住三个函数是在哪里调用的,谁调用谁的。

Usage:
    $ python path/to/models/yolo.py --cfg yolov5s.yaml
"""

import argparse # 解析命令行参数模块
import sys  # sys系统模块 包含了与Python解释器和它的环境有关的函数
from copy import deepcopy    # 数据拷贝模块 深拷贝
from pathlib import Path    # Path将str转换为Path对象 使字符串路径易于操作的模块

FILE = Path(__file__).resolve()
ROOT = FILE.parents[1]  # YOLOv5 root directory
if str(ROOT) not in sys.path:
    sys.path.append(str(ROOT))  # add ROOT to PATH
# ROOT = ROOT.relative_to(Path.cwd())  # relative

from models.common import *
from models.experimental import *
from utils.autoanchor import check_anchor_order
from utils.general import LOGGER, check_version, check_yaml, make_divisible, print_args
from utils.plots import feature_visualization
from utils.torch_utils import fuse_conv_and_bn, initialize_weights, model_info, scale_img, select_device, time_sync

# 导入thop包 用于计算FLOPs
try:
    import thop  # for FLOPs computation
except ImportError:
    thop = None


class Detect(nn.Module):
    """
        Detect模块是用来构建Detect层的,将输入feature map 通过一个卷积操作和公式计算到我们想要的shape, 为后面的计算损失或者NMS作准备

    """
    stride = None  # strides computed during build
    onnx_dynamic = False  # ONNX export parameter   再export中这个参数会重新设为True

    def __init__(self, nc=80, anchors=(), ch=(), inplace=True):  # detection layer
        super().__init__()
        """
                detection layer 相当于yolov3中的YOLOLayer层
                :params nc: number of classes
                :params anchors: 传入3个feature map上的所有anchor的大小(P3、P4、P5)
                :params ch: [128, 256, 512] 3个输出feature map的channel
        """
        self.nc = nc  # number of classes,若是VOC,则类别为20
        self.no = nc + 5  # number of outputs per anchor。   若是VOC: 5+20=25  该数为:xywhc+classes
        self.nl = len(anchors)   # number of detection layers   Detect的个数 3
        self.na = len(anchors[0]) // 2   # number of anchors  每个feature map的anchor个数 3
        self.grid = [torch.zeros(1)] * self.nl  # init grid  {list: 3}  tensor([0.]) X 3
        self.anchor_grid = [torch.zeros(1)] * self.nl  # init anchor grid

        # a=[3, 3, 2]  anchors以[w, h]对的形式存储  3个feature map 每个feature map上有三个anchor(w,h)
        # a = torch.tensor(anchors).float().view(self.nl, -1, 2)

        # register_buffer
        # 模型中需要保存的参数一般有两种:
        # 一种是反向传播需要被optimizer更新的,即参与训练的参数称为parameter,optim.step只能更新nn.parameter类型的参数
        # 另一种不要被更新,即不参与训练的参数称为buffer,buffer的参数更新是在forward中。
        # shape(nl,na,2)
        # self.register_buffer('anchors', a)
        self.register_buffer('anchors', torch.tensor(anchors).float().view(self.nl, -1, 2))  # shape(nl,na,2)


        # output conv 对每个输出的feature map都要调用一次conv1x1
        self.m = nn.ModuleList(nn.Conv2d(x, self.no * self.na, 1) for x in ch)  # output conv
        # use in-place ops (e.g. slice assignment) 一般都是True 默认不使用AWS Inferentia加速
        self.inplace = inplace  # use in-place ops (e.g. slice assignment)

    def forward(self, x):   # x:[[],[],[]]分别对应1/8 1/16 1/32 三个维度大小的宽高输入
        # forward函数在Model类的forward_once中调用
        """
            :return
                    train: 一个tensor list 存放三个元素   [bs, anchor_num, grid_w, grid_h, xywh+c+20classes]
                           分别是 [1, 3, 80, 80, 25] [1, 3, 40, 40, 25] [1, 3, 20, 20, 25]
                    inference: 0 [1, 19200+4800+1200, 25] = [bs, anchor_num*grid_w*grid_h, xywh+c+20classes]
                               1 一个tensor list 存放三个元素 [bs, anchor_num, grid_w, grid_h, xywh+c+20classes]
                                 [1, 3, 80, 80, 25] [1, 3, 40, 40, 25] [1, 3, 20, 20, 25]
        """
        z = []  # inference output
        for i in range(self.nl):    # 对三个feature map分别进行处理,遍历一共多少层
            x[i] = self.m[i](x[i])  # conv   xi[bs, 128/256/512, 80, 80] to [bs, 75, 80, 80]
            bs, _, ny, nx = x[i].shape  # x(bs,255,20,20) to x(bs,3,20,20,85)
            x[i] = x[i].view(bs, self.na, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous()

            # inference,预测部分
            if not self.training:  # inference
                # 构造网格
                # 因为推理返回的不是归一化后的网格偏移量 需要再加上网格的位置 得到最终的推理坐标 再送入nms
                # 所以这里构建网格就是为了记录每个grid的网格坐标 方面后面使用
                if self.onnx_dynamic or self.grid[i].shape[2:4] != x[i].shape[2:4]: # 第一次运行时候,会实例化这两个属性
                    self.grid[i], self.anchor_grid[i] = self._make_grid(nx, ny, i)  # 拿到左上角的坐标

                y = x[i].sigmoid()  # 将每一层的特征归一化到0到1之间

                if self.inplace:
                    # 默认执行 不使用AWS Inferentia
                    # 这里的公式和yolov3、v4中使用的不一样 是yolov5作者自己用的效果更好,边框预测公式,ppt有
                    # 计算中心点坐标,将0到1之间处理到原图大小的区间
                    y[..., 0:2] = (y[..., 0:2] * 2 - 0.5 + self.grid[i]) * self.stride[i]  # xy # xy||||| × self.stride[i]是为了放大到原图
                    # 计算宽高,将0到1之间处理到原图大小的区间
                    y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i]  # wh
                else:  # for YOLOv5 on AWS Inferentia https://github.com/ultralytics/yolov5/pull/2953
                    xy = (y[..., 0:2] * 2 - 0.5 + self.grid[i]) * self.stride[i]  # xy
                    wh = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i]  # wh
                    y = torch.cat((xy, wh, y[..., 4:]), -1)
                # z是一个tensor list 三个元素 分别是[1, 19200, 25] [1, 4800, 25] [1, 1200, 25]
                z.append(y.view(bs, -1, self.no))

        return x if self.training else (torch.cat(z, 1), x)

    def _make_grid(self, nx=20, ny=20, i=0):
        """
                构造网格
        """
        d = self.anchors[i].device
        if check_version(torch.__version__, '1.10.0'):  # torch>=1.10.0 meshgrid workaround for torch>=0.7 compatibility
            yv, xv = torch.meshgrid([torch.arange(ny, device=d), torch.arange(nx, device=d)], indexing='ij')
        else:
            yv, xv = torch.meshgrid([torch.arange(ny, device=d), torch.arange(nx, device=d)])
        grid = torch.stack((xv, yv), 2).expand((1, self.na, ny, nx, 2)).float()
        anchor_grid = (self.anchors[i].clone() * self.stride[i]) \
            .view((1, self.na, 1, 1, 2)).expand((1, self.na, ny, nx, 2)).float()
        return grid, anchor_grid


class Model(nn.Module):
    def __init__(self, cfg='yolov5s.yaml', ch=3, nc=None, anchors=None):  # model, input channels, number of classes
        """
                Model主要包含模型的搭建与扩展功能,yolov5的作者将这个模块的功能写的很全,
                    扩展功能如:特征可视化,打印模型信息、TTA推理增强、融合Conv+Bn加速推理、模型搭载nms功能、autoshape函数:
                    模型搭建包含前处理、推理、后处理的模块(预处理 + 推理 + nms)。
                感兴趣的可以仔细看看,不感兴趣的可以直接看__init__和__forward__两个函数即可。

                :params cfg:模型配置文件
                :params ch: input img channels 一般是3 RGB文件
                :params nc: number of classes 数据集的类别个数
                :anchors: 一般是None
        """
        super().__init__()
        if isinstance(cfg, dict):
            self.yaml = cfg  # model dict
        else:  # is *.yaml
            # is *.yaml  一般执行这里
            import yaml  # for torch hub
            self.yaml_file = Path(cfg).name # cfg file name = yolov5s.yaml
            # 如果配置文件中有中文,打开时要加encoding参数
            with open(cfg, encoding='ascii', errors='ignore') as f:
                # model dict  取到配置文件中每条的信息(没有注释内容)
                self.yaml = yaml.safe_load(f)  # model dict

        # Define model
        ch = self.yaml['ch'] = self.yaml.get('ch', ch)  # input channels
        # 设置类别数 一般不执行, 因为nc=self.yaml['nc']恒成立
        if nc and nc != self.yaml['nc']:
            LOGGER.info(f"Overriding model.yaml nc={self.yaml['nc']} with nc={nc}")
            self.yaml['nc'] = nc  # override yaml value
        # 重写anchor,一般不执行, 因为传进来的anchors一般都是None
        if anchors:
            LOGGER.info(f'Overriding model.yaml anchors with anchors={anchors}')
            self.yaml['anchors'] = round(anchors)  # override yaml value

        # 创建网络模型
        # self.model: 初始化的整个网络模型(包括Detect层结构)
        # self.save: 所有层结构中from不等于-1的序号,并排好序  [4, 6, 10, 14, 17, 20, 23]
        self.model, self.save = parse_model(deepcopy(self.yaml), ch=[ch])  # model, savelist

        # default class names ['0', '1', '2',..., '19']
        self.names = [str(i) for i in range(self.yaml['nc'])]  # default names

        # self.inplace=True  默认True  不使用加速推理
        # AWS Inferentia Inplace compatiability
        # https://github.com/ultralytics/yolov5/pull/2953
        self.inplace = self.yaml.get('inplace', True)

        # 获取Detect模块的stride(相对输入图像的下采样率)和anchors在当前Detect输出的feature map的尺度
        # Build strides, anchors
        m = self.model[-1]  # Detect()
        if isinstance(m, Detect):
            s = 256  # 2x min stride
            m.inplace = self.inplace
            # 计算三个feature map下采样的倍率  [8, 16, 32]
            # 假设640X640的图片大小,在最后三层时分别乘1/8 1/16 1/32,得到80,40,20
            m.stride = torch.tensor([s / x.shape[-2] for x in self.forward(torch.zeros(1, ch, s, s))])  # 前向传播的处理,为了得到最后输出的stride的大小 # forward
            # 将当前图片的大小处理成相对当前feature map的anchor大小 如[10, 13]/8 -> [1.25, 1.625]
            m.anchors /= m.stride.view(-1, 1, 1)
            # 检查anchor顺序与stride顺序是否一致
            check_anchor_order(m)
            self.stride = m.stride
            self._initialize_biases()  # only run once # only run once 初始化偏置
            # logger.info('Strides: %s' % m.stride.tolist())

        # Init weights, biases
        initialize_weights(self)     # 调用torch_utils.py下initialize_weights初始化模型权重
        self.info() # 打印模型信息
        LOGGER.info('')

    def forward(self, x, augment=False, profile=False, visualize=False):
        # augmented inference, None  上下flip/左右flip
        # 是否在测试时也使用数据增强  Test Time Augmentation(TTA)
        if augment:
            return self._forward_augment(x)  # augmented inference, None

        # 默认执行 正常前向推理
        # single-scale inference, train
        return self._forward_once(x, profile, visualize)  # single-scale inference, train

    def _forward_augment(self, x):
        img_size = x.shape[-2:]  # height, width
        s = [1, 0.83, 0.67]  # scales
        f = [None, 3, None]  # flips (2-ud, 3-lr)
        y = []  # outputs
        for si, fi in zip(s, f):
            # scale_img缩放图片尺寸
            xi = scale_img(x.flip(fi) if fi else x, si, gs=int(self.stride.max()))
            yi = self._forward_once(xi)[0]  # forward
            # cv2.imwrite(f'img_{si}.jpg', 255 * xi[0].cpu().numpy().transpose((1, 2, 0))[:, :, ::-1])  # save
            # _descale_pred将推理结果恢复到相对原图图片尺寸
            yi = self._descale_pred(yi, fi, si, img_size)
            y.append(yi)
        y = self._clip_augmented(y)  # clip augmented tails
        return torch.cat(y, 1), None  # augmented inference, train

    def _forward_once(self, x, profile=False, visualize=False):
        """
            :params x: 输入图像
            :params profile: True 可以做一些性能评估
            :params feature_vis: True 可以做一些特征可视化
            :return train: 一个tensor list 存放三个元素   [bs, anchor_num, grid_w, grid_h, xywh+c+20classes]
                           分别是 [1, 3, 80, 80, 25] [1, 3, 40, 40, 25] [1, 3, 20, 20, 25]
                    inference: 0 [1, 19200+4800+1200, 25] = [bs, anchor_num*grid_w*grid_h, xywh+c+20classes]
                               1 一个tensor list 存放三个元素 [bs, anchor_num, grid_w, grid_h, xywh+c+20classes]
                                 [1, 3, 80, 80, 25] [1, 3, 40, 40, 25] [1, 3, 20, 20, 25]
        """
        # y: 存放着self.save=True的每一层的输出,因为后面的层结构concat等操作要用到
        # dt: 在profile中做性能评估时使用
        y, dt = [], []  # outputs
        for m in self.model:
            # 前向推理每一层结构   m.i=index   m.f=from   m.type=类名   m.np=number of params
            # if not from previous layer   m.f=当前层的输入来自哪一层的输出  s的m.f都是-1
            if m.f != -1:  # if not from previous layer
                # 这里需要做4个concat操作和1个Detect操作
                # concat操作如m.f=[-1,6] x就有两个元素,一个是上一层的输出,另一个是index=6的层的输出 再送到x=m(x)做concat操作
                # Detect操作m.f=[17, 20, 23] x有三个元素,分别存放第17层第20层第23层的输出 再送到x=m(x)做Detect的forward
                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

            # 打印日志信息  FLOPs time等
            # 打印日志信息  前向推理时间
            if profile:
                self._profile_one_layer(m, x, dt)

            x = m(x)  # run正向推理  执行每一层的forward函数(除Concat和Detect操作)
            # print('层数',i,'特征图大小',x.shape)

            # 存放着self.save的每一层的输出,因为后面需要用来作concat等操作要用到  不在self.save层的输出就为None
            y.append(x if m.i in self.save else None)  # save output

            # 特征可视化 可以自己改动想要哪层的特征进行可视化
            if visualize:
                feature_visualization(x, m.type, m.i, save_dir=visualize)
        return x

    def _descale_pred(self, p, flips, scale, img_size):
        """
            用在上面的__init__函数上
                将推理结果恢复到原图图片尺寸  Test Time Augmentation(TTA)中用到
                de-scale predictions following augmented inference (inverse operation)
                :params p: 推理结果
                :params flips:
                :params scale:
                :params img_size:
        """
        # 不同的方式前向推理使用公式不同 具体可看Detect函数
        # de-scale predictions following augmented inference (inverse operation)
        if self.inplace:    # 默认执行 不使用AWS Inferentia
            p[..., :4] /= scale  # de-scale
            if flips == 2:
                p[..., 1] = img_size[0] - p[..., 1]  # de-flip ud
            elif flips == 3:
                p[..., 0] = img_size[1] - p[..., 0]  # de-flip lr
        else:
            x, y, wh = p[..., 0:1] / scale, p[..., 1:2] / scale, p[..., 2:4] / scale  # de-scale
            if flips == 2:
                y = img_size[0] - y  # de-flip ud
            elif flips == 3:
                x = img_size[1] - x  # de-flip lr
            p = torch.cat((x, y, wh, p[..., 4:]), -1)
        return p

    def _clip_augmented(self, y):
        # Clip YOLOv5 augmented inference tails
        nl = self.model[-1].nl  # number of detection layers (P3-P5)
        g = sum(4 ** x for x in range(nl))  # grid points
        e = 1  # exclude layer count
        i = (y[0].shape[1] // g) * sum(4 ** x for x in range(e))  # indices
        y[0] = y[0][:, :-i]  # large
        i = (y[-1].shape[1] // g) * sum(4 ** (nl - 1 - x) for x in range(e))  # indices
        y[-1] = y[-1][:, i:]  # small
        return y

    def _profile_one_layer(self, m, x, dt):
        c = isinstance(m, Detect)  # is final layer, copy input as inplace fix
        o = thop.profile(m, inputs=(x.copy() if c else x,), verbose=False)[0] / 1E9 * 2 if thop else 0  # FLOPs
        t = time_sync()
        for _ in range(10):
            m(x.copy() if c else x)
        dt.append((time_sync() - t) * 100)
        if m == self.model[0]:
            LOGGER.info(f"{'time (ms)':>10s} {'GFLOPs':>10s} {'params':>10s}  {'module'}")
        LOGGER.info(f'{dt[-1]:10.2f} {o:10.2f} {m.np:10.0f}  {m.type}')
        if c:
            LOGGER.info(f"{sum(dt):10.2f} {'-':>10s} {'-':>10s}  Total")

    def _initialize_biases(self, cf=None):  # initialize biases into Detect(), cf is class frequency
        """用在上面的__init__函数上
                initialize biases into Detect(), cf is class frequency
                https://arxiv.org/abs/1708.02002 section 3.3
                """
        # cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1.
        m = self.model[-1]  # Detect() module
        for mi, s in zip(m.m, m.stride):  # from
            b = mi.bias.view(m.na, -1)  # conv.bias(255) to (3,85)
            b.data[:, 4] += math.log(8 / (640 / s) ** 2)  # obj (8 objects per 640 image)
            b.data[:, 5:] += math.log(0.6 / (m.nc - 0.999999)) if cf is None else torch.log(cf / cf.sum())  # cls
            mi.bias = torch.nn.Parameter(b.view(-1), requires_grad=True)

    def _print_biases(self):
        """
                打印模型中最后Detect层的偏置bias信息(也可以任选哪些层bias信息)
        """
        m = self.model[-1]  # Detect() module
        for mi in m.m:  # from
            b = mi.bias.detach().view(m.na, -1).T  # conv.bias(255) to (3,85)
            LOGGER.info(
                ('%6g Conv2d.bias:' + '%10.3g' * 6) % (mi.weight.shape[1], *b[:5].mean(1).tolist(), b[5:].mean()))

    # def _print_weights(self):
    #     for m in self.model.modules():
    #         if type(m) is Bottleneck:
    #             LOGGER.info('%10.3g' % (m.w.detach().sigmoid() * 2))  # shortcut weights

    def fuse(self):  # fuse model Conv2d() + BatchNorm2d() layers
        """用在detect.py、val.py
               fuse model Conv2d() + BatchNorm2d() layers
               调用torch_utils.py中的fuse_conv_and_bn函数和common.py中Conv模块的fuseforward函数
               """
        LOGGER.info('Fusing layers... ')     # 日志
        # 遍历每一层结构
        for m in self.model.modules():
            # 如果当前层是卷积层Conv且有bn结构, 那么就调用fuse_conv_and_bn函数讲conv和bn进行融合, 加速推理
            if isinstance(m, (Conv, DWConv)) and hasattr(m, 'bn'):
                m.conv = fuse_conv_and_bn(m.conv, m.bn)  # 融合 update conv
                delattr(m, 'bn')  # 移除bn remove batchnorm
                m.forward = m.forward_fuse  # 更新前向传播 update forward (反向传播不用管, 因为这种推理只用在推理阶段)
        self.info() # 打印conv+bn融合后的模型信息
        return self

    def info(self, verbose=False, img_size=640):  # print model information
        """
            用在上面的__init__函数上
                调用torch_utils.py下model_info函数打印模型信息
        """
        model_info(self, verbose, img_size)

    def _apply(self, fn):
        # Apply to(), cpu(), cuda(), half() to model tensors that are not parameters or registered buffers
        self = super()._apply(fn)
        m = self.model[-1]  # Detect()
        if isinstance(m, Detect):
            m.stride = fn(m.stride)
            m.grid = list(map(fn, m.grid))
            if isinstance(m.anchor_grid, list):
                m.anchor_grid = list(map(fn, m.anchor_grid))
        return self


def parse_model(d, ch):  # model_dict, input_channels(3)
    """
        主要功能:parse_model模块用来解析模型文件(从Model中传来的字典形式),并搭建网络结构。
        在上面Model模块的__init__函数中调用

        这个函数其实主要做的就是: 更新当前层的args(参数),计算c2(当前层的输出channel) =>
                              使用当前层的参数搭建当前层 =>
                              生成 layers + save

        :params d: model_dict 模型文件 字典形式 {dict:7}  yolov5s.yaml中的6个元素 + ch
        :params ch: 记录模型每一层的输出channel 初始ch=[3] 后面会删除
        :return nn.Sequential(*layers): 网络的每一层的层结构
        :return sorted(save): 把所有层结构中from不是-1的值记下 并排序 [4, 6, 10, 14, 17, 20, 23]
    """

    LOGGER.info(f"\n{'':>3}{'from':>18}{'n':>3}{'params':>10}  {'module':<40}{'arguments':<30}")
    # 读取d字典中的anchors和parameters(nc、depth_multiple、width_multiple)
    #  nc(number of classes)数据集类别个数;
    # depth_multiple,通过深度参数depth gain在搭建每一层的时候,实际深度 = 理论深度(每一层的参数n) * depth_multiple,起到一个动态调整模型深度的作用。
    # width_multiple,在模型中间层的每一层的实际输出channel = 理论channel(每一层的参数c2) * width_multiple,起到一个动态调整模型宽度的作用。
    anchors, nc, gd, gw = d['anchors'], d['nc'], d['depth_multiple'], d['width_multiple']

    # na: number of anchors 每一个predict head上的anchor数 = 3
    na = (len(anchors[0]) // 2) if isinstance(anchors, list) else anchors  # number of anchors

    # no: number of outputs 每一个predict head层的输出channel = anchors * (classes + 5) = 75(VOC)
    no = na * (nc + 5)  #总共预测的anchors个数 number of outputs = anchors * (classes + 5)

    # 开始搭建网络
    # layers: 保存每一层的层结构
    # save: 记录下所有层结构中from中不是-1的层结构序号
    # c2: 保存当前层的输出channel
    layers, save, c2 = [], [], ch[-1]  # layers, savelist, ch out
    # from(当前层输入来自哪些层), number(当前层次数 初定), module(当前层类别), args(当前层类参数 初定)
    for i, (f, n, m, args) in enumerate(d['backbone'] + d['head']):  # 遍历backbone和head的每一层  # from, number, module, args
        # eval(string) 得到当前层的真实类名
        # 例如: m= Focus -> 
        m = eval(m) if isinstance(m, str) else m  # 将字符串处理成一个类名 或者 字符串,即实现名字向类的转换

        for j, a in enumerate(args):    # 主要照顾 yolo.yaml文件中最后一列的, [nc, anchors]
            try:
                args[j] = eval(a) if isinstance(a, str) else a  # eval strings,当他是一个字符串,就试图将它处理成一个变量名
            except NameError:
                pass
        # ------------------- 更新当前层的args(参数),计算c2(当前层的输出channel) -------------------
        # depth gain 控制深度  如v5s: n*0.33   n: 当前模块的次数(间接控制深度)
        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]:
            # c1: 当前层的输入的channel数
            # c2: 当前层的输出的channel数(初定)
            # ch: 记录着所有层的输出channel,f代表该ch中文最后一个,即对一下一层来说,这就是-1层的输入
            c1, c2 = ch[f], args[0] #  args[0]为[-1, 1, Conv, [128, 3, 2]]这的128

            # if not output  no=75  只有最后一层c2=no  最后一层不用控制宽度,输出channel必须是no
            if c2 != no:  # if not output
                # width gain 控制宽度  如v5s: c2*width_multiple(yolo.yaml)
                # c2: 当前层的最终输出的channel数(间接控制宽度)
                c2 = make_divisible(c2 * gw, 8)

            # 在初始arg的基础上更新 加入当前层的输入channel并更新当前层
            # [in_channel, out_channel, *args[1:]]
            args = [c1, c2, *args[1:]]  # [-1, 1, Conv, [128, 3, 2]] 变为 [-1, 1, Conv, [-1的值,128 × width_multiple , 3, 2]]
            # 如果当前层是BottleneckCSP/C3/C3TR, 则需要在args中加入bottleneck的个数
            # [in_channel, out_channel, Bottleneck的个数n, bool(True表示有shortcut 默认,反之无)]
            if m in [BottleneckCSP, C3, C3TR, C3Ghost]: # 因为这几个类的定义中,初始化中有n=1这个参数,整个过程就是在初始化卷积的参数罢了
                args.insert(2, n)  # 在第二个位置插入bottleneck个数n
                n = 1   # 恢复默认值1
        elif m is nn.BatchNorm2d:
            # BN层只需要返回上一层的输出channel
            args = [ch[f]]
        elif m is Concat:
            # Concat层则将f中所有的输出累加得到这层的输出channel
            c2 = sum(ch[x] for x in f)  # 因为这个[[-1, 6], 1, Concat, [1]] 的第一个是个列表,所以需要遍历,然后将-1, 6层的输入加起来
        elif m is Detect:    # Detect(YOLO Layer)层
            # 在args中加入三个Detect层的输出channel
            args.append([ch[x] for x in f])
            if isinstance(args[1], int):   # number of anchors  几乎不执行
                args[1] = [list(range(args[1] * 2))] * len(f)
        elif m is Contract:
            c2 = ch[f] * args[0] ** 2
        elif m is Expand:
            c2 = ch[f] // args[0] ** 2
        else:
            # Upsample
            c2 = ch[f]   # args不变

        # m_: 得到当前层module  如果n>1就创建多个m(当前层结构), 如果n=1就创建一个m
        # n只有在[BottleneckCSP, C3, C3TR]中才会用到
        m_ = nn.Sequential(*(m(*args) for _ in range(n))) if n > 1 else m(*args)  # module

        # 打印当前层结构的一些基本信息
        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, 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

        # append to savelist  把所有层结构中from不是-1的值记下  [6, 4, 14, 10, 17, 20, 23]
        save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1)  # append to savelist

        # 将当前层结构module加入layers中
        layers.append(m_)

        if i == 0:
            ch = []  # 去除输入channel [3]

        # 把当前层的输出channel数加入ch
        ch.append(c2)

    return nn.Sequential(*layers), sorted(save) # nn.Sequential(*layers) 处理成一个模型


if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--cfg', type=str, default='yolov5s.yaml', help='model.yaml')
    parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
    parser.add_argument('--profile', action='store_true', help='profile model speed')
    parser.add_argument('--test', action='store_true', help='test all yolo*.yaml')
    opt = parser.parse_args()
    opt.cfg = check_yaml(opt.cfg)  # check YAML
    print_args(FILE.stem, opt)
    device = select_device(opt.device)

    # Create model
    model = Model(opt.cfg).to(device)
    model.train()

    # Profile
    if opt.profile:
        img = torch.rand(8 if torch.cuda.is_available() else 1, 3, 640, 640).to(device)
        y = model(img, profile=True)

    # Test all models
    if opt.test:
        for cfg in Path(ROOT / 'models').rglob('yolo*.yaml'):
            try:
                _ = Model(cfg)
            except Exception as e:
                print(f'Error in {cfg}: {e}')

    # Tensorboard (not working https://github.com/ultralytics/yolov5/issues/2898)
    # from torch.utils.tensorboard import SummaryWriter
    # tb_writer = SummaryWriter('.')
    # LOGGER.info("Run 'tensorboard --logdir=models' to view tensorboard at http://localhost:6006/")
    # tb_writer.add_graph(torch.jit.trace(model, img, strict=False), [])  # add model graph

你可能感兴趣的:(#,python,深度学习,pytorch,人工智能,神经网络)