深度学习Week15-common.py文件解读（YOLOv5）

目录

 简介

一.基本组件

1.1autopad

1.2Conv

1.3 Focus

 1.4Bottleneck

1.5BottleneckCSP

1.6 C3

1.7 SPP

1.8Concat

1.9Contract、Expand

二、重要类

2.1非极大值抑制（NMS）

2.2AutoShape

2.3 Detections 

2.4 Classify

三、实验

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🍨 本文为[🔗365天深度学习训练营]内部限免文章（版权归 *K同学啊* 所有）
🍖 作者：[K同学啊]

这周接着详细解析小白YOLOv5全流程-训练+实现数字识别_牛大了2022的博客-CSDN博客_yolov5识别数字，之前入门教大家下载配置环境，如果没有的话请参考这篇的文章深度学习Week11-调用官方权重进行检测（YOLOv5）_yolov5权重_牛大了2022的博客-CSDN博客

📌 本周任务：将YOLOv5s网络模型中的C3模块按照下图方式修改，并跑通YOLOv5。
💫 任务提示：仅需修改./models/common.yaml文件。

 简介

common.py文件位置在./models/commonpy

 需要导入的基础包和配置：

import ast # 抽象语法树 
import contextlib  #为with语句分配资源的实用程序
import json #用于json和Python数据之间的相互转换
import math  # 数学函数模块
import platform #获取操作系统的信息
import warnings #警告程序员关于语言或库功能的变化的方法
import zipfile #zip格式编码的压缩和解压缩
from collections import OrderedDict, namedtuple #集合模块
from copy import copy   # 数据拷贝模块 分浅拷贝和深拷贝
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
from urllib.parse import urlparse  #urllib为http请求库，parse 用来编码和解码import cv2   # opencv-python
import numpy as np   # numpy数组操作模块
import pandas as pd   # panda数组操作模块
import requests # Python的HTTP客户端库
import torch  # pytorch深度学习框架
import torch.nn as nn   ## 专门为神经网络设计的模块化接口
from IPython.display import display 
from PIL import Image   # 图像基础操作模块
from torch.cuda import amp # 混合精度训练模块from utils import TryExcept  
from utils.dataloaders import exif_transpose, letterbox
from utils.general import (LOGGER, ROOT, Profile, check_requirements, check_suffix, check_version, colorstr,increment_path, is_notebook, make_divisible, non_max_suppression, scale_boxes, xywh2xyxy,xyxy2xywh, yaml_load)
from utils.plots import Annotator, colors, save_one_box
from utils.torch_utils import copy_attr, smart_inference_mode

一.基本组件

1.1autopad

根据输入的卷积核计算该卷积模块所需的pad值。

def autopad(k, p=None, d=1):  # kernel, padding, dilation# Pad to 'same' shape outputsif d > 1:k = d * (k - 1) + 1 if isinstance(k, int) else [d * (x - 1) + 1 for x in k]  # actual kernel-sizeif p is None:p = k // 2 if isinstance(k, int) else [x // 2 for x in k]  # auto-padreturn p

1.2Conv

最基础的模块组成: 卷积层 + BN层 + 激活函数。

class Conv(nn.Module):# Standard convolution with args(ch_in, ch_out, kernel, stride, padding, groups, dilation, activation)default_act = nn.SiLU()  # default activationdef __init__(self, c1, c2, k=1, s=1, p=None, g=1, d=1, act=True):super().__init__()self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p, d), groups=g, dilation=d, bias=False)self.bn = nn.BatchNorm2d(c2)self.act = self.default_act if act is True else act if isinstance(act, nn.Module) else nn.Identity()def forward(self, x):return self.act(self.bn(self.conv(x)))def forward_fuse(self, x):return self.act(self.conv(x))

其中有一个特殊函数 fuseforward ，这是一个前向加速推理模块，在前向传播过程中，通过融合conv + bn层，达到加速推理的作用，一般用于测试或验证阶段。

1.3 Focus

将输入图像切为4份（即宽高各减半），再聚合到通道处。

class Focus(nn.Module):# Focus wh information into c-spacedef __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True):  # ch_in, ch_out, kernel, stride, padding, groupssuper().__init__()self.conv = Conv(c1 * 4, c2, k, s, p, g, act=act)# self.contract = Contract(gain=2)def forward(self, x):  # x(b,c,w,h) -> y(b,4c,w/2,h/2)return self.conv(torch.cat((x[..., ::2, ::2], x[..., 1::2, ::2], x[..., ::2, 1::2], x[..., 1::2, 1::2]), 1))# return self.conv(self.contract(x))

 1.4Bottleneck

由1x1conv、3x3conv、残差块组成。

class Bottleneck(nn.Module):# Standard bottleneckdef __init__(self, c1, c2, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, shortcut, groups, expansionsuper().__init__()c_ = int(c2 * e)  # hidden channelsself.cv1 = Conv(c1, c_, 1, 1)self.cv2 = Conv(c_, c2, 3, 1, g=g)self.add = shortcut and c1 == c2def forward(self, x):return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))

1.5BottleneckCSP

由Bottleneck模块和CSP结构组成。

class BottleneckCSP(nn.Module):# CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworksdef __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, number, shortcut, groups, expansionsuper().__init__()c_ = int(c2 * e)  # hidden channelsself.cv1 = Conv(c1, c_, 1, 1)self.cv2 = nn.Conv2d(c1, c_, 1, 1, bias=False)self.cv3 = nn.Conv2d(c_, c_, 1, 1, bias=False)self.cv4 = Conv(2 * c_, c2, 1, 1)self.bn = nn.BatchNorm2d(2 * c_)  # applied to cat(cv2, cv3)self.act = nn.SiLU()self.m = nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)))def forward(self, x):y1 = self.cv3(self.m(self.cv1(x)))y2 = self.cv2(x)return self.cv4(self.act(self.bn(torch.cat((y1, y2), 1))))

1.6 C3

一种简化版的BottleneckCSP, 由三个卷积块和N个Bottleneck组成。

class C3(nn.Module):# CSP Bottleneck with 3 convolutionsdef __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, number, shortcut, groups, expansionsuper().__init__()c_ = int(c2 * e)  # hidden channelsself.cv1 = Conv(c1, c_, 1, 1)self.cv2 = Conv(c1, c_, 1, 1)self.cv3 = Conv(2 * c_, c2, 1)  # optional act=FReLU(c2)self.m = nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)))def forward(self, x):return self.cv3(torch.cat((self.m(self.cv1(x)), self.cv2(x)), 1))

1.7 SPP

空间金字塔池化，将更多不同分辨率的特征进行融合，得到更多的信息。

经典的空间金字塔池化模块首先将输入的卷积特征分成不同的尺寸，然后每个尺寸提取固定维度的特征，最后将这些特征拼接成一个固定的维度，如图1所示。输入的卷积特征图的大小为(w,h)，第一层空间金字塔采用4×4的刻度对特征图进行划分，其将输入的特征图分成了16个块，每块的大小为(w/4, h/4)；第二层空间金字塔采用2×2刻度对特征图进行划分，其将特征图分为4个快，每块大小为(w/2,h/2)；第三层空间金字塔将整张特征图作为一块，进行特征提取操作，最终的特征向量为21=16+4+1维。

class SPP(nn.Module):# Spatial Pyramid Pooling (SPP) layer https://arxiv.org/abs/1406.4729def __init__(self, c1, c2, k=(5, 9, 13)):super().__init__()c_ = c1 // 2  # hidden channelsself.cv1 = Conv(c1, c_, 1, 1)self.cv2 = Conv(c_ * (len(k) + 1), c2, 1, 1)self.m = nn.ModuleList([nn.MaxPool2d(kernel_size=x, stride=1, padding=x // 2) for x in k])def forward(self, x):x = self.cv1(x)with warnings.catch_warnings():warnings.simplefilter('ignore')  # suppress torch 1.9.0 max_pool2d() warningreturn self.cv2(torch.cat([x] + [m(x) for m in self.m], 1))

1.8Concat

按照某个维度进行concat，常用来合并前后两个feature map（也是上周提到的yolov5s结构图中的Concat）

class Concat(nn.Module):# Concatenate a list of tensors along dimensiondef __init__(self, dimension=1):super().__init__()self.d = dimensiondef forward(self, x):return torch.cat(x, self.d)

1.9Contract、Expand

用于改变feature map的维度，Contract将feature map的w和h维度(缩小)的数据收缩到channel维度上(放大)，Expand将channel维度(变小)的数据扩展到W和H维度(变大)。

class Contract(nn.Module):# Contract width-height into channels, i.e. x(1,64,80,80) to x(1,256,40,40)def __init__(self, gain=2):super().__init__()self.gain = gaindef forward(self, x):b, c, h, w = x.size()  # assert (h / s == 0) and (W / s == 0), 'Indivisible gain's = self.gainx = x.view(b, c, h // s, s, w // s, s)  # x(1,64,40,2,40,2)x = x.permute(0, 3, 5, 1, 2, 4).contiguous()  # x(1,2,2,64,40,40)return x.view(b, c * s * s, h // s, w // s)  # x(1,256,40,40)class Expand(nn.Module):# Expand channels into width-height, i.e. x(1,64,80,80) to x(1,16,160,160)def __init__(self, gain=2):super().__init__()self.gain = gaindef forward(self, x):b, c, h, w = x.size()  # assert C / s ** 2 == 0, 'Indivisible gain's = self.gainx = x.view(b, s, s, c // s ** 2, h, w)  # x(1,2,2,16,80,80)x = x.permute(0, 3, 4, 1, 5, 2).contiguous()  # x(1,16,80,2,80,2)return x.view(b, c // s ** 2, h * s, w * s)  # x(1,16,160,160)

二、重要类

下面的几个函数都是属于模型的扩展模块。yolov5的作者将搭建模型的函数功能写的很齐全。不光包含搭建模型部分，还考虑到了各个方面其他的功能，比如给模型搭载nms功能、给模型封装成包含前处理、推理、后处理的模块(预处理 + 推理 + nms)、二次分类等等功能。

2.1非极大值抑制（NMS）

非极大抑制，保留哪些框.

class NMS(nn.Module):"""在yolo.py中Model类的nms函数中使用NMS非极大值抑制 Non-Maximum Suppression (NMS) module给模型model封装nms  增加模型的扩展功能  但是我们一般不用 一般是在前向推理结束后再调用non_max_suppression函数"""conf = 0.25     # 置信度阈值              confidence thresholdiou = 0.45      # iou阈值                IoU thresholdclasses = None  # 是否nms后只保留特定的类别 (optional list) filter by classmax_det = 1000  # 每张图片的最大目标个数    maximum number of detections per imagedef __init__(self):super(NMS, self).__init__()def forward(self, x):""":params x[0]: [batch, num_anchors(3个yolo预测层), (x+y+w+h+1+num_classes)]直接调用的是general.py中的non_max_suppression函数给model扩展nms功能"""return non_max_suppression(x[0], self.conf, iou_thres=self.iou, classes=self.classes, max_det=self.max_det)

2.2AutoShape

给模型封装成包含前处理、推理、后处理的模块(预处理 + 推理 + nms) （代码很长，参考看）

class AutoShape(nn.Module):# YOLOv5 input-robust model wrapper for passing cv2/np/PIL/torch inputs. Includes preprocessing, inference and NMSconf = 0.25  # NMS confidence thresholdiou = 0.45  # NMS IoU thresholdagnostic = False  # NMS class-agnosticmulti_label = False  # NMS multiple labels per boxclasses = None  # (optional list) filter by class, i.e. = [0, 15, 16] for COCO persons, cats and dogsmax_det = 1000  # maximum number of detections per imageamp = False  # Automatic Mixed Precision (AMP) inferencedef __init__(self, model, verbose=True):super().__init__()if verbose:LOGGER.info('Adding AutoShape... ')copy_attr(self, model, include=('yaml', 'nc', 'hyp', 'names', 'stride', 'abc'), exclude=())  # copy attributesself.dmb = isinstance(model, DetectMultiBackend)  # DetectMultiBackend() instanceself.pt = not self.dmb or model.pt  # PyTorch modelself.model = model.eval()if self.pt:m = self.model.model.model[-1] if self.dmb else self.model.model[-1]  # Detect()m.inplace = False  # Detect.inplace=False for safe multithread inferencem.export = True  # do not output loss valuesdef _apply(self, fn):# Apply to(), cpu(), cuda(), half() to model tensors that are not parameters or registered buffersself = super()._apply(fn)if self.pt:m = self.model.model.model[-1] if self.dmb else self.model.model[-1]  # 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@smart_inference_mode()def forward(self, ims, size=640, augment=False, profile=False):# Inference from various sources. For size(height=640, width=1280), RGB images example inputs are:#   file:        ims = 'data/images/zidane.jpg'  # str or PosixPath#   URI:             = 'https://ultralytics.com/images/zidane.jpg'#   OpenCV:          = cv2.imread('image.jpg')[:,:,::-1]  # HWC BGR to RGB x(640,1280,3)#   PIL:             = Image.open('image.jpg') or ImageGrab.grab()  # HWC x(640,1280,3)#   numpy:           = np.zeros((640,1280,3))  # HWC#   torch:           = torch.zeros(16,3,320,640)  # BCHW (scaled to size=640, 0-1 values)#   multiple:        = [Image.open('image1.jpg'), Image.open('image2.jpg'), ...]  # list of imagesdt = (Profile(), Profile(), Profile())with dt[0]:if isinstance(size, int):  # expandsize = (size, size)p = next(self.model.parameters()) if self.pt else torch.empty(1, device=self.model.device)  # paramautocast = self.amp and (p.device.type != 'cpu')  # Automatic Mixed Precision (AMP) inferenceif isinstance(ims, torch.Tensor):  # torchwith amp.autocast(autocast):return self.model(ims.to(p.device).type_as(p), augment=augment)  # inference# Pre-processn, ims = (len(ims), list(ims)) if isinstance(ims, (list, tuple)) else (1, [ims])  # number, list of imagesshape0, shape1, files = [], [], []  # image and inference shapes, filenamesfor i, im in enumerate(ims):f = f'image{i}'  # filenameif isinstance(im, (str, Path)):  # filename or uriim, f = Image.open(requests.get(im, stream=True).raw if str(im).startswith('http') else im), imim = np.asarray(exif_transpose(im))elif isinstance(im, Image.Image):  # PIL Imageim, f = np.asarray(exif_transpose(im)), getattr(im, 'filename', f) or ffiles.append(Path(f).with_suffix('.jpg').name)if im.shape[0] < 5:  # image in CHWim = im.transpose((1, 2, 0))  # reverse dataloader .transpose(2, 0, 1)im = im[..., :3] if im.ndim == 3 else cv2.cvtColor(im, cv2.COLOR_GRAY2BGR)  # enforce 3ch inputs = im.shape[:2]  # HWCshape0.append(s)  # image shapeg = max(size) / max(s)  # gainshape1.append([y * g for y in s])ims[i] = im if im.data.contiguous else np.ascontiguousarray(im)  # updateshape1 = [make_divisible(x, self.stride) for x in np.array(shape1).max(0)] if self.pt else size  # inf shapex = [letterbox(im, shape1, auto=False)[0] for im in ims]  # padx = np.ascontiguousarray(np.array(x).transpose((0, 3, 1, 2)))  # stack and BHWC to BCHWx = torch.from_numpy(x).to(p.device).type_as(p) / 255  # uint8 to fp16/32with amp.autocast(autocast):# Inferencewith dt[1]:y = self.model(x, augment=augment)  # forward# Post-processwith dt[2]:y = non_max_suppression(y if self.dmb else y[0],self.conf,self.iou,self.classes,self.agnostic,self.multi_label,max_det=self.max_det)  # NMSfor i in range(n):scale_boxes(shape1, y[i][:, :4], shape0[i])return Detections(ims, y, files, dt, self.names, x.shape)

2.3 Detections 

对推理结果进行处理（代码很长，参考看）

class Detections:# YOLOv5 detections class for inference resultsdef __init__(self, ims, pred, files, times=(0, 0, 0), names=None, shape=None):super().__init__()d = pred[0].device  # devicegn = [torch.tensor([*(im.shape[i] for i in [1, 0, 1, 0]), 1, 1], device=d) for im in ims]  # normalizationsself.ims = ims  # list of images as numpy arraysself.pred = pred  # list of tensors pred[0] = (xyxy, conf, cls)self.names = names  # class namesself.files = files  # image filenamesself.times = times  # profiling timesself.xyxy = pred  # xyxy pixelsself.xywh = [xyxy2xywh(x) for x in pred]  # xywh pixelsself.xyxyn = [x / g for x, g in zip(self.xyxy, gn)]  # xyxy normalizedself.xywhn = [x / g for x, g in zip(self.xywh, gn)]  # xywh normalizedself.n = len(self.pred)  # number of images (batch size)self.t = tuple(x.t / self.n * 1E3 for x in times)  # timestamps (ms)self.s = tuple(shape)  # inference BCHW shapedef _run(self, pprint=False, show=False, save=False, crop=False, render=False, labels=True, save_dir=Path('')):s, crops = '', []for i, (im, pred) in enumerate(zip(self.ims, self.pred)):s += f'\nimage {i + 1}/{len(self.pred)}: {im.shape[0]}x{im.shape[1]} '  # stringif pred.shape[0]:for c in pred[:, -1].unique():n = (pred[:, -1] == c).sum()  # detections per classs += f"{n} {self.names[int(c)]}{'s' * (n > 1)}, "  # add to strings = s.rstrip(', ')if show or save or render or crop:annotator = Annotator(im, example=str(self.names))for *box, conf, cls in reversed(pred):  # xyxy, confidence, classlabel = f'{self.names[int(cls)]} {conf:.2f}'if crop:file = save_dir / 'crops' / self.names[int(cls)] / self.files[i] if save else Nonecrops.append({'box': box,'conf': conf,'cls': cls,'label': label,'im': save_one_box(box, im, file=file, save=save)})else:  # all othersannotator.box_label(box, label if labels else '', color=colors(cls))im = annotator.imelse:s += '(no detections)'im = Image.fromarray(im.astype(np.uint8)) if isinstance(im, np.ndarray) else im  # from npif show:im.show(self.files[i])  # showif save:f = self.files[i]im.save(save_dir / f)  # saveif i == self.n - 1:LOGGER.info(f"Saved {self.n} image{'s' * (self.n > 1)} to {colorstr('bold', save_dir)}")if render:self.ims[i] = np.asarray(im)if pprint:s = s.lstrip('\n')return f'{s}\nSpeed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {self.s}' % self.tif crop:if save:LOGGER.info(f'Saved results to {save_dir}\n')return cropsdef show(self, labels=True):self._run(show=True, labels=labels)  # show resultsdef save(self, labels=True, save_dir='runs/detect/exp', exist_ok=False):save_dir = increment_path(save_dir, exist_ok, mkdir=True)  # increment save_dirself._run(save=True, labels=labels, save_dir=save_dir)  # save resultsdef crop(self, save=True, save_dir='runs/detect/exp', exist_ok=False):save_dir = increment_path(save_dir, exist_ok, mkdir=True) if save else Nonereturn self._run(crop=True, save=save, save_dir=save_dir)  # crop resultsdef render(self, labels=True):self._run(render=True, labels=labels)  # render resultsreturn self.imsdef pandas(self):# return detections as pandas DataFrames, i.e. print(results.pandas().xyxy[0])new = copy(self)  # return copyca = 'xmin', 'ymin', 'xmax', 'ymax', 'confidence', 'class', 'name'  # xyxy columnscb = 'xcenter', 'ycenter', 'width', 'height', 'confidence', 'class', 'name'  # xywh columnsfor k, c in zip(['xyxy', 'xyxyn', 'xywh', 'xywhn'], [ca, ca, cb, cb]):a = [[x[:5] + [int(x[5]), self.names[int(x[5])]] for x in x.tolist()] for x in getattr(self, k)]  # updatesetattr(new, k, [pd.DataFrame(x, columns=c) for x in a])return newdef tolist(self):# return a list of Detections objects, i.e. 'for result in results.tolist():'r = range(self.n)  # iterablex = [Detections([self.ims[i]], [self.pred[i]], [self.files[i]], self.times, self.names, self.s) for i in r]# for d in x:#    for k in ['ims', 'pred', 'xyxy', 'xyxyn', 'xywh', 'xywhn']:#        setattr(d, k, getattr(d, k)[0])  # pop out of listreturn xdef print(self):LOGGER.info(self.__str__())def __len__(self):  # override len(results)return self.ndef __str__(self):  # override print(results)return self._run(pprint=True)  # print resultsdef __repr__(self):return f'YOLOv5 {self.__class__} instance\n' + self.__str__()class Proto(nn.Module):# YOLOv5 mask Proto module for segmentation modelsdef __init__(self, c1, c_=256, c2=32):  # ch_in, number of protos, number of maskssuper().__init__()self.cv1 = Conv(c1, c_, k=3)self.upsample = nn.Upsample(scale_factor=2, mode='nearest')self.cv2 = Conv(c_, c_, k=3)self.cv3 = Conv(c_, c2)def forward(self, x):return self.cv3(self.cv2(self.upsample(self.cv1(x))))class Classify(nn.Module):# YOLOv5 classification head, i.e. x(b,c1,20,20) to x(b,c2)def __init__(self, c1, c2, k=1, s=1, p=None, g=1):  # ch_in, ch_out, kernel, stride, padding, groupssuper().__init__()c_ = 1280  # efficientnet_b0 sizeself.conv = Conv(c1, c_, k, s, autopad(k, p), g)self.pool = nn.AdaptiveAvgPool2d(1)  # to x(b,c_,1,1)self.drop = nn.Dropout(p=0.0, inplace=True)self.linear = nn.Linear(c_, c2)  # to x(b,c2)def forward(self, x):if isinstance(x, list):x = torch.cat(x, 1)return self.linear(self.drop(self.pool(self.conv(x)).flatten(1)))

2.4 Classify

二级分类，比如要做识别人脸面部表情，先要识别出人脸，如果想识别出人的面部表情，就需要二级分类进一步检测。

class Classify(nn.Module):# YOLOv5 classification head, i.e. x(b,c1,20,20) to x(b,c2)def __init__(self, c1, c2, k=1, s=1, p=None, g=1):  # ch_in, ch_out, kernel, stride, padding, groupssuper().__init__()c_ = 1280  # efficientnet_b0 sizeself.conv = Conv(c1, c_, k, s, autopad(k, p), g)self.pool = nn.AdaptiveAvgPool2d(1)  # to x(b,c_,1,1)self.drop = nn.Dropout(p=0.0, inplace=True)self.linear = nn.Linear(c_, c2)  # to x(b,c2)def forward(self, x):if isinstance(x, list):x = torch.cat(x, 1)return self.linear(self.drop(self.pool(self.conv(x)).flatten(1)))

三、实验

要求：

应该是移除cv3卷积层即可，注释掉

class C3(nn.Module):# CSP Bottleneck with 3 convolutionsdef __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, number, shortcut, groups, expansionsuper().__init__()c_ = int(c2 * e)  # hidden channelsself.cv1 = Conv(c1, c_, 1, 1)self.cv2 = Conv(c1, c_, 1, 1)self.cv3 = Conv(2 * c_, c2, 1)  # optional act=FReLU(c2)self.m = nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)))def forward(self, x):return self.cv3(torch.cat((self.m(self.cv1(x)), self.cv2(x)), 1))