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雄安免费网站建设方案,成都市城乡和住房建设局,房产网站制作方案,查看wordpress使用什么主题目录 简介 需要的基础包和配置 二、主要组件介绍 2.1 parse_model 2.2Detect类 2.3DetectionModel类 三、实验 #x1f368; 本文为[#x1f517;365天深度学习训练营]内部限免文章#xff08;版权归 K同学啊 所有#xff09; #x1f356; 作者#xff1a;[K同学啊…目录 简介 需要的基础包和配置 二、主要组件介绍 2.1 parse_model 2.2Detect类 2.3DetectionModel类 三、实验 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形成C2模块并将C2模块插入第2层与第3层之间且跑通YOLOv5s。 ● 任务提示   ○提示1需要修改common.yaml、yolo.py、yolov5s.yaml文件。   ○提示2C2模块与C3模块是非常相似的两个模块我们要插入C2到模型当中只需要找到哪里有C3模块然后在其附近加上C2即可。 简介 common.py文件位置在./models/yolo.py 需要的基础包和配置 import argparse import contextlib import os import platform import sys from copy import deepcopy from pathlib import PathFILE 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 if platform.system() ! Windows:ROOT Path(os.path.relpath(ROOT, Path.cwd())) # relativefrom 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, profile, scale_img, select_device,time_sync)try:import thop # for FLOPs computation except ImportError:thop None 二、主要组件介绍 2.1 parse_model 用于将模型的模块拼接起来搭建完成的网络模型。不是固定的改框架也要改这个函数。 def parse_model(d, ch): # model_dict, input_channels(3)# Parse a YOLOv5 model.yaml dictionaryLOGGER.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.defaultact nn.SiLU()LOGGER.info(f{colorstr(activation:)} {act}) # printna (len(anchors[0]) // 2) if isinstance(anchors, list) else anchors # number of anchorsno na * (nc 5) # number of outputs anchors * (classes 5)layers, save, c2 [], [], ch[-1] # layers, savelist, ch outfor i, (f, n, m, args) in enumerate(d[backbone] d[head]): # from, number, module, argsm eval(m) if isinstance(m, str) else m # eval stringsfor j, a in enumerate(args):with contextlib.suppress(NameError):args[j] eval(a) if isinstance(a, str) else a # eval stringsn n max(round(n * gd), 1) if n 1 else n # depth gainif 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 outputc2 make_divisible(c2 * gw, 8)args [c1, c2, *args[1:]]if m in {BottleneckCSP, C3, C3TR, C3Ghost, C3x}:args.insert(2, n) # number of repeatsn 1elif m is nn.BatchNorm2d:args [ch[f]]elif m is Concat:c2 sum(ch[x] for x in f)# TODO: channel, gw, gdelif m in {Detect, Segment}:args.append([ch[x] for x in f])if isinstance(args[1], int): # number of anchorsargs[1] [list(range(args[1] * 2))] * len(f)if m is Segment:args[3] makedivisible(args[3] * gw, 8)elif m is Contract:c2 ch[f] * args[0] ** 2elif m is Expand:c2 ch[f] // args[0] ** 2else:c2 ch[f]m nn.Sequential(*(m(*args) for _ in range(n))) if n 1 else m(*args) # modulet str(m)[8:-2].replace(main., ) # module typenp sum(x.numel() for x in m.parameters()) # number paramsm.i, m.f, m.type, m.np i, f, t, np # attach index, from index, type, number paramsLOGGER.info(f{i:3}{str(f):18}{n:3}{np:10.0f} {t:40}{str(args):30}) # printsave.extend(x % i for x in ([f] if isinstance(f, int) else f) if x ! -1) # append to savelistlayers.append(m_)if i 0:ch []ch.append(c2)return nn.Sequential(*layers), sorted(save) 2.2Detect类 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。 class Detect(nn.Module):# YOLOv5 Detect head for detection modelsstride None # strides computed during builddynamic False # force grid reconstructionexport False # export modedef init(self, nc80, anchors(), ch(), inplaceTrue): # detection layersuper().init()self.nc nc # number of classesself.no nc 5 # number of outputs per anchorself.nl len(anchors) # number of detection layersself.na len(anchors[0]) // 2 # number of anchorsself.grid [torch.empty(0) for _ in range(self.nl)] # init gridself.anchor_grid [torch.empty(0) for _ in range(self.nl)] # init anchor gridself.register_buffer(anchors, torch.tensor(anchors).float().view(self.nl, -1, 2)) # shape(nl,na,2)self.m nn.ModuleList(nn.Conv2d(x, self.no * self.na, 1) for x in ch) # output convself.inplace inplace # use inplace ops (e.g. slice assignment)def forward(self, x):z [] # inference outputfor i in range(self.nl):x[i] self.mi # convbs, _, 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()if not self.training: # inferenceif self.dynamic or self.grid[i].shape[2:4] ! x[i].shape[2:4]:self.grid[i], self.anchor_grid[i] self._make_grid(nx, ny, i)if isinstance(self, Segment): # (boxes masks)xy, wh, conf, mask x[i].split((2, 2, self.nc 1, self.no - self.nc - 5), 4)xy (xy.sigmoid() * 2 self.grid[i]) * self.stride[i] # xywh (wh.sigmoid() * 2) ** 2 * self.anchor_grid[i] # why torch.cat((xy, wh, conf.sigmoid(), mask), 4)else: # Detect (boxes only)xy, wh, conf x[i].sigmoid().split((2, 2, self.nc 1), 4)xy (xy * 2 self.grid[i]) * self.stride[i] # xywh (wh * 2) ** 2 * self.anchor_grid[i] # why torch.cat((xy, wh, conf), 4)z.append(y.view(bs, self.na * nx * ny, self.no))return x if self.training else (torch.cat(z, 1),) if self.export else (torch.cat(z, 1), x)def _make_grid(self, nx20, ny20, i0, torch_1_10check_version(torch.version, 1.10.0)):d self.anchors[i].devicet self.anchors[i].dtypeshape 1, self.na, ny, nx, 2 # grid shapey, x torch.arange(ny, deviced, dtypet), torch.arange(nx, deviced, dtypet)yv, xv torch.meshgrid(y, x, indexingij) if torch_1_10 else torch.meshgrid(y, x) # torch0.7 compatibilitygrid torch.stack((xv, yv), 2).expand(shape) - 0.5 # add grid offset, i.e. y 2.0 * x - 0.5anchor_grid (self.anchors[i] * self.stride[i]).view((1, self.na, 1, 1, 2)).expand(shape)return grid, anchor_grid 2.3DetectionModel类 模型的构建工场,指定模型的yaml文件,以及一系列的训练参数 class DetectionModel(BaseModel):# YOLOv5 detection modeldef init(self, cfgyolov5s.yaml, ch3, ncNone, anchorsNone): # model, input channels, number of classessuper().init()if isinstance(cfg, dict):self.yaml cfg # model dictelse: # is *.yamlimport yaml # for torch hubself.yaml_file Path(cfg).namewith open(cfg, encodingascii, errorsignore) as f:self.yaml yaml.safe_load(f) # model dict# Define modelch self.yaml[ch] self.yaml.get(ch, ch) # input channelsif nc and nc ! self.yaml[nc]:LOGGER.info(fOverriding model.yaml nc{self.yaml[nc]} with nc{nc})self.yaml[nc] nc # override yaml valueif anchors:LOGGER.info(fOverriding model.yaml anchors with anchors{anchors})self.yaml[anchors] round(anchors) # override yaml valueself.model, self.save parse_model(deepcopy(self.yaml), ch[ch]) # model, savelistself.names [str(i) for i in range(self.yaml[nc])] # default namesself.inplace self.yaml.get(inplace, True)# Build strides, anchorsm self.model[-1] # Detect()if isinstance(m, (Detect, Segment)):s 256 # 2x min stridem.inplace self.inplaceforward lambda x: self.forward(x)[0] if isinstance(m, Segment) else self.forward(x)m.stride torch.tensor([s / x.shape[-2] for x in forward(torch.zeros(1, ch, s, s))]) # forwardcheck_anchor_order(m)m.anchors / m.stride.view(-1, 1, 1)self.stride m.strideself._initialize_biases() # only run once# Init weights, biasesinitialize_weights(self)self.info()LOGGER.info()def forward(self, x, augmentFalse, profileFalse, visualizeFalse):if augment:return self._forward_augment(x) # augmented inference, Nonereturn self._forward_once(x, profile, visualize) # single-scale inference, traindef _forward_augment(self, x):img_size x.shape[-2:] # height, widths [1, 0.83, 0.67] # scalesf [None, 3, None] # flips (2-ud, 3-lr)y [] # outputsfor si, fi in zip(s, f):xi scale_img(x.flip(fi) if fi else x, si, gsint(self.stride.max()))yi self._forwardonce(xi)[0] # forward# cv2.imwrite(fimg{si}.jpg, 255 * xi[0].cpu().numpy().transpose((1, 2, 0))[:, :, ::-1]) # saveyi self._descale_pred(yi, fi, si, img_size)y.append(yi)y self._clip_augmented(y) # clip augmented tailsreturn torch.cat(y, 1), None # augmented inference, traindef _descale_pred(self, p, flips, scale, img_size):# de-scale predictions following augmented inference (inverse operation)if self.inplace:p[…, :4] / scale # de-scaleif flips 2:p[…, 1] img_size[0] - p[…, 1] # de-flip udelif flips 3:p[…, 0] img_size[1] - p[…, 0] # de-flip lrelse:x, y, wh p[…, 0:1] / scale, p[…, 1:2] / scale, p[…, 2:4] / scale # de-scaleif flips 2:y img_size[0] - y # de-flip udelif flips 3:x img_size[1] - x # de-flip lrp torch.cat((x, y, wh, p[…, 4:]), -1)return pdef _clip_augmented(self, y):# Clip YOLOv5 augmented inference tailsnl self.model[-1].nl # number of detection layers (P3-P5)g sum(4 ** x for x in range(nl)) # grid pointse 1 # exclude layer counti (y[0].shape[1] // g) * sum(4 ** x for x in range(e)) # indicesy[0] y[0][:, :-i] # largei (y[-1].shape[1] // g) * sum(4 ** (nl - 1 - x) for x in range(e)) # indicesy[-1] y[-1][:, i:] # smallreturn ydef _initialize_biases(self, cfNone): # 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(), minlengthnc) 1.m self.model[-1] # Detect() modulefor mi, s in zip(m.m, m.stride): # fromb 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[:, 5:5 m.nc] math.log(0.6 / (m.nc - 0.99999)) if cf is None else torch.log(cf / cf.sum()) # clsmi.bias torch.nn.Parameter(b.view(-1), requires_gradTrue) 三、实验 C2 模块结构图任务:如图所示将C3模块改为C2模块并将C2模块插入到第二层与第三层之间并跑通代码。 common.py中  yolov5s.yaml yolo.py 注意添加修改会涉及多处地方。