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课程链接
?Run/Debug Configurations
Script path：D:\yolov5\train.py(train.py文件会调用Detect类)
Parameters：--data data/voc-new.yaml --cfg model/yolov5s-voc.yaml --weights weights/yolov5s.pt --batch-size 16 --epochs 200
import argparse
import logging
import sys
from copy import deepcopy
from pathlib import Path
import math
sys.path.append('./') # to run '$ python *.py' files in subdirectories
logger = logging.getLogger(__name__)
import torch
import torch.nn as nn
from models.common import Conv, Bottleneck, SPP, DWConv, Focus, BottleneckCSP, Concat, NMS, autoShape
from models.experimental import MixConv2d, CrossConv, C3
from utils.general import check_anchor_order, make_divisible, check_file, set_logging
from utils.torch_utils import time_synchronized, fuse_conv_and_bn, model_info, scale_img, initialize_weights, \
select_device, copy_attr
# 对特征图进行检测的类,指的是yolohead中的detect
class Detect(nn.Module):
stride = None # strides computed during build
export = False # onnx export
def __init__(self, nc=80, anchors=(), ch=()): # detection layer
super(Detect, self).__init__()
self.nc = nc # number of classes
self.no = nc + 5 # number of outputs per anchor. VOC: 20+5=25,20为类别得分,5分别是4个坐标信息和1个目标性得分,每个anchor有25个值
self.nl = len(anchors) # number of detection layers = 3,anchor有3个尺度的列表
self.na = len(anchors[0]) // 2 # number of anchors =3
self.grid = [torch.zeros(1)] * self.nl # init grid
a = torch.tensor(anchors).float().view(self.nl, -1, 2)
# 模型中需要保存下来的参数包括两种: 一种是反向传播需要被optimizer更新的，称之为 parameter;
# 一种是反向传播不需要被optimizer更新，称之为 buffer。
# 第二种参数我们需要创建tensor, 然后将tensor通过register_buffer()进行注册，
# 可以通过model.buffers() 返回，注册完后参数也会自动保存到OrderDict中去。
# 注意：buffer的更新在forward中，optim.step只能更新nn.parameter类型的参数
self.register_buffer('anchors', a) # shape(nl,na,2)
self.register_buffer('anchor_grid', a.clone().view(self.nl, 1, -1, 1, 1, 2)) # shape(nl,1,na,1,1,2)
self.m = nn.ModuleList(nn.Conv2d(x, self.no * self.na, 1) for x in ch) # output conv 1*1卷积,1*1卷积是把特征图经过卷积运算,得到预测后的值,包括预测框的坐标信息、目标性得分以及分类的概率,x为通道的取值(128,256,512)
def forward(self, x):
# x = x.copy() # for profiling
z = [] # inference output
self.training |= self.export
for i in range(self.nl):
x[i] = self.m[i](x[i]) # conv
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() # 调整信息变量的顺序,再将其变为连续的变量
if not self.training: # inference
if self.grid[i].shape[2:4] != x[i].shape[2:4]:
self.grid[i] = self._make_grid(nx, ny).to(x[i].device) # 构造网格
y = x[i].sigmoid()
y[..., 0:2] = (y[..., 0:2] * 2. - 0.5 + self.grid[i].to(x[i].device)) * self.stride[i] # xy
y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i] # wh
z.append(y.view(bs, -1, self.no)) # 预测框坐标信息
return x if self.training else (torch.cat(z, 1), x) # 预测框坐标, obj, cls
@staticmethod
def _make_grid(nx=20, ny=20):
# 划分为单元网格
yv, xv = torch.meshgrid([torch.arange(ny), torch.arange(nx)])
return torch.stack((xv, yv), 2).view((1, 1, ny, nx, 2)).float()
# 网络模型类
class Model(nn.Module):
def __init__(self, cfg='yolov5s.yaml', ch=3, nc=None): # model, input channels, number of classes
super(Model, self).__init__()
if isinstance(cfg, dict): # 判断cfg是否为字典
self.yaml = cfg # model dict
else: # is *.yaml
import yaml # for torch hub
self.yaml_file = Path(cfg).name
with open(cfg) as f:
self.yaml = yaml.load(f, Loader=yaml.FullLoader) # model dict
# Define model
if nc and nc != self.yaml['nc']:
print('Overriding model.yaml nc=%g with nc=%g' % (self.yaml['nc'], nc))
self.yaml['nc'] = nc # override yaml value
self.model, self.save = parse_model(deepcopy(self.yaml), ch=[ch]) # model, savelist, ch_out
# print([x.shape for x in self.forward(torch.zeros(1, ch, 64, 64))])
# Build strides, anchors
m = self.model[-1] # Detect()
if isinstance(m, Detect):
s = 128 # 2x min stride
# m.stride = [8,16,32],下采样
m.stride = torch.tensor([s / x.shape[-2] for x in self.forward(torch.zeros(1, ch, s, s))]) # forward
# anchor大小计算, 例如 [10, 13] --> [1.25, 1.625]
m.anchors /= m.stride.view(-1, 1, 1)
check_anchor_order(m) # 检查anchor顺序和stride顺序是否一致
self.stride = m.stride
self._initialize_biases() # 初始化偏置 only run once
# print('Strides: %s' % m.stride.tolist())
# Init weights, biases
initialize_weights(self) # 初始化权重
self.info()
print('')
def forward(self, x, augment=False, profile=False):
if augment: # TTA (Test Time Augmentation) 判断是否是测试时的数据增强。测试的时候也可以对数据进行数据增强,有助于提高检测的性能。在执行命令时在后面添加--img 832 --augment(test.py detect.py),但此时image size也要增大,有助于提高检测的性能,但检测的速度会减慢
img_size = x.shape[-2:] # height, width
s = [1, 0.83, 0.67] # scales
f = [None, 3, None] # flips (2-ud, 3-lr),3代表左右的翻转,2代表上下的翻转
y = [] # outputs
for si, fi in zip(s, f):
xi = scale_img(x.flip(fi) if fi else x, si) # 改变图像尺寸
yi = self.forward_once(xi)[0] # forward
# cv2.imwrite('img%g.jpg' % s, 255 * xi[0].numpy().transpose((1, 2, 0))[:, :, ::-1]) # save
yi[..., :4] /= si # de-scale
if fi == 2: # 撤回翻转
yi[..., 1] = img_size[0] - yi[..., 1] # de-flip ud
elif fi == 3:
yi[..., 0] = img_size[1] - yi[..., 0] # de-flip lr
y.append(yi)
return torch.cat(y, 1), None # augmented inference, train
else:
return self.forward_once(x, profile) # single-scale inference, train
def forward_once(self, x, profile=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: # 性能评估,默认false
try:
import thop # THOP: PyTorch-OpCounter 估算PyTorch模型的FLOPs模块
o = thop.profile(m, inputs=(x,), verbose=False)[0] / 1E9 * 2 # FLOPS
except:
o = 0
t = time_synchronized()
for _ in range(10):
_ = m(x)
dt.append((time_synchronized() - t) * 100)
print('%10.1f%10.0f%10.1fms %-40s' % (o, m.np, dt[-1], m.type))
x = m(x) # 执行网络组件操作
y.append(x if m.i in self.save else None) # save output
if profile:
print('%.1fms total' % sum(dt))
return x
def _initialize_biases(self, cf=None): # 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[:, 4] += math.log(8 / (640 / s) ** 2) # obj (8 objects per 640 image)
b[:, 5:] += math.log(0.6 / (m.nc - 0.99)) 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):
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)
print(('%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:
# print('%10.3g' % (m.w.detach().sigmoid() * 2)) # shortcut weights
def fuse(self): # fuse model Conv2d() + BatchNorm2d() layers
print('Fusing layers... ')
for m in self.model.modules():
if type(m) is Conv and hasattr(m, 'bn'):
m._non_persistent_buffers_set = set() # pytorch 1.6.0 compatability
m.conv = fuse_conv_and_bn(m.conv, m.bn) # update conv
delattr(m, 'bn') # remove batchnorm
m.forward = m.fuseforward # update forward
self.info()
return self
def nms(self, mode=True): # add or remove NMS module
present = type(self.model[-1]) is NMS # last layer is NMS
if mode and not present: # true则添加,默认为true
print('Adding NMS... ')
m = NMS() # module
m.f = -1 # from
m.i = self.model[-1].i + 1 # index
self.model.add_module(name='%s' % m.i, module=m) # add
self.eval()
elif not mode and present: # false则不使用nms
print('Removing NMS... ')
self.model = self.model[:-1] # remove
return self
def autoshape(self): # add autoShape module
print('Adding autoShape... ')
m = autoShape(self) # wrap model
copy_attr(m, self, include=('yaml', 'nc', 'hyp', 'names', 'stride'), exclude=()) # copy attributes
return m
def info(self, verbose=False): # print model information
model_info(self, verbose)
# 解析网络模型配置文件并构建模型
def parse_model(d, ch): # model_dict, input_channels(3)
logger.info('\n%3s%18s%3s%10s %-40s%-30s' % ('', 'from', 'n', 'params', 'module', 'arguments'))
#将模型结构的depth_multiple，width_multiple提取出，赋值给gd (yolov5s: 0.33)，gw (yolov5s:0.50)
anchors, nc, gd, gw = d['anchors'], d['nc'], d['depth_multiple'], d['width_multiple']
na = (len(anchors[0]) // 2) if isinstance(anchors, list) else anchors # number of anchors =3
no = na * (nc + 5) # number of outputs = anchors * (classes + 5); VOC : 75
layers, save, c2 = [], [], ch[-1] # layers, savelist, ch out = 3
for i, (f, n, m, args) in enumerate(d['backbone'] + d['head']): # from, number, module, args
m = eval(m) if isinstance(m, str) else m # eval strings
for j, a in enumerate(args):
try:
args[j] = eval(a) if isinstance(a, str) else a # eval strings
except:
pass
# 控制深度的代码
n = max(round(n * gd), 1) if n > 1 else n # depth gain
if m in [Conv, Bottleneck, SPP, DWConv, MixConv2d, Focus, CrossConv, BottleneckCSP, C3]:
c1, c2 = ch[f], args[0]
# Normal
# if i > 0 and args[0] != no: # channel expansion factor
# ex = 1.75 # exponential (default 2.0)
# e = math.log(c2 / ch[1]) / math.log(2)
# c2 = int(ch[1] * ex ** e)
# if m != Focus:
# 控制宽度(卷积核个数)的代码
c2 = make_divisible(c2 * gw, 8) if c2 != no else c2
# Experimental
# if i > 0 and args[0] != no: # channel expansion factor
# ex = 1 + gw # exponential (default 2.0)
# ch1 = 32 # ch[1]
# e = math.log(c2 / ch1) / math.log(2) # level 1-n
# c2 = int(ch1 * ex ** e)
# if m != Focus:
# c2 = make_divisible(c2, 8) if c2 != no else c2
args = [c1, c2, *args[1:]]
if m in [BottleneckCSP, C3]:
args.insert(2, n)
n = 1
elif m is nn.BatchNorm2d:
args = [ch[f]]
elif m is Concat:
c2 = sum([ch[-1 if x == -1 else x + 1] for x in f])
elif m is Detect:
args.append([ch[x + 1] for x in f])
if isinstance(args[1], int): # number of anchors
args[1] = [list(range(args[1] * 2))] * len(f)
else:
c2 = ch[f]
# *args表示接收任意个数量的参数，调用时会将实际参数打包为一个元组传入实参
# 通过Sequential来构造bottleneckCSP中的组件,即其中bottleneck的个数
# *[m(*args)中第一个星号可以返回列表中的逐个元素,然后传给sequential
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, f, t, np # attach index, 'from' index, type, number params
logger.info('%3s%18s%3s%10.0f %-40s%-30s' % (i, f, n, np, t, args)) # print
save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1) # append to savelist
layers.append(m_)
ch.append(c2)
return nn.Sequential(*layers), sorted(save)
if __name__ == '__main__':
# 建立参数解析对象parser
parser = argparse.ArgumentParser()
# 添加属性：给xx实例增加一个aa属性，如 xx.add_argument("aa")
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')
# 采用parser对象的parse_args函数获取解析的参数
opt = parser.parse_args()
opt.cfg = check_file(opt.cfg) # check file
set_logging()
device = select_device(opt.device)
# Create model
model = Model(opt.cfg).to(device)
model.train()
# Profile
# img = torch.rand(8 if torch.cuda.is_available() else 1, 3, 640, 640).to(device)
# y = model(img, profile=True)
# Tensorboard
# from torch.utils.tensorboard import SummaryWriter
# tb_writer = SummaryWriter()
# print("Run 'tensorboard --logdir=models/runs' to view tensorboard at http://localhost:6006/")
# tb_writer.add_graph(model.model, img) # add model to tensorboard
# tb_writer.add_image('test', img[0], dataformats='CWH') # add model to tensorboard
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