YOLOV5训练代码train.py注释与解析
YOLOV5训练代码train.py注释与解析
- 训练参数以及main函数解析
- train函数解析
本文主要对ultralytics\yolov5的训练代码train.py的解析，由于yolov5还在开发当中，平常多多少少都会修复一些bug或者有一些代码和功能的更新，但基本上不会有很大的改动，故以下注释与解析都是适用的；当然如果有大改动，笔者也会更新注释。
训练参数以及main函数解析
训练的时候可以设置进行超参数进化算法(默认不使用)。
值得一提的是，由于现在yolov5还在开发当中，训练文件的–resume还不是100%的完善，不建议打断训练再resume。具体可以参照issue292。
if __name__ == '__main__':
# 因为yolov5还在开发当中，check_git_status()检查你的代码版本是否为最新的(不适用于windows系统)
check_git_status()
"""
opt参数解析：
cfg:模型配置文件，网络结构
data:数据集配置文件，数据集路径，类名等
hyp:超参数文件
epochs:训练总轮次
batch-size:批次大小
img-size:输入图片分辨率大小
rect:是否采用矩形训练，默认False
resume:接着打断训练上次的结果接着训练
nosave:不保存模型，默认False
notest:不进行test，默认False
noautoanchor:不自动调整anchor，默认False
evolve:是否进行超参数进化，默认False
bucket:谷歌云盘bucket，一般不会用到
cache-images:是否提前缓存图片到内存，以加快训练速度，默认False
weights:加载的权重文件
name:数据集名字，如果设置：results.txt to results_name.txt，默认无
device:训练的设备，cpu；0(表示一个gpu设备cuda:0)；0,1,2,3(多个gpu设备)
multi-scale:是否进行多尺度训练，默认False
single-cls:数据集是否只有一个类别，默认False
"""
parser = argparse.ArgumentParser()
parser.add_argument('--cfg', type=str, default='models/yolov5x_landslide.yaml', help='model.yaml path')
parser.add_argument('--data', type=str, default='data/landslide.yaml', help='data.yaml path')
parser.add_argument('--hyp', type=str, default='', help='hyp.yaml path (optional)')
parser.add_argument('--epochs', type=int, default=300)
parser.add_argument('--batch-size', type=int, default=8)
parser.add_argument('--img-size', nargs='+', type=int, default=[416, 416], help='train,test sizes')
parser.add_argument('--rect', action='store_true', help='rectangular training')
parser.add_argument('--resume', nargs='?', const='get_last', default='runs/exp0/weights/last.pt',
help='resume from given path/to/last.pt, or most recent run if blank.')
parser.add_argument('--nosave', action='store_true', help='only save final checkpoint')
parser.add_argument('--notest', action='store_true', help='only test final epoch')
parser.add_argument('--noautoanchor', action='store_true', help='disable autoanchor check')
parser.add_argument('--evolve', action='store_true', help='evolve hyperparameters')
parser.add_argument('--bucket', type=str, default='', help='gsutil bucket')
parser.add_argument('--cache-images', action='store_true', help='cache images for faster training')
parser.add_argument('--weights', type=str, default='', help='initial weights path')
parser.add_argument('--name', default='', help='renames results.txt to results_name.txt if supplied')
parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
parser.add_argument('--multi-scale', action='store_true', help='vary img-size +/- 50%%')
parser.add_argument('--single-cls', action='store_true', help='train as single-class dataset')
opt = parser.parse_args()
"""
resume时获取last.pt的路径
get_latest_run()函数获取runs文件夹中最近的last.pt
注意：进行resume时，不要设置opt.weights(除非设置opt.weights='last.pt')，否则会重新开始训练
"""
last = get_latest_run() if opt.resume == 'get_last' else opt.resume # resume from most recent run
if last and not opt.weights:
print(f'Resuming training from {last}')
opt.weights = last if opt.resume and not opt.weights else opt.weights
# check_file检查文件是否存在
opt.cfg = check_file(opt.cfg) # check file
opt.data = check_file(opt.data) # check file
opt.hyp = check_file(opt.hyp) if opt.hyp else '' # check file
print(opt)
# 扩展image_size为[image_size, image_size]一个是训练size，一个是测试size
opt.img_size.extend([opt.img_size[-1]] * (2 - len(opt.img_size))) # extend to 2 sizes (train, test)
# 选择设备
device = torch_utils.select_device(opt.device, apex=mixed_precision, batch_size=opt.batch_size)
if device.type == 'cpu':
mixed_precision = False
# Train
# 如果不进行超参数进化，则直接调用train()函数，开始训练
if not opt.evolve:
print('Start Tensorboard with "tensorboard --logdir=runs", view at http://localhost:6006/')
# 创建tensorboard
tb_writer = SummaryWriter(log_dir=increment_dir('runs' + os.sep + 'exp', opt.name))
# 如果设置了超参数文件路径，则加载新的超参数文件
if opt.hyp: # update hyps
with open(opt.hyp) as f:
hyp.update(yaml.load(f, Loader=yaml.FullLoader))
train(hyp)
# Evolve hyperparameters (optional)
# 根据训练结果进行超参数的进化
else:
tb_writer = None
# 设置不测试不保存模型
opt.notest, opt.nosave = True, True # only test/save final epoch
if opt.bucket:
os.system('gsutil cp gs://%s/evolve.txt .' % opt.bucket) # download evolve.txt if exists
# 默认进化十次
"""
这里的进化算法是：根据之前训练时的hyp来确定一个base hyp再进行突变；
如何根据？通过之前每次进化得到的results来确定之前每个hyp的权重
有了每个hyp和每个hyp的权重之后有两种进化方式；
1.根据每个hyp的权重随机选择一个之前的hyp作为base hyp，random.choices(range(n), weights=w)
2.根据每个hyp的权重对之前所有的hyp进行融合获得一个base hyp，(x * w.reshape(n, 1)).sum(0) / w.sum()
evolve.txt会记录每次进化之后的results+hyp
每次进化时，hyp会根据之前的results进行从大到小的排序；
再根据fitness函数计算之前每次进化得到的hyp的权重
再确定哪一种进化方式，从而进行进化
"""
for _ in range(10): # generations to evolve
if os.path.exists('evolve.txt'): # if evolve.txt exists: select best hyps and mutate
# Select parent(s)
# 选择进化方式
parent = 'single' # parent selection method: 'single' or 'weighted'
# 加载evolve.txt
x = np.loadtxt('evolve.txt', ndmin=2)
# 选取至多前5次进化的结果
n = min(5, len(x)) # number of previous results to consider
x = x[np.argsort(-fitness(x))][:n] # top n mutations
# 根据results计算hyp的权重
w = fitness(x) - fitness(x).min() # weights
# 根据不同进化方式获得base hyp
if parent == 'single' or len(x) == 1:
# x = x[random.randint(0, n - 1)] # random selection
x = x[random.choices(range(n), weights=w)[0]] # weighted selection
elif parent == 'weighted':
x = (x * w.reshape(n, 1)).sum(0) / w.sum() # weighted combination
# Mutate
# 超参数进化
mp, s = 0.9, 0.2 # mutation probability, sigma
npr = np.random
npr.seed(int(time.time()))
g = np.array([1, 1, 1, 1, 1, 1, 1, 0, .1, 1, 0, 1, 1, 1, 1, 1, 1, 1]) # gains
ng = len(g)
v = np.ones(ng)
# 设置突变
while all(v == 1): # mutate until a change occurs (prevent duplicates)
v = (g * (npr.random(ng) < mp) * npr.randn(ng) * npr.random() * s + 1).clip(0.3, 3.0)
# 将突变添加到base hyp上
# [i+7]是因为x中前七个数字为results的指标(P, R, mAP, F1, test_losses=(GIoU, obj, cls))，之后才是超参数hyp
for i, k in enumerate(hyp.keys()): # plt.hist(v.ravel(), 300)
hyp[k] = x[i + 7] * v[i] # mutate
# Clip to limits
# 修剪hyp在规定范围里
keys = ['lr0', 'iou_t', 'momentum', 'weight_decay', 'hsv_s', 'hsv_v', 'translate', 'scale', 'fl_gamma']
limits = [(1e-5, 1e-2), (0.00, 0.70), (0.60, 0.98), (0, 0.001), (0, .9), (0, .9), (0, .9), (0, .9), (0, 3)]
for k, v in zip(keys, limits):
hyp[k] = np.clip(hyp[k], v[0], v[1])
# Train mutation
# 训练
results = train(hyp.copy())
# Write mutation results
"""
写入results和对应的hyp到evolve.txt
evolve.txt文件每一行为一次进化的结果
一行中前七个数字为(P, R, mAP, F1, test_losses=(GIoU, obj, cls))，之后为hyp
"""
print_mutation(hyp, results, opt.bucket)
# Plot results
# plot_evolution_results(hyp)
train函数解析
import argparse
import torch.distributed as dist
import torch.nn.functional as F
import torch.optim as optim
import torch.optim.lr_scheduler as lr_scheduler
import torch.utils.data
from torch.utils.tensorboard import SummaryWriter
import test # import test.py to get mAP after each epoch
from models.yolo import Model
from utils import google_utils
from utils.datasets import *
from utils.utils import *
# 设置混精度训练，需要安装英伟达的apex，默认为True,笔者没用到就设置为False
mixed_precision = False
try: # Mixed precision training https://github.com/NVIDIA/apex
from apex import amp
except:
print('Apex recommended for faster mixed precision training: https://github.com/NVIDIA/apex')
mixed_precision = False # not installed
# 超参数
hyp = {'optimizer': 'SGD', # 优化器['adam', 'SGD', None] if none, default is SGD
'lr0': 0.01, # 学习率initial learning rate (SGD=1E-2, Adam=1E-3)
'momentum': 0.937, # 学习率动量SGD momentum/Adam beta1
'weight_decay': 5e-4, # 权重衰减系数optimizer weight decay
'giou': 0.05, # giou损失的系数giou loss gain
'cls': 0.58, # 分类损失的系数cls loss gain
'cls_pw': 1.0, # 分类BCELoss中正样本的权重cls BCELoss positive_weight
'obj': 1.0, # 有无物体损失的系数obj loss gain (*=img_size/320 if img_size != 320)
'obj_pw': 1.0, # 有无物体BCELoss中正样本的权重obj BCELoss positive_weight
'iou_t': 0.20, # 标签与anchors的iou阈值iou training threshold
'anchor_t': 4.0, # 标签的长h宽w/anchor的长h_a宽w_a阈值, 即h/h_a, w/w_a都要在(1/4, 4)之间anchor-multiple threshold
'fl_gamma': 0.0, # focal loss gamma, 设为0则表示不使用focal loss(efficientDet default is gamma=1.5)
# 下面是一些数据增强的系数, 包括颜色空间和图片空间
'hsv_h': 0.014, # image HSV-Hue augmentation (fraction)
'hsv_s': 0.68, # image HSV-Saturation augmentation (fraction)
'hsv_v': 0.36, # image HSV-Value augmentation (fraction)
'degrees': 0.0, # image rotation (+/- deg)
'translate': 0.0, # image translation (+/- fraction)
'scale': 0.5, # image scale (+/- gain)
'shear': 0.0} # image shear (+/- deg)
def train(hyp):
print(f'Hyperparameters {hyp}')
# 获取记录训练日志的路径
"""
训练日志包括：权重、tensorboard文件、超参数hyp、设置的训练参数opt(也就是epochs,batch_size等),result.txt
result.txt包括: 占GPU内存、训练集的GIOU loss, objectness loss, classification loss, 总loss,
targets的数量, 输入图片分辨率, 准确率TP/(TP+FP),召回率TP/P ;
测试集的mAP50, [email protected]:0.95, GIOU loss, objectness loss, classification loss.
还会保存batch<3的ground truth
"""
log_dir = tb_writer.log_dir # run directory
# 设置保存权重的路径
wdir = str(Path(log_dir) / 'weights') + os.sep # weights directory
os.makedirs(wdir, exist_ok=True)
last = wdir + 'last.pt'
best = wdir + 'best.pt'
# 设置保存results的路径
results_file = log_dir + os.sep + 'results.txt'
# Save run settings
# 保存hyp和opt
with open(Path(log_dir) / 'hyp.yaml', 'w') as f:
yaml.dump(hyp, f, sort_keys=False)
with open(Path(log_dir) / 'opt.yaml', 'w') as f:
yaml.dump(vars(opt), f, sort_keys=False)
# 设置轮次、批次、权重
epochs = opt.epochs # 300
batch_size = opt.batch_size # 64
weights = opt.weights # initial training weights
# Configure
# 设置随机种子
init_seeds(1)
# 加载数据配置信息
with open(opt.data) as f:
data_dict = yaml.load(f, Loader=yaml.FullLoader) # model dict
# 获取训练集、测试集图片路径
train_path = data_dict['train']
test_path = data_dict['val']
# 获取类别数量和类别名字
# 如果设置了opt.single_cls则为一类
nc, names = (1, ['item']) if opt.single_cls else (int(data_dict['nc']), data_dict['names']) # number classes, names
assert len(names) == nc, '%g names found for nc=%g dataset in %s' % (len(names), nc, opt.data) # check
# Remove previous results
# 移除之前的图片结果
for f in glob.glob('*_batch*.jpg') + glob.glob(results_file):
os.remove(f)
# Create model
# 创建模型
model = Model(opt.cfg, nc=nc).to(device)
# Image sizes
# 获取模型总步长和模型输入图片分辨率
gs = int(max(model.stride)) # grid size (max stride)
# 检查输入图片分辨率确保能够整除总步长gs
imgsz, imgsz_test = [check_img_size(x, gs) for x in opt.img_size] # verify imgsz are gs-multiples
# Optimizer
"""
nbs为模拟的batch_size;
就比如默认的话上面设置的opt.batch_size为16,这个nbs就为64，
也就是模型梯度累积了64/16=4(accumulate)次之后
再更新一次模型，变相的扩大了batch_size
"""
nbs = 32 # nominal batch size
accumulate = max(round(nbs / batch_size), 1) # accumulate loss before optimizing
# 根据accumulate设置权重衰减系数
hyp['weight_decay'] *= batch_size * accumulate / nbs # scale weight_decay
pg0, pg1, pg2 = [], [], [] # optimizer parameter groups
# 将模型分成三组(weight、bn, bias, 其他所有参数)优化
for k, v in model.named_parameters():
if v.requires_grad:
if '.bias' in k:
pg2.append(v) # biases
elif '.weight' in k and '.bn' not in k:
pg1.append(v) # apply weight decay
else:
pg0.append(v) # all else
# 选用优化器，并设置pg0组的优化方式
if hyp['optimizer'] == 'adam': # https://pytorch.org/docs/stable/_modules/torch/optim/lr_scheduler.html#OneCycleLR
optimizer = optim.Adam(pg0, lr=hyp['lr0'], betas=(hyp['momentum'], 0.999)) # adjust beta1 to momentum
else:
optimizer = optim.SGD(pg0, lr=hyp['lr0'], momentum=hyp['momentum'], nesterov=True)
# 设置weight、bn的优化方式
optimizer.add_param_group({'params': pg1, 'weight_decay': hyp['weight_decay']}) # add pg1 with weight_decay
# 设置biases的优化方式
optimizer.add_param_group({'params': pg2}) # add pg2 (biases)
print('Optimizer groups: %g .bias, %g conv.weight, %g other' % (len(pg2), len(pg1), len(pg0)))
del pg0, pg1, pg2
# 设置学习率衰减，这里为余弦退火方式进行衰减
# 就是根据以下公式lf与epoch进行衰减
# Scheduler https://arxiv.org/pdf/1812.01187.pdf
lf = lambda x: (((1 + math.cos(x * math.pi / epochs)) / 2) ** 1.0) * 0.9 + 0.1 # cosine
scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf)
# plot_lr_scheduler(optimizer, scheduler, epochs, save_dir=log_dir)
# Load Model
# 加载模型，从google云盘中自动下载模型
# 但通常会下载失败，建议提前下载下来放进weights目录
google_utils.attempt_download(weights)
# 初始化开始训练的epoch和最好的结果
# best_fitness是以[0.0, 0.0, 0.1, 0.9]为系数并乘以[精确度, 召回率, [email protected], [email protected]:0.95]再求和所得
# 根据best_fitness来保存best.pt
start_epoch, best_fitness = 0, 0.0
if weights.endswith('.pt'): # pytorch format
# 加载检查点
ckpt = torch.load(weights, map_location=device) # load checkpoint
# load model
# 加载模型
try:
ckpt['model'] = {k: v for k, v in ckpt['model'].float().state_dict().items()
if model.state_dict()[k].shape == v.shape} # to FP32, filter
model.load_state_dict(ckpt['model'], strict=False)
except KeyError as e:
s = "%s is not compatible with %s. This may be due to model differences or %s may be out of date. " \
"Please delete or update %s and try again, or use --weights '' to train from scratch." \
% (opt.weights, opt.cfg, opt.weights, opt.weights)
raise KeyError(s) from e
# load optimizer
# 加载优化器与best_fitness
if ckpt['optimizer'] is not None:
optimizer.load_state_dict(ckpt['optimizer'])
best_fitness = ckpt['best_fitness']
# load results
# 加载训练结果result.txt
if ckpt.get('training_results') is not None:
with open(results_file, 'w') as file:
file.write(ckpt['training_results']) # write results.txt
# epochs
# 加载训练的轮次
start_epoch = ckpt['epoch'] + 1
"""
如果新设置epochs小于加载的epoch，
则视新设置的epochs为需要再训练的轮次数而不再是总的轮次数
"""
if epochs < start_epoch:
print('%s has been trained for %g epochs. Fine-tuning for %g additional epochs.' %
(opt.weights, ckpt['epoch'], epochs))
epochs += ckpt['epoch'] # finetune additional epochs
del ckpt
# Mixed precision training https://github.com/NVIDIA/apex
# 如果设置混精度训练，初始化混精度训练
if mixed_precision:
model, optimizer = amp.initialize(model, optimizer, opt_level='O1', verbosity=0)
# Distributed training
# 如果不在cpu上计算且gpu数量大于1且pytorch允许分布式，则设置分布式训练
if device.type != 'cpu' and torch.cuda.device_count() > 1 and torch.distributed.is_available():
dist.init_process_group(backend='nccl', # distributed backend
init_method='tcp://127.0.0.1:9999', # init method
world_size=1, # number of nodes
rank=0) # node rank
model = torch.nn.parallel.DistributedDataParallel(model)
# pip install torch==1.4.0+cu100 torchvision==0.5.0+cu100 -f https://download.pytorch.org/whl/torch_stable.html
# Trainloader
# 创建训练集dataloader
dataloader, dataset = create_dataloader(train_path, imgsz, batch_size, gs, opt,
hyp=hyp, augment=True, cache=opt.cache_images, rect=opt.rect)
"""
获取标签中最大的类别值，并于类别数作比较
如果小于类别数则表示有问题
"""
mlc = np.concatenate(dataset.labels, 0)[:, 0].max() # max label class
nb = len(dataloader) # number of batches
assert mlc < nc, 'Label class %g exceeds nc=%g in %s. Correct your labels or your model.' % (mlc, nc, opt.cfg)
# Testloader
# 创建测试集dataloader
testloader = create_dataloader(test_path, imgsz_test, batch_size, gs, opt,
hyp=hyp, augment=False, cache=opt.cache_images, rect=True)[0]
# Model parameters
# 根据自己数据集的类别数设置分类损失的系数
hyp['cls'] *= nc / 80. # scale coco-tuned hyp['cls'] to current dataset
# 设置类别数，超参数
model.nc = nc # attach number of classes to model
model.hyp = hyp # attach hyperparameters to model
"""
设置giou的值在objectness loss中做标签的系数, 使用代码如下
tobj[b, a, gj, gi] = (1.0 - model.gr) + model.gr * giou.detach().clamp(0).type(tobj.dtype)
这里model.gr=1，也就是说完全使用标签框与预测框的giou值来作为该预测框的objectness标签
"""
model.gr = 1.0 # giou loss ratio (obj_loss = 1.0 or giou)
# 根据labels初始化图片采样权重
model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device) # attach class weights
# 获取类别的名字
model.names = names
# Class frequency
# 将所有样本的标签拼接到一起shape为(total, 5)，统计后做可视化
labels = np.concatenate(dataset.labels, 0)
# 获得所有样本的类别
c = torch.tensor(labels[:, 0]) # classes
# cf = torch.bincount(c.long(), minlength=nc) + 1.
# model._initialize_biases(cf.to(device))
# 根据上面的统计对所有样本的类别，中心点xy位置，长宽wh做可视化
plot_labels(labels, save_dir=log_dir)
# 添加类别的直方图到tensorboard中
if tb_writer:
tb_writer.add_histogram('classes', c, 0)
# Check anchors
"""
计算默认锚点anchor与数据集标签框的长宽比值
标签的长h宽w与anchor的长h_a宽w_a的比值, 即h/h_a, w/w_a都要在(1/hyp['anchor_t'], hyp['anchor_t'])是可以接受的
如果标签框满足上面条件的数量小于总数的99%，则根据k-mean算法聚类新的锚点anchor
"""
if not opt.noautoanchor:
check_anchors(dataset, model=model, thr=hyp['anchor_t'], imgsz=imgsz)
# Exponential moving average
# 为模型创建EMA指数滑动平均
ema = torch_utils.ModelEMA(model, updates=start_epoch * nb / accumulate)
print(ema.updates)
# Start training
t0 = time.time()
# 获取热身训练的迭代次数
nw = max(3 * nb, 1e3) # number of warmup iterations, max(3 epochs, 1k iterations)
# 初始化mAP和results
maps = np.zeros(nc) # mAP per class
results = (0, 0, 0, 0, 0, 0, 0) # 'P', 'R', 'mAP', 'F1', 'val GIoU', 'val Objectness', 'val Classification'
"""
设置学习率衰减所进行到的轮次，
目的是打断训练后，--resume接着训练也能正常的衔接之前的训练进行学习率衰减
"""
scheduler.last_epoch = start_epoch - 1 # do not move
"""
打印训练和测试输入图片分辨率
加载图片时调用的cpu进程数
从哪个epoch开始训练
"""
print('Image sizes %g train, %g test' % (imgsz, imgsz_test))
print('Using %g dataloader workers' % dataloader.num_workers)
print('Starting training for %g epochs...' % epochs)
# torch.autograd.set_detect_anomaly(True)
# 训练
for epoch in range(start_epoch, epochs): # epoch ------------------------------------------------------------------
# if epoch == 250:
# exit()
model.train()
# Update image weights (optional)
"""
如果设置进行图片采样策略，
则根据前面初始化的图片采样权重model.class_weights以及maps配合每张图片包含的类别数
通过random.choices生成图片索引indices从而进行采样
"""
if dataset.image_weights:
w = model.class_weights.cpu().numpy() * (1 - maps) ** 2 # class weights
image_weights = labels_to_image_weights(dataset.labels, nc=nc, class_weights=w)
dataset.indices = random.choices(range(dataset.n), weights=image_weights, k=dataset.n) # rand weighted idx
# Update mosaic border
# b = int(random.uniform(0.25 * imgsz, 0.75 * imgsz + gs) // gs * gs)
# dataset.mosaic_border = [b - imgsz, -b] # height, width borders
# 初始化训练时打印的平均损失信息
mloss = torch.zeros(4, device=device) # mean losses
print(('\n' + '%10s' * 8) % ('Epoch', 'gpu_mem', 'GIoU', 'obj', 'cls', 'total', 'targets', 'img_size'))
# tqdm 创建进度条，方便训练时 信息的展示
pbar = tqdm(enumerate(dataloader), total=nb) # progress bar
for i, (imgs, targets, paths, _) in pbar: # batch -------------------------------------------------------------
# 计算迭代的次数iteration
ni = i + nb * epoch # number integrated batches (since train start)
imgs = imgs.to(device).float() / 255.0 # uint8 to float32, 0 - 255 to 0.0 - 1.0
# Warmup
"""
热身训练(前nw次迭代)
在前nw次迭代中，根据以下方式选取accumulate和学习率
"""
if ni <= nw:
xi = [0, nw] # x interp
# model.gr = np.interp(ni, xi, [0.0, 1.0]) # giou loss ratio (obj_loss = 1.0 or giou)
accumulate = max(1, np.interp(ni, xi, [1, nbs / batch_size]).round())
for j, x in enumerate(optimizer.param_groups):
# bias lr falls from 0.1 to lr0, all other lrs rise from 0.0 to lr0
"""
bias的学习率从0.1下降到基准学习率lr*lf(epoch)，
其他的参数学习率从0增加到lr*lf(epoch).
lf为上面设置的余弦退火的衰减函数
"""
x['lr'] = np.interp(ni, xi, [0.1 if j == 2 else 0.0, x['initial_lr'] * lf(epoch)])
# 动量momentum也从0.9慢慢变到hyp['momentum'](default=0.937)
if 'momentum' in x:
x['momentum'] = np.interp(ni, xi, [0.9, hyp['momentum']])
# Multi-scale
# 设置多尺度训练，从imgsz * 0.5, imgsz * 1.5 + gs随机选取尺寸
if opt.multi_scale:
sz = random.randrange(imgsz * 0.5, imgsz * 1.5 + gs) // gs * gs # size
sf = sz / max(imgs.shape[2:]) # scale factor
if sf != 1:
ns = [math.ceil(x * sf / gs) * gs for x in imgs.shape[2:]] # new shape (stretched to gs-multiple)
imgs = F.interpolate(imgs, size=ns, mode='bilinear', align_corners=False)
# Forward
pred = model(imgs)
# Loss
# 计算损失，包括分类损失，objectness损失，框的回归损失
# loss为总损失值，loss_items为一个元组，包含分类损失，objectness损失，框的回归损失和总损失
loss, loss_items = compute_loss(pred, targets.to(device), model)
# 检查loss是否无穷大(可能时梯度爆炸，或者计算损失梯度时存在log(score)->log(0)->无穷大)
if not torch.isfinite(loss):
print('WARNING: non-finite loss, ending training ', loss_items)
return results
# Backward
# 如果设置混精度训练，混合精度反向传播
if mixed_precision:
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
else:
loss.backward()
# Optimize
# 模型反向传播accumulate次之后再根据累积的梯度更新一次参数
if ni % accumulate == 0:
optimizer.step()
optimizer.zero_grad()
ema.update(model)
# Print
# 打印显存，进行的轮次，损失，target的数量和图片的size等信息
mloss = (mloss * i + loss_items) / (i + 1) # update mean losses
mem = '%.3gG' % (torch.cuda.memory_cached() / 1E9 if torch.cuda.is_available() else 0) # (GB)
s = ('%10s' * 2 + '%10.4g' * 6) % (
'%g/%g' % (epoch, epochs - 1), mem, *mloss, targets.shape[0], imgs.shape[-1])
# 进度条显示以上信息
pbar.set_description(s)
# Plot
# 将前三次迭代batch的标签框在图片上画出来并保存
if ni < 3:
f = str(Path(log_dir) / ('train_batch%g.jpg' % ni)) # filename
result = plot_images(images=imgs, targets=targets, paths=paths, fname=f)
if tb_writer and result is not None:
tb_writer.add_image(f, result, dataformats='HWC', global_step=epoch)
# tb_writer.add_graph(model, imgs) # add model to tensorboard
# end batch ------------------------------------------------------------------------------------------------
# Scheduler
# 进行学习率衰减
scheduler.step()
# mAP
# 更新EMA的属性
ema.update_attr(model)
# 判断该epoch是否为最后一轮
final_epoch = epoch + 1 == epochs
# 对测试集进行测试，计算mAP等指标
# 测试时使用的是EMA模型
if not opt.notest or final_epoch: # Calculate mAP
results, maps, times = test.test(opt.data,
batch_size=batch_size,
imgsz=imgsz_test,
save_json=final_epoch and opt.data.endswith(os.sep + 'coco.yaml'),
model=ema.ema,
single_cls=opt.single_cls,
dataloader=testloader,
save_dir=log_dir)
# Write
# 将指标写入result.txt
with open(results_file, 'a') as f:
f.write(s + '%10.4g' * 7 % results + '\n') # P, R, mAP, F1, test_losses=(GIoU, obj, cls)
# 如果设置opt.bucket, 上传results.txt到谷歌云盘
if len(opt.name) and opt.bucket:
os.system('gsutil cp results.txt gs://%s/results/results%s.txt' % (opt.bucket, opt.name))
# Tensorboard
# 添加指标，损失等信息到tensorboard显示
if tb_writer:
tags = ['train/giou_loss', 'train/obj_loss', 'train/cls_loss',
'metrics/precision', 'metrics/recall', 'metrics/mAP_0.5', 'metrics/F1',
'val/giou_loss', 'val/obj_loss', 'val/cls_loss']
for x, tag in zip(list(mloss[:-1]) + list(results), tags):
tb_writer.add_scalar(tag, x, epoch)
# Update best mAP
# 更新best_fitness
fi = fitness(np.array(results).reshape(1, -1)) # fitness_i = weighted combination of [P, R, mAP, F1]
if fi > best_fitness:
best_fitness = fi
# Save model
"""
保存模型，还保存了epoch，results，optimizer等信息，
optimizer将不会在最后一轮完成后保存
model保存的是EMA的模型
"""
save = (not opt.nosave) or (final_epoch and not opt.evolve)
if save:
with open(results_file, 'r') as f: # create checkpoint
ckpt = {'epoch': epoch,
'best_fitness': best_fitness,
'training_results': f.read(),
'model': ema.ema,
'optimizer': None if final_epoch else optimizer.state_dict()}
# Save last, best and delete
torch.save(ckpt, last)
if (best_fitness == fi) and not final_epoch:
torch.save(ckpt, best)
del ckpt
# end epoch ----------------------------------------------------------------------------------------------------
# end training
# Strip optimizers
"""
模型训练完后，strip_optimizer函数将optimizer从ckpt中去除；
并且对模型进行model.half(), 将Float32的模型->Float16，
可以减少模型大小，提高inference速度
"""
n = ('_' if len(opt.name) and not opt.name.isnumeric() else '') + opt.name
fresults, flast, fbest = 'results%s.txt' % n, wdir + 'last%s.pt' % n, wdir + 'best%s.pt' % n
for f1, f2 in zip([wdir + 'last.pt', wdir + 'best.pt', 'results.txt'], [flast, fbest, fresults]):
if os.path.exists(f1):
os.rename(f1, f2) # rename
ispt = f2.endswith('.pt') # is *.pt
strip_optimizer(f2) if ispt else None # strip optimizer
# 上传结果到谷歌云盘
os.system('gsutil cp %s gs://%s/weights' % (f2, opt.bucket)) if opt.bucket and ispt else None # upload
# Finish
# 可视化results.txt文件
if not opt.evolve:
plot_results(save_dir=log_dir) # save as results.png
print('%g epochs completed in %.3f hours.\n' % (epoch - start_epoch + 1, (time.time() - t0) / 3600))
# 释放显存
dist.destroy_process_group() if device.type != 'cpu' and torch.cuda.device_count() > 1 else None
torch.cuda.empty_cache()
return results
以上我根据ultralytics\yolov5的train.py代码对其整体流程做一个梳理，讲解每个部分的代码的作用，但是对于一些细节函数还没做详细解析，就比如说计算损失的compute_loss()函数等，这些函数在utils.py文件里，之后更新解析utils.py。