660 lines
38 KiB
Python
660 lines
38 KiB
Python
import argparse
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import logging
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import math
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import os
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import random
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import time
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from copy import deepcopy
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from pathlib import Path
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from threading import Thread
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import numpy as np
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import torch.distributed as dist
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import torch.nn as nn
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import torch.nn.functional as F
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import torch.optim as optim
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import torch.optim.lr_scheduler as lr_scheduler
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import torch.utils.data
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import yaml
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from torch.cuda import amp
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from torch.nn.parallel import DistributedDataParallel as DDP
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from torch.utils.tensorboard import SummaryWriter
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from tqdm import tqdm
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import test # import test.py to get mAP after each epoch
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from models.experimental import attempt_load
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from models.yolo import Model
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from utils.autoanchor import check_anchors
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from utils.datasets import create_dataloader
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from utils.general import labels_to_class_weights, increment_path, labels_to_image_weights, init_seeds, \
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fitness, strip_optimizer, get_latest_run, check_dataset, check_file, check_git_status, check_img_size, \
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check_requirements, print_mutation, set_logging, one_cycle, colorstr
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from utils.google_utils import attempt_download
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from utils.loss import ComputeLoss
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from utils.plots import plot_images, plot_labels, plot_results, plot_evolution
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from utils.torch_utils import ModelEMA, select_device, intersect_dicts, torch_distributed_zero_first, is_parallel
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from utils.wandb_logging.wandb_utils import WandbLogger, check_wandb_resume
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logger = logging.getLogger(__name__)
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def train(hyp, opt, device, tb_writer=None):
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logger.info(colorstr('hyperparameters: ') + ', '.join(f'{k}={v}' for k, v in hyp.items()))
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save_dir, epochs, batch_size, total_batch_size, weights, rank = \
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Path(opt.save_dir), opt.epochs, opt.batch_size, opt.total_batch_size, opt.weights, opt.global_rank
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# Directories
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wdir = save_dir / 'weights'
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wdir.mkdir(parents=True, exist_ok=True) # make dir
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last = wdir / 'last.pt'
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best = wdir / 'best.pt'
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results_file = save_dir / 'results.txt'
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# Save run settings
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with open(save_dir / 'hyp.yaml', 'w') as f: # wdir 通常是一个 Path 对象（来自 pathlib 模块），表示文件夹路径。 parents=True 如果路径中包含不存在的父文件夹，mkdir 会自动创建这些父文件夹。 exist_ok=True 如果指定的文件夹已经存在，则不会抛出错误。
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yaml.dump(hyp, f, sort_keys=False) #把hyp的数据写入到f中 #yaml 模块：一个 Python 库，用于读取和写入 YAML 格式的文件。
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with open(save_dir / 'opt.yaml', 'w') as f:#在 save_dir 路径下创建一个名为 opt.yaml 的文件。'w' 表示以写入模式打开文件。如果文件已存在，它会被覆盖。
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yaml.dump(vars(opt), f, sort_keys=False) #vars() 是一个内置函数，如果传入的是一个对象，vars() 会返回该对象的所有属性及其值，返回对象的 __dict__ 属性。在这里，vars(opt) 会将 opt 对象的所有属性转换为字典，便于保存为 YAML 文件。
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#sort_keys=False：不按键排序，保持原始顺序。
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# Configure
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plots = not opt.evolve # create plots #是否在超参数优化模式下运行。 not opt.evolve 表示仅在非进化模式下创建绘图。
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cuda = device.type != 'cpu'
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init_seeds(2 + rank) #设置随机种子（seed），确保代码在多次运行时产生可重复的结果。
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with open(opt.data) as f:
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data_dict = yaml.load(f, Loader=yaml.SafeLoader) # data dict #从文件 f 中加载 YAML 格式的数据，并将其解析为 Python 的数据结构（如字典、列表等）传给data_dict 。 yaml.SafeLoader 是 PyYAML 提供的一个安全加载器。 它只加载简单的 YAML 数据结构，防止执行潜在的恶意代码。 对比：yaml.Loader 可以解析更复杂的对象，但可能存在安全隐患。
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is_coco = opt.data.endswith('coco.yaml')#检查字符串是否以指定的子字符串结尾。 如果是，返回 True；否则，返回 False。
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# Logging- Doing this before checking the dataset. Might update data_dict
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loggers = {'wandb': None} # loggers dict
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if rank in [-1, 0]:
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opt.hyp = hyp # add hyperparameters
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run_id = torch.load(weights).get('wandb_id') if weights.endswith('.pt') and os.path.isfile(weights) else None
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wandb_logger = WandbLogger(opt, Path(opt.save_dir).stem, run_id, data_dict)
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loggers['wandb'] = wandb_logger.wandb
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data_dict = wandb_logger.data_dict
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if wandb_logger.wandb:
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weights, epochs, hyp = opt.weights, opt.epochs, opt.hyp # WandbLogger might update weights, epochs if resuming
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nc = 1 if opt.single_cls else int(data_dict['nc']) # number of classes #opt.single_cls：布尔值，表示是否进行单类别训练。
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names = ['item'] if opt.single_cls and len(data_dict['names']) != 1 else data_dict['names'] # class names #'item' 是一个默认的占位符，表示“单类别”的名称。此处假设数据集中只有一种类别时，没有必要详细命名具体类别。
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assert len(names) == nc, '%g names found for nc=%g dataset in %s' % (len(names), nc, opt.data) # check
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# Model
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pretrained = weights.endswith('.pt')
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if pretrained:
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with torch_distributed_zero_first(rank):
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attempt_download(weights) # download if not found locally
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ckpt = torch.load(weights, map_location=device) # load checkpoint #weights 指向 .pt 文件（通常是 PyTorch 的模型文件）。 ckpt（Checkpoint）：是一个 Python 字典，包含保存的模型信息和状态。
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model = Model(opt.cfg or ckpt['model'].yaml, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device) # create or 会返回第一个非空（非 None 或 False）的值。
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exclude = ['anchor'] if (opt.cfg or hyp.get('anchors')) and not opt.resume else [] # exclude keys
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state_dict = ckpt['model'].float().state_dict() # to FP32
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state_dict = intersect_dicts(state_dict, model.state_dict(), exclude=exclude) # intersect
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model.load_state_dict(state_dict, strict=False) # load
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logger.info('Transferred %g/%g items from %s' % (len(state_dict), len(model.state_dict()), weights)) # report
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else:
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model = Model(opt.cfg, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device) # create
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with torch_distributed_zero_first(rank): #在分布式数据并行（DDP）模式下，确保某些操作（如检查数据集）只在 rank=0 的主进程上执行
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check_dataset(data_dict) # check
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train_path = data_dict['train']
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test_path = data_dict['val']
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# Freeze
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freeze = [] # parameter names to freeze (full or partial)
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for k, v in model.named_parameters(): #返回的是元组 包括名字+神经网络对应的参数大小【layer1.weight layer1.bias】（超参数不在named_parameters()中）
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v.requires_grad = True # train all layers
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if any(x in k for x in freeze):
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print('freezing %s' % k)
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v.requires_grad = False
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# Optimizer
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nbs = 64 # nominal batch size
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accumulate = max(round(nbs / total_batch_size), 1) # accumulate loss before optimizing
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hyp['weight_decay'] *= total_batch_size * accumulate / nbs # scale weight_decay #weight_decay 是优化器中的一个超参数，常用于实现 L2 正则化，以防止模型过拟合。它通过在更新参数时引入一个额外的惩罚项，抑制模型参数（权重）变得过大。
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logger.info(f"Scaled weight_decay = {hyp['weight_decay']}")
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pg0, pg1, pg2 = [], [], [] # optimizer parameter groups
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#pg0：包含不需要权重衰减（weight_decay）的参数（如批归一化的权重）。
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#pg1：包含需要权重衰减的参数（如卷积层的权重）。
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#pg2：包含偏置参数（通常不适用权重衰减）。
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#named_modules()是 PyTorch 的 nn.Module 提供的一个方法.返回模型中的所有模块及其名称。
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#与 named_parameters() 不同：named_modules() 返回的是模块（如卷积层对象本身）。
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#named_parameters() 返回的是参数（如权重和偏置的张量对象）。
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for k, v in model.named_modules():
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if hasattr(v, 'bias') and isinstance(v.bias, nn.Parameter): #hasattr(v, 'bias')：检查模块是否有 bias 属性。 isinstance(v.bias, nn.Parameter)：确保 bias 是一个 nn.Parameter 对象。
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pg2.append(v.bias) # biases
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if isinstance(v, nn.BatchNorm2d):
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pg0.append(v.weight) # no decay
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elif hasattr(v, 'weight') and isinstance(v.weight, nn.Parameter):
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pg1.append(v.weight) # apply decay
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if opt.adam:
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optimizer = optim.Adam(pg0, lr=hyp['lr0'], betas=(hyp['momentum'], 0.999)) # adjust beta1 to momentum
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else:
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optimizer = optim.SGD(pg0, lr=hyp['lr0'], momentum=hyp['momentum'], nesterov=True)
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optimizer.add_param_group({'params': pg1, 'weight_decay': hyp['weight_decay']}) # add pg1 with weight_decay
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optimizer.add_param_group({'params': pg2}) # add pg2 (biases)
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logger.info('Optimizer groups: %g .bias, %g conv.weight, %g other' % (len(pg2), len(pg1), len(pg0)))
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del pg0, pg1, pg2
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# Scheduler https://arxiv.org/pdf/1812.01187.pdf
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# https://pytorch.org/docs/stable/_modules/torch/optim/lr_scheduler.html#OneCycleLR
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if opt.linear_lr:
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lf = lambda x: (1 - x / (epochs - 1)) * (1.0 - hyp['lrf']) + hyp['lrf'] # linear
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else:
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lf = one_cycle(1, hyp['lrf'], epochs) # cosine 1->hyp['lrf']
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scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf)
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# plot_lr_scheduler(optimizer, scheduler, epochs)
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# EMA
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ema = ModelEMA(model) if rank in [-1, 0] else None
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# Resume
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start_epoch, best_fitness = 0, 0.0
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if pretrained:
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# Optimizer
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if ckpt['optimizer'] is not None:
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optimizer.load_state_dict(ckpt['optimizer'])
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best_fitness = ckpt['best_fitness']
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# EMA
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if ema and ckpt.get('ema'):
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ema.ema.load_state_dict(ckpt['ema'].float().state_dict())
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ema.updates = ckpt['updates']
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# Results
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if ckpt.get('training_results') is not None:
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results_file.write_text(ckpt['training_results']) # write results.txt
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# Epochs
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start_epoch = ckpt['epoch'] + 1
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if opt.resume:
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assert start_epoch > 0, '%s training to %g epochs is finished, nothing to resume.' % (weights, epochs)
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if epochs < start_epoch:
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logger.info('%s has been trained for %g epochs. Fine-tuning for %g additional epochs.' %
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(weights, ckpt['epoch'], epochs))
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epochs += ckpt['epoch'] # finetune additional epochs
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del ckpt, state_dict
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# Image sizes
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gs = max(int(model.stride.max()), 32) # grid size (max stride) #stride是8 16 32 最后是32
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nl = model.model[-1].nl # number of detection layers (used for scaling hyp['obj'])
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imgsz, imgsz_test = [check_img_size(x, gs) for x in opt.img_size] # verify imgsz are gs-multiples
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#opt.img_size 是一个列表，通常包含两个图像尺寸，一个用于训练，一个用于测试。
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# DP mode
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if cuda and rank == -1 and torch.cuda.device_count() > 1:
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model = torch.nn.DataParallel(model)
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# SyncBatchNorm
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if opt.sync_bn and cuda and rank != -1:
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model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model).to(device)
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logger.info('Using SyncBatchNorm()')
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# Trainloader
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dataloader, dataset = create_dataloader(train_path, imgsz, batch_size, gs, opt,
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hyp=hyp, augment=True, cache=opt.cache_images, rect=opt.rect, rank=rank,
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world_size=opt.world_size, workers=opt.workers,
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image_weights=opt.image_weights, quad=opt.quad, prefix=colorstr('train: '))
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mlc = np.concatenate(dataset.labels, 0)[:, 0].max() # max label class
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nb = len(dataloader) # number of batches
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# assert mlc < nc, 'Label class %g exceeds nc=%g in %s. Possible class labels are 0-%g' % (mlc, nc, opt.data, nc - 1)
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# Process 0
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if rank in [-1, 0]:
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testloader = create_dataloader(test_path, imgsz_test, batch_size * 2, gs, opt, # testloader
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hyp=hyp, cache=opt.cache_images and not opt.notest, rect=True, rank=-1,
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world_size=opt.world_size, workers=opt.workers,
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pad=0.5, prefix=colorstr('val: '))[0]
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if not opt.resume:
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labels = np.concatenate(dataset.labels, 0)
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c = torch.tensor(labels[:, 0]) # classes
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# cf = torch.bincount(c.long(), minlength=nc) + 1. # frequency
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# model._initialize_biases(cf.to(device))
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if plots:
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plot_labels(labels, names, save_dir, loggers)
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if tb_writer:
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tb_writer.add_histogram('classes', c, 0)
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# Anchors
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if not opt.noautoanchor:
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check_anchors(dataset, model=model, thr=hyp['anchor_t'], imgsz=imgsz)
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model.half().float() # pre-reduce anchor precision
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# DDP mode
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if cuda and rank != -1:
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model = DDP(model, device_ids=[opt.local_rank], output_device=opt.local_rank,
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# nn.MultiheadAttention incompatibility with DDP https://github.com/pytorch/pytorch/issues/26698
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find_unused_parameters=any(isinstance(layer, nn.MultiheadAttention) for layer in model.modules()))
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# Model parameters
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hyp['box'] *= 3. / nl # scale to layers
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hyp['cls'] *= nc / 80. * 3. / nl # scale to classes and layers
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hyp['obj'] *= (imgsz / 640) ** 2 * 3. / nl # scale to image size and layers
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hyp['label_smoothing'] = opt.label_smoothing
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model.nc = nc # attach number of classes to model
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model.hyp = hyp # attach hyperparameters to model
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model.gr = 1.0 # iou loss ratio (obj_loss = 1.0 or iou)
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model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device) * nc # attach class weights
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model.names = names
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# Start training
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t0 = time.time()
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nw = max(round(hyp['warmup_epochs'] * nb), 1000) # number of warmup iterations, max(3 epochs, 1k iterations)
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# nw = min(nw, (epochs - start_epoch) / 2 * nb) # limit warmup to < 1/2 of training
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maps = np.zeros(nc) # mAP per class
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results = (0, 0, 0, 0, 0, 0, 0) # P, R, mAP@.5, mAP@.5-.95, val_loss(box, obj, cls)
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scheduler.last_epoch = start_epoch - 1 # do not move
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scaler = amp.GradScaler(enabled=cuda)
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compute_loss = ComputeLoss(model) # init loss class
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logger.info(f'Image sizes {imgsz} train, {imgsz_test} test\n'
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f'Using {dataloader.num_workers} dataloader workers\n'
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f'Logging results to {save_dir}\n'
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f'Starting training for {epochs} epochs...')
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for epoch in range(start_epoch, epochs): # epoch ------------------------------------------------------------------
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model.train()
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# Update image weights (optional)
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if opt.image_weights:
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# Generate indices
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if rank in [-1, 0]:
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cw = model.class_weights.cpu().numpy() * (1 - maps) ** 2 / nc # class weights
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iw = labels_to_image_weights(dataset.labels, nc=nc, class_weights=cw) # image weights
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dataset.indices = random.choices(range(dataset.n), weights=iw, k=dataset.n) # rand weighted idx
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# Broadcast if DDP
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if rank != -1:
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indices = (torch.tensor(dataset.indices) if rank == 0 else torch.zeros(dataset.n)).int()
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dist.broadcast(indices, 0)
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if rank != 0:
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dataset.indices = indices.cpu().numpy()
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# Update mosaic border
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# b = int(random.uniform(0.25 * imgsz, 0.75 * imgsz + gs) // gs * gs)
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# dataset.mosaic_border = [b - imgsz, -b] # height, width borders
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mloss = torch.zeros(4, device=device) # mean losses
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if rank != -1:
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dataloader.sampler.set_epoch(epoch)
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pbar = enumerate(dataloader)
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logger.info(('\n' + '%10s' * 8) % ('Epoch', 'gpu_mem', 'box', 'obj', 'cls', 'total', 'labels', 'img_size'))
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if rank in [-1, 0]:
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pbar = tqdm(pbar, total=nb) # progress bar
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optimizer.zero_grad()
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for i, (imgs, targets, paths, _) in pbar: # batch -------------------------------------------------------------
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ni = i + nb * epoch # number integrated batches (since train start)
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imgs = imgs.to(device, non_blocking=True).float() / 255.0 # uint8 to float32, 0-255 to 0.0-1.0
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# Warmup
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if ni <= nw:
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xi = [0, nw] # x interp
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# model.gr = np.interp(ni, xi, [0.0, 1.0]) # iou loss ratio (obj_loss = 1.0 or iou)
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accumulate = max(1, np.interp(ni, xi, [1, nbs / total_batch_size]).round())
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for j, x in enumerate(optimizer.param_groups):
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# bias lr falls from 0.1 to lr0, all other lrs rise from 0.0 to lr0
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x['lr'] = np.interp(ni, xi, [hyp['warmup_bias_lr'] if j == 2 else 0.0, x['initial_lr'] * lf(epoch)])
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if 'momentum' in x:
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x['momentum'] = np.interp(ni, xi, [hyp['warmup_momentum'], hyp['momentum']])
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# Multi-scale
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if opt.multi_scale:
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sz = random.randrange(imgsz * 0.5, imgsz * 1.5 + gs) // gs * gs # size
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sf = sz / max(imgs.shape[2:]) # scale factor
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if sf != 1:
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ns = [math.ceil(x * sf / gs) * gs for x in imgs.shape[2:]] # new shape (stretched to gs-multiple)
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imgs = F.interpolate(imgs, size=ns, mode='bilinear', align_corners=False)
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# Forward
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with amp.autocast(enabled=cuda):
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pred = model(imgs) # forward
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loss, loss_items = compute_loss(pred, targets.to(device)) # loss scaled by batch_size
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if rank != -1:
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loss *= opt.world_size # gradient averaged between devices in DDP mode
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if opt.quad:
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loss *= 4.
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# Backward
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scaler.scale(loss).backward()
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# Optimize
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if ni % accumulate == 0:
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scaler.step(optimizer) # optimizer.step
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scaler.update()
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optimizer.zero_grad()
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if ema:
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ema.update(model)
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# Print
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if rank in [-1, 0]:
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mloss = (mloss * i + loss_items) / (i + 1) # update mean losses
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mem = '%.3gG' % (torch.cuda.memory_reserved() / 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])
|
||
pbar.set_description(s)
|
||
|
||
# Plot
|
||
if plots and ni < 3:
|
||
f = save_dir / f'train_batch{ni}.jpg' # filename
|
||
Thread(target=plot_images, args=(imgs, targets, paths, f), daemon=True).start()
|
||
# if tb_writer:
|
||
# tb_writer.add_image(f, result, dataformats='HWC', global_step=epoch)
|
||
# tb_writer.add_graph(torch.jit.trace(model, imgs, strict=False), []) # add model graph
|
||
elif plots and ni == 10 and wandb_logger.wandb:
|
||
wandb_logger.log({"Mosaics": [wandb_logger.wandb.Image(str(x), caption=x.name) for x in
|
||
save_dir.glob('train*.jpg') if x.exists()]})
|
||
|
||
# end batch ------------------------------------------------------------------------------------------------
|
||
# end epoch ----------------------------------------------------------------------------------------------------
|
||
|
||
# Scheduler
|
||
lr = [x['lr'] for x in optimizer.param_groups] # for tensorboard
|
||
scheduler.step()
|
||
|
||
# DDP process 0 or single-GPU
|
||
if rank in [-1, 0]:
|
||
# mAP
|
||
ema.update_attr(model, include=['yaml', 'nc', 'hyp', 'gr', 'names', 'stride', 'class_weights'])
|
||
final_epoch = epoch + 1 == epochs
|
||
if not opt.notest or final_epoch: # Calculate mAP
|
||
wandb_logger.current_epoch = epoch + 1
|
||
results, maps, times = test.test(data_dict,
|
||
batch_size=batch_size * 2,
|
||
imgsz=imgsz_test,
|
||
model=ema.ema,
|
||
single_cls=opt.single_cls,
|
||
dataloader=testloader,
|
||
save_dir=save_dir,
|
||
verbose=nc < 50 and final_epoch,
|
||
plots=plots and final_epoch,
|
||
wandb_logger=wandb_logger,
|
||
compute_loss=compute_loss,
|
||
is_coco=is_coco)
|
||
|
||
# Write
|
||
with open(results_file, 'a') as f:
|
||
f.write(s + '%10.4g' * 7 % results + '\n') # append metrics, val_loss
|
||
if len(opt.name) and opt.bucket:
|
||
os.system('gsutil cp %s gs://%s/results/results%s.txt' % (results_file, opt.bucket, opt.name))
|
||
|
||
# Log
|
||
tags = ['train/box_loss', 'train/obj_loss', 'train/cls_loss', # train loss
|
||
'metrics/precision', 'metrics/recall', 'metrics/mAP_0.5', 'metrics/mAP_0.5:0.95',
|
||
'val/box_loss', 'val/obj_loss', 'val/cls_loss', # val loss
|
||
'x/lr0', 'x/lr1', 'x/lr2'] # params
|
||
for x, tag in zip(list(mloss[:-1]) + list(results) + lr, tags):
|
||
if tb_writer:
|
||
tb_writer.add_scalar(tag, x, epoch) # tensorboard
|
||
if wandb_logger.wandb:
|
||
wandb_logger.log({tag: x}) # W&B
|
||
|
||
# Update best mAP
|
||
fi = fitness(np.array(results).reshape(1, -1)) # weighted combination of [P, R, mAP@.5, mAP@.5-.95]
|
||
if fi > best_fitness:
|
||
best_fitness = fi
|
||
wandb_logger.end_epoch(best_result=best_fitness == fi)
|
||
|
||
# Save model
|
||
if (not opt.nosave) or (final_epoch and not opt.evolve): # if save
|
||
ckpt = {'epoch': epoch,
|
||
'best_fitness': best_fitness,
|
||
'training_results': results_file.read_text(),
|
||
'model': deepcopy(model.module if is_parallel(model) else model).half(),
|
||
'ema': deepcopy(ema.ema).half(),
|
||
'updates': ema.updates,
|
||
'optimizer': optimizer.state_dict(),
|
||
'wandb_id': wandb_logger.wandb_run.id if wandb_logger.wandb else None}
|
||
|
||
# Save last, best and delete
|
||
torch.save(ckpt, last)
|
||
if best_fitness == fi:
|
||
torch.save(ckpt, best)
|
||
if wandb_logger.wandb:
|
||
if ((epoch + 1) % opt.save_period == 0 and not final_epoch) and opt.save_period != -1:
|
||
wandb_logger.log_model(
|
||
last.parent, opt, epoch, fi, best_model=best_fitness == fi)
|
||
del ckpt
|
||
|
||
# end epoch ----------------------------------------------------------------------------------------------------
|
||
# end training
|
||
if rank in [-1, 0]:
|
||
# Plots
|
||
if plots:
|
||
plot_results(save_dir=save_dir) # save as results.png
|
||
if wandb_logger.wandb:
|
||
files = ['results.png', 'confusion_matrix.png', *[f'{x}_curve.png' for x in ('F1', 'PR', 'P', 'R')]]
|
||
wandb_logger.log({"Results": [wandb_logger.wandb.Image(str(save_dir / f), caption=f) for f in files
|
||
if (save_dir / f).exists()]})
|
||
# Test best.pt
|
||
logger.info('%g epochs completed in %.3f hours.\n' % (epoch - start_epoch + 1, (time.time() - t0) / 3600))
|
||
if opt.data.endswith('coco.yaml') and nc == 80: # if COCO
|
||
for m in (last, best) if best.exists() else (last): # speed, mAP tests
|
||
results, _, _ = test.test(opt.data,
|
||
batch_size=batch_size * 2,
|
||
imgsz=imgsz_test,
|
||
conf_thres=0.001,
|
||
iou_thres=0.7,
|
||
model=attempt_load(m, device).half(),
|
||
single_cls=opt.single_cls,
|
||
dataloader=testloader,
|
||
save_dir=save_dir,
|
||
save_json=True,
|
||
plots=False,
|
||
is_coco=is_coco)
|
||
|
||
# Strip optimizers
|
||
final = best if best.exists() else last # final model
|
||
for f in last, best:
|
||
if f.exists():
|
||
strip_optimizer(f) # strip optimizers
|
||
if opt.bucket:
|
||
os.system(f'gsutil cp {final} gs://{opt.bucket}/weights') # upload
|
||
if wandb_logger.wandb and not opt.evolve: # Log the stripped model
|
||
wandb_logger.wandb.log_artifact(str(final), type='model',
|
||
name='run_' + wandb_logger.wandb_run.id + '_model',
|
||
aliases=['last', 'best', 'stripped'])
|
||
wandb_logger.finish_run()
|
||
else:
|
||
dist.destroy_process_group()
|
||
torch.cuda.empty_cache()
|
||
return results
|
||
|
||
|
||
if __name__ == '__main__':
|
||
parser = argparse.ArgumentParser() #ArgumentParser 是 argparse 模块中的一个类，用于创建一个解析器对象。这个对象会处理命令行参数，并将它们转换为 Python 数据类型（如字符串、整数等），或者是一些更复杂的数据结构（如列表）。
|
||
parser.add_argument('--weights', type=str, default='runs/train/exp33/weights/best.pt', help='initial weights path')
|
||
parser.add_argument('--cfg', type=str, default='models/yolov5s.yaml', help='model.yaml path')
|
||
parser.add_argument('--data', type=str, default='data/mydata.yaml', help='data.yaml path')
|
||
parser.add_argument('--hyp', type=str, default='runs/train/exp33/hyp.yaml', help='hyperparameters path') #'data/hyp.scratch.yaml'
|
||
parser.add_argument('--epochs', type=int, default=2)
|
||
parser.add_argument('--batch-size', type=int, default=1, help='total batch size for all GPUs')
|
||
parser.add_argument('--img-size', nargs='+', type=int, default=[640, 640], help='[train, test] image sizes')
|
||
parser.add_argument('--rect', action='store_true', help='rectangular training')
|
||
parser.add_argument('--resume', nargs='?', const=True, default=False, help='resume most recent training')
|
||
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('--image-weights', action='store_true', help='use weighted image selection for training')
|
||
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 multi-class data as single-class')
|
||
parser.add_argument('--adam', action='store_true', help='use torch.optim.Adam() optimizer')
|
||
parser.add_argument('--sync-bn', action='store_true', help='use SyncBatchNorm, only available in DDP mode')
|
||
parser.add_argument('--local_rank', type=int, default=-1, help='DDP parameter, do not modify')
|
||
parser.add_argument('--workers', type=int, default=0, help='maximum number of dataloader workers')
|
||
parser.add_argument('--project', default='runs/train', help='save to project/name')
|
||
parser.add_argument('--entity', default=None, help='W&B entity')
|
||
parser.add_argument('--name', default='exp', help='save to project/name')
|
||
parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
|
||
parser.add_argument('--quad', action='store_true', help='quad dataloader')
|
||
parser.add_argument('--linear-lr', action='store_true', help='linear LR')
|
||
parser.add_argument('--label-smoothing', type=float, default=0.0, help='Label smoothing epsilon')
|
||
parser.add_argument('--upload_dataset', action='store_true', help='Upload dataset as W&B artifact table')
|
||
parser.add_argument('--bbox_interval', type=int, default=-1, help='Set bounding-box image logging interval for W&B')
|
||
parser.add_argument('--save_period', type=int, default=-1, help='Log model after every "save_period" epoch')
|
||
parser.add_argument('--artifact_alias', type=str, default="latest", help='version of dataset artifact to be used')
|
||
opt = parser.parse_args()
|
||
|
||
# Set DDP variables DDP (Distributed Data Parallel) 是 PyTorch 的一种分布式训练模式
|
||
opt.world_size = int(os.environ['WORLD_SIZE']) if 'WORLD_SIZE' in os.environ else 1
|
||
opt.global_rank = int(os.environ['RANK']) if 'RANK' in os.environ else -1
|
||
set_logging(opt.global_rank) #调用日志设置函数
|
||
if opt.global_rank in [-1, 0]: #仅在非分布式环境 (global_rank == -1) 或主进程 (global_rank == 0) 中执行
|
||
check_git_status() #检查代码库的 Git 状态，例如是否有未提交的更改。 确保训练使用的是当前代码库的最新状态。
|
||
check_requirements() #检查所需的 Python 包是否安装，版本是否满足要求。
|
||
|
||
# Resume
|
||
wandb_run = check_wandb_resume(opt)
|
||
if opt.resume and not wandb_run: # resume an interrupted run
|
||
ckpt = opt.resume if isinstance(opt.resume, str) else get_latest_run() # specified or most recent path
|
||
assert os.path.isfile(ckpt), 'ERROR: --resume checkpoint does not exist'
|
||
apriori = opt.global_rank, opt.local_rank
|
||
with open(Path(ckpt).parent.parent / 'opt.yaml') as f:
|
||
opt = argparse.Namespace(**yaml.load(f, Loader=yaml.SafeLoader)) # replace
|
||
opt.cfg, opt.weights, opt.resume, opt.batch_size, opt.global_rank, opt.local_rank = '', ckpt, True, opt.total_batch_size, *apriori # reinstate
|
||
logger.info('Resuming training from %s' % ckpt)
|
||
else:
|
||
# opt.hyp = opt.hyp or ('hyp.finetune.yaml' if opt.weights else 'hyp.scratch.yaml')
|
||
opt.data, opt.cfg, opt.hyp = check_file(opt.data), check_file(opt.cfg), check_file(opt.hyp) # check files
|
||
assert len(opt.cfg) or len(opt.weights), 'either --cfg or --weights must be specified'
|
||
opt.img_size.extend([opt.img_size[-1]] * (2 - len(opt.img_size))) # extend to 2 sizes (train, test)
|
||
opt.name = 'evolve' if opt.evolve else opt.name
|
||
opt.save_dir = increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok | opt.evolve) # increment run
|
||
#Path(opt.project) / opt.name 相当于 os.path.join(opt.project, opt.name)。
|
||
# DDP mode
|
||
opt.total_batch_size = opt.batch_size
|
||
device = select_device(opt.device, batch_size=opt.batch_size)
|
||
if opt.local_rank != -1:
|
||
assert torch.cuda.device_count() > opt.local_rank
|
||
torch.cuda.set_device(opt.local_rank)
|
||
device = torch.device('cuda', opt.local_rank)
|
||
dist.init_process_group(backend='nccl', init_method='env://') # distributed backend
|
||
assert opt.batch_size % opt.world_size == 0, '--batch-size must be multiple of CUDA device count'
|
||
opt.batch_size = opt.total_batch_size // opt.world_size
|
||
|
||
# Hyperparameters
|
||
with open(opt.hyp) as f:
|
||
hyp = yaml.load(f, Loader=yaml.SafeLoader) # load hyps
|
||
|
||
# Train
|
||
logger.info(opt) #打印 opt 对象的内容，通常是运行配置的详细信息。
|
||
if not opt.evolve:
|
||
tb_writer = None # init loggers
|
||
if opt.global_rank in [-1, 0]:
|
||
prefix = colorstr('tensorboard: ')
|
||
logger.info(f"{prefix}Start with 'tensorboard --logdir {opt.project}', view at http://localhost:6006/")
|
||
tb_writer = SummaryWriter(opt.save_dir) # Tensorboard
|
||
train(hyp, opt, device, tb_writer)
|
||
|
||
# Evolve hyperparameters (optional)
|
||
else:
|
||
# Hyperparameter evolution metadata (mutation scale 0-1, lower_limit, upper_limit)
|
||
meta = {'lr0': (1, 1e-5, 1e-1), # initial learning rate (SGD=1E-2, Adam=1E-3)
|
||
'lrf': (1, 0.01, 1.0), # final OneCycleLR learning rate (lr0 * lrf)
|
||
'momentum': (0.3, 0.6, 0.98), # SGD momentum/Adam beta1
|
||
'weight_decay': (1, 0.0, 0.001), # optimizer weight decay
|
||
'warmup_epochs': (1, 0.0, 5.0), # warmup epochs (fractions ok)
|
||
'warmup_momentum': (1, 0.0, 0.95), # warmup initial momentum
|
||
'warmup_bias_lr': (1, 0.0, 0.2), # warmup initial bias lr
|
||
'box': (1, 0.02, 0.2), # box loss gain
|
||
'cls': (1, 0.2, 4.0), # cls loss gain
|
||
'cls_pw': (1, 0.5, 2.0), # cls BCELoss positive_weight
|
||
'obj': (1, 0.2, 4.0), # obj loss gain (scale with pixels)
|
||
'obj_pw': (1, 0.5, 2.0), # obj BCELoss positive_weight
|
||
'iou_t': (0, 0.1, 0.7), # IoU training threshold
|
||
'anchor_t': (1, 2.0, 8.0), # anchor-multiple threshold
|
||
'anchors': (2, 2.0, 10.0), # anchors per output grid (0 to ignore)
|
||
'fl_gamma': (0, 0.0, 2.0), # focal loss gamma (efficientDet default gamma=1.5)
|
||
'hsv_h': (1, 0.0, 0.1), # image HSV-Hue augmentation (fraction)
|
||
'hsv_s': (1, 0.0, 0.9), # image HSV-Saturation augmentation (fraction)
|
||
'hsv_v': (1, 0.0, 0.9), # image HSV-Value augmentation (fraction)
|
||
'degrees': (1, 0.0, 45.0), # image rotation (+/- deg)
|
||
'translate': (1, 0.0, 0.9), # image translation (+/- fraction)
|
||
'scale': (1, 0.0, 0.9), # image scale (+/- gain)
|
||
'shear': (1, 0.0, 10.0), # image shear (+/- deg)
|
||
'perspective': (0, 0.0, 0.001), # image perspective (+/- fraction), range 0-0.001
|
||
'flipud': (1, 0.0, 1.0), # image flip up-down (probability)
|
||
'fliplr': (0, 0.0, 1.0), # image flip left-right (probability)
|
||
'mosaic': (1, 0.0, 1.0), # image mixup (probability)
|
||
'mixup': (1, 0.0, 1.0)} # image mixup (probability)
|
||
|
||
assert opt.local_rank == -1, 'DDP mode not implemented for --evolve'
|
||
opt.notest, opt.nosave = True, True # only test/save final epoch
|
||
# ei = [isinstance(x, (int, float)) for x in hyp.values()] # evolvable indices
|
||
yaml_file = Path(opt.save_dir) / 'hyp_evolved.yaml' # save best result here
|
||
if opt.bucket:
|
||
os.system('gsutil cp gs://%s/evolve.txt .' % opt.bucket) # download evolve.txt if exists
|
||
|
||
for _ in range(300): # generations to evolve
|
||
if Path('evolve.txt').exists(): # if evolve.txt exists: select best hyps and mutate
|
||
# Select parent(s)
|
||
parent = 'single' # parent selection method: 'single' or 'weighted'
|
||
x = np.loadtxt('evolve.txt', ndmin=2)
|
||
n = min(5, len(x)) # number of previous results to consider
|
||
x = x[np.argsort(-fitness(x))][:n] # top n mutations
|
||
w = fitness(x) - fitness(x).min() # weights
|
||
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
|
||
mp, s = 0.8, 0.2 # mutation probability, sigma
|
||
npr = np.random
|
||
npr.seed(int(time.time()))
|
||
g = np.array([x[0] for x in meta.values()]) # gains 0-1
|
||
ng = len(meta)
|
||
v = np.ones(ng)
|
||
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)
|
||
for i, k in enumerate(hyp.keys()): # plt.hist(v.ravel(), 300)
|
||
hyp[k] = float(x[i + 7] * v[i]) # mutate
|
||
|
||
# Constrain to limits
|
||
for k, v in meta.items():
|
||
hyp[k] = max(hyp[k], v[1]) # lower limit
|
||
hyp[k] = min(hyp[k], v[2]) # upper limit
|
||
hyp[k] = round(hyp[k], 5) # significant digits
|
||
|
||
# Train mutation
|
||
results = train(hyp.copy(), opt, device)
|
||
|
||
# Write mutation results
|
||
print_mutation(hyp.copy(), results, yaml_file, opt.bucket)
|
||
|
||
# Plot results
|
||
plot_evolution(yaml_file)
|
||
print(f'Hyperparameter evolution complete. Best results saved as: {yaml_file}\n'
|
||
f'Command to train a new model with these hyperparameters: $ python train.py --hyp {yaml_file}')
|
||
|
||
#
|
||
# wandb 是 Weights & Biases 的缩写，这是一个流行的机器学习实验跟踪和可视化工具，常用于记录训练日志、可视化超参数影响、保存模型等。
|
||
#
|
||
# 核心功能：
|
||
# 跟踪训练过程中的指标（如 loss、accuracy 等）。
|
||
# 可视化超参数搜索。
|
||
# 保存训练日志和模型文件到云端。
|
||
# 在代码中，wandb 通常通过一个对象（如 wandb_logger）进行集成和使用。
|
||
#
|
||
# #logger 是一个日志记录对象，由 Python 的内置模块 logging 提供。
|
||
# 日志记录可以用来：
|
||
# 调试：追踪程序的执行过程。
|
||
# 信息记录：记录关键操作和结果。
|
||
# 错误检测：捕获异常或潜在问题。
|
||
# #2. logger 的初始化
|
||
# logger = logging.getLogger(__name__)
|
||
# logging.getLogger(__name__)：
|
||
#
|
||
# __name__ 是当前模块的名称，例如如果模块名是 train.py，则 __name__ 是 'train'。
|
||
# 作用：
|
||
# 创建一个与模块绑定的日志对象。
|
||
# 如果已经有一个同名的日志器存在，它会返回现有的实例，而不是创建新实例。
|
||
# 优点：
|
||
# 每个模块有自己独立的日志对象，方便定位日志来源。
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# 可以通过模块名对日志级别、格式等进行定制。 |