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train.py
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train.py
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# -*- coding: utf-8 -*-
import argparse # FileNotFoundError: file "/userhome/mmdetection/configs/a-voc-mini/faster_rcnn_r101_fpn_1x_viped_4gpu.py" does not exist
import logging
import os
import random
import time
from pathlib import Path
from threading import Thread
from warnings import warn
import math
import numpy as np
import torch.distributed as dist
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torch.optim.lr_scheduler as lr_scheduler
import torch.utils.data
import yaml
from torch.cuda import amp
from torch.nn.parallel import DistributedDataParallel as DDP
from torch.utils.tensorboard import SummaryWriter
from tqdm import tqdm
#import test # import test.py to get mAP after each epoch
import test_MMD
from models.experimental import attempt_load
from models.yolo import Model
from models.yolo_feature import Model_feature # 为了返回最后的特征 额外定义的特征
from utils.MMD import get_feature, get_feature_train, MMD_distance, choice_topk
from torch.backends import cudnn
from utils.autoanchor import check_anchors
from utils.datasets import create_dataloader
from utils.general import labels_to_class_weights, increment_path, labels_to_image_weights, init_seeds, \
fitness, strip_optimizer, get_latest_run, check_dataset, check_file, check_git_status, check_img_size, \
print_mutation, set_logging
from utils.google_utils import attempt_download
from utils.loss import compute_loss
from utils.plots import plot_images, plot_labels, plot_results, plot_evolution
from utils.torch_utils import ModelEMA, select_device, intersect_dicts, torch_distributed_zero_first
from utils.copy_paste_da import copy_paste_api
logger = logging.getLogger(__name__)
try:
import wandb
except ImportError:
wandb = None
logger.info("Install Weights & Biases for experiment logging via 'pip install wandb' (recommended)")
def train(hyp, opt, device, tb_writer=None, wandb=None): # opt.data 是数据集加载的yaml文件信息
logger.info(f'Hyperparameters {hyp}') #在DDP模式下 batch_size 为每一张卡分到的batch数目 total_batch_size 是设置的总batch的数目
save_dir, epochs, batch_size, total_batch_size, weights, rank = \
Path(opt.save_dir), opt.epochs, opt.batch_size, opt.total_batch_size, opt.weights, opt.global_rank
# Directories
wdir = save_dir / 'weights'
wdir.mkdir(parents=True, exist_ok=True) # make dir
last = wdir / 'last.pt'
best = wdir / 'best.pt'
results_file = save_dir / 'results.txt'
# Save run settings
with open(save_dir / 'hyp.yaml', 'w') as f:
yaml.dump(hyp, f, sort_keys=False)
with open(save_dir / 'opt.yaml', 'w') as f:
yaml.dump(vars(opt), f, sort_keys=False)
# Configure
plots = not opt.evolve # create plots
cuda = device.type != 'cpu'
init_seeds(2 + rank) # 每台机器的随机种子设置为不同
with open(opt.data) as f:
data_dict = yaml.load(f, Loader=yaml.FullLoader) # data dict
with torch_distributed_zero_first(rank):
check_dataset(data_dict) # check
# domain adaption path here
train_path = data_dict['train_source'] # train_path 为list 里面包含所有train_set的数据集
train_target_path = data_dict['train_target']
test_path = data_dict['val']
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
# Model
pretrained = weights.endswith('.pt') # 加载预训练模型 weights是按照路径加载的
if pretrained:
with torch_distributed_zero_first(rank):
attempt_download(weights) # download if not found locally
ckpt = torch.load(weights, map_location=device) # load checkpoint
if hyp.get('anchors'):
ckpt['model'].yaml['anchors'] = round(hyp['anchors']) # force autoanchor
model = Model_feature(opt.cfg or ckpt['model'].yaml, ch=3, nc=nc).to(device) # create
#model_feature = Model_feature(opt.cfg or ckpt['model'].yaml, ch=3, nc=nc).to(device) # create output feature model
exclude = ['anchor'] if opt.cfg or hyp.get('anchors') else [] # exclude keys
state_dict = ckpt['model'].float().state_dict() # to FP32
state_dict = intersect_dicts(state_dict, model.state_dict(), exclude=exclude) # intersect
#model_feature.load_state_dict(state_dict, strict=False) # 同步加载预训练的参数
model.load_state_dict(state_dict, strict=False) # load para
logger.info('Transferred %g/%g items from %s' % (len(state_dict), len(model.state_dict()), weights)) # report
else:
model = Model_feature(opt.cfg, ch=3, nc=nc).to(device) # create and initialize
#model_feature = Model_feature(opt.cfg, ch=3, nc=nc).to(device)
# Freeze
freeze = [] # parameter names to freeze (full or partial)
for k, v in model.named_parameters():
v.requires_grad = True # train all layers
if any(x in k for x in freeze):
print('freezing %s' % k)
v.requires_grad = False
# Optimizer
nbs = 64 # nominal batch size
accumulate = max(round(nbs / total_batch_size), 1) # accumulate loss before optimizing
hyp['weight_decay'] *= total_batch_size * accumulate / nbs # scale weight_decay
pg0, pg1, pg2 = [], [], [] # optimizer parameter groups
for k, v in model.named_modules():
if hasattr(v, 'bias') and isinstance(v.bias, nn.Parameter):
pg2.append(v.bias) # biases
if isinstance(v, nn.BatchNorm2d):
pg0.append(v.weight) # no decay
elif hasattr(v, 'weight') and isinstance(v.weight, nn.Parameter):
pg1.append(v.weight) # apply decay
if opt.adam:
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)
optimizer.add_param_group({'params': pg1, 'weight_decay': hyp['weight_decay']}) # add pg1 with weight_decay
optimizer.add_param_group({'params': pg2}) # add pg2 (biases)
logger.info('Optimizer groups: %g .bias, %g conv.weight, %g other' % (len(pg2), len(pg1), len(pg0)))
del pg0, pg1, pg2
lf = lambda x: ((1 + math.cos(x * math.pi / epochs)) / 2) * (1 - hyp['lrf']) + hyp['lrf'] # cosine
scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf)
# plot_lr_scheduler(optimizer, scheduler, epochs)
# Logging
if wandb and wandb.run is None:
opt.hyp = hyp # add hyperparameters
wandb_run = wandb.init(config=opt, resume="allow",
project='YOLOv5' if opt.project == 'runs/train' else Path(opt.project).stem,
name=save_dir.stem,
id=ckpt.get('wandb_id') if 'ckpt' in locals() else None)
loggers = {'wandb': wandb} # loggers dict
# Resume
start_epoch, best_fitness = 0, 0.0
if pretrained:
# Optimizer
if ckpt['optimizer'] is not None:
optimizer.load_state_dict(ckpt['optimizer'])
best_fitness = ckpt['best_fitness']
# Results
if ckpt.get('training_results') is not None:
with open(results_file, 'w') as file:
file.write(ckpt['training_results']) # write results.txt
# Epochs
start_epoch = ckpt['epoch'] + 1
if opt.resume:
assert start_epoch > 0, '%s training to %g epochs is finished, nothing to resume.' % (weights, epochs)
if epochs < start_epoch:
logger.info('%s has been trained for %g epochs. Fine-tuning for %g additional epochs.' %
(weights, ckpt['epoch'], epochs))
epochs += ckpt['epoch'] # finetune additional epochs
del ckpt, state_dict
# Image sizes
gs = int(max(model.stride)) # grid size (max stride)
imgsz, imgsz_test = [check_img_size(x, gs) for x in opt.img_size] # verify imgsz are gs-multiples
# DP mode 模型并行
if cuda and rank == -1 and torch.cuda.device_count() > 1:
model = torch.nn.DataParallel(model)
# SyncBatchNorm
if opt.sync_bn and cuda and rank != -1:
model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model).to(device)
logger.info('Using SyncBatchNorm()')
# EMA
ema = ModelEMA(model) if rank in [-1, 0] else None
# DDP mode
if cuda and rank != -1:
model = DDP(model, device_ids=[opt.local_rank], output_device=opt.local_rank)
# Trainloader !!!!!!!!!!!! 通过列表的train_path 构建dataloader
# for 2 domains half the total_batch_size
print("batchsize is ", batch_size)
# 确定是batchsize的问题 尝试采用一个新的dataloader来进行赋值
# 这里 batch_size变量 在DDP模式下 是每张显卡上面的batchsize个数
bs_source = int(batch_size*0.98) # source dataloader built by bs but choose topk for training
#bs_target = math.ceil(batch_size*0.5) # bs_source + bs_target = batch_size
bs_target = math.ceil(batch_size*0.02)
bs_topk = int(bs_source*opt.k_por)
bs_add = bs_source - bs_topk # bs_source = bs_topk + bs_add
if bs_add > bs_topk: # 这里当前的写法其实不好 先这样做50%的实验 之后需要完善这里 需要完善成重复选取的版本
bs_add = bs_topk
print("bs_target is", bs_target)
print("bs_source is", bs_source)
print("bs_topk is ", bs_topk)
print("bs_add is ", bs_add)
dataloader, dataset = create_dataloader(train_path, imgsz, bs_source, gs, opt,
hyp=hyp, augment=True, cache=opt.cache_images, rect=opt.rect, rank=rank,
world_size=opt.world_size, workers=opt.workers,
image_weights=opt.image_weights)
dataloader_target, dataset_target = create_dataloader(train_target_path, imgsz, bs_target, gs, opt,
hyp=hyp, augment=True, cache=opt.cache_images, rect=opt.rect, rank=rank,
world_size=opt.world_size, workers=opt.workers,
image_weights=opt.image_weights)
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. Possible class labels are 0-%g' % (mlc, nc, opt.data, nc - 1)
# Process 0 全局master节点时运行以下程序
if rank in [-1, 0]:
ema.updates = start_epoch * nb // accumulate # set EMA updates
testloader = create_dataloader(test_path, imgsz_test, total_batch_size, gs, opt, # testloader
hyp=hyp, cache=opt.cache_images and not opt.notest, rect=True,
rank=-1, world_size=opt.world_size, workers=opt.workers, pad=0.5)[0]
if not opt.resume:
labels = np.concatenate(dataset.labels, 0)
c = torch.tensor(labels[:, 0]) # classes
# cf = torch.bincount(c.long(), minlength=nc) + 1. # frequency
# model._initialize_biases(cf.to(device))
if plots:
Thread(target=plot_labels, args=(labels, save_dir, loggers), daemon=True).start()
if tb_writer:
tb_writer.add_histogram('classes', c, 0)
# Anchors
if not opt.noautoanchor:
check_anchors(dataset, model=model, thr=hyp['anchor_t'], imgsz=imgsz)
# Model parameters
hyp['cls'] *= nc / 80. # scale coco-tuned hyp['cls'] to current dataset
model.nc = nc # attach number of classes to model
model.hyp = hyp # attach hyperparameters to model
model.gr = 1.0 # iou loss ratio (obj_loss = 1.0 or iou)
model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device) # attach class weights
model.names = names
# Start training
t0 = time.time()
nw = max(round(hyp['warmup_epochs'] * nb), 1000) # number of warmup iterations, max(3 epochs, 1k iterations)
# nw = min(nw, (epochs - start_epoch) / 2 * nb) # limit warmup to < 1/2 of training
maps = np.zeros(nc) # mAP per class
results = (0, 0, 0, 0, 0, 0, 0) # P, R, [email protected], [email protected], val_loss(box, obj, cls)
scheduler.last_epoch = start_epoch - 1 # do not move
scaler = amp.GradScaler(enabled=cuda)
logger.info('Image sizes %g train, %g test\n'
'Using %g dataloader workers\nLogging results to %s\n'
'Starting training for %g epochs...' % (imgsz, imgsz_test, dataloader.num_workers, save_dir, epochs))
for epoch in range(start_epoch, epochs): # epoch ------------------------------------------------------------------
# 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
model.eval()
mloss = torch.zeros(5, device=device) # mean losses | original 4 + added lkl
pbar = list(enumerate(dataloader)) # pbar 最终整合了整个dataloader
pbar_target = list(enumerate(dataloader_target)) # 这里把少量的样本当成目标域吧
target_feature = torch.tensor([]).to(device) # 不确定要不要.to(device)
print("caculating target dataset mean discranpancy")
imgs_t = torch.tensor([]).to(device)
for i, (imgs, targets, paths, space) in pbar_target: # 这里的imgs是torch.tensor 这段代码没有问题
imgs_t_i = imgs.to(device, non_blocking=True).float() / 255.0
imgs_feature = model(imgs_t_i)[1].detach().mean(3).mean(2) # imgs_feature [B, 1280]
imgs_t = torch.cat((imgs_t, imgs_t_i))
target_feature = torch.cat((target_feature, imgs_feature))
imgs_t_ = imgs_t.clone()
feature_t = target_feature.clone() # detach
# target_feature_mean = [8, 1280] target_feature.requires_grid = True
target_feature_mean = target_feature
torch.cuda.empty_cache()
print(target_feature_mean.shape)
print("merging dataloader to pbar") # 对pbar迭代 batch in pbar
for i, (imgs, targets, paths, _) in pbar: # pbar [(0, (2, 3)), (1, (2, 3))]
pbar[i] = list(pbar[i]) # pbar [[0, (2, 3)], [1, (2, 3)]]
pbar[i][1] = list(pbar[i][1]) # pbar [[0, [2, 3]], [1, [2, 3]]] 到现在pbar可以改变
i_tar, (imgs_tar, targets_tar, paths_tar, _) = random.sample(list(pbar_target) ,1)[0] # 选择一个batch的图片
imgs_tar_ = imgs_tar.clone()
targets_tar_ = targets_tar.clone() # targets_tar 把几张图片的目标全部都结合到了一起 [N, 6] 所以需要进行根据索引的筛选
# imgs_feature [bs, 1280]
imgs_feature = model(imgs.to(device, non_blocking=True).float() / 255.0)[1].detach().mean(3).mean(2)
imgs_topk_index = MMD_distance(target_feature_mean, imgs_feature, bs_topk)
if bs_add <= len(paths): # 增加对最后一个迭代的判断
imgs_add_index = torch.arange(0, bs_add)
else:
imgs_add_index = torch.arange(0, len(paths)) # 如果bs_add 大于 paths中有的元素个数 则全部复制paths中的样本
imgs, targets, paths = choice_topk(imgs, targets, paths, imgs_topk_index.cpu())
imgs_add, targets_add, paths_add = choice_topk(imgs, targets, paths, imgs_add_index)
#if opt.cross_domain_cp:
# imgs, imgs_tar_, targets, targets_tar_ = copy_paste_api(imgs, imgs_tar_, targets, targets_tar_)
targets_tar_[:, 0] = targets_tar_[:, 0] + imgs.shape[0]
targets_add[:, 0] = targets_add[:, 0] + imgs.shape[0] + imgs_tar_.shape[0]
imgs = torch.cat((imgs, imgs_tar_, imgs_add), 0) # refine
targets = torch.cat((targets, targets_tar_, targets_add), 0) #refine
paths = paths + paths_tar + paths_add # refine
#print("imgs shape is", imgs.shape)
#print("targets max is", max(targets[:,0]))
#print("paths len is", len(paths))
pbar[i][1][0] = imgs # 对pbar进行覆盖
pbar[i][1][1] = targets
pbar[i][1][2] = paths
print("merging end")
model.train()
if rank != -1:
dataloader.sampler.set_epoch(epoch)
dataloader_target.sampler.set_epoch(epoch)
logger.info(('\n' + '%10s' * 9) % ('Epoch', 'gpu_mem', 'box', 'obj', 'cls', 'mmd', 'total', 'targets', 'img_size'))
if rank in [-1, 0]:
pbar = tqdm(pbar, total=nb) # progress bar
optimizer.zero_grad()
for i, (imgs, targets, paths, _) in pbar: # batch -------------------------------------------------------------
ni = i + nb * epoch # number integrated batches (since train start)
imgs = imgs.to(device, non_blocking=True).float() / 255.0 # uint8 to float32, 0-255 to 0.0-1.0
# Warmup
if ni <= nw:
xi = [0, nw] # x interp
# model.gr = np.interp(ni, xi, [0.0, 1.0]) # iou loss ratio (obj_loss = 1.0 or iou)
accumulate = max(1, np.interp(ni, xi, [1, nbs / total_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
x['lr'] = np.interp(ni, xi, [hyp['warmup_bias_lr'] if j == 2 else 0.0, x['initial_lr'] * lf(epoch)])
if 'momentum' in x:
x['momentum'] = np.interp(ni, xi, [hyp['warmup_momentum'], hyp['momentum']])
# Multi-scale
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 target samples
# now updating source and target samples everytime
# 添加下面这段代码会导致 label分配到图片出现问题
#feature_t = torch.tensor([]).to(device)
#for i, (imgs_t, targets, paths, space) in pbar_target: # 这里的imgs是torch.tensor 这段代码没有问题
# feature_t_i = model(imgs_t.to(device, non_blocking=True).float() / 255.0)[1].mean(3).mean(2) # imgs_feature [B, 1280]
# feature_t = torch.cat((feature_t, feature_t_i))
#feature_t = model(imgs_t_)[1].mean(3).mean(2)
# Forward
with amp.autocast(enabled=cuda):
# pred: detection preds
# feature_s: [B, 1280, H, W] H = W = 20 for size 640 from backbone_feature
pred, feature_s = model(imgs)
loss, loss_items = compute_loss(pred, targets.to(device), model, feature_s.mean(3).mean(2), feature_t) # loss scaled by batch_size
if rank != -1:
loss *= opt.world_size # gradient averaged between devices in DDP mode
# Backward
scaler.scale(loss).backward()
# Optimize
if ni % accumulate == 0:
scaler.step(optimizer) # optimizer.step
scaler.update()
optimizer.zero_grad()
if ema:
ema.update(model)
# Print
if rank in [-1, 0]:
mloss = (mloss * i + loss_items) / (i + 1) # update mean losses
mem = '%.3gG' % (torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0) # (GB)
s = ('%10s' * 2 + '%10.4g' * 7) % (
'%g/%g' % (epoch, epochs - 1), mem, *mloss, targets.shape[0], imgs.shape[-1])
pbar.set_description(s)
# Plot
if plots and ni < 20:
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(model, imgs) # add model to tensorboard
elif plots and ni == 8 and wandb:
wandb.log({"Mosaics": [wandb.Image(str(x), caption=x.name) for x in save_dir.glob('train*.jpg')]})
#break # for debug mode
# 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
if ema:
ema.update_attr(model, include=['yaml', 'nc', 'hyp', 'gr', 'names', 'stride'])
final_epoch = epoch + 1 == epochs
if not opt.notest or final_epoch: # Calculate mAP
results, maps, times = test_MMD.test(opt.data,
batch_size=total_batch_size,
imgsz=imgsz_test,
model=ema.ema,
single_cls=opt.single_cls,
dataloader=testloader,
save_dir=save_dir,
plots=plots and final_epoch,
log_imgs=opt.log_imgs if wandb else 0)
# Write
with open(results_file, 'a') as f:
f.write(s + '%10.4g' * 7 % results + '\n') # P, R, [email protected], [email protected], val_loss(box, obj, cls)
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:
wandb.log({tag: x}) # W&B
# Update best mAP
fi = fitness(np.array(results).reshape(1, -1)) # weighted combination of [P, R, [email protected], [email protected]]
if fi > best_fitness:
best_fitness = fi
# Save model
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(),
'wandb_id': wandb_run.id if wandb else None}
# Save last, best and delete
torch.save(ckpt, last)
if best_fitness == fi:
torch.save(ckpt, best)
del ckpt
# end epoch ----------------------------------------------------------------------------------------------------
# end training
if rank in [-1, 0]:
# Strip optimizers
for f in [last, best]:
if f.exists(): # is *.pt
strip_optimizer(f) # strip optimizer
os.system('gsutil cp %s gs://%s/weights' % (f, opt.bucket)) if opt.bucket else None # upload
# Plots
if plots:
plot_results(save_dir=save_dir) # save as results.png
if wandb:
files = ['results.png', 'precision_recall_curve.png', 'confusion_matrix.png']
wandb.log({"Results": [wandb.Image(str(save_dir / f), caption=f) for f in files
if (save_dir / f).exists()]})
logger.info('%g epochs completed in %.3f hours.\n' % (epoch - start_epoch + 1, (time.time() - t0) / 3600))
# Test best.pt
if opt.data.endswith('coco.yaml') and nc == 80: # if COCO
results, _, _ = test_MMD.test(opt.data,
batch_size=total_batch_size,
imgsz=imgsz_test,
model=attempt_load(best if best.exists() else last, device).half(),
single_cls=opt.single_cls,
dataloader=testloader,
save_dir=save_dir,
save_json=True, # use pycocotools
plots=False)
else:
dist.destroy_process_group()
wandb.run.finish() if wandb and wandb.run else None
torch.cuda.empty_cache()
return results
# opt.cross_domain_cp
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--weights', type=str, default='', help='initial weights path') #修改默认为从头开始训练
parser.add_argument('--cfg', type=str, default='', help='model.yaml path')
parser.add_argument('--data', type=str, default='data/coco128.yaml', help='data.yaml path')
parser.add_argument('--hyp', type=str, default='data/hyp.scratch.yaml', help='hyperparameters path')
parser.add_argument('--cross_domain_cp', type=int, default=1, help = 'whether use cross_domain_copy_paste, default is using')
parser.add_argument('--epochs', type=int, default=300)
parser.add_argument('--batch-size', type=int, default=16, help='total batch size for all GPUs')
parser.add_argument('--k_por', type=float, default=0.8, help='filtering proportion ration for distribution optimization')
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 as single-class dataset')
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('--log-imgs', type=int, default=16, help='number of images for W&B logging, max 100')
parser.add_argument('--workers', type=int, default=8, help='maximum number of dataloader workers')
parser.add_argument('--project', default='runs/train', help='save to project/name')
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')
opt = parser.parse_args()
# Set DDP variables
opt.total_batch_size = opt.batch_size
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)
if opt.global_rank in [-1, 0]:
check_git_status()
# Resume
if opt.resume: # 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'
with open(Path(ckpt).parent.parent / 'opt.yaml') as f:
opt = argparse.Namespace(**yaml.load(f, Loader=yaml.FullLoader)) # replace
opt.cfg, opt.weights, opt.resume = '', ckpt, True
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
# DDP mode
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.FullLoader) # load hyps
if 'box' not in hyp:
warn('Compatibility: %s missing "box" which was renamed from "giou" in %s' %
(opt.hyp, 'https://github.com/ultralytics/yolov5/pull/1120'))
hyp['box'] = hyp.pop('giou')
# Train
logger.info(opt)
if not opt.evolve:
tb_writer = None # init loggers
if opt.global_rank in [-1, 0]:
logger.info(f'Start Tensorboard with "tensorboard --logdir {opt.project}", view at http://localhost:6006/')
tb_writer = SummaryWriter(opt.save_dir) # Tensorboard
train(hyp, opt, device, tb_writer, wandb)