YOLOV7剪枝流程

1、训练

1）划分数据集进行训练前的准备，按正常的划分流程即可

2）修改train.py文件

第一次处在参数列表里添加剪枝的参数，正常训练时设置为False，剪枝后微调时设置为True

parser.add_argument('--pruned', action='store_true', default=True, help='pruned model fine train')

第二处位置在

# Resume

下面修改代码，源代码为：

   # Resumestart_epoch, best_fitness = 0, 0.0if pretrained:# Optimizerif ckpt['optimizer'] is not None:optimizer.load_state_dict(ckpt['optimizer'])best_fitness = ckpt['best_fitness']# EMAif ema and ckpt.get('ema'):ema.ema.load_state_dict(ckpt['ema'].float().state_dict())ma.updates = ckpt['updates']# Resultsif ckpt.get('training_results') is not None:results_file.write_text(ckpt['training_results'])  # write results.txt

修改为：

   # Resumestart_epoch, best_fitness = 0, 0.0if pretrained:if not opt.pruned:# Optimizerif ckpt['optimizer'] is not None:optimizer.load_state_dict(ckpt['optimizer'])best_fitness = ckpt['best_fitness']# EMAif ema and ckpt.get('ema'):ema.ema.load_state_dict(ckpt['ema'].float().state_dict())ema.updates = ckpt['updates']# Resultsif ckpt.get('training_results') is not None:results_file.write_text(ckpt['training_results'])  # write results.txt

第三处位置在

# Epochs

源代码为：

         # Epochsstart_epoch = ckpt['epoch'] + 1if 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 epochsdel ckpt, state_dict

修改为

        # Epochsif not opt.pruned:start_epoch = ckpt['epoch'] + 1if opt.resume:assert start_epoch > 0, '%s training to %g epochs is finished, nothing to resume.' % (weights, epochs)elif opt.pruned:ckpt['epoch'] = 0start_epoch = ckpt['epoch'] + 1if 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 epochsif not opt.pruned:del ckpt, state_dictelif opt.pruned:del ckpt

第四处位置在

# Save model

源代码为：

         # Save modelif (not opt.nosave) or (final_epoch and not opt.evolve):  # if saveckpt = {'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}

修改为：

            # Save modelif (not opt.nosave) or (final_epoch and not opt.evolve):  # if saveif opt.pruned:ckpt = {'model': deepcopy(model.module if is_parallel(model) else model).half(),}elif not opt.pruned: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}

修改后的train.py整体代码如下：

import argparse
import logging
import math
import os
import random
import time
from copy import deepcopy
from pathlib import Path
from threading import Threadimport 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 tqdmimport test  # import test.py to get mAP after each epoch
from models.experimental import attempt_load
from models.yolo import Model
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, \check_requirements, print_mutation, set_logging, one_cycle, colorstr
from utils.google_utils import attempt_download
from utils.loss import ComputeLoss, ComputeLossOTA
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, is_parallel
from utils.wandb_logging.wandb_utils import WandbLogger, check_wandb_resumelogger = logging.getLogger(__name__)def train(hyp, opt, device, tb_writer=None):logger.info(colorstr('hyperparameters: ') + ', '.join(f'{k}={v}' for k, v in hyp.items()))save_dir, epochs, batch_size, total_batch_size, weights, rank, freeze = \Path(opt.save_dir), opt.epochs, opt.batch_size, opt.total_batch_size, opt.weights, opt.global_rank, opt.freeze# Directorieswdir = save_dir / 'weights'wdir.mkdir(parents=True, exist_ok=True)  # make dirlast = wdir / 'last.pt'best = wdir / 'best.pt'results_file = save_dir / 'results.txt'# Save run settingswith 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)# Configureplots = not opt.evolve  # create plotscuda = device.type != 'cpu'init_seeds(2 + rank)with open(opt.data) as f:data_dict = yaml.load(f, Loader=yaml.SafeLoader)  # data dictis_coco = opt.data.endswith('coco.yaml')# Logging- Doing this before checking the dataset. Might update data_dictloggers = {'wandb': None}  # loggers dictif rank in [-1, 0]:opt.hyp = hyp  # add hyperparametersrun_id = torch.load(weights, map_location=device).get('wandb_id') if weights.endswith('.pt') and os.path.isfile(weights) else Nonewandb_logger = WandbLogger(opt, Path(opt.save_dir).stem, run_id, data_dict)loggers['wandb'] = wandb_logger.wandbdata_dict = wandb_logger.data_dictif wandb_logger.wandb:weights, epochs, hyp = opt.weights, opt.epochs, opt.hyp  # WandbLogger might update weights, epochs if resumingnc = 1 if opt.single_cls else int(data_dict['nc'])  # number of classesnames = ['item'] if opt.single_cls and len(data_dict['names']) != 1 else data_dict['names']  # class namesassert len(names) == nc, '%g names found for nc=%g dataset in %s' % (len(names), nc, opt.data)  # check# Modelpretrained = weights.endswith('.pt')if pretrained:if opt.pruned:from models.yolo import attempt_load#model = attempt_load(weights,map_location=device)ckpt = torch.load(weights, map_location=device)model = ckpt['model']else:with torch_distributed_zero_first(rank):attempt_download(weights)  # download if not found locallyckpt = torch.load(weights, map_location=device)  # 加载模型# 模型的定义model = Model(opt.cfg or ckpt['model'].yaml, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device)  # createexclude = ['anchor'] if (opt.cfg or hyp.get('anchors'))else []  # exclude keysstate_dict = ckpt['model'].float().state_dict()  # to FP32 获得预权重的权值state_dict = intersect_dicts(state_dict, model.state_dict(), exclude=exclude)  # intersect# 将权重加载到模型内model.load_state_dict(state_dict, strict=False)  # loadlogger.info('Transferred %g/%g items from %s' % (len(state_dict), len(model.state_dict()), weights))  # report# with torch_distributed_zero_first(rank):#     attempt_download(weights)  # download if not found locally# ckpt = torch.load(weights, map_location=device)  # load checkpoint# model = Model(opt.cfg or ckpt['model'].yaml, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device)  # create# exclude = ['anchor'] if (opt.cfg or hyp.get('anchors')) and not opt.resume 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.load_state_dict(state_dict, strict=False)  # load# logger.info('Transferred %g/%g items from %s' % (len(state_dict), len(model.state_dict()), weights))  # reportelse:model = Model(opt.cfg, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device)  # createwith torch_distributed_zero_first(rank):check_dataset(data_dict)  # checktrain_path = data_dict['train']test_path = data_dict['val']# Freezefreeze = [f'model.{x}.' for x in (freeze if len(freeze) > 1 else range(freeze[0]))]  # parameter names to freeze (full or partial)for k, v in model.named_parameters():v.requires_grad = True  # train all layersif any(x in k for x in freeze):print('freezing %s' % k)v.requires_grad = False# Optimizernbs = 64  # nominal batch sizeaccumulate = max(round(nbs / total_batch_size), 1)  # accumulate loss before optimizinghyp['weight_decay'] *= total_batch_size * accumulate / nbs  # scale weight_decaylogger.info(f"Scaled weight_decay = {hyp['weight_decay']}")pg0, pg1, pg2 = [], [], []  # optimizer parameter groupsfor k, v in model.named_modules():if hasattr(v, 'bias') and isinstance(v.bias, nn.Parameter):pg2.append(v.bias)  # biasesif isinstance(v, nn.BatchNorm2d):pg0.append(v.weight)  # no decayelif hasattr(v, 'weight') and isinstance(v.weight, nn.Parameter):pg1.append(v.weight)  # apply decayif hasattr(v, 'im'):if hasattr(v.im, 'implicit'):           pg0.append(v.im.implicit)else:for iv in v.im:pg0.append(iv.implicit)if hasattr(v, 'imc'):if hasattr(v.imc, 'implicit'):           pg0.append(v.imc.implicit)else:for iv in v.imc:pg0.append(iv.implicit)if hasattr(v, 'imb'):if hasattr(v.imb, 'implicit'):           pg0.append(v.imb.implicit)else:for iv in v.imb:pg0.append(iv.implicit)if hasattr(v, 'imo'):if hasattr(v.imo, 'implicit'):           pg0.append(v.imo.implicit)else:for iv in v.imo:pg0.append(iv.implicit)if hasattr(v, 'ia'):if hasattr(v.ia, 'implicit'):           pg0.append(v.ia.implicit)else:for iv in v.ia:pg0.append(iv.implicit)if hasattr(v, 'attn'):if hasattr(v.attn, 'logit_scale'):   pg0.append(v.attn.logit_scale)if hasattr(v.attn, 'q_bias'):   pg0.append(v.attn.q_bias)if hasattr(v.attn, 'v_bias'):  pg0.append(v.attn.v_bias)if hasattr(v.attn, 'relative_position_bias_table'):  pg0.append(v.attn.relative_position_bias_table)if hasattr(v, 'rbr_dense'):if hasattr(v.rbr_dense, 'weight_rbr_origin'):  pg0.append(v.rbr_dense.weight_rbr_origin)if hasattr(v.rbr_dense, 'weight_rbr_avg_conv'): pg0.append(v.rbr_dense.weight_rbr_avg_conv)if hasattr(v.rbr_dense, 'weight_rbr_pfir_conv'):  pg0.append(v.rbr_dense.weight_rbr_pfir_conv)if hasattr(v.rbr_dense, 'weight_rbr_1x1_kxk_idconv1'): pg0.append(v.rbr_dense.weight_rbr_1x1_kxk_idconv1)if hasattr(v.rbr_dense, 'weight_rbr_1x1_kxk_conv2'):   pg0.append(v.rbr_dense.weight_rbr_1x1_kxk_conv2)if hasattr(v.rbr_dense, 'weight_rbr_gconv_dw'):   pg0.append(v.rbr_dense.weight_rbr_gconv_dw)if hasattr(v.rbr_dense, 'weight_rbr_gconv_pw'):   pg0.append(v.rbr_dense.weight_rbr_gconv_pw)if hasattr(v.rbr_dense, 'vector'):   pg0.append(v.rbr_dense.vector)if opt.adam:optimizer = optim.Adam(pg0, lr=hyp['lr0'], betas=(hyp['momentum'], 0.999))  # adjust beta1 to momentumelse: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_decayoptimizer.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# Scheduler https://arxiv.org/pdf/1812.01187.pdf# https://pytorch.org/docs/stable/_modules/torch/optim/lr_scheduler.html#OneCycleLRif opt.linear_lr:lf = lambda x: (1 - x / (epochs - 1)) * (1.0 - hyp['lrf']) + hyp['lrf']  # linearelse:lf = one_cycle(1, hyp['lrf'], epochs)  # cosine 1->hyp['lrf']scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf)# plot_lr_scheduler(optimizer, scheduler, epochs)# EMAema = ModelEMA(model) if rank in [-1, 0] else None# Resumestart_epoch, best_fitness = 0, 0.0if pretrained:if not opt.pruned:# Optimizerif ckpt['optimizer'] is not None:optimizer.load_state_dict(ckpt['optimizer'])best_fitness = ckpt['best_fitness']# EMAif ema and ckpt.get('ema'):ema.ema.load_state_dict(ckpt['ema'].float().state_dict())ema.updates = ckpt['updates']# Resultsif ckpt.get('training_results') is not None:results_file.write_text(ckpt['training_results'])  # write results.txt# Epochsif not opt.pruned:start_epoch = ckpt['epoch'] + 1if opt.resume:assert start_epoch > 0, '%s training to %g epochs is finished, nothing to resume.' % (weights, epochs)elif opt.pruned:ckpt['epoch'] = 0start_epoch = ckpt['epoch'] + 1if 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 epochsif not opt.pruned:del ckpt, state_dictelif opt.pruned:del ckpt# 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 sizesgs = max(int(model.stride.max()), 32)  # grid size (max stride)nl = model.model[-1].nl  # number of detection layers (used for scaling hyp['obj'])imgsz, imgsz_test = [check_img_size(x, gs) for x in opt.img_size]  # verify imgsz are gs-multiples# DP modeif cuda and rank == -1 and torch.cuda.device_count() > 1:model = torch.nn.DataParallel(model)# SyncBatchNormif opt.sync_bn and cuda and rank != -1:model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model).to(device)logger.info('Using SyncBatchNorm()')# Trainloaderdataloader, dataset = create_dataloader(train_path, imgsz, batch_size, 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, quad=opt.quad, prefix=colorstr('train: '))mlc = np.concatenate(dataset.labels, 0)[:, 0].max()  # max label classnb = len(dataloader)  # number of batchesassert mlc < nc, 'Label class %g exceeds nc=%g in %s. Possible class labels are 0-%g' % (mlc, nc, opt.data, nc - 1)# Process 0if rank in [-1, 0]:testloader = create_dataloader(test_path, imgsz_test, batch_size * 2, gs, opt,  # testloaderhyp=hyp, cache=opt.cache_images and not opt.notest, rect=True, rank=-1,world_size=opt.world_size, workers=opt.workers,pad=0.5, prefix=colorstr('val: '))[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:#plot_labels(labels, names, save_dir, loggers)if tb_writer:tb_writer.add_histogram('classes', c, 0)# Anchorsif not opt.noautoanchor:check_anchors(dataset, model=model, thr=hyp['anchor_t'], imgsz=imgsz)model.half().float()  # pre-reduce anchor precision# DDP modeif cuda and rank != -1:model = DDP(model, device_ids=[opt.local_rank], output_device=opt.local_rank,# nn.MultiheadAttention incompatibility with DDP https://github.com/pytorch/pytorch/issues/26698find_unused_parameters=any(isinstance(layer, nn.MultiheadAttention) for layer in model.modules()))# Model parametershyp['box'] *= 3. / nl  # scale to layershyp['cls'] *= nc / 80. * 3. / nl  # scale to classes and layershyp['obj'] *= (imgsz / 640) ** 2 * 3. / nl  # scale to image size and layershyp['label_smoothing'] = opt.label_smoothingmodel.nc = nc  # attach number of classes to modelmodel.hyp = hyp  # attach hyperparameters to modelmodel.gr = 1.0  # iou loss ratio (obj_loss = 1.0 or iou)model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device) * nc  # attach class weightsmodel.names = names# Start trainingt0 = 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 trainingmaps = np.zeros(nc)  # mAP per classresults = (0, 0, 0, 0, 0, 0, 0)  # P, R, mAP@.5, mAP@.5-.95, val_loss(box, obj, cls)scheduler.last_epoch = start_epoch - 1  # do not movescaler = amp.GradScaler(enabled=cuda)compute_loss_ota = ComputeLossOTA(model)  # init loss classcompute_loss = ComputeLoss(model)  # init loss classlogger.info(f'Image sizes {imgsz} train, {imgsz_test} test\n'f'Using {dataloader.num_workers} dataloader workers\n'f'Logging results to {save_dir}\n'f'Starting training for {epochs} epochs...')torch.save(model, wdir / 'init.pt')for epoch in range(start_epoch, epochs):  # epoch ------------------------------------------------------------------model.train()# Update image weights (optional)if opt.image_weights:# Generate indicesif rank in [-1, 0]:cw = model.class_weights.cpu().numpy() * (1 - maps) ** 2 / nc  # class weightsiw = labels_to_image_weights(dataset.labels, nc=nc, class_weights=cw)  # image weightsdataset.indices = random.choices(range(dataset.n), weights=iw, k=dataset.n)  # rand weighted idx# Broadcast if DDPif rank != -1:indices = (torch.tensor(dataset.indices) if rank == 0 else torch.zeros(dataset.n)).int()dist.broadcast(indices, 0)if rank != 0:dataset.indices = indices.cpu().numpy()# Update mosaic border# b = int(random.uniform(0.25 * imgsz, 0.75 * imgsz + gs) // gs * gs)# dataset.mosaic_border = [b - imgsz, -b]  # height, width bordersmloss = torch.zeros(4, device=device)  # mean lossesif rank != -1:dataloader.sampler.set_epoch(epoch)pbar = enumerate(dataloader)logger.info(('\n' + '%10s' * 8) % ('Epoch', 'gpu_mem', 'box', 'obj', 'cls', 'total', 'labels', 'img_size'))if rank in [-1, 0]:pbar = tqdm(pbar, total=nb)  # progress baroptimizer.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# Warmupif 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 lr0x['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-scaleif opt.multi_scale:sz = random.randrange(imgsz * 0.5, imgsz * 1.5 + gs) // gs * gs  # sizesf = sz / max(imgs.shape[2:])  # scale factorif 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)# Forwardwith amp.autocast(enabled=cuda):pred = model(imgs)  # forwardif 'loss_ota' not in hyp or hyp['loss_ota'] == 1:loss, loss_items = compute_loss_ota(pred, targets.to(device), imgs)  # loss scaled by batch_sizeelse:loss, loss_items = compute_loss(pred, targets.to(device))  # loss scaled by batch_sizeif rank != -1:loss *= opt.world_size  # gradient averaged between devices in DDP modeif opt.quad:loss *= 4.# Backwardscaler.scale(loss).backward()# Optimizeif ni % accumulate == 0:scaler.step(optimizer)  # optimizer.stepscaler.update()optimizer.zero_grad()if ema:ema.update(model)# Printif rank in [-1, 0]:mloss = (mloss * i + loss_items) / (i + 1)  # update mean lossesmem = '%.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)# Plotif plots and ni < 10:f = save_dir / f'train_batch{ni}.jpg'  # filenameThread(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 graphelif plots and ni == 10 and wandb_logger.wandb:wandb_logger.log({"Mosaics": [wandb_logger.wandb.Image(str(x), caption=x.name) for x insave_dir.glob('train*.jpg') if x.exists()]})# end batch ------------------------------------------------------------------------------------------------# end epoch ----------------------------------------------------------------------------------------------------# Schedulerlr = [x['lr'] for x in optimizer.param_groups]  # for tensorboardscheduler.step()# DDP process 0 or single-GPUif rank in [-1, 0]:# mAPema.update_attr(model, include=['yaml', 'nc', 'hyp', 'gr', 'names', 'stride', 'class_weights'])final_epoch = epoch + 1 == epochsif not opt.notest or final_epoch:  # Calculate mAPwandb_logger.current_epoch = epoch + 1results, 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,v5_metric=opt.v5_metric)# Writewith open(results_file, 'a') as f:f.write(s + '%10.4g' * 7 % results + '\n')  # append metrics, val_lossif len(opt.name) and opt.bucket:os.system('gsutil cp %s gs://%s/results/results%s.txt' % (results_file, opt.bucket, opt.name))# Logtags = ['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']  # paramsfor x, tag in zip(list(mloss[:-1]) + list(results) + lr, tags):if tb_writer:tb_writer.add_scalar(tag, x, epoch)  # tensorboardif wandb_logger.wandb:wandb_logger.log({tag: x})  # W&B# Update best mAPfi = fitness(np.array(results).reshape(1, -1))  # weighted combination of [P, R, mAP@.5, mAP@.5-.95]if fi > best_fitness:best_fitness = fiwandb_logger.end_epoch(best_result=best_fitness == fi)# Save modelif (not opt.nosave) or (final_epoch and not opt.evolve):  # if saveif opt.pruned:ckpt = {'model': deepcopy(model.module if is_parallel(model) else model).half(),}elif not opt.pruned: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 deletetorch.save(ckpt, last)if best_fitness == fi:torch.save(ckpt, best)if (best_fitness == fi) and (epoch >= 200):torch.save(ckpt, wdir / 'best_{:03d}.pt'.format(epoch))if epoch == 0:torch.save(ckpt, wdir / 'epoch_{:03d}.pt'.format(epoch))elif ((epoch+1) % 25) == 0:torch.save(ckpt, wdir / 'epoch_{:03d}.pt'.format(epoch))elif epoch >= (epochs-5):torch.save(ckpt, wdir / 'epoch_{:03d}.pt'.format(epoch))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 trainingif rank in [-1, 0]:# Plotsif plots:plot_results(save_dir=save_dir)  # save as results.pngif 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 filesif (save_dir / f).exists()]})# Test best.ptlogger.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 COCOfor m in (last, best) if best.exists() else (last):  # speed, mAP testsresults, _, _ = 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,v5_metric=opt.v5_metric)# Strip optimizersfinal = best if best.exists() else last  # final modelfor f in last, best:if f.exists():strip_optimizer(f)  # strip optimizersif opt.bucket:os.system(f'gsutil cp {final} gs://{opt.bucket}/weights')  # uploadif wandb_logger.wandb and not opt.evolve:  # Log the stripped modelwandb_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 resultsif __name__ == '__main__':parser = argparse.ArgumentParser()parser.add_argument('--weights', type=str, default='my_dataset/layer_pruning.pt', help='initial weights path')parser.add_argument('--cfg', type=str, default='cfg/training/yolov7-tiny.yaml', help='model.yaml path')parser.add_argument('--data', type=str, default='my_dataset/coco.yaml', help='data.yaml path')parser.add_argument('--hyp', type=str, default='data/hyp.scratch.p5.yaml', help='hyperparameters path')parser.add_argument('--epochs', type=int, default=600)parser.add_argument('--batch-size', type=int, default=32, 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='0', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')parser.add_argument('--multi-scale', action='store_true', default=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=16, 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=2, 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')parser.add_argument('--freeze', nargs='+', type=int, default=[0], help='Freeze layers: backbone of yolov7=50, first3=0 1 2')parser.add_argument('--v5-metric', action='store_true', help='assume maximum recall as 1.0 in AP calculation')parser.add_argument('--pruned', action='store_true', default=True, help='pruned model fine train')opt = parser.parse_args()# Set DDP variablesopt.world_size = int(os.environ['WORLD_SIZE']) if 'WORLD_SIZE' in os.environ else 1opt.global_rank = int(os.environ['RANK']) if 'RANK' in os.environ else -1set_logging(opt.global_rank)#if opt.global_rank in [-1, 0]:#    check_git_status()#    check_requirements()# Resumewandb_run = check_wandb_resume(opt)if opt.resume and not wandb_run:  # resume an interrupted runckpt = opt.resume if isinstance(opt.resume, str) else get_latest_run()  # specified or most recent pathassert os.path.isfile(ckpt), 'ERROR: --resume checkpoint does not exist'apriori = opt.global_rank, opt.local_rankwith open(Path(ckpt).parent.parent / 'opt.yaml') as f:opt = argparse.Namespace(**yaml.load(f, Loader=yaml.SafeLoader))  # replaceopt.save_dir = Path(ckpt).parent.parent  # increment runopt.cfg, opt.weights, opt.resume, opt.batch_size, opt.global_rank, opt.local_rank = '', ckpt, True, opt.total_batch_size, *apriori  # reinstatelogger.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 filesassert 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.nameopt.save_dir = increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok | opt.evolve)  # increment run# DDP modeopt.total_batch_size = opt.batch_sizedevice = select_device(opt.device, batch_size=opt.batch_size)if opt.local_rank != -1:assert torch.cuda.device_count() > opt.local_ranktorch.cuda.set_device(opt.local_rank)device = torch.device('cuda', opt.local_rank)dist.init_process_group(backend='nccl', init_method='env://')  # distributed backendassert 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# Hyperparameterswith open(opt.hyp) as f:hyp = yaml.load(f, Loader=yaml.SafeLoader)  # load hyps# Trainlogger.info(opt)if not opt.evolve:tb_writer = None  # init loggersif 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)  # Tensorboardtrain(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)'copy_paste': (1, 0.0, 1.0),  # segment copy-paste (probability)'paste_in': (1, 0.0, 1.0)}    # segment copy-paste (probability)with open(opt.hyp, errors='ignore') as f:hyp = yaml.safe_load(f)  # load hyps dictif 'anchors' not in hyp:  # anchors commented in hyp.yamlhyp['anchors'] = 3assert 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 indicesyaml_file = Path(opt.save_dir) / 'hyp_evolved.yaml'  # save best result hereif opt.bucket:os.system('gsutil cp gs://%s/evolve.txt .' % opt.bucket)  # download evolve.txt if existsfor _ in range(300):  # generations to evolveif 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 considerx = x[np.argsort(-fitness(x))][:n]  # top n mutationsw = fitness(x) - fitness(x).min()  # weightsif parent == 'single' or len(x) == 1:# x = x[random.randint(0, n - 1)]  # random selectionx = x[random.choices(range(n), weights=w)[0]]  # weighted selectionelif parent == 'weighted':x = (x * w.reshape(n, 1)).sum(0) / w.sum()  # weighted combination# Mutatemp, s = 0.8, 0.2  # mutation probability, sigmanpr = np.randomnpr.seed(int(time.time()))g = np.array([x[0] for x in meta.values()])  # gains 0-1ng = 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 limitsfor k, v in meta.items():hyp[k] = max(hyp[k], v[1])  # lower limithyp[k] = min(hyp[k], v[2])  # upper limithyp[k] = round(hyp[k], 5)  # significant digits# Train mutationresults = train(hyp.copy(), opt, device)# Write mutation resultsprint_mutation(hyp.copy(), results, yaml_file, opt.bucket)# Plot resultsplot_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}')

2、剪枝

剪枝方法

YOLOv4剪枝【附代码】_strategy = tp.strategy.l1strategy()-CSDN博客参考此篇博文进行的通道剪枝。Pruning Filters for Efficient ConvNets这篇论文的技术

添加prunmodel.py文件

下载torch_pruning模块时一定要使用0.2.5版本，

pip install torch_pruning==0.2.7
pip install loguru

把训练好的模型路径放进去

layer_pruning('/home/jovyan/exp_3046/runs/train/best.pt')  

定义剪枝后保存的模型路径

torch.save(model_, '/home/jovyan/exp_3047/data/layer_pruning.pt')

修改prunmodel.py文件中需要剪枝的权重路径。重点修改58~62行。这里是以修改model的前10层为例。head层不能剪，我们选择backbone的层。

  included_layers = []for layer in model.model[:10]:  # 获取backboneif type(layer) is Conv:included_layers.append(layer.conv)included_layers.append(layer.bn)

下面代码是剪枝conv和BN层。【重点是tp.prune_conv】，自己修改amout也就是剪枝率。

        if isinstance(m, nn.Conv2d) and m in included_layers:# amount是剪枝率# 卷积剪枝pruning_plan = DG.get_pruning_plan(m, tp.prune_conv, idxs=strategy(m.weight, amount=0.8))logger.info(pruning_plan)# 执行剪枝pruning_plan.exec()if isinstance(m, nn.BatchNorm2d) and m in included_layers:# BN层剪枝pruning_plan = DG.get_pruning_plan(m, tp.prune_batchnorm, idxs=strategy(m.weight, amount=0.8))logger.info(pruning_plan)pruning_plan.exec()

出现以下内容说明剪枝成功
2023-03-15 14:57:40.825 | INFO     | __main__:layer_pruning:84 -   Params: 37196556 => 36839795
​
2023-03-15 14:57:41.176 | INFO     | __main__:layer_pruning:95 - 剪枝完成

代码如下：

import syssys.path.append("/home/jovyan/exp_3046")
# print(sys.path)import torch_pruning as tp
from loguru import logger
from models.common import *
from models.experimental import Ensemble
from utils.torch_utils import select_device"""
剪枝的时候根据模型结构去剪，不要盲目的猜
剪枝完需要进行一个微调训练
"""# 加载模型
def attempt_load(weights, map_location=None, inplace=True):from models.yolo import Detect, Modelmodel = Ensemble()ckpt = torch.load(weights, map_location=map_location)  # load weightsmodel.append(ckpt['ema' if ckpt.get('ema') else 'model'].float().eval())  # without layer fuse  权值的加载# Compatibility updatesfor m in model.modules():  # 取出每一层if type(m) in [nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU, Detect, Model]:m.inplace = inplace  # pytorch 1.7.0 compatibilityif type(m) is Detect:  # 判断是否为目标检测if not isinstance(m.anchor_grid, list):  # new Detect Layer compatibilitydelattr(m, 'anchor_grid')setattr(m, 'anchor_grid', [torch.zeros(1)] * m.nl)elif type(m) is Conv:  # 卷积层m._non_persistent_buffers_set = set()  # pytorch 1.6.0 compatibilityif len(model) == 1:return model[-1], ckpt  # return modelelse:print(f'Ensemble created with {weights}\n')for k in ['names']:setattr(model, k, getattr(model[-1], k))model.stride = model[torch.argmax(torch.tensor([m.stride.max() for m in model])).int()].stride  # max stridereturn model, ckpt  # return ensemble@logger.catch
def layer_pruning(weights):logger.add('../logs/layer_pruning.log', rotation='1 MB')device = select_device('cpu')model, ckpt = attempt_load(weights, map_location=device)for para in model.parameters():para.requires_grad = True# 创建输入样例，可在此修改输入大小x = torch.zeros(1, 3, 640, 640)# -----------------对整个模型的剪枝--------------------strategy = tp.strategy.L1Strategy()  # L1策略DG = tp.DependencyGraph()  # 依赖图DG = DG.build_dependency(model, example_inputs=x)"""这里写要剪枝的层这里以backbone为例"""included_layers = []for layer in model.model[:41]:  # 获取backboneif type(layer) is Conv:included_layers.append(layer.conv)included_layers.append(layer.bn)logger.info(included_layers)# 获取未剪枝之前的参数量num_params_before_pruning = tp.utils.count_params(model)# 模型遍历for m in model.modules():# 判断是否为卷积并且是否在需要剪枝的层里if isinstance(m, nn.Conv2d) and m in included_layers:# amount是剪枝率# 卷积剪枝# 层剪枝(需要筛选出不需要剪枝的层，比如yolo需要把头部的预测部分取出来，这个是不需要剪枝的) pruning_plan = DG.get_pruning_plan(m, tp.prune_conv, idxs=strategy(m.weight, amount=0.4))logger.info(pruning_plan)# 执行剪枝pruning_plan.exec()if isinstance(m, nn.BatchNorm2d) and m in included_layers:# BN层剪枝pruning_plan = DG.get_pruning_plan(m, tp.prune_batchnorm, idxs=strategy(m.weight, amount=0.3))logger.info(pruning_plan)pruning_plan.exec()# 获得剪枝以后的参数量num_params_after_pruning = tp.utils.count_params(model)# 输出一下剪枝前后的参数量logger.info("  Params: %s => %s\n" % (num_params_before_pruning, num_params_after_pruning))# 剪枝完以后模型的保存(不要用torch.save(model.state_dict(),...))model_ = {'model': model.half(),# 'optimizer': ckpt['optimizer'],# 'training_results': ckpt['training_results'],'epoch': ckpt['epoch']}torch.save(model_, '/home/jovyan/exp_3046/my_dataset/layer_pruning.pt')del model_, ckptlogger.info("剪枝完成\n")layer_pruning('/home/jovyan/exp_3046/runs/train/best.pt')  

3、微调

运行train.py

权重为剪枝之后的layer_pruning.pt

parser.add_argument('--pruned', action='store_true', default=True, help='pruned model fine train')

4、效果

使用初始训练好的权重进行预测用时 249.217s

使用剪枝之后的模型进行预测用时172.418s

p.prune_batchnorm, idxs=strategy(m.weight, amount=0.3))logger.info(pruning_plan)pruning_plan.exec()# 获得剪枝以后的参数量num_params_after_pruning = tp.utils.count_params(model)# 输出一下剪枝前后的参数量logger.info("  Params: %s => %s\n" % (num_params_before_pruning, num_params_after_pruning))# 剪枝完以后模型的保存(不要用torch.save(model.state_dict(),...))model_ = {'model': model.half(),# 'optimizer': ckpt['optimizer'],# 'training_results': ckpt['training_results'],'epoch': ckpt['epoch']}torch.save(model_, '/home/jovyan/exp_3046/my_dataset/layer_pruning.pt')del model_, ckptlogger.info("剪枝完成\n")layer_pruning('/home/jovyan/exp_3046/runs/train/best.pt')  

3、微调

运行train.py

权重为剪枝之后的layer_pruning.pt

parser.add_argument('--pruned', action='store_true', default=True, help='pruned model fine train')

4、效果

使用初始训练好的权重进行预测用时 249.217s

使用剪枝之后的模型进行预测用时172.418s