基于depgraph的模型压缩——使用coco128+yolov5验证_depgraph: towards any structural pruning

参考论文是23年发表在cvpr的论文，DepGraph: Towards Any Structural Pruning，是一种结构化的模型压缩算法。凭记忆写的，可能会有些小问题...

一、torch_pruning环境搭建

1. 创建conda虚拟环境

conda create -n ModelPruning python=3.9

2. 到官网下载代码解压，cd到对应文件夹里安装环境

pip install torch-pruning 

3. 测试一下

# 下载预训练模型
wget https://github.com/VainF/Torch-Pruning/releases/download/v1.1.4/cifar10_resnet56.pth
 
# 按提示装包
 
# 把模型放到相应文件夹里，运行depgraph算法
python benchmarks/main.py --mode prune --model resnet56 --batch-size 128 --restore models/trained/cifar10_resnet56.pth --dataset cifar10  --method group_sl --speed-up 2.11 --global-pruning 

二、模型与数据集

仍然使用上面的conda环境

1. 下载yolov5的代码（官网），安装依赖

pip install -U -r requirements.txt

我的这条命令会报错找不到对应版本的包，通过关掉vpn解决了。

2. 下载预训练模型（yolov5s）与coco128数据集，放到yolov5的文件夹中

3. 各种命令

# 训练
python train.py --img 640 --batch 16 --epochs 5 --data ./data/coco128.yaml --cfg ./models/yolov5s.yaml --weights ./yolov5s.pt
 
# 预测
python detect.py --source inference/images/ --weights ./yolov5s.pt
 
# 测试
python val.py --weights yolov5s.pt --data coco128.yaml --img 640

三、使用depgraph算法压缩yolov5模型

import argparse
import copy
import math
import os
import random
import subprocess
import sys
import time
from copy import deepcopy
from datetime import datetime
from pathlib import Path
from functools import partial
 
try:
    import comet_ml  # must be imported before torch (if installed)
except ImportError:
    comet_ml = None
 
import numpy as np
import torch
import torch.distributed as dist
import torch.nn as nn
import yaml
from torch.optim import lr_scheduler
from tqdm import tqdm
 
FILE = Path(__file__).resolve()
ROOT = FILE.parents[0].parents[0]  # YOLOv5 root directory
if str(ROOT) not in sys.path:
    sys.path.append(str(ROOT))  # add ROOT to PATH
ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative
 
import val as validate  # for end-of-epoch mAP
from models.experimental import attempt_load
from models.yolo import Model
from utils.autoanchor import check_anchors
from utils.autobatch import check_train_batch_size
from utils.callbacks import Callbacks
from utils.dataloaders import create_dataloader
from utils.downloads import attempt_download, is_url
from utils.general import (LOGGER, TQDM_BAR_FORMAT, check_amp, check_dataset, check_file, check_git_info,
                           check_git_status, check_img_size, check_requirements, check_suffix, check_yaml, colorstr,
                           get_latest_run, increment_path, init_seeds, intersect_dicts, labels_to_class_weights,
                           labels_to_image_weights, methods, one_cycle, print_args, print_mutation, strip_optimizer,
                           yaml_save)
from utils.loggers import Loggers
from utils.loggers.comet.comet_utils import check_comet_resume
from utils.loss import ComputeLoss
from utils.metrics import fitness
from utils.plots import plot_evolve
from utils.torch_utils import (EarlyStopping, ModelEMA, de_parallel, select_device, smart_DDP, smart_optimizer,
                               smart_resume, torch_distributed_zero_first)
 
LOCAL_RANK = int(os.getenv('LOCAL_RANK', -1))  # https://pytorch.org/docs/stable/elastic/run.html
RANK = int(os.getenv('RANK', -1))
WORLD_SIZE = int(os.getenv('WORLD_SIZE', 1))
GIT_INFO = check_git_info()
 
 
## global variables ##
nc = 1  # number of classes
w = ''  # path of pretrained model
last = ''  # path of last.pt
best = ''  # path of best.pt
plots = True  # figure
cuda =True  # if using gpu
names = {}  # class name
is_coco = False  # if val with coco dataset
amp = True  # if training/valuating model with amp
batch_size = 64
loggers = None  # logs
train_path = ''
val_path = ''
pretrained = True # if load pretained model
imgsz = 320
ckpt = None  # the loaded model
csd = None  # checkpoint state_dict as FP32
labels = None # torch tensor
gs = 0 # image size
data_dict = ''
compute_loss = None
 
# 运行命令
# python pruning/MagnitudePrunner_train_after_pruning.py --weights yolov5s.pt --data data/coco128.yaml --target-prune-rate 0.1 --epochs 50
 
def preparing(hyp, opt, device, callbacks):
    global loggers, nc, last, best, plots, cuda, names, is_coco, train_path, val_path, ckpt, data_dict, w
 
    save_dir, epochs, batch_size, weights, single_cls, evolve, data, cfg, resume, noval, nosave, workers, freeze = \
        Path(opt.save_dir), opt.epochs, opt.batch_size, opt.weights, opt.single_cls, opt.evolve, opt.data, opt.cfg, \
            opt.resume, opt.noval, opt.nosave, opt.workers, opt.freeze
    callbacks.run('on_pretrain_routine_start')
 
    # Directories
    w = save_dir / 'weights'  # weights dir
    (w.parent if evolve else w).mkdir(parents=True, exist_ok=True)  # make dir
    last, best = w / 'last.pt', w / 'best.pt'
 
    # Hyperparameters
    if isinstance(hyp, str):
        with open(hyp, errors='ignore') as f:
            hyp = yaml.safe_load(f)  # load hyps dict
    LOGGER.info(colorstr('hyperparameters: ') + ', '.join(f'{k}={v}' for k, v in hyp.items()))
    opt.hyp = hyp.copy()  # for saving hyps to checkpoints
 
    # Save run settings
    if not evolve:
        yaml_save(save_dir / 'hyp.yaml', hyp)
        yaml_save(save_dir / 'opt.yaml', vars(opt))
 
    # Loggers
    data_dict = None
    if RANK in {-1, 0}:
        loggers = Loggers(save_dir, weights, opt, hyp, LOGGER)  # loggers instance
 
        # Register actions
        for k in methods(loggers):
            callbacks.register_action(k, callback=getattr(loggers, k))
 
        # Process custom dataset artifact link
        data_dict = loggers.remote_dataset
        if resume:  # If resuming runs from remote artifact
            weights, epochs, hyp, batch_size = opt.weights, opt.epochs, opt.hyp, opt.batch_size
 
    # Config
    plots = not evolve and not opt.noplots  # create plots
    cuda = device.type != 'cpu'
    init_seeds(opt.seed + 1 + RANK, deterministic=True)
    with torch_distributed_zero_first(LOCAL_RANK):
        data_dict = data_dict or check_dataset(data)  # check if None
    train_path, val_path = data_dict['train'], data_dict['val']
    nc = 1 if single_cls else int(data_dict['nc'])  # number of classes
    names = {0: 'item'} if single_cls and len(data_dict['names']) != 1 else data_dict['names']  # class names
    is_coco = isinstance(val_path, str) and val_path.endswith('coco/val2017.txt')  # COCO dataset
 
def load_model(hyp, opt, device):
    weights, cfg, resume, freeze, single_cls = \
        opt.weights, opt.cfg, opt.resume, opt.freeze, opt.single_cls
 
    global pretrained, ckpt, csd
 
    # Model
    check_suffix(weights, '.pt')  # check weights
    pretrained = weights.endswith('.pt')
    if pretrained:
        with torch_distributed_zero_first(LOCAL_RANK):
            weights = attempt_download(weights)  # download if not found locally
        ckpt = torch.load(weights, map_location='cpu')  # load checkpoint to CPU to avoid CUDA memory leak
        model = Model(cfg or ckpt['model'].yaml, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device)  # create
        exclude = ['anchor'] if (cfg or hyp.get('anchors')) and not resume else []  # exclude keys
        csd = ckpt['model'].float().state_dict()  # checkpoint state_dict as FP32
        csd = intersect_dicts(csd, model.state_dict(), exclude=exclude)  # intersect
        model.load_state_dict(csd, strict=False)  # load
        LOGGER.info(f'Transferred {len(csd)}/{len(model.state_dict())} items from {weights}')  # report
    else:
        model = Model(cfg, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device)  # create
 
    global amp
    amp = check_amp(model)  # check AMP
 
    # Freeze
    freeze = [f'model.{x}.' for x in (freeze if len(freeze) > 1 else range(freeze[0]))]  # layers to freeze
    for k, v in model.named_parameters():
        v.requires_grad = True  # train all layers
        # v.register_hook(lambda x: torch.nan_to_num(x))  # NaN to 0 (commented for erratic training results)
        if any(x in k for x in freeze):
            LOGGER.info(f'freezing {k}')
            v.requires_grad = False
 
    # DP mode
    if cuda and RANK == -1 and torch.cuda.device_count() > 1:
        LOGGER.warning(
            'WARNING ⚠️ DP not recommended, use torch.distributed.run for best DDP Multi-GPU results.\n'
            'See Multi-GPU Tutorial at https://docs.ultralytics.com/yolov5/tutorials/multi_gpu_training to get started.'
        )
        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()')
 
    return model
 
def load_dataset(hyp, opt, model):
    global batch_size, imgsz, labels, gs
    single_cls, workers, data, noval = opt.single_cls, opt.workers, opt.data, opt.noval
    # Image size
    gs = max(int(model.stride.max()), 32)  # grid size (max stride)
    imgsz = check_img_size(opt.imgsz, gs, floor=gs * 2)  # verify imgsz is gs-multiple
 
    # Batch size
    if RANK == -1 and batch_size == -1:  # single-GPU only, estimate best batch size
        batch_size = check_train_batch_size(model, imgsz, amp)
        loggers.on_params_update({'batch_size': batch_size})
 
 
    # Trainloader
    train_loader, dataset = create_dataloader(train_path,
                                              imgsz,
                                              batch_size // WORLD_SIZE,
                                              gs,
                                              single_cls,
                                              hyp=hyp,
                                              augment=True,
                                              cache=None if opt.cache == 'val' else opt.cache,
                                              rect=opt.rect,
                                              rank=LOCAL_RANK,
                                              workers=workers,
                                              image_weights=opt.image_weights,
                                              quad=opt.quad,
                                              prefix=colorstr('train: '),
                                              shuffle=True,
                                              seed=opt.seed)
    labels = np.concatenate(dataset.labels, 0)
    mlc = int(labels[:, 0].max())  # max label class
    assert mlc < nc, f'Label class {mlc} exceeds nc={nc} in {data}. Possible class labels are 0-{nc - 1}'
 
    # Process 0
    val_loader = None
    if RANK in {-1, 0}:
        val_loader = create_dataloader(val_path,
                                       imgsz,
                                       batch_size // WORLD_SIZE * 2,
                                       gs,
                                       single_cls,
                                       hyp=hyp,
                                       cache=None if noval else opt.cache,
                                       rect=True,
                                       rank=-1,
                                       workers=workers * 2,
                                       pad=0.5,
                                       prefix=colorstr('val: '))[0]
 
 
    return dataset, train_loader, val_loader
 
def get_pruner(opt, model, device):
    import torch_pruning as tp
    example_inputs = torch.randn(1, 3, imgsz, imgsz).to(device)
 
    ignored_layers = []
    from models.yolo import Detect
    for m in model.modules():
        if isinstance(m, (Detect)):
            ignored_layers.append(m.m)
 
    opt.sparsity_learning = True
    imp = tp.importance.GroupNormImportance(p=2, normalizer='max')  # normalized by the maximum score for CIFAR
    pruner_entry = partial(tp.pruner.GroupNormPruner, reg=opt.reg, global_pruning=opt.global_pruning)
 
    pruning_ratio_dict = {}
    unwrapped_parameters = []
    pruner = pruner_entry(
        model,
        example_inputs,
        importance=imp,
        iterative_steps=20,
        pruning_ratio=1.0,
        pruning_ratio_dict=pruning_ratio_dict,
        max_pruning_ratio=0.5,
        ignored_layers=ignored_layers,
        unwrapped_parameters=unwrapped_parameters,
    )
    return pruner
 
def pruning(opt, model, device, pruner):
    import torch_pruning as tp
    model.eval()
    # print(model)
 
    example_inputs = torch.randn(1, 3, imgsz, imgsz).to(device)
    base_macs, base_nparams = tp.utils.count_ops_and_params(model, example_inputs)
 
    # progress
    current_speed_up = 1
    while current_speed_up < opt.speed_up:
        pruner.step()
        pruned_ops, _ = tp.utils.count_ops_and_params(model, example_inputs=example_inputs)
        current_speed_up = float(base_macs) / pruned_ops
        if pruner.current_step == pruner.iterative_steps:
            break
 
    pruned_macs, pruned_nparams = tp.utils.count_ops_and_params(model, example_inputs)
    # print(model)
 
    print("Before Pruning: MACs=%f G, #Params=%f G" % (base_macs / 1e9, base_nparams / 1e9))
    print("After Pruning: MACs=%f G, #Params=%f G" % (pruned_macs / 1e9, pruned_nparams / 1e9))
 
    return model
 
def eval(model, val_loader, op, callbacks, save_dir):
    single_cls = op.single_cls
    save_dir.mkdir(parents=True, exist_ok=True)
 
    LOGGER.info("eval model...")
    validate.run(
        data_dict,
        batch_size=batch_size // WORLD_SIZE * 2,
        imgsz=imgsz,
        model=model,
        iou_thres=0.65 if is_coco else 0.60,  # best pycocotools at iou 0.65
        single_cls=single_cls,
        dataloader=val_loader,
        save_dir=save_dir,
        save_json=is_coco,
        verbose=True,
        plots=plots,
        callbacks=callbacks,
        compute_loss=compute_loss,
    )
 
from enum import Enum
class Mode(Enum):
    FINE_TUNING = False
    SPARSE_LEARNING = True
 
def train(hyp, opt, device,
          model, dataset, train_loader, val_loader,
          callbacks,
          mode=Mode.FINE_TUNING,
          pruner=None):  # hyp is path/to/hyp.yaml or hyp dictionary
    weights, epochs, resume, save_dir, single_cls, noval, nosave, evolve = (
        opt.weights, opt.epochs, opt.resume, Path(opt.save_dir), opt.single_cls, opt.noval, opt.nosave, opt.evolve)
 
    if mode == Mode.SPARSE_LEARNING:
        epochs = opt.sl_epochs
 
    # Optimizer
    nbs = 64  # nominal batch size
    accumulate = max(round(nbs / batch_size), 1)  # accumulate loss before optimizing
    hyp['weight_decay'] *= batch_size * accumulate / nbs  # scale weight_decay
    optimizer = smart_optimizer(model, opt.optimizer, hyp['lr0'], hyp['momentum'], hyp['weight_decay'])
 
    # Scheduler
    if opt.cos_lr:
        lf = one_cycle(1, hyp['lrf'], epochs)  # cosine 1->hyp['lrf']
    else:
        lf = lambda x: (1 - x / epochs) * (1.0 - hyp['lrf']) + hyp['lrf']  # linear
    scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf)  # plot_lr_scheduler(optimizer, scheduler, epochs)
 
    # EMA
    ema = ModelEMA(model) if RANK in {-1, 0} else None
 
    # Resume
    best_fitness, start_epoch = 0.0, 0
 
    global ckpt, csd
    if pretrained:
        if resume:
            best_fitness, start_epoch, epochs = smart_resume(ckpt, optimizer, ema, weights, epochs, resume)
 
        try:
            del ckpt, csd
        except NameError:
            print("ckpt, csd have been deleted!")
 
    if RANK in {-1, 0}:
        if not resume:
            if not opt.noautoanchor:
                check_anchors(dataset, model=model, thr=hyp['anchor_t'], imgsz=imgsz)  # run AutoAnchor
            model.half().float()  # pre-reduce anchor precision
 
        callbacks.run('on_pretrain_routine_end', labels, names)
 
    # DDP mode
    if cuda and RANK != -1:
        model = smart_DDP(model)
 
    # Model attributes
    nl = de_parallel(model).model[-1].nl  # number of detection layers (to scale hyps)
    hyp['box'] *= 3 / nl  # scale to layers
    hyp['cls'] *= nc / 80 * 3 / nl  # scale to classes and layers
    hyp['obj'] *= (imgsz / 640) ** 2 * 3 / nl  # scale to image size and layers
    hyp['label_smoothing'] = opt.label_smoothing
    model.nc = nc  # attach number of classes to model
    model.hyp = hyp  # attach hyperparameters to model
    model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device) * nc  # attach class weights
    model.names = names
 
    # Start training
    t0 = time.time()
    nb = len(train_loader)  # number of batches
    nw = max(round(hyp['warmup_epochs'] * nb), 100)  # number of warmup iterations, max(3 epochs, 100 iterations)
    # nw = min(nw, (epochs - start_epoch) / 2 * nb)  # limit warmup to < 1/2 of training
    last_opt_step = -1
    maps = np.zeros(nc)  # mAP per class
    results = (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 move
    scaler = torch.cuda.amp.GradScaler(enabled=amp)
    stopper, stop = EarlyStopping(patience=opt.patience), False
 
    global compute_loss
    compute_loss = ComputeLoss(model)  # init loss class
    callbacks.run('on_train_start')
    LOGGER.info(f'Image sizes {imgsz} train, {imgsz} val\n'
                f'Using {train_loader.num_workers * WORLD_SIZE} dataloader workers\n'
                f"Logging results to {colorstr('bold', save_dir)}\n"
                f'Starting training for {epochs} epochs...')
 
    for epoch in range(start_epoch, epochs):  # epoch ------------------------------------------------------------------
        callbacks.run('on_train_epoch_start')
        model.train()
 
        # Update image weights (optional, single-GPU only)
        if opt.image_weights:
            cw = model.class_weights.cpu().numpy() * (1 - maps) ** 2 / nc  # class weights
            iw = labels_to_image_weights(dataset.labels, nc=nc, class_weights=cw)  # image weights
            dataset.indices = random.choices(range(dataset.n), weights=iw, k=dataset.n)  # rand weighted idx
 
        # Update mosaic border (optional)
        # b = int(random.uniform(0.25 * imgsz, 0.75 * imgsz + gs) // gs * gs)
        # dataset.mosaic_border = [b - imgsz, -b]  # height, width borders
 
        mloss = torch.zeros(3, device=device)  # mean losses
        if RANK != -1:
            train_loader.sampler.set_epoch(epoch)
        pbar = enumerate(train_loader)
        LOGGER.info(('\n' + '%11s' * 7) % ('Epoch', 'GPU_mem', 'box_loss', 'obj_loss', 'cls_loss', 'Instances', 'Size'))
        if RANK in {-1, 0}:
            pbar = tqdm(pbar, total=nb, bar_format=TQDM_BAR_FORMAT)  # progress bar
        optimizer.zero_grad()
        for i, (imgs, targets, paths, _) in pbar:  # batch -------------------------------------------------------------
            callbacks.run('on_train_batch_start')
            ni = i + nb * epoch  # number integrated batches (since train start)
            imgs = imgs.to(device, non_blocking=True).float() / 255  # uint8 to float32, 0-255 to 0.0-1.0
 
            # Warmup
            if ni <= nw:
                xi = [0, nw]  # x interp
                # compute_loss.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 / 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 == 0 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(int(imgsz * 0.5), int(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 = nn.functional.interpolate(imgs, size=ns, mode='bilinear', align_corners=False)
 
            # Forward
            with torch.cuda.amp.autocast(amp):
                pred = model(imgs)  # forward
                loss, loss_items = compute_loss(pred, targets.to(device))  # loss scaled by batch_size
                if RANK != -1:
                    loss *= WORLD_SIZE  # gradient averaged between devices in DDP mode
                if opt.quad:
                    loss *= 4.
 
            # Backward
            scaler.scale(loss).backward()
 
            # sparse learning
            if mode == Mode.SPARSE_LEARNING:
                pruner.regularize(model)
 
            # Optimize - https://pytorch.org/docs/master/notes/amp_examples.html
            if ni - last_opt_step >= accumulate:
                scaler.unscale_(optimizer)  # unscale gradients
                torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=10.0)  # clip gradients
                scaler.step(optimizer)  # optimizer.step
                scaler.update()
                optimizer.zero_grad()
                if ema:
                    ema.update(model)
                last_opt_step = ni
 
            # Log
            if RANK in {-1, 0}:
                mloss = (mloss * i + loss_items) / (i + 1)  # update mean losses
                mem = f'{torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0:.3g}G'  # (GB)
                pbar.set_description(('%11s' * 2 + '%11.4g' * 5) %
                                     (f'{epoch}/{epochs - 1}', mem, *mloss, targets.shape[0], imgs.shape[-1]))
                callbacks.run('on_train_batch_end', model, ni, imgs, targets, paths, list(mloss))
                if callbacks.stop_training:
                    return
            # end batch ------------------------------------------------------------------------------------------------
 
        # Scheduler
        lr = [x['lr'] for x in optimizer.param_groups]  # for loggers
        scheduler.step()
 
        if RANK in {-1, 0}:
            # mAP
            callbacks.run('on_train_epoch_end', epoch=epoch)
            ema.update_attr(model, include=['yaml', 'nc', 'hyp', 'names', 'stride', 'class_weights'])
            final_epoch = (epoch + 1 == epochs) or stopper.possible_stop
            if not noval or final_epoch:  # Calculate mAP
                results, maps, _ = validate.run(data_dict,
                                                batch_size=batch_size // WORLD_SIZE * 2,
                                                imgsz=imgsz,
                                                half=amp,
                                                model=ema.ema,
                                                single_cls=single_cls,
                                                dataloader=val_loader,
                                                save_dir=save_dir,
                                                plots=False,
                                                callbacks=callbacks,
                                                compute_loss=compute_loss)
 
            # Update best mAP
            fi = fitness(np.array(results).reshape(1, -1))  # weighted combination of [P, R, mAP@.5, mAP@.5-.95]
            stop = stopper(epoch=epoch, fitness=fi)  # early stop check
            if fi > best_fitness:
                best_fitness = fi
            log_vals = list(mloss) + list(results) + lr
            callbacks.run('on_fit_epoch_end', log_vals, epoch, best_fitness, fi)
 
            # Save model
            if (not nosave) or (final_epoch and not evolve):  # if save
                ckpt = {
                    'epoch': epoch,
                    'best_fitness': best_fitness,
                    'model': deepcopy(de_parallel(model)).half(),
                    'ema': deepcopy(ema.ema).half(),
                    'updates': ema.updates,
                    'optimizer': optimizer.state_dict(),
                    'opt': vars(opt),
                    'git': GIT_INFO,  # {remote, branch, commit} if a git repo
                    'date': datetime.now().isoformat()}
 
                # Save last, best and delete
                torch.save(ckpt, last)
                if best_fitness == fi:
                    torch.save(ckpt, best)
                if opt.save_period > 0 and epoch % opt.save_period == 0:
                    torch.save(ckpt, w / f'epoch{epoch}.pt')
                del ckpt
                callbacks.run('on_model_save', last, epoch, final_epoch, best_fitness, fi)
 
        # EarlyStopping
        if RANK != -1:  # if DDP training
            broadcast_list = [stop if RANK == 0 else None]
            dist.broadcast_object_list(broadcast_list, 0)  # broadcast 'stop' to all ranks
            if RANK != 0:
                stop = broadcast_list[0]
        if stop:
            break  # must break all DDP ranks
 
        # end epoch ----------------------------------------------------------------------------------------------------
 
    # end training -----------------------------------------------------------------------------------------------------
    if RANK in {-1, 0}:
        LOGGER.info(f'\n{epoch - start_epoch + 1} epochs completed in {(time.time() - t0) / 3600:.3f} hours.')
        for f in last, best:
            if f.exists():
                strip_optimizer(f)  # strip optimizers
                if f is best:
                    LOGGER.info(f'\nValidating {f}...')
                    results, _, _ = validate.run(
                        data_dict,
                        batch_size=batch_size // WORLD_SIZE * 2,
                        imgsz=imgsz,
                        model=attempt_load(f, device).half(),
                        iou_thres=0.65 if is_coco else 0.60,  # best pycocotools at iou 0.65
                        single_cls=single_cls,
                        dataloader=val_loader,
                        save_dir=save_dir,
                        save_json=is_coco,
                        verbose=True,
                        plots=plots,
                        callbacks=callbacks,
                        compute_loss=compute_loss)  # val best model with plots
                    if is_coco:
                        callbacks.run('on_fit_epoch_end', list(mloss) + list(results) + lr, epoch, best_fitness, fi)
 
        callbacks.run('on_train_end', last, best, epoch, results)
 
    torch.cuda.empty_cache()
    return results
 
 
def parse_opt(known=False):
    parser = argparse.ArgumentParser()
    parser.add_argument('--weights', type=str, default=ROOT / 'yolov5s.pt', help='initial weights path')
    parser.add_argument('--cfg', type=str, default='', help='model.yaml path')
    parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='dataset.yaml path')
    parser.add_argument('--hyp', type=str, default=ROOT / 'data/hyps/hyp.scratch-low.yaml', help='hyperparameters path')
    parser.add_argument('--epochs', type=int, default=5, help='total training epochs')
    parser.add_argument('--batch-size', type=int, default=16, help='total batch size for all GPUs, -1 for autobatch')
    parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=640, help='train, val image size (pixels)')
    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('--noval', action='store_true', help='only validate final epoch')
    parser.add_argument('--noautoanchor', action='store_true', help='disable AutoAnchor')
    parser.add_argument('--noplots', action='store_true', help='save no plot files')
    parser.add_argument('--evolve', type=int, nargs='?', const=300, help='evolve hyperparameters for x generations')
    parser.add_argument('--bucket', type=str, default='', help='gsutil bucket')
    parser.add_argument('--cache', type=str, nargs='?', const='ram', help='image --cache ram/disk')
    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('--optimizer', type=str, choices=['SGD', 'Adam', 'AdamW'], default='SGD', help='optimizer')
    parser.add_argument('--sync-bn', action='store_true', help='use SyncBatchNorm, only available in DDP mode')
    parser.add_argument('--workers', type=int, default=8, help='max dataloader workers (per RANK in DDP mode)')
    parser.add_argument('--project', default=ROOT / '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')
    parser.add_argument('--quad', action='store_true', help='quad dataloader')
    parser.add_argument('--cos-lr', action='store_true', help='cosine LR scheduler')
    parser.add_argument('--label-smoothing', type=float, default=0.0, help='Label smoothing epsilon')
    parser.add_argument('--patience', type=int, default=100, help='EarlyStopping patience (epochs without improvement)')
    parser.add_argument('--freeze', nargs='+', type=int, default=[0], help='Freeze layers: backbone=10, first3=0 1 2')
    parser.add_argument('--save-period', type=int, default=-1, help='Save checkpoint every x epochs (disabled if < 1)')
    parser.add_argument('--seed', type=int, default=0, help='Global training seed')
    parser.add_argument('--local_rank', type=int, default=-1, help='Automatic DDP Multi-GPU argument, do not modify')
    # Logger arguments
    parser.add_argument('--entity', default=None, help='Entity')
    parser.add_argument('--upload_dataset', nargs='?', const=True, default=False, help='Upload data, "val" option')
    parser.add_argument('--bbox_interval', type=int, default=-1, help='Set bounding-box image logging interval')
    parser.add_argument('--artifact_alias', type=str, default='latest', help='Version of dataset artifact to use')
 
    ## for pruning
    parser.add_argument('--target-prune-rate', default=0.5, type=float, help='Target pruning rate')
    parser.add_argument("--speed-up", type=float, default=2)
    parser.add_argument("--global-pruning", action="store_true", default=False)
    parser.add_argument("--iterative-steps", default=20, type=int)
    parser.add_argument("--max-pruning-ratio", type=float, default=1.0)
    parser.add_argument("--soft-keeping-ratio", type=float, default=0.0)
 
    parser.add_argument("--finetune", action="store_true", default=False, help='whether finetune or not')
    # parser.add_argument("--total-epochs", type=int, default=100)
    parser.add_argument("--lr-decay-milestones", default="60,80", type=str, help="milestones for learning rate decay")
    parser.add_argument("--lr-decay-gamma", default=0.1, type=float)
    parser.add_argument("--lr", default=0.01, type=float, help="learning rate")
    parser.add_argument("--weight-decay", type=float, default=5e-4)  # optimizer的参数，限制模型权重
 
    parser.add_argument("--sparse-learning", action="store_true", default=False, help='whether sparse learning or not')
    parser.add_argument("--sl-epochs", type=int, default=5, help="epochs for sparsity learning")
    parser.add_argument("--sl-lr", default=0.01, type=float, help="learning rate for sparsity learning")
    parser.add_argument("--sl-lr-decay-milestones", default="60,80", type=str, help="milestones for sparsity learning")
    parser.add_argument("--sl-restore", type=str, default=None)
    parser.add_argument("--reg", type=float, default=5e-4)
 
    return parser.parse_known_args()[0] if known else parser.parse_args()
 
 
def main(opt, callbacks=Callbacks()):
    # Checks
    if RANK in {-1, 0}:
        print_args(vars(opt))
        check_git_status()
        check_requirements(ROOT / 'requirements.txt')
 
    # Resume (from specified or most recent last.pt)
    if opt.resume and not check_comet_resume(opt) and not opt.evolve:
        global last
        last = Path(check_file(opt.resume) if isinstance(opt.resume, str) else get_latest_run())
        opt_yaml = last.parent.parent / 'opt.yaml'  # train options yaml
        opt_data = opt.data  # original dataset
        if opt_yaml.is_file():
            with open(opt_yaml, errors='ignore') as f:
                d = yaml.safe_load(f)
        else:
            d = torch.load(last, map_location='cpu')['opt']
        opt = argparse.Namespace(**d)  # replace
        opt.cfg, opt.weights, opt.resume = '', str(last), True  # reinstate
        if is_url(opt_data):
            opt.data = check_file(opt_data)  # avoid HUB resume auth timeout
    else:
        opt.data, opt.cfg, opt.hyp, opt.weights, opt.project = \
            check_file(opt.data), check_yaml(opt.cfg), check_yaml(opt.hyp), str(opt.weights), str(opt.project)  # checks
        assert len(opt.cfg) or len(opt.weights), 'either --cfg or --weights must be specified'
        if opt.evolve:
            if opt.project == str(ROOT / 'runs/train'):  # if default project name, rename to runs/evolve
                opt.project = str(ROOT / 'runs/evolve')
            opt.exist_ok, opt.resume = opt.resume, False  # pass resume to exist_ok and disable resume
        if opt.name == 'cfg':
            opt.name = Path(opt.cfg).stem  # use model.yaml as name
        opt.save_dir = str(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok))
 
    # DDP mode
    device = select_device(opt.device, batch_size=opt.batch_size)
    if LOCAL_RANK != -1:
        msg = 'is not compatible with YOLOv5 Multi-GPU DDP training'
        assert not opt.image_weights, f'--image-weights {msg}'
        assert not opt.evolve, f'--evolve {msg}'
        assert opt.batch_size != -1, f'AutoBatch with --batch-size -1 {msg}, please pass a valid --batch-size'
        assert opt.batch_size % WORLD_SIZE == 0, f'--batch-size {opt.batch_size} must be multiple of WORLD_SIZE'
        assert torch.cuda.device_count() > LOCAL_RANK, 'insufficient CUDA devices for DDP command'
        torch.cuda.set_device(LOCAL_RANK)
        device = torch.device('cuda', LOCAL_RANK)
        dist.init_process_group(backend='nccl' if dist.is_nccl_available() else 'gloo')
 
    # Train
    if not opt.evolve:
        # 设置参数
        preparing(opt.hyp, opt, device, callbacks)
        # 加载预训练模型
        model = copy.deepcopy(load_model(opt.hyp, opt, device))
        # 加载数据集
        dataset, train_loader, val_loader = load_dataset(opt.hyp, opt, model)
 
        # 测试原模型
        eval(model, val_loader, opt, callbacks, save_dir=Path(opt.save_dir) / 'original_eval')
 
        ## 用DepGraph算法剪枝
        # 稀疏训练
        # model.train()
        pruner = get_pruner(opt, model, device)
        if opt.sparse_learning:
            train(opt.hyp, opt, device, model, dataset, train_loader, val_loader, callbacks, Mode.SPARSE_LEARNING, pruner)
        # 剪枝
        pruning(opt, model, device, pruner)
        # 模型微调
        if opt.finetune:
            train(opt.hyp, opt, device, model, dataset, train_loader, val_loader, callbacks)
 
        # 测试新模型
        # eval(model, val_loader, opt, callbacks, save_dir=Path(opt.save_dir) / 'pruned_eval')
 
    # 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)
 
        with open(opt.hyp, errors='ignore') as f:
            hyp = yaml.safe_load(f)  # load hyps dict
            if 'anchors' not in hyp:  # anchors commented in hyp.yaml
                hyp['anchors'] = 3
        if opt.noautoanchor:
            del hyp['anchors'], meta['anchors']
        opt.noval, opt.nosave, save_dir = True, True, Path(opt.save_dir)  # only val/save final epoch
        # ei = [isinstance(x, (int, float)) for x in hyp.values()]  # evolvable indices
        evolve_yaml, evolve_csv = save_dir / 'hyp_evolve.yaml', save_dir / 'evolve.csv'
        if opt.bucket:
            # download evolve.csv if exists
            subprocess.run([
                'gsutil',
                'cp',
                f'gs://{opt.bucket}/evolve.csv',
                str(evolve_csv), ])
 
        for _ in range(opt.evolve):  # generations to evolve
            if evolve_csv.exists():  # if evolve.csv exists: select best hyps and mutate
                # Select parent(s)
                parent = 'single'  # parent selection method: 'single' or 'weighted'
                x = np.loadtxt(evolve_csv, ndmin=2, delimiter=',', skiprows=1)
                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() + 1E-6  # weights (sum > 0)
                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([meta[k][0] for k in hyp.keys()])  # 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, callbacks)
            callbacks = Callbacks()
            # Write mutation results
            keys = ('metrics/precision', 'metrics/recall', 'metrics/mAP_0.5', 'metrics/mAP_0.5:0.95', 'val/box_loss',
                    'val/obj_loss', 'val/cls_loss')
            print_mutation(keys, results, hyp.copy(), save_dir, opt.bucket)
 
        # Plot results
        plot_evolve(evolve_csv)
        LOGGER.info(f'Hyperparameter evolution finished {opt.evolve} generations\n'
                    f"Results saved to {colorstr('bold', save_dir)}\n"
                    f'Usage example: $ python train.py --hyp {evolve_yaml}')
 
 
def run(**kwargs):
    # Usage: import train; train.run(data='coco128.yaml', imgsz=320, weights='yolov5m.pt')
    opt = parse_opt(True)
    for k, v in kwargs.items():
        setattr(opt, k, v)
    main(opt)
    return opt
 
 
if __name__ == '__main__':
    opt = parse_opt()
    main(opt)

目前没有区别Sparse Learning和Fine-tuing的optimizer和scheduler，需要的话，可以增加if语句为两种情况创建不同的优化器。