381 lines
16 KiB
Python
381 lines
16 KiB
Python
import argparse
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import time
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import torch.distributed as dist
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import torch.optim as optim
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import torch.optim.lr_scheduler as lr_scheduler
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from torch.utils.data import DataLoader
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import test # import test.py to get mAP after each epoch
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from models import *
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from utils.datasets import *
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from utils.utils import *
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# Hyperparameters: train.py --evolve --epochs 2 --img-size 320, Metrics: 0.204 0.302 0.175 0.234 (square smart)
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hyp = {'giou': .035, # giou loss gain
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'xy': 0.20, # xy loss gain
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'wh': 0.10, # wh loss gain
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'cls': 0.035, # cls loss gain
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'conf': 1.61, # conf loss gain
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'conf_bpw': 3.53, # conf BCELoss positive_weight
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'iou_t': 0.29, # iou target-anchor training threshold
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'lr0': 0.001, # initial learning rate
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'lrf': -4., # final learning rate = lr0 * (10 ** lrf)
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'momentum': 0.90, # SGD momentum
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'weight_decay': 0.0005} # optimizer weight decay
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def train(
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cfg,
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data_cfg,
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img_size=416,
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resume=False,
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epochs=100, # 500200 batches at bs 4, 117263 images = 68 epochs
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batch_size=16,
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accumulate=4, # effective bs = 64 = batch_size * accumulate
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freeze_backbone=False,
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transfer=False # Transfer learning (train only YOLO layers)
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):
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init_seeds()
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weights = 'weights' + os.sep
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latest = weights + 'latest.pt'
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best = weights + 'best.pt'
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device = torch_utils.select_device()
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img_size_test = img_size # image size for testing
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multi_scale = not opt.single_scale
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if multi_scale:
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img_size_min = round(img_size / 32 / 1.5)
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img_size_max = round(img_size / 32 * 1.5)
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img_size = img_size_max * 32 # initiate with maximum multi_scale size
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# Configure run
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data_dict = parse_data_cfg(data_cfg)
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train_path = data_dict['train']
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nc = int(data_dict['classes']) # number of classes
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# Initialize model
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model = Darknet(cfg).to(device)
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# Optimizer
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optimizer = optim.SGD(model.parameters(), lr=hyp['lr0'], momentum=hyp['momentum'], weight_decay=hyp['weight_decay'])
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cutoff = -1 # backbone reaches to cutoff layer
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start_epoch = 0
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best_loss = float('inf')
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nf = int(model.module_defs[model.yolo_layers[0] - 1]['filters']) # yolo layer size (i.e. 255)
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if resume: # Load previously saved model
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if transfer: # Transfer learning
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chkpt = torch.load(weights + 'yolov3-spp.pt', map_location=device)
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model.load_state_dict({k: v for k, v in chkpt['model'].items() if v.numel() > 1 and v.shape[0] != 255},
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strict=False)
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for p in model.parameters():
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p.requires_grad = True if p.shape[0] == nf else False
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else: # resume from latest.pt
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chkpt = torch.load(latest, map_location=device) # load checkpoint
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model.load_state_dict(chkpt['model'])
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start_epoch = chkpt['epoch'] + 1
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if chkpt['optimizer'] is not None:
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optimizer.load_state_dict(chkpt['optimizer'])
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best_loss = chkpt['best_loss']
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del chkpt
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else: # Initialize model with backbone (optional)
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if '-tiny.cfg' in cfg:
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cutoff = load_darknet_weights(model, weights + 'yolov3-tiny.conv.15')
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else:
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cutoff = load_darknet_weights(model, weights + 'darknet53.conv.74')
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# Remove old results
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for f in glob.glob('*_batch*.jpg') + glob.glob('results.txt'):
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os.remove(f)
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# Scheduler https://github.com/ultralytics/yolov3/issues/238
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# lf = lambda x: 1 - x / epochs # linear ramp to zero
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# lf = lambda x: 10 ** (hyp['lrf'] * x / epochs) # exp ramp
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# lf = lambda x: 1 - 10 ** (hyp['lrf'] * (1 - x / epochs)) # inverse exp ramp
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# scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf)
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scheduler = lr_scheduler.MultiStepLR(optimizer, milestones=[round(opt.epochs * x) for x in (0.8, 0.9)], gamma=0.1)
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scheduler.last_epoch = start_epoch - 1
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# # Plot lr schedule
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# y = []
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# for _ in range(epochs):
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# scheduler.step()
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# y.append(optimizer.param_groups[0]['lr'])
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# plt.plot(y, label='LambdaLR')
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# plt.xlabel('epoch')
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# plt.ylabel('LR')
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# plt.tight_layout()
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# plt.savefig('LR.png', dpi=300)
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# Dataset
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rectangular_training = False
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dataset = LoadImagesAndLabels(train_path,
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img_size,
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batch_size,
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augment=True,
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rect=rectangular_training)
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# Initialize distributed training
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if torch.cuda.device_count() > 1:
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dist.init_process_group(backend=opt.backend, init_method=opt.dist_url, world_size=opt.world_size, rank=opt.rank)
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model = torch.nn.parallel.DistributedDataParallel(model)
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# sampler = torch.utils.data.distributed.DistributedSampler(dataset)
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# Dataloader
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dataloader = DataLoader(dataset,
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batch_size=batch_size,
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num_workers=opt.num_workers,
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shuffle=not rectangular_training, # Shuffle=True unless rectangular training is used
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pin_memory=True,
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collate_fn=dataset.collate_fn)
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# Mixed precision training https://github.com/NVIDIA/apex
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try:
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from apex import amp
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model, optimizer = amp.initialize(model, optimizer, opt_level='O1')
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mixed_precision = True
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except: # not installed: install help: https://github.com/NVIDIA/apex/issues/259
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mixed_precision = False
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# Start training
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model.hyp = hyp # attach hyperparameters to model
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# model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device) # attach class weights
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model_info(model, report='summary') # 'full' or 'summary'
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nb = len(dataloader)
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maps = np.zeros(nc) # mAP per class
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results = (0, 0, 0, 0, 0) # P, R, mAP, F1, test_loss
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n_burnin = min(round(nb / 5 + 1), 1000) # burn-in batches
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t, t0 = time.time(), time.time()
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for epoch in range(start_epoch, epochs):
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model.train()
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print(('\n%8s%12s' + '%10s' * 7) % ('Epoch', 'Batch', 'xy', 'wh', 'conf', 'cls', 'total', 'targets', 'time'))
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# Update scheduler
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scheduler.step()
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# Freeze backbone at epoch 0, unfreeze at epoch 1 (optional)
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if freeze_backbone and epoch < 2:
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for name, p in model.named_parameters():
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if int(name.split('.')[1]) < cutoff: # if layer < 75
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p.requires_grad = False if epoch == 0 else True
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# # Update image weights (optional)
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# w = model.class_weights.cpu().numpy() * (1 - maps) # class weights
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# image_weights = labels_to_image_weights(dataset.labels, nc=nc, class_weights=w)
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# dataset.indices = random.choices(range(dataset.n), weights=image_weights, k=dataset.n) # random weighted index
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mloss = torch.zeros(5).to(device) # mean losses
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for i, (imgs, targets, _, _) in enumerate(dataloader):
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imgs = imgs.to(device)
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targets = targets.to(device)
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# Multi-Scale training
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if multi_scale:
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if (i + 1 + nb * epoch) / accumulate % 10 == 0: # adjust (67% - 150%) every 10 batches
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img_size = random.choice(range(img_size_min, img_size_max + 1)) * 32
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print('img_size = %g' % img_size)
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scale_factor = img_size / max(imgs.shape[-2:])
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imgs = F.interpolate(imgs, scale_factor=scale_factor, mode='bilinear', align_corners=False)
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# Plot images with bounding boxes
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if epoch == 0 and i == 0:
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plot_images(imgs=imgs, targets=targets, fname='train_batch%g.jpg' % i)
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# SGD burn-in
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if epoch == 0 and i <= n_burnin:
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lr = hyp['lr0'] * (i / n_burnin) ** 4
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for x in optimizer.param_groups:
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x['lr'] = lr
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# Run model
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pred = model(imgs)
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# Compute loss
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loss, loss_items = compute_loss(pred, targets, model, giou_loss=opt.giou)
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if torch.isnan(loss):
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print('WARNING: nan loss detected, ending training')
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return results
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# Compute gradient
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if mixed_precision:
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with amp.scale_loss(loss, optimizer) as scaled_loss:
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scaled_loss.backward()
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else:
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loss.backward()
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# Accumulate gradient for x batches before optimizing
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if (i + 1) % accumulate == 0 or (i + 1) == nb:
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optimizer.step()
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optimizer.zero_grad()
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# Print batch results
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mloss = (mloss * i + loss_items) / (i + 1) # update mean losses
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s = ('%8s%12s' + '%10.3g' * 7) % (
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'%g/%g' % (epoch, epochs - 1),
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'%g/%g' % (i, nb - 1), *mloss, len(targets), time.time() - t)
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t = time.time()
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print(s)
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# Report time
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dt = (time.time() - t0) / 3600
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print('%g epochs completed in %.3f hours.' % (epoch - start_epoch + 1, dt))
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# Calculate mAP (always test final epoch, skip first 5 if opt.nosave)
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if not (opt.notest or (opt.nosave and epoch < 10)) or epoch == epochs - 1:
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with torch.no_grad():
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results, maps = test.test(cfg, data_cfg, batch_size=batch_size*2, img_size=img_size_test, model=model,
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conf_thres=0.1)
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# Write epoch results
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with open('results.txt', 'a') as file:
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file.write(s + '%11.3g' * 5 % results + '\n') # P, R, mAP, F1, test_loss
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# Update best loss
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test_loss = results[4]
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if test_loss < best_loss:
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best_loss = test_loss
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# Save training results
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save = (not opt.nosave) or (epoch == epochs - 1)
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if save:
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# Create checkpoint
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chkpt = {'epoch': epoch,
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'best_loss': best_loss,
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'model': model.module.state_dict() if type(
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model) is nn.parallel.DistributedDataParallel else model.state_dict(),
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'optimizer': optimizer.state_dict()}
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# Save latest checkpoint
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torch.save(chkpt, latest)
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# Save best checkpoint
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if best_loss == test_loss:
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torch.save(chkpt, best)
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# Save backup every 10 epochs (optional)
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if epoch > 0 and epoch % 10 == 0:
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torch.save(chkpt, weights + 'backup%g.pt' % epoch)
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# Delete checkpoint
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del chkpt
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return results
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def print_mutation(hyp, results):
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# Write mutation results
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a = '%11s' * len(hyp) % tuple(hyp.keys()) # hyperparam keys
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b = '%11.4g' * len(hyp) % tuple(hyp.values()) # hyperparam values
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c = '%11.3g' * len(results) % results # results (P, R, mAP, F1, test_loss)
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print('\n%s\n%s\nEvolved fitness: %s\n' % (a, b, c))
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with open('evolve.txt', 'a') as f:
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f.write(c + b + '\n')
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument('--epochs', type=int, default=100, help='number of epochs')
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parser.add_argument('--batch-size', type=int, default=8, help='batch size')
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parser.add_argument('--accumulate', type=int, default=8, help='number of batches to accumulate before optimizing')
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parser.add_argument('--cfg', type=str, default='cfg/yolov3-spp.cfg', help='cfg file path')
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parser.add_argument('--data-cfg', type=str, default='data/coco_64img.data', help='coco.data file path')
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parser.add_argument('--single-scale', action='store_true', help='train at fixed size (no multi-scale)')
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parser.add_argument('--img-size', type=int, default=416, help='inference size (pixels)')
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parser.add_argument('--resume', action='store_true', help='resume training flag')
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parser.add_argument('--transfer', action='store_true', help='transfer learning flag')
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parser.add_argument('--num-workers', type=int, default=4, help='number of Pytorch DataLoader workers')
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parser.add_argument('--dist-url', default='tcp://127.0.0.1:9999', type=str, help='distributed training init method')
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parser.add_argument('--rank', default=0, type=int, help='distributed training node rank')
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parser.add_argument('--world-size', default=1, type=int, help='number of nodes for distributed training')
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parser.add_argument('--backend', default='nccl', type=str, help='distributed backend')
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parser.add_argument('--nosave', action='store_true', help='only save final checkpoint')
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parser.add_argument('--notest', action='store_true', help='only test final epoch')
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parser.add_argument('--giou', action='store_true', help='use GIoU loss instead of xy, wh loss')
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parser.add_argument('--evolve', action='store_true', help='run hyperparameter evolution')
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parser.add_argument('--var', default=0, type=int, help='debug variable')
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opt = parser.parse_args()
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print(opt)
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if opt.evolve:
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opt.notest = True # only test final epoch
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opt.nosave = True # only save final checkpoint
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# Train
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results = train(
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opt.cfg,
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opt.data_cfg,
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img_size=opt.img_size,
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resume=opt.resume or opt.transfer,
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transfer=opt.transfer,
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epochs=opt.epochs,
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batch_size=opt.batch_size,
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accumulate=opt.accumulate,
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)
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# Evolve hyperparameters (optional)
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if opt.evolve:
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best_fitness = results[2] # use mAP for fitness
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# Write mutation results
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print_mutation(hyp, results)
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gen = 1000 # generations to evolve
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for _ in range(gen):
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# Mutate hyperparameters
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old_hyp = hyp.copy()
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init_seeds(seed=int(time.time()))
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s = [.4, .4, .4, .4, .4, .4, .4, .4 * 0, .4 * 0, .04 * 0, .4 * 0] # fractional sigmas
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for i, k in enumerate(hyp.keys()):
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x = (np.random.randn(1) * s[i] + 1) ** 1.1 # plt.hist(x.ravel(), 100)
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hyp[k] = hyp[k] * float(x) # vary by about 30% 1sigma
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# Clip to limits
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keys = ['lr0', 'iou_t', 'momentum', 'weight_decay']
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limits = [(1e-4, 1e-2), (0, 0.70), (0.70, 0.98), (0, 0.01)]
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for k, v in zip(keys, limits):
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hyp[k] = np.clip(hyp[k], v[0], v[1])
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# Determine mutation fitness
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results = train(
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opt.cfg,
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opt.data_cfg,
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img_size=opt.img_size,
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resume=opt.resume or opt.transfer,
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transfer=opt.transfer,
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epochs=opt.epochs,
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batch_size=opt.batch_size,
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accumulate=opt.accumulate,
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)
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mutation_fitness = results[2]
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# Write mutation results
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print_mutation(hyp, results)
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# Update hyperparameters if fitness improved
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if mutation_fitness > best_fitness:
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# Fitness improved!
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print('Fitness improved!')
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best_fitness = mutation_fitness
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else:
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hyp = old_hyp.copy() # reset hyp to
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# # Plot results
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# import numpy as np
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# import matplotlib.pyplot as plt
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# a = np.loadtxt('evolve_1000val.txt')
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# x = a[:, 2] * a[:, 3] # metric = mAP * F1
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# weights = (x - x.min()) ** 2
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# fig = plt.figure(figsize=(14, 7))
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# for i in range(len(hyp)):
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# y = a[:, i + 5]
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# mu = (y * weights).sum() / weights.sum()
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# plt.subplot(2, 5, i+1)
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# plt.plot(x.max(), mu, 'o')
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# plt.plot(x, y, '.')
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# print(list(hyp.keys())[i],'%.4g' % mu)
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