361 lines
14 KiB
Python
361 lines
14 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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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
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hyp = {'k': 10.11, # loss multiple
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'xy': 0.1509, # xy loss fraction
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'wh': 0.008077, # wh loss fraction
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'cls': 0.01082, # cls loss fraction
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'conf': 0.8302, # conf loss fraction
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'iou_t': 0.05892, # iou target-anchor training threshold
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'lr0': 0.001475, # initial learning rate
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'lrf': -3.371, # final learning rate = lr0 * (10 ** lrf)
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'momentum': 0.8733, # SGD momentum
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'weight_decay': 0.0006636, # optimizer weight decay
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}
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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=273, # 500200 batches at bs 64, dataset length 117263
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batch_size=16,
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accumulate=1,
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multi_scale=False,
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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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if multi_scale:
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img_size = 608 # initiate with maximum multi_scale size
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opt.num_workers = 0 # bug https://github.com/ultralytics/yolov3/issues/174
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else:
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torch.backends.cudnn.benchmark = True # unsuitable for multiscale
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# Configure run
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train_path = parse_data_cfg(data_cfg)['train']
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# Initialize model
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model = Darknet(cfg, img_size).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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# Scheduler (reduce lr at epochs 218, 245, i.e. batches 400k, 450k)
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# lf = lambda x: 1 - x / epochs # linear ramp to zero
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# lf = lambda x: 10 ** (-2 * x / epochs) # exp ramp to lr0 * 1e-2
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lf = lambda x: 1 - 10 ** (hyp['lrf'] * (1 - x / epochs)) # inv exp ramp to lr0 * 1e-2
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scheduler = optim.lr_scheduler.LambdaLR(optimizer, lr_lambda=lf, last_epoch=start_epoch - 1)
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# scheduler = optim.lr_scheduler.MultiStepLR(optimizer, milestones=[218, 245], gamma=0.1, 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)
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# Dataset
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dataset = LoadImagesAndLabels(train_path, img_size=img_size, augment=True)
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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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else:
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sampler = None
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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=True,
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pin_memory=True,
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collate_fn=dataset.collate_fn,
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sampler=sampler)
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# Mixed precision training https://github.com/NVIDIA/apex
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# install help: https://github.com/NVIDIA/apex/issues/259
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mixed_precision = False
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if mixed_precision:
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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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# Start training
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t = time.time()
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model.hyp = hyp # attach hyperparameters to model
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model_info(model)
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nb = len(dataloader)
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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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os.remove('train_batch0.jpg') if os.path.exists('train_batch0.jpg') else None
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os.remove('test_batch0.jpg') if os.path.exists('test_batch0.jpg') else None
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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', 'nTargets', '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
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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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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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nt = len(targets)
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# if nt == 0: # if no targets continue
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# continue
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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_batch0.jpg')
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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)
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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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# Update running mean of tracked metrics
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mloss = (mloss * i + loss_items) / (i + 1)
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# Print batch results
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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, nt, time.time() - t)
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t = time.time()
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print(s)
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# Multi-Scale training (320 - 608 pixels) every 10 batches
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if multi_scale and (i + 1) % 10 == 0:
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dataset.img_size = random.choice(range(10, 20)) * 32
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print('multi_scale img_size = %g' % dataset.img_size)
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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 < 5)) or epoch == epochs - 1:
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with torch.no_grad():
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results = test.test(cfg, data_cfg, batch_size=batch_size, img_size=img_size, 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 = True and not opt.nosave
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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=273, help='number of epochs')
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parser.add_argument('--batch-size', type=int, default=16, help='size of each image batch')
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parser.add_argument('--accumulate', type=int, default=1, help='accumulate gradient x batches 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.data', help='coco.data file path')
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parser.add_argument('--multi-scale', action='store_true', help='random image sizes per batch 320 - 608')
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parser.add_argument('--img-size', type=int, default=416, help='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='do not save training results')
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parser.add_argument('--notest', action='store_true', help='only test final epoch')
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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, end='\n\n')
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if opt.evolve:
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opt.notest = True # save time by only testing final epoch
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opt.nosave = True # do not save checkpoints
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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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multi_scale=opt.multi_scale,
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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 = 30 # 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 = [.2, .2, .2, .2, .2, .2, .2, .2, .02, .2]
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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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# Apply limits
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hyp['iou_t'] = np.clip(hyp['iou_t'], 0, 0.90)
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hyp['momentum'] = np.clip(hyp['momentum'], 0.8, 0.95)
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hyp['weight_decay'] = np.clip(hyp['weight_decay'], 0, 0.01)
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# Normalize loss components (sum to 1)
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lcf = ['xy', 'wh', 'cls', 'conf']
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s = sum([v for k, v in hyp.items() if k in lcf])
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for k in lcf:
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hyp[k] /= s
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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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multi_scale=opt.multi_scale,
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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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#
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# a = np.loadtxt('evolve.txt')
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# x = a[:, 3]
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# fig = plt.figure(figsize=(14, 7))
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# for i in range(1, 10):
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# plt.subplot(2, 5, i)
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# plt.plot(x, a[:, i + 5], '.')
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