208 lines
7.1 KiB
Python
208 lines
7.1 KiB
Python
import argparse
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import time
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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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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,
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batch_size=16,
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accumulated_batches=1,
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multi_scale=False,
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freeze_backbone=False,
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var=0,
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):
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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: # pass maximum multi_scale size
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img_size = 608
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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)
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# Get dataloader
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dataloader = LoadImagesAndLabels(train_path, batch_size, img_size, multi_scale=multi_scale, augment=True)
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lr0 = 0.001
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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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if resume:
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checkpoint = torch.load(latest, map_location='cpu')
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# Load weights to resume from
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model.load_state_dict(checkpoint['model'])
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# if torch.cuda.device_count() > 1:
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# model = nn.DataParallel(model)
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model.to(device).train()
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# Transfer learning (train only YOLO layers)
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# for i, (name, p) in enumerate(model.named_parameters()):
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# p.requires_grad = True if (p.shape[0] == 255) else False
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# Set optimizer
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optimizer = torch.optim.SGD(filter(lambda x: x.requires_grad, model.parameters()), lr=lr0, momentum=.9)
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start_epoch = checkpoint['epoch'] + 1
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if checkpoint['optimizer'] is not None:
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optimizer.load_state_dict(checkpoint['optimizer'])
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best_loss = checkpoint['best_loss']
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del checkpoint # current, saved
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else:
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# Initialize model with backbone (optional)
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if cfg.endswith('yolov3.cfg'):
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load_darknet_weights(model, weights + 'darknet53.conv.74')
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cutoff = 75
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elif cfg.endswith('yolov3-tiny.cfg'):
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load_darknet_weights(model, weights + 'yolov3-tiny.conv.15')
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cutoff = 15
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# if torch.cuda.device_count() > 1:
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# model = nn.DataParallel(model)
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model.to(device).train()
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# Set optimizer
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optimizer = torch.optim.SGD(filter(lambda x: x.requires_grad, model.parameters()), lr=lr0, momentum=.9)
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# Set scheduler
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# scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=[54, 61], gamma=0.1)
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# Start training
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t0 = time.time()
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model_info(model)
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n_burnin = min(round(dataloader.nB / 5 + 1), 1000) # number of burn-in batches
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for epoch in range(epochs):
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epoch += start_epoch
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print(('%8s%12s' + '%10s' * 7) % (
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'Epoch', 'Batch', 'xy', 'wh', 'conf', 'cls', 'total', 'nTargets', 'time'))
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# Update scheduler (automatic)
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# scheduler.step()
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# Update scheduler (manual) at 0, 54, 61 epochs to 1e-3, 1e-4, 1e-5
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if epoch > 50:
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lr = lr0 / 10
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else:
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lr = lr0
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for g in optimizer.param_groups:
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g['lr'] = lr
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# Freeze darknet53.conv.74 for first epoch
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if freeze_backbone and (epoch < 2):
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for i, (name, p) in enumerate(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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ui = -1
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rloss = defaultdict(float) # running loss
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optimizer.zero_grad()
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for i, (imgs, targets, _, _) in enumerate(dataloader):
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if sum([len(x) for x in targets]) < 1: # if no targets continue
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continue
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# SGD burn-in
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if (epoch == 0) & (i <= n_burnin):
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lr = lr0 * (i / n_burnin) ** 4
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for g in optimizer.param_groups:
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g['lr'] = lr
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# Compute loss
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loss = model(imgs.to(device), targets, var=var)
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# Compute gradient
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loss.backward()
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# Accumulate gradient for x batches before optimizing
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if ((i + 1) % accumulated_batches == 0) or (i == len(dataloader) - 1):
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optimizer.step()
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optimizer.zero_grad()
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# Running epoch-means of tracked metrics
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ui += 1
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for key, val in model.losses.items():
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rloss[key] = (rloss[key] * ui + val) / (ui + 1)
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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, len(dataloader) - 1),
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rloss['xy'], rloss['wh'], rloss['conf'],
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rloss['cls'], rloss['loss'],
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model.losses['nT'], time.time() - t0)
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t0 = time.time()
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print(s)
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# Update best loss
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if rloss['loss'] < best_loss:
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best_loss = rloss['loss']
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# Save latest checkpoint
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checkpoint = {'epoch': epoch,
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'best_loss': best_loss,
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'model': model.state_dict(),
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'optimizer': optimizer.state_dict()}
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torch.save(checkpoint, latest)
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# Save best checkpoint
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if best_loss == rloss['loss']:
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os.system('cp ' + latest + ' ' + best)
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# Save backup weights every 5 epochs (optional)
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# if (epoch > 0) & (epoch % 5 == 0):
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# os.system('cp ' + latest + ' ' + weights + 'backup{}.pt'.format(epoch)))
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# Calculate mAP
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with torch.no_grad():
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mAP, R, P = test.test(cfg, data_cfg, weights=latest, batch_size=batch_size, img_size=img_size)
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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' * 3 % (mAP, P, R) + '\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=16, help='size of each image batch')
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parser.add_argument('--accumulated-batches', type=int, default=1, help='number of batches before optimizer step')
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parser.add_argument('--cfg', type=str, default='cfg/yolov3.cfg', help='cfg file path')
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parser.add_argument('--data-cfg', type=str, default='cfg/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=32 * 13, help='pixels')
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parser.add_argument('--resume', action='store_true', help='resume training flag')
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parser.add_argument('--var', type=float, default=0, help='test variable')
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opt = parser.parse_args()
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print(opt, end='\n\n')
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init_seeds()
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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,
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epochs=opt.epochs,
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batch_size=opt.batch_size,
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accumulated_batches=opt.accumulated_batches,
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multi_scale=opt.multi_scale,
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var=opt.var,
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)
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