203 lines
7.3 KiB
Python
203 lines
7.3 KiB
Python
import argparse
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import time
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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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parser = argparse.ArgumentParser()
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parser.add_argument('-epochs', type=int, default=68, 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('-data_config_path', type=str, default='cfg/coco.data', help='data config file path')
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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('-img_size', type=int, default=32 * 13, help='size of each image dimension')
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parser.add_argument('-resume', default=False, help='resume training flag')
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opt = parser.parse_args()
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print(opt)
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cuda = torch.cuda.is_available()
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device = torch.device('cuda:0' if cuda else 'cpu')
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random.seed(0)
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np.random.seed(0)
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torch.manual_seed(0)
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if cuda:
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torch.cuda.manual_seed(0)
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torch.cuda.manual_seed_all(0)
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torch.backends.cudnn.benchmark = True
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def main(opt):
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os.makedirs('weights', exist_ok=True)
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# Configure run
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data_config = parse_data_config(opt.data_config_path)
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num_classes = int(data_config['classes'])
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if platform == 'darwin': # MacOS (local)
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train_path = data_config['train']
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else: # linux (cloud, i.e. gcp)
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train_path = '../coco/trainvalno5k.part'
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# Initialize model
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model = Darknet(opt.cfg, opt.img_size)
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# Get dataloader
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dataloader = load_images_and_labels(train_path, batch_size=opt.batch_size, img_size=opt.img_size, augment=True)
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# Reload saved optimizer state
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start_epoch = 0
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best_loss = float('inf')
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if opt.resume:
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checkpoint = torch.load('weights/latest.pt', map_location='cpu')
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model.load_state_dict(checkpoint['model'])
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if torch.cuda.device_count() > 1:
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print('Using ', torch.cuda.device_count(), ' GPUs')
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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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# if p.shape[0] != 650: # not YOLO layer
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# p.requires_grad = False
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# Set optimizer
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# optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad, model.parameters()))
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optimizer = torch.optim.SGD(filter(lambda p: p.requires_grad, model.parameters()), lr=1e-3,
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momentum=.9, weight_decay=5e-4, nesterov=True)
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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 darknet53 weights (optional)
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if not os.path.isfile('weights/darknet53.conv.74'):
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os.system('wget https://pjreddie.com/media/files/darknet53.conv.74 -P weights')
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load_weights(model, 'weights/darknet53.conv.74')
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if torch.cuda.device_count() > 1:
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print('Using ', torch.cuda.device_count(), ' GPUs')
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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.Adam(model.parameters(), lr=1e-4, weight_decay=5e-4)
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optimizer = torch.optim.SGD(model.parameters(), lr=1e-3, momentum=.9, weight_decay=5e-4, nesterov=True)
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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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model_info(model)
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t0, t1 = time.time(), time.time()
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mean_recall, mean_precision = 0, 0
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print('%10s' * 16 % (
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'Epoch', 'Batch', 'x', 'y', 'w', 'h', 'conf', 'cls', 'total', 'P', 'R', 'nTargets', 'TP', 'FP', 'FN', 'time'))
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for epoch in range(opt.epochs):
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epoch += start_epoch
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# Multi-Scale YOLO Training
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# img_size = random.choice(range(10, 20)) * 32 # 320 - 608 pixels
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# dataloader = load_images_and_labels(train_path, batch_size=opt.batch_size, img_size=img_size, augment=True)
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# print('Running this epoch with image size %g' % img_size)
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# Update scheduler (automatic)
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# scheduler.step()
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# Update scheduler (manual)
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if epoch < 54:
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lr = 1e-3
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elif epoch < 61:
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lr = 1e-4
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else:
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lr = 1e-5
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for g in optimizer.param_groups:
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g['lr'] = lr
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ui = -1
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rloss = defaultdict(float) # running loss
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metrics = torch.zeros(3, num_classes)
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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 <= 1000):
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lr = 1e-3 * (i / 1000) ** 4
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for g in optimizer.param_groups:
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g['lr'] = lr
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# Compute loss, compute gradient, update parameters
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loss = model(imgs.to(device), targets, requestPrecision=True)
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loss.backward()
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# accumulated_batches = 4 # accumulate gradient for 4 batches before stepping optimizer
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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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# Compute running epoch-means of tracked metrics
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ui += 1
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metrics += model.losses['metrics']
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TP, FP, FN = metrics
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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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# Precision
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precision = TP / (TP + FP)
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k = (TP + FP) > 0
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if k.sum() > 0:
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mean_precision = precision[k].mean()
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# Recall
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recall = TP / (TP + FN)
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k = (TP + FN) > 0
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if k.sum() > 0:
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mean_recall = recall[k].mean()
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s = ('%10s%10s' + '%10.3g' * 14) % (
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'%g/%g' % (epoch, opt.epochs - 1), '%g/%g' % (i, len(dataloader) - 1), rloss['x'],
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rloss['y'], rloss['w'], rloss['h'], rloss['conf'], rloss['cls'],
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rloss['loss'], mean_precision, mean_recall, model.losses['nT'], model.losses['TP'],
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model.losses['FP'], model.losses['FN'], time.time() - t1)
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t1 = time.time()
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print(s)
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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 + '\n')
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# Update best loss
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loss_per_target = rloss['loss'] / rloss['nT']
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if loss_per_target < best_loss:
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best_loss = loss_per_target
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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, 'weights/latest.pt')
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# Save best checkpoint
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if best_loss == loss_per_target:
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os.system('cp weights/latest.pt weights/best.pt')
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# Save backup weights every 5 epochs
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if (epoch > 0) & (epoch % 5 == 0):
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os.system('cp weights/latest.pt weights/backup' + str(epoch) + '.pt')
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# Save final model
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dt = time.time() - t0
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print('Finished %g epochs in %.2fs (%.2fs/epoch)' % (epoch, dt, dt / (epoch + 1)))
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if __name__ == '__main__':
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torch.cuda.empty_cache()
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main(opt)
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torch.cuda.empty_cache()
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