import argparse
import time
import test # Import test.py to get mAP after each epoch
from models import *
from utils.datasets import *
from utils.utils import *
def train(
cfg,
data_cfg,
img_size=416,
resume=False,
epochs=100,
batch_size=16,
accumulate=1,
multi_scale=False,
freeze_backbone=False,
):
weights = 'weights' + os.sep
latest = weights + 'latest.pt'
best = weights + 'best.pt'
device = torch_utils.select_device()
if multi_scale: # pass maximum multi_scale size
img_size = 608
else:
torch.backends.cudnn.benchmark = True # unsuitable for multiscale
# Configure run
train_path = parse_data_cfg(data_cfg)['train']
# Initialize model
model = Darknet(cfg, img_size)
# Get dataloader
dataloader = LoadImagesAndLabels(train_path, batch_size, img_size, augment=True)
lr0 = 0.001 # initial learning rate
cutoff = -1 # backbone reaches to cutoff layer
start_epoch = 0
best_loss = float('inf')
if resume:
checkpoint = torch.load(latest, map_location='cpu')
# Load weights to resume from
model.load_state_dict(checkpoint['model'])
# Transfer learning (train only YOLO layers)
# for i, (name, p) in enumerate(model.named_parameters()):
# p.requires_grad = True if (p.shape[0] == 255) else False
# Set optimizer
optimizer = torch.optim.SGD(filter(lambda x: x.requires_grad, model.parameters()), lr=lr0, momentum=.9)
start_epoch = checkpoint['epoch'] + 1
if checkpoint['optimizer'] is not None:
optimizer.load_state_dict(checkpoint['optimizer'])
best_loss = checkpoint['best_loss']
del checkpoint # current, saved
else:
# Initialize model with backbone (optional)
if cfg.endswith('yolov3.cfg'):
cutoff = load_darknet_weights(model, weights + 'darknet53.conv.74')
elif cfg.endswith('yolov3-tiny.cfg'):
cutoff = load_darknet_weights(model, weights + 'yolov3-tiny.conv.15')
# Set optimizer
optimizer = torch.optim.SGD(model.parameters(), lr=lr0, momentum=.9)
if torch.cuda.device_count() > 1:
model = nn.DataParallel(model)
model.to(device).train()
# Set scheduler
# scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=[54, 61], gamma=0.1)
# Start training
t0 = time.time()
model_info(model)
n_burnin = min(round(dataloader.nB / 5 + 1), 1000) # number of burn-in batches
for epoch in range(epochs):
model.train()
epoch += start_epoch
print(('\n%8s%12s' + '%10s' * 7) % (
'Epoch', 'Batch', 'xy', 'wh', 'conf', 'cls', 'total', 'nTargets', 'time'))
# Update scheduler (automatic)
# scheduler.step()
# Update scheduler (manual)
if epoch > 250:
lr = lr0 / 10
else:
lr = lr0
for x in optimizer.param_groups:
x['lr'] = lr
# Freeze backbone at epoch 0, unfreeze at epoch 1
if freeze_backbone and epoch < 2:
for i, (name, p) in enumerate(model.named_parameters()):
if int(name.split('.')[1]) < cutoff: # if layer < 75
p.requires_grad = False if (epoch == 0) else True
ui = -1
rloss = defaultdict(float)
for i, (imgs, targets, _, _) in enumerate(dataloader):
targets = targets.to(device)
nT = targets.shape[0]
if nT == 0: # if no targets continue
continue
# SGD burn-in
if (epoch == 0) and (i <= n_burnin):
lr = lr0 * (i / n_burnin) ** 4
for x in optimizer.param_groups:
x['lr'] = lr
# Run model
pred = model(imgs.to(device))
# Build targets
target_list = build_targets(model, targets, pred)
# Compute loss
loss, loss_dict = compute_loss(pred, target_list)
# Compute gradient
loss.backward()
# Accumulate gradient for x batches before optimizing
if (i + 1) % accumulate == 0 or (i + 1) == len(dataloader):
optimizer.step()
optimizer.zero_grad()
# Running epoch-means of tracked metrics
ui += 1
for key, val in loss_dict.items():
rloss[key] = (rloss[key] * ui + val) / (ui + 1)
s = ('%8s%12s' + '%10.3g' * 7) % (
'%g/%g' % (epoch, epochs - 1),
'%g/%g' % (i, len(dataloader) - 1),
rloss['xy'], rloss['wh'], rloss['conf'],
rloss['cls'], rloss['total'],
nT, time.time() - t0)
t0 = time.time()
print(s)
# Multi-Scale training (320 - 608 pixels) every 10 batches
if multi_scale and (i + 1) % 10 == 0:
dataloader.img_size = random.choice(range(10, 20)) * 32
print('multi_scale img_size = %g' % dataloader.img_size)
# Update best loss
if rloss['total'] < best_loss:
best_loss = rloss['total']
# Save training results
save = True
if save:
# Save latest checkpoint
checkpoint = {'epoch': epoch,
'best_loss': best_loss,
'model': model.module.state_dict() if type(model) is nn.DataParallel else model.state_dict(),
'optimizer': optimizer.state_dict()}
torch.save(checkpoint, latest)
# Save best checkpoint
if best_loss == rloss['total']:
os.system('cp ' + latest + ' ' + best)
# Save backup weights every 5 epochs (optional)
if (epoch > 0) and (epoch % 5 == 0):
os.system('cp ' + latest + ' ' + weights + 'backup{}.pt'.format(epoch))
# Calculate mAP
with torch.no_grad():
P, R, mAP = test.test(cfg, data_cfg, weights=latest, batch_size=batch_size, img_size=img_size, model=model)
# Write epoch results
with open('results.txt', 'a') as file:
file.write(s + '%11.3g' * 3 % (P, R, mAP) + '\n')
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--epochs', type=int, default=270, help='number of epochs')
parser.add_argument('--batch-size', type=int, default=16, help='size of each image batch')
parser.add_argument('--accumulate', type=int, default=1, help='accumulate gradient x batches before optimizing')
parser.add_argument('--cfg', type=str, default='cfg/yolov3.cfg', help='cfg file path')
parser.add_argument('--data-cfg', type=str, default='cfg/coco.data', help='coco.data file path')
parser.add_argument('--multi-scale', action='store_true', help='random image sizes per batch 320 - 608')
parser.add_argument('--img-size', type=int, default=32 * 13, help='pixels')
parser.add_argument('--resume', action='store_true', help='resume training flag')
opt = parser.parse_args()
print(opt, end='\n\n')
init_seeds()
train(
opt.cfg,
opt.data_cfg,
img_size=opt.img_size,
resume=opt.resume,
epochs=opt.epochs,
batch_size=opt.batch_size,
accumulate=opt.accumulate,
multi_scale=opt.multi_scale,
)