225 lines
8.1 KiB
Python
225 lines
8.1 KiB
Python
import argparse
|
|
import time
|
|
|
|
from torch.utils.data import DataLoader
|
|
|
|
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=270,
|
|
batch_size=16,
|
|
accumulate=1,
|
|
multi_scale=False,
|
|
freeze_backbone=False,
|
|
num_workers=0
|
|
):
|
|
weights = 'weights' + os.sep
|
|
latest = weights + 'latest.pt'
|
|
best = weights + 'best.pt'
|
|
device = torch_utils.select_device()
|
|
|
|
if multi_scale:
|
|
img_size = 608 # initiate with maximum multi_scale size
|
|
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).to(device)
|
|
|
|
# Optimizer
|
|
lr0 = 0.001 # initial learning rate
|
|
optimizer = torch.optim.SGD(model.parameters(), lr=lr0, momentum=.9)
|
|
|
|
# Dataloader
|
|
dataset = LoadImagesAndLabels(train_path, img_size=img_size, augment=True)
|
|
dataloader = DataLoader(dataset, batch_size=batch_size, num_workers=num_workers)
|
|
|
|
cutoff = -1 # backbone reaches to cutoff layer
|
|
start_epoch = 0
|
|
best_loss = float('inf')
|
|
if resume: # Load previously saved PyTorch model
|
|
checkpoint = torch.load(latest, map_location=device) # load checkpoint
|
|
model.load_state_dict(checkpoint['model'])
|
|
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')
|
|
|
|
if torch.cuda.device_count() > 1:
|
|
model = nn.DataParallel(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 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(len(dataloader) / 5 + 1), 1000) # 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
|
|
|
|
rloss = defaultdict(float)
|
|
for i, (imgs, targets, _, _) in enumerate(dataloader):
|
|
# Unpad and collate targets
|
|
for j, t in enumerate(targets):
|
|
t[:, 0] = j
|
|
targets = torch.cat([t[t[:, 5].nonzero()] for t in targets], 0).squeeze(1)
|
|
|
|
nT = len(targets)
|
|
if nT == 0: # if no targets continue
|
|
continue
|
|
|
|
# Plot images with bounding boxes
|
|
plot_images = False
|
|
if plot_images:
|
|
import matplotlib.pyplot as plt
|
|
plt.figure(figsize=(10, 10))
|
|
for ip in range(batch_size):
|
|
labels = xywh2xyxy(targets[targets[:, 0] == ip, 2:6]).numpy() * img_size
|
|
plt.subplot(3, 3, ip + 1).imshow(imgs[ip].numpy().transpose(1, 2, 0))
|
|
plt.plot(labels[:, [0, 2, 2, 0, 0]].T, labels[:, [1, 1, 3, 3, 1]].T, '.-')
|
|
plt.axis('off')
|
|
|
|
# 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.to(device), 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
|
|
for key, val in loss_dict.items():
|
|
rloss[key] = (rloss[key] * i + val) / (i + 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:
|
|
dataset.img_size = random.choice(range(10, 20)) * 32
|
|
print('multi_scale img_size = %g' % dataset.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')
|
|
parser.add_argument('--num-workers', type=int, default=4, help='number of Pytorch DataLoader workers')
|
|
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,
|
|
num_workers=opt.num_workers
|
|
)
|