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,
accumulated_batches=1,
weights='weights',
multi_scale=False,
freeze_backbone=True,
var=0,
):
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
latest = os.path.join(weights, 'latest.pt')
best = os.path.join(weights, 'best.pt')
# 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, multi_scale=multi_scale, augment=True)
lr0 = 0.001
if resume:
checkpoint = torch.load(latest, map_location='cpu')
# Load weights to resume from
model.load_state_dict(checkpoint['model'])
# if torch.cuda.device_count() > 1:
# model = nn.DataParallel(model)
model.to(device).train()
# # Transfer learning (train only YOLO layers)
# for i, (name, p) in enumerate(model.named_parameters()):
# if p.shape[0] != 650: # not YOLO layer
# p.requires_grad = 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:
start_epoch = 0
best_loss = float('inf')
# Initialize model with darknet53 weights (optional)
load_darknet_weights(model, os.path.join(weights, 'darknet53.conv.74'))
# if torch.cuda.device_count() > 1:
# model = nn.DataParallel(model)
model.to(device).train()
# Set optimizer
optimizer = torch.optim.SGD(filter(lambda x: x.requires_grad, model.parameters()), lr=lr0, momentum=.9)
# Set scheduler
# scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=[54, 61], gamma=0.1)
model_info(model)
t0 = time.time()
for epoch in range(epochs):
epoch += start_epoch
print(('%8s%12s' + '%10s' * 7) % (
'Epoch', 'Batch', 'xy', 'wh', 'conf', 'cls', 'total', 'nTargets', 'time'))
# Update scheduler (automatic)
# scheduler.step()
# Update scheduler (manual) at 0, 54, 61 epochs to 1e-3, 1e-4, 1e-5
if epoch > 50:
lr = lr0 / 10
else:
lr = lr0
for g in optimizer.param_groups:
g['lr'] = lr
# Freeze darknet53.conv.74 for first epoch
if freeze_backbone:
if epoch == 0:
for i, (name, p) in enumerate(model.named_parameters()):
if int(name.split('.')[1]) < 75: # if layer < 75
p.requires_grad = False
elif epoch == 1:
for i, (name, p) in enumerate(model.named_parameters()):
if int(name.split('.')[1]) < 75: # if layer < 75
p.requires_grad = True
ui = -1
rloss = defaultdict(float) # running loss
optimizer.zero_grad()
for i, (imgs, targets) in enumerate(dataloader):
if sum([len(x) for x in targets]) < 1: # if no targets continue
continue
# SGD burn-in
if (epoch == 0) & (i <= 1000):
lr = lr0 * (i / 1000) ** 4
for g in optimizer.param_groups:
g['lr'] = lr
# Compute loss, compute gradient, update parameters
loss = model(imgs.to(device), targets, var=var)
loss.backward()
# accumulate gradient for x batches before optimizing
if ((i + 1) % accumulated_batches == 0) or (i == len(dataloader) - 1):
optimizer.step()
optimizer.zero_grad()
# Running epoch-means of tracked metrics
ui += 1
for key, val in model.losses.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['loss'], model.losses['nT'], time.time() - t0)
t0 = time.time()
print(s)
# Update best loss
loss_per_target = rloss['loss'] / rloss['nT']
if loss_per_target < best_loss:
best_loss = loss_per_target
# Save latest checkpoint
checkpoint = {'epoch': epoch,
'best_loss': best_loss,
'model': model.state_dict(),
'optimizer': optimizer.state_dict()}
torch.save(checkpoint, latest)
# Save best checkpoint
if best_loss == loss_per_target:
os.system('cp ' + latest + ' ' + best)
# Save backup weights every 5 epochs (optional)
# if (epoch > 0) & (epoch % 5 == 0):
# os.system('cp ' + latest + ' ' + os.path.join(weights, 'backup{}.pt'.format(epoch)))
# Calculate mAP
with torch.no_grad():
mAP, R, P = test.test(cfg, data_cfg, weights=latest, batch_size=batch_size, img_size=img_size)
# Write epoch results
with open('results.txt', 'a') as file:
file.write(s + '%11.3g' * 3 % (mAP, P, R) + '\n')
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--epochs', type=int, default=100, help='number of epochs')
parser.add_argument('--batch-size', type=int, default=16, help='size of each image batch')
parser.add_argument('--accumulated-batches', type=int, default=1, help='number of batches before optimizer step')
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('--weights', type=str, default='weights', help='path to store weights')
parser.add_argument('--resume', action='store_true', help='resume training flag')
parser.add_argument('--var', type=float, default=0, help='test variable')
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,
accumulated_batches=opt.accumulated_batches,
weights=opt.weights,
multi_scale=opt.multi_scale,
var=opt.var,
)