Towards-Realtime-MOT/train.py

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2019-09-27 07:37:47 +02:00
import argparse
import json
import time
import test # Import test.py to get mAP after each epoch
from models import *
from utils.datasets import JointDataset, collate_fn
from utils.utils import *
from torchvision.transforms import transforms as T
def train(
cfg,
data_cfg,
img_size=416,
resume=False,
epochs=100,
batch_size=16,
accumulated_batches=1,
freeze_backbone=False,
var=0,
opt=None,
):
weights = 'weights' + os.sep
latest = weights + 'latest.pt'
best = weights + 'best.pt'
device = torch_utils.select_device()
torch.backends.cudnn.benchmark = True # unsuitable for multiscale
# Configure run
f = open(data_cfg)
trainset_paths = json.load(f)['train']
f.close()
transforms = T.Compose([T.ToTensor()])
# Get dataloader
dataset = JointDataset(trainset_paths, img_size, augment=True, transforms=transforms)
dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=True,
num_workers=8, pin_memory=True, drop_last=True, collate_fn=collate_fn)
# Initialize model
model = Darknet(cfg, img_size, dataset.nID)
lr0 = opt.lr
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'])
model.to(device).train()
# 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'):
load_darknet_weights(model, weights + 'darknet53.conv.74')
cutoff = 75
elif cfg.endswith('yolov3-tiny.cfg'):
load_darknet_weights(model, weights + 'yolov3-tiny.conv.15')
cutoff = 15
model.to(device).train()
# Set optimizer
optimizer = torch.optim.SGD(filter(lambda x: x.requires_grad, model.parameters()), lr=lr0, momentum=.9, weight_decay=1e-4)
model = torch.nn.DataParallel(model)
# Set scheduler
scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer,
milestones=[int(0.5*opt.epochs), int(0.75*opt.epochs)], gamma=0.1)
# An important trick for detection: freeze bn during fine-tuning
if not opt.unfreeze_bn:
for i, (name, p) in enumerate(model.named_parameters()):
p.requires_grad = False if 'batch_norm' in name else True
model_info(model)
t0 = time.time()
for epoch in range(epochs):
epoch += start_epoch
print(('%8s%12s' + '%10s' * 6) % (
'Epoch', 'Batch', 'box', 'conf', 'id', 'total', 'nTargets', 'time'))
# Update scheduler (automatic)
scheduler.step()
# Freeze darknet53.conv.74 for first epoch
if freeze_backbone and (epoch < 2):
for i, (name, p) in enumerate(model.named_parameters()):
if int(name.split('.')[2]) < cutoff: # if layer < 75
p.requires_grad = False if (epoch == 0) else True
ui = -1
rloss = defaultdict(float) # running loss
optimizer.zero_grad()
for i, (imgs, targets, _, _, targets_len) in enumerate(dataloader):
if sum([len(x) for x in targets]) < 1: # if no targets continue
continue
# SGD burn-in
burnin = min(1000, len(dataloader))
if (epoch == 0) & (i <= burnin):
lr = lr0 * (i / burnin) **4
for g in optimizer.param_groups:
g['lr'] = lr
# Compute loss, compute gradient, update parameters
loss, components = model(imgs.cuda(), targets.cuda(), targets_len.cuda())
components = torch.mean(components.view(4,-1),dim=0)
loss = torch.mean(loss)
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 ii, key in enumerate(model.module.loss_names):
rloss[key] = (rloss[key] * ui + components[ii]) / (ui + 1)
s = ('%8s%12s' + '%10.3g' * 6) % (
'%g/%g' % (epoch, epochs - 1),
'%g/%g' % (i, len(dataloader) - 1),
rloss['box'], rloss['conf'],
rloss['id'],rloss['loss'],
rloss['nT'], time.time() - t0)
t0 = time.time()
if i % opt.print_interval == 0:
print(s)
# Save latest checkpoint
checkpoint = {'epoch': epoch,
# 'best_loss': best_loss,
'model': model.module.state_dict(),
'optimizer': optimizer.state_dict()}
torch.save(checkpoint, latest)
# Calculate mAP
if epoch % opt.test_interval ==0:
with torch.no_grad():
mAP, R, P = test.test(cfg, data_cfg, weights=latest, batch_size=batch_size, img_size=img_size, print_interval=40, nID=dataset.nID)
test.test_emb(cfg, data_cfg, weights=latest, batch_size=batch_size, img_size=img_size, print_interval=40, nID=dataset.nID)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--epochs', type=int, default=30, help='number of epochs')
parser.add_argument('--batch-size', type=int, default=32, 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/ccmcpe.json', help='coco.data file path')
parser.add_argument('--img-size', type=int, default=(1088, 608), help='pixels')
parser.add_argument('--resume', action='store_true', help='resume training flag')
parser.add_argument('--var', type=float, default=0, help='test variable')
parser.add_argument('--print-interval', type=int, default=40, help='print interval')
parser.add_argument('--test-interval', type=int, default=9, help='test interval')
parser.add_argument('--lr', type=float, default=1e-2, help='init lr')
parser.add_argument('--idw', type=float, default=0.1, help='loss id weight')
parser.add_argument('--unfreeze-bn', action='store_true', help='unfreeze bn')
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,
var=opt.var,
opt=opt,
)