Towards-Realtime-MOT/train.py
Parthesh Soni 24f351d1b5
Documentation (#95)
* Added documentation

* Added docstrings and comments

* Removed unused imports

* Removed unused imports

* Added functionality of saving checkpoints during training process

* Update train.py

* Update multitracker.py
2020-03-14 10:24:27 +08:00

219 lines
8.9 KiB
Python

import argparse
import json
import time
from time import gmtime, strftime
import test
from models import *
from shutil import copyfile
from utils.datasets import JointDataset, collate_fn
from utils.utils import *
from utils.log import logger
from torchvision.transforms import transforms as T
def train(
cfg,
data_cfg,
weights_from="",
weights_to="",
save_every=10,
img_size=(1088, 608),
resume=False,
epochs=100,
batch_size=16,
accumulated_batches=1,
freeze_backbone=False,
opt=None,
):
# The function starts
timme = strftime("%Y-%d-%m %H:%M:%S", gmtime())
timme = timme[5:-3].replace('-', '_')
timme = timme.replace(' ', '_')
timme = timme.replace(':', '_')
weights_to = osp.join(weights_to, 'run' + timme)
mkdir_if_missing(weights_to)
if resume:
latest_resume = osp.join(weights_from, 'latest.pt')
torch.backends.cudnn.benchmark = True # unsuitable for multiscale
# Configure run
f = open(data_cfg)
data_config = json.load(f)
trainset_paths = data_config['train']
dataset_root = data_config['root']
f.close()
transforms = T.Compose([T.ToTensor()])
# Get dataloader
dataset = JointDataset(dataset_root, 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, dataset.nID)
cutoff = -1 # backbone reaches to cutoff layer
start_epoch = 0
if resume:
checkpoint = torch.load(latest_resume, map_location='cpu')
# Load weights to resume from
model.load_state_dict(checkpoint['model'])
model.cuda().train()
# Set optimizer
optimizer = torch.optim.SGD(filter(lambda x: x.requires_grad, model.parameters()), lr=opt.lr, momentum=.9)
start_epoch = checkpoint['epoch'] + 1
if checkpoint['optimizer'] is not None:
optimizer.load_state_dict(checkpoint['optimizer'])
del checkpoint # current, saved
else:
# Initialize model with backbone (optional)
if cfg.endswith('yolov3.cfg'):
load_darknet_weights(model, osp.join(weights_from, 'darknet53.conv.74'))
cutoff = 75
elif cfg.endswith('yolov3-tiny.cfg'):
load_darknet_weights(model, osp.join(weights_from, 'yolov3-tiny.conv.15'))
cutoff = 15
model.cuda().train()
# Set optimizer
optimizer = torch.optim.SGD(filter(lambda x: x.requires_grad, model.parameters()), lr=opt.lr, 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
logger.info(('%8s%12s' + '%10s' * 6) % (
'Epoch', 'Batch', 'box', 'conf', 'id', 'total', 'nTargets', 'time'))
# 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 = opt.lr * (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(-1, 5), 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)
# rloss indicates running loss values with mean updated at every epoch
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:
logger.info(s)
# Save latest checkpoint
checkpoint = {'epoch': epoch,
'model': model.module.state_dict(),
'optimizer': optimizer.state_dict()}
copyfile(cfg, weights_to + '/cfg/yolo3.cfg')
copyfile(data_cfg, weights_to + '/cfg/ccmcpe.json')
latest = osp.join(weights_to, 'latest.pt')
torch.save(checkpoint, latest)
if epoch % save_every == 0 and epoch != 0:
# making the checkpoint lite
checkpoint["optimizer"] = []
torch.save(checkpoint, osp.join(weights_to, "weights_epoch_" + str(epoch) + ".pt"))
# 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)
# Call scheduler.step() after opimizer.step() with pytorch > 1.1.0
scheduler.step()
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('--weights-from', type=str, default='weights/',
help='Path for getting the trained model for resuming training (Should only be used with '
'--resume)')
parser.add_argument('--weights-to', type=str, default='weights/',
help='Store the trained weights after resuming training session. It will create a new folder '
'with timestamp in the given path')
parser.add_argument('--save-model-after', type=int, default=10,
help='Save a checkpoint of model at given interval of epochs')
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], nargs='+', help='pixels')
parser.add_argument('--resume', action='store_true', help='resume training flag')
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('--unfreeze-bn', action='store_true', help='unfreeze bn')
opt = parser.parse_args()
init_seeds()
train(
opt.cfg,
opt.data_cfg,
weights_from=opt.weights_from,
weights_to=opt.weights_to,
save_every=opt.save_model_after,
img_size=opt.img_size,
resume=opt.resume,
epochs=opt.epochs,
batch_size=opt.batch_size,
accumulated_batches=opt.accumulated_batches,
opt=opt,
)