def train(epoch):
train_loader = torch.utils.data.DataLoader(
lmdb_utils.lmdbDataset(traindb, shape=(model.module.width, model.module.height),
shuffle=True,
train=True, seen=model.module.seen),
batch_size=batch_size, shuffle=False, **kwargs)
logging('epoch %d : processed %d samples' % (epoch, epoch * len(train_loader.dataset)))
model.train()
adjust_learning_rate(optimizer, epoch)
for batch_idx, (data, target) in enumerate(train_loader):
if (batch_idx+1) % 70 == 0:
sys.stdout.write('.')
if use_cuda:
data = data.cuda()
#target= target.cuda()
data, target = Variable(data), Variable(target)
optimizer.zero_grad()
output = model(data)
loss = region_loss(output, target)
loss.backward()
optimizer.step()
print('')
logging('save weights to %s/%06d.weights' % (backupdir, epoch+1))
model.module.seen = (epoch + 1) * len(train_loader.dataset)
model.module.save_weights('%s/%06d.weights' % (backupdir, epoch+1))
###############
torch.manual_seed(seed)
if use_cuda:
torch.cuda.manual_seed(seed)
model = Darknet(cfgfile)
region_loss = model.loss
model.load_weights(weightfile)
model.print_network()
init_epoch = model.seen / nsamples
kwargs = {'num_workers': 8, 'pin_memory': True} if use_cuda else {}
test_loader = torch.utils.data.DataLoader(
lmdb_utils.lmdbDataset(testdb, shape=(160, 160),
shuffle=False,
transform=None,
train=False),
batch_size=batch_size, shuffle=False, **kwargs)
if use_cuda:
model = torch.nn.DataParallel(model).cuda()
optimizer = optim.SGD(model.parameters(), lr=learning_rate, momentum=momentum)
def adjust_learning_rate(optimizer, epoch):
"""Sets the learning rate to the initial LR decayed by 10 every 30 epochs"""
lr = learning_rate * (0.1 ** (epoch // 50))
for param_group in optimizer.param_groups:
param_group['lr'] = lr
logging('set lr=%f' % (lr))
eps = 1e-5
###############
torch.manual_seed(seed)
if use_cuda:
torch.cuda.manual_seed(seed)
model = Darknet(cfgfile)
region_loss = model.loss
model.load_weights(weightfile)
model.print_network()
init_epoch = model.seen / nsamples
kwargs = {'num_workers': 8, 'pin_memory': True} if use_cuda else {}
test_loader = torch.utils.data.DataLoader(lmdb_utils.lmdbDataset(
testdb, shape=(160, 160), shuffle=False, transform=None, train=False),
batch_size=batch_size,
shuffle=False,
**kwargs)
if use_cuda:
model = torch.nn.DataParallel(model).cuda()
optimizer = optim.SGD(model.parameters(), lr=learning_rate, momentum=momentum)
def adjust_learning_rate(optimizer, epoch):
"""Sets the learning rate to the initial LR decayed by 10 every 30 epochs"""
lr = learning_rate * (0.1**(epoch // 50))
for param_group in optimizer.param_groups:
param_group['lr'] = lr
