transform=composed_transforms_tr)
db_train = combine_dbs.CombineDBs([voc_train, sbd_train],
excluded=[voc_val])
else:
db_train = voc_train
trainloader = DataLoader(db_train,
batch_size=p['trainBatch'],
shuffle=True,
num_workers=0)
testloader = DataLoader(voc_val,
batch_size=testBatch,
shuffle=False,
num_workers=0)
utils.generate_param_report(os.path.join(save_dir, exp_name + '.txt'), p)
num_img_tr = len(trainloader)
num_img_ts = len(testloader)
running_loss_tr = 0.0
running_loss_ts = 0.0
aveGrad = 0
global_step = 0
print("Training Network")
# Main Training and Testing Loop
for epoch in range(resume_epoch, nEpochs):
start_time = timeit.default_timer()
if epoch % p['epoch_size'] == p['epoch_size'] - 1:
lr_ = utils.lr_poly(p['lr'], epoch, nEpochs, 0.9)
tr.ToTensor()])
voc_train = pascal.VOCSegmentation(split='train', transform=composed_transforms_tr)
voc_val = pascal.VOCSegmentation(split='val', transform=composed_transforms_ts)
if use_sbd:
print("Using SBD dataset")
sbd_train = sbd.SBDSegmentation(split=['train', 'val'], transform=composed_transforms_tr)
db_train = combine_dbs.CombineDBs([voc_train, sbd_train], excluded=[voc_val])
else:
db_train = voc_train
trainloader = DataLoader(db_train, batch_size=p['trainBatch'], shuffle=True, num_workers=2)
testloader = DataLoader(voc_val, batch_size=testBatch, shuffle=False, num_workers=2)
utils.generate_param_report(os.path.join(save_dir, exp_name + '.txt'), p)
num_img_tr = len(trainloader)
num_img_ts = len(testloader)
running_loss_tr = 0.0
running_loss_ts = 0.0
aveGrad = 0
print("Training Network")
# Main Training and Testing Loop
for epoch in range(resume_epoch, nEpochs):
start_time = timeit.default_timer()
if epoch % p['epoch_size'] == p['epoch_size'] - 1:
lr_ = utils.lr_poly(p['lr'], epoch, nEpochs, 0.9)
print('(poly lr policy) learning rate: ', lr_)