def train(args):
data_shape = cityscape.train_data_shape()
num_classes = cityscape.num_classes()
# define network
images = fluid.layers.data(name='image', shape=data_shape, dtype='float32')
label_sub1 = fluid.layers.data(name='label_sub1', shape=[1], dtype='int32')
label_sub2 = fluid.layers.data(name='label_sub2', shape=[1], dtype='int32')
label_sub4 = fluid.layers.data(name='label_sub4', shape=[1], dtype='int32')
mask_sub1 = fluid.layers.data(name='mask_sub1', shape=[-1], dtype='int32')
mask_sub2 = fluid.layers.data(name='mask_sub2', shape=[-1], dtype='int32')
mask_sub4 = fluid.layers.data(name='mask_sub4', shape=[-1], dtype='int32')
sub4_out, sub24_out, sub124_out = icnet(
images, num_classes,
np.array(data_shape[1:]).astype("float32"))
loss_sub4 = create_loss(sub4_out, label_sub4, mask_sub4, num_classes)
loss_sub24 = create_loss(sub24_out, label_sub2, mask_sub2, num_classes)
loss_sub124 = create_loss(sub124_out, label_sub1, mask_sub1, num_classes)
reduced_loss = LAMBDA1 * loss_sub4 + LAMBDA2 * loss_sub24 + LAMBDA3 * loss_sub124
regularizer = fluid.regularizer.L2Decay(0.0001)
optimizer = fluid.optimizer.Momentum(learning_rate=poly_decay(),
momentum=0.9,
regularization=regularizer)
_, params_grads = optimizer.minimize(reduced_loss, no_grad_set=no_grad_set)
# prepare environment
place = fluid.CPUPlace()
if args.use_gpu:
place = fluid.CUDAPlace(0)
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
if args.init_model is not None:
print("load model from: %s" % args.init_model)
def if_exist(var):
return os.path.exists(os.path.join(args.init_model, var.name))
fluid.io.load_vars(exe, args.init_model, predicate=if_exist)
iter_id = 0
t_loss = 0.
sub4_loss = 0.
sub24_loss = 0.
sub124_loss = 0.
train_reader = cityscape.train(args.batch_size,
flip=args.random_mirror,
scaling=args.random_scaling)
start_time = time.time()
while True:
# train a pass
for data in train_reader():
if iter_id > TOTAL_STEP:
end_time = time.time()
print("kpis train_duration %f" % (end_time - start_time))
return
iter_id += 1
results = exe.run(
feed=get_feeder_data(data, place),
fetch_list=[reduced_loss, loss_sub4, loss_sub24, loss_sub124])
t_loss += results[0]
sub4_loss += results[1]
sub24_loss += results[2]
sub124_loss += results[3]
# training log
if iter_id % LOG_PERIOD == 0:
print(
"Iter[%d]; train loss: %.3f; sub4_loss: %.3f; sub24_loss: %.3f; sub124_loss: %.3f"
% (iter_id, t_loss / LOG_PERIOD, sub4_loss / LOG_PERIOD,
sub24_loss / LOG_PERIOD, sub124_loss / LOG_PERIOD))
print("kpis train_cost %f" % (t_loss / LOG_PERIOD))
t_loss = 0.
sub4_loss = 0.
sub24_loss = 0.
sub124_loss = 0.
sys.stdout.flush()
if iter_id % CHECKPOINT_PERIOD == 0 and args.checkpoint_path is not None:
dir_name = args.checkpoint_path + "/" + str(iter_id)
fluid.io.save_persistables(exe, dirname=dir_name)
print("Saved checkpoint: %s" % (dir_name))
def train(args):
data_shape = cityscape.train_data_shape()