def test_milestone2(self):
milestone1 = [20, 10, 1]
with pytest.raises(ValueError):
dr.piecewise_constant_lr(milestone1, learning_rates)
milestone2 = [1.0, 2.0, True]
with pytest.raises(TypeError):
dr.piecewise_constant_lr(milestone2, learning_rates)
def train():
context.set_context(
mode=context.GRAPH_MODE,
device_target="Ascend",
#save_graphs=True,
#save_graphs_path="/home/work/user-job-dir/EAST/",
#enable_reduce_precision=False,
#device_id=5
)
epoch = 600
my_dataset.download_dataset()
train_img_path = os.path.abspath('/cache/train_img')
train_gt_path = os.path.abspath('/cache/train_gt')
#my_dataset.data_to_mindrecord_byte_image(train_img_path, train_gt_path, mindrecord_dir='/cache', prefix='icdar_train.mindrecord',file_num=1)
#dataset = my_dataset.create_icdar_train_dataset(mindrecord_file=['icdar_train.mindrecord0','icdar_train.mindrecord1','icdar_train.mindrecord2','icdar_train.mindrecord3'], batch_size=32, repeat_num=epoch,
# is_training=True, num_parallel_workers=8, length=512, scale=0.25)
#dataset = my_dataset.create_icdar_train_dataset(mindrecord_file='/cache/icdar_train.mindrecord', batch_size=32, repeat_num=epoch,
# is_training=True, num_parallel_workers=24, length=512, scale=0.25)
#dataset = my_dataset.create_demo_dataset(batch_size=21, repeat_num=2)
#train_img_path = os.path.abspath('/home/licheng/gpzlx1/ICDAR_2015/train/img')
#train_gt_path = os.path.abspath('/home/licheng/gpzlx1/ICDAR_2015/train/gt')
dataset = datasetV2.create_icdar_train_dataset(train_img_path,
train_gt_path,
batch_size=14,
repeat_num=1,
is_training=True,
num_parallel_workers=24)
#dataset = datasetV3.create_icdar_train_dataset(train_img_path, train_gt_path, batch_size=14, repeat_num=1, is_training=True, num_parallel_workers=24)
dataset_size = dataset.get_dataset_size()
print("Create dataset done!, dataset_size: ", dataset_size)
#east = EAST.EAST()
net = EAST_VGG.EAST()
#ckpt_config = CheckpointConfig(save_checkpoint_steps=dataset_size * 20)
#ckpoint_cb = ModelCheckpoint(prefix='EAST', directory='/cache', config=ckpt_config)
milestone = [100, 300]
learning_rates = [1e-3, 1e-4]
lr = piecewise_constant_lr(milestone, learning_rates)
opt = nn.Adam(filter(lambda x: x.requires_grad, net.get_parameters()),
learning_rate=lr)
net = my_loss.EASTWithLossCell(net)
net = my_loss.TrainingWrapper(net, opt)
net.set_train(True)
callback = [TimeMonitor(data_size=dataset_size),
LossMonitor()] #, ckpoint_cb]
model = Model(net)
dataset_sink_mode = False
print("start trainig")
model.train(epoch,
dataset,
callbacks=callback,
dataset_sink_mode=dataset_sink_mode)
def test_learning_rates2(self):
lr = [1, 2, 1]
with pytest.raises(TypeError):
dr.piecewise_constant_lr(milestone, lr)
def test_milestone1(self):
milestone1 = 1
with pytest.raises(TypeError):
dr.piecewise_constant_lr(milestone1, learning_rates)
def test_learning_rate():
lr = dr.piecewise_constant_lr(milestone, learning_rates)
assert len(lr) == milestone[-1]
dataset = create_dataset("/home/dingfeifei/datasets/faces_webface_112x112_raw_image", \
p=config.p, k=config.k)
step_size = dataset.get_dataset_size()
base_lr = config.learning_rate
warm_up_epochs = config.lr_warmup_epochs
lr_decay_epochs = config.lr_decay_epochs
lr_decay_factor = config.lr_decay_factor
lr_decay_steps = []
lr_decay = []
for i, v in enumerate(lr_decay_epochs):
lr_decay_steps.append(v * step_size)
lr_decay.append(base_lr * lr_decay_factor**i)
lr_1 = warmup_lr(base_lr, step_size * warm_up_epochs, step_size,
warm_up_epochs)
lr_2 = piecewise_constant_lr(lr_decay_steps, lr_decay)
lr = lr_1 + lr_2
train_net = TrainStepWrap(loss_net, lr, config.momentum)
test_net = TestStepWrap(net)
f = open("checkpoints/pretrained_resnet50.pkl", "rb")
param_dict = pickle.load(f)
load_param_into_net(net=train_net, parameter_dict=param_dict)
model = Model(train_net,
eval_network=test_net,
metrics={"Accuracy": Accuracy()})
loss_cb = LossMonitor()
cb = [loss_cb]
def test_learning_rates1(self):
lr = True
with pytest.raises(ValueError):
dr.piecewise_constant_lr(milestone, lr)
