def test_control_C():
# 用于测试 ControlC , 再两次训练时用 Control+C 进行退出，如果最后不显示 "Test failed!" 则通过测试
from fastNLP import ControlC, Callback
import time
line1 = "\n\n\n\n\n*************************"
line2 = "*************************\n\n\n\n\n"
class Wait(Callback):
def on_epoch_end(self):
time.sleep(5)
data_set, model = prepare_env()
print(line1 + "Test starts!" + line2)
trainer = Trainer(data_set, model, optimizer=SGD(lr=0.1), loss=BCELoss(pred="predict", target="y"),
batch_size=32, n_epochs=20, dev_data=data_set,
metrics=AccuracyMetric(pred="predict", target="y"), use_tqdm=True,
callbacks=[Wait(), ControlC(False)], check_code_level=2)
trainer.train()
print(line1 + "Program goes on ..." + line2)
trainer = Trainer(data_set, model, optimizer=SGD(lr=0.1), loss=BCELoss(pred="predict", target="y"),
batch_size=32, n_epochs=20, dev_data=data_set,
metrics=AccuracyMetric(pred="predict", target="y"), use_tqdm=True,
callbacks=[Wait(), ControlC(True)], check_code_level=2)
trainer.train()
print(line1 + "Test failed!" + line2)
def test_control_C_callback(self):
class Raise(Callback):
def on_epoch_end(self):
raise KeyboardInterrupt
flags = [False]
def set_flag():
flags[0] = not flags[0]
data_set, model = prepare_env()
trainer = Trainer(data_set,
model,
optimizer=SGD(lr=0.1),
loss=BCELoss(pred="predict", target="y"),
batch_size=32,
n_epochs=20,
dev_data=data_set,
metrics=AccuracyMetric(pred="predict", target="y"),
use_tqdm=True,
callbacks=[Raise(),
ControlC(False, set_flag)],
check_code_level=2)
trainer.train()
self.assertEqual(flags[0], False)
trainer = Trainer(data_set,
model,
optimizer=SGD(lr=0.1),
loss=BCELoss(pred="predict", target="y"),
batch_size=32,
n_epochs=20,
dev_data=data_set,
metrics=AccuracyMetric(pred="predict", target="y"),
use_tqdm=True,
callbacks=[Raise(),
ControlC(True, set_flag)],
check_code_level=2)
trainer.train()
self.assertEqual(flags[0], True)
def test_KeyBoardInterrupt(self):
data_set, model = prepare_env()
trainer = Trainer(data_set,
model,
loss=BCELoss(pred="predict", target="y"),
n_epochs=5,
batch_size=32,
print_every=50,
optimizer=SGD(lr=0.1),
check_code_level=2,
use_tqdm=False,
callbacks=[ControlC(False)])
trainer.train()