def test_metric_tracker_save_and_load():
metric_tracker = MetricTracker(patient=1)
for metric in METRICS:
metric_tracker.add_metric(**metric)
if metric["epoch"] > 4:
ASSERT.assertTrue(metric_tracker.early_stopping(metric["epoch"]))
else:
ASSERT.assertFalse(metric_tracker.early_stopping(metric["epoch"]))
if metric_tracker.early_stopping(metric["epoch"]):
break
saved_file_path = os.path.join(ROOT_PATH, "data/easytext/tests/trainer/metric_tracker.json")
metric_tracker.save(saved_file_path)
loaded_metric_tracker = MetricTracker.from_file(saved_file_path)
best = metric_tracker.best()
loaded_best = loaded_metric_tracker.best()
ASSERT.assertEqual(best.epoch, loaded_best.epoch)
ASSERT.assertDictEqual(best.train_metric, loaded_best.train_metric)
ASSERT.assertDictEqual(best.validation_metric, loaded_best.validation_metric)
ASSERT.assertEqual(best.train_model_target_metric.name, loaded_best.train_model_target_metric.name)
ASSERT.assertEqual(best.train_model_target_metric.value, loaded_best.train_model_target_metric.value)
ASSERT.assertEqual(best.validation_model_target_metric.name, loaded_best.validation_model_target_metric.name)
ASSERT.assertEqual(best.validation_model_target_metric.value, loaded_best.validation_model_target_metric.value)
def test_metric_tracker_patient():
metric_tracker = MetricTracker(patient=1)
for metric in METRICS:
metric_tracker.add_metric(**metric)
if metric["epoch"] > 4:
ASSERT.assertTrue(metric_tracker.early_stopping(metric["epoch"]))
else:
ASSERT.assertFalse(metric_tracker.early_stopping(metric["epoch"]))
if metric_tracker.early_stopping(metric["epoch"]):
break
expect = {"epoch": 3,
"train_metric": {"acc": 0.85},
"train_model_target_metric": ModelTargetMetric(metric_name="acc", metric_value=0.85),
"validation_metric": {"acc": 0.60},
"validation_model_target_metric": ModelTargetMetric(metric_name="acc", metric_value=0.60)}
best = metric_tracker.best()
ASSERT.assertEqual(expect["epoch"], best.epoch)
ASSERT.assertDictEqual(expect["train_metric"], best.train_metric)
ASSERT.assertDictEqual(expect["validation_metric"], best.validation_metric)
ASSERT.assertEqual(expect["train_model_target_metric"].name, best.train_model_target_metric.name)
ASSERT.assertEqual(expect["train_model_target_metric"].value, best.train_model_target_metric.value)
ASSERT.assertEqual(expect["validation_model_target_metric"].name, best.validation_model_target_metric.name)
ASSERT.assertEqual(expect["validation_model_target_metric"].value, best.validation_model_target_metric.value)
def test_is_tensor_equal():
"""
测试两个 tensor 是否相等
:return:
"""
x = torch.tensor([1, 2, 3])
y = torch.tensor([1, 2, 3])
equal = tensor_util.is_tensor_equal(tensor1=x, tensor2=y, epsilon=0)
ASSERT.assertTrue(equal)
x = torch.tensor([1, 2, 3])
y = torch.tensor([2, 2, 3])
equal = tensor_util.is_tensor_equal(tensor1=x, tensor2=y, epsilon=0)
ASSERT.assertFalse(equal)
x = torch.tensor([1.0001, 2.0001, 3.0001])
y = torch.tensor([1., 2., 3.])
equal = tensor_util.is_tensor_equal(tensor1=x, tensor2=y, epsilon=1e-3)
ASSERT.assertTrue(equal)
equal = tensor_util.is_tensor_equal(tensor1=x, tensor2=y, epsilon=1e-4)
ASSERT.assertFalse(equal)
