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Trainer multi label (#7191)

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* Trainer accep multiple labels

* Missing import

* Fix dosctrings
This commit is contained in:
Sylvain Gugger
2020-09-17 08:15:37 -04:00
committed by GitHub
parent 709745927b
commit 492bb6aa48
4 changed files with 110 additions and 29 deletions
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33
tests/test_trainer.py
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@@ -24,17 +24,21 @@ PATH_SAMPLE_TEXT = f"{get_tests_dir()}/fixtures/sample_text.txt"
class RegressionDataset:
def __init__(self, a=2, b=3, length=64, seed=42):
def __init__(self, a=2, b=3, length=64, seed=42, label_names=None):
np.random.seed(seed)
self.label_names = ["labels"] if label_names is None else label_names
self.length = length
self.x = np.random.normal(size=(length,)).astype(np.float32)
self.y = a * self.x + b + np.random.normal(scale=0.1, size=(length,))
self.ys = [a * self.x + b + np.random.normal(scale=0.1, size=(length,)) for _ in self.label_names]
self.ys = [y.astype(np.float32) for y in self.ys]
def __len__(self):
return self.length
def __getitem__(self, i):
return {"input_x": self.x[i], "label": self.y[i]}
result = {name: y[i] for name, y in zip(self.label_names, self.ys)}
result["input_x"] = self.x[i]
return result
class AlmostAccuracy:
@@ -68,7 +72,7 @@ if is_torch_available():
self.double_output = double_output
self.config = None
def forward(self, input_x=None, labels=None):
def forward(self, input_x=None, labels=None, **kwargs):
y = input_x * self.a + self.b
if labels is None:
return (y, y) if self.double_output else (y,)
@@ -76,8 +80,9 @@ if is_torch_available():
return (loss, y, y) if self.double_output else (loss, y)
def get_regression_trainer(a=0, b=0, double_output=False, train_len=64, eval_len=64, **kwargs):
train_dataset = RegressionDataset(length=train_len)
eval_dataset = RegressionDataset(length=eval_len)
label_names = kwargs.get("label_names", None)
train_dataset = RegressionDataset(length=train_len, label_names=label_names)
eval_dataset = RegressionDataset(length=eval_len, label_names=label_names)
model = RegressionModel(a, b, double_output)
compute_metrics = kwargs.pop("compute_metrics", None)
data_collator = kwargs.pop("data_collator", None)
@@ -174,7 +179,7 @@ class TrainerIntegrationTest(unittest.TestCase):
trainer = get_regression_trainer(a=1.5, b=2.5, compute_metrics=AlmostAccuracy())
results = trainer.evaluate()
x, y = trainer.eval_dataset.x, trainer.eval_dataset.y
x, y = trainer.eval_dataset.x, trainer.eval_dataset.ys[0]
pred = 1.5 * x + 2.5
expected_loss = ((pred - y) ** 2).mean()
self.assertAlmostEqual(results["eval_loss"], expected_loss)
@@ -185,7 +190,7 @@ class TrainerIntegrationTest(unittest.TestCase):
trainer = get_regression_trainer(a=1.5, b=2.5, eval_len=66, compute_metrics=AlmostAccuracy())
results = trainer.evaluate()
x, y = trainer.eval_dataset.x, trainer.eval_dataset.y
x, y = trainer.eval_dataset.x, trainer.eval_dataset.ys[0]
pred = 1.5 * x + 2.5
expected_loss = ((pred - y) ** 2).mean()
self.assertAlmostEqual(results["eval_loss"], expected_loss)
@@ -212,6 +217,18 @@ class TrainerIntegrationTest(unittest.TestCase):
self.assertTrue(np.allclose(preds[0], 1.5 * x + 2.5))
self.assertTrue(np.allclose(preds[1], 1.5 * x + 2.5))
# With more than one output/label of the model
trainer = get_regression_trainer(a=1.5, b=2.5, double_output=True, label_names=["labels", "labels_2"])
outputs = trainer.predict(trainer.eval_dataset)
preds = outputs.predictions
labels = outputs.label_ids
x = trainer.eval_dataset.x
self.assertTrue(len(preds), 2)
self.assertTrue(np.allclose(preds[0], 1.5 * x + 2.5))
self.assertTrue(np.allclose(preds[1], 1.5 * x + 2.5))
self.assertTrue(np.array_equal(labels[0], trainer.eval_dataset.ys[0]))
self.assertTrue(np.array_equal(labels[1], trainer.eval_dataset.ys[1]))
def test_trainer_with_datasets(self):
np.random.seed(42)
x = np.random.normal(size=(64,)).astype(np.float32)