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Fix #5114 (#5122)

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Sylvain Gugger 2020-06-18 19:20:04 -04:00 committed by GitHub
parent a258982af3
commit 5f721ad6e4
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2 changed files with 16 additions and 4 deletions
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4
src/transformers/data/data_collator.py
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@ -42,10 +42,10 @@ def default_data_collator(features: List[InputDataClass]) -> Dict[str, torch.Ten
# Special handling for labels.
# Ensure that tensor is created with the correct type
# (it should be automatically the case, but let's make sure of it.)
if "label" in first:
if "label" in first and first["label"] is not None:
dtype = torch.long if type(first["label"]) is int else torch.float
batch["labels"] = torch.tensor([f["label"] for f in features], dtype=dtype)
elif "label_ids" in first:
elif "label_ids" in first and first["label_ids"] is not None:
if isinstance(first["label_ids"], torch.Tensor):
batch["labels"] = torch.stack([f["label_ids"] for f in features])
else:

16
tests/test_trainer.py
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@ -25,7 +25,7 @@ PATH_SAMPLE_TEXT = "./tests/fixtures/sample_text.txt"
@require_torch
class DataCollatorIntegrationTest(unittest.TestCase):
def test_default_with_dict(self):
features = [{"labels": i, "inputs": [0, 1, 2, 3, 4, 5]} for i in range(8)]
features = [{"label": i, "inputs": [0, 1, 2, 3, 4, 5]} for i in range(8)]
batch = default_data_collator(features)
self.assertTrue(batch["labels"].equal(torch.tensor(list(range(8)))))
self.assertEqual(batch["labels"].dtype, torch.long)
@ -39,12 +39,24 @@ class DataCollatorIntegrationTest(unittest.TestCase):
self.assertEqual(batch["inputs"].shape, torch.Size([8, 6]))
# Features can already be tensors
features = [{"labels": i, "inputs": torch.randint(10, [10])} for i in range(8)]
features = [{"label": i, "inputs": torch.randint(10, [10])} for i in range(8)]
batch = default_data_collator(features)
self.assertTrue(batch["labels"].equal(torch.tensor(list(range(8)))))
self.assertEqual(batch["labels"].dtype, torch.long)
self.assertEqual(batch["inputs"].shape, torch.Size([8, 10]))
def test_default_with_no_labels(self):
features = [{"label": None, "inputs": [0, 1, 2, 3, 4, 5]} for i in range(8)]
batch = default_data_collator(features)
self.assertTrue("labels" not in batch)
self.assertEqual(batch["inputs"].shape, torch.Size([8, 6]))
# With label_ids
features = [{"label_ids": None, "inputs": [0, 1, 2, 3, 4, 5]} for i in range(8)]
batch = default_data_collator(features)
self.assertTrue("labels" not in batch)
self.assertEqual(batch["inputs"].shape, torch.Size([8, 6]))
def test_default_classification(self):
MODEL_ID = "bert-base-cased-finetuned-mrpc"
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)