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move sparse_categorical_crossentropy to test_ops (#11083)

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also flattened the tests
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chenyu
2025-07-03 21:40:54 -04:00
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parent 7c8ccb0267
commit a2f5a54458
2 changed files with 33 additions and 23 deletions
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33
test/test_ops.py
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@@ -2950,6 +2950,39 @@ class TestOps(unittest.TestCase):
helper_test_op([(32,10)], lambda x: torch.nn.functional.cross_entropy(x, torch.tensor(classes), label_smoothing=ls),
lambda x: x.cross_entropy(Tensor(classes), label_smoothing=ls))
def test_sparse_categorical_crossentropy(self):
classes = np.random.randint(0, 10, (12,), dtype=np.int32).tolist()
helper_test_op([(12,10)], lambda x: torch.nn.CrossEntropyLoss()(x, torch.tensor(classes)),
lambda x: x.sparse_categorical_crossentropy(Tensor(classes)))
# combine args
helper_test_op([(12,10)],
lambda x: torch.nn.CrossEntropyLoss(reduction="mean", ignore_index=classes[0], label_smoothing=0.3)(x, torch.tensor(classes)),
lambda x: x.sparse_categorical_crossentropy(Tensor(classes), reduction="mean", ignore_index=classes[0], label_smoothing=0.3))
# with batch. somehow this does not match torch
classes = np.random.randint(0, 10, (3,12), dtype=np.int32).tolist()
helper_test_op([(3,12,10)], lambda x: torch.nn.CrossEntropyLoss()(x.permute(0,2,1), torch.tensor(classes)),
lambda x: x.sparse_categorical_crossentropy(Tensor(classes)))
def test_sparse_categorical_crossentropy_reductions(self):
for r in ("mean", "sum", "none"):
classes = np.random.randint(0, 10, (12,), dtype=np.int32).tolist()
helper_test_op([(12,10)], lambda x: torch.nn.CrossEntropyLoss(reduction=r)(x, torch.tensor(classes)),
lambda x: x.sparse_categorical_crossentropy(Tensor(classes), reduction=r))
def test_sparse_categorical_crossentropy_ignore_index(self):
classes = [0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3]
for i in (-1, 0, 3):
helper_test_op([(12,10)], lambda x: torch.nn.CrossEntropyLoss(ignore_index=i)(x, torch.tensor(classes)),
lambda x: x.sparse_categorical_crossentropy(Tensor(classes), ignore_index=i))
def test_sparse_categorical_crossentropy_label_smoothing(self):
for s in (0.3, 0.9):
classes = np.random.randint(0, 10, (12,), dtype=np.int32).tolist()
helper_test_op([(12,10)], lambda x: torch.nn.CrossEntropyLoss(label_smoothing=s)(x, torch.tensor(classes)),
lambda x: x.sparse_categorical_crossentropy(Tensor(classes), label_smoothing=s))
def test_nll_loss(self):
target = np.random.randint(0, 10, (32,), dtype=np.int32).tolist()
helper_test_op([(32,10)],