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Fix Graphormer test suite (#21419)

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* [FIX] path for Graphormer checkpoint

* [FIX] Test suite for graphormer

* [FIX] Update graphormer default num_classes
This commit is contained in:
Clémentine Fourrier 2023-02-02 16:29:13 +01:00 committed by GitHub
parent e006ab51ac
commit 67a3920d85
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2 changed files with 9 additions and 13 deletions
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6
src/transformers/models/graphormer/configuration_graphormer.py
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@ -39,8 +39,8 @@ class GraphormerConfig(PretrainedConfig):
Args:
num_classes (`int`, *optional*, defaults to 2):
Number of target classes or labels, set to 1 if the task is a regression task.
num_classes (`int`, *optional*, defaults to 1):
Number of target classes or labels, set to n for binary classification of n tasks.
num_atoms (`int`, *optional*, defaults to 512*9):
Number of node types in the graphs.
num_edges (`int`, *optional*, defaults to 512*3):
@ -134,7 +134,7 @@ class GraphormerConfig(PretrainedConfig):
def __init__(
self,
num_classes: int = 2,
num_classes: int = 1,
num_atoms: int = 512 * 9,
num_edges: int = 512 * 3,
num_in_degree: int = 512,

16
tests/models/graphormer/test_modeling_graphormer.py
View File

@ -40,7 +40,7 @@ class GraphormerModelTester:
def __init__(
self,
parent,
num_classes=2,
num_classes=1,
num_atoms=512 * 9,
num_edges=512 * 3,
num_in_degree=512,
@ -614,7 +614,7 @@ class GraphormerModelIntegrationTest(unittest.TestCase):
[3, 3, 4, 3, 3, 3, 3, 4, 4, 3, 4, 2, 2, 0, 0, 0, 0],
]
),
"x": tensor(
"input_nodes": tensor(
[
[[3], [3], [3], [3], [3], [3], [3], [3], [3], [3], [3], [3], [3], [3], [3], [3], [3]],
[[3], [3], [3], [3], [3], [3], [3], [3], [3], [3], [3], [3], [3], [0], [0], [0], [0]],
@ -1279,15 +1279,11 @@ class GraphormerModelIntegrationTest(unittest.TestCase):
output = model(**model_input)["logits"]
print(output.shape)
print(output)
expected_shape = torch.Size(())
expected_shape = torch.Size((2, 1))
self.assertEqual(output.shape, expected_shape)
# TODO Replace values below with what was printed above.
expected_slice = torch.tensor(
[[[-0.0483, 0.1188, -0.0313], [-0.0606, 0.1435, 0.0199], [-0.0235, 0.1519, 0.0175]]]
expected_logs = torch.tensor(
[[7.6060], [7.4126]]
)
self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=1e-4))
self.assertTrue(torch.allclose(output, expected_logs, atol=1e-4))