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Set the dataset format used by test_trainer to float32 (#28920)

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Co-authored-by: unit_test <test@unit.com>
This commit is contained in:
Huazhong Ji
2024-02-14 21:55:12 +08:00
committed by GitHub
parent 7252e8d937
commit 69ca640dd6
1 changed files with 3 additions and 3 deletions
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tests/trainer/test_trainer.py
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@@ -176,8 +176,8 @@ class DynamicShapesDataset:
np.random.seed(seed) np.random.seed(seed)
sizes = np.random.randint(1, 20, (length // batch_size,)) sizes = np.random.randint(1, 20, (length // batch_size,))
# For easy batching, we make every batch_size consecutive samples the same size. # For easy batching, we make every batch_size consecutive samples the same size.
self.xs = [np.random.normal(size=(s,)) for s in sizes.repeat(batch_size)] self.xs = [np.random.normal(size=(s,)).astype(np.float32) for s in sizes.repeat(batch_size)]
self.ys = [np.random.normal(size=(s,)) for s in sizes.repeat(batch_size)] self.ys = [np.random.normal(size=(s,)).astype(np.float32) for s in sizes.repeat(batch_size)]
def __len__(self): def __len__(self):
return self.length return self.length
@@ -547,7 +547,7 @@ class TrainerIntegrationPrerunTest(TestCasePlus, TrainerIntegrationCommon):
np.random.seed(42) np.random.seed(42)
x = np.random.normal(size=(64,)).astype(np.float32) x = np.random.normal(size=(64,)).astype(np.float32)
y = 2.0 * x + 3.0 + np.random.normal(scale=0.1, size=(64,)) y = 2.0 * x + 3.0 + np.random.normal(scale=0.1, size=(64,)).astype(np.float32)
train_dataset = datasets.Dataset.from_dict({"input_x": x, "label": y}) train_dataset = datasets.Dataset.from_dict({"input_x": x, "label": y})
# Base training. Should have the same results as test_reproducible_training # Base training. Should have the same results as test_reproducible_training