29 lines
1.1 KiB
Python
29 lines
1.1 KiB
Python
import pytest
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import torch
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from pytorch_lightning.metrics.utils import class_reduce, reduce
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def test_reduce():
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start_tensor = torch.rand(50, 40, 30)
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assert torch.allclose(reduce(start_tensor, 'elementwise_mean'), torch.mean(start_tensor))
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assert torch.allclose(reduce(start_tensor, 'sum'), torch.sum(start_tensor))
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assert torch.allclose(reduce(start_tensor, 'none'), start_tensor)
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with pytest.raises(ValueError):
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reduce(start_tensor, 'error_reduction')
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def test_class_reduce():
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num = torch.randint(1, 10, (100, )).float()
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denom = torch.randint(10, 20, (100, )).float()
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weights = torch.randint(1, 100, (100, )).float()
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assert torch.allclose(class_reduce(num, denom, weights, 'micro'), torch.sum(num) / torch.sum(denom))
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assert torch.allclose(class_reduce(num, denom, weights, 'macro'), torch.mean(num / denom))
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assert torch.allclose(
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class_reduce(num, denom, weights, 'weighted'), torch.sum(num / denom * (weights / torch.sum(weights)))
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)
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assert torch.allclose(class_reduce(num, denom, weights, 'none'), num / denom)
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