49 lines
1.4 KiB
Python
49 lines
1.4 KiB
Python
import torch
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import pytest
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from pina.loss import PowerLoss
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input = torch.tensor([[3.0], [1.0], [-8.0]])
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target = torch.tensor([[6.0], [4.0], [2.0]])
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available_reductions = ["str", "mean", "none"]
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def test_PowerLoss_constructor():
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# test reduction
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for reduction in available_reductions:
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PowerLoss(reduction=reduction)
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# test p
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for p in [float("inf"), -float("inf"), 1, 10, -8]:
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PowerLoss(p=p)
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def test_PowerLoss_forward():
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# l2 loss
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loss = PowerLoss(p=2, reduction="mean")
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l2_loss = torch.mean((input - target).pow(2))
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assert loss(input, target) == l2_loss
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# l1 loss
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loss = PowerLoss(p=1, reduction="sum")
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l1_loss = torch.sum(torch.abs(input - target))
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assert loss(input, target) == l1_loss
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def test_LpRelativeLoss_constructor():
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# test reduction
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for reduction in available_reductions:
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PowerLoss(reduction=reduction, relative=True)
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# test p
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for p in [float("inf"), -float("inf"), 1, 10, -8]:
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PowerLoss(p=p, relative=True)
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def test_LpRelativeLoss_forward():
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# l2 relative loss
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loss = PowerLoss(p=2, reduction="mean", relative=True)
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l2_loss = (input - target).pow(2) / input.pow(2)
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assert loss(input, target) == torch.mean(l2_loss)
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# l1 relative loss
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loss = PowerLoss(p=1, reduction="sum", relative=True)
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l1_loss = torch.abs(input - target) / torch.abs(input)
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assert loss(input, target) == torch.sum(l1_loss)
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