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.loss_interface import *
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input = torch.tensor([[3.], [1.], [-8.]])
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target = torch.tensor([[6.], [4.], [2.]])
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available_reductions = ['str', 'mean', 'none']
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def test_LpLoss_constructor():
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# test reduction
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for reduction in available_reductions:
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LpLoss(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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LpLoss(p=p)
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def test_LpLoss_forward():
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# l2 loss
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loss = LpLoss(p=2, reduction='mean')
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l2_loss = torch.mean(torch.sqrt((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 = LpLoss(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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LpLoss(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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LpLoss(p=p, relative=True)
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def test_LpRelativeLoss_forward():
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# l2 relative loss
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loss = LpLoss(p=2, reduction='mean', relative=True)
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l2_loss = torch.sqrt((input - target).pow(2)) / torch.sqrt(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 = LpLoss(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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