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Documentation for v0.1 version (#199)

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* Adding Equations, solving typos
* improve _code.rst
* the team rst and restuctore index.rst
* fixing errors

---------

Co-authored-by: Dario Coscia <dariocoscia@dhcp-015.eduroam.sissa.it>
This commit is contained in:
Dario Coscia
2023-11-08 14:39:00 +01:00
committed by Nicola Demo
parent 3f9305d475
commit 8b7b61b3bd
144 changed files with 2741 additions and 1766 deletions
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21
tests/test_loss/test_lploss.py
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@@ -16,34 +16,33 @@ def test_LpLoss_constructor():
for p in [float('inf'), -float('inf'), 1, 10, -8]:
LpLoss(p=p)
def test_LpLoss_forward():
# l2 loss
loss = LpLoss(p=2, reduction='mean')
l2_loss = torch.mean(torch.sqrt((input-target).pow(2)))
l2_loss = torch.mean(torch.sqrt((input - target).pow(2)))
assert loss(input, target) == l2_loss
# l1 loss
loss = LpLoss(p=1, reduction='sum')
l1_loss = torch.sum(torch.abs(input-target))
l1_loss = torch.sum(torch.abs(input - target))
assert loss(input, target) == l1_loss
def test_LpRelativeLoss_constructor():
# test reduction
for reduction in available_reductions:
LpLoss(reduction=reduction, relative=True)
# test p
for p in [float('inf'), -float('inf'), 1, 10, -8]:
LpLoss(p=p,relative=True)
LpLoss(p=p, relative=True)
def test_LpRelativeLoss_forward():
# l2 relative loss
loss = LpLoss(p=2, reduction='mean',relative=True)
l2_loss = torch.sqrt((input-target).pow(2))/torch.sqrt(input.pow(2))
loss = LpLoss(p=2, reduction='mean', relative=True)
l2_loss = torch.sqrt((input - target).pow(2)) / torch.sqrt(input.pow(2))
assert loss(input, target) == torch.mean(l2_loss)
# l1 relative loss
loss = LpLoss(p=1, reduction='sum',relative=True)
l1_loss = torch.abs(input-target)/torch.abs(input)
loss = LpLoss(p=1, reduction='sum', relative=True)
l1_loss = torch.abs(input - target) / torch.abs(input)
assert loss(input, target) == torch.sum(l1_loss)

21
tests/test_loss/test_powerloss.py
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@@ -16,34 +16,33 @@ def test_PowerLoss_constructor():
for p in [float('inf'), -float('inf'), 1, 10, -8]:
PowerLoss(p=p)
def test_PowerLoss_forward():
# l2 loss
loss = PowerLoss(p=2, reduction='mean')
l2_loss = torch.mean((input-target).pow(2))
l2_loss = torch.mean((input - target).pow(2))
assert loss(input, target) == l2_loss
# l1 loss
loss = PowerLoss(p=1, reduction='sum')
l1_loss = torch.sum(torch.abs(input-target))
l1_loss = torch.sum(torch.abs(input - target))
assert loss(input, target) == l1_loss
def test_LpRelativeLoss_constructor():
# test reduction
for reduction in available_reductions:
PowerLoss(reduction=reduction, relative=True)
# test p
for p in [float('inf'), -float('inf'), 1, 10, -8]:
PowerLoss(p=p,relative=True)
PowerLoss(p=p, relative=True)
def test_LpRelativeLoss_forward():
# l2 relative loss
loss = PowerLoss(p=2, reduction='mean',relative=True)
l2_loss = (input-target).pow(2)/input.pow(2)
loss = PowerLoss(p=2, reduction='mean', relative=True)
l2_loss = (input - target).pow(2) / input.pow(2)
assert loss(input, target) == torch.mean(l2_loss)
# l1 relative loss
loss = PowerLoss(p=1, reduction='sum',relative=True)
l1_loss = torch.abs(input-target)/torch.abs(input)
loss = PowerLoss(p=1, reduction='sum', relative=True)
l1_loss = torch.abs(input - target) / torch.abs(input)
assert loss(input, target) == torch.sum(l1_loss)