Lightining update (#104)

* multiple functions for version 0.0
* lightining update
* minor changes
* data pinn  loss added
---------

Co-authored-by: Nicola Demo <demo.nicola@gmail.com>
Co-authored-by: Dario Coscia <dariocoscia@cli-10-110-3-125.WIFIeduroamSTUD.units.it>
Co-authored-by: Dario Coscia <dariocoscia@Dario-Coscia.station>
Co-authored-by: Dario Coscia <dariocoscia@Dario-Coscia.local>
Co-authored-by: Dario Coscia <dariocoscia@192.168.1.38>

tests/test_loss.py

@@ -0,0 +1,49 @@
+import torch
+import pytest
+
+from pina.loss import *
+
+input = torch.tensor([[3.], [1.], [-8.]])
+target = torch.tensor([[6.], [4.], [2.]])
+available_reductions = ['str', 'mean', 'none']
+
+
+def test_LpLoss_constructor():
+    # test reduction
+    for reduction in available_reductions:
+        LpLoss(reduction=reduction)
+    # test p
+    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)))
+    assert loss(input, target) == l2_loss
+    # l1 loss
+    loss = LpLoss(p=1, reduction='sum')
+    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)
+
+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))
+    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)
+    assert loss(input, target) == torch.sum(l1_loss)
+
+
+
+