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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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33
tests/test_model/test_deeponet.py
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@@ -32,36 +32,39 @@ def test_constructor_fails_when_invalid_inner_layer_size():
reduction='+',
aggregator='*')
def test_forward_extract_str():
branch_net = FeedForward(input_dimensions=1, output_dimensions=10)
trunk_net = FeedForward(input_dimensions=2, output_dimensions=10)
model = DeepONet(branch_net=branch_net,
trunk_net=trunk_net,
input_indeces_branch_net=['a'],
input_indeces_trunk_net=['b', 'c'],
reduction='+',
aggregator='*')
trunk_net=trunk_net,
input_indeces_branch_net=['a'],
input_indeces_trunk_net=['b', 'c'],
reduction='+',
aggregator='*')
model(input_)
def test_forward_extract_int():
branch_net = FeedForward(input_dimensions=1, output_dimensions=10)
trunk_net = FeedForward(input_dimensions=2, output_dimensions=10)
model = DeepONet(branch_net=branch_net,
trunk_net=trunk_net,
input_indeces_branch_net=[0],
input_indeces_trunk_net=[1, 2],
reduction='+',
aggregator='*')
trunk_net=trunk_net,
input_indeces_branch_net=[0],
input_indeces_trunk_net=[1, 2],
reduction='+',
aggregator='*')
model(data)
def test_forward_extract_str_wrong():
branch_net = FeedForward(input_dimensions=1, output_dimensions=10)
trunk_net = FeedForward(input_dimensions=2, output_dimensions=10)
model = DeepONet(branch_net=branch_net,
trunk_net=trunk_net,
input_indeces_branch_net=['a'],
input_indeces_trunk_net=['b', 'c'],
reduction='+',
aggregator='*')
trunk_net=trunk_net,
input_indeces_branch_net=['a'],
input_indeces_trunk_net=['b', 'c'],
reduction='+',
aggregator='*')
with pytest.raises(RuntimeError):
model(data)

14
tests/test_model/test_fnn.py
View File

@@ -3,7 +3,6 @@ import pytest
from pina.model import FeedForward
data = torch.rand((20, 3))
input_vars = 3
output_vars = 4
@@ -13,19 +12,24 @@ def test_constructor():
FeedForward(input_vars, output_vars)
FeedForward(input_vars, output_vars, inner_size=10, n_layers=20)
FeedForward(input_vars, output_vars, layers=[10, 20, 5, 2])
FeedForward(input_vars, output_vars, layers=[10, 20, 5, 2],
FeedForward(input_vars,
output_vars,
layers=[10, 20, 5, 2],
func=torch.nn.ReLU)
FeedForward(input_vars, output_vars, layers=[10, 20, 5, 2],
FeedForward(input_vars,
output_vars,
layers=[10, 20, 5, 2],
func=[torch.nn.ReLU, torch.nn.ReLU, None, torch.nn.Tanh])
def test_constructor_wrong():
with pytest.raises(RuntimeError):
FeedForward(input_vars, output_vars, layers=[10, 20, 5, 2],
FeedForward(input_vars,
output_vars,
layers=[10, 20, 5, 2],
func=[torch.nn.ReLU, torch.nn.ReLU])
def test_forward():
dim_in, dim_out = 3, 2
fnn = FeedForward(dim_in, dim_out)

57
tests/test_model/test_fno.py
View File

@@ -1,7 +1,6 @@
import torch
from pina.model import FNO
output_channels = 5
batch_size = 15
resolution = [30, 40, 50]
@@ -11,7 +10,7 @@ lifting_dim = 128
def test_constructor():
input_channels = 3
lifting_net = torch.nn.Linear(input_channels, lifting_dim)
projecting_net = torch.nn.Linear(60, output_channels)
projecting_net = torch.nn.Linear(60, output_channels)
# simple constructor
FNO(lifting_net=lifting_net,
@@ -20,7 +19,7 @@ def test_constructor():
dimensions=3,
inner_size=60,
n_layers=5)
# simple constructor with n_modes list
FNO(lifting_net=lifting_net,
projecting_net=projecting_net,
@@ -36,53 +35,61 @@ def test_constructor():
dimensions=3,
inner_size=60,
n_layers=2)
# simple constructor with n_modes list of list
projecting_net = torch.nn.Linear(50, output_channels)
projecting_net = torch.nn.Linear(50, output_channels)
FNO(lifting_net=lifting_net,
projecting_net=projecting_net,
n_modes=5,
dimensions=3,
layers=[50, 50])
def test_1d_forward():
input_channels = 1
input_ = torch.rand(batch_size, resolution[0], input_channels)
lifting_net = torch.nn.Linear(input_channels, lifting_dim)
projecting_net = torch.nn.Linear(60, output_channels)
fno = FNO(lifting_net=lifting_net,
projecting_net=projecting_net,
n_modes=5,
dimensions=1,
inner_size=60,
n_layers=2)
projecting_net=projecting_net,
n_modes=5,
dimensions=1,
inner_size=60,
n_layers=2)
out = fno(input_)
assert out.shape == torch.Size([batch_size, resolution[0], output_channels])
def test_2d_forward():
input_channels = 2
input_ = torch.rand(batch_size, resolution[0], resolution[1], input_channels)
input_ = torch.rand(batch_size, resolution[0], resolution[1],
input_channels)
lifting_net = torch.nn.Linear(input_channels, lifting_dim)
projecting_net = torch.nn.Linear(60, output_channels)
fno = FNO(lifting_net=lifting_net,
projecting_net=projecting_net,
n_modes=5,
dimensions=2,
inner_size=60,
n_layers=2)
projecting_net=projecting_net,
n_modes=5,
dimensions=2,
inner_size=60,
n_layers=2)
out = fno(input_)
assert out.shape == torch.Size([batch_size, resolution[0], resolution[1], output_channels])
assert out.shape == torch.Size(
[batch_size, resolution[0], resolution[1], output_channels])
def test_3d_forward():
input_channels = 3
input_ = torch.rand(batch_size, resolution[0], resolution[1], resolution[2], input_channels)
input_ = torch.rand(batch_size, resolution[0], resolution[1], resolution[2],
input_channels)
lifting_net = torch.nn.Linear(input_channels, lifting_dim)
projecting_net = torch.nn.Linear(60, output_channels)
fno = FNO(lifting_net=lifting_net,
projecting_net=projecting_net,
n_modes=5,
dimensions=3,
inner_size=60,
n_layers=2)
projecting_net=projecting_net,
n_modes=5,
dimensions=3,
inner_size=60,
n_layers=2)
out = fno(input_)
assert out.shape == torch.Size([batch_size, resolution[0], resolution[1], resolution[2], output_channels])
assert out.shape == torch.Size([
batch_size, resolution[0], resolution[1], resolution[2], output_channels
])

43
tests/test_model/test_mionet.py
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@@ -14,59 +14,42 @@ def test_constructor():
branch_net1 = FeedForward(input_dimensions=1, output_dimensions=10)
branch_net2 = FeedForward(input_dimensions=2, output_dimensions=10)
trunk_net = FeedForward(input_dimensions=1, output_dimensions=10)
networks = {branch_net1 : ['x'],
branch_net2 : ['x', 'y'],
trunk_net : ['z']}
MIONet(networks=networks,
reduction='+',
aggregator='*')
networks = {branch_net1: ['x'], branch_net2: ['x', 'y'], trunk_net: ['z']}
MIONet(networks=networks, reduction='+', aggregator='*')
def test_constructor_fails_when_invalid_inner_layer_size():
branch_net1 = FeedForward(input_dimensions=1, output_dimensions=10)
branch_net2 = FeedForward(input_dimensions=2, output_dimensions=10)
trunk_net = FeedForward(input_dimensions=1, output_dimensions=12)
networks = {branch_net1 : ['x'],
branch_net2 : ['x', 'y'],
trunk_net : ['z']}
networks = {branch_net1: ['x'], branch_net2: ['x', 'y'], trunk_net: ['z']}
with pytest.raises(ValueError):
MIONet(networks=networks,
reduction='+',
aggregator='*')
MIONet(networks=networks, reduction='+', aggregator='*')
def test_forward_extract_str():
branch_net1 = FeedForward(input_dimensions=1, output_dimensions=10)
branch_net2 = FeedForward(input_dimensions=1, output_dimensions=10)
trunk_net = FeedForward(input_dimensions=1, output_dimensions=10)
networks = {branch_net1 : ['a'],
branch_net2 : ['b'],
trunk_net : ['c']}
model = MIONet(networks=networks,
reduction='+',
aggregator='*')
networks = {branch_net1: ['a'], branch_net2: ['b'], trunk_net: ['c']}
model = MIONet(networks=networks, reduction='+', aggregator='*')
model(input_)
def test_forward_extract_int():
branch_net1 = FeedForward(input_dimensions=1, output_dimensions=10)
branch_net2 = FeedForward(input_dimensions=1, output_dimensions=10)
trunk_net = FeedForward(input_dimensions=1, output_dimensions=10)
networks = {branch_net1 : [0],
branch_net2 : [1],
trunk_net : [2]}
model = MIONet(networks=networks,
reduction='+',
aggregator='*')
networks = {branch_net1: [0], branch_net2: [1], trunk_net: [2]}
model = MIONet(networks=networks, reduction='+', aggregator='*')
model(data)
def test_forward_extract_str_wrong():
branch_net1 = FeedForward(input_dimensions=1, output_dimensions=10)
branch_net2 = FeedForward(input_dimensions=1, output_dimensions=10)
trunk_net = FeedForward(input_dimensions=1, output_dimensions=10)
networks = {branch_net1 : ['a'],
branch_net2 : ['b'],
trunk_net : ['c']}
model = MIONet(networks=networks,
reduction='+',
aggregator='*')
networks = {branch_net1: ['a'], branch_net2: ['b'], trunk_net: ['c']}
model = MIONet(networks=networks, reduction='+', aggregator='*')
with pytest.raises(RuntimeError):
model(data)

12
tests/test_model/test_residualfnn.py
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@@ -2,21 +2,25 @@ import torch
import pytest
from pina.model import ResidualFeedForward
def test_constructor():
# simple constructor
ResidualFeedForward(input_dimensions=2, output_dimensions=1)
# wrong transformer nets (not 2)
with pytest.raises(ValueError):
ResidualFeedForward(input_dimensions=2, output_dimensions=1, transformer_nets=[torch.nn.Linear(2, 20)])
ResidualFeedForward(input_dimensions=2,
output_dimensions=1,
transformer_nets=[torch.nn.Linear(2, 20)])
# wrong transformer nets (not nn.Module)
with pytest.raises(ValueError):
ResidualFeedForward(input_dimensions=2, output_dimensions=1, transformer_nets=[2, 2])
ResidualFeedForward(input_dimensions=2,
output_dimensions=1,
transformer_nets=[2, 2])
def test_forward():
x = torch.rand(10, 2)
model = ResidualFeedForward(input_dimensions=2, output_dimensions=1)
model(x)