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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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79
tests/test_layers/test_conv.py
View File

@@ -10,6 +10,7 @@ def prod(iterable):
def make_grid(x):
def _transform_image(image):
# extracting image info
@@ -17,11 +18,13 @@ def make_grid(x):
# initializing transfomed image
coordinates = torch.zeros(
[channels, prod(dimension), len(dimension) + 1]).to(image.device)
[channels, prod(dimension),
len(dimension) + 1]).to(image.device)
# creating the n dimensional mesh grid
values_mesh = [torch.arange(0, dim).float().to(
image.device) for dim in dimension]
values_mesh = [
torch.arange(0, dim).float().to(image.device) for dim in dimension
]
mesh = torch.meshgrid(values_mesh)
coordinates_mesh = [x.reshape(-1, 1) for x in mesh]
coordinates_mesh.append(0)
@@ -40,11 +43,9 @@ class MLP(torch.nn.Module):
def __init__(self) -> None:
super().__init__()
self. model = torch.nn.Sequential(torch.nn.Linear(2, 8),
torch.nn.ReLU(),
torch.nn.Linear(8, 8),
torch.nn.ReLU(),
torch.nn.Linear(8, 1))
self.model = torch.nn.Sequential(torch.nn.Linear(2, 8), torch.nn.ReLU(),
torch.nn.Linear(8, 8), torch.nn.ReLU(),
torch.nn.Linear(8, 1))
def forward(self, x):
return self.model(x)
@@ -56,10 +57,12 @@ channel_output = 6
batch = 2
N = 10
dim = [3, 3]
stride = {"domain": [10, 10],
"start": [0, 0],
"jumps": [3, 3],
"direction": [1, 1.]}
stride = {
"domain": [10, 10],
"start": [0, 0],
"jumps": [3, 3],
"direction": [1, 1.]
}
dim_filter = len(dim)
dim_input = (batch, channel_input, 10, dim_filter)
dim_output = (batch, channel_output, 4, dim_filter)
@@ -71,15 +74,15 @@ def test_constructor():
model = MLP
conv = ContinuousConvBlock(channel_input,
channel_output,
dim,
stride,
model=model)
channel_output,
dim,
stride,
model=model)
conv = ContinuousConvBlock(channel_input,
channel_output,
dim,
stride,
model=None)
channel_output,
dim,
stride,
model=None)
def test_forward():
@@ -87,19 +90,19 @@ def test_forward():
# simple forward
conv = ContinuousConvBlock(channel_input,
channel_output,
dim,
stride,
model=model)
channel_output,
dim,
stride,
model=model)
conv(x)
# simple forward with optimization
conv = ContinuousConvBlock(channel_input,
channel_output,
dim,
stride,
model=model,
optimize=True)
channel_output,
dim,
stride,
model=model,
optimize=True)
conv(x)
@@ -108,16 +111,16 @@ def test_transpose():
# simple transpose
conv = ContinuousConvBlock(channel_input,
channel_output,
dim,
stride,
model=model)
channel_output,
dim,
stride,
model=model)
conv2 = ContinuousConvBlock(channel_output,
channel_input,
dim,
stride,
model=model)
channel_input,
dim,
stride,
model=model)
integrals = conv(x)
conv2.transpose(integrals[..., -1], x)
@@ -137,4 +140,4 @@ def test_transpose():
# no_overlap=True)
# integrals = conv(x)
# conv.transpose(integrals[..., -1], x)
# conv.transpose(integrals[..., -1], x)

35
tests/test_layers/test_fourier.py
View File

@@ -5,39 +5,44 @@ input_numb_fields = 3
output_numb_fields = 4
batch = 5
def test_constructor_1d():
FourierBlock1D(input_numb_fields=input_numb_fields,
output_numb_fields=output_numb_fields,
n_modes=5)
output_numb_fields=output_numb_fields,
n_modes=5)
def test_forward_1d():
sconv = FourierBlock1D(input_numb_fields=input_numb_fields,
output_numb_fields=output_numb_fields,
n_modes=4)
output_numb_fields=output_numb_fields,
n_modes=4)
x = torch.rand(batch, input_numb_fields, 10)
sconv(x)
def test_constructor_2d():
FourierBlock2D(input_numb_fields=input_numb_fields,
output_numb_fields=output_numb_fields,
n_modes=[5, 4])
output_numb_fields=output_numb_fields,
n_modes=[5, 4])
def test_forward_2d():
sconv = FourierBlock2D(input_numb_fields=input_numb_fields,
output_numb_fields=output_numb_fields,
n_modes=[5, 4])
output_numb_fields=output_numb_fields,
n_modes=[5, 4])
x = torch.rand(batch, input_numb_fields, 10, 10)
sconv(x)
def test_constructor_3d():
FourierBlock3D(input_numb_fields=input_numb_fields,
output_numb_fields=output_numb_fields,
n_modes=[5, 4, 4])
output_numb_fields=output_numb_fields,
n_modes=[5, 4, 4])
def test_forward_3d():
sconv = FourierBlock3D(input_numb_fields=input_numb_fields,
output_numb_fields=output_numb_fields,
n_modes=[5, 4, 4])
output_numb_fields=output_numb_fields,
n_modes=[5, 4, 4])
x = torch.rand(batch, input_numb_fields, 10, 10, 10)
sconv(x)

69
tests/test_layers/test_residual.py
View File

@@ -1,26 +1,69 @@
from pina.model.layers import ResidualBlock
from pina.model.layers import ResidualBlock, EnhancedLinear
import torch
import torch.nn as nn
def test_constructor():
def test_constructor_residual_block():
res_block = ResidualBlock(input_dim=10, output_dim=3, hidden_dim=4)
res_block = ResidualBlock(input_dim=10,
output_dim=3,
hidden_dim=4)
res_block = ResidualBlock(input_dim=10,
output_dim=3,
hidden_dim=4,
spectral_norm=True)
def test_forward():
def test_forward_residual_block():
res_block = ResidualBlock(input_dim=10, output_dim=3, hidden_dim=4)
res_block = ResidualBlock(input_dim=10,
output_dim=3,
hidden_dim=4)
x = torch.rand(size=(80, 10))
y = res_block(x)
assert y.shape[1]==3
assert y.shape[0]==x.shape[0]
assert y.shape[1] == 3
assert y.shape[0] == x.shape[0]
def test_constructor_no_activation_no_dropout():
linear_layer = nn.Linear(10, 20)
enhanced_linear = EnhancedLinear(linear_layer)
assert len(list(enhanced_linear.parameters())) == len(list(linear_layer.parameters()))
def test_constructor_with_activation_no_dropout():
linear_layer = nn.Linear(10, 20)
activation = nn.ReLU()
enhanced_linear = EnhancedLinear(linear_layer, activation)
assert len(list(enhanced_linear.parameters())) == len(list(linear_layer.parameters())) + len(list(activation.parameters()))
def test_constructor_no_activation_with_dropout():
linear_layer = nn.Linear(10, 20)
dropout_prob = 0.5
enhanced_linear = EnhancedLinear(linear_layer, dropout=dropout_prob)
assert len(list(enhanced_linear.parameters())) == len(list(linear_layer.parameters()))
def test_constructor_with_activation_with_dropout():
linear_layer = nn.Linear(10, 20)
activation = nn.ReLU()
dropout_prob = 0.5
enhanced_linear = EnhancedLinear(linear_layer, activation, dropout_prob)
assert len(list(enhanced_linear.parameters())) == len(list(linear_layer.parameters())) + len(list(activation.parameters()))
def test_forward_enhanced_linear_no_dropout():
enhanced_linear = EnhancedLinear(nn.Linear(10, 3))
x = torch.rand(size=(80, 10))
y = enhanced_linear(x)
assert y.shape[1] == 3
assert y.shape[0] == x.shape[0]
def test_forward_enhanced_linear_dropout():
enhanced_linear = EnhancedLinear(nn.Linear(10, 3), dropout=0.5)
x = torch.rand(size=(80, 10))
y = enhanced_linear(x)
assert y.shape[1] == 3
assert y.shape[0] == x.shape[0]

11
tests/test_layers/test_spectral_conv.py
View File

@@ -5,11 +5,13 @@ input_numb_fields = 3
output_numb_fields = 4
batch = 5
def test_constructor_1d():
SpectralConvBlock1D(input_numb_fields=input_numb_fields,
output_numb_fields=output_numb_fields,
n_modes=5)
def test_forward_1d():
sconv = SpectralConvBlock1D(input_numb_fields=input_numb_fields,
output_numb_fields=output_numb_fields,
@@ -22,7 +24,8 @@ def test_constructor_2d():
SpectralConvBlock2D(input_numb_fields=input_numb_fields,
output_numb_fields=output_numb_fields,
n_modes=[5, 4])
def test_forward_2d():
sconv = SpectralConvBlock2D(input_numb_fields=input_numb_fields,
output_numb_fields=output_numb_fields,
@@ -30,11 +33,13 @@ def test_forward_2d():
x = torch.rand(batch, input_numb_fields, 10, 10)
sconv(x)
def test_constructor_3d():
SpectralConvBlock3D(input_numb_fields=input_numb_fields,
output_numb_fields=output_numb_fields,
n_modes=[5, 4, 4])
def test_forward_3d():
sconv = SpectralConvBlock3D(input_numb_fields=input_numb_fields,
output_numb_fields=output_numb_fields,