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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
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@@ -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)