chemprop.nn.transforms#

Classes#

`ScaleTransform`	Base class for all neural network modules.
`UnscaleTransform`	Base class for all neural network modules.
`GraphTransform`	Base class for all neural network modules.

Module Contents#

class chemprop.nn.transforms.ScaleTransform(mean, scale, pad=0)[source]#

Bases: _ScaleTransformMixin

Base class for all neural network modules.

Your models should also subclass this class.

Modules can also contain other Modules, allowing them to be nested in a tree structure. You can assign the submodules as regular attributes:

import torch.nn as nn
import torch.nn.functional as F


class Model(nn.Module):
    def __init__(self) -> None:
        super().__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))

Submodules assigned in this way will be registered, and will also have their parameters converted when you call to(), etc.

Note

As per the example above, an __init__() call to the parent class must be made before assignment on the child.

Variables:

training (bool) – Boolean represents whether this module is in training or evaluation mode.

Parameters:

mean (numpy.typing.ArrayLike)
scale (numpy.typing.ArrayLike)
pad (int)

forward(X)[source]#

Parameters:: X (torch.Tensor)
Return type:: torch.Tensor

class chemprop.nn.transforms.UnscaleTransform(mean, scale, pad=0)[source]#

Bases: _ScaleTransformMixin

Base class for all neural network modules.

Your models should also subclass this class.

Modules can also contain other Modules, allowing them to be nested in a tree structure. You can assign the submodules as regular attributes:

import torch.nn as nn
import torch.nn.functional as F


class Model(nn.Module):
    def __init__(self) -> None:
        super().__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))

Submodules assigned in this way will be registered, and will also have their parameters converted when you call to(), etc.

Note

As per the example above, an __init__() call to the parent class must be made before assignment on the child.

Variables:

training (bool) – Boolean represents whether this module is in training or evaluation mode.

Parameters:

mean (numpy.typing.ArrayLike)
scale (numpy.typing.ArrayLike)
pad (int)

forward(X)[source]#

Parameters:: X (torch.Tensor)
Return type:: torch.Tensor

transform_variance(var)[source]#

Parameters:: var (torch.Tensor)
Return type:: torch.Tensor

class chemprop.nn.transforms.GraphTransform(V_transform, E_transform)[source]#

Bases: torch.nn.Module

Base class for all neural network modules.

Your models should also subclass this class.

Modules can also contain other Modules, allowing them to be nested in a tree structure. You can assign the submodules as regular attributes:

import torch.nn as nn
import torch.nn.functional as F


class Model(nn.Module):
    def __init__(self) -> None:
        super().__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))

Submodules assigned in this way will be registered, and will also have their parameters converted when you call to(), etc.

Note

As per the example above, an __init__() call to the parent class must be made before assignment on the child.

Variables:

training (bool) – Boolean represents whether this module is in training or evaluation mode.

Parameters:

V_transform (ScaleTransform)
E_transform (ScaleTransform)

V_transform#

E_transform#

forward(bmg)[source]#

Parameters:: bmg (chemprop.data.collate.BatchMolGraph)
Return type:: chemprop.data.collate.BatchMolGraph