deepmd.pt.utils.utils

Module Contents

Classes

ActivationFn

Base class for all neural network modules.

Functions

to_numpy_array(…)
to_torch_tensor(…)
dict_to_device(sample_dict)

class deepmd.pt.utils.utils.ActivationFn(activation: str | None)

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 to nest them 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):
        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 have their parameters converted too 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.

forward(x: torch.Tensor) → torch.Tensor

Returns the tensor after applying activation function corresponding to activation.

deepmd.pt.utils.utils.to_numpy_array(xx: torch.Tensor) → numpy.ndarray

deepmd.pt.utils.utils.to_numpy_array(xx: None) → None

deepmd.pt.utils.utils.to_torch_tensor(xx: numpy.ndarray) → torch.Tensor

deepmd.pt.utils.utils.to_torch_tensor(xx: None) → None

deepmd.pt.utils.utils.dict_to_device(sample_dict)