deepmd.pt.model.task.denoise

Module Contents

Classes

DenoiseNet

Base class for all neural network modules.

class deepmd.pt.model.task.denoise.DenoiseNet(feature_dim, ntypes, attn_head=8, prefactor=[0.5, 0.5], activation_function='gelu', **kwargs)

Bases: deepmd.pt.model.task.fitting.Fitting

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.

output_def()

Returns the output def of the fitting net.

forward(pair_weights, diff, nlist_mask, features, sw, masked_tokens: torch.Tensor | None = None)

Calculate the updated coord. Args: - coord: Input noisy coord with shape [nframes, nloc, 3]. - pair_weights: Input pair weights with shape [nframes, nloc, nnei, head]. - diff: Input pair relative coord list with shape [nframes, nloc, nnei, 3]. - nlist_mask: Input nlist mask with shape [nframes, nloc, nnei].

Returns:

• denoised_coord: Denoised updated coord with shape [nframes, nloc, 3].