core.models.base

core.models.base#

Copyright (c) Meta Platforms, Inc. and affiliates.

This source code is licensed under the MIT license found in the LICENSE file in the root directory of this source tree.

Classes#

HeadInterface

BackboneInterface

HydraModel

Base class for all neural network modules.

HydraModelV2

Base class for all neural network modules.

Module Contents#

class core.models.base.HeadInterface#
property use_amp#
abstract forward(data: fairchem.core.datasets.atomic_data.AtomicData, emb: dict[str, torch.Tensor]) → dict[str, torch.Tensor]#

Head forward.

Parameters:

  • data (AtomicData) – Atomic systems as input

  • emb (dict[str->torch.Tensor]) – Embeddings of the input as generated by the backbone

Returns:

outputs – Return one or more targets generated by this head

Return type:

dict[str->torch.Tensor]

class core.models.base.BackboneInterface#
abstract forward(data: fairchem.core.datasets.atomic_data.AtomicData) → dict[str, torch.Tensor]#

Backbone forward.

Parameters:

data (AtomicData) – Atomic systems as input

Returns:

embedding – Return backbone embeddings for the given input

Return type:

dict[str->torch.Tensor]

class core.models.base.HydraModel(backbone: dict | None = None, heads: dict | None = None, finetune_config: dict | None = None, otf_graph: bool = True, pass_through_head_outputs: bool = False, freeze_backbone: bool = False)#

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.

device = None#
otf_graph#
pass_through_head_outputs#
forward(data: fairchem.core.datasets.atomic_data.AtomicData)#
class core.models.base.HydraModelV2(backbone: BackboneInterface, heads: dict[str, HeadInterface], freeze_backbone: bool = False)#

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.

backbone#
output_heads#
device = None#
forward(data)#
Contents