Modulus Models
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Core v0.1.0

Fully Connected Network

class modulus.models.mlp.fully_connected.FullyConnected(in_features: int = 512, layer_size: int = 512, out_features: int = 512, num_layers: int = 6, activation_fn: Union[Module, List[Module]] = SiLU(), skip_connections: bool = False, adaptive_activations: bool = False, weight_norm: bool = False)[source]

Bases: Module

A densely-connected MLP architecture

Parameters

  • in_features (int, optional) – Size of input features, by default 512

  • layer_size (int, optional) – Size of every hidden layer, by default 512

  • out_features (int, optional) – Size of output features, by default 512

  • num_layers (int, optional) – Number of hidden layers, by default 6

  • activation_fn (Union[nn.Module, List[nn.Module]], optional) – Activation function to use, by default nn.SILU

  • skip_connections (bool, optional) – Add skip connections every 2 hidden layers, by default False

  • adaptive_activations (bool, optional) – Use an adaptive activation function, by default False

  • weight_norm (bool, optional) – Use weight norm on fully connected layers, by default False

Example

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>>> model = modulus.models.mlp.FullyConnected(in_features=32, out_features=64)
>>> input = torch.randn(128, 32)
>>> output = model(input)
>>> output.size()
torch.Size([128, 64])

forward(x: Tensor) → Tensor[source]

Defines the computation performed at every call.

Should be overridden by all subclasses.
class modulus.models.mlp.fully_connected.MetaData(name: str = 'FullyConnected', jit: bool = True, cuda_graphs: bool = True, amp: bool = True, amp_cpu: bool = None, amp_gpu: bool = None, torch_fx: bool = True, onnx: bool = True, onnx_gpu: bool = None, onnx_cpu: bool = None, onnx_runtime: bool = True, trt: bool = False, var_dim: int = -1, func_torch: bool = True, auto_grad: bool = True)[source]

Bases: ModelMetaData

Fourier Neural Operators

class modulus.models.fno.fno.FNO(decoder_net: Module, in_channels: int, dimension: int, latent_channels: int = 32, num_fno_layers: int = 4, num_fno_modes: Union[int, List[int]] = 16, padding: int = 8, padding_type: str = 'constant', activation_fn: Module = GELU(approximate='none'), coord_features: bool = True)[source]

Bases: Module

Fourier neural operator (FNO) model.

Note

The FNO architecture supports options for 1D, 2D and 3D fields which can be controlled using the dimension parameter.

Parameters

  • decoder_net (modulus.Module) – Pointwise decoder network, input feature size should match latent_channels

  • in_channels (int) – Number of input channels

  • dimension (int) – Model dimensionality (supports 1, 2, 3).

  • latent_channels (int, optional) – Latent features size in spectral convolutions, by default 32

  • num_fno_layers (int, optional) – Number of spectral convolutional layers, by default 4

  • num_fno_modes (Union[int, List[int]], optional) – Number of Fourier modes kept in spectral convolutions, by default 16

  • padding (int, optional) – Domain padding for spectral convolutions, by default 8

  • padding_type (str, optional) – Type of padding for spectral convolutions, by default “constant”

  • activation_fn (nn.Module, optional) – Activation function, by default nn.GELU

  • coord_features (bool, optional) – Use coordinate grid as additional feature map, by default True

Example

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>>> # define the decoder net
>>> decoder = modulus.models.mlp.FullyConnected(
...     in_features=32,
...     out_features=3,
...     num_layers=2,
...     layer_size=16,
... )
>>> # define the 2d FNO model
>>> model = modulus.models.fno.FNO(
...     decoder_net=decoder,
...     in_channels=4,
...     dimension=2,
...     latent_channels=32,
...     num_fno_layers=2,
...     padding=0,
... )
>>> input = torch.randn(32, 4, 32, 32) #(N, C, H, W)
>>> output = model(input)
>>> output.size()
torch.Size([32, 3, 32, 32])

Note

Reference: Li, Zongyi, et al. “Fourier neural operator for parametric partial differential equations.” arXiv preprint arXiv:2010.08895 (2020).

forward(x: Tensor) → Tensor[source]

Defines the computation performed at every call.

Should be overridden by all subclasses.
class modulus.models.fno.fno.FNO1DEncoder(in_channels: int = 1, num_fno_layers: int = 4, fno_layer_size: int = 32, num_fno_modes: Union[int, List[int]] = 16, padding: Union[int, List[int]] = 8, padding_type: str = 'constant', activation_fn: Module = GELU(approximate='none'), coord_features: bool = True)[source]

Bases: Module

1D Spectral encoder for FNO

Parameters

  • in_channels (int, optional) – Number of input channels, by default 1

  • num_fno_layers (int, optional) – Number of spectral convolutional layers, by default 4

  • fno_layer_size (int, optional) – Latent features size in spectral convolutions, by default 32

  • num_fno_modes (Union[int, List[int]], optional) – Number of Fourier modes kept in spectral convolutions, by default 16

  • padding (Union[int, List[int]], optional) – Domain padding for spectral convolutions, by default 8

  • padding_type (str, optional) – Type of padding for spectral convolutions, by default “constant”

  • activation_fn (nn.Module, optional) – Activation function, by default nn.GELU

  • coord_features (bool, optional) – Use coordinate grid as additional feature map, by default True
forward(x: Tensor) → Tensor[source]

Defines the computation performed at every call.

Should be overridden by all subclasses.
meshgrid(shape: List[int], device: device) → Tensor[source]

Creates 1D meshgrid feature

Parameters

  • shape (List[int]) – Tensor shape

  • device (torch.device) – Device model is on
Returns

Meshgrid tensor
Return type

Tensor
class modulus.models.fno.fno.FNO2DEncoder(in_channels: int = 1, num_fno_layers: int = 4, fno_layer_size: int = 32, num_fno_modes: Union[int, List[int]] = 16, padding: Union[int, List[int]] = 8, padding_type: str = 'constant', activation_fn: Module = GELU(approximate='none'), coord_features: bool = True)[source]

Bases: Module

2D Spectral encoder for FNO

Parameters

  • in_channels (int, optional) – Number of input channels, by default 1

  • num_fno_layers (int, optional) – Number of spectral convolutional layers, by default 4

  • fno_layer_size (int, optional) – Latent features size in spectral convolutions, by default 32

  • num_fno_modes (Union[int, List[int]], optional) – Number of Fourier modes kept in spectral convolutions, by default 16

  • padding (Union[int, List[int]], optional) – Domain padding for spectral convolutions, by default 8

  • padding_type (str, optional) – Type of padding for spectral convolutions, by default “constant”

  • activation_fn (nn.Module, optional) – Activation function, by default nn.GELU

  • coord_features (bool, optional) – Use coordinate grid as additional feature map, by default True
forward(x: Tensor) → Tensor[source]

Defines the computation performed at every call.

Should be overridden by all subclasses.
meshgrid(shape: List[int], device: device) → Tensor[source]

Creates 2D meshgrid feature

Parameters

  • shape (List[int]) – Tensor shape

  • device (torch.device) – Device model is on
Returns

Meshgrid tensor
Return type

Tensor
class modulus.models.fno.fno.FNO3DEncoder(in_channels: int = 1, num_fno_layers: int = 4, fno_layer_size: int = 32, num_fno_modes: Union[int, List[int]] = 16, padding: Union[int, List[int]] = 8, padding_type: str = 'constant', activation_fn: Module = GELU(approximate='none'), coord_features: bool = True)[source]

Bases: Module

3D Spectral encoder for FNO

Parameters

  • in_channels (int, optional) – Number of input channels, by default 1

  • num_fno_layers (int, optional) – Number of spectral convolutional layers, by default 4

  • fno_layer_size (int, optional) – Latent features size in spectral convolutions, by default 32

  • num_fno_modes (Union[int, List[int]], optional) – Number of Fourier modes kept in spectral convolutions, by default 16

  • padding (Union[int, List[int]], optional) – Domain padding for spectral convolutions, by default 8

  • padding_type (str, optional) – Type of padding for spectral convolutions, by default “constant”

  • activation_fn (nn.Module, optional) – Activation function, by default nn.GELU

  • coord_features (bool, optional) – Use coordinate grid as additional feature map, by default True
forward(x: Tensor) → Tensor[source]

Defines the computation performed at every call.

Should be overridden by all subclasses.
meshgrid(shape: List[int], device: device) → Tensor[source]

Creates 3D meshgrid feature

Parameters

  • shape (List[int]) – Tensor shape

  • device (torch.device) – Device model is on
Returns

Meshgrid tensor
Return type

Tensor
class modulus.models.fno.fno.MetaData(name: str = 'FourierNeuralOperator', jit: bool = True, cuda_graphs: bool = True, amp: bool = False, amp_cpu: bool = None, amp_gpu: bool = None, torch_fx: bool = False, onnx: bool = False, onnx_gpu: bool = False, onnx_cpu: bool = False, onnx_runtime: bool = False, trt: bool = False, var_dim: int = 1, func_torch: bool = False, auto_grad: bool = False)[source]

Bases: ModelMetaData
class modulus.models.afno.afno.AFNO(img_size: Tuple[int, int], in_channels: int, out_channels: int, patch_size: Tuple[int, int] = (16, 16), embed_dim: int = 256, depth: int = 4, mlp_ratio: float = 4.0, drop_rate: float = 0.0, num_blocks: int = 16, sparsity_threshold: float = 0.01, hard_thresholding_fraction: float = 1.0)[source]

Bases: Module

Adaptive Fourier neural operator (AFNO) model.

Note

AFNO is a model that is designed for 2D images only.

Parameters

  • img_size (Tuple[int, int]) – Input image dimensions (height, width)

  • in_channels (int) – Number of input channels

  • out_channels (int) – Number of output channels

  • patch_size (Tuple[int, int], optional) – Size of image patches, by default (16, 16)

  • embed_dim (int, optional) – Embedded channel size, by default 256

  • depth (int, optional) – Number of AFNO layers, by default 4

  • mlp_ratio (float, optional) – Ratio of layer MLP latent variable size to input feature size, by default 4.0

  • drop_rate (float, optional) – Drop out rate in layer MLPs, by default 0.0

  • num_blocks (int, optional) – Number of blocks in the block-diag frequency weight matrices, by default 16

  • sparsity_threshold (float, optional) – Sparsity threshold (softshrink) of spectral features, by default 0.01

  • hard_thresholding_fraction (float, optional) – Threshold for limiting number of modes used [0,1], by default 1

Example

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>>> model = modulus.models.afno.AFNO(
...     img_size=(32, 32),
...     in_channels=2,
...     out_channels=1,
...     patch_size=(8, 8),
...     embed_dim=16,
...     depth=2,
...     num_blocks=2,
... )
>>> input = torch.randn(32, 2, 32, 32) #(N, C, H, W)
>>> output = model(input)
>>> output.size()
torch.Size([32, 1, 32, 32])

Note

Reference: Guibas, John, et al. “Adaptive fourier neural operators: Efficient token mixers for transformers.” arXiv preprint arXiv:2111.13587 (2021).

forward(x: Tensor) → Tensor[source]

Defines the computation performed at every call.

Should be overridden by all subclasses.
forward_features(x: Tensor) → Tensor[source]

Forward pass of core AFNO
class modulus.models.afno.afno.AFNO2DLayer(hidden_size: int, num_blocks: int = 8, sparsity_threshold: float = 0.01, hard_thresholding_fraction: float = 1, hidden_size_factor: int = 1)[source]

Bases: Module

AFNO spectral convolution layer

Parameters

  • hidden_size (int) – Feature dimensionality

  • num_blocks (int, optional) – Number of blocks used in the block diagonal weight matrix, by default 8

  • sparsity_threshold (float, optional) – Sparsity threshold (softshrink) of spectral features, by default 0.01

  • hard_thresholding_fraction (float, optional) – Threshold for limiting number of modes used [0,1], by default 1

  • hidden_size_factor (int, optional) – Factor to increase spectral features by after weight multiplication, by default 1
forward(x: Tensor) → Tensor[source]

Defines the computation performed at every call.

Should be overridden by all subclasses.
class modulus.models.afno.afno.AFNOMlp(in_features: int, latent_features: int, out_features: int, activation_fn: Module = GELU(approximate='none'), drop: float = 0.0)[source]

Bases: Module

Fully-connected Multi-layer perception used inside AFNO

Parameters

  • in_features (int) – Input feature size

  • latent_features (int) – Latent feature size

  • out_features (int) – Output feature size

  • activation_fn (nn.Module, optional) – Activation function, by default nn.GELU

  • drop (float, optional) – Drop out rate, by default 0.0
forward(x: Tensor) → Tensor[source]

Defines the computation performed at every call.

Should be overridden by all subclasses.
class modulus.models.afno.afno.Block(embed_dim: int, num_blocks: int = 8, mlp_ratio: float = 4.0, drop: float = 0.0, activation_fn: ~torch.nn.modules.module.Module = GELU(approximate='none'), norm_layer: ~torch.nn.modules.module.Module = <class 'torch.nn.modules.normalization.LayerNorm'>, double_skip: bool = True, sparsity_threshold: float = 0.01, hard_thresholding_fraction: float = 1.0)[source]

Bases: Module

AFNO block, spectral convolution and MLP

Parameters

  • embed_dim (int) – Embedded feature dimensionality

  • num_blocks (int, optional) – Number of blocks used in the block diagonal weight matrix, by default 8

  • mlp_ratio (float, optional) – Ratio of MLP latent variable size to input feature size, by default 4.0

  • drop (float, optional) – Drop out rate in MLP, by default 0.0

  • activation_fn (nn.Module, optional) – Activation function used in MLP, by default nn.GELU

  • norm_layer (nn.Module, optional) – Normalization function, by default nn.LayerNorm

  • double_skip (bool, optional) – Residual, by default True

  • sparsity_threshold (float, optional) – Sparsity threshold (softshrink) of spectral features, by default 0.01

  • hard_thresholding_fraction (float, optional) – Threshold for limiting number of modes used [0,1], by default 1
forward(x: Tensor) → Tensor[source]

Defines the computation performed at every call.

Should be overridden by all subclasses.
class modulus.models.afno.afno.MetaData(name: str = 'AFNO', jit: bool = False, cuda_graphs: bool = True, amp: bool = True, amp_cpu: bool = None, amp_gpu: bool = None, torch_fx: bool = False, onnx: bool = False, onnx_gpu: bool = True, onnx_cpu: bool = False, onnx_runtime: bool = True, trt: bool = False, var_dim: int = 1, func_torch: bool = False, auto_grad: bool = False)[source]

Bases: ModelMetaData
class modulus.models.afno.afno.PatchEmbed(img_size: Tuple[int, int], in_channels: int, patch_size: Tuple[int, int] = (16, 16), embed_dim: int = 256)[source]

Bases: Module

Patch embedding layer

Converts 2D patch into a 1D vector for input to AFNO

Parameters

  • img_size (Tuple[int, int]) – Input image dimensions (height, width)

  • in_channels (int) – Number of input channels

  • patch_size (Tuple[int, int], optional) – Size of image patches, by default (16, 16)

  • embed_dim (int, optional) – Embedded channel size, by default 256
forward(x: Tensor) → Tensor[source]

Defines the computation performed at every call.

Should be overridden by all subclasses.

Graph Neural Networks

class modulus.models.meshgraphnet.meshgraphnet.MeshGraphNet(input_dim_nodes: int, input_dim_edges: int, output_dim: int, processor_size: int = 15, num_layers_node_processor: int = 2, num_layers_edge_processor: int = 2, hidden_dim_node_encoder: int = 128, num_layers_node_encoder: int = 2, hidden_dim_edge_encoder: int = 128, num_layers_edge_encoder: int = 2, hidden_dim_node_decoder: int = 128, num_layers_node_decoder: int = 2)[source]

Bases: Module

MeshGraphNet network architecture

Parameters

  • input_dim_nodes (int) – Number of node features

  • input_dim_edges (int) – Number of edge features

  • output_dim (int) – Number of outputs

  • processor_size (int, optional) – Number of message passing blocks, by default 15

  • num_layers_node_processor (int, optional) – Number of MLP layers for processing nodes in each message passing block, by default 2

  • num_layers_edge_processor (int, optional) – Number of MLP layers for processing edge features in each message passing block, by default 2

  • hidden_dim_node_encoder (int, optional) – Hidden layer size for the node feature encoder, by default 128

  • num_layers_node_encoder (int, optional) – Number of MLP layers for the node feature encoder, by default 2

  • hidden_dim_edge_encoder (int, optional) – Hidden layer size for the edge feature encoder, by default 128

  • num_layers_edge_encoder (int, optional) – Number of MLP layers for the edge feature encoder, by default 2

  • hidden_dim_node_decoder (int, optional) – Hidden layer size for the node feature decoder, by default 128

  • num_layers_node_decoder (int, optional) – Number of MLP layers for the node feature decoder, by default 2

Example

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>>> model = modulus.models.meshgraphnet.MeshGraphNet(
...         input_dim_nodes=4,
...         input_dim_edges=3,
...         output_dim=2,
...     )
>>> graph = dgl.rand_graph(10, 5)
>>> node_features = torch.randn(10, 4)
>>> edge_features = torch.randn(5, 3)
>>> output = model(graph, node_features, edge_features)
>>> output.size()
torch.Size([10, 2])

Note

Reference: Pfaff, Tobias, et al. “Learning mesh-based simulation with graph networks.” arXiv preprint arXiv:2010.03409 (2020).

forward(graph: Union[DGLGraph, List[DGLGraph]], node_features: Tensor, edge_features: Tensor) → Tensor[source]

Defines the computation performed at every call.

Should be overridden by all subclasses.
class modulus.models.meshgraphnet.meshgraphnet.MetaData(name: str = 'MeshGraphNet', jit: bool = True, cuda_graphs: bool = False, amp: bool = False, amp_cpu: bool = False, amp_gpu: bool = True, torch_fx: bool = False, onnx: bool = False, onnx_gpu: bool = None, onnx_cpu: bool = None, onnx_runtime: bool = False, trt: bool = False, var_dim: int = -1, func_torch: bool = True, auto_grad: bool = True)[source]

Bases: ModelMetaData
class modulus.models.graphcast.graph_cast_net.GraphCastNet(meshgraph_path: str, static_dataset_path: str, input_res: tuple = (721, 1440), input_dim_grid_nodes: int = 474, input_dim_mesh_nodes: int = 3, input_dim_edges: int = 4, output_dim_grid_nodes: int = 227, processor_layers: int = 16, hidden_layers: int = 1, hidden_dim: int = 512, aggregation: str = 'sum', activation_fn: Module = SiLU(), norm_type: str = 'LayerNorm', use_cugraphops_encoder: bool = False, use_cugraphops_processor: bool = False, use_cugraphops_decoder: bool = False, do_concat_trick: bool = False, recompute_activation: bool = False)[source]

Bases: Module

GraphCast network architecture

Parameters

  • meshgraph_path (str) – Path to the meshgraph file. If not provided, the meshgraph will be created using PyMesh.

  • static_dataset_path (str) – Path to the static dataset file.

  • input_res (Tuple[int, int]) – Input resolution of the latitude-longitude grid

  • input_dim_grid_nodes (int, optional) – Input dimensionality of the grid node features, by default 474

  • input_dim_mesh_nodes (int, optional) – Input dimensionality of the mesh node features, by default 3

  • input_dim_edges (int, optional) – Input dimensionality of the edge features, by default 4

  • output_dim_grid_nodes (int, optional) – Final output dimensionality of the edge features, by default 227

  • processor_layers (int, optional) – Number of processor layers, by default 16

  • hidden_layers (int, optional) – Number of hiddel layers, by default 1

  • hidden_dim (int, optional) – Number of neurons in each hidden layer, by default 512

  • aggregation (str, optional) – Message passing aggregation method (“sum”, “mean”), by default “sum”

  • activation_fn (nn.Module, optional) – Type of activation function, by default nn.SiLU()

  • norm_type (str, optional) – Normalization type, by default “LayerNorm”

  • use_cugraphops_encoder (bool, default=False) – Flag to select cugraphops kernels in encoder

  • use_cugraphops_processor (bool, default=False) – Flag to select cugraphops kernels in the processor

  • use_cugraphops_decoder (bool, default=False) – Flag to select cugraphops kernels in the decoder

  • do_conat_trick (: bool, default=False) – Whether to replace concat+MLP with MLP+idx+sum

  • recompute_activation (bool, optional) – Flag for recomputing activation in backward to save memory, by default False. Currently, only SiLU is supported.
Note

Based on these papers: - “GraphCast: Learning skillful medium-range global weather forecasting”

https://arxiv.org/abs/2212.12794

custom_forward(grid_nfeat: Tensor) → Tensor[source]

GraphCast forward method with support for gradient checkpointing.

Parameters

grid_nfeat (Tensor) – Node features of the latitude-longitude graph.
Returns

grid_nfeat_finale – Predicted node features of the latitude-longitude graph.
Return type

Tensor
decoder_forward(mesh_efeat_processed: Tensor, mesh_nfeat_processed: Tensor, grid_nfeat_encoded: Tensor) → Tensor[source]

Forward method for the last layer of the processor, the decoder, and the final MLP.

Parameters

  • mesh_efeat_processed (Tensor) – Multimesh edge features processed by the processor.

  • mesh_nfeat_processed (Tensor) – Multi-mesh node features processed by the processor.

  • grid_nfeat_encoded (Tensor) – The encoded node features for the latitude-longitude grid.
Returns

grid_nfeat_finale – The final node features for the latitude-longitude grid.
Return type

Tensor
encoder_forward(grid_nfeat: Tensor) → Tensor[source]

Forward method for the embedder, encoder, and the first of the processor.

Parameters

grid_nfeat (Tensor) – Node features for the latitude-longitude grid.
Returns

  • mesh_efeat_processed (Tensor) – Processed edge features for the multimesh.

  • mesh_nfeat_processed (Tensor) – Processed node features for the multimesh.

  • grid_nfeat_encoded (Tensor) – Encoded node features for the latitude-longitude grid.
forward(grid_nfeat: Tensor) → Tensor[source]

Defines the computation performed at every call.

Should be overridden by all subclasses.
prepare_input(invar: Tensor) → Tensor[source]

Prepares the input to the model in the required shape.

Parameters

invar (Tensor) – Input in the shape [N, C, H, W].
Returns

Reshaped input.
Return type

Tensor
prepare_output(outvar: Tensor) → Tensor[source]

Prepares the output of the model in the shape [N, C, H, W].

Parameters

outvar (Tensor) – Output of the final MLP of the model.
Returns

The reshaped output of the model.
Return type

Tensor
set_checkpoint_decoder(checkpoint_flag: bool)[source]

Sets checkpoint function for the last layer of the processor, the decoder, and the final MLP.

This function returns the appropriate checkpoint function based on the provided checkpoint_flag flag. If checkpoint_flag is True, the function returns the checkpoint function from PyTorch’s torch.utils.checkpoint. Otherwise, it returns an identity function that simply passes the inputs through the given layer.

Parameters

checkpoint_flag (bool) – Whether to use checkpointing for gradient computation. Checkpointing can reduce memory usage during backpropagation at the cost of increased computation time.
Returns

The selected checkpoint function to use for gradient computation.
Return type

Callable
set_checkpoint_encoder(checkpoint_flag: bool)[source]

Sets checkpoint function for the embedder, encoder, and the first of the processor.

This function returns the appropriate checkpoint function based on the provided checkpoint_flag flag. If checkpoint_flag is True, the function returns the checkpoint function from PyTorch’s torch.utils.checkpoint. Otherwise, it returns an identity function that simply passes the inputs through the given layer.

Parameters

checkpoint_flag (bool) – Whether to use checkpointing for gradient computation. Checkpointing can reduce memory usage during backpropagation at the cost of increased computation time.
Returns

The selected checkpoint function to use for gradient computation.
Return type

Callable
set_checkpoint_model(checkpoint_flag: bool)[source]

Sets checkpoint function for the entire model.

This function returns the appropriate checkpoint function based on the provided checkpoint_flag flag. If checkpoint_flag is True, the function returns the checkpoint function from PyTorch’s torch.utils.checkpoint. In this case, all the other gradient checkpoitings will be disabled. Otherwise, it returns an identity function that simply passes the inputs through the given layer.

Parameters

checkpoint_flag (bool) – Whether to use checkpointing for gradient computation. Checkpointing can reduce memory usage during backpropagation at the cost of increased computation time.
Returns

The selected checkpoint function to use for gradient computation.
Return type

Callable
set_checkpoint_processor(checkpoint_segments: int)[source]

Sets checkpoint function for the processor excluding the first and last layers.

This function returns the appropriate checkpoint function based on the provided checkpoint_segments flag. If checkpoint_segments is positive,

the function returns the checkpoint function from PyTorch’s

torch.utils.checkpoint, with number of checkpointing segments equal to checkpoint_segments. Otherwise, it returns an identity function that simply passes the inputs through the given layer.

Parameters

checkpoint_segments (int) – Number of checkpointing segments for gradient computation. Checkpointing can reduce memory usage during backpropagation at the cost of increased computation time.
Returns

The selected checkpoint function to use for gradient computation.
Return type

Callable
to(*args: Any, **kwargs: Any) → GraphCastNet[source]

Moves the object to the specified device, dtype, or format. This method moves the object and its underlying graph and graph features to the specified device, dtype, or format, and returns the updated object.

Parameters

  • *args (Any) – Positional arguments to be passed to the torch._C._nn._parse_to function.

  • **kwargs (Any) – Keyword arguments to be passed to the torch._C._nn._parse_to function.
Returns

The updated object after moving to the specified device, dtype, or format.
Return type

GraphCastNet
class modulus.models.graphcast.graph_cast_net.MetaData(name: str = 'GraphCastNet', jit: bool = False, cuda_graphs: bool = False, amp: bool = False, amp_cpu: bool = False, amp_gpu: bool = True, torch_fx: bool = False, onnx: bool = False, onnx_gpu: bool = None, onnx_cpu: bool = None, onnx_runtime: bool = False, trt: bool = False, var_dim: int = -1, func_torch: bool = False, auto_grad: bool = False)[source]

Bases: ModelMetaData

Pix2Pix Net

class modulus.models.pix2pix.pix2pix.MetaData(name: str = 'Pix2Pix', jit: bool = True, cuda_graphs: bool = True, amp: bool = False, amp_cpu: bool = False, amp_gpu: bool = True, torch_fx: bool = False, onnx: bool = True, onnx_gpu: bool = None, onnx_cpu: bool = None, onnx_runtime: bool = False, trt: bool = False, var_dim: int = 1, func_torch: bool = True, auto_grad: bool = True)[source]

Bases: ModelMetaData
class modulus.models.pix2pix.pix2pix.Pix2Pix(in_channels: int, out_channels: int, dimension: int, conv_layer_size: int = 64, n_downsampling: int = 3, n_upsampling: int = 3, n_blocks: int = 3, activation_fn: Union[Module, List[Module]] = ReLU(), batch_norm: bool = False, padding_type: str = 'reflect')[source]

Bases: Module

Convolutional encoder-decoder based on pix2pix generator models.

Note

The pix2pix architecture supports options for 1D, 2D and 3D fields which can be constroled using the dimension parameter.

Parameters

  • in_channels (int) – Number of input channels

  • out_channels (Union[int, Any], optional) – Number of outout channels

  • dimension (int) – Model dimensionality (supports 1, 2, 3).

  • conv_layer_size (int, optional) – Latent channel size after first convolution, by default 64

  • n_downsampling (int, optional) – Number of downsampling blocks, by default 3

  • n_upsampling (int, optional) – Number of upsampling blocks, by default 3

  • n_blocks (int, optional) – Number of residual blocks in middle of model, by default 3

  • activation_fn (Activation, optional) – Activation function, by default ReLU

  • batch_norm (bool, optional) – Batch normalization, by default False

  • padding_type (str, optional) – Padding type (‘reflect’, ‘replicate’ or ‘zero’), by default “reflect”

Example

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>>> #2D convolutional encoder decoder
>>> model = modulus.models.pix2pix.Pix2Pix(
... in_channels=1,
... out_channels=2,
... dimension=2,
... conv_layer_size=4)
>>> input = torch.randn(4, 1, 32, 32) #(N, C, H, W)
>>> output = model(input)
>>> output.size()
torch.Size([4, 2, 32, 32])

Note

Reference: Isola, Phillip, et al. “Image-To-Image translation with conditional adversarial networks” Conference on Computer Vision and Pattern Recognition, 2017. https://arxiv.org/abs/1611.07004

Reference: Wang, Ting-Chun, et al. “High-Resolution image synthesis and semantic manipulation with conditional GANs” Conference on Computer Vision and Pattern Recognition, 2018. https://arxiv.org/abs/1711.11585

Note

Based on the implementation: https://github.com/NVIDIA/pix2pixHD

forward(input: Tensor) → Tensor[source]

Defines the computation performed at every call.

Should be overridden by all subclasses.
class modulus.models.pix2pix.pix2pix.ResnetBlock(dimension: int, channels: int, padding_type: str = 'reflect', activation: Module = ReLU(), use_batch_norm: bool = False, use_dropout: bool = False)[source]

Bases: Module

A simple ResNet block

Parameters

  • dimension (int) – Model dimensionality (supports 1, 2, 3).

  • channels (int) – Number of feature channels

  • padding_type (str, optional) – Padding type (‘reflect’, ‘replicate’ or ‘zero’), by default “reflect”

  • activation (nn.Module, optional) – Activation function, by default nn.ReLU()

  • use_batch_norm (bool, optional) – Batch normalization, by default False
forward(x: Tensor) → Tensor[source]

Defines the computation performed at every call.

Should be overridden by all subclasses.

Recurrent Neural Networks

class modulus.models.rnn.rnn_one2many.MetaData(name: str = 'One2ManyRNN', jit: bool = False, cuda_graphs: bool = False, amp: bool = True, amp_cpu: bool = None, amp_gpu: bool = None, torch_fx: bool = True, onnx: bool = False, onnx_gpu: bool = None, onnx_cpu: bool = None, onnx_runtime: bool = False, trt: bool = False, var_dim: int = -1, func_torch: bool = False, auto_grad: bool = False)[source]

Bases: ModelMetaData
class modulus.models.rnn.rnn_one2many.One2ManyRNN(input_channels: int, dimension: int = 2, nr_latent_channels: int = 512, nr_residual_blocks: int = 2, activation_fn: Union[Module, List[Module]] = ReLU(), nr_downsamples: int = 2, nr_tsteps: int = 32)[source]

Bases: Module

A RNN model with encoder/decoder for 2d/3d problems that provides predictions based on single initial condition.

Parameters

  • input_channels (int) – Number of channels in the input

  • dimension (int, optional) – Spatial dimension of the input. Only 2d and 3d are supported, by default 2

  • nr_latent_channels (int, optional) – Channels for encoding/decoding, by default 512

  • nr_residual_blocks (int, optional) – Number of residual blocks, by default 2

  • activation_fn (Union[nn.Module, List[nn.Module]], optional) – Activation function to use, by default nn.ReLU()

  • nr_downsamples (int, optional) – Number of downsamples, by default 2

  • nr_tsteps (int, optional) – Time steps to predict, by default 32

Example

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>>> model = modulus.models.rnn.One2ManyRNN(
... input_channels=6,
... dimension=2,
... nr_latent_channels=32,
... activation_fn=torch.nn.ReLU(),
... nr_downsamples=2,
... nr_tsteps=16,
... )
>>> input = invar = torch.randn(4, 6, 1, 16, 16) # [N, C, T, H, W]
>>> output = model(input)
>>> output.size()
torch.Size([4, 6, 16, 16, 16])

forward(x: Tensor) → Tensor[source]

Forward pass

Parameters

x (Tensor) – Expects a tensor of size [N, C, 1, H, W] for 2D or [N, C, 1, D, H, W] for 3D Where, N is the batch size, C is the number of channels, 1 is the number of input timesteps and D, H, W are spatial dimensions.
Returns

Size [N, C, T, H, W] for 2D or [N, C, T, D, H, W] for 3D. Where, T is the number of timesteps being predicted.
Return type

Tensor
class modulus.models.rnn.rnn_seq2seq.MetaData(name: str = 'Seq2SeqRNN', jit: bool = False, cuda_graphs: bool = False, amp: bool = True, amp_cpu: bool = None, amp_gpu: bool = None, torch_fx: bool = True, onnx: bool = False, onnx_gpu: bool = None, onnx_cpu: bool = None, onnx_runtime: bool = False, trt: bool = False, var_dim: int = -1, func_torch: bool = False, auto_grad: bool = False)[source]

Bases: ModelMetaData
class modulus.models.rnn.rnn_seq2seq.Seq2SeqRNN(input_channels: int, dimension: int = 2, nr_latent_channels: int = 512, nr_residual_blocks: int = 2, activation_fn: Union[Module, List[Module]] = ReLU(), nr_downsamples: int = 2, nr_tsteps: int = 32)[source]

Bases: Module

A RNN model with encoder/decoder for 2d/3d problems. Given input 0 to t-1, predicts signal t to t + nr_tsteps

Parameters

  • input_channels (int) – Number of channels in the input

  • dimension (int, optional) – Spatial dimension of the input. Only 2d and 3d are supported, by default 2

  • nr_latent_channels (int, optional) – Channels for encoding/decoding, by default 512

  • nr_residual_blocks (int, optional) – Number of residual blocks, by default 2

  • activation_fn (Union[nn.Module, List[nn.Module]], optional) – Activation function to use, by default nn.ReLU()

  • nr_downsamples (int, optional) – Number of downsamples, by default 2

  • nr_tsteps (int, optional) – Time steps to predict, by default 32

Example

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>>> model = modulus.models.rnn.Seq2SeqRNN(
... input_channels=6,
... dimension=2,
... nr_latent_channels=32,
... activation_fn=torch.nn.ReLU(),
... nr_downsamples=2,
... nr_tsteps=16,
... )
>>> input = invar = torch.randn(4, 6, 16, 16, 16) # [N, C, T, H, W]
>>> output = model(input)
>>> output.size()
torch.Size([4, 6, 16, 16, 16])

forward(x: Tensor) → Tensor[source]

Forward pass

Parameters

x (Tensor) – Expects a tensor of size [N, C, T, H, W] for 2D or [N, C, T, D, H, W] for 3D Where, N is the batch size, C is the number of channels, T is the number of input timesteps and D, H, W are spatial dimensions. Currently, this requires input time steps to be same as predicted time steps.
Returns

Size [N, C, T, H, W] for 2D or [N, C, T, D, H, W] for 3D. Where, T is the number of timesteps being predicted.
Return type

Tensor

Super Resolution Network

class modulus.models.srrn.super_res_net.ConvolutionalBlock3d(in_channels: int, out_channels: int, kernel_size: int, stride: int = 1, batch_norm: bool = False, activation_fn: Module = Identity())[source]

Bases: Module

3D convolutional block

Parameters

  • in_channels (int) – Input channels

  • out_channels (int) – Output channels

  • kernel_size (int) – Kernel size

  • stride (int, optional) – Convolutional stride, by default 1

  • batch_norm (bool, optional) – Use batchnorm, by default False
forward(input: Tensor) → Tensor[source]

Defines the computation performed at every call.

Should be overridden by all subclasses.
class modulus.models.srrn.super_res_net.MetaData(name: str = 'SuperResolution', jit: bool = True, cuda_graphs: bool = False, amp: bool = False, amp_cpu: bool = False, amp_gpu: bool = False, torch_fx: bool = False, onnx: bool = True, onnx_gpu: bool = None, onnx_cpu: bool = None, onnx_runtime: bool = False, trt: bool = False, var_dim: int = 1, func_torch: bool = True, auto_grad: bool = True)[source]

Bases: ModelMetaData
class modulus.models.srrn.super_res_net.PixelShuffle3d(scale: int)[source]

Bases: Module

3D pixel-shuffle operation

Parameters

scale (int) – Factor to downscale channel count by
Note

Reference: http://www.multisilicon.com/blog/a25332339.html

forward(input: Tensor) → Tensor[source]

Defines the computation performed at every call.

Should be overridden by all subclasses.
class modulus.models.srrn.super_res_net.ResidualConvBlock3d(n_layers: int = 1, kernel_size: int = 3, conv_layer_size: int = 64, activation_fn: Module = Identity())[source]

Bases: Module

3D ResNet block

Parameters

  • n_layers (int, optional) – Number of convolutional layers, by default 1

  • kernel_size (int, optional) – Kernel size, by default 3

  • conv_layer_size (int, optional) – Latent channel size, by default 64

  • activation_fn (nn.Module, optional) – Activation function, by default nn.Identity()
forward(input: Tensor) → Tensor[source]

Defines the computation performed at every call.

Should be overridden by all subclasses.
class modulus.models.srrn.super_res_net.SRResNet(in_channels: int, out_channels: int, large_kernel_size: int = 7, small_kernel_size: int = 3, conv_layer_size: int = 32, n_resid_blocks: int = 8, scaling_factor: int = 8, activation_fn: Module = PReLU(num_parameters=1))[source]

Bases: Module

3D convolutional super-resolution network

Parameters

  • in_channels (int) – Number of input channels

  • out_channels (int) – Number of outout channels

  • large_kernel_size (int, optional) – convolutional kernel size for first and last convolution, by default 7

  • small_kernel_size (int, optional) – convolutional kernel size for internal convolutions, by default 3

  • conv_layer_size (int, optional) – Latent channel size, by default 32

  • n_resid_blocks (int, optional) – Number of residual blocks before , by default 8

  • scaling_factor (int, optional) – Scaling factor to increase the output feature size compared to the input (2, 4, or 8), by default 8

  • activation_fn (Activation, optional) – Activation function, by default Activation.PRELU

Example

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>>> #3D convolutional encoder decoder
>>> model = modulus.models.srrn.SRResNet(
... in_channels=1,
... out_channels=2,
... conv_layer_size=4,
... scaling_factor=2)
>>> input = torch.randn(4, 1, 8, 8, 8) #(N, C, D, H, W)
>>> output = model(input)
>>> output.size()
torch.Size([4, 2, 16, 16, 16])

Note

Based on the implementation: https://github.com/sgrvinod/a-PyTorch-Tutorial-to-Super-Resolution

forward(in_vars: Tensor) → Tensor[source]

Defines the computation performed at every call.

Should be overridden by all subclasses.
class modulus.models.srrn.super_res_net.SubPixel_ConvolutionalBlock3d(kernel_size: int = 3, conv_layer_size: int = 64, scaling_factor: int = 2)[source]

Bases: Module

Convolutional block with Pixel Shuffle operation

Parameters

  • kernel_size (int, optional) – Kernel size, by default 3

  • conv_layer_size (int, optional) – Latent channel size, by default 64

  • scaling_factor (int, optional) – Pixel shuffle scaling factor, by default 2
forward(input: Tensor) → Tensor[source]

Defines the computation performed at every call.

Should be overridden by all subclasses.
© Copyright 2023, NVIDIA Modulus Team. Last updated on Aug 8, 2023.