spatialvi.module.SpatialVAEModule

class spatialvi.module.SpatialVAEModule(n_input, n_batch=0, n_hidden=128, n_latent=10, n_layers=1, dropout_rate=0.1, dispersion='gene', gene_likelihood='zinb', latent_distribution='normal', use_spatial=True, spatial_weight=1.0, library_log_means=None, library_log_vars=None, n_cats_per_cov=None, n_continuous_cov=0, use_batch_norm='both', use_layer_norm='none', **kwargs)

Bases: BaseSpatialModule

Spatial VAE module for gene expression modeling.

This module implements a VAE that incorporates spatial context for improved latent representations.

Parameters:

• n_input (int) – Number of input genes.

• n_batch (int) – Number of batches.

• n_hidden (int) – Number of nodes per hidden layer.

• n_latent (int) – Dimensionality of the latent space.

• n_layers (int) – Number of hidden layers.

• dropout_rate (float) – Dropout rate for neural networks.

• dispersion (Literal['gene', 'gene-batch', 'gene-cell']) – Dispersion parameter type.

• gene_likelihood (Literal['zinb', 'nb', 'poisson']) – Distribution for gene expression.

• latent_distribution (Literal['normal', 'ln']) – Distribution for latent space.

• use_spatial (bool) – Whether to use spatial information.

• spatial_weight (float) – Weight for spatial regularization.

• library_log_means (ndarray | None) – Log means of library sizes.

• library_log_vars (ndarray | None) – Log variances of library sizes.

• n_cats_per_cov (list[int] | None) – Number of categories per categorical covariate.

• n_continuous_cov (int) – Number of continuous covariates.

• use_batch_norm (Literal['encoder', 'decoder', 'none', 'both']) – Whether to use batch normalization.

• use_layer_norm (Literal['encoder', 'decoder', 'none', 'both']) – Whether to use layer normalization.

• n_input (int)

• n_batch (int)

• n_hidden (int)

• n_latent (int)

• n_layers (int)

• dropout_rate (float)

• dispersion (Literal['gene', 'gene-batch', 'gene-cell'])

• gene_likelihood (Literal['zinb', 'nb', 'poisson'])

• latent_distribution (Literal['normal', 'ln'])

• use_spatial (bool)

• spatial_weight (float)

• library_log_means (np.ndarray | None)

• library_log_vars (np.ndarray | None)

• n_cats_per_cov (list[int] | None)

• n_continuous_cov (int)

• use_batch_norm (Literal['encoder', 'decoder', 'none', 'both'])

• use_layer_norm (Literal['encoder', 'decoder', 'none', 'both'])

__init__(n_input, n_batch=0, n_hidden=128, n_latent=10, n_layers=1, dropout_rate=0.1, dispersion='gene', gene_likelihood='zinb', latent_distribution='normal', use_spatial=True, spatial_weight=1.0, library_log_means=None, library_log_vars=None, n_cats_per_cov=None, n_continuous_cov=0, use_batch_norm='both', use_layer_norm='none', **kwargs)

Initialize internal Module state, shared by both nn.Module and ScriptModule.

Parameters:

• n_input (int)

• n_batch (int)

• n_hidden (int)

• n_latent (int)

• n_layers (int)

• dropout_rate (float)

• dispersion (Literal['gene', 'gene-batch', 'gene-cell'])

• gene_likelihood (Literal['zinb', 'nb', 'poisson'])

• latent_distribution (Literal['normal', 'ln'])

• use_spatial (bool)

• spatial_weight (float)

• library_log_means (ndarray | None)

• library_log_vars (ndarray | None)

• n_cats_per_cov (list[int] | None)

• n_continuous_cov (int)

• use_batch_norm (Literal['encoder', 'decoder', 'none', 'both'])

• use_layer_norm (Literal['encoder', 'decoder', 'none', 'both'])

Methods

__init__(n_input[, n_batch, n_hidden, ...])	Initialize internal Module state, shared by both nn.Module and ScriptModule.
add_module(name, module)	Add a child module to the current module.
apply(fn)	Apply fn recursively to every submodule (as returned by .children()) as well as self.
bfloat16()	Casts all floating point parameters and buffers to bfloat16 datatype.
buffers([recurse])	Return an iterator over module buffers.
children()	Return an iterator over immediate children modules.
compile(*args, **kwargs)	Compile this Module's forward using torch.compile().
cpu()	Move all model parameters and buffers to the CPU.
cuda([device])	Move all model parameters and buffers to the GPU.
double()	Casts all floating point parameters and buffers to double datatype.
eval()	Set the module in evaluation mode.
extra_repr()	Return the extra representation of the module.
float()	Casts all floating point parameters and buffers to float datatype.
forward(tensors[, ...])	Forward pass through the module.
generative(z, library[, batch_index, ...])	Run the generative network.
get_buffer(target)	Return the buffer given by target if it exists, otherwise throw an error.
get_extra_state()	Return any extra state to include in the module's state_dict.
get_parameter(target)	Return the parameter given by target if it exists, otherwise throw an error.
get_submodule(target)	Return the submodule given by target if it exists, otherwise throw an error.
half()	Casts all floating point parameters and buffers to half datatype.
inference(x[, batch_index, cont_covs, ...])	Run the inference network.
ipu([device])	Move all model parameters and buffers to the IPU.
load_state_dict(state_dict[, strict, assign])	Copy parameters and buffers from state_dict into this module and its descendants.
loss(tensors, inference_outputs, ...[, ...])	Compute the loss.
modules()	Return an iterator over all modules in the network.
mtia([device])	Move all model parameters and buffers to the MTIA.
named_buffers([prefix, recurse, ...])	Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.
named_children()	Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself.
named_modules([memo, prefix, remove_duplicate])	Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.
named_parameters([prefix, recurse, ...])	Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.
on_load(model, **kwargs)	Callback function run in load().
parameters([recurse])	Return an iterator over module parameters.
register_backward_hook(hook)	Register a backward hook on the module.
register_buffer(name, tensor[, persistent])	Add a buffer to the module.
register_forward_hook(hook, *[, prepend, ...])	Register a forward hook on the module.
register_forward_pre_hook(hook, *[, ...])	Register a forward pre-hook on the module.
register_full_backward_hook(hook[, prepend])	Register a backward hook on the module.
register_full_backward_pre_hook(hook[, prepend])	Register a backward pre-hook on the module.
register_load_state_dict_post_hook(hook)	Register a post-hook to be run after module's load_state_dict() is called.
register_load_state_dict_pre_hook(hook)	Register a pre-hook to be run before module's load_state_dict() is called.
register_module(name, module)	Alias for add_module().
register_parameter(name, param)	Add a parameter to the module.
register_state_dict_post_hook(hook)	Register a post-hook for the state_dict() method.
register_state_dict_pre_hook(hook)	Register a pre-hook for the state_dict() method.
requires_grad_([requires_grad])	Change if autograd should record operations on parameters in this module.
sample(tensors[, n_samples])	Sample from the model.
set_extra_state(state)	Set extra state contained in the loaded state_dict.
set_submodule(target, module[, strict])	Set the submodule given by target if it exists, otherwise throw an error.
share_memory()	See torch.Tensor.share_memory_().
state_dict(*args[, destination, prefix, ...])	Return a dictionary containing references to the whole state of the module.
to(*args, **kwargs)	Move and/or cast the parameters and buffers.
to_empty(*, device[, recurse])	Move the parameters and buffers to the specified device without copying storage.
train([mode])	Set the module in training mode.
type(dst_type)	Casts all parameters and buffers to dst_type.
xpu([device])	Move all model parameters and buffers to the XPU.
zero_grad([set_to_none])	Reset gradients of all model parameters.

Attributes

T_destination
call_super_init
device
dump_patches
training

inference(x, batch_index=None, cont_covs=None, cat_covs=None, spatial_coords=None, neighbor_indices=None, n_samples=1, **kwargs)

Run the inference network.

Parameters:

• x (Tensor) – Gene expression tensor.

• batch_index (Tensor | None) – Batch indices.

• cont_covs (Tensor | None) – Continuous covariates.

• cat_covs (Tensor | None) – Categorical covariates.

• spatial_coords (Tensor | None) – Spatial coordinates.

• neighbor_indices (Tensor | None) – Neighbor indices.

• n_samples (int) – Number of samples from latent distribution.

• x (Tensor)

• batch_index (Tensor | None)

• cont_covs (Tensor | None)

• cat_covs (Tensor | None)

• spatial_coords (Tensor | None)

• neighbor_indices (Tensor | None)

• n_samples (int)

Return type:

dict[str, Tensor | Distribution]

Returns:

Dictionary of inference outputs.

generative(z, library, batch_index=None, cont_covs=None, cat_covs=None, **kwargs)

Run the generative network.

Parameters:

• z (Tensor) – Latent representation.

• library (Tensor) – Library size.

• batch_index (Tensor | None) – Batch indices.

• cont_covs (Tensor | None) – Continuous covariates.

• cat_covs (Tensor | None) – Categorical covariates.

• z (Tensor)

• library (Tensor)

• batch_index (Tensor | None)

• cont_covs (Tensor | None)

• cat_covs (Tensor | None)

Return type:

dict[str, Tensor | Distribution]

Returns:

Dictionary of generative outputs.

loss(tensors, inference_outputs, generative_outputs, kl_weight=1.0)

Compute the loss.

Parameters:

• tensors (dict[str, Tensor]) – Dictionary of input tensors.

• inference_outputs (dict[str, Tensor | Distribution]) – Dictionary of inference outputs.

• generative_outputs (dict[str, Tensor | Distribution]) – Dictionary of generative outputs.

• kl_weight (float) – Weight for KL divergence term.

• tensors (dict[str, Tensor])

• inference_outputs (dict[str, Tensor | Distribution])

• generative_outputs (dict[str, Tensor | Distribution])

• kl_weight (float)

Return type:

LossOutput

Returns:

LossOutput containing reconstruction loss, KL divergence, etc.

sample(tensors, n_samples=1)

Sample from the model.

Parameters:

• tensors (dict[str, Tensor]) – Dictionary of input tensors.

• n_samples (int) – Number of samples.

• tensors (dict[str, Tensor])

• n_samples (int)

Return type:

Tensor

Returns:

Sampled gene expression.