hyrax.models.image_dcae

hyrax.models.image_dcae#

Classes#

`ArcsinhActivation`	Helper module for ImageDCAE to use the arcsinh function
`ImageDCAE`	This is an autoencoder with skipconnections that should work with

Module Contents#

class ArcsinhActivation(*args: Any, **kwargs: Any)[source]#

Bases: torch.nn.Module

Helper module for ImageDCAE to use the arcsinh function

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

forward(x)[source]#

class ImageDCAE(config, data_sample=None)[source]#

Bases: torch.nn.Module

This is an autoencoder with skipconnections that should work with arbitarily sized images with arbitrary number of channels.

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

input_shape[source]#

config[source]#

latent_dim[source]#

base_channel_size[source]#

conv_output_size[source]#

encoder1[source]#

encoder2[source]#

encoder3[source]#

encoder4[source]#

pool[source]#

latent_encoder[source]#

latent_decoder[source]#

decoder4[source]#

decoder3[source]#

decoder2[source]#

decoder1[source]#

activation[source]#

_calculate_conv_output_size()[source]#: Calculate the output size after all convolutional layers for the linear bottleneck.

encode(x)[source]#: Encode input to latent space with skip connections.

decode(latent, skip_connections, encoded_shape)[source]#: Decode from latent space to image with skip connections.

forward(x)[source]#: Forward pass - returns latent representation for anomaly detection.

reconstruct(x)[source]#: Full reconstruction for evaluation and anomaly detection.

train_batch(batch)[source]#

This function contains the logic for a single training step.

Parameters:: batch (tuple) – A tuple containing the input data for the current batch, possibly with labels that are ignored.
Returns:: Current loss value – Dictionary containing the loss value for the current batch.
Return type:: dict

validate_batch(batch)[source]#

This function contains the logic for a single validation step that will process a single batch of data.

Parameters:: batch (tuple) – A tuple containing the input data for the current batch, possibly with labels that are ignored.
Returns:: Current loss value – Dictionary containing the loss value for the current batch.
Return type:: dict

test_batch(batch)[source]#

This function contains the logic for a single testing step that will process a single batch of data. In this case, it is identical to validate_batch.

Parameters:: batch (tuple) – A tuple containing the input data for the current batch, possibly with labels that are ignored.
Returns:: Current loss value – Dictionary containing the loss value for the current batch.
Return type:: dict

infer_batch(batch)[source]#

This function contains the logic for a single inference step.

Parameters:: batch (tuple) – A tuple containing the input data for the current batch, possibly with labels that are ignored.
Returns:: Reconstructed images – Tensor containing the reconstructed images for the current batch.
Return type:: torch.Tensor

static prepare_inputs(data_dict)[source]#

Extract the image array from the batch dictionary.

This static method is the interface between the data pipeline and the model. Override it on the model class to reshape or select fields from the collated batch to match the inputs your model expects.

Hyrax will convert the returned array to a PyTorch tensor and move it to the appropriate device automatically.

Parameters:: data_dict (dict) – The collated batch dictionary produced by the data pipeline. Expected to contain a "data" key with an "image" field.
Returns:: image – The image array extracted from the batch.
Return type:: numpy.ndarray