hyrax.models.hyrax_cnn

hyrax.models.hyrax_cnn#

Attributes#

logger

Classes#

HyraxCNN

This CNN is designed to work with datasets that are prepared with Hyrax's HSC Data Set class.

Module Contents#

logger[source]#

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

Bases: torch.nn.Module

This CNN is designed to work with datasets that are prepared with Hyrax’s HSC Data Set class.

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

config[source]#

conv1[source]#

pool[source]#

conv2[source]#

fc1[source]#

fc2[source]#

fc3[source]#

conv2d_output_size(input_size, kernel_size, padding=0, stride=1, dilation=1) → int[source]#

pool2d_output_size(input_size, kernel_size, stride, padding=0, dilation=1) → int[source]#

forward(x)[source]#

train_batch(batch)[source]#

This function contains the logic for a single training step that will process a single batch of data. i.e. the contents of the inner loop of a ML training process.

Parameters:: batch (tuple) – A tuple containing the inputs and labels for the current batch.
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. i.e. the contents of the inner loop of a ML validation process. In this case it is identical to test_batch.

Parameters:: batch (tuple) – A tuple containing the inputs and labels for the current batch.
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. i.e. the contents of the inner loop of a ML testing process. In this case, it is identical to validate_batch.

Parameters:: batch (tuple) – A tuple containing the inputs and labels for the current batch.
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 that will process a single batch of data. i.e. the contents of the inner loop of a ML inference process.

Parameters:: batch (tuple) – A tuple containing the inputs and labels for the current batch.
Returns:: Model outputs – Tensor containing the model outputs for the current batch.
Return type:: Tensor

static prepare_inputs(data_dict) → tuple[source]#: Does NOT convert to PyTorch Tensors. This works exclusively with numpy data types and returns a tuple of numpy data types.