minerva.models.nets.deeplabv3

Classes

`DeepLabV3`	A DeeplabV3 with a ResNet50 backbone
`DeepLabV3Backbone`	A ResNet50 backbone for DeepLabV3
`DeepLabV3PredictionHead`	The prediction head for DeepLabV3

Module Contents

class minerva.models.nets.deeplabv3.DeepLabV3(backbone=None, pred_head=None, loss_fn=None, learning_rate=0.001, num_classes=6, train_metrics=None, val_metrics=None, test_metrics=None)

Bases: minerva.models.nets.base.SimpleSupervisedModel

A DeeplabV3 with a ResNet50 backbone

References

Liang-Chieh Chen, George Papandreou, Florian Schroff, Hartwig Adam. “Rethinking Atrous Convolution for Semantic Image Segmentation”, 2017

Initializes a DeepLabV3 model.

Parameters

backbone: Optional[nn.Module]: The backbone network. Defaults to None.
pred_head: Optional[nn.Module]: The prediction head network. Defaults to None.
loss_fn: Optional[nn.Module]: The loss function. Defaults to None.
learning_rate: float: The learning rate for the optimizer. Defaults to 0.001.
num_classes: int: The number of classes for prediction. Defaults to 6.
train_metrics: Optional[Dict[str, Metric]]: The metrics to be computed during training. Defaults to None.
val_metrics: Optional[Dict[str, Metric]]: The metrics to be computed during validation. Defaults to None.
test_metrics: Optional[Dict[str, Metric]]: The metrics to be computed during testing. Defaults to None.

_loss_func(y_hat, y)

Calculate the loss between the output and the input data.

Parameters

y_hattorch.Tensor: The output data from the forward pass.
ytorch.Tensor: The input data/label.

Returns

torch.Tensor: The loss value.

Parameters:

y_hat (torch.Tensor)
y (torch.Tensor)

Return type:

torch.Tensor

configure_optimizers()

forward(x)

Perform a forward pass with the input data on the backbone model.

Parameters

xtorch.Tensor: The input data.

Returns

torch.Tensor: The output data from the forward pass.

Parameters:: x (torch.Tensor)
Return type:: torch.Tensor

Parameters:

backbone (Optional[torch.nn.Module])
pred_head (Optional[torch.nn.Module])
loss_fn (Optional[torch.nn.Module])
learning_rate (float)
num_classes (int)
train_metrics (Optional[Dict[str, torchmetrics.Metric]])
val_metrics (Optional[Dict[str, torchmetrics.Metric]])
test_metrics (Optional[Dict[str, torchmetrics.Metric]])

class minerva.models.nets.deeplabv3.DeepLabV3Backbone(num_classes=6)

Bases: torch.nn.Module

A ResNet50 backbone for DeepLabV3

Initializes the DeepLabV3 model.

Parameters

num_classes: int: The number of classes for classification. Default is 6.

forward(x)

freeze_weights()

unfreeze_weights()

Parameters:: num_classes (int)

class minerva.models.nets.deeplabv3.DeepLabV3PredictionHead(in_channels=2048, num_classes=6, atrous_rates=(12, 24, 36))

Bases: torch.nn.Sequential

The prediction head for DeepLabV3

Initializes the DeepLabV3 model.

Parameters

in_channels: int: Number of input channels. Defaults to 2048.
num_classes: int: Number of output classes. Defaults to 6.
atrous_rates: Sequence[int]: A sequence of atrous rates for the ASPP module. Defaults to (12, 24, 36).

Parameters:

in_channels (int)
num_classes (int)
atrous_rates (Sequence[int])