minerva.models.nets.image.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.image.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)[source]

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)[source]

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()[source]

Choose what optimizers and learning-rate schedulers to use in your optimization. Normally you’d need one. But in the case of GANs or similar you might have multiple. Optimization with multiple optimizers only works in the manual optimization mode.

Return:

Any of these 6 options.

Single optimizer.
List or Tuple of optimizers.
Two lists - The first list has multiple optimizers, and the second has multiple LR schedulers (or multiple lr_scheduler_config).
Dictionary, with an "optimizer" key, and (optionally) a "lr_scheduler" key whose value is a single LR scheduler or lr_scheduler_config.
None - Fit will run without any optimizer.

The lr_scheduler_config is a dictionary which contains the scheduler and its associated configuration. The default configuration is shown below.

lr_scheduler_config = {
    # REQUIRED: The scheduler instance
    "scheduler": lr_scheduler,
    # The unit of the scheduler's step size, could also be 'step'.
    # 'epoch' updates the scheduler on epoch end whereas 'step'
    # updates it after a optimizer update.
    "interval": "epoch",
    # How many epochs/steps should pass between calls to
    # `scheduler.step()`. 1 corresponds to updating the learning
    # rate after every epoch/step.
    "frequency": 1,
    # Metric to monitor for schedulers like `ReduceLROnPlateau`
    "monitor": "val_loss",
    # If set to `True`, will enforce that the value specified 'monitor'
    # is available when the scheduler is updated, thus stopping
    # training if not found. If set to `False`, it will only produce a warning
    "strict": True,
    # If using the `LearningRateMonitor` callback to monitor the
    # learning rate progress, this keyword can be used to specify
    # a custom logged name
    "name": None,
}

When there are schedulers in which the .step() method is conditioned on a value, such as the torch.optim.lr_scheduler.ReduceLROnPlateau scheduler, Lightning requires that the lr_scheduler_config contains the keyword "monitor" set to the metric name that the scheduler should be conditioned on.

Metrics can be made available to monitor by simply logging it using self.log('metric_to_track', metric_val) in your LightningModule.

Note:

Some things to know:

Lightning calls .backward() and .step() automatically in case of automatic optimization.
If a learning rate scheduler is specified in configure_optimizers() with key "interval" (default “epoch”) in the scheduler configuration, Lightning will call the scheduler’s .step() method automatically in case of automatic optimization.
If you use 16-bit precision (precision=16), Lightning will automatically handle the optimizer.
If you use torch.optim.LBFGS, Lightning handles the closure function automatically for you.
If you use multiple optimizers, you will have to switch to ‘manual optimization’ mode and step them yourself.
If you need to control how often the optimizer steps, override the optimizer_step() hook.

forward(x)[source]

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.image.deeplabv3.DeepLabV3Backbone(num_classes=6)[source]

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.

RN50model

forward(x)[source]

freeze_weights()[source]

unfreeze_weights()[source]

Parameters:: num_classes (int)

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

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])