minerva.models.nets

Submodules

Classes

`DeepLabV3`	A DeeplabV3 with a ResNet50 backbone
`MLP`	A multilayer perceptron (MLP) implemented as a subclass of nn.Sequential.
`SETR_PUP`	SET-R model with PUP head for image segmentation.
`SimpleSupervisedModel`	Simple pipeline for supervised models.
`UNet`	This class is a simple implementation of the U-Net model, which is a
`WiseNet`	Simple pipeline for supervised models.

Package Contents

class minerva.models.nets.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.MLP(layer_sizes, activation_cls=nn.ReLU, *args, **kwargs)[source]

Bases: torch.nn.Sequential

A multilayer perceptron (MLP) implemented as a subclass of nn.Sequential.

This MLP is composed of a sequence of linear layers interleaved with ReLU activation functions, except for the final layer which remains purely linear.

Example

>>> mlp = MLP(10, 20, 30, 40)
>>> print(mlp)
MLP(
    (0): Linear(in_features=10, out_features=20, bias=True)
    (1): ReLU()
    (2): Linear(in_features=20, out_features=30, bias=True)
    (3): ReLU()
    (4): Linear(in_features=30, out_features=40, bias=True)
)

Initializes the MLP with specified layer sizes.

Parameters

layer_sizesSequence[int]: A sequence of positive integers indicating the size of each layer. At least two integers are required, representing the input and output layers.
activation_clstype: The class of the activation function to use between layers. Default is nn.ReLU.
*args: Additional arguments passed to the activation function.
**kwargs: Additional keyword arguments passed to the activation function.

Raises

AssertionError: If fewer than two layer sizes are provided or if any layer size is not a positive integer.
AssertionError: If activation_cls does not inherit from torch.nn.Module.

Parameters:

layer_sizes (Sequence[int])
activation_cls (type)

class minerva.models.nets.SETR_PUP(image_size=512, patch_size=16, num_layers=24, num_heads=16, hidden_dim=1024, mlp_dim=4096, encoder_dropout=0.1, num_classes=1000, norm_layer=None, decoder_channels=256, num_convs=4, up_scale=2, kernel_size=3, align_corners=False, decoder_dropout=0.1, conv_norm=None, conv_act=None, interpolate_mode='bilinear', loss_fn=None, optimizer_type=None, optimizer_params=None, train_metrics=None, val_metrics=None, test_metrics=None, aux_output=True, aux_output_layers=None, aux_weights=None, load_backbone_path=None, freeze_backbone_on_load=True, learning_rate=0.001, loss_weights=None, original_resolution=None, head_lr_factor=1.0, test_engine=None)[source]

Bases: lightning.pytorch.LightningModule

SET-R model with PUP head for image segmentation.

Methods

forward(x: torch.Tensor) -> torch.Tensor: Forward pass of the model.
_compute_metrics(y_hat: torch.Tensor, y: torch.Tensor, step_name: str): Compute metrics for the given step.
_loss_func(y_hat: Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]], y: torch.Tensor) -> torch.Tensor: Calculate the loss between the output and the input data.
_single_step(batch: torch.Tensor, batch_idx: int, step_name: str): Perform a single step of the training/validation loop.
training_step(batch: torch.Tensor, batch_idx: int): Perform a single training step.
validation_step(batch: torch.Tensor, batch_idx: int): Perform a single validation step.
test_step(batch: torch.Tensor, batch_idx: int): Perform a single test step.
predict_step(batch: torch.Tensor, batch_idx: int, dataloader_idx: Optional[int] = None): Perform a single prediction step.
load_backbone(path: str, freeze: bool = False): Load a pre-trained backbone.
configure_optimizers(): Configure the optimizer for the model.
create_from_dict(config: Dict) -> “SETR_PUP”: Create an instance of SETR_PUP from a configuration dictionary.

Initialize the SETR model with Progressive Upsampling Head.

Parameters

image_sizeUnion[int, Tuple[int, int]], optional: Size of the input image, by default 512.
patch_sizeint, optional: Size of the patches to be extracted from the input image, by default 16.
num_layersint, optional: Number of transformer layers, by default 24.
num_headsint, optional: Number of attention heads, by default 16.
hidden_dimint, optional: Dimension of the hidden layer, by default 1024.
mlp_dimint, optional: Dimension of the MLP layer, by default 4096.
encoder_dropoutfloat, optional: Dropout rate for the encoder, by default 0.1.
num_classesint, optional: Number of output classes, by default 1000.
norm_layerOptional[nn.Module], optional: Normalization layer, by default None.
decoder_channelsint, optional: Number of channels in the decoder, by default 256.
num_convsint, optional: Number of convolutional layers in the decoder, by default 4.
up_scaleint, optional: Upscaling factor for the decoder, by default 2.
kernel_sizeint, optional: Kernel size for the convolutional layers, by default 3.
align_cornersbool, optional: Whether to align corners when interpolating, by default False.
decoder_dropoutfloat, optional: Dropout rate for the decoder, by default 0.1.
conv_normOptional[nn.Module], optional: Normalization layer for the convolutional layers, by default None.
conv_actOptional[nn.Module], optional: Activation function for the convolutional layers, by default None.
interpolate_modestr, optional: Interpolation mode, by default “bilinear”.
loss_fnOptional[nn.Module], optional: Loss function, when None defaults to nn.CrossEntropyLoss, by default None.
optimizer_typeOptional[type], optional: Type of optimizer, by default None.
optimizer_paramsOptional[Dict], optional: Parameters for the optimizer, by default None.
train_metricsOptional[Dict[str, Metric]], optional: Metrics for training, by default None.
val_metricsOptional[Dict[str, Metric]], optional: Metrics for validation, by default None.
test_metricsOptional[Dict[str, Metric]], optional: Metrics for testing, by default None.
aux_outputbool, optional: Whether to use auxiliary outputs, by default True.
aux_output_layerslist[int], optional: Layers for auxiliary outputs, when None it defaults to [9, 14, 19].
aux_weightslist[float], optional: Weights for auxiliary outputs, when None it defaults [0.3, 0.3, 0.3].
load_backbone_pathOptional[str], optional: Path to load the backbone model, by default None.
freeze_backbone_on_loadbool, optional: Whether to freeze the backbone model on load, by default True.
learning_ratefloat, optional: Learning rate, by default 1e-3.
loss_weightsOptional[list[float]], optional: Weights for the loss function, by default None.
original_resolutionOptional[Tuple[int, int]], optional: The original resolution of the input image in the pre-training weights. When None, positional embeddings will not be interpolated. Defaults to None.
head_lr_factorfloat, optional: Learning rate factor for the head. used if you need different learning rates for backbone and prediction head, by default 1.0.
test_engineOptional[_Engine], optional: Engine used for test and validation steps. When None, behavior of all steps, training, testing and validation is the same, by default None.

_compute_metrics(y_hat, y, step_name)[source]

Parameters:

y_hat (torch.Tensor)
y (torch.Tensor)
step_name (str)

_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 (Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]])
y (torch.Tensor)

Return type:

torch.Tensor

_single_step(batch, batch_idx, step_name)[source]

Perform a single step of the training/validation loop.

Parameters

batchtorch.Tensor: The input data.
batch_idxint: The index of the batch.
step_namestr: The name of the step, either “train” or “val”.

Returns

torch.Tensor: The loss value.

Parameters:

batch (torch.Tensor)
batch_idx (int)
step_name (str)

aux_weights = None

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.

static create_from_dict(config)[source]

Parameters:: config (Dict)
Return type:: SETR_PUP

forward(x)[source]

Same as torch.nn.Module.forward().

Args:: *args: Whatever you decide to pass into the forward method. **kwargs: Keyword arguments are also possible.
Return:: Your model’s output

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

head_lr_factor = 1.0

learning_rate = 0.001

load_backbone(path, freeze=False)[source]

Parameters:

path (str)
freeze (bool)

loss_fn = None

metrics

model

num_classes = 1000

optimizer_type = None

predict_step(batch, batch_idx, dataloader_idx=None)[source]

Step function called during predict(). By default, it calls forward(). Override to add any processing logic.

The predict_step() is used to scale inference on multi-devices.

To prevent an OOM error, it is possible to use BasePredictionWriter callback to write the predictions to disk or database after each batch or on epoch end.

The BasePredictionWriter should be used while using a spawn based accelerator. This happens for Trainer(strategy="ddp_spawn") or training on 8 TPU cores with Trainer(accelerator="tpu", devices=8) as predictions won’t be returned.

Args:: batch: The output of your data iterable, normally a DataLoader. batch_idx: The index of this batch. dataloader_idx: The index of the dataloader that produced this batch.

(only if multiple dataloaders used)
Return:: Predicted output (optional).

Example

class MyModel(LightningModule):

    def predict_step(self, batch, batch_idx, dataloader_idx=0):
        return self(batch)

dm = ...
model = MyModel()
trainer = Trainer(accelerator="gpu", devices=2)
predictions = trainer.predict(model, dm)

Parameters:

batch (torch.Tensor)
batch_idx (int)
dataloader_idx (Optional[int])

test_engine = None

test_step(batch, batch_idx)[source]

Operates on a single batch of data from the test set. In this step you’d normally generate examples or calculate anything of interest such as accuracy.

Args:

batch: The output of your data iterable, normally a DataLoader. batch_idx: The index of this batch. dataloader_idx: The index of the dataloader that produced this batch.

(only if multiple dataloaders used)

Return:

Tensor - The loss tensor
dict - A dictionary. Can include any keys, but must include the key 'loss'.
None - Skip to the next batch.

# if you have one test dataloader:
def test_step(self, batch, batch_idx): ...


# if you have multiple test dataloaders:
def test_step(self, batch, batch_idx, dataloader_idx=0): ...

Examples:

# CASE 1: A single test dataset
def test_step(self, batch, batch_idx):
    x, y = batch

    # implement your own
    out = self(x)
    loss = self.loss(out, y)

    # log 6 example images
    # or generated text... or whatever
    sample_imgs = x[:6]
    grid = torchvision.utils.make_grid(sample_imgs)
    self.logger.experiment.add_image('example_images', grid, 0)

    # calculate acc
    labels_hat = torch.argmax(out, dim=1)
    test_acc = torch.sum(y == labels_hat).item() / (len(y) * 1.0)

    # log the outputs!
    self.log_dict({'test_loss': loss, 'test_acc': test_acc})

If you pass in multiple test dataloaders, test_step() will have an additional argument. We recommend setting the default value of 0 so that you can quickly switch between single and multiple dataloaders.

# CASE 2: multiple test dataloaders
def test_step(self, batch, batch_idx, dataloader_idx=0):
    # dataloader_idx tells you which dataset this is.
    ...

Note:: If you don’t need to test you don’t need to implement this method.
Note:: When the test_step() is called, the model has been put in eval mode and PyTorch gradients have been disabled. At the end of the test epoch, the model goes back to training mode and gradients are enabled.

Parameters:

batch (torch.Tensor)
batch_idx (int)

training_step(batch, batch_idx)[source]

Here you compute and return the training loss and some additional metrics for e.g. the progress bar or logger.

Args:

batch: The output of your data iterable, normally a DataLoader. batch_idx: The index of this batch. dataloader_idx: The index of the dataloader that produced this batch.

(only if multiple dataloaders used)

Return:

Tensor - The loss tensor
dict - A dictionary which can include any keys, but must include the key 'loss' in the case of automatic optimization.
None - In automatic optimization, this will skip to the next batch (but is not supported for multi-GPU, TPU, or DeepSpeed). For manual optimization, this has no special meaning, as returning the loss is not required.

In this step you’d normally do the forward pass and calculate the loss for a batch. You can also do fancier things like multiple forward passes or something model specific.

Example:

def training_step(self, batch, batch_idx):
    x, y, z = batch
    out = self.encoder(x)
    loss = self.loss(out, x)
    return loss

To use multiple optimizers, you can switch to ‘manual optimization’ and control their stepping:

def __init__(self):
    super().__init__()
    self.automatic_optimization = False


# Multiple optimizers (e.g.: GANs)
def training_step(self, batch, batch_idx):
    opt1, opt2 = self.optimizers()

    # do training_step with encoder
    ...
    opt1.step()
    # do training_step with decoder
    ...
    opt2.step()

Note:: When accumulate_grad_batches > 1, the loss returned here will be automatically normalized by accumulate_grad_batches internally.

Parameters:

batch (torch.Tensor)
batch_idx (int)

validation_step(batch, batch_idx)[source]

Operates on a single batch of data from the validation set. In this step you’d might generate examples or calculate anything of interest like accuracy.

Args:

batch: The output of your data iterable, normally a DataLoader. batch_idx: The index of this batch. dataloader_idx: The index of the dataloader that produced this batch.

(only if multiple dataloaders used)

Return:

Tensor - The loss tensor
dict - A dictionary. Can include any keys, but must include the key 'loss'.
None - Skip to the next batch.

# if you have one val dataloader:
def validation_step(self, batch, batch_idx): ...


# if you have multiple val dataloaders:
def validation_step(self, batch, batch_idx, dataloader_idx=0): ...

Examples:

# CASE 1: A single validation dataset
def validation_step(self, batch, batch_idx):
    x, y = batch

    # implement your own
    out = self(x)
    loss = self.loss(out, y)

    # log 6 example images
    # or generated text... or whatever
    sample_imgs = x[:6]
    grid = torchvision.utils.make_grid(sample_imgs)
    self.logger.experiment.add_image('example_images', grid, 0)

    # calculate acc
    labels_hat = torch.argmax(out, dim=1)
    val_acc = torch.sum(y == labels_hat).item() / (len(y) * 1.0)

    # log the outputs!
    self.log_dict({'val_loss': loss, 'val_acc': val_acc})

If you pass in multiple val dataloaders, validation_step() will have an additional argument. We recommend setting the default value of 0 so that you can quickly switch between single and multiple dataloaders.

# CASE 2: multiple validation dataloaders
def validation_step(self, batch, batch_idx, dataloader_idx=0):
    # dataloader_idx tells you which dataset this is.
    ...

Note:: If you don’t need to validate you don’t need to implement this method.
Note:: When the validation_step() is called, the model has been put in eval mode and PyTorch gradients have been disabled. At the end of validation, the model goes back to training mode and gradients are enabled.

Parameters:

batch (torch.Tensor)
batch_idx (int)

Parameters:

image_size (Union[int, Tuple[int, int]])
patch_size (int)
num_layers (int)
num_heads (int)
hidden_dim (int)
mlp_dim (int)
encoder_dropout (float)
num_classes (int)
norm_layer (Optional[torch.nn.Module])
decoder_channels (int)
num_convs (int)
up_scale (int)
kernel_size (int)
align_corners (bool)
decoder_dropout (float)
conv_norm (Optional[torch.nn.Module])
conv_act (Optional[torch.nn.Module])
interpolate_mode (str)
loss_fn (Optional[torch.nn.Module])
optimizer_type (Optional[type])
optimizer_params (Optional[Dict])
train_metrics (Optional[Dict[str, torchmetrics.Metric]])
val_metrics (Optional[Dict[str, torchmetrics.Metric]])
test_metrics (Optional[Dict[str, torchmetrics.Metric]])
aux_output (bool)
aux_output_layers (Optional[list[int]])
aux_weights (Optional[list[float]])
load_backbone_path (Optional[str])
freeze_backbone_on_load (bool)
learning_rate (float)
loss_weights (Optional[list[float]])
original_resolution (Optional[Tuple[int, int]])
head_lr_factor (float)
test_engine (Optional[minerva.engines.engine._Engine])

class minerva.models.nets.SimpleSupervisedModel(backbone, fc, loss_fn, adapter=None, learning_rate=0.001, flatten=True, train_metrics=None, val_metrics=None, test_metrics=None, freeze_backbone=False)[source]

Bases: lightning.LightningModule

Simple pipeline for supervised models.

This class implements a very common deep learning pipeline, which is composed by the following steps:

Make a forward pass with the input data on the backbone model;
Make a forward pass with the input data on the fc model;
Compute the loss between the output and the label data;
Optimize the model (backbone and FC) parameters with respect to the loss.

This reduces the code duplication for autoencoder models, and makes it easier to implement new models by only changing the backbone model. More complex models, that does not follow this pipeline, should not inherit from this class. Note that, for this class the input data is a tuple of tensors, where the first tensor is the input data and the second tensor is the mask or label.

Initialize the model with the backbone, fc, loss function and metrics. Metrics are used to evaluate the model during training, validation, testing or prediction. It will be logged using lightning logger at the end of each epoch. Metrics should implement the torchmetrics.Metric interface.

Parameters

backbonetorch.nn.Module: The backbone model. Usually the encoder/decoder part of the model.
fctorch.nn.Module: The fully connected model, usually used to classification tasks. Use torch.nn.Identity() if no FC model is needed.
loss_fntorch.nn.Module: The function used to compute the loss.
learning_ratefloat, optional: The learning rate to Adam optimizer, by default 1e-3
flattenbool, optional: If True the input data will be flattened before passing through the fc model, by default True
train_metricsDict[str, Metric], optional: The metrics to be used during training, by default None
val_metricsDict[str, Metric], optional: The metrics to be used during validation, by default None
test_metricsDict[str, Metric], optional: The metrics to be used during testing, by default None
predict_metricsDict[str, Metric], optional: The metrics to be used during prediction, by default None

_compute_metrics(y_hat, y, step_name)[source]

Calculate the metrics for the given step.

Parameters

y_hattorch.Tensor: The output data from the forward pass.
ytorch.Tensor: The input data/label.
step_namestr: Name of the step. It will be used to get the metrics from the self.metrics attribute.

Returns

Dict[str, torch.Tensor]: A dictionary with the metrics values.

Parameters:

y_hat (torch.Tensor)
y (torch.Tensor)
step_name (str)

Return type:

Dict[str, torch.Tensor]

_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

_single_step(batch, batch_idx, step_name)[source]

Perform a single train/validation/test step. It consists in making a forward pass with the input data on the backbone model, computing the loss between the output and the input data, and logging the loss.

Parameters

batchtorch.Tensor: The input data. It must be a 2-element tuple of tensors, where the first tensor is the input data and the second tensor is the mask.
batch_idxint: The index of the batch.
step_namestr: The name of the step. It will be used to log the loss. The possible values are: “train”, “val” and “test”. The loss will be logged as “{step_name}_loss”.

Returns

torch.Tensor: A tensor with the loss value.

Parameters:

batch (torch.Tensor)
batch_idx (int)
step_name (str)

Return type:

torch.Tensor

adapter = None

backbone

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.

fc

flatten = True

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

freeze_backbone = False

learning_rate = 0.001

loss_fn

metrics

predict_step(batch, batch_idx, dataloader_idx=None)[source]

Step function called during predict(). By default, it calls forward(). Override to add any processing logic.

The predict_step() is used to scale inference on multi-devices.

To prevent an OOM error, it is possible to use BasePredictionWriter callback to write the predictions to disk or database after each batch or on epoch end.

The BasePredictionWriter should be used while using a spawn based accelerator. This happens for Trainer(strategy="ddp_spawn") or training on 8 TPU cores with Trainer(accelerator="tpu", devices=8) as predictions won’t be returned.

Args:: batch: The output of your data iterable, normally a DataLoader. batch_idx: The index of this batch. dataloader_idx: The index of the dataloader that produced this batch.

(only if multiple dataloaders used)
Return:: Predicted output (optional).

Example

class MyModel(LightningModule):

    def predict_step(self, batch, batch_idx, dataloader_idx=0):
        return self(batch)

dm = ...
model = MyModel()
trainer = Trainer(accelerator="gpu", devices=2)
predictions = trainer.predict(model, dm)

test_step(batch, batch_idx)[source]

Operates on a single batch of data from the test set. In this step you’d normally generate examples or calculate anything of interest such as accuracy.

Args:

batch: The output of your data iterable, normally a DataLoader. batch_idx: The index of this batch. dataloader_idx: The index of the dataloader that produced this batch.

(only if multiple dataloaders used)

Return:

Tensor - The loss tensor
dict - A dictionary. Can include any keys, but must include the key 'loss'.
None - Skip to the next batch.

# if you have one test dataloader:
def test_step(self, batch, batch_idx): ...


# if you have multiple test dataloaders:
def test_step(self, batch, batch_idx, dataloader_idx=0): ...

Examples:

# CASE 1: A single test dataset
def test_step(self, batch, batch_idx):
    x, y = batch

    # implement your own
    out = self(x)
    loss = self.loss(out, y)

    # log 6 example images
    # or generated text... or whatever
    sample_imgs = x[:6]
    grid = torchvision.utils.make_grid(sample_imgs)
    self.logger.experiment.add_image('example_images', grid, 0)

    # calculate acc
    labels_hat = torch.argmax(out, dim=1)
    test_acc = torch.sum(y == labels_hat).item() / (len(y) * 1.0)

    # log the outputs!
    self.log_dict({'test_loss': loss, 'test_acc': test_acc})

If you pass in multiple test dataloaders, test_step() will have an additional argument. We recommend setting the default value of 0 so that you can quickly switch between single and multiple dataloaders.

# CASE 2: multiple test dataloaders
def test_step(self, batch, batch_idx, dataloader_idx=0):
    # dataloader_idx tells you which dataset this is.
    ...

Note:: If you don’t need to test you don’t need to implement this method.
Note:: When the test_step() is called, the model has been put in eval mode and PyTorch gradients have been disabled. At the end of the test epoch, the model goes back to training mode and gradients are enabled.

Parameters:

batch (torch.Tensor)
batch_idx (int)

training_step(batch, batch_idx)[source]

Here you compute and return the training loss and some additional metrics for e.g. the progress bar or logger.

Args:

batch: The output of your data iterable, normally a DataLoader. batch_idx: The index of this batch. dataloader_idx: The index of the dataloader that produced this batch.

(only if multiple dataloaders used)

Return:

Tensor - The loss tensor
dict - A dictionary which can include any keys, but must include the key 'loss' in the case of automatic optimization.
None - In automatic optimization, this will skip to the next batch (but is not supported for multi-GPU, TPU, or DeepSpeed). For manual optimization, this has no special meaning, as returning the loss is not required.

In this step you’d normally do the forward pass and calculate the loss for a batch. You can also do fancier things like multiple forward passes or something model specific.

Example:

def training_step(self, batch, batch_idx):
    x, y, z = batch
    out = self.encoder(x)
    loss = self.loss(out, x)
    return loss

To use multiple optimizers, you can switch to ‘manual optimization’ and control their stepping:

def __init__(self):
    super().__init__()
    self.automatic_optimization = False


# Multiple optimizers (e.g.: GANs)
def training_step(self, batch, batch_idx):
    opt1, opt2 = self.optimizers()

    # do training_step with encoder
    ...
    opt1.step()
    # do training_step with decoder
    ...
    opt2.step()

Note:: When accumulate_grad_batches > 1, the loss returned here will be automatically normalized by accumulate_grad_batches internally.

Parameters:

batch (torch.Tensor)
batch_idx (int)

validation_step(batch, batch_idx)[source]

Operates on a single batch of data from the validation set. In this step you’d might generate examples or calculate anything of interest like accuracy.

Args:

batch: The output of your data iterable, normally a DataLoader. batch_idx: The index of this batch. dataloader_idx: The index of the dataloader that produced this batch.

(only if multiple dataloaders used)

Return:

Tensor - The loss tensor
dict - A dictionary. Can include any keys, but must include the key 'loss'.
None - Skip to the next batch.

# if you have one val dataloader:
def validation_step(self, batch, batch_idx): ...


# if you have multiple val dataloaders:
def validation_step(self, batch, batch_idx, dataloader_idx=0): ...

Examples:

# CASE 1: A single validation dataset
def validation_step(self, batch, batch_idx):
    x, y = batch

    # implement your own
    out = self(x)
    loss = self.loss(out, y)

    # log 6 example images
    # or generated text... or whatever
    sample_imgs = x[:6]
    grid = torchvision.utils.make_grid(sample_imgs)
    self.logger.experiment.add_image('example_images', grid, 0)

    # calculate acc
    labels_hat = torch.argmax(out, dim=1)
    val_acc = torch.sum(y == labels_hat).item() / (len(y) * 1.0)

    # log the outputs!
    self.log_dict({'val_loss': loss, 'val_acc': val_acc})

If you pass in multiple val dataloaders, validation_step() will have an additional argument. We recommend setting the default value of 0 so that you can quickly switch between single and multiple dataloaders.

# CASE 2: multiple validation dataloaders
def validation_step(self, batch, batch_idx, dataloader_idx=0):
    # dataloader_idx tells you which dataset this is.
    ...

Note:: If you don’t need to validate you don’t need to implement this method.
Note:: When the validation_step() is called, the model has been put in eval mode and PyTorch gradients have been disabled. At the end of validation, the model goes back to training mode and gradients are enabled.

Parameters:

batch (torch.Tensor)
batch_idx (int)

Parameters:

backbone (Union[torch.nn.Module, minerva.models.loaders.LoadableModule])
fc (Union[torch.nn.Module, minerva.models.loaders.LoadableModule])
loss_fn (torch.nn.Module)
adapter (Optional[Callable[[torch.Tensor], torch.Tensor]])
learning_rate (float)
flatten (bool)
train_metrics (Optional[Dict[str, torchmetrics.Metric]])
val_metrics (Optional[Dict[str, torchmetrics.Metric]])
test_metrics (Optional[Dict[str, torchmetrics.Metric]])
freeze_backbone (bool)

class minerva.models.nets.UNet(n_channels=1, bilinear=False, learning_rate=0.001, loss_fn=None, **kwargs)[source]

Bases: minerva.models.nets.base.SimpleSupervisedModel

This class is a simple implementation of the U-Net model, which is a convolutional neural network used for image segmentation. The model consists of a contracting path (encoder) and an expansive path (decoder). The contracting path follows the typical architecture of a convolutional neural network, with repeated applications of convolutions and max pooling layers. The expansive path consists of up-convolutions and concatenation of feature maps from the contracting path. The model also has skip connections, which allows the expansive path to use information from the contracting path at multiple resolutions. The U-Net model was originally proposed by Ronneberger, Fischer, and Brox in 2015.

This architecture, handles arbitrary input sizes, and returns an output of the same size as the input. The expected input size is (N, C, H, W), where N is the batch size, C is the number of channels, H is the height of the input image, and W is the width of the input image.

Note that, for this implementation, the input batch is a single tensor and not a tuple of tensors (e.g., data and label).

Note that this class wrappers the _UNet class, which is the actual implementation of the U-Net model, into a SimpleReconstructionNet class, which is a simple autoencoder pipeline for reconstruction tasks.

References

Ronneberger, Olaf, Philipp Fischer, and Thomas Brox. “U-net: Convolutional networks for biomedical image segmentation.” Medical Image Computing and Computer-Assisted Intervention-MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18. Springer International Publishing, 2015.

Wrapper implementation of the U-Net model.

Parameters

n_channelsint, optional: The number of channels of the input, by default 1
bilinearbool, optional: If True use bilinear interpolation for upsampling, by default False.
learning_ratefloat, optional: The learning rate to Adam optimizer, by default 1e-3
loss_fntorch.nn.Module, optional: The function used to compute the loss. If None, it will be used the MSELoss, by default None.
kwargsDict: Additional arguments to be passed to the SimpleSupervisedModel class.

Parameters:

n_channels (int)
bilinear (bool)
learning_rate (float)
loss_fn (Optional[torch.nn.Module])

class minerva.models.nets.WiseNet(in_channels=1, out_channels=1, loss_fn=None, learning_rate=0.001, **kwargs)[source]

Bases: minerva.models.nets.base.SimpleSupervisedModel

Simple pipeline for supervised models.

This class implements a very common deep learning pipeline, which is composed by the following steps:

Make a forward pass with the input data on the backbone model;
Make a forward pass with the input data on the fc model;
Compute the loss between the output and the label data;
Optimize the model (backbone and FC) parameters with respect to the loss.

This reduces the code duplication for autoencoder models, and makes it easier to implement new models by only changing the backbone model. More complex models, that does not follow this pipeline, should not inherit from this class. Note that, for this class the input data is a tuple of tensors, where the first tensor is the input data and the second tensor is the mask or label.

Initialize the model with the backbone, fc, loss function and metrics. Metrics are used to evaluate the model during training, validation, testing or prediction. It will be logged using lightning logger at the end of each epoch. Metrics should implement the torchmetrics.Metric interface.

Parameters

backbonetorch.nn.Module: The backbone model. Usually the encoder/decoder part of the model.
fctorch.nn.Module: The fully connected model, usually used to classification tasks. Use torch.nn.Identity() if no FC model is needed.
loss_fntorch.nn.Module: The function used to compute the loss.
learning_ratefloat, optional: The learning rate to Adam optimizer, by default 1e-3
flattenbool, optional: If True the input data will be flattened before passing through the fc model, by default True
train_metricsDict[str, Metric], optional: The metrics to be used during training, by default None
val_metricsDict[str, Metric], optional: The metrics to be used during validation, by default None
test_metricsDict[str, Metric], optional: The metrics to be used during testing, by default None
predict_metricsDict[str, Metric], optional: The metrics to be used during prediction, by default None

_single_step(batch, batch_idx, step_name)[source]

Perform a single train/validation/test step. It consists in making a forward pass with the input data on the backbone model, computing the loss between the output and the input data, and logging the loss.

Parameters

batchtorch.Tensor: The input data. It must be a 2-element tuple of tensors, where the first tensor is the input data and the second tensor is the mask.
batch_idxint: The index of the batch.
step_namestr: The name of the step. It will be used to log the loss. The possible values are: “train”, “val” and “test”. The loss will be logged as “{step_name}_loss”.

Returns

torch.Tensor: A tensor with the loss value.

Parameters:

batch (torch.Tensor)
batch_idx (int)
step_name (str)

Return type:

torch.Tensor

predict_step(batch, batch_idx, dataloader_idx=None)[source]

Step function called during predict(). By default, it calls forward(). Override to add any processing logic.

The predict_step() is used to scale inference on multi-devices.

To prevent an OOM error, it is possible to use BasePredictionWriter callback to write the predictions to disk or database after each batch or on epoch end.

The BasePredictionWriter should be used while using a spawn based accelerator. This happens for Trainer(strategy="ddp_spawn") or training on 8 TPU cores with Trainer(accelerator="tpu", devices=8) as predictions won’t be returned.

Args:: batch: The output of your data iterable, normally a DataLoader. batch_idx: The index of this batch. dataloader_idx: The index of the dataloader that produced this batch.

(only if multiple dataloaders used)
Return:: Predicted output (optional).

Example

class MyModel(LightningModule):

    def predict_step(self, batch, batch_idx, dataloader_idx=0):
        return self(batch)

dm = ...
model = MyModel()
trainer = Trainer(accelerator="gpu", devices=2)
predictions = trainer.predict(model, dm)

Parameters:

in_channels (int)
out_channels (int)
loss_fn (torch.nn.Module)
learning_rate (float)