minerva.models.nets.base
Classes
Simple pipeline for supervised models. |
Module Contents
- class minerva.models.nets.base.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 orlr_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 thetorch.optim.lr_scheduler.ReduceLROnPlateau
scheduler, Lightning requires that thelr_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 yourLightningModule
.- 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 callsforward()
. 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 forTrainer(strategy="ddp_spawn")
or training on 8 TPU cores withTrainer(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 tensordict
- 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 tensordict
- 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 byaccumulate_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 tensordict
- 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)