tasks.DivaHisDB package

Submodules

tasks.DivaHisDB.semantic_segmentation_cropped module

class SemanticSegmentationCroppedHisDB(model: Module, optimizer: Optimizer, loss_fn: Optional[Callable] = None, metric_train: Optional[Metric] = None, metric_val: Optional[Metric] = None, metric_test: Optional[Metric] = None, test_output_path: Optional[Union[str, Path]] = 'test_output', predict_output_path: Optional[Union[str, Path]] = 'predict_output', confusion_matrix_val: Optional[bool] = False, confusion_matrix_test: Optional[bool] = False, confusion_matrix_log_every_n_epoch: Optional[int] = 1, lr: float = 0.001)

Bases: AbstractTask

Semantic Segmentation task for cropped images of the HisDB dataset. The output for the test are also patches that can be stitched together with the :class: CroppedOutputMerger and are in the HisDB format as well as raw prediction of the network in numpy format.

Parameters:

• model (nn.Module) – The model to train, validate and test.

• optimizer (torch.optim.Optimizer) – The optimizer used during training.

• loss_fn (Callable) – The loss function used during training, validation, and testing.

• metric_train (torchmetrics.Metric) – The metric used during training.

• metric_val (torchmetrics.Metric) – The metric used during validation.

• metric_test (torchmetrics.Metric) – The metric used during testing.

• confusion_matrix_val (bool) – Whether to compute the confusion matrix during validation.

• confusion_matrix_test (bool) – Whether to compute the confusion matrix during testing.

• confusion_matrix_log_every_n_epoch (int) – The frequency of logging the confusion matrix.

• lr (float) – The learning rate.

on_test_end() → None

Called at the end of testing.

setup(stage: str) → None

Called at the beginning of fit (train + validate), validate, test, or predict. This is a good hook when you need to build models dynamically or adjust something about them. This hook is called on every process when using DDP.

Parameters:

stage – either 'fit', 'validate', 'test', or 'predict'

Example:

class LitModel(...):
    def __init__(self):
        self.l1 = None

    def prepare_data(self):
        download_data()
        tokenize()

        # don't do this
        self.something = else

    def setup(self, stage):
        data = load_data(...)
        self.l1 = nn.Linear(28, data.num_classes)

test_step(batch, batch_idx, **kwargs)

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.

# the pseudocode for these calls
test_outs = []
for test_batch in test_data:
    out = test_step(test_batch)
    test_outs.append(out)
test_epoch_end(test_outs)

Parameters:

• batch – The output of your DataLoader.

• batch_idx – The index of this batch.

• dataloader_id – The index of the dataloader that produced this batch. (only if multiple test dataloaders used).

Returns:

Any of.

• Any object or value

• None - Testing will 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.

static to_metrics_format(x: Tensor, **kwargs) → Tensor

Convert the output of the model to the format needed for the metrics.

Parameters:

• x (torch.Tensor) – the output of the model

• kwargs (Any) – additional arguments

Returns:

the output in the format needed for the metrics

Return type:

torch.Tensor

training: bool

training_step(batch, batch_idx, **kwargs)

The training step. Calls the step method and logs the metrics and loss.

Parameters:

• batch (Any) – The current batch to train on.

• batch_idx (int) – The index of the current batch.

• kwargs (Any) – Additional arguments.

Returns:

The output of the step method.

Return type:

Any

validation_step(batch, batch_idx, **kwargs)

the validation step. Calls the step method and logs the metrics and loss.

Parameters:

• batch (Any) – The current batch to validate on.

• batch_idx (int) – The index of the current batch.

• kwargs (Any) – Additional arguments.

Returns:

The output of the step method.

Return type:

Any

Module contents