biapy.engine.classification

Classification workflow for BiaPy.

This module defines the Classification_Workflow class, which implements the training, validation, and inference pipeline for image classification tasks in BiaPy. It handles data loading, model setup, metrics, predictions, and result saving for single-label classification problems.

class biapy.engine.classification.Classification_Workflow(cfg, job_identifier, device, system_dict, args, **kwargs)[source]

Bases: Base_Workflow

Classification workflow where the goal of this workflow is to assing a label to the input image.

More details in our documentation.

Parameters:

cfg (YACS configuration) – Running configuration.
Job_identifier (str) – Complete name of the running job.
device (Torch device) – Device used.
args (argpase class) – Arguments used in BiaPy’s call.

define_activations_and_channels()[source]

Define the activations to be applied to the model output and the channels that the model will output.

This function must define the following variables:

self.model_output_channelsList of int: Number of channels for each output head of the model. E.g. [3] for a model with one head outputting 3 channels, [1, 5] for a model with two heads outputting 1 and 5 channels respectively, etc.
self.model_output_channel_infoList of str: Information about the output channels. A value per output head of the model must be defined.
self.separated_class_channelbool: Whether if we should expect a separated output channel for classification.
self.head_activationsList of str: Activations to be applied to the model output. A value per output channel (not output head) of the model must be defined. “linear” and “ce_sigmoid” will not be applied. E.g. [“linear”] for a model with one channel, [“linear”, “sigmoid”] for a model with two channels, etc.

Example of a correct definition of the function for a model with two output heads: 1) the first one will be predicting foreground and contours; 2) the second one will classify into 3 classes the predicted objects. In this case the following definition would be correct:

self.model_output_channels = [1, 3]
self.model_output_channel_info = ["mask", "class"]
self.separated_class_channel = True
self.head_activations = ["ce_sigmoid", "ce_sigmoid", "ce_softmax", "ce_softmax", "ce_softmax"]

define_metrics()[source]

Define the metrics to be used during training and test/inference.

This function must define the following variables:

self.train_metricsList of functions: Metrics to be calculated during model’s training.
self.train_metric_namesList of str: Names of the metrics calculated during training.
self.train_metric_bestList of str: To know which value should be considered as the best one. Options must be: “max” or “min”.
self.test_metricsList of functions: Metrics to be calculated during model’s test/inference.
self.test_metric_namesList of str: Names of the metrics calculated during test/inference.
self.lossFunction: Loss function used during training and test.

metric_calculation(output: ndarray[tuple[int, ...], dtype[_ScalarType_co]] | Tensor, targets: ndarray[tuple[int, ...], dtype[_ScalarType_co]] | Tensor, train: bool = True, metric_logger: MetricLogger | None = None) → Dict[source]

Execute the calculation of metrics defined in define_metrics() function.

Parameters:

output (Torch Tensor/List of ints) – Prediction of the model.
targets (Torch Tensor/List of ints) – Ground truth to compare the prediction with.
train (bool, optional) – Whether to calculate train or test metrics.
metric_logger (MetricLogger, optional) – Class to be updated with the new metric(s) value(s) calculated.

Returns:

out_metrics – Value of the metrics for the given prediction.

Return type:

dict

prepare_targets(targets, batch)[source]

Perform any necessary data transformations to targets before calculating the loss.

Parameters:

targets (Torch Tensor) – Ground truth to compare the prediction with.
batch (Torch Tensor) – Prediction of the model. Not used here.

Returns:

targets – Resulting targets.

Return type:

Torch tensor

load_train_data()[source]: Load training and validation data.

load_test_data()[source]: Load test data.

process_test_sample()[source]: Process a sample in the inference phase.

torchvision_model_call(in_img: Tensor, is_train: bool = False) → Tensor | None[source]

Call a regular Pytorch model.

Parameters:

in_img (torch.Tensors) – Input image to pass through the model.
is_train (bool, optional) – Whether if the call is during training or inference.

Returns:

prediction – Image prediction.

Return type:

torch.Tensor

after_all_images()[source]: Execute steps that are needed after predicting all images.

print_stats(image_counter)[source]

Print statistics.

Parameters:: image_counter (int) – Number of images to call normalize_stats.

after_merge_patches(pred)[source]

Execute steps that are needed after merging all predicted patches into the original image.

Parameters:: pred (Torch Tensor) – Model prediction.

after_full_image(pred: ndarray[tuple[int, ...], dtype[_ScalarType_co]])[source]

Execute steps that are needed after generating the prediction by supplying the entire image to the model.

Parameters:: pred (NDArray) – Model prediction.

after_all_chunk_prediction_workflow_process()[source]: Place any code that needs to be done after predicting all patches in “by chunks” setting. This function is called on all ranks.

after_all_chunk_prediction_workflow_process_master_rank()[source]: Place any code that needs to be done after predicting all patches in “by chunks” setting, but only on the master rank. This function is called only on the master rank.