tfm.vision.MaskRCNNTask

A single-replica view of training procedure.

Inherits From: Task

tfm.vision.MaskRCNNTask(
    params, logging_dir: Optional[str] = None, name: Optional[str] = None
)

Mask R-CNN task provides artifacts for training/evalution procedures, including loading/iterating over Datasets, initializing the model, calculating the loss, post-processing, and customized metrics with reduction.

params the task configuration instance, which can be any of dataclass, ConfigDict, namedtuple, etc.
logging_dir a string pointing to where the model, summaries etc. will be saved. You can also write additional stuff in this directory.
name the task name.

logging_dir

task_config

Methods

aggregate_logs

View source

aggregate_logs(
    state: Optional[Any] = None, step_outputs: Optional[Dict[str, Any]] = None
) -> Optional[Any]

Optional aggregation over logs returned from a validation step.

build_inputs

View source

build_inputs(
    params: tfm.vision.configs.maskrcnn.DataConfig,
    input_context: Optional[tf.distribute.InputContext] = None,
    dataset_fn: Optional[dataset_fn_lib.PossibleDatasetType] = None
) -> tf.data.Dataset

Builds input dataset.

build_losses

View source

build_losses(
    outputs: Mapping[str, Any],
    labels: Mapping[str, Any],
    aux_losses: Optional[Any] = None
) -> Dict[str, tf.Tensor]

Builds Mask R-CNN losses.

build_metrics

View source

build_metrics(
    training: bool = True
)

Builds detection metrics.

build_model

View source

build_model()

Builds Mask R-CNN model.

create_optimizer

View source

@classmethod
create_optimizer(
    optimizer_config: tfm.optimization.OptimizationConfig,
    runtime_config: Optional[tfm.core.base_task.RuntimeConfig] = None,
    dp_config: Optional[tfm.core.base_task.DifferentialPrivacyConfig] = None
)

Creates an TF optimizer from configurations.

Args
optimizer_config the parameters of the Optimization settings.
runtime_config the parameters of the runtime.
dp_config the parameter of differential privacy.

Returns
A tf.optimizers.Optimizer object.

inference_step

View source

inference_step(
    inputs, model: tf.keras.Model
)

Performs the forward step.

With distribution strategies, this method runs on devices.

Args
inputs a dictionary of input tensors.
model the keras.Model.

Returns
Model outputs.

initialize

View source

initialize(
    model: tf.keras.Model
)

Loads pretrained checkpoint.

process_compiled_metrics

View source

process_compiled_metrics(
    compiled_metrics, labels, model_outputs
)

Process and update compiled_metrics.

call when using compile/fit API.

Args
compiled_metrics the compiled metrics (model.compiled_metrics).
labels a tensor or a nested structure of tensors.
model_outputs a tensor or a nested structure of tensors. For example, output of the keras model built by self.build_model.

process_metrics

View source

process_metrics(
    metrics, labels, model_outputs, **kwargs
)

Process and update metrics.

Called when using custom training loop API.

Args
metrics a nested structure of metrics objects. The return of function self.build_metrics.
labels a tensor or a nested structure of tensors.
model_outputs a tensor or a nested structure of tensors. For example, output of the keras model built by self.build_model.
**kwargs other args.

reduce_aggregated_logs

View source

reduce_aggregated_logs(
    aggregated_logs: Dict[str, Any], global_step: Optional[tf.Tensor] = None
) -> Dict[str, tf.Tensor]

Optional reduce of aggregated logs over validation steps.

train_step

View source

train_step(
    inputs: Tuple[Any, Any],
    model: tf.keras.Model,
    optimizer: tf.keras.optimizers.Optimizer,
    metrics: Optional[List[Any]] = None
)

Does forward and backward.

Args
inputs a dictionary of input tensors.
model the model, forward pass definition.
optimizer the optimizer for this training step.
metrics a nested structure of metrics objects.

Returns
A dictionary of logs.

validation_step

View source

validation_step(
    inputs: Tuple[Any, Any],
    model: tf.keras.Model,
    metrics: Optional[List[Any]] = None
)

Validatation step.

Args
inputs a dictionary of input tensors.
model the keras.Model.
metrics a nested structure of metrics objects.

Returns
A dictionary of logs.

loss 'loss'