mmedit.engine.runner.gen_loops

Module Contents

Classes

GenValLoop	Validation loop for generative models. This class support evaluate
GenTestLoop	Validation loop for generative models. This class support evaluate

class mmedit.engine.runner.gen_loops.GenValLoop(runner: mmengine.runner.Runner, dataloader: Union[torch.utils.data.DataLoader, Dict], evaluator: Union[mmengine.evaluator.Evaluator, Dict, List])

Bases: mmengine.runner.ValLoop

Validation loop for generative models. This class support evaluate metrics with different sample mode.

参数

• runner (Runner) – A reference of runner.

• dataloader (Dataloader or dict) – A dataloader object or a dict to build a dataloader.

• evaluator (Evaluator or dict or list) – Used for computing metrics.

run()

Launch validation. The evaluation process consists of four steps.

1. Prepare pre-calculated items for all metrics by calling self.evaluator.prepare_metrics().

2. Get a list of metrics-sampler pair. Each pair contains a list of metrics with the same sampler mode and a shared sampler.

3. Generate images for the each metrics group. Loop for elements in each sampler and feed to the model as input by calling self.run_iter().

4. Evaluate all metrics by calling self.evaluator.evaluate().

run_iter(idx, data_batch: dict, metrics: Sequence[mmengine.evaluator.BaseMetric])

Iterate one mini-batch and feed the output to corresponding metrics.

参数

• idx (int) – Current idx for the input data.

• data_batch (dict) – Batch of data from dataloader.

• metrics (Sequence[BaseMetric]) – Specific metrics to evaluate.

class mmedit.engine.runner.gen_loops.GenTestLoop(runner: mmengine.runner.Runner, dataloader: Union[torch.utils.data.DataLoader, Dict], evaluator: Union[mmengine.evaluator.Evaluator, Dict, List])

Bases: mmengine.runner.TestLoop

Validation loop for generative models. This class support evaluate metrics with different sample mode.

参数

• runner (Runner) – A reference of runner.

• dataloader (Dataloader or dict) – A dataloader object or a dict to build a dataloader.

• evaluator (Evaluator or dict or list) – Used for computing metrics.

run()

Launch validation. The evaluation process consists of four steps.

1. Prepare pre-calculated items for all metrics by calling self.evaluator.prepare_metrics().

2. Get a list of metrics-sampler pair. Each pair contains a list of metrics with the same sampler mode and a shared sampler.

3. Generate images for the each metrics group. Loop for elements in each sampler and feed to the model as input by calling self.run_iter().

4. Evaluate all metrics by calling self.evaluator.evaluate().

run_iter(idx, data_batch: dict, metrics: Sequence[mmengine.evaluator.BaseMetric])

Iterate one mini-batch and feed the output to corresponding metrics.

参数

• idx (int) – Current idx for the input data.

• data_batch (dict) – Batch of data from dataloader.

• metrics (Sequence[BaseMetric]) – Specific metrics to evaluate.