def create_inference_pipeline(model_config: SegmentationModelBase,
full_path_to_checkpoints: List[Path],
use_gpu: bool = True) \
-> Tuple[FullImageInferencePipelineBase, SegmentationModelBase]:
"""
Create pipeline for inference, this can be a single model inference pipeline or an ensemble, if multiple
checkpoints provided.
:param model_config: Model config to use to create the pipeline.
:param full_path_to_checkpoints: Checkpoints to use for model inference.
:param use_gpu: If GPU should be used or not.
"""
model_config.use_gpu = use_gpu
logging.info('test_config: ' + model_config.model_name)
inference_pipeline: Optional[FullImageInferencePipelineBase]
if len(full_path_to_checkpoints) == 1:
inference_pipeline = InferencePipeline.create_from_checkpoint(
path_to_checkpoint=full_path_to_checkpoints[0],
model_config=model_config)
else:
inference_pipeline = EnsemblePipeline.create_from_checkpoints(
path_to_checkpoints=full_path_to_checkpoints,
model_config=model_config)
if inference_pipeline is None:
raise ValueError("Cannot create inference pipeline")
return inference_pipeline, model_config
def create_pipeline_from_checkpoint_paths(
config: ModelConfigBase,
checkpoint_paths: List[Path]) -> Optional[InferencePipelineBase]:
"""
Attempt to create a pipeline from the provided checkpoint paths. If the files referred to by the paths
do not exist, or if there are no paths, None will be returned.
"""
if len(checkpoint_paths) > 1:
if config.is_segmentation_model:
assert isinstance(config, SegmentationModelBase)
return EnsemblePipeline.create_from_checkpoints(
path_to_checkpoints=checkpoint_paths, model_config=config)
elif config.is_scalar_model:
assert isinstance(config, ScalarModelBase)
return ScalarEnsemblePipeline.create_from_checkpoint(
paths_to_checkpoint=checkpoint_paths, config=config)
else:
raise NotImplementedError(
"Cannot create inference pipeline for unknown model type")
if len(checkpoint_paths) == 1:
if config.is_segmentation_model:
assert isinstance(config, SegmentationModelBase)
return InferencePipeline.create_from_checkpoint(
path_to_checkpoint=checkpoint_paths[0], model_config=config)
elif config.is_scalar_model:
assert isinstance(config, ScalarModelBase)
return ScalarInferencePipeline.create_from_checkpoint(
path_to_checkpoint=checkpoint_paths[0], config=config)
else:
raise NotImplementedError(
"Cannot create ensemble pipeline for unknown model type")
return None
def create_inference_pipeline(config: ModelConfigBase,
checkpoint_paths: List[Path]) -> Optional[InferencePipelineBase]:
"""
If multiple checkpoints are found in run_recovery then create EnsemblePipeline otherwise InferencePipeline.
If no checkpoint files exist in the run recovery or current run checkpoint folder, None will be returned.
:param config: Model related configs.
:param epoch: The epoch for which to create pipeline for.
:param run_recovery: RunRecovery data if applicable
:return: FullImageInferencePipelineBase or ScalarInferencePipelineBase
"""
if not checkpoint_paths:
return None
if len(checkpoint_paths) > 1:
if config.is_segmentation_model:
assert isinstance(config, SegmentationModelBase)
return EnsemblePipeline.create_from_checkpoints(path_to_checkpoints=checkpoint_paths, model_config=config)
elif config.is_scalar_model:
assert isinstance(config, ScalarModelBase)
return ScalarEnsemblePipeline.create_from_checkpoint(paths_to_checkpoint=checkpoint_paths, config=config)
else:
raise NotImplementedError("Cannot create inference pipeline for unknown model type")
if len(checkpoint_paths) == 1:
if config.is_segmentation_model:
assert isinstance(config, SegmentationModelBase)
return InferencePipeline.create_from_checkpoint(path_to_checkpoint=checkpoint_paths[0],
model_config=config)
elif config.is_scalar_model:
assert isinstance(config, ScalarModelBase)
return ScalarInferencePipeline.create_from_checkpoint(path_to_checkpoint=checkpoint_paths[0],
config=config)
else:
raise NotImplementedError("Cannot create ensemble pipeline for unknown model type")
return None