# This implements the workflow for applying the network to a directory of images and measuring network performance with metrics.

# these transforms are used for inference to load and regularise inputs
transforms:
- _target_: AsDiscreted
  keys: ['@pred', '@label']
  argmax: [true, false]
  to_onehot: '@num_classes'
- _target_: ToTensord
  keys: ['@pred', '@label']
  device: '@device'

postprocessing:
  _target_: Compose
  transforms: $@transforms

# inference handlers to load checkpoint, gather statistics
val_handlers:
- _target_: CheckpointLoader
  _disabled_: $not os.path.exists(@ckpt_path)
  load_path: '@ckpt_path'
  load_dict:
    model: '@network'
- _target_: StatsHandler
  name: null  # use engine.logger as the Logger object to log to
  output_transform: '$lambda x: None'
- _target_: MetricsSaver
  save_dir: '@output_dir'
  metrics: ['val_accuracy']
  metric_details: ['val_accuracy']
  batch_transform: "$lambda x: [xx['image'].meta for xx in x]"
  summary_ops: "*"

initialize:
- "$monai.utils.set_determinism(seed=123)"
- "$setattr(torch.backends.cudnn, 'benchmark', True)"
run:
- $@evaluator.run()