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# 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()
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