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5821 Add interface for bundle workflows (#5822)
Fixes #5821 . ### Description The main idea of this PR is to solve 3 pain points of the current bundle training or inference workflow: 1. Training or inference is defined in a "rigid" `training` or `evaluation` list, so 3rd party package can't flexibly initialize and run the workflows separately, see: Project-MONAI/model-zoo#294. This PR splits it into `initialize`, `run` and `finalize`, and still compatible with current existing bundles. 2. 3rd party packages need to access the bundle config directly with specifed keys based on `ConfigParser`, but some developers of bundles don't want to write JSON or YAML configs. This PR implemented the interface to support both config-based and python-based workflows. 3. MONAI Label, MONAI deploy, MONAI-FL require many fixed properties of a training or inference workflow, but we don't have definition for them in the code, just described in the developer guide doc of model-zoo. This PR implemented the `TrainProperties` and `inferenceProperties` interfaces and check tool. ### Types of changes <!--- Put an `x` in all the boxes that apply, and remove the not applicable items --> - [x] Non-breaking change (fix or new feature that would not break existing functionality). - [ ] Breaking change (fix or new feature that would cause existing functionality to change). - [ ] New tests added to cover the changes. - [ ] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [ ] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. - [ ] In-line docstrings updated. - [ ] Documentation updated, tested `make html` command in the `docs/` folder. --------- Signed-off-by: Nic Ma <nma@nvidia.com> Signed-off-by: root <root@apt-sh-ai.nvidia.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: root <root@apt-sh-ai.nvidia.com>
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‎monai/bundle/__init__.py‎

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from .config_item import ComponentLocator, ConfigComponent, ConfigExpression, ConfigItem, Instantiable
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from .config_parser import ConfigParser
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from .properties import InferProperties, TrainProperties
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from .reference_resolver import ReferenceResolver
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from .scripts import (
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ckpt_export,
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get_bundle_versions,
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init_bundle,
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load,
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patch_bundle_tracking,
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run,
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verify_metadata,
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verify_net_in_out,
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MACRO_KEY,
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load_bundle_config,
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)
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from .workflows import BundleWorkflow, ConfigWorkflow

‎monai/bundle/properties.py‎

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# Copyright (c) MONAI Consortium
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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# http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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The predefined properties for a bundle workflow, other applications can leverage the properties
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to interact with the bundle workflow.
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Some properties are required and some are optional, optional properties mean: if some component of the
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bundle workflow refer to the property, the property must be defined, otherwise, the property can be None.
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Every item in this `TrainProperties` or `InferProperties` dictionary is a property,
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the key is the property name and the values include:
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1. description.
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2. whether it's a required property.
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3. config item ID name (only applicable when the bundle workflow is defined in config).
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4. reference config item ID name (only applicable when the bundle workflow is defined in config).
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"""
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from __future__ import annotations
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from monai.bundle.utils import ID_SEP_KEY
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from monai.utils import BundleProperty, BundlePropertyConfig
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TrainProperties = {
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"bundle_root": {
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BundleProperty.DESC: "root path of the bundle.",
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BundleProperty.REQUIRED: True,
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BundlePropertyConfig.ID: "bundle_root",
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},
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"device": {
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BundleProperty.DESC: "target device to execute the bundle workflow.",
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BundleProperty.REQUIRED: True,
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BundlePropertyConfig.ID: "device",
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},
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"dataset_dir": {
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BundleProperty.DESC: "directory path of the dataset.",
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BundleProperty.REQUIRED: True,
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BundlePropertyConfig.ID: "dataset_dir",
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},
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"trainer": {
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BundleProperty.DESC: "training workflow engine.",
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BundleProperty.REQUIRED: True,
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BundlePropertyConfig.ID: f"train{ID_SEP_KEY}trainer",
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},
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"max_epochs": {
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BundleProperty.DESC: "max number of epochs to execute the training.",
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BundleProperty.REQUIRED: True,
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BundlePropertyConfig.ID: f"train{ID_SEP_KEY}trainer{ID_SEP_KEY}max_epochs",
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},
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"train_dataset": {
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BundleProperty.DESC: "PyTorch dataset object for the training logic.",
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BundleProperty.REQUIRED: True,
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BundlePropertyConfig.ID: f"train{ID_SEP_KEY}dataset",
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},
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"train_dataset_data": {
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BundleProperty.DESC: "data source for the training dataset.",
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BundleProperty.REQUIRED: True,
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BundlePropertyConfig.ID: f"train{ID_SEP_KEY}dataset{ID_SEP_KEY}data",
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},
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"train_inferer": {
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BundleProperty.DESC: "MONAI Inferer object to execute the model computation in training.",
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BundleProperty.REQUIRED: True,
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BundlePropertyConfig.ID: f"train{ID_SEP_KEY}inferer",
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},
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"train_handlers": {
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BundleProperty.DESC: "event-handlers for the training logic.",
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BundleProperty.REQUIRED: False,
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BundlePropertyConfig.ID: f"train{ID_SEP_KEY}handlers",
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BundlePropertyConfig.REF_ID: f"train{ID_SEP_KEY}trainer{ID_SEP_KEY}train_handlers",
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},
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"train_preprocessing": {
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BundleProperty.DESC: "preprocessing for the training input data.",
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BundleProperty.REQUIRED: False,
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BundlePropertyConfig.ID: f"train{ID_SEP_KEY}preprocessing",
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BundlePropertyConfig.REF_ID: f"train{ID_SEP_KEY}dataset{ID_SEP_KEY}transform",
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},
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"train_postprocessing": {
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BundleProperty.DESC: "postprocessing for the training model output data.",
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BundleProperty.REQUIRED: False,
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BundlePropertyConfig.ID: f"train{ID_SEP_KEY}postprocessing",
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BundlePropertyConfig.REF_ID: f"train{ID_SEP_KEY}trainer{ID_SEP_KEY}postprocessing",
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},
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"train_key_metric": {
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BundleProperty.DESC: "key metric to compute on the training data.",
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BundleProperty.REQUIRED: False,
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BundlePropertyConfig.ID: f"train{ID_SEP_KEY}key_metric",
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BundlePropertyConfig.REF_ID: f"train{ID_SEP_KEY}trainer{ID_SEP_KEY}key_train_metric",
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},
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"evaluator": {
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BundleProperty.DESC: "validation workflow engine.",
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BundleProperty.REQUIRED: False,
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BundlePropertyConfig.ID: f"validate{ID_SEP_KEY}evaluator",
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BundlePropertyConfig.REF_ID: "validator", # this REF_ID is the arg name of `ValidationHandler`
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},
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"val_interval": {
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BundleProperty.DESC: "validation interval during the training.",
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BundleProperty.REQUIRED: False,
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BundlePropertyConfig.ID: "val_interval",
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BundlePropertyConfig.REF_ID: "interval", # this REF_ID is the arg name of `ValidationHandler`
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},
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"val_handlers": {
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BundleProperty.DESC: "event-handlers for the validation logic.",
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BundleProperty.REQUIRED: False,
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BundlePropertyConfig.ID: f"validate{ID_SEP_KEY}handlers",
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BundlePropertyConfig.REF_ID: f"validate{ID_SEP_KEY}evaluator{ID_SEP_KEY}val_handlers",
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},
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"val_dataset": {
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BundleProperty.DESC: "PyTorch dataset object for the validation logic.",
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BundleProperty.REQUIRED: False,
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BundlePropertyConfig.ID: f"validate{ID_SEP_KEY}dataset",
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BundlePropertyConfig.REF_ID: f"validate{ID_SEP_KEY}dataloader{ID_SEP_KEY}dataset",
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},
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"val_dataset_data": {
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BundleProperty.DESC: "data source for the validation dataset.",
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BundleProperty.REQUIRED: False,
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BundlePropertyConfig.ID: f"validate{ID_SEP_KEY}dataset{ID_SEP_KEY}data",
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BundlePropertyConfig.REF_ID: None, # no reference to this ID
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},
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"val_inferer": {
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BundleProperty.DESC: "MONAI Inferer object to execute the model computation in validation.",
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BundleProperty.REQUIRED: False,
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BundlePropertyConfig.ID: f"validate{ID_SEP_KEY}inferer",
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BundlePropertyConfig.REF_ID: f"validate{ID_SEP_KEY}evaluator{ID_SEP_KEY}inferer",
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},
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"val_preprocessing": {
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BundleProperty.DESC: "preprocessing for the validation input data.",
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BundleProperty.REQUIRED: False,
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BundlePropertyConfig.ID: f"validate{ID_SEP_KEY}preprocessing",
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BundlePropertyConfig.REF_ID: f"validate{ID_SEP_KEY}dataset{ID_SEP_KEY}transform",
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},
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"val_postprocessing": {
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BundleProperty.DESC: "postprocessing for the validation model output data.",
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BundleProperty.REQUIRED: False,
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BundlePropertyConfig.ID: f"validate{ID_SEP_KEY}postprocessing",
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BundlePropertyConfig.REF_ID: f"validate{ID_SEP_KEY}evaluator{ID_SEP_KEY}postprocessing",
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},
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"val_key_metric": {
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BundleProperty.DESC: "key metric to compute on the validation data.",
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BundleProperty.REQUIRED: False,
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BundlePropertyConfig.ID: f"validate{ID_SEP_KEY}key_metric",
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BundlePropertyConfig.REF_ID: f"validate{ID_SEP_KEY}evaluator{ID_SEP_KEY}key_val_metric",
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},
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}
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InferProperties = {
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"bundle_root": {
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BundleProperty.DESC: "root path of the bundle.",
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BundleProperty.REQUIRED: True,
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BundlePropertyConfig.ID: "bundle_root",
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},
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"device": {
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BundleProperty.DESC: "target device to execute the bundle workflow.",
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BundleProperty.REQUIRED: True,
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BundlePropertyConfig.ID: "device",
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},
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"network_def": {
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BundleProperty.DESC: "network module for the inference.",
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BundleProperty.REQUIRED: True,
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BundlePropertyConfig.ID: "network_def",
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},
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"inferer": {
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BundleProperty.DESC: "MONAI Inferer object to execute the model computation in inference.",
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BundleProperty.REQUIRED: True,
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BundlePropertyConfig.ID: "inferer",
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},
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"preprocessing": {
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BundleProperty.DESC: "preprocessing for the input data.",
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BundleProperty.REQUIRED: False,
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BundlePropertyConfig.ID: "preprocessing",
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BundlePropertyConfig.REF_ID: f"dataset{ID_SEP_KEY}transform",
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},
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"postprocessing": {
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BundleProperty.DESC: "postprocessing for the model output data.",
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BundleProperty.REQUIRED: False,
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BundlePropertyConfig.ID: "postprocessing",
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BundlePropertyConfig.REF_ID: f"evaluator{ID_SEP_KEY}postprocessing",
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},
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"key_metric": {
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BundleProperty.DESC: "the key metric during evaluation.",
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BundleProperty.REQUIRED: False,
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BundlePropertyConfig.ID: "key_metric",
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BundlePropertyConfig.REF_ID: f"evaluator{ID_SEP_KEY}key_val_metric",
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},
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}

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