Add pyndantic config

GANDLF/config_manager.py

@@ -11,6 +11,7 @@
 
 from GANDLF.metrics import surface_distance_ids
 from importlib.metadata import version
+from GANDLF.utils.pydantic_config import Parameters
 
 ## dictionary to define defaults for appropriate options, which are evaluated
 parameter_defaults = {
@@ -653,6 +654,7 @@ def _parseConfig(
     if "opt" in params:
         print("DeprecationWarning: 'opt' has been superseded by 'optimizer'")
         params["optimizer"] = params["opt"]
+        params.pop("opt")
 
     # initialize defaults for patch sampler
     temp_patch_sampler_dict = {
@@ -747,7 +749,10 @@ def ConfigManager(
         dict: The parameter dictionary.
     """
     try:
-        return _parseConfig(config_file_path, version_check_flag)
+        parameters = Parameters(
+            **_parseConfig(config_file_path, version_check_flag)
+        ).model_dump()
+        return parameters
     except Exception as e:
         ## todo: ensure logging captures assertion errors
         assert (

GANDLF/models/imagenet_unet.py

@@ -253,7 +253,7 @@ def __init__(self, parameters) -> None:
         )
 
         # all BatchNorm should be replaced with InstanceNorm for DP experiments
-        if "differential_privacy" in parameters:
+        if parameters["differential_privacy"] is not None:
             self.replace_batchnorm(self.model)
 
         if self.n_dimensions == 3:

GANDLF/utils/data_splitter.py

@@ -22,12 +22,12 @@ def split_data(
         "nested_training" in parameters
     ), "`nested_training` key missing in parameters"
     # populate the headers
-    if "headers" not in parameters:
+    if parameters["headers"] is None:
         _, parameters["headers"] = parseTrainingCSV(full_dataset)
 
     parameters = (
         populate_header_in_parameters(parameters, parameters["headers"])
-        if "problem_type" not in parameters
+        if parameters["problem_type"] is None
         else parameters
     )
 

GANDLF/utils/pydantic_config.py

@@ -0,0 +1,78 @@
+from pydantic import BaseModel, ConfigDict, Field
+from typing import Dict, Any, List, Optional
+from enum import Enum
+from GANDLF.models.modelBase import ModelBase
+from typing import Union
+
+
+class Version(BaseModel):
+    minimum: str
+    maximum: str
+
+
+class Model(BaseModel):
+    dimension: int
+    base_filters: int
+    architecture: str
+    norm_type: str
+    final_layer: str
+    class_list: list[Union[int, str]]
+    ignore_label_validation: Union[int, None]
+    amp: bool
+    print_summary: bool
+    type: str
+    data_type: str
+    save_at_every_epoch: bool
+    num_channels: Optional[int] = None
+
+
+class Parameters(BaseModel):
+    model_config = ConfigDict(extra="forbid")
+    version: Version
+    model: Model
+    modality: str
+    scheduler: dict
+    learning_rate: float
+    weighted_loss: bool
+    verbose: bool
+    q_verbose: bool
+    medcam_enabled: bool
+    save_training: bool
+    save_output: bool
+    in_memory: bool
+    pin_memory_dataloader: bool
+    scaling_factor: Union[float, int]
+    q_max_length: int
+    q_samples_per_volume: int
+    q_num_workers: int
+    num_epochs: int
+    patience: int
+    batch_size: int
+    learning_rate: float
+    clip_grad: Union[None, float]
+    track_memory_usage: bool
+    memory_save_mode: bool
+    print_rgb_label_warning: bool
+    data_postprocessing: Dict  # TODO: maybe is better to create a class
+    data_preprocessing: Dict  # TODO: maybe is better to create a class
+    grid_aggregator_overlap: str
+    determinism: bool
+    previous_parameters: None
+    metrics: Union[List, dict]
+    patience: int
+    parallel_compute_command: Union[str, bool, None]
+    loss_function: Union[str, Dict]
+    data_augmentation: dict  # TODO: maybe is better to create a class
+    nested_training: dict  # TODO: maybe is better to create a class
+    optimizer: Union[dict, str]
+    patch_sampler: Union[dict, str]
+    patch_size: Union[List[int], int]
+    clip_mode: Union[str, None]
+    inference_mechanism: dict
+    data_postprocessing_after_reverse_one_hot_encoding: dict
+    enable_padding: Optional[Union[dict, bool]] = None
+    headers: Optional[dict] = None
+    output_dir: Optional[str] = ""
+    problem_type: Optional[str] = None
+    differential_privacy: Optional[dict] = None
+    # opt: Optional[Union[dict, str]] = {}  # TODO find a better way

samples/test_config_file.yaml

@@ -0,0 +1,10 @@
+version:
+  {
+    minimum: 0.1.2-dev,
+    maximum: 0.1.2-dev # this should NOT be made a variable, but should be tested after every tag is created
+  }
+model:
+    {
+      dimension: 3, # the dimension of the model and dataset: defines dimensionality of computations
+      base_filters: "adsa"
+    }

testing/test_full.py

@@ -20,6 +20,7 @@
     get_patch_size_in_microns,
     convert_to_tiff,
 )
+from GANDLF.utils.pydantic_config import Parameters
 from GANDLF.config_manager import ConfigManager
 from GANDLF.parseConfig import parseConfig
 from GANDLF.training_manager import TrainingManager