API ideas (#681)

clinicadl/API/complicated_case.py

@@ -0,0 +1,144 @@
+from pathlib import Path
+
+import torchio.transforms as transforms
+
+from clinicadl.dataset.caps_dataset import (
+    CapsDatasetPatch,
+    CapsDatasetRoi,
+    CapsDatasetSlice,
+)
+from clinicadl.dataset.caps_reader import CapsReader
+from clinicadl.dataset.concat import ConcatDataset
+from clinicadl.dataset.config.extraction import ExtractionConfig
+from clinicadl.dataset.config.preprocessing import (
+    PreprocessingConfig,
+    T1PreprocessingConfig,
+)
+from clinicadl.dataset.dataloader_config import DataLoaderConfig
+from clinicadl.experiment_manager.experiment_manager import ExperimentManager
+from clinicadl.losses.config import CrossEntropyLossConfig
+from clinicadl.losses.factory import get_loss_function
+from clinicadl.model.clinicadl_model import ClinicaDLModel
+from clinicadl.networks.config import ImplementedNetworks
+from clinicadl.networks.factory import (
+    ConvEncoderOptions,
+    create_network_config,
+    get_network_from_config,
+)
+from clinicadl.optimization.optimizer.config import AdamConfig, OptimizerConfig
+from clinicadl.optimization.optimizer.factory import get_optimizer
+from clinicadl.predictor.predictor import Predictor
+from clinicadl.splitter.kfold import KFolder
+from clinicadl.splitter.split import get_single_split, split_tsv
+from clinicadl.trainer.trainer import Trainer
+from clinicadl.transforms.transforms import Transforms
+
+# Create the Maps Manager / Read/write manager /
+maps_path = Path("/")
+manager = ExperimentManager(
+    maps_path, overwrite=False
+)  # a ajouter dans le manager: mlflow/ profiler/ etc ...
+
+caps_directory = Path("caps_directory")  # output of clinica pipelines
+caps_reader = CapsReader(caps_directory, manager=manager)
+
+preprocessing_1 = caps_reader.get_preprocessing("t1-linear")
+caps_reader.prepare_data(
+    preprocessing=preprocessing_1, data_tsv=Path(""), n_proc=2
+)  # don't return anything -> just extract the image tensor and compute some information for each images
+
+transforms_1 = Transforms(
+    object_augmentation=[transforms.Crop, transforms.Transform],
+    image_augmentation=[transforms.Crop, transforms.Transform],
+    extraction=ExtractionPatchConfig(patch_size=3),
+    image_transforms=[transforms.Blur, transforms.Ghosting],
+    object_transforms=[transforms.BiasField, transforms.Motion],
+)  # not mandatory
+
+preprocessing_2 = caps_reader.get_preprocessing("pet-linear")
+caps_reader.prepare_data(
+    preprocessing=preprocessing_1, data_tsv=Path(""), n_proc=2
+)  # to extract the tensor of the PET file this time
+transforms_2 = Transforms(
+    object_augmentation=[transforms.Crop, transforms.Transform],
+    image_augmentation=[transforms.Crop, transforms.Transform],
+    extraction=ExtractionSliceConfig(),
+    image_transforms=[transforms.Blur, transforms.Ghosting],
+    object_transforms=[transforms.BiasField, transforms.Motion],
+)
+
+sub_ses_tsv_1 = Path("")
+split_dir_1 = split_tsv(sub_ses_tsv_1)  # -> creer un test.tsv et un train.tsv
+
+sub_ses_tsv_2 = Path("")
+split_dir_2 = split_tsv(sub_ses_tsv_2)
+
+dataset_t1_roi = caps_reader.get_dataset(
+    preprocessing=preprocessing_1,
+    sub_ses_tsv=split_dir_1 / "train.tsv",
+    transforms=transforms_1,
+)  # do we give config or object for transforms ?
+dataset_pet_patch = caps_reader.get_dataset(
+    preprocessing=preprocessing_2,
+    sub_ses_tsv=split_dir_2 / "train.tsv",
+    transforms=transforms_2,
+)
+
+dataset_multi_modality_multi_extract = ConcatDataset(
+    [
+        dataset_t1_roi,
+        dataset_pet_patch,
+        caps_reader.get_dataset_from_json(json_path=Path("dataset.json")),
+    ]
+)  # 3 train.tsv en entrée qu'il faut concat et pareil pour les transforms à faire attention
+
+# TODO : think about adding transforms in extract_json
+
+
+config_file = Path("config_file")
+trainer = Trainer.from_json(config_file=config_file, manager=manager)
+
+# CAS CROSS-VALIDATION
+splitter = KFolder(caps_dataset=dataset_multi_modality_multi_extract, manager=manager)
+split_dir = splitter.make_splits(
+    n_splits=3, output_dir=Path(""), subset_name="validation", stratification=""
+)  # Optional data tsv and output_dir
+
+dataloader_config = DataLoaderConfig(n_procs=3, batch_size=10)
+
+for split in splitter.get_splits(splits=(0, 3, 4), dataloader_config=dataloader_config):
+    # bien définir ce qu'il y a dans l'objet split
+
+    network_config = create_network_config(ImplementedNetworks.CNN)(
+        in_shape=[2, 2, 2],
+        num_outputs=1,
+        conv_args=ConvEncoderOptions(channels=[3, 2, 2]),
+    )
+    model = ClinicaDLModelClassif.from_config(
+        network_config=network_config,
+        loss_config=CrossEntropyLossConfig(),
+        optimizer_config=AdamConfig(),
+    )
+
+    trainer.train(model, split)
+    # le trainer va instancier un predictor/valdiator dans le train ou dans le init
+
+# TEST
+
+preprocessing_test = caps_reader.get_preprocessing("pet-linear")
+transforms_test = Transforms(
+    object_augmentation=[transforms.Crop, transforms.Transform],
+    image_augmentation=[transforms.Crop, transforms.Transform],
+    extraction=ExtractioImageConfig(),
+    image_transforms=[transforms.Blur, transforms.Ghosting],
+    object_transforms=[transforms.BiasField, transforms.Motion],
+)
+
+dataset_test = caps_reader.get_dataset(
+    preprocessing=preprocessing_test,
+    sub_ses_tsv=split_dir_1 / "test.tsv",  # test only on data from the first dataset
+    transforms=transforms_test,
+)
+
+predictor = Predictor(model=model, manager=manager)
+predictor.predict(dataset_test=dataset_test, split_number=2)

clinicadl/API/cross_val.py

@@ -0,0 +1,80 @@
+from pathlib import Path
+
+import torchio.transforms as transforms
+
+from clinicadl.dataset.caps_dataset import (
+    CapsDatasetPatch,
+    CapsDatasetRoi,
+    CapsDatasetSlice,
+)
+from clinicadl.dataset.caps_reader import CapsReader
+from clinicadl.dataset.concat import ConcatDataset
+from clinicadl.dataset.config.extraction import ExtractionConfig
+from clinicadl.dataset.config.preprocessing import (
+    PreprocessingConfig,
+    T1PreprocessingConfig,
+)
+from clinicadl.dataset.dataloader_config import DataLoaderConfig
+from clinicadl.experiment_manager.experiment_manager import ExperimentManager
+from clinicadl.losses.config import CrossEntropyLossConfig
+from clinicadl.losses.factory import get_loss_function
+from clinicadl.model.clinicadl_model import ClinicaDLModel
+from clinicadl.networks.config import ImplementedNetworks
+from clinicadl.networks.factory import (
+    ConvEncoderOptions,
+    create_network_config,
+    get_network_from_config,
+)
+from clinicadl.optimization.optimizer.config import AdamConfig, OptimizerConfig
+from clinicadl.optimization.optimizer.factory import get_optimizer
+from clinicadl.predictor.predictor import Predictor
+from clinicadl.splitter.kfold import KFolder
+from clinicadl.splitter.split import get_single_split, split_tsv
+from clinicadl.trainer.trainer import Trainer
+from clinicadl.transforms.config import TransformsConfig
+
+# SIMPLE EXPERIMENT WITH A CAPS ALREADY EXISTING
+
+maps_path = Path("/")
+manager = ExperimentManager(maps_path, overwrite=False)
+
+dataset_t1_image = CapsDatasetPatch.from_json(
+    extraction=Path("test.json"),
+    sub_ses_tsv=Path("split_dir") / "train.tsv",
+)
+config_file = Path("config_file")
+trainer = Trainer.from_json(
+    config_file=config_file, manager=manager
+)  # gpu, amp, fsdp, seed
+
+# CAS CROSS-VALIDATION
+splitter = KFolder(caps_dataset=dataset_t1_image, manager=manager)
+split_dir = splitter.make_splits(
+    n_splits=3,
+    output_dir=Path(""),
+    data_tsv=Path("labels.tsv"),
+    subset_name="validation",
+    stratification="",
+)  # Optional data tsv and output_dir
+# n_splits must be >1
+# for the single split case, this method output a path to the directory containing the train and test tsv files so we should have the same output here
+
+# CAS EXISTING CROSS-VALIDATION
+splitter = KFolder.from_split_dir(caps_dataset=dataset_t1_image, manager=manager)
+
+# define the needed parameters for the dataloader
+dataloader_config = DataLoaderConfig(n_procs=3, batch_size=10)
+
+for split in splitter.get_splits(splits=(0, 3, 4), dataloader_config=dataloader_config):
+    # bien définir ce qu'il y a dans l'objet split
+
+    network_config = create_network_config(ImplementedNetworks.CNN)(
+        in_shape=[2, 2, 2],
+        num_outputs=1,
+        conv_args=ConvEncoderOptions(channels=[3, 2, 2]),
+    )
+    optimizer, _ = get_optimizer(network, AdamConfig())
+    model = ClinicaDLModel(network=network_config, loss=nn.MSE(), optimizer=optimizer)
+
+    trainer.train(model, split)
+    # le trainer va instancier un predictor/valdiator dans le train ou dans le init

clinicadl/API/single_split.py

@@ -0,0 +1,90 @@
+from pathlib import Path
+
+import torchio.transforms as transforms
+
+from clinicadl.dataset.caps_reader import CapsReader
+from clinicadl.dataset.concat import ConcatDataset
+from clinicadl.dataset.config.extraction import ExtractionConfig, ExtractionPatchConfig
+from clinicadl.dataset.config.preprocessing import (
+    PreprocessingConfig,
+    T1PreprocessingConfig,
+)
+from clinicadl.experiment_manager.experiment_manager import ExperimentManager
+from clinicadl.losses.config import CrossEntropyLossConfig
+from clinicadl.losses.factory import get_loss_function
+from clinicadl.model.clinicadl_model import ClinicaDLModel
+from clinicadl.networks.config import ImplementedNetworks
+from clinicadl.networks.factory import (
+    ConvEncoderOptions,
+    create_network_config,
+    get_network_from_config,
+)
+from clinicadl.optimization.optimizer.config import AdamConfig, OptimizerConfig
+from clinicadl.optimization.optimizer.factory import get_optimizer
+from clinicadl.predictor.predictor import Predictor
+from clinicadl.splitter.kfold import KFolder
+from clinicadl.splitter.split import get_single_split, split_tsv
+from clinicadl.trainer.trainer import Trainer
+from clinicadl.transforms.config import TransformsConfig
+from clinicadl.transforms.transforms import Transforms
+from clinicadl.utils.enum import ExtractionMethod
+
+# SIMPLE EXPERIMENT
+
+
+caps_directory = Path("caps_directory")  # output of clinica pipelines
+caps_reader = CapsReader(caps_directory)
+# un peu bizarre de passer un maps_path a cet endroit via le manager pq on veut pas forcmeent faire un entrainement ??
+
+preprocessing_t1 = caps_reader.get_preprocessing("t1-linear")
+caps_reader.prepare_data(
+    preprocessing=preprocessing_t1,
+    data_tsv=Path(""),
+    n_proc=2,
+    use_uncropped_images=False,
+)
+transforms_1 = Transforms(
+    object_augmentation=[transforms.RandomMotion()],  # default = no transforms
+    image_augmentation=[transforms.RandomMotion()],  # default = no transforms
+    object_transforms=[transforms.Blur((0.4, 0.5, 0.6))],  # default = none
+    image_transforms=[transforms.Noise(0.2, 0.5, 3)],  # default = MiniMax
+    extraction=ExtractionPatchConfig(patch_size=30, stride_size=20),  # default = Image
+)  # not mandatory
+
+sub_ses_tsv = Path("")
+split_dir = split_tsv(sub_ses_tsv)  # -> creer un test.tsv et un train.tsv
+
+dataset_t1_image = caps_reader.get_dataset(
+    preprocessing=preprocessing_t1,
+    sub_ses_tsv=split_dir / "train.tsv",
+    transforms=transforms_1,
+)  # do we give config or ob  -> dataset.json
+# we can create a dataset.json in the CAPS ? or elsewhere ?
+# but maybe we need to create a json file with the infos from the dataset (preprocessing, tsv file, transforms options and caps_directory)
+dataset_t1_image = caps_reader.get_dataset_from_json("dataset.json")
+
+# CAS SINGLE SPLIT
+split = get_single_split(
+    n_subject_validation=0,
+    caps_dataset=dataset_t1_image,
+    # manager=manager,
+)  # as we said, maybe we do not need to pass the manager in this function
+
+maps_path = Path("/")
+manager = ExperimentManager(maps_path, overwrite=False)
+
+config_file = Path("config_file")
+trainer = Trainer.from_json(config_file=config_file, manager=manager)
+# how to create the trainer not from a config file ?
+
+network_config = create_network_config(ImplementedNetworks.CNN)(
+    in_shape=[2, 2, 2], num_outputs=1, conv_args=ConvEncoderOptions(channels=[3, 2, 2])
+)
+model = ClinicaDLModelClassif.from_config(
+    network_config=network_config,
+    loss_config=CrossEntropyLossConfig(),
+    optimizer_config=AdamConfig(),
+)
+
+trainer.train(model, split)
+# le trainer va instancier un predictor/valdiator dans le train ou dans le init