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Cleaning #689

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Dec 18, 2024
Merged

Cleaning #689

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57407fb
some cleaning for next step of the refactoring
camillebrianceau Dec 18, 2024
3b9a3ec
rebase
camillebrianceau Dec 18, 2024
d469f81
change module dataset to data
camillebrianceau Dec 18, 2024
f422ca3
make split or kfold from df
camillebrianceau Dec 18, 2024
575b55d
comment trainconfig and randomsearchconfig test
camillebrianceau Dec 18, 2024
d9b7e8a
remove unused module
camillebrianceau Dec 18, 2024
2d0edf1
remove unused module part 2
camillebrianceau Dec 18, 2024
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88 changes: 25 additions & 63 deletions clinicadl/API/complicated_case.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2,41 +2,26 @@

import torchio.transforms as transforms

from clinicadl.dataset.dataloader_config import DataLoaderConfig
from clinicadl.dataset.datasets.caps_dataset import CapsDataset
from clinicadl.dataset.datasets.concat import ConcatDataset
from clinicadl.dataset.preprocessing import (
PreprocessingCustom,
from clinicadl.data.dataloader import DataLoaderConfig
from clinicadl.data.datasets.caps_dataset import CapsDataset
from clinicadl.data.datasets.concat import ConcatDataset
from clinicadl.data.preprocessing import (
PreprocessingPET,
PreprocessingT1,
)
from clinicadl.dataset.readers.caps_reader import CapsReader
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.networks.config.resnet import ResNetConfig
from clinicadl.optim.optimizers.config import AdamConfig
from clinicadl.splitter import KFold, make_kfold, make_split
from clinicadl.trainer.trainer import Trainer
from clinicadl.transforms.extraction import ROI, BaseExtraction, Image, Patch, Slice
from clinicadl.transforms.extraction import Extraction, Image, Patch, Slice
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_directory = Path(
"/Users/camille.brianceau/aramis/CLINICADL/caps"
) # output of clinica pipelines

sub_ses_t1 = Path("/Users/camille.brianceau/aramis/CLINICADL/caps/subjects_t1.tsv")
preprocessing_t1 = PreprocessingT1()
Expand All @@ -60,7 +45,7 @@
sub_ses_pet_45 = Path(
"/Users/camille.brianceau/aramis/CLINICADL/caps/subjects_pet_18FAV45.tsv"
)
preprocessing_pet_45 = PreprocessingPET(tracer="18FAV45", suvr_reference_region="pons2")
preprocessing_pet_45 = PreprocessingPET(tracer="18FAV45", suvr_reference_region="pons2") # type: ignore

dataset_pet_image = CapsDataset(
caps_directory=caps_directory,
Expand All @@ -79,47 +64,27 @@
) # 3 train.tsv en entrée qu'il faut concat et pareil pour les transforms à faire attention


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)
split_dir = make_split(sub_ses_t1, n_test=0.2) # Optional data tsv and output_dir
fold_dir = make_kfold(split_dir / "train.tsv", n_splits=2)

splitter = KFold(fold_dir)

# CAS 1

# Prérequis : déjà avoir des fichiers avec les listes train et validation
split_dir = make_kfold(
"dataset.tsv"
) # lit dataset.tsv => fait le kfold => ecrit la sortie dans split_dir
splitter = KFolder(
dataset_multi_modality, split_dir
) # c'est plutôt un iterable de dataloader
maps_path = Path("/")
manager = ExperimentManager(maps_path, overwrite=False)

# CAS 2
splitter = KFolder(caps_dataset=dataset_t1_image)
splitter.make_splits(n_splits=3)
splitter.write(split_dir)
config_file = Path("config_file")
trainer = Trainer.from_json(config_file=config_file, manager=manager)

# or
splitter = KFolder(caps_dataset=dataset_t1_image)
splitter.read(split_dir)

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
for split in splitter.get_splits(dataset=dataset_t1_image):
train_loader = split.build_train_loader(batch_size=2)
val_loader = split.build_val_loader(DataLoaderConfig())

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,
model = ClinicaDLModel.from_config(
network_config=ResNetConfig(num_outputs=1, spatial_dims=1, in_channels=1),
loss_config=CrossEntropyLossConfig(),
optimizer_config=AdamConfig(),
)
Expand All @@ -133,9 +98,6 @@
dataset_test = CapsDataset(
caps_directory=caps_directory,
preprocessing=preprocessing_t1,
sub_ses_tsv=Path("test.tsv"), # test only on data from the first dataset
data=Path("test.tsv"), # test only on data from the first dataset
transforms=transforms_image,
)

predictor = Predictor(model=model, manager=manager)
predictor.predict(dataset_test=dataset_test, split_number=2)
27 changes: 12 additions & 15 deletions clinicadl/API/cross_val.py
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Original file line number Diff line number Diff line change
@@ -1,11 +1,10 @@
from pathlib import Path

from clinicadl.dataset.datasets.caps_dataset import CapsDataset
from clinicadl.experiment_manager.experiment_manager import ExperimentManager
from clinicadl.predictor.predictor import Predictor
from clinicadl.splitter.new_splitter.dataloader import DataLoaderConfig
from clinicadl.splitter.new_splitter.splitter.kfold import KFold
from clinicadl.trainer.trainer import Trainer
from clinicadl.data.dataloader import DataLoaderConfig
from clinicadl.data.datasets import CapsDataset
from clinicadl.experiment_manager import ExperimentManager
from clinicadl.splitter import KFold, make_kfold, make_split
from clinicadl.trainer import Trainer

# SIMPLE EXPERIMENT WITH A CAPS ALREADY EXISTING

Expand All @@ -19,20 +18,18 @@
config_file=config_file, manager=manager
) # gpu, amp, fsdp, seed

splitter = KFold(dataset=dataset_t1_image)
splitter.make_splits(n_splits=3)
split_dir = Path("")
splitter.write(split_dir)
split_dir = make_split(
dataset_t1_image.df, n_test=0.2, subset_name="validation", output_dir="test"
) # Optional data tsv and output_dir
fold_dir = make_kfold(split_dir / "train.tsv", n_splits=2)

splitter = KFold(fold_dir)

splitter.read(split_dir)

# define the needed parameters for the dataloader
dataloader_config = DataLoaderConfig(num_workers=3, batch_size=10)


for split in splitter.get_splits(splits=(0, 3, 4)):
print(split)
for split in splitter.get_splits(dataset=dataset_t1_image):
split.build_train_loader(dataloader_config)
split.build_val_loader(num_workers=3, batch_size=10)

print(split)
61 changes: 26 additions & 35 deletions clinicadl/API/dataset_test.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2,26 +2,25 @@

import torchio.transforms as transforms

from clinicadl.dataset.datasets.caps_dataset import CapsDataset
from clinicadl.dataset.datasets.concat import ConcatDataset
from clinicadl.dataset.preprocessing import (
from clinicadl.data.datasets import CapsDataset, ConcatDataset
from clinicadl.data.preprocessing import (
BasePreprocessing,
PreprocessingFlair,
PreprocessingPET,
PreprocessingT1,
)
from clinicadl.experiment_manager.experiment_manager import ExperimentManager
from clinicadl.data.preprocessing.pet import SUVRReferenceRegions, Tracer
from clinicadl.experiment_manager import ExperimentManager
from clinicadl.losses.config import CrossEntropyLossConfig
from clinicadl.model.clinicadl_model import ClinicaDLModel
from clinicadl.networks.factory import (
ConvEncoderOptions,
create_network_config,
get_network_from_config,
)
from clinicadl.splitter.kfold import KFolder
from clinicadl.splitter.split import get_single_split, split_tsv
from clinicadl.transforms.extraction import ROI, Image, Patch, Slice
from clinicadl.transforms.transforms import Transforms
from clinicadl.splitter import KFold, make_kfold, make_split
from clinicadl.transforms import Transforms
from clinicadl.transforms.extraction import Image, Patch, Slice

sub_ses_t1 = Path("/Users/camille.brianceau/aramis/CLINICADL/caps/subjects_t1.tsv")
sub_ses_pet_45 = Path(
Expand All @@ -38,8 +37,12 @@
"/Users/camille.brianceau/aramis/CLINICADL/caps"
) # output of clinica pipelines

preprocessing_pet_45 = PreprocessingPET(tracer="18FAV45", suvr_reference_region="pons2")
preprocessing_pet_11 = PreprocessingPET(tracer="11CPIB", suvr_reference_region="pons2")
preprocessing_pet_45 = PreprocessingPET(
tracer=Tracer.FAV45, suvr_reference_region=SUVRReferenceRegions.PONS2
)
preprocessing_pet_11 = PreprocessingPET(
tracer=Tracer.CPIB, suvr_reference_region=SUVRReferenceRegions.PONS2
)

preprocessing_t1 = PreprocessingT1()
preprocessing_flair = PreprocessingFlair()
Expand All @@ -55,18 +58,6 @@

transforms_slice = Transforms(extraction=Slice())

transforms_roi = Transforms(
object_augmentation=[transforms.Ghosting(2, 1, 0.1, 0.1)],
object_transforms=[transforms.RandomMotion()],
extraction=ROI(
roi_list=["leftHippocampusBox", "rightHippocampusBox"],
roi_mask_location=Path(
"/Users/camille.brianceau/aramis/CLINICADL/caps/masks/tpl-MNI152NLin2009cSym"
),
roi_crop_input=True,
),
)

transforms_image = Transforms(
image_augmentation=[transforms.RandomMotion()],
extraction=Image(),
Expand Down Expand Up @@ -96,25 +87,25 @@
)


print("Pet 11 and ROI ")
print("Pet 11 and Image ")

dataset_pet_11_roi = CapsDataset(
dataset_pet_11_image = CapsDataset(
caps_directory=caps_directory,
data=sub_ses_pet_11,
preprocessing=preprocessing_pet_11,
transforms=transforms_roi,
transforms=transforms_image,
)
dataset_pet_11_roi.prepare_data(
dataset_pet_11_image.prepare_data(
n_proc=2
) # to extract the tensor of the PET file this time

print(dataset_pet_11_roi)
print(dataset_pet_11_roi.__len__())
print(dataset_pet_11_roi._get_meta_data(0))
print(dataset_pet_11_roi._get_meta_data(1))
# print(dataset_pet_11_roi._get_full_image())
print(dataset_pet_11_roi.__getitem__(1).elem_idx)
print(dataset_pet_11_roi.elem_per_image)
print(dataset_pet_11_image)
print(dataset_pet_11_image.__len__())
print(dataset_pet_11_image._get_meta_data(0))
print(dataset_pet_11_image._get_meta_data(1))
# print(dataset_pet_11_image._get_full_image())
print(dataset_pet_11_image.__getitem__(1).elem_idx)
print(dataset_pet_11_image.elem_per_image)


print("T1 and image ")
Expand Down Expand Up @@ -161,7 +152,7 @@

lity_multi_extract = ConcatDataset(
[
dataset_t1,
dataset_pet,
dataset_t1_image,
dataset_pet_11_image,
]
) # 3 train.tsv en entrée qu'il faut concat et pareil pour les transforms à faire attention
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