Update clinicadl/API_test_v2.py

clinicadl/API_test_v2.py

@@ -1,102 +1,112 @@
-# %% class
-class MapsIO:
+from pathlib import Path
+from clinicadl.caps_dataset2.config.preprocessing import PreprocessingConfig
+from clinicadl.caps_dataset2.config.extraction import ExtractionConfig
+from clinicadl.caps_dataset2.data import CapsDatasetRoi, CapsDatasetPatch, CapsDatasetSlice
+from clinicadl.transforms.config import TransformsConfig
+import torchio
+from clinicadl import tsvtools
+from clinicadl.trainer.trainer import Trainer
+
+class ExperimentManager:
     pass
 
-
-class CapsDataset:
+class CapsReader:
     pass
 
-
-class Splitter:
+class Transforms:
     pass
 
-
-class ClinicaDLModels:
+class Predictor:
     pass
 
+class ClinicaDLModel:
+    pass
 
-class Networks:
+class KFolder:
     pass
 
+def get_loss_function():
+    pass
 
-class VAE(Networks):
+def get_network_from_config():
     pass
 
+def create_network_config():
+    pass
 
-class Optimizer:
+def get_single_split():
     pass
 
+# Create the Maps Manager / Read/write manager /
+maps_path = Path("/")
+manager = ExperimentManager(maps_path, overwrite = False)
 
-class Loss:
-    pass
+caps_directory = Path("caps_directory") # output of clinica pipelines
+caps_reader = CapsReader(caps_directory, manager= manager)
 
+preprocessing_1: PreprocessingConfig = caps_reader.get_preprocessing("t1-linear")
+extraction_1: ExtractionConfig = caps_reader.extract_slice(preprocessing=preprocessing_1, arg_slice = 2)
+transforms_1 = Transforms(data_augmentation = [torchio.t1, torchio.t2], image_transforms= [torchio.t1, torchio.t2], object_transforms=[torchio.t1, torchio.t2]) # not mandatory 
 
-class Metrics:
-    pass
+preprocessing_2: PreprocessingConfig = caps_reader.get_preprocessing("pet-linear")
+extraction_2: ExtractionConfig= caps_reader.extract_patch(preprocessing=preprocessing_2, arg_patch = 2)
+transforms_2 = Transforms(data_augmentation = [torchio.t2], image_transforms= [torchio.t1], object_transforms=[torchio.t1, torchio.t2]) 
 
+sub_ses_tsv = Path("")
+split_dir = tsvtools.split_tsv(sub_ses_tsv) #-> creer un test.tsv et un train.tsv
 
-class Trainer:
-    pass
+dataset_t1_roi: CapsDatasetRoi  = caps_reader.get_dataset( extraction = extraction_1, preprocessing = preprocessing_1, sub_ses_tsv = split_dir / "train.tsv", transforms = transforms_1)
+dataset_pet_patch: CapsDatasetPatch = caps_reader.get_dataset(extraction = extraction_2, preprocessing= preprocessing_2, sub_ses_tsv = split_dir / "train.tsv", transforms = transforms_2)
 
+dataset_multi_modality_multi_extract = concat_dataset(dataset_t1, dataset_pet) # 2 train.tsv en entrée qu'il faut concat et pareil pour les transforms à faire attention
 
-class Validator:
-    pass
+config_file = Path("config_file")
+trainer = Trainer.from_json(config_file=config_file, manager=manager)
 
+# CAS CROSS-VALIDATION
+splitter = KFolder(n_splits = 3, caps_dataset=dataset_multi_modality_multi_extract, manager = manager)
 
-# %% maps
-maps = MapsIO("/path/to/maps")  # Crée un dossier
+for split in splitter.split_iterator(split_list = [0,1]):
+    # bien définir ce qu'il y a dans l'objet split
+
+    loss, loss_config = get_loss_function(CrossEntropyLossConfig())
+    network_config = create_network_config(ImplementedNetworks.CNN)(in_shape = [2,2,2], num_outputs = 1, conv_args = ConvEncoderOptions(channels = [3, 2, 2]))
+    network = get_network_from_config(network_config)
+
+    model = ClinicaDLModel(
+        network= network,
+        loss=loss,
+        optimizer= AdamConfig()
+    )
 
-# %% Dataset
-DataConfig = {
-    "caps_dir": "",
-    "tsv": "",
-    "mode": "",
-}
-capsdataset = CapsDataset(DataConfig, maps)  # torch.dataset
+    trainer.train(model, split)
+    # le trainer va instancier un predictor/valdiator dans le train ou dans le init
 
-# %% Model
-network = VAE()  # nn.module
-loss = Loss()
-optimizer = ClinicaDLOptim(
-    Adam()
-)  # get_optimizer({"name": "Adam", "par1": 0.5}) # torch.optim
-# model = ClinicaDLModels(
-#     network,
-#     loss,
-#     optimizer,
-# )
 
-# %% Cross val
-SplitConfig = SplitterConfig()
-splitter = Splitter(SplitConfig, capsdataset)
+# CAS SINGLE SPLIT
+split = get_single_split(n_subject_validation = 0, caps_dataset=dataset_multi_modality_multi_extract, manager = manager)
 
-# %% Metrics
-metrics1 = Metrics("MAE")  # monai.metric
-metrics2 = Metrics("MSE")  # monai.metric
+loss, loss_config = get_loss_function(CrossEntropyLossConfig())
+network_config = create_network_config(ImplementedNetworks.CNN)(in_shape = [2,2,2], num_outputs = 1, conv_args = ConvEncoderOptions(channels = [3, 2, 2]))
+network = get_network_from_config(network_config)
+
+model = ClinicaDLModel(
+    network= network,
+    loss=loss,
+    optimizer= AdamConfig()
+)
 
+trainer.train(model, split)
+# le trainer va instancier un predictor/valdiator dans le train ou dans le init
 
-# %% Option 1
-for split in splitter.iterate():
-    trainer = Trainer(split, maps, (optimizer))
-    validator = Validator(split, [metrics1, metrics2], maps)
 
-    trainer.train(validator, model)
+# TEST 
 
+preprocessing_test: PreprocessingConfig = caps_reader.get_preprocessing("pet-linear")
+extraction_test: ExtractionConfig= caps_reader.extract_patch(preprocessing=preprocessing_2, arg_patch = 2)
+transforms_test = Transforms(data_augmentation = [torchio.t2], image_transforms= [torchio.t1], object_transforms=[torchio.t1, torchio.t2]) 
 
-# %% Option 2
-val = Validator([metrics1, metrics2], maps)
-trainer = Trainer(validator, maps)
-for split in splitter.iterate():
-    trainer.train(model, split)
+dataset_test: CapsDatasetROI  = caps_reader.get_dataset( extraction = extraction_test, preprocessing = preprocessing_test, sub_ses_tsv = split_dir / "test.tsv", transforms = transforms_test)
 
-# %% Option 3
-trainer = Trainer(
-    maps, [metrics1, metrics2]
-)  # Initialise un maps manager + initialise un validator
-for split in splitter.iterate():
-    model = ClinicaDLModels(
-        network,
-        loss,
-        optimizer,
-    )
-    trainer.train(model, split)
+predictor = Predictor(manager= manager)
+predictor.predict(dataset_test= dataset_test, split = 2)