add API

clinicadl/API_test_v2.py

@@ -0,0 +1,102 @@
+# %% class
+class MapsIO:
+    pass
+
+
+class CapsDataset:
+    pass
+
+
+class Splitter:
+    pass
+
+
+class ClinicaDLModels:
+    pass
+
+
+class Networks:
+    pass
+
+
+class VAE(Networks):
+    pass
+
+
+class Optimizer:
+    pass
+
+
+class Loss:
+    pass
+
+
+class Metrics:
+    pass
+
+
+class Trainer:
+    pass
+
+
+class Validator:
+    pass
+
+
+# %% maps
+maps = MapsIO("/path/to/maps")  # Crée un dossier
+
+# %% Dataset
+DataConfig = {
+    "caps_dir": "",
+    "tsv": "",
+    "mode": "",
+}
+capsdataset = CapsDataset(DataConfig, maps)  # torch.dataset
+
+# %% 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)
+
+# %% Metrics
+metrics1 = Metrics("MAE")  # monai.metric
+metrics2 = Metrics("MSE")  # monai.metric
+
+
+# %% Option 1
+for split in splitter.iterate():
+    trainer = Trainer(split, maps, (optimizer))
+    validator = Validator(split, [metrics1, metrics2], maps)
+
+    trainer.train(validator, model)
+
+
+# %% Option 2
+val = Validator([metrics1, metrics2], maps)
+trainer = Trainer(validator, maps)
+for split in splitter.iterate():
+    trainer.train(model, split)
+
+# %% 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)