def test_connect_loops_recursive():
"""Test Trainer references in a nested loop assigned to a Trainer."""
main_loop = NestedLoop()
child0 = NestedLoop()
child1 = NestedLoop()
main_loop.connect(child0, child1)
assert main_loop.trainer is None
assert main_loop.child_loop0.trainer is None
trainer = Trainer()
trainer.fit_loop = main_loop
assert child0.trainer is trainer
assert child1.trainer is trainer
def test_connect_loops_recursive():
"""Test Trainer references in a nested loop assigned to a Trainer."""
main_loop = NestedLoop()
child0 = NestedLoop()
child1 = NestedLoop()
main_loop.connect(child0, child1)
with pytest.raises(RuntimeError, match="The loop is not attached to a Trainer"):
_ = main_loop.trainer
with pytest.raises(RuntimeError, match="The loop is not attached to a Trainer"):
_ = main_loop.child_loop0.trainer
trainer = Trainer()
trainer.fit_loop = main_loop
assert child0.trainer is trainer
assert child1.trainer is trainer
self.val_acc(logits, y)
self.log("val_acc", self.val_acc)
self.log("val_loss", loss)
#############################################################################################
# Step 5 / 5: Connect the KFoldLoop to the Trainer #
# After creating the `KFoldDataModule` and our model, the `KFoldLoop` is being connected to #
# the Trainer. #
# Finally, use `trainer.fit` to start the cross validation training. #
#############################################################################################
if __name__ == "__main__":
seed_everything(42)
model = LitImageClassifier()
datamodule = MNISTKFoldDataModule()
trainer = Trainer(
max_epochs=10,
limit_train_batches=2,
limit_val_batches=2,
limit_test_batches=2,
num_sanity_val_steps=0,
devices=2,
accelerator="auto",
strategy="ddp",
)
internal_fit_loop = trainer.fit_loop
trainer.fit_loop = KFoldLoop(5, export_path="./")
trainer.fit_loop.connect(internal_fit_loop)
trainer.fit(model, datamodule)