Tests and miscellaneous (#4)

* linter

* Add tests

* Add restore test

* Type hints

* PyTorch dependency

* One fewer temp buffer

1 files changed, 96 insertions, 0 deletions

diff --git a/tests/test_ema.py b/tests/test_ema.py
new file mode 100644
index 0000000..6d7e43e
--- /dev/null
+++ b/tests/test_ema.py
@@ -0,0 +1,96 @@
+import pytest
+
+import torch
+
+from torch_ema import ExponentialMovingAverage
+
+
+@pytest.mark.parametrize("decay", [0.995, 0.9])
+@pytest.mark.parametrize("use_num_updates", [True, False])
+def test_val_error(decay, use_num_updates):
+    """Confirm that EMA validation error is lower than raw validation error."""
+    torch.manual_seed(0)
+    x_train = torch.rand((100, 10))
+    y_train = torch.rand(100).round().long()
+    x_val = torch.rand((100, 10))
+    y_val = torch.rand(100).round().long()
+    model = torch.nn.Linear(10, 2)
+    optimizer = torch.optim.Adam(model.parameters(), lr=1e-2)
+    ema = ExponentialMovingAverage(
+        model.parameters(),
+        decay=decay,
+        use_num_updates=use_num_updates
+    )
+
+    # Train for a few epochs
+    model.train()
+    for _ in range(20):
+        logits = model(x_train)
+        loss = torch.nn.functional.cross_entropy(logits, y_train)
+        optimizer.zero_grad()
+        loss.backward()
+        optimizer.step()
+        ema.update(model.parameters())
+
+    # Validation: original
+    model.eval()
+    logits = model(x_val)
+    loss_orig = torch.nn.functional.cross_entropy(logits, y_val)
+    print(f"Original loss: {loss_orig}")
+
+    # Validation: with EMA
+    # First save original parameters before replacing with EMA version
+    ema.store(model.parameters())
+    # Copy EMA parameters to model
+    ema.copy_to(model.parameters())
+    logits = model(x_val)
+    loss_ema = torch.nn.functional.cross_entropy(logits, y_val)
+
+    print(f"EMA loss: {loss_ema}")
+    assert loss_ema < loss_orig, "EMA loss wasn't lower"
+
+    # Test restore
+    ema.restore(model.parameters())
+    model.eval()
+    logits = model(x_val)
+    loss_orig2 = torch.nn.functional.cross_entropy(logits, y_val)
+    assert torch.allclose(loss_orig, loss_orig2), \
+        "Restored model wasn't the same as stored model"
+
+
+@pytest.mark.parametrize("decay", [0.995, 0.9, 0.0, 1.0])
+@pytest.mark.parametrize("use_num_updates", [True, False])
+def test_store_restore(decay, use_num_updates):
+    model = torch.nn.Linear(10, 2)
+    ema = ExponentialMovingAverage(
+        model.parameters(),
+        decay=decay,
+        use_num_updates=use_num_updates
+    )
+    orig_weight = model.weight.clone().detach()
+    ema.store(model.parameters())
+    with torch.no_grad():
+        model.weight.uniform_(0.0, 1.0)
+    ema.restore(model.parameters())
+    assert torch.all(model.weight == orig_weight)
+
+
+@pytest.mark.parametrize("decay", [0.995, 0.9, 0.0, 1.0])
+def test_update(decay):
+    model = torch.nn.Linear(10, 2, bias=False)
+    with torch.no_grad():
+        model.weight.fill_(0.0)
+    ema = ExponentialMovingAverage(
+        model.parameters(),
+        decay=decay,
+        use_num_updates=False
+    )
+    with torch.no_grad():
+        model.weight.fill_(1.0)
+    ema.update(model.parameters())
+    assert torch.all(model.weight == 1.0), "ema.update changed model weights"
+    ema.copy_to(model.parameters())
+    assert torch.allclose(
+        model.weight,
+        torch.full(size=(1,), fill_value=(1.0 - decay))
+    ), "average was wrong"