Warning on wrong call order

CHANGELOG.md

@@ -34,6 +34,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Changed `ExplainedVariance` from storing all preds/targets to tracking 5 statistics ([#68](https://github.com/PyTorchLightning/metrics/pull/68))
 - Changed behaviour of `confusionmatrix` for multilabel data to better match `multilabel_confusion_matrix` from sklearn ([#134](https://github.com/PyTorchLightning/metrics/pull/134))
 - Updated FBeta arguments ([#111](https://github.com/PyTorchLightning/metrics/pull/111))
+- Calling `compute` before `update` will now return 0 and give an warning ([#164](https://github.com/PyTorchLightning/metrics/pull/164))
 
 ### Deprecated
 

tests/bases/test_composition.py

@@ -27,6 +27,7 @@ def __init__(self, val_to_return):
         super().__init__()
         self._num_updates = 0
         self._val_to_return = val_to_return
+        self._update_called = True
 
     def update(self, *args, **kwargs) -> None:
         self._num_updates += 1
@@ -57,6 +58,9 @@ def test_metrics_add(second_operand, expected_result):
     assert isinstance(final_add, CompositionalMetric)
     assert isinstance(final_radd, CompositionalMetric)
 
+    final_add.update()
+    final_radd.update()
+
     assert torch.allclose(expected_result, final_add.compute())
     assert torch.allclose(expected_result, final_radd.compute())
 
@@ -75,6 +79,8 @@ def test_metrics_and(second_operand, expected_result):
     assert isinstance(final_and, CompositionalMetric)
     assert isinstance(final_rand, CompositionalMetric)
 
+    final_and.update()
+    final_rand.update()
     assert torch.allclose(expected_result, final_and.compute())
     assert torch.allclose(expected_result, final_rand.compute())
 
@@ -95,6 +101,7 @@ def test_metrics_eq(second_operand, expected_result):
 
     assert isinstance(final_eq, CompositionalMetric)
 
+    final_eq.update()
     # can't use allclose for bool tensors
     assert (expected_result == final_eq.compute()).all()
 
@@ -116,6 +123,7 @@ def test_metrics_floordiv(second_operand, expected_result):
 
     assert isinstance(final_floordiv, CompositionalMetric)
 
+    final_floordiv.update()
     assert torch.allclose(expected_result, final_floordiv.compute())
 
 
@@ -135,6 +143,7 @@ def test_metrics_ge(second_operand, expected_result):
 
     assert isinstance(final_ge, CompositionalMetric)
 
+    final_ge.update()
     # can't use allclose for bool tensors
     assert (expected_result == final_ge.compute()).all()
 
@@ -155,6 +164,7 @@ def test_metrics_gt(second_operand, expected_result):
 
     assert isinstance(final_gt, CompositionalMetric)
 
+    final_gt.update()
     # can't use allclose for bool tensors
     assert (expected_result == final_gt.compute()).all()
 
@@ -175,6 +185,7 @@ def test_metrics_le(second_operand, expected_result):
 
     assert isinstance(final_le, CompositionalMetric)
 
+    final_le.update()
     # can't use allclose for bool tensors
     assert (expected_result == final_le.compute()).all()
 
@@ -195,6 +206,7 @@ def test_metrics_lt(second_operand, expected_result):
 
     assert isinstance(final_lt, CompositionalMetric)
 
+    final_lt.update()
     # can't use allclose for bool tensors
     assert (expected_result == final_lt.compute()).all()
 
@@ -210,6 +222,7 @@ def test_metrics_matmul(second_operand, expected_result):
 
     assert isinstance(final_matmul, CompositionalMetric)
 
+    final_matmul.update()
     assert torch.allclose(expected_result, final_matmul.compute())
 
 
@@ -228,6 +241,8 @@ def test_metrics_mod(second_operand, expected_result):
     final_mod = first_metric % second_operand
 
     assert isinstance(final_mod, CompositionalMetric)
+
+    final_mod.update()
     # prevent Runtime error for PT 1.8 - Long did not match Float
     assert torch.allclose(expected_result.to(float), final_mod.compute().to(float))
 
@@ -250,6 +265,8 @@ def test_metrics_mul(second_operand, expected_result):
     assert isinstance(final_mul, CompositionalMetric)
     assert isinstance(final_rmul, CompositionalMetric)
 
+    final_mul.update()
+    final_rmul.update()
     assert torch.allclose(expected_result, final_mul.compute())
     assert torch.allclose(expected_result, final_rmul.compute())
 
@@ -270,6 +287,7 @@ def test_metrics_ne(second_operand, expected_result):
 
     assert isinstance(final_ne, CompositionalMetric)
 
+    final_ne.update()
     # can't use allclose for bool tensors
     assert (expected_result == final_ne.compute()).all()
 
@@ -288,6 +306,8 @@ def test_metrics_or(second_operand, expected_result):
     assert isinstance(final_or, CompositionalMetric)
     assert isinstance(final_ror, CompositionalMetric)
 
+    final_or.update()
+    final_ror.update()
     assert torch.allclose(expected_result, final_or.compute())
     assert torch.allclose(expected_result, final_ror.compute())
 
@@ -308,6 +328,7 @@ def test_metrics_pow(second_operand, expected_result):
 
     assert isinstance(final_pow, CompositionalMetric)
 
+    final_pow.update()
     assert torch.allclose(expected_result, final_pow.compute())
 
 
@@ -322,6 +343,8 @@ def test_metrics_rfloordiv(first_operand, expected_result):
     final_rfloordiv = first_operand // second_operand
 
     assert isinstance(final_rfloordiv, CompositionalMetric)
+
+    final_rfloordiv.update()
     assert torch.allclose(expected_result, final_rfloordiv.compute())
 
 
@@ -336,6 +359,7 @@ def test_metrics_rmatmul(first_operand, expected_result):
 
     assert isinstance(final_rmatmul, CompositionalMetric)
 
+    final_rmatmul.update()
     assert torch.allclose(expected_result, final_rmatmul.compute())
 
 
@@ -350,6 +374,7 @@ def test_metrics_rmod(first_operand, expected_result):
 
     assert isinstance(final_rmod, CompositionalMetric)
 
+    final_rmod.update()
     assert torch.allclose(expected_result, final_rmod.compute())
 
 
@@ -367,7 +392,7 @@ def test_metrics_rpow(first_operand, expected_result):
     final_rpow = first_operand**second_operand
 
     assert isinstance(final_rpow, CompositionalMetric)
-
+    final_rpow.update()
     assert torch.allclose(expected_result, final_rpow.compute())
 
 
@@ -386,7 +411,7 @@ def test_metrics_rsub(first_operand, expected_result):
     final_rsub = first_operand - second_operand
 
     assert isinstance(final_rsub, CompositionalMetric)
-
+    final_rsub.update()
     assert torch.allclose(expected_result, final_rsub.compute())
 
 
@@ -406,7 +431,7 @@ def test_metrics_rtruediv(first_operand, expected_result):
     final_rtruediv = first_operand / second_operand
 
     assert isinstance(final_rtruediv, CompositionalMetric)
-
+    final_rtruediv.update()
     assert torch.allclose(expected_result, final_rtruediv.compute())
 
 
@@ -425,7 +450,7 @@ def test_metrics_sub(second_operand, expected_result):
     final_sub = first_metric - second_operand
 
     assert isinstance(final_sub, CompositionalMetric)
-
+    final_sub.update()
     assert torch.allclose(expected_result, final_sub.compute())
 
 
@@ -445,7 +470,7 @@ def test_metrics_truediv(second_operand, expected_result):
     final_truediv = first_metric / second_operand
 
     assert isinstance(final_truediv, CompositionalMetric)
-
+    final_truediv.update()
     assert torch.allclose(expected_result, final_truediv.compute())
 
 
@@ -463,6 +488,8 @@ def test_metrics_xor(second_operand, expected_result):
     assert isinstance(final_xor, CompositionalMetric)
     assert isinstance(final_rxor, CompositionalMetric)
 
+    final_xor.update()
+    final_rxor.update()
     assert torch.allclose(expected_result, final_xor.compute())
     assert torch.allclose(expected_result, final_rxor.compute())
 
@@ -473,7 +500,7 @@ def test_metrics_abs():
     final_abs = abs(first_metric)
 
     assert isinstance(final_abs, CompositionalMetric)
-
+    final_abs.update()
     assert torch.allclose(tensor(1), final_abs.compute())
 
 
@@ -482,6 +509,7 @@ def test_metrics_invert():
 
     final_inverse = ~first_metric
     assert isinstance(final_inverse, CompositionalMetric)
+    final_inverse.update()
     assert torch.allclose(tensor(-2), final_inverse.compute())
 
 
@@ -490,6 +518,7 @@ def test_metrics_neg():
 
     final_neg = neg(first_metric)
     assert isinstance(final_neg, CompositionalMetric)
+    final_neg.update()
     assert torch.allclose(tensor(-1), final_neg.compute())
 
 
@@ -498,6 +527,7 @@ def test_metrics_pos():
 
     final_pos = pos(first_metric)
     assert isinstance(final_pos, CompositionalMetric)
+    final_pos.update()
     assert torch.allclose(tensor(1), final_pos.compute())
 
 
@@ -510,6 +540,7 @@ def test_metrics_getitem(value, idx, expected_result):
 
     final_getitem = first_metric[idx]
     assert isinstance(final_getitem, CompositionalMetric)
+    final_getitem.update()
     assert torch.allclose(expected_result, final_getitem.compute())
 
 

tests/bases/test_metric.py

@@ -259,3 +259,25 @@ def test_device_and_dtype_transfer(tmpdir):
 
     metric = metric.half()
     assert metric.x.dtype == torch.float16
+
+
+def test_warning_on_compute_before_update():
+    metric = DummyMetricSum()
+
+    # make sure everything is fine with forward
+    with pytest.warns(None) as record:
+        val = metric(1)
+    assert not record
+
+    metric.reset()
+
+    with pytest.warns(UserWarning, match=r'The ``compute`` method of metric .*'):
+        val = metric.compute()
+        assert val == 0.0
+
+    # after update things should be fine
+    metric.update(2.0)
+    with pytest.warns(None) as record:
+        val = metric.compute()
+    assert not record
+    assert val == 2.0

torchmetrics/metric.py

@@ -21,7 +21,7 @@
 import torch
 from torch import Tensor, nn
 
-from torchmetrics.utilities import apply_to_collection
+from torchmetrics.utilities import apply_to_collection, rank_zero_warn
 from torchmetrics.utilities.data import _flatten, dim_zero_cat, dim_zero_mean, dim_zero_sum
 from torchmetrics.utilities.distributed import gather_all_tensors
 
@@ -79,6 +79,7 @@ def __init__(
         self.compute = self._wrap_compute(self.compute)
         self._computed = None
         self._forward_cache = None
+        self._update_called = False
 
         # initialize state
         self._defaults = {}
@@ -198,6 +199,7 @@ def _wrap_update(self, update):
         @functools.wraps(update)
         def wrapped_func(*args, **kwargs):
             self._computed = None
+            self._update_called = True
             return update(*args, **kwargs)
 
         return wrapped_func
@@ -206,6 +208,15 @@ def _wrap_compute(self, compute):
 
         @functools.wraps(compute)
         def wrapped_func(*args, **kwargs):
+            if not self._update_called:
+                rank_zero_warn(
+                    f"The ``compute`` method of metric {self.__class__.__name__}"
+                    " was called before the ``update`` method which may lead to errors,"
+                    " as metric states have not yet been updated. Will return 0.0 instead.",
+                    UserWarning
+                )
+                return 0.0
+
             # return cached value
             if self._computed is not None:
                 return self._computed
@@ -255,6 +266,7 @@ def reset(self):
         This method automatically resets the metric state variables to their default value.
         """
         self._computed = None
+        self._update_called = False
 
         for attr, default in self._defaults.items():
             current_val = getattr(self, attr)