[WIP] Fix missing _is_reduced in precision/recall with tests

pytorch · Aug 7, 2019 · 0bcc287 · 0bcc287
1 parent ad8375c
commit 0bcc287
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Showing 3 changed files with 20 additions and 5 deletions.
diff --git a/ignite/metrics/precision.py b/ignite/metrics/precision.py
@@ -41,6 +41,7 @@ def compute(self):
         if not (self._type == "multilabel" and not self._average):
             self._true_positives = self._sync_all_reduce(self._true_positives)
             self._positives = self._sync_all_reduce(self._positives)
+            self._is_reduced = True
 
         result = self._true_positives / (self._positives + self.eps)
 

diff --git a/tests/ignite/metrics/test_precision.py b/tests/ignite/metrics/test_precision.py
@@ -806,8 +806,13 @@ def update(engine, i):
 
             assert "pr" in engine.state.metrics
             res = engine.state.metrics['pr']
+            res2 = pr.compute()
             if isinstance(res, torch.Tensor):
                 res = res.cpu().numpy()
+                res2 = res2.cpu().numpy()
+                assert (res == res2).all()
+            else:
+                assert res == res2
 
             with warnings.catch_warnings():
                 warnings.simplefilter("ignore", category=UndefinedMetricWarning)
@@ -828,7 +833,9 @@ def update(engine, i):
         y_pred = torch.randint(0, 2, size=(10, 5, 18, 16))
         y = torch.randint(0, 2, size=(10, 5, 18, 16)).long()
         pr.update((y_pred, y))
-        pr_compute = pr.compute()
-        assert len(pr_compute) == 10 * 18 * 16
+        pr_compute1 = pr.compute()
+        pr_compute2 = pr.compute()
+        assert len(pr_compute1) == 10 * 18 * 16
+        assert (pr_compute1 == pr_compute2).all()
 
     test_distrib_itegration_multilabel()
diff --git a/tests/ignite/metrics/test_recall.py b/tests/ignite/metrics/test_recall.py
@@ -806,16 +806,21 @@ def update(engine, i):
 
             assert "re" in engine.state.metrics
             res = engine.state.metrics['re']
+            res2 = re.compute()
             if isinstance(res, torch.Tensor):
                 res = res.cpu().numpy()
+                res2 = res2.cpu().numpy()
+                assert (res == res2).all()
+            else:
+                assert res == res2
 
             with warnings.catch_warnings():
                 warnings.simplefilter("ignore", category=UndefinedMetricWarning)
                 true_res = recall_score(to_numpy_multilabel(y_true),
                                         to_numpy_multilabel(y_preds),
                                         average='samples' if average else None)
 
-            assert pytest.approx(res) == true_res
+            assert pytest.approx(res) == true_res            
 
         for _ in range(5):
             _test(average=True, n_epochs=1)
@@ -828,7 +833,9 @@ def update(engine, i):
         y_pred = torch.randint(0, 2, size=(10, 5, 18, 16))
         y = torch.randint(0, 2, size=(10, 5, 18, 16)).long()
         re.update((y_pred, y))
-        re_compute = re.compute()
-        assert len(re_compute) == 10 * 18 * 16
+        re_compute1 = re.compute()
+        re_compute2 = re.compute()
+        assert len(re_compute1) == 10 * 18 * 16
+        assert (re_compute1 == re_compute2).all()
 
     test_distrib_itegration_multilabel()