From 77f6aa43e51434f8f510f60e884fc7457320e99a Mon Sep 17 00:00:00 2001
From: Philip E Blair <peblairman@gmail.com>
Date: Thu, 18 Feb 2021 10:54:12 +0100
Subject: [PATCH] Fix: Allow hashing of metrics with lists in their state
 (#5939)

* Fix: Allow hashing of metrics with lists in their state

* Add test case and modify semantics of Metric __hash__ in order to be compatible with structural equality checks

* Fix pep8 style issue

Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com>
Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com>
---
 pytorch_lightning/metrics/metric.py |  8 +++++++-
 tests/metrics/test_metric.py        | 26 ++++++++++++++++++++++++++
 2 files changed, 33 insertions(+), 1 deletion(-)

diff --git a/pytorch_lightning/metrics/metric.py b/pytorch_lightning/metrics/metric.py
index 2c910edb8e404..ab198356f7279 100644
--- a/pytorch_lightning/metrics/metric.py
+++ b/pytorch_lightning/metrics/metric.py
@@ -333,7 +333,13 @@ def __hash__(self):
         hash_vals = [self.__class__.__name__]
 
         for key in self._defaults.keys():
-            hash_vals.append(getattr(self, key))
+            val = getattr(self, key)
+            # Special case: allow list values, so long
+            # as their elements are hashable
+            if hasattr(val, '__iter__') and not isinstance(val, torch.Tensor):
+                hash_vals.extend(val)
+            else:
+                hash_vals.append(val)
 
         return hash(tuple(hash_vals))
 
diff --git a/tests/metrics/test_metric.py b/tests/metrics/test_metric.py
index 03b79633e3eb7..04c7ce64beed0 100644
--- a/tests/metrics/test_metric.py
+++ b/tests/metrics/test_metric.py
@@ -154,6 +154,32 @@ def compute(self):
     assert a.compute() == 5
 
 
+def test_hash():
+
+    class A(Dummy):
+        pass
+
+    class B(DummyList):
+        pass
+
+    a1 = A()
+    a2 = A()
+    assert hash(a1) != hash(a2)
+
+    b1 = B()
+    b2 = B()
+    assert hash(b1) == hash(b2)
+    assert isinstance(b1.x, list) and len(b1.x) == 0
+    b1.x.append(torch.tensor(5))
+    assert isinstance(hash(b1), int)  # <- check that nothing crashes
+    assert isinstance(b1.x, list) and len(b1.x) == 1
+    b2.x.append(torch.tensor(5))
+    # Sanity:
+    assert isinstance(b2.x, list) and len(b2.x) == 1
+    # Now that they have tensor contents, they should have different hashes:
+    assert hash(b1) != hash(b2)
+
+
 def test_forward():
 
     class A(Dummy):