Fix references for ResultCollection.extra and improve str and `re…

…pr` (#8622)

CHANGELOG.md

@@ -32,7 +32,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Saved checkpoints will no longer use the type of a `Callback` as the key to avoid issues with unpickling ([#6886](https://github.com/PyTorchLightning/pytorch-lightning/pull/6886))
 
 
--
+- Improved string conversion for `ResultCollection` ([#8622](https://github.com/PyTorchLightning/pytorch-lightning/pull/8622))
 
 
 -
@@ -74,7 +74,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 ### Fixed
 
--
+- Fixed references for `ResultCollection.extra` ([#8622](https://github.com/PyTorchLightning/pytorch-lightning/pull/8622))
 
 
 -

pytorch_lightning/trainer/connectors/logger_connector/result.py

@@ -377,9 +377,8 @@ def minimize(self) -> Optional[torch.Tensor]:
 
     @minimize.setter
     def minimize(self, loss: Optional[torch.Tensor]) -> None:
-        if loss is not None:
-            if not isinstance(loss, torch.Tensor):
-                raise ValueError(f"`Result.minimize` must be a `torch.Tensor`, found: {loss}")
+        if loss is not None and not isinstance(loss, torch.Tensor):
+            raise ValueError(f"`Result.minimize` must be a `torch.Tensor`, found: {loss}")
         self._minimize = loss
 
     @property
@@ -388,7 +387,8 @@ def extra(self) -> Dict[str, Any]:
         Extras are any keys other than the loss returned by
         :meth:`~pytorch_lightning.core.lightning.LightningModule.training_step`
         """
-        return self.get("_extra", {})
+        self.setdefault("_extra", {})
+        return self["_extra"]
 
     @extra.setter
     def extra(self, extra: Dict[str, Any]) -> None:
@@ -605,7 +605,16 @@ def cpu(self) -> "ResultCollection":
         return self.to(device="cpu")
 
     def __str__(self) -> str:
-        return f"{self.__class__.__name__}({self.training}, {self.device}, {repr(self)})"
+        # sample output: `ResultCollection(minimize=1.23, {})`
+        minimize = f"minimize={self.minimize}, " if self.minimize is not None else ""
+        # remove empty values
+        self_str = str({k: v for k, v in self.items() if v})
+        return f"{self.__class__.__name__}({minimize}{self_str})"
+
+    def __repr__(self):
+        # sample output: `{True, cpu, minimize=tensor(1.23 grad_fn=<SumBackward0>), {'_extra': {}}}`
+        minimize = f"minimize={repr(self.minimize)}, " if self.minimize is not None else ""
+        return f"{{{self.training}, {repr(self.device)}, " + minimize + f"{super().__repr__()}}}"
 
     def __getstate__(self, drop_value: bool = True) -> dict:
         d = self.__dict__.copy()

tests/core/test_metric_result_integration.py

@@ -132,13 +132,28 @@ def test_result_metric_integration():
 
         assert epoch_log == {"b": cumulative_sum, "a_epoch": cumulative_sum}
 
+    result.minimize = torch.tensor(1.0)
+    result.extra = {}
     assert str(result) == (
-        "ResultCollection(True, cpu, {"
+        "ResultCollection("
+        "minimize=1.0, "
+        "{"
         "'h.a': ResultMetric('a', value=DummyMetric()), "
         "'h.b': ResultMetric('b', value=DummyMetric()), "
         "'h.c': ResultMetric('c', value=DummyMetric())"
         "})"
     )
+    assert repr(result) == (
+        "{"
+        "True, "
+        "device(type='cpu'), "
+        "minimize=tensor(1.), "
+        "{'h.a': ResultMetric('a', value=DummyMetric()), "
+        "'h.b': ResultMetric('b', value=DummyMetric()), "
+        "'h.c': ResultMetric('c', value=DummyMetric()), "
+        "'_extra': {}}"
+        "}"
+    )
 
 
 def test_result_collection_simple_loop():
@@ -332,3 +347,9 @@ def on_save_checkpoint(self, checkpoint) -> None:
         gpus=1 if device == "cuda" else 0,
     )
     trainer.fit(model)
+
+
+def test_result_collection_extra_reference():
+    """Unit-test to check that the `extra` dict reference is properly set."""
+    rc = ResultCollection(True)
+    assert rc.extra is rc["_extra"]