[Metrics] Disable default reset after compute

CHANGELOG.md

@@ -64,6 +64,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Changed `iou` [func] to allow float input ([#4704](https://github.com/PyTorchLightning/pytorch-lightning/pull/4704))
 
 
+- Metric `compute()` method will no longer automatically call `reset()` ([#5409](https://github.com/PyTorchLightning/pytorch-lightning/pull/5409/))
+
+
 - Set PyTorch 1.4 as min requirements, also for testing and examples `torchvision>=0.5` and `torchtext>=0.5` ([#5418](https://github.com/PyTorchLightning/pytorch-lightning/pull/5418))
 
 

docs/source/metrics.rst

@@ -20,6 +20,11 @@ The metrics API provides ``update()``, ``compute()``, ``reset()`` functions to t
 serves the dual purpose of calling ``update()`` on its input and simultaneously returning the value of the metric over the
 provided input.
 
+.. warning::
+    From v1.2 onward ``compute()`` will no longer automatically call ``reset()``,
+    and it is up to the user to reset metrics between epochs, except in the case where the
+    metric is directly passed to ``LightningModule``s ``self.log``.
+
 These metrics work with DDP in PyTorch and PyTorch Lightning by default. When ``.compute()`` is called in
 distributed mode, the internal state of each metric is synced and reduced across each process, so that the
 logic present in ``.compute()`` is applied to state information from all processes.

pytorch_lightning/core/step_result.py

@@ -306,7 +306,6 @@ def get_epoch_log_metrics(self, add_dataloader_idx=False) -> dict:
         Gets the metrics to log at the end of epoch
         """
         result = {}
-
         meta = self['meta']
         for k, options in meta.items():
             if k == '_internal':
@@ -320,12 +319,16 @@ def get_epoch_log_metrics(self, add_dataloader_idx=False) -> dict:
             if options['logger'] and options['on_epoch']:
                 if isinstance(self[k], Metric):
                     result[dl_key] = self[k].compute().detach()
+                    self[k].reset()
                 else:
                     result[dl_key] = self[k]
 
             if k in self and not options['on_epoch'] and isinstance(self[k], Metric):
-                # compute metric on epoch anyway so state does not accumulate
+                # reset metric anyway so state does not accumulate
+                # NOTE: we must compute before reseting just in case the computed value is needed
+                # later (i.e. if the step metric gets visited first, and then the epoch metric)
                 self[k].compute()
+                self[k].reset()
 
         return result
 
@@ -348,12 +351,16 @@ def get_epoch_pbar_metrics(self, add_dataloader_idx=False):
             if options['prog_bar'] and options['on_epoch']:
                 if isinstance(self[k], Metric):
                     result[dl_key] = self[k].compute().detach()
+                    self[k].reset()
                 else:
                     result[dl_key] = self[k]
 
             if k in self and not options['on_epoch'] and isinstance(self[k], Metric):
-                # compute metric on epoch anyway so state does not accumulate
+                # reset metric anyway so state does not accumulate
+                # NOTE: we must compute before reseting just in case the computed value is needed
+                # later (i.e. if the step metric gets visited first, and then the epoch metric)
                 self[k].compute()
+                self[k].reset()
 
         return result
 
@@ -373,6 +380,7 @@ def get_forked_metrics(self, add_dataloader_idx=False):
             if options['forked']:
                 if isinstance(self[k], Metric):
                     result[dl_key] = self[k].compute().detach()
+                    self[k].reset()
                 else:
                     result[dl_key] = self[k]
 

pytorch_lightning/metrics/metric.py

@@ -126,9 +126,7 @@ def add_state(
             and not isinstance(default, list)  # noqa: W503
             or (isinstance(default, list) and len(default) != 0)  # noqa: W503
         ):
-            raise ValueError(
-                "state variable must be a tensor or any empty list (where you can append tensors)"
-            )
+            raise ValueError("state variable must be a tensor or any empty list (where you can append tensors)")
 
         if dist_reduce_fx == "sum":
             dist_reduce_fx = dim_zero_sum
@@ -137,9 +135,7 @@ def add_state(
         elif dist_reduce_fx == "cat":
             dist_reduce_fx = dim_zero_cat
         elif dist_reduce_fx is not None and not isinstance(dist_reduce_fx, Callable):
-            raise ValueError(
-                "`dist_reduce_fx` must be callable or one of ['mean', 'sum', 'cat', None]"
-            )
+            raise ValueError("`dist_reduce_fx` must be callable or one of ['mean', 'sum', 'cat', None]")
 
         setattr(self, name, default)
 
@@ -161,15 +157,15 @@ def forward(self, *args, **kwargs):
             self._to_sync = self.dist_sync_on_step
 
             # save context before switch
-            self._cache = {attr: getattr(self, attr) for attr in self._defaults.keys()}
+            cache = {attr: getattr(self, attr) for attr in self._defaults.keys()}
 
             # call reset, update, compute, on single batch
             self.reset()
             self.update(*args, **kwargs)
             self._forward_cache = self.compute()
 
             # restore context
-            for attr, val in self._cache.items():
+            for attr, val in cache.items():
                 setattr(self, attr, val)
             self._to_sync = True
             self._computed = None
@@ -201,6 +197,7 @@ def _wrap_update(self, update):
         def wrapped_func(*args, **kwargs):
             self._computed = None
             return update(*args, **kwargs)
+
         return wrapped_func
 
     def _wrap_compute(self, compute):
@@ -211,19 +208,24 @@ def wrapped_func(*args, **kwargs):
                 return self._computed
 
             dist_sync_fn = self.dist_sync_fn
-            if (
-                dist_sync_fn is None
-                and torch.distributed.is_available()
-                and torch.distributed.is_initialized()
-            ):
+            if dist_sync_fn is None and torch.distributed.is_available() and torch.distributed.is_initialized():
                 # User provided a bool, so we assume DDP if available
                 dist_sync_fn = gather_all_tensors
 
+            synced = False
             if self._to_sync and dist_sync_fn is not None:
+                # cache prior to syncing
+                cache = {attr: getattr(self, attr) for attr in self._defaults.keys()}
+
+                # sync
                 self._sync_dist(dist_sync_fn)
+                synced = True
 
             self._computed = compute(*args, **kwargs)
-            self.reset()
+            if synced:
+                # if we synced, restore to cache so that we can continue to accumulate un-synced state
+                for attr, val in cache.items():
+                    setattr(self, attr, val)
 
             return self._computed
 
@@ -270,8 +272,8 @@ def __setstate__(self, state):
         self.compute = self._wrap_compute(self.compute)
 
     def _apply(self, fn):
-        """ Overwrite _apply function such that we can also move metric states
-            to the correct device when `.to`, `.cuda`, etc methods are called
+        """Overwrite _apply function such that we can also move metric states
+        to the correct device when `.to`, `.cuda`, etc methods are called
         """
         self = super()._apply(fn)
         # Also apply fn to metric states
@@ -282,13 +284,15 @@ def _apply(self, fn):
             elif isinstance(current_val, Sequence):
                 setattr(self, key, [fn(cur_v) for cur_v in current_val])
             else:
-                raise TypeError('Expected metric state to be either a torch.Tensor'
-                                f'or a list of torch.Tensor, but encountered {current_val}')
+                raise TypeError(
+                    "Expected metric state to be either a torch.Tensor"
+                    f"or a list of torch.Tensor, but encountered {current_val}"
+                )
         return self
 
     def persistent(self, mode: bool = False):
-        """ Method for post-init to change if metric states should be saved to
-            its state_dict
+        """Method for post-init to change if metric states should be saved to
+        its state_dict
         """
         for key in self._persistent.keys():
             self._persistent[key] = mode

tests/metrics/test_metric.py

@@ -139,8 +139,8 @@ def compute(self):
     assert a._computed == 1
     a.update(2)
     assert a._computed is None
-    assert a.compute() == 2
-    assert a._computed == 2
+    assert a.compute() == 3
+    assert a._computed == 3
 
     # called without update, should return cached value
     a._computed = 5
@@ -192,7 +192,7 @@ def test_pickle(tmpdir):
     assert metric_loaded.compute() == 1
 
     metric_loaded.update(5)
-    assert metric_loaded.compute() == 5
+    assert metric_loaded.compute() == 6
 
     metric_pickled = cloudpickle.dumps(a)
     metric_loaded = cloudpickle.loads(metric_pickled)

tests/metrics/test_metric_lightning.py

@@ -46,6 +46,7 @@ def training_step(self, batch, batch_idx):
         def training_epoch_end(self, outs):
             assert torch.allclose(self.sum, self.metric.compute())
             self.sum = 0.0
+            self.metric.reset()
 
     model = TestModel()
     model.val_dataloader = None