[bug-fix] Metric reduction with Logging

pytorch_lightning/callbacks/early_stopping.py

@@ -19,14 +19,16 @@
 Monitor a metric and stop training when it stops improving.
 
 """
+import numbers
 import os
 
 import numpy as np
 import torch
 
 from pytorch_lightning import _logger as log
 from pytorch_lightning.callbacks.base import Callback
-from pytorch_lightning.utilities import rank_zero_info, rank_zero_warn, TPU_AVAILABLE
+from pytorch_lightning.metrics.metric import Metric
+from pytorch_lightning.utilities import TPU_AVAILABLE, rank_zero_info, rank_zero_warn
 
 
 class EarlyStopping(Callback):
@@ -201,8 +203,11 @@ def _run_early_stopping_check(self, trainer, pl_module):
         # when in dev debugging
         trainer.dev_debugger.track_early_stopping_history(self, current)
 
-        if not isinstance(current, torch.Tensor):
-            current = torch.tensor(current, device=pl_module.device)
+        if current is not None:
+            if isinstance(current, Metric):
+                current = current.compute()
+            elif isinstance(current, numbers.Number):
+                current = torch.tensor(current, device=pl_module.device, dtype=torch.float)
 
         if trainer.use_tpu and TPU_AVAILABLE:
             current = current.cpu()

pytorch_lightning/callbacks/model_checkpoint.py

@@ -20,6 +20,7 @@
 
 """
 
+import numbers
 import os
 import re
 from copy import deepcopy
@@ -32,8 +33,9 @@
 
 from pytorch_lightning import _logger as log
 from pytorch_lightning.callbacks.base import Callback
-from pytorch_lightning.utilities import rank_zero_info, rank_zero_only, rank_zero_warn
+from pytorch_lightning.metrics.metric import Metric
 from pytorch_lightning.plugins.rpc_plugin import RPCPlugin
+from pytorch_lightning.utilities import rank_zero_info, rank_zero_only, rank_zero_warn
 from pytorch_lightning.utilities.cloud_io import get_filesystem
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
 
@@ -580,8 +582,11 @@ def _save_top_k_checkpoints(self, trainer, pl_module, metrics):
         epoch = metrics.get("epoch")
         step = metrics.get("step")
 
-        if not isinstance(current, torch.Tensor) and current is not None:
-            current = torch.tensor(current, device=pl_module.device)
+        if current is not None:
+            if isinstance(current, Metric):
+                current = current.compute()
+            elif isinstance(current, numbers.Number):
+                current = torch.tensor(current, device=pl_module.device, dtype=torch.float)
 
         if self.check_monitor_top_k(current):
             self._update_best_and_save(current, epoch, step, trainer, pl_module, metrics)

pytorch_lightning/core/step_result.py

@@ -367,7 +367,10 @@ def get_forked_metrics(self, add_dataloader_idx=False):
             dl_key = self._add_dataloader_idx(k, options["dataloader_idx"], add_dataloader_idx)
 
             if options['forked']:
-                result[dl_key] = self[k]
+                if isinstance(self[k], Metric):
+                    result[dl_key] = self[k].compute().detach()
+                else:
+                    result[dl_key] = self[k]
 
         return result
 

tests/trainer/logging_tests/test_train_loop_logging_1_0.py

@@ -26,8 +26,8 @@
 from torch.utils.data import Dataset
 
 import pytorch_lightning as pl
-from pytorch_lightning import Trainer, callbacks
-from pytorch_lightning.callbacks import ModelCheckpoint
+from pytorch_lightning import callbacks, Trainer
+from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
 from pytorch_lightning.core.lightning import LightningModule
 from tests.base.boring_model import BoringModel, RandomDictDataset, RandomDictStringDataset
 from tests.base.deterministic_model import DeterministicModel
@@ -771,3 +771,48 @@ def on_train_epoch_end(self, *_):
     trainer.fit(model)
     assert model.epoch_end_called
     assert model.on_train_epoch_end_called
+
+
+@pytest.mark.skipif(not torch.cuda.is_available(), reason="requires GPU machine")
+def test_metric_are_properly_reduced(tmpdir):
+    class TestingModel(BoringModel):
+        def __init__(self, *args, **kwargs):
+            super().__init__()
+            self.train_acc = pl.metrics.Accuracy()
+            self.val_acc = pl.metrics.Accuracy()
+
+        def training_step(self, batch, batch_idx):
+            self.train_acc(torch.rand(1, 3, device=self.device), torch.randint(0, 2, (1,), device=self.device))
+            self.log('train_acc', self.train_acc, on_step=True, on_epoch=True)
+            return super().training_step(batch, batch_idx)
+
+        def validation_step(self, batch, batch_idx):
+            preds = torch.tensor(0, device=self.device)
+            targets = torch.tensor(1, device=self.device)
+            if batch_idx < 8:
+                targets = preds
+            self.val_acc(preds, targets)
+            self.log('val_acc', self.val_acc, on_step=True, on_epoch=True)
+            return super().validation_step(batch, batch_idx)
+
+    early_stop = EarlyStopping(monitor='val_acc', mode='max')
+
+    checkpoint = ModelCheckpoint(
+        monitor='val_acc',
+        save_last=True,
+        save_top_k=2,
+        mode='max',
+    )
+
+    model = TestingModel()
+    trainer = Trainer(
+        default_root_dir=tmpdir,
+        gpus=1,
+        max_epochs=2,
+        limit_train_batches=5,
+        limit_val_batches=32,
+        callbacks=[early_stop, checkpoint])
+    trainer.fit(model)
+
+    assert trainer.callback_metrics["val_acc"] == 8 / 32.
+    assert "train_acc" in trainer.callback_metrics