Convert progress bar metrics to float

pytorch_lightning/callbacks/progress.py

@@ -293,7 +293,6 @@ def init_validation_tqdm(self) -> tqdm:
 
     def init_test_tqdm(self, trainer=None) -> tqdm:
         """ Override this to customize the tqdm bar for testing. """
-        desc = "Testing"
         desc = "Predicting" if trainer is not None and getattr(trainer, "is_predicting", False) else "Testing"
         bar = tqdm(
             desc=desc,

pytorch_lightning/trainer/connectors/logger_connector/logger_connector.py

@@ -36,7 +36,7 @@ def __init__(self, trainer):
         self._callback_metrics = MetricsHolder()
         self._evaluation_callback_metrics = MetricsHolder(to_float=True)
         self._logged_metrics = MetricsHolder()
-        self._progress_bar_metrics = MetricsHolder()
+        self._progress_bar_metrics = MetricsHolder(to_float=True)
         self.eval_loop_results = []
         self._cached_results = {stage: EpochResultStore(trainer, stage) for stage in RunningStage}
         self._cached_results[None] = EpochResultStore(trainer, None)
@@ -87,7 +87,7 @@ def get_metrics(self, key: str) -> Dict:
         )
         return metrics_holder.metrics
 
-    def set_metrics(self, key: str, val: Any) -> None:
+    def set_metrics(self, key: str, val: Dict) -> None:
         metrics_holder = getattr(self, f"_{key}", None)
         metrics_holder.reset(val)
 

pytorch_lightning/trainer/connectors/logger_connector/metrics_holder.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import numbers
-from typing import Any
+from typing import Any, Dict
 
 import torch
 
@@ -33,13 +33,13 @@ def __init__(self, to_float: bool = False):
         self.metrics = {}
         self._to_float = to_float
 
-    def update(self, metrics):
+    def update(self, metrics: Dict):
         self.metrics.update(metrics)
 
-    def pop(self, key, default):
+    def pop(self, key: Any, default: Any):
         return self.metrics.pop(key, default)
 
-    def reset(self, metrics):
+    def reset(self, metrics: Dict):
         self.metrics = metrics
 
     def convert(self, use_tpu: bool, device: torch.device):
@@ -48,10 +48,10 @@ def convert(self, use_tpu: bool, device: torch.device):
 
     def _convert(self, current: Any, use_tpu: bool, device: torch.device):
         if self._to_float:
-            return self._convert_to_float(current, use_tpu, device)
+            return self._convert_to_float(current)
         return self._convert_to_tensor(current, use_tpu, device)
 
-    def _convert_to_float(self, current, use_tpu: bool, device: torch.device):
+    def _convert_to_float(self, current):
         if isinstance(current, Metric):
             current = current.compute().detach()
 

tests/callbacks/test_progress_bar.py

@@ -16,6 +16,7 @@
 from unittest.mock import call, Mock
 
 import pytest
+import torch
 
 from pytorch_lightning import Trainer
 from pytorch_lightning.callbacks import ModelCheckpoint, ProgressBar, ProgressBarBase
@@ -340,3 +341,25 @@ def test_test_progress_bar_update_amount(tmpdir, test_batches, refresh_rate, tes
     )
     trainer.test(model)
     progress_bar.test_progress_bar.update.assert_has_calls([call(delta) for delta in test_deltas])
+
+
+def test_test_progress_bar_thing(tmpdir):
+    """Check tensor gets converted to float"""
+    class TestModel(BoringModel):
+        def training_step(self, batch, batch_idx):
+            self.log('foo', torch.tensor(0.123), prog_bar=True)
+            self.log('bar', {"baz": torch.tensor([1])}, prog_bar=True)
+            return super().training_step(batch, batch_idx)
+
+    trainer = Trainer(
+        default_root_dir=tmpdir,
+        max_epochs=1,
+        limit_train_batches=2,
+        logger=False,
+        checkpoint_callback=False,
+    )
+    trainer.fit(TestModel())
+
+    pbar = trainer.progress_bar_callback.main_progress_bar
+    actual = str(pbar.postfix)
+    assert actual.endswith("foo=0.123, bar={'baz': tensor([1])}")