Convert progress bar metrics to float (#5692)

* MetricsHolder(to_float=True)

* Update CHANGELOG

* Update tests/callbacks/test_progress_bar.py

* flake8

Co-authored-by: Jirka Borovec <jirka.borovec@seznam.cz>

CHANGELOG.md

@@ -103,7 +103,10 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Changed the default of `find_unused_parameters` to `False` in DDP ([#5185](https://github.com/PyTorchLightning/pytorch-lightning/pull/5185))
 
 
-- Changed `ModelCheckpoint` version suffixes to start at 1 ([5008](https://github.com/PyTorchLightning/pytorch-lightning/pull/5008))
+- Changed `ModelCheckpoint` version suffixes to start at 1 ([#5008](https://github.com/PyTorchLightning/pytorch-lightning/pull/5008))
+
+
+- Progress bar metrics tensors are now converted to float ([#5692](https://github.com/PyTorchLightning/pytorch-lightning/pull/5692))
 
 
 - Changed the default value for the `progress_bar_refresh_rate` Trainer argument in Google COLAB notebooks to 20 ([#5516](https://github.com/PyTorchLightning/pytorch-lightning/pull/5516))

pytorch_lightning/trainer/connectors/logger_connector/logger_connector.py

@@ -14,7 +14,7 @@
 import os
 from copy import deepcopy
 from pprint import pprint
-from typing import Any, Dict, Iterable, Union
+from typing import Dict, Iterable, Union
 
 import torch
 
@@ -37,7 +37,7 @@ 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)
@@ -88,7 +88,7 @@ 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)
 

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
@@ -349,3 +350,27 @@ 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_tensor_to_float_conversion(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])}")