Fix progress bar print error when called before training (#7674)

* Check progress bar existence before printing

* Add tests for predict_progres_bar

* Add tests for progress_bar printing without training

* Update changelog

Author: ryanking13

CHANGELOG.md

@@ -138,6 +138,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Fixed setting correct `DistribType` for `ddp_cpu` (spawn) backend ([#7492](https://github.com/PyTorchLightning/pytorch-lightning/pull/7492))
 
 
+- Fixed print errors in `ProgressBar` when `trainer.fit` is not called ([#7674](https://github.com/PyTorchLightning/pytorch-lightning/pull/7674))
+
+
 ## [1.3.1] - 2021-05-11
 
 ### Fixed

pytorch_lightning/callbacks/progress.py

@@ -473,12 +473,14 @@ def print(
     ):
         active_progress_bar = None
 
-        if not self.main_progress_bar.disable:
+        if self.main_progress_bar is not None and not self.main_progress_bar.disable:
             active_progress_bar = self.main_progress_bar
-        elif not self.val_progress_bar.disable:
+        elif self.val_progress_bar is not None and not self.val_progress_bar.disable:
             active_progress_bar = self.val_progress_bar
-        elif not self.test_progress_bar.disable:
+        elif self.test_progress_bar is not None and not self.test_progress_bar.disable:
             active_progress_bar = self.test_progress_bar
+        elif self.predict_progress_bar is not None and not self.predict_progress_bar.disable:
+            active_progress_bar = self.predict_progress_bar
 
         if active_progress_bar is not None:
             s = sep.join(map(str, args))

tests/callbacks/test_progress_bar.py

@@ -433,6 +433,10 @@ def test_step(self, *args, **kwargs):
         self.print("test_step")
         return super().test_step(*args, **kwargs)
 
+    def predict_step(self, *args, **kwargs):
+        self.print("predict_step")
+        return super().predict_step(*args, **kwargs)
+
 
 @mock.patch("pytorch_lightning.callbacks.progress.tqdm.write")
 def test_progress_bar_print(tqdm_write, tmpdir):
@@ -445,16 +449,45 @@ def test_progress_bar_print(tqdm_write, tmpdir):
         limit_train_batches=1,
         limit_val_batches=1,
         limit_test_batches=1,
+        limit_predict_batches=1,
         max_steps=1,
         callbacks=[bar],
     )
     trainer.fit(model)
     trainer.test(model)
-    assert tqdm_write.call_count == 3
+    trainer.predict(model)
+    assert tqdm_write.call_count == 4
     assert tqdm_write.call_args_list == [
         call("training_step", end="", file=None, nolock=False),
         call("validation_step", end=os.linesep, file=sys.stderr, nolock=False),
         call("test_step", end=os.linesep, file=None, nolock=False),
+        call("predict_step", end=os.linesep, file=None, nolock=False),
+    ]
+
+
+@mock.patch("pytorch_lightning.callbacks.progress.tqdm.write")
+def test_progress_bar_print_no_train(tqdm_write, tmpdir):
+    """ Test that printing in the LightningModule redirects arguments to the progress bar without training. """
+    model = PrintModel()
+    bar = ProgressBar()
+    trainer = Trainer(
+        default_root_dir=tmpdir,
+        num_sanity_val_steps=0,
+        limit_val_batches=1,
+        limit_test_batches=1,
+        limit_predict_batches=1,
+        max_steps=1,
+        callbacks=[bar],
+    )
+
+    trainer.validate(model)
+    trainer.test(model)
+    trainer.predict(model)
+    assert tqdm_write.call_count == 3
+    assert tqdm_write.call_args_list == [
+        call("validation_step", end=os.linesep, file=sys.stderr, nolock=False),
+        call("test_step", end=os.linesep, file=None, nolock=False),
+        call("predict_step", end=os.linesep, file=None, nolock=False),
     ]
 
 
@@ -470,17 +503,20 @@ def test_progress_bar_print_disabled(tqdm_write, mock_print, tmpdir):
         limit_train_batches=1,
         limit_val_batches=1,
         limit_test_batches=1,
+        limit_predict_batches=1,
         max_steps=1,
         callbacks=[bar],
     )
     bar.disable()
     trainer.fit(model)
-    trainer.test(model)
+    trainer.test(model, verbose=False)
+    trainer.predict(model)
 
     mock_print.assert_has_calls([
         call("training_step", end=""),
         call("validation_step", file=ANY),
         call("test_step"),
+        call("predict_step"),
     ])
     tqdm_write.assert_not_called()