Centralize progress logging and progress bar logic

DeepSpeech.py

@@ -22,7 +22,7 @@ from tensorflow.python.tools import freeze_graph
 from util.config import Config, initialize_globals
 from util.feeding import create_dataset, samples_to_mfccs, audiofile_to_features
 from util.flags import create_flags, FLAGS
-from util.logging import log_info, log_error, log_debug
+from util.logging import log_info, log_error, log_debug, log_progress, create_progressbar
 
 
 # Graph Creation
@@ -425,15 +425,6 @@ def train():
 
     initializer = tf.global_variables_initializer()
 
-    # Disable progress logging if needed
-    if FLAGS.show_progressbar:
-        pbar_class = progressbar.ProgressBar
-        def log_progress(*args, **kwargs):
-            pass
-    else:
-        pbar_class = progressbar.NullBar
-        log_progress = log_info
-
     with tf.Session(config=Config.session_config) as session:
         log_debug('Session opened.')
 
@@ -479,7 +470,7 @@ def train():
                        ' | Steps: ', progressbar.widgets.Counter(),
                        ' | ', LossWidget()]
             suffix = ' | Dataset: {}'.format(dataset) if dataset else None
-            pbar = pbar_class(prefix=prefix, widgets=widgets, suffix=suffix, fd=sys.stdout).start()
+            pbar = create_progressbar(prefix=prefix, widgets=widgets, suffix=suffix).start()
 
             # Initialize iterator to the appropriate dataset
             session.run(init_op)

evaluate.py

@@ -18,7 +18,7 @@ from util.config import Config, initialize_globals
 from util.evaluate_tools import calculate_report
 from util.feeding import create_dataset
 from util.flags import create_flags, FLAGS
-from util.logging import log_error
+from util.logging import log_error, log_progress, create_progressbar
 from util.text import levenshtein
 
 
@@ -93,9 +93,9 @@ def evaluate(test_csvs, create_model, try_loading):
             seq_lengths = []
             ground_truths = []
 
-            bar = progressbar.ProgressBar(prefix='Computing acoustic model predictions | ',
-                                          widgets=['Steps: ', progressbar.Counter(), ' | ', progressbar.Timer()],
-                                          fd=sys.stdout).start()
+            bar = create_progressbar(prefix='Computing acoustic model predictions | ',
+                                     widgets=['Steps: ', progressbar.Counter(), ' | ', progressbar.Timer()]).start()
+            log_progress('Computing acoustic model predictions...')
 
             step_count = 0
 
@@ -121,9 +121,9 @@ def evaluate(test_csvs, create_model, try_loading):
 
             predictions = []
 
-            bar = progressbar.ProgressBar(max_value=step_count,
-                                          prefix='Decoding predictions | ',
-                                          fd=sys.stdout).start()
+            bar = create_progressbar(max_value=step_count,
+                                     prefix='Decoding predictions | ').start()
+            log_progress('Decoding predictions...')
 
             # Second pass, decode logits and compute WER and edit distance metrics
             for logits, seq_length in bar(zip(logitses, seq_lengths)):

util/logging.py

@@ -1,5 +1,8 @@
 from __future__ import print_function
 
+import progressbar
+import sys
+
 from util.flags import FLAGS
 
 
@@ -28,3 +31,19 @@ def log_warn(message):
 def log_error(message):
     if FLAGS.log_level <= 3:
         prefix_print('E ', message)
+
+
+def create_progressbar(*args, **kwargs):
+    # Progress bars in stdout by default
+    if 'fd' not in kwargs:
+        kwargs['fd'] = sys.stdout
+
+    if FLAGS.show_progressbar:
+        return progressbar.ProgressBar(*args, **kwargs)
+
+    return progressbar.NullBar(*args, **kwargs)
+
+
+def log_progress(message):
+    if not FLAGS.show_progressbar:
+        log_info(message)