From 0dcb1f87c59263a07b1836a39824badeb71a9719 Mon Sep 17 00:00:00 2001
From: Reuben Morais <reuben.morais@gmail.com>
Date: Sun, 11 Nov 2018 18:04:00 -0200
Subject: [PATCH] Clean up evaluate.py

---
 evaluate.py | 46 +++++++++++++++++++++++-----------------------
 1 file changed, 23 insertions(+), 23 deletions(-)

diff --git a/evaluate.py b/evaluate.py
index 50234f17..1f0fe291 100755
--- a/evaluate.py
+++ b/evaluate.py
@@ -159,29 +159,29 @@ def evaluate(test_data, inference_graph, alphabet):
             logitses.append(logits)
             losses.extend(loss)
 
-        ground_truths = []
-        predictions = []
-        distances = []
+    ground_truths = []
+    predictions = []
 
-        print('Decoding predictions...')
-        bar = progressbar.ProgressBar(max_value=batch_count,
-                                      widget=progressbar.AdaptiveETA)
+    print('Decoding predictions...')
+    bar = progressbar.ProgressBar(max_value=batch_count,
+                                  widget=progressbar.AdaptiveETA)
 
-        # Get number of accessible CPU cores for this process
-        try:
-            num_processes = cpu_count()
-        except:
-            num_processes = 1
+    # Get number of accessible CPU cores for this process
+    try:
+        num_processes = cpu_count()
+    except:
+        num_processes = 1
 
-        # Second pass, decode logits and compute WER and edit distance metrics
-        for logits, batch in bar(zip(logitses, split_data(test_data, FLAGS.test_batch_size))):
-            seq_lengths = batch['features_len'].values.astype(np.int32)
-            decoded = ctc_beam_search_decoder_batch(logits, seq_lengths, alphabet, FLAGS.beam_width,
-                                                    num_processes=num_processes, scorer=scorer)
+    # Second pass, decode logits and compute WER and edit distance metrics
+    for logits, batch in bar(zip(logitses, split_data(test_data, FLAGS.test_batch_size))):
+        seq_lengths = batch['features_len'].values.astype(np.int32)
+        decoded = ctc_beam_search_decoder_batch(logits, seq_lengths, alphabet, FLAGS.beam_width,
+                                                num_processes=num_processes, scorer=scorer)
 
-            ground_truths.extend(alphabet.decode(l) for l in batch['transcript'])
-            predictions.extend(d[0][1] for d in decoded)
-            distances.extend(levenshtein(a, b) for a, b in zip(labels, predictions))
+        ground_truths.extend(alphabet.decode(l) for l in batch['transcript'])
+        predictions.extend(d[0][1] for d in decoded)
+
+    distances = [levenshtein(a, b) for a, b in zip(ground_truths, predictions)]
 
     wer, samples = calculate_report(ground_truths, predictions, distances, losses)
     mean_edit_distance = np.mean(distances)
@@ -190,12 +190,12 @@ def evaluate(test_data, inference_graph, alphabet):
     # Take only the first report_count items
     report_samples = itertools.islice(samples, FLAGS.report_count)
 
-    print('Test - WER: %f, loss: %f, mean edit distance: %f' %
-          (wer, mean_loss, mean_edit_distance))
+    print('Test - WER: %f, CER: %f, loss: %f' %
+          (wer, mean_edit_distance, mean_loss))
     print('-' * 80)
     for sample in report_samples:
-        print('WER: %f, loss: %f, edit distance: %f' %
-              (sample.wer, sample.loss, sample.distance))
+        print('WER: %f, CER: %f, loss: %f' %
+              (sample.wer, sample.distance, sample.loss))
         print(' - src: "%s"' % sample.src)
         print(' - res: "%s"' % sample.res)
         print('-' * 80)