Fix Pylint issues

tests/generic_utils_text.py

@@ -2,7 +2,7 @@ import unittest
 import torch as T
 
 from utils.generic_utils import save_checkpoint, save_best_model
-from layers.tacotron import Prenet, CBHG, Decoder, Encoder
+from layers.tacotron import Prenet
 
 OUT_PATH = '/tmp/test.pth.tar'
 
@@ -11,14 +11,14 @@ class ModelSavingTests(unittest.TestCase):
     def save_checkpoint_test(self):
         # create a dummy model
         model = Prenet(128, out_features=[256, 128])
-        model = T.nn.DataParallel(layer)
+        model = T.nn.DataParallel(layer) #FIXME: undefined variable layer
 
         # save the model
-        save_checkpoint(model, None, 100, OUTPATH, 1, 1)
+        save_checkpoint(model, None, 100, OUT_PATH, 1, 1)
 
         # load the model to CPU
-        model_dict = torch.load(
-            MODEL_PATH, map_location=lambda storage, loc: storage)
+        model_dict = T.load(
+            MODEL_PATH, map_location=lambda storage, loc: storage) #FIXME: undefined variable MODEL_PATH
         model.load_state_dict(model_dict['model'])
 
     def save_best_model_test(self):
@@ -27,9 +27,9 @@ class ModelSavingTests(unittest.TestCase):
         model = T.nn.DataParallel(layer)
 
         # save the model
-        best_loss = save_best_model(model, None, 0, 100, OUT_PATH, 10, 1)
+        save_best_model(model, None, 0, 100, OUT_PATH, 10, 1)
 
         # load the model to CPU
-        model_dict = torch.load(
+        model_dict = T.load(
             MODEL_PATH, map_location=lambda storage, loc: storage)
         model.load_state_dict(model_dict['model'])

tests/test_audio.py

@@ -1,7 +1,5 @@
 import os
 import unittest
-import numpy as np
-import torch as T
 
 from tests import get_tests_path, get_tests_input_path, get_tests_output_path
 from utils.audio import AudioProcessor

tests/test_layers.py

@@ -19,6 +19,7 @@ class PrenetTests(unittest.TestCase):
 
 class CBHGTests(unittest.TestCase):
     def test_in_out(self):
+        #pylint: disable=attribute-defined-outside-init
         layer = self.cbhg = CBHG(
             128,
             K=8,
@@ -38,7 +39,7 @@ class CBHGTests(unittest.TestCase):
 
 class DecoderTests(unittest.TestCase):
     def test_in_out(self):
-        layer = Decoder(in_features=256, memory_dim=80, r=2, memory_size=4, attn_windowing=False, attn_norm="sigmoid")
+        layer = Decoder(in_features=256, memory_dim=80, r=2, memory_size=4, attn_windowing=False, attn_norm="sigmoid") #FIXME: several missing required parameters for Decoder ctor
         dummy_input = T.rand(4, 8, 256)
         dummy_memory = T.rand(4, 2, 80)
 

tests/test_loader.py

@@ -1,7 +1,6 @@
 import os
 import unittest
 import shutil
-import numpy as np
 
 from torch.utils.data import DataLoader
 from utils.generic_utils import load_config
@@ -132,7 +131,7 @@ class TestTTSDataset(unittest.TestCase):
                 self.ap.save_wav(wav, OUTPATH + '/mel_inv_dataloader.wav')
                 shutil.copy(item_idx[0], OUTPATH + '/mel_target_dataloader.wav')
 
-                # check linear-spec 
+                # check linear-spec
                 linear_spec = linear_input[0].cpu().numpy()
                 wav = self.ap.inv_spectrogram(linear_spec.T)
                 self.ap.save_wav(wav, OUTPATH + '/linear_inv_dataloader.wav')

tests/test_tacotron2_model.py

@@ -37,7 +37,7 @@ class TacotronTrainTest(unittest.TestCase):
 
         criterion = MSELossMasked().to(device)
         criterion_st = nn.BCEWithLogitsLoss().to(device)
-        model = Tacotron2(24, c.r).to(device)
+        model = Tacotron2(24, c.r).to(device) #FIXME: missing num_speakers parameter to Tacotron2 ctor
         model.train()
         model_ref = copy.deepcopy(model)
         count = 0

tests/test_tacotron_model.py

@@ -2,7 +2,6 @@ import os
 import copy
 import torch
 import unittest
-import numpy as np
 
 from torch import optim
 from torch import nn
@@ -48,7 +47,7 @@ class TacotronTrainTest(unittest.TestCase):
             linear_dim=c.audio['num_freq'],
             mel_dim=c.audio['num_mels'],
             r=c.r,
-            memory_size=c.memory_size).to(device)
+            memory_size=c.memory_size).to(device) #FIXME: missing num_speakers parameter to Tacotron ctor
         model.train()
         print(" > Num parameters for Tacotron model:%s"%(count_parameters(model)))
         model_ref = copy.deepcopy(model)
@@ -58,7 +57,7 @@ class TacotronTrainTest(unittest.TestCase):
             assert (param - param_ref).sum() == 0, param
             count += 1
         optimizer = optim.Adam(model.parameters(), lr=c.lr)
-        for i in range(5):
+        for _ in range(5):
             mel_out, linear_out, align, stop_tokens = model.forward(
                 input, input_lengths, mel_spec)
             optimizer.zero_grad()
@@ -77,4 +76,4 @@ class TacotronTrainTest(unittest.TestCase):
             assert (param != param_ref).any(
             ), "param {} with shape {} not updated!! \n{}\n{}".format(
                 count, param.shape, param, param_ref)
-            count += 1
+            count += 1

tests/test_text_processing.py

@@ -69,7 +69,6 @@ def test_phoneme_to_sequence():
 
 def test_text2phone():
     text = "Recent research at Harvard has shown meditating for as little as 8 weeks can actually increase, the grey matter in the parts of the brain responsible for emotional regulation and learning!"
-    text_cleaner = ["phoneme_cleaners"]
     gt = "ɹ|iː|s|ə|n|t| |ɹ|ɪ|s|ɜː|tʃ| |æ|t| |h|ɑːɹ|v|ɚ|d| |h|ɐ|z| |ʃ|oʊ|n| |m|ɛ|d|ᵻ|t|eɪ|ɾ|ɪ|ŋ| |f|ɔː|ɹ| |æ|z| |l|ɪ|ɾ|əl| |æ|z| |eɪ|t| |w|iː|k|s| |k|æ|n| |æ|k|tʃ|uː|əl|i|| |ɪ|n|k|ɹ|iː|s|,| |ð|ə| |ɡ|ɹ|eɪ| |m|æ|ɾ|ɚ|ɹ| |ɪ|n|ð|ə| |p|ɑːɹ|t|s| |ʌ|v|ð|ə| |b|ɹ|eɪ|n| |ɹ|ɪ|s|p|ɑː|n|s|ə|b|əl| |f|ɔː|ɹ| |ɪ|m|oʊ|ʃ|ə|n|əl| |ɹ|ɛ|ɡ|j|uː|l|eɪ|ʃ|ə|n||| |æ|n|d| |l|ɜː|n|ɪ|ŋ|!"
     lang = "en-us"
     phonemes = text2phone(text, lang)