Revert "Use explicit relative import syntax for python 3 compatibility in audioset."

research/audioset/vggish_inference_demo.py

@@ -46,16 +46,16 @@ Usage:
 
 from __future__ import print_function
 
-from . import vggish_input
-from . import vggish_params
-from . import vggish_postprocess
-from . import vggish_slim
-
 import numpy as np
 from scipy.io import wavfile
 import six
 import tensorflow as tf
 
+import vggish_input
+import vggish_params
+import vggish_postprocess
+import vggish_slim
+
 flags = tf.app.flags
 
 flags.DEFINE_string(

research/audioset/vggish_input.py

@@ -15,13 +15,13 @@
 
 """Compute input examples for VGGish from audio waveform."""
 
-from . import mel_features
-from . import vggish_params
-
 import numpy as np
 import resampy
 from scipy.io import wavfile
 
+import mel_features
+import vggish_params
+
 
 def waveform_to_examples(data, sample_rate):
   """Converts audio waveform into an array of examples for VGGish.

research/audioset/vggish_postprocess.py

@@ -15,10 +15,10 @@
 
 """Post-process embeddings from VGGish."""
 
-from . import vggish_params
-
 import numpy as np
 
+import vggish_params
+
 
 class Postprocessor(object):
   """Post-processes VGGish embeddings.

research/audioset/vggish_slim.py

@@ -30,8 +30,8 @@ For comparison, here is TF-Slim's VGG definition:
 https://github.com/tensorflow/models/blob/master/research/slim/nets/vgg.py
 """
 
-from . import vggish_params as params
 import tensorflow as tf
+import vggish_params as params
 
 slim = tf.contrib.slim
 

research/audioset/vggish_smoke_test.py

@@ -31,14 +31,14 @@ Usage:
 
 from __future__ import print_function
 
-from . import vggish_input
-from . import vggish_params
-from . import vggish_postprocess
-from . import vggish_slim
-
 import numpy as np
 import tensorflow as tf
 
+import vggish_input
+import vggish_params
+import vggish_postprocess
+import vggish_slim
+
 print('\nTesting your install of VGGish\n')
 
 # Paths to downloaded VGGish files.

research/audioset/vggish_train_demo.py

@@ -47,13 +47,13 @@ from __future__ import print_function
 
 from random import shuffle
 
-from . import vggish_input
-from . import vggish_params
-from . import vggish_slim
-
 import numpy as np
 import tensorflow as tf
 
+import vggish_input
+import vggish_params
+import vggish_slim
+
 flags = tf.app.flags
 slim = tf.contrib.slim