resolve comments

texar/losses/__init__.py

@@ -1,4 +1,4 @@
-# Copyright 2018 The Texar Authors. All Rights Reserved.
+# Copyright 2019 The Texar Authors. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -15,10 +15,6 @@
 Modules of texar losses.
 """
 
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
 # pylint: disable=wildcard-import
 
 from texar.losses.losses_utils import *

texar/losses/adv_losses.py

@@ -1,4 +1,4 @@
-# Copyright 2018 The Texar Authors. All Rights Reserved.
+# Copyright 2019 The Texar Authors. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -14,11 +14,9 @@
 """
 Adversarial losses.
 """
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
 
 import torch
+import torch.nn.functional as F
 
 
 def binary_adversarial_losses(real_data,
@@ -58,14 +56,14 @@ def binary_adversarial_losses(real_data,
     real_logits = discriminator_fn(real_data)
     if isinstance(real_logits, (list, tuple)):
         real_logits = real_logits[0]
-    real_loss = torch.nn.BCEWithLogitsLoss(reduction='mean')
-    real_loss = real_loss(real_logits, torch.ones_like(real_logits))
+    real_loss = F.binary_cross_entropy_with_logits(
+        real_logits, torch.ones_like(real_logits))
 
     fake_logits = discriminator_fn(fake_data)
     if isinstance(fake_logits, (list, tuple)):
         fake_logits = fake_logits[0]
-    fake_loss = torch.nn.BCEWithLogitsLoss(reduction='mean')
-    fake_loss = fake_loss(fake_logits, torch.zeros_like(fake_logits))
+    fake_loss = F.binary_cross_entropy_with_logits(
+        fake_logits, torch.zeros_like(fake_logits))
 
     d_loss = real_loss + fake_loss
 

texar/losses/adv_losses_test.py

@@ -4,11 +4,6 @@
 Unit tests for adv_losses.
 """
 
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-from __future__ import unicode_literals
-
 # pylint: disable=invalid-name
 
 import unittest

texar/losses/entropy.py

@@ -1,4 +1,4 @@
-# Copyright 2018 The Texar Authors. All Rights Reserved.
+# Copyright 2019 The Texar Authors. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -15,10 +15,6 @@
 Various entropies.
 """
 
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
 import torch
 import torch.nn.functional as F
 

texar/losses/entropy_test.py

@@ -4,11 +4,6 @@
 Unit tests for entropy.
 """
 
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-from __future__ import unicode_literals
-
 # pylint: disable=invalid-name
 
 import unittest

texar/losses/losses_utils.py

@@ -1,4 +1,4 @@
-# Copyright 2018 The Texar Authors. All Rights Reserved.
+# Copyright 2019 The Texar Authors. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -15,11 +15,6 @@
 Various utilities for losses.
 """
 
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-
 import torch
 
 from texar.utils.shapes import transpose_batch_time, mask_sequences

texar/losses/mle_losses.py

@@ -1,4 +1,4 @@
-# Copyright 2018 The Texar Authors. All Rights Reserved.
+# Copyright 2019 The Texar Authors. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -15,10 +15,6 @@
 Various losses
 """
 
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
 import torch
 import torch.nn.functional as F
 
@@ -45,8 +41,7 @@ def sequence_softmax_cross_entropy(labels,
                                    sum_over_batch=False,
                                    sum_over_timesteps=True,
                                    time_major=False,
-                                   stop_gradient_to_label=False,
-                                   name=None):
+                                   stop_gradient_to_label=False):
     """Computes softmax cross entropy for each time step of sequence
     predictions.
 
@@ -122,8 +117,7 @@ def sequence_sparse_softmax_cross_entropy(labels,
                                           average_across_timesteps=False,
                                           sum_over_batch=False,
                                           sum_over_timesteps=True,
-                                          time_major=False,
-                                          name=None):
+                                          time_major=False):
     """Computes sparse softmax cross entropy for each time step of sequence
     predictions.
 
@@ -213,8 +207,7 @@ def sequence_sigmoid_cross_entropy(labels,
                                    sum_over_timesteps=True,
                                    sum_over_classes=False,
                                    time_major=False,
-                                   stop_gradient_to_label=False,
-                                   name=None):
+                                   stop_gradient_to_label=False):
     """Computes sigmoid cross entropy for each time step of sequence
     predictions.
 
@@ -281,9 +274,6 @@ class dimension. Must not set `average_across_classes`
     losses = losses(logits, labels.type(logits.dtype))
 
     rank = shapes.get_rank(logits) or shapes.get_rank(labels)
-    if rank is None:
-        raise ValueError(
-            'Cannot determine the rank of `logits` or `labels`.')
 
     losses = mask_and_reduce(losses,
                              sequence_length,
@@ -305,8 +295,7 @@ def binary_sigmoid_cross_entropy(pos_logits=None,
                                  average_across_classes=True,
                                  sum_over_batch=False,
                                  sum_over_classes=False,
-                                 return_pos_neg_losses=False,
-                                 name=None):
+                                 return_pos_neg_losses=False):
     """Computes sigmoid cross entropy of binary predictions.
 
     Args:
@@ -384,8 +373,7 @@ def binary_sigmoid_cross_entropy_with_clas(clas_fn,
                                            average_across_classes=True,
                                            sum_over_batch=False,
                                            sum_over_classes=False,
-                                           return_pos_neg_losses=False,
-                                           name=None):
+                                           return_pos_neg_losses=False):
     """Computes sigmoid cross entropy of binary classifier.
 
     .. role:: python(code)
@@ -454,5 +442,4 @@ class dimension. Must not set `average_across_classes`
         average_across_classes=average_across_classes,
         sum_over_batch=sum_over_batch,
         sum_over_classes=sum_over_classes,
-        return_pos_neg_losses=return_pos_neg_losses,
-        name=name)
+        return_pos_neg_losses=return_pos_neg_losses)

texar/losses/mle_losses_test.py

@@ -4,11 +4,6 @@
 Unit tests for mle losses.
 """
 
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-from __future__ import unicode_literals
-
 # pylint: disable=invalid-name
 
 import unittest

texar/losses/pg_losses.py

@@ -1,4 +1,4 @@
-# Copyright 2018 The Texar Authors. All Rights Reserved.
+# Copyright 2019 The Texar Authors. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -15,10 +15,6 @@
 Various loss functions for policy gradients.
 """
 
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
 import torch
 import torch.nn.functional as F
 

texar/losses/pg_losses_test.py

@@ -4,11 +4,6 @@
 Unit tests for pg losses.
 """
 
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-from __future__ import unicode_literals
-
 # pylint: disable=invalid-name
 
 import unittest