Standardize some names from TFGAN -> TF-GAN.

A noop.

PiperOrigin-RevId: 227273663

tensorflow/contrib/gan/BUILD

@@ -1,4 +1,4 @@
-# Files for using TFGAN framework.
+# Files for using TF-GAN framework.
 load("//tensorflow:tensorflow.bzl", "py_test")
 
 package(default_visibility = [

tensorflow/contrib/gan/python/estimator/python/gan_estimator_impl.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""A TFGAN-backed GAN Estimator."""
+"""A TF-GAN-backed GAN Estimator."""
 
 from __future__ import absolute_import
 from __future__ import division
@@ -56,10 +56,10 @@ _summary_type_map = {
 class GANEstimator(estimator.Estimator):
   """An estimator for Generative Adversarial Networks (GANs).
 
-  This Estimator is backed by TFGAN. The network functions follow the TFGAN API
-  except for one exception: if either `generator_fn` or `discriminator_fn` have
-  an argument called `mode`, then the tf.Estimator mode is passed in for that
-  argument. This helps with operations like batch normalization, which have
+  This Estimator is backed by TF-GAN. The network functions follow the TF-GAN
+  API except for one exception: if either `generator_fn` or `discriminator_fn`
+  have an argument called `mode`, then the tf.Estimator mode is passed in for
+  that argument. This helps with operations like batch normalization, which have
   different train and evaluation behavior.
 
   Example:
@@ -68,7 +68,7 @@ class GANEstimator(estimator.Estimator):
       import tensorflow as tf
       tfgan = tf.contrib.gan
 
-      # See TFGAN's `train.py` for a description of the generator and
+      # See TF-GAN's `train.py` for a description of the generator and
       # discriminator API.
       def generator_fn(generator_inputs):
         ...
@@ -123,13 +123,13 @@ class GANEstimator(estimator.Estimator):
         to continue training a previously saved model.
       generator_fn: A python function that takes a Tensor, Tensor list, or
         Tensor dictionary as inputs and returns the outputs of the GAN
-        generator. See `TFGAN` for more details and examples. Additionally, if
+        generator. See `TF-GAN` for more details and examples. Additionally, if
         it has an argument called `mode`, the Estimator's `mode` will be passed
         in (ex TRAIN, EVAL, PREDICT). This is useful for things like batch
         normalization.
       discriminator_fn: A python function that takes the output of
         `generator_fn` or real data in the GAN setup, and `generator_inputs`.
-        Outputs a Tensor in the range [-inf, inf]. See `TFGAN` for more details
+        Outputs a Tensor in the range [-inf, inf]. See `TF-GAN` for more details
         and examples.
       generator_loss_fn: The loss function on the generator. Takes a `GANModel`
         tuple.

tensorflow/contrib/gan/python/losses/python/losses_impl.py

@@ -28,7 +28,7 @@ wasserstein_gradient_penalty
 All losses must be able to accept 1D or 2D Tensors, so as to be compatible with
 patchGAN style losses (https://arxiv.org/abs/1611.07004).
 
-To make these losses usable in the TFGAN framework, please create a tuple
+To make these losses usable in the TF-GAN framework, please create a tuple
 version of the losses with `losses_utils.py`.
 """
 
@@ -320,7 +320,7 @@ def wasserstein_gradient_penalty(
     generated_data: Output of the generator.
     generator_inputs: Exact argument to pass to the generator, which is used
       as optional conditioning to the discriminator.
-    discriminator_fn: A discriminator function that conforms to TFGAN API.
+    discriminator_fn: A discriminator function that conforms to TF-GAN API.
     discriminator_scope: If not `None`, reuse discriminators from this scope.
     epsilon: A small positive number added for numerical stability when
       computing the gradient norm.

tensorflow/contrib/gan/python/losses/python/tuple_losses_impl.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""TFGAN utilities for loss functions that accept GANModel namedtuples.
+"""TF-GAN utilities for loss functions that accept GANModel namedtuples.
 
 The losses and penalties in this file all correspond to losses in
 `losses_impl.py`. Losses in that file take individual arguments, whereas in this

tensorflow/contrib/gan/python/namedtuples.py

@@ -12,10 +12,10 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Named tuples for TFGAN.
+"""Named tuples for TF-GAN.
 
-TFGAN training occurs in four steps, and each step communicates with the next
-step via one of these named tuples. At each step, you can either use a TFGAN
+TF-GAN training occurs in four steps, and each step communicates with the next
+step via one of these named tuples. At each step, you can either use a TF-GAN
 helper function in `train.py`, or you can manually construct a tuple.
 """
 

tensorflow/contrib/gan/python/train.py

@@ -12,12 +12,12 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""The TFGAN project provides a lightweight GAN training/testing framework.
+"""The TF-GAN project provides a lightweight GAN training/testing framework.
 
 This file contains the core helper functions to create and train a GAN model.
 See the README or examples in `tensorflow_models` for details on how to use.
 
-TFGAN training occurs in four steps:
+TF-GAN training occurs in four steps:
 1) Create a model
 2) Add a loss
 3) Create train ops
@@ -645,9 +645,10 @@ def gan_loss(
         type(model))
 
   # Optionally create pooled model.
-  pooled_model = (
-      _tensor_pool_adjusted_model(model, tensor_pool_fn)
-      if tensor_pool_fn else model)
+  if tensor_pool_fn:
+    pooled_model = _tensor_pool_adjusted_model(model, tensor_pool_fn)
+  else:
+    pooled_model = model
 
   # Create standard losses.
   gen_loss = generator_loss_fn(model, add_summaries=add_summaries)
@@ -665,10 +666,11 @@ def gan_loss(
   if _use_aux_loss(mutual_information_penalty_weight):
     gen_info_loss = tfgan_losses.mutual_information_penalty(
         model, add_summaries=add_summaries)
-    dis_info_loss = (
-        gen_info_loss
-        if tensor_pool_fn is None else tfgan_losses.mutual_information_penalty(
-            pooled_model, add_summaries=add_summaries))
+    if tensor_pool_fn is None:
+      dis_info_loss = gen_info_loss
+    else:
+      dis_info_loss = tfgan_losses.mutual_information_penalty(
+          pooled_model, add_summaries=add_summaries)
     gen_loss += mutual_information_penalty_weight * gen_info_loss
     dis_loss += mutual_information_penalty_weight * dis_info_loss
   if _use_aux_loss(aux_cond_generator_weight):
@@ -929,7 +931,7 @@ def gan_train_ops(
     **kwargs):
   """Returns GAN train ops.
 
-  The highest-level call in TFGAN. It is composed of functions that can also
+  The highest-level call in TF-GAN. It is composed of functions that can also
   be called, should a user require more control over some part of the GAN
   training process.