Suspicious diffgan changes

-def _transform_imagnet_image(image, target_image_shape, crop_method, seed, random_flip):
+def _transform_imagnet_image(image, target_image_shape, crop_method, seed):
   """Preprocesses ImageNet images to have a target image shape.

   Args:
@@ -473,28 +469,10 @@ def _transform_imagnet_image(image, target_image_shape, crop_method, seed, rando
     begin = tf.cast(begin, tf.int32)
     begin = tf.concat([begin, [0]], axis=0)  # Add channel dimension.
     image = tf.slice(image, begin, [size, size, 3])
-  elif crop_method == "zoomed":
-    shape = tf.shape(image)
-    h, w = shape[0], shape[1]
-    size = tf.minimum(h, w)
-    begin, size, _ = tf.image.sample_distorted_bounding_box(
-        [size, size, shape[2]],
-        tf.zeros([0, 0, 4], tf.float32),
-        aspect_ratio_range=[1.0, 1.0],
-        area_range=[0.5, 1.0],
-        use_image_if_no_bounding_boxes=True,
-        seed=seed)
-    begin = tf.cast([h - size[0], w - size[1], 0], tf.int32) // 2
-    image = tf.slice(image, begin, size)
-    # Unfortunately, the above operation loses the depth-dimension. So we need
-    # to restore it the manual way.
-    image.set_shape([None, None, target_image_shape[-1]])
   elif crop_method != "none":
     raise ValueError("Unsupported crop method: {}".format(crop_method))
   image = tf.image.resize_images(
       image, [target_image_shape[0], target_image_shape[1]])
-  if random_flip:
-    image = tf.image.random_flip_left_right(image, seed=seed)
   image.set_shape(target_image_shape)
   return image

diff --git a/DiffAugment-biggan-imagenet/compare_gan/eval_gan_lib.py b/DiffAugment-biggan-imagenet/compare_gan/eval_gan_lib.py
index cb7af7d..aa84e59 100644
--- a/DiffAugment-biggan-imagenet/compare_gan/eval_gan_lib.py
+++ b/DiffAugment-biggan-imagenet/compare_gan/eval_gan_lib.py
@@ -93,7 +93,7 @@ def _update_bn_accumulators(sess, generated, num_accu_examples):


 def evaluate_tfhub_module(module_spec, eval_tasks, use_tpu,
-                          num_averaging_runs, update_bn_accumulators=True, use_tags=True):
+                          num_averaging_runs):
   """Evaluate model at given checkpoint_path.

   Args:
@@ -129,7 +129,7 @@ def evaluate_tfhub_module(module_spec, eval_tasks, use_tpu,
         generator = hub.Module(
             module_spec,
             name="gen_module",
-            tags={"gen", "bs{}".format(batch_size)} if use_tags else None)
+            tags={"gen", "bs{}".format(batch_size)})
         logging.info("Generator inputs: %s", generator.get_input_info_dict())
         z_dim = generator.get_input_info_dict()["z"].get_shape()[1].value
         z = z_generator(shape=[batch_size, z_dim])
@@ -153,7 +153,7 @@ def evaluate_tfhub_module(module_spec, eval_tasks, use_tpu,
       tf.global_variables_initializer().run()


-      if update_bn_accumulators and _update_bn_accumulators(sess, generated, num_accu_examples=204800):
+      if _update_bn_accumulators(sess, generated, num_accu_examples=204800):
         saver = tf.train.Saver()
         save_path = os.path.join(module_spec, "model-with-accu.ckpt")
         checkpoint_path = saver.save(