Bug fixes.

Author: otiliastr

research/gam/gam/experiments/helper.py

@@ -62,7 +62,7 @@ def get_model_cls(model_name,
         num_classes=data.num_classes,
         lrelu_leakiness=0.1,
         horizontal_flip=dataset_name in ('cifar10',),
-        random_translation=True,
+        random_translation=False,
         gaussian_noise=dataset_name not in ('svhn', 'svhn_cropped'),
         width=2,
         num_residual_units=4,
@@ -125,7 +125,7 @@ def get_model_agr(model_name,
         num_classes=1,
         lrelu_leakiness=0.1,
         horizontal_flip=dataset_name in ('cifar10',),
-        random_translation=True,
+        random_translation=False,
         gaussian_noise=dataset_name not in ('svhn', 'svhn_cropped'),
         width=2,
         num_residual_units=4,

research/gam/gam/models/models_base.py

@@ -142,7 +142,7 @@ class attribute. The valid options are:
     else:
       raise NotImplementedError()
 
-  def _project(self, inputs, reuse=tf.AUTO_REUSE):
+  def _project(self, inputs, reuse=tf.compat.v1.AUTO_REUSE):
     """Projects the provided inputs using a multilayer perceptron.
 
     Arguments:

research/gam/gam/models/wide_resnet.py

@@ -22,7 +22,6 @@
 
 import numpy as np
 import tensorflow as tf
-import tensorflow_addons as tfa
 
 
 def fast_flip(images, is_training):
@@ -38,27 +37,6 @@ def func(inp):
   return tf.cond(is_training, lambda: func(images), lambda: images)
 
 
-def jitter(input_data, is_training):
-  """Applies random noise to input data when training."""
-
-  def func(inp):
-    """Wrap functionality in a subfunction."""
-    bsz = tf.shape(inp)[0]
-    inp = tf.pad(inp, [[0, 0], [2, 2], [2, 2], [0, 0]], mode="REFLECT")
-    base = tf.constant([1, 0, 0, 0, 1, 0, 0, 0], shape=[1, 8], dtype=tf.float32)
-    base = tf.tile(base, [bsz, 1])
-    mask = tf.constant([0, 0, 1, 0, 0, 1, 0, 0], shape=[1, 8], dtype=tf.float32)
-    mask = tf.tile(mask, [bsz, 1])
-    jit = tf.random_uniform([bsz, 8], minval=-2, maxval=3, dtype=tf.int32)
-    jit = tf.cast(jit, tf.float32)
-    xforms = base + jit * mask
-    processed_data = tfa.image.transform(images=inp, transforms=xforms)
-    cropped_data = processed_data[:, 2:-2, 2:-2, :]
-    return cropped_data
-
-  return tf.cond(is_training, lambda: func(input_data), lambda: input_data)
-
-
 class WideResnet(Model):
   """Resnet implementation from `Realistic Evaluation of Deep SSL Algorithms`.
 
@@ -289,7 +267,7 @@ def _residual(x,
     if self.horizontal_flip:
       x = fast_flip(x, is_training=is_train)
     if self.random_translation:
-      x = jitter(x, is_training=is_train)
+      raise NotImplementedError('Random translations are not implemented yet.')
     if self.gaussian_noise:
       x = tf.cond(is_train, lambda: x + tf.random_normal(tf.shape(x)) * 0.15,
                   lambda: x)

research/gam/gam/trainer/trainer_classification_gcn.py

@@ -21,8 +21,8 @@
 import logging
 import os
 
-from .adversarial_dense import entropy_y_x
-from .adversarial_dense import get_loss_vat
+from .adversarial_sparse import entropy_y_x
+from .adversarial_sparse import get_loss_vat
 import numpy as np
 import tensorflow as tf
 from .trainer_base import batch_iterator
@@ -869,7 +869,7 @@ def train(self, data, session=None, **kwargs):
 
   def predict(self, session, indices, is_train):
     """Make predictions for the provided sample indices."""
-    if not indices:
+    if len(indices) == 0:
       return np.zeros((0, self.data.num_classes), dtype=np.float32)
     feed_dict = {
         self.input_indices: indices,