Add compat.v1 to support TF 2.0 in mnist

official/mnist/dataset.py

@@ -35,7 +35,7 @@ def read32(bytestream):
 
 def check_image_file_header(filename):
   """Validate that filename corresponds to images for the MNIST dataset."""
-  with tf.gfile.Open(filename, 'rb') as f:
+  with tf.io.gfile.GFile(filename, 'rb') as f:
     magic = read32(f)
     read32(f)  # num_images, unused
     rows = read32(f)
@@ -51,7 +51,7 @@ def check_image_file_header(filename):
 
 def check_labels_file_header(filename):
   """Validate that filename corresponds to labels for the MNIST dataset."""
-  with tf.gfile.Open(filename, 'rb') as f:
+  with tf.io.gfile.GFile(filename, 'rb') as f:
     magic = read32(f)
     read32(f)  # num_items, unused
     if magic != 2049:
@@ -62,17 +62,17 @@ def check_labels_file_header(filename):
 def download(directory, filename):
   """Download (and unzip) a file from the MNIST dataset if not already done."""
   filepath = os.path.join(directory, filename)
-  if tf.gfile.Exists(filepath):
+  if tf.io.gfile.exists(filepath):
     return filepath
-  if not tf.gfile.Exists(directory):
-    tf.gfile.MakeDirs(directory)
+  if not tf.io.gfile.exists(directory):
+    tf.io.gfile.mkdir(directory)
   # CVDF mirror of http://yann.lecun.com/exdb/mnist/
   url = 'https://storage.googleapis.com/cvdf-datasets/mnist/' + filename + '.gz'
   _, zipped_filepath = tempfile.mkstemp(suffix='.gz')
   print('Downloading %s to %s' % (url, zipped_filepath))
   urllib.request.urlretrieve(url, zipped_filepath)
   with gzip.open(zipped_filepath, 'rb') as f_in, \
-      tf.gfile.Open(filepath, 'wb') as f_out:
+      tf.io.gfile.GFile(filepath, 'wb') as f_out:
     shutil.copyfileobj(f_in, f_out)
   os.remove(zipped_filepath)
   return filepath
@@ -89,13 +89,13 @@ def dataset(directory, images_file, labels_file):
 
   def decode_image(image):
     # Normalize from [0, 255] to [0.0, 1.0]
-    image = tf.decode_raw(image, tf.uint8)
+    image = tf.io.decode_raw(image, tf.uint8)
     image = tf.cast(image, tf.float32)
     image = tf.reshape(image, [784])
     return image / 255.0
 
   def decode_label(label):
-    label = tf.decode_raw(label, tf.uint8)  # tf.string -> [tf.uint8]
+    label = tf.io.decode_raw(label, tf.uint8)  # tf.string -> [tf.uint8]
     label = tf.reshape(label, [])  # label is a scalar
     return tf.cast(label, tf.int32)
 

official/mnist/mnist.py

@@ -125,11 +125,12 @@ def model_fn(features, labels, mode, params):
             'classify': tf.estimator.export.PredictOutput(predictions)
         })
   if mode == tf.estimator.ModeKeys.TRAIN:
-    optimizer = tf.train.AdamOptimizer(learning_rate=LEARNING_RATE)
+    optimizer = tf.compat.v1.train.AdamOptimizer(learning_rate=LEARNING_RATE)
 
     logits = model(image, training=True)
-    loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits)
-    accuracy = tf.metrics.accuracy(
+    loss = tf.compat.v1.losses.sparse_softmax_cross_entropy(labels=labels,
+                                                            logits=logits)
+    accuracy = tf.compat.v1.metrics.accuracy(
         labels=labels, predictions=tf.argmax(logits, axis=1))
 
     # Name tensors to be logged with LoggingTensorHook.
@@ -143,7 +144,8 @@ def model_fn(features, labels, mode, params):
     return tf.estimator.EstimatorSpec(
         mode=tf.estimator.ModeKeys.TRAIN,
         loss=loss,
-        train_op=optimizer.minimize(loss, tf.train.get_or_create_global_step()))
+        train_op=optimizer.minimize(loss,
+            tf.compat.v1.train.get_or_create_global_step()))
   if mode == tf.estimator.ModeKeys.EVAL:
     logits = model(image, training=False)
     loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits)
@@ -166,7 +168,7 @@ def run_mnist(flags_obj):
   model_helpers.apply_clean(flags_obj)
   model_function = model_fn
 
-  session_config = tf.ConfigProto(
+  session_config = tf.compat.v1.ConfigProto(
       inter_op_parallelism_threads=flags_obj.inter_op_parallelism_threads,
       intra_op_parallelism_threads=flags_obj.intra_op_parallelism_threads,
       allow_soft_placement=True)
@@ -227,7 +229,7 @@ def run_mnist(flags_obj):
 
   # Export the model
   if flags_obj.export_dir is not None:
-    image = tf.placeholder(tf.float32, [None, 28, 28])
+    image = tf.compat.v1.placeholder(tf.float32, [None, 28, 28])
     input_fn = tf.estimator.export.build_raw_serving_input_receiver_fn({
         'image': image,
     })
@@ -240,6 +242,6 @@ def main(_):
 
 
 if __name__ == '__main__':
-  tf.logging.set_verbosity(tf.logging.INFO)
+  tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.INFO)
   define_mnist_flags()
   absl_app.run(main)

official/mnist/mnist_test.py

@@ -29,8 +29,8 @@ BATCH_SIZE = 100
 
 
 def dummy_input_fn():
-  image = tf.random_uniform([BATCH_SIZE, 784])
-  labels = tf.random_uniform([BATCH_SIZE, 1], maxval=9, dtype=tf.int32)
+  image = tf.random.uniform([BATCH_SIZE, 784])
+  labels = tf.random.uniform([BATCH_SIZE, 1], maxval=9, dtype=tf.int32)
   return image, labels
 
 
@@ -64,7 +64,7 @@ class Tests(tf.test.TestCase):
     self.assertEqual(2, global_step)
     self.assertEqual(accuracy.shape, ())
 
-    input_fn = lambda: tf.random_uniform([3, 784])
+    input_fn = lambda: tf.random.uniform([3, 784])
     predictions_generator = classifier.predict(input_fn)
     for _ in range(3):
       predictions = next(predictions_generator)