TensorFlow v2 & linting

README.md

@@ -25,10 +25,10 @@ EfficientNets rely on AutoML and compound scaling to achieve superior performanc
 <table border="0">
 <tr>
     <td>
-    <img src="https://raw.githubusercontent.com/tensorflow/tpu/master/models/official/efficientnet/g3doc/params.png" width="100%" />
+    <img src="https://raw.githubusercontent.com/tensorflow/tpu/master/models/official/efficientnet/g3doc/params.png" alt="Params" width="100%" />
     </td>
     <td>
-    <img src="https://raw.githubusercontent.com/tensorflow/tpu/master/models/official/efficientnet/g3doc/flops.png", width="90%" />
+    <img src="https://raw.githubusercontent.com/tensorflow/tpu/master/models/official/efficientnet/g3doc/flops.png" alt="Flops" width="90%" />
     </td>
 </tr>
 </table>

efficientnet/__init__.py

@@ -35,6 +35,7 @@ def get_submodules_from_kwargs(kwargs):
 
 def inject_keras_modules(func):
     import keras
+
     @functools.wraps(func)
     def wrapper(*args, **kwargs):
         kwargs['backend'] = keras.backend
@@ -48,6 +49,7 @@ def wrapper(*args, **kwargs):
 
 def inject_tfkeras_modules(func):
     import tensorflow.keras as tfkeras
+
     @functools.wraps(func)
     def wrapper(*args, **kwargs):
         kwargs['backend'] = tfkeras.backend

efficientnet/model.py

@@ -27,15 +27,12 @@
 from __future__ import print_function
 
 import os
-import json
 import math
 import string
 import collections
-import numpy as np
 
 from six.moves import xrange
 from keras_applications.imagenet_utils import _obtain_input_shape
-from keras_applications.imagenet_utils import decode_predictions
 from keras_applications.imagenet_utils import preprocess_input as _preprocess_input
 
 from . import get_submodules_from_kwargs
@@ -139,6 +136,7 @@ def preprocess_input(x, **kwargs):
 
 def get_swish(**kwargs):
     backend, layers, models, keras_utils = get_submodules_from_kwargs(kwargs)
+
     def swish(x):
         """Swish activation function: x * sigmoid(x).
         Reference: [Searching for Activation Functions](https://arxiv.org/abs/1710.05941)
@@ -153,7 +151,7 @@ def swish(x):
                 pass
 
         return x * backend.sigmoid(x)
-    return  swish
+    return swish
 
 
 def get_dropout(**kwargs):

efficientnet/preprocessing.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-import numpy as np
+
 from skimage.transform import resize
 
 MAP_INTERPOLATION_TO_ORDER = {

scripts/load_efficientnet.py

@@ -17,8 +17,6 @@
 import argparse
 import sys
 
-import numpy as np
-
 import tensorflow as tf
 import efficientnet.keras
 from keras.layers import BatchNormalization, Conv2D, Dense
@@ -93,14 +91,14 @@ def convert_tensorflow_model(
     """ Loads and saves a TensorFlow model. """
     image_files = [example_img]
     eval_ckpt_driver = eval_ckpt_main.EvalCkptDriver(model_name)
-    with tf.Graph().as_default(), tf.Session() as sess:
+    with tf.Graph().as_default(), tf.compat.v1.Session() as sess:
         images, _ = eval_ckpt_driver.build_dataset(
             image_files, [0] * len(image_files), False
         )
         eval_ckpt_driver.build_model(images, is_training=False)
-        sess.run(tf.global_variables_initializer())
+        sess.run(tf.compat.v1.global_variables_initializer())
         eval_ckpt_driver.restore_model(sess, model_ckpt)
-        global_variables = tf.global_variables()
+        global_variables = tf.compat.v1.global_variables()
         weights = dict()
         for variable in global_variables:
             try:
@@ -149,15 +147,15 @@ def convert_tensorflow_model(
         default="true",
         help="Whether to include metadata in the serialized Keras model",
     )
-    args = parser.parse_args()
+    cli_args = parser.parse_args()
 
-    sys.path.append(args.source)
+    sys.path.append(cli_args.source)
     import eval_ckpt_main
 
     true_values = ("yes", "true", "t", "1", "y")
     convert_tensorflow_model(
-        model_name=args.model_name,
-        model_ckpt=args.tf_checkpoint,
-        output_file=args.output_file,
-        weights_only=args.weights_only in true_values,
+        model_name=cli_args.model_name,
+        model_ckpt=cli_args.tf_checkpoint,
+        output_file=cli_args.output_file,
+        weights_only=cli_args.weights_only in true_values,
     )

setup.py

@@ -1,3 +1,6 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
 # Copyright 2019 The TensorFlow Authors, Pavel Yakubovskiy. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -12,8 +15,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
 
 # Note: To use the 'upload' functionality of this file, you must:
 #   $ pip install twine

tests/test_model.py

@@ -71,4 +71,4 @@ def test_models_result(args):
 
 
 if __name__ == "__main__":
-    pytest.main([__file__])
+    pytest.main([__file__])