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diff --git a/resnet_preprocessing.py b/resnet_preprocessing.py
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+# Trimmed by Vasil Zlatanov
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""ImageNet preprocessing for ResNet."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import tensorflow as tf
+
+def resize_or_crop_image(image, target_height, target_width):
+  image_height = tf.shape(image)[0]
+  image_width = tf.shape(image)[1]
+  # If the viewport is long but the width is right simply crop the length of the page
+  # Otherwise we just resize the image bilinearly
+  image = tf.cond(
+          tf.logical_and(tf.greater(image_height, target_height),tf.equal(target_width, image_width)),
+          lambda: tf.cast(tf.image.crop_to_bounding_box(image, 0, 0, target_height, target_width),dtype=tf.float32),
+          lambda: tf.image.resize_images(image, [target_height,target_width], align_corners=True)
+          )
+  return image
+
+def preprocess_for_train(image_bytes, target_width, target_height, resize, use_bfloat16):
+  """Preprocesses the given image for evaluation.
+
+  Args:
+    image_bytes: `Tensor` representing an image binary of arbitrary size.
+    use_bfloat16: `bool` for whether to use bfloat16.
+
+  Returns:
+    A preprocessed image `Tensor`.
+  """
+  image = tf.image.decode_png(image_bytes, channels=3)
+  if resize:
+      image = resize_or_crop_image(image, target_height, target_width)
+  else:
+      image = tf.cast(image, tf.float32)
+
+  return image
+
+
+def preprocess_for_eval(image_bytes, target_width, target_height, resize, use_bfloat16):
+  """Preprocesses the given image for evaluation.
+
+  Args:
+    image_bytes: `Tensor` representing an image binary of arbitrary size.
+    use_bfloat16: `bool` for whether to use bfloat16.
+
+  Returns:
+    A preprocessed image `Tensor`.
+  """
+  image = tf.image.decode_png(image_bytes, channels=3)
+  if resize:
+      image = resize_or_crop_image(image, target_height, target_width)
+  else:
+      image = tf.cast(image, tf.float32)
+
+  return image
+
+
+def preprocess_image(image_bytes, width, height, resize, is_training=False, use_bfloat16=False):
+  """Preprocesses the given image.
+
+  Args:
+    image_bytes: `Tensor` representing an image binary of arbitrary size.
+    is_training: `bool` for whether the preprocessing is for training.
+    use_bfloat16: `bool` for whether to use bfloat16.
+
+  Returns:
+    A preprocessed image `Tensor`.
+  """
+  if is_training:
+    return preprocess_for_train(image_bytes, width, height, resize, use_bfloat16)
+  else:
+    return preprocess_for_eval(image_bytes, width, height, resize, use_bfloat16)