Add training and evaluation code.

Author: richardaecn

.gitignore

@@ -1,6 +1,7 @@
 *.pyc
 .ipynb_checkpoints
 test.ipynb
+run.sh
 feature/
 data/
 checkpoints/

LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2018 Yin Cui
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

@@ -20,7 +20,7 @@ Notice that we used a mini validation set (./inat_minival.txt) contains 9,697 im
 
 
 ## Preparation:
-+ Clone the repo with recursive
++ Clone the repo with recursive:
 ```bash
 git clone --recursive https://github.com/richardaecn/cvpr18-inaturalist-transfer.git
 ```
@@ -94,6 +94,25 @@ DomainSimilarityDemo.ipynb
 ```
 
 
+## Training and Evaluation
++ Convert dataset into '.tfrecord':
+```
+python convert_dataset.py --dataset_name=cub_200 --num_shards=10
+```
++ Train (fine-tune) the model on 1 GPU:
+```
+CUDA_VISIBLE_DEVICES=0 ./train.sh
+```
++ Evaluate the model on another GPU simultaneously:
+```
+CUDA_VISIBLE_DEVICES=1 ./eval.sh
+```
++ Run Tensorboard for visualization:
+```
+tensorboard --logdir=./checkpoints/cub_200/ --port=6006
+```
+
+
 ## Citation
 If you find our work helpful in your research, please cite it as:
 ```latex

convert_dataset.py

@@ -0,0 +1,161 @@
+"""Converts data to TFRecords of TF-Example protos.
+python convert_dataset.py --dataset_name=cub_200 --num_shards=10
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import math
+import os
+import random
+import sys
+
+import tensorflow as tf
+
+sys.path.insert(0, './slim/')
+from datasets import dataset_utils
+
+FLAGS = tf.app.flags.FLAGS
+
+tf.app.flags.DEFINE_string(
+    'dataset_name',
+    None,
+    'The name of the dataset to convert, one of "ILSVRC2012", "inat2017", '
+    '"aircraft", "cub_200", "flower_102", "food_101", "nabirds", '
+    '"stanford_cars", "stanford_dogs"')
+
+tf.app.flags.DEFINE_integer(
+    'num_shards', 10, 'The number of shards per dataset split.')
+
+
+class ImageReader(object):
+  """Helper class that provides TensorFlow image coding utilities."""
+
+  def __init__(self):
+    # Initializes function that decodes RGB JPEG data.
+    self._decode_jpeg_data = tf.placeholder(dtype=tf.string)
+    self._decode_jpeg = tf.image.decode_jpeg(self._decode_jpeg_data, channels=3)
+
+  def read_image_dims(self, sess, image_data):
+    image = self.decode_jpeg(sess, image_data)
+    return image.shape[0], image.shape[1]
+
+  def decode_jpeg(self, sess, image_data):
+    image = sess.run(self._decode_jpeg,
+                     feed_dict={self._decode_jpeg_data: image_data})
+    assert len(image.shape) == 3
+    assert image.shape[2] == 3
+    return image
+
+
+def _get_filenames_and_labels(dataset_dir):
+  train_filenames = []
+  val_filenames = []
+  train_labels = []
+  val_labels = []
+  for line in open(os.path.join(dataset_dir, 'train.txt'), 'r'):
+    line_list = line.strip().split(': ')
+    train_filenames.append(os.path.join(dataset_dir, line_list[0]))
+    train_labels.append(int(line_list[1]))
+  for line in open(os.path.join(dataset_dir, 'val.txt'), 'r'):
+    line_list = line.strip().split(': ')
+    val_filenames.append(os.path.join(dataset_dir, line_list[0]))
+    val_labels.append(int(line_list[1]))
+  return train_filenames, val_filenames, train_labels, val_labels
+
+
+def _get_dataset_filename(dataset_dir, split_name, shard_id):
+  output_filename = '%s_%05d-of-%05d.tfrecord' % (
+      split_name, shard_id, FLAGS.num_shards)
+  return os.path.join(dataset_dir, output_filename)
+
+
+def _convert_dataset(split_name, filenames, labels, dataset_dir):
+  """Converts the given filenames to a TFRecord dataset.
+
+  Args:
+    split_name: The name of the dataset, either 'train' or 'validation'.
+    filenames: A list of absolute paths to png or jpg images.
+    labels: A list of class ids (integers start with 0).
+    dataset_dir: The directory where the converted datasets are stored.
+  """
+  assert split_name in ['train', 'validation']
+
+  num_per_shard = int(math.ceil(len(filenames) / float(FLAGS.num_shards)))
+
+  with tf.Graph().as_default():
+    image_reader = ImageReader()
+
+    with tf.Session('') as sess:
+
+      for shard_id in range(FLAGS.num_shards):
+        output_filename = _get_dataset_filename(
+            dataset_dir, split_name, shard_id)
+
+        with tf.python_io.TFRecordWriter(output_filename) as tfrecord_writer:
+          start_ndx = shard_id * num_per_shard
+          end_ndx = min((shard_id+1) * num_per_shard, len(filenames))
+          for i in range(start_ndx, end_ndx):
+            sys.stdout.write('\r>> Converting %s image %d/%d shard %d' % (
+                split_name, i+1, len(filenames), shard_id))
+            sys.stdout.flush()
+
+            # Read the filename and label:
+            image_data = tf.gfile.FastGFile(filenames[i], 'rb').read()
+            height, width = image_reader.read_image_dims(sess, image_data)
+            class_id = labels[i]
+
+            example = dataset_utils.image_to_tfexample(
+                image_data, b'jpg', height, width, class_id)
+            tfrecord_writer.write(example.SerializeToString())
+
+  sys.stdout.write('\n')
+  sys.stdout.flush()
+
+
+def _dataset_exists(dataset_dir):
+  for split_name in ['train', 'validation']:
+    for shard_id in range(FLAGS.num_shards):
+      output_filename = _get_dataset_filename(
+          dataset_dir, split_name, shard_id)
+      if not tf.gfile.Exists(output_filename):
+        return False
+  return True
+
+
+def run(dataset_dir):
+  """Runs the conversion operation.
+
+  Args:
+    dataset_dir: The dataset directory where the dataset is stored.
+  """
+  if not tf.gfile.Exists(dataset_dir):
+    tf.gfile.MakeDirs(dataset_dir)
+
+  if _dataset_exists(dataset_dir):
+    print('Dataset files already exist. Exiting without re-creating them.')
+    return
+
+  train_filenames, val_filenames, train_labels, val_labels = \
+      _get_filenames_and_labels(dataset_dir)
+
+  train_idx = list(zip(train_filenames, train_labels))
+  random.shuffle(train_idx)
+  train_filenames, train_labels = zip(*train_idx)
+
+  _convert_dataset('train', train_filenames, train_labels, dataset_dir)
+  _convert_dataset('validation', val_filenames, val_labels, dataset_dir)
+
+  print('\nFinished converting the dataset!')
+
+
+def main(_):
+  if not FLAGS.dataset_name:
+    raise ValueError('You must supply the dataset name with --dataset_name')
+
+  run(os.path.join('./data', FLAGS.dataset_name))
+
+
+if __name__ == '__main__':
+  tf.app.run()