removed dependency to download models

retrain.py

@@ -84,21 +84,7 @@
 from tensorflow.python.util import compat
 
 FLAGS = None
-
-# Input and output file flags.
-
-# Details of the training configuration.
-
-# File-system cache locations.
-
-# Controls the distortions used during training.
-
-# These are all parameters that are tied to the particular model architecture
-# we're using for Inception v3. These include things like tensor names and their
-# sizes. If you want to adapt this script to work with another model, you will
-# need to update these to reflect the values in the network you're using.
-# pylint: disable=line-too-long
-DATA_URL = 'http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz'
+current_dir_path = os.path.dirname(os.path.realpath(__file__))
 # pylint: enable=line-too-long
 BOTTLENECK_TENSOR_NAME = 'pool_3/_reshape:0'
 BOTTLENECK_TENSOR_SIZE = 2048
@@ -284,37 +270,6 @@ def run_bottleneck_on_image(sess, image_data, image_data_tensor,
   bottleneck_values = np.squeeze(bottleneck_values)
   return bottleneck_values
 
-
-def maybe_download_and_extract():
-  """Download and extract model tar file.
-
-  If the pretrained model we're using doesn't already exist, this function
-  downloads it from the TensorFlow.org website and unpacks it into a directory.
-  """
-  dest_directory = FLAGS.model_dir
-  print (dest_directory)
-  if not os.path.exists(dest_directory):
-    os.makedirs(dest_directory)
-  filename = DATA_URL.split('/')[-1]
-  filepath = os.path.join(dest_directory, filename)
-  print ("filepath is:" + filepath)
-  if not os.path.exists(filepath):
-    print ("file not found" + filepath)
-    def _progress(count, block_size, total_size):
-      sys.stdout.write('\r>> Downloading %s %.1f%%' %
-                       (filename,
-                        float(count * block_size) / float(total_size) * 100.0))
-      sys.stdout.flush()
-
-    filepath, _ = urllib.request.urlretrieve(DATA_URL,
-                                             filepath,
-                                             _progress)
-    print()
-    statinfo = os.stat(filepath)
-    print('Successfully downloaded', filename, statinfo.st_size, 'bytes.')
-  tarfile.open(filepath, 'r:gz').extractall(dest_directory)
-
-
 def ensure_dir_exists(dir_name):
   """Makes sure the folder exists on disk.
 
@@ -749,8 +704,6 @@ def main(_):
     tf.gfile.DeleteRecursively(FLAGS.summaries_dir)
   tf.gfile.MakeDirs(FLAGS.summaries_dir)
 
-  # Set up the pre-trained graph.
-  maybe_download_and_extract()
   graph, bottleneck_tensor, jpeg_data_tensor, resized_image_tensor = (
       create_inception_graph())