mdb

src/autoencoder.py

@@ -11,20 +11,21 @@
 
 from tflearn import is_training
 
-from general_tools.in_out.basics import create_dir, pickle_data, unpickle_data
+from . in_out import create_dir, pickle_data, unpickle_data
+
 from general_tools.simpletons import iterate_in_chunks
 
 from . in_out import apply_augmentations
-from .. neural_net import Neural_Net
+from . neural_net import Neural_Net
 
 model_saver_id = 'models.ckpt'
 
 
 class Configuration():
     def __init__(self, n_input, encoder, decoder, encoder_args={}, decoder_args={},
                  training_epochs=200, batch_size=10, learning_rate=0.001, denoising=False,
-                 saver_step=None, train_dir=None, z_rotate=False, loss='l2', gauss_augment=None,
-                 saver_max_to_keep=None, loss_display_step=1, spatial_trans=False, debug=False,
+                 saver_step=None, train_dir=None, z_rotate=False, loss='chamfer', gauss_augment=None,
+                 saver_max_to_keep=None, loss_display_step=1, debug=False,
                  n_z=None, n_output=None, latent_vs_recon=1.0, consistent_io=None):
 
         # Parameters for any AE

src/in_out.py

@@ -8,7 +8,7 @@
 import os
 import os.path as osp
 import re
-
+from six.moves import cPickle
 
 def create_dir(dir_path):
     ''' Creates a directory (or nested directories) if they don't exist.
@@ -19,6 +19,28 @@ def create_dir(dir_path):
     return dir_path
 
 
+
+def pickle_data(file_name, *args):
+    '''Using (c)Pickle to save multiple python objects in a single file.
+    '''
+    myFile = open(file_name, 'wb')
+    cPickle.dump(len(args), myFile, protocol=2)
+    for item in args:
+        cPickle.dump(item, myFile, protocol=2)
+    myFile.close()
+
+
+def unpickle_data(file_name):
+    '''Restore data previously saved with pickle_data().
+    '''
+    inFile = open(file_name, 'rb')
+    size = cPickle.load(inFile)
+    for _ in xrange(size):
+        yield cPickle.load(inFile)
+    inFile.close()
+
+
+
 def files_in_subdirs(top_dir, search_pattern):
     regex = re.compile(search_pattern)
     for path, _, files in os.walk(top_dir):

src/neural_net.py

@@ -15,8 +15,7 @@ class Neural_Net(object):
     def __init__(self, name, graph):
         if graph is None:
             graph = tf.get_default_graph()
-            # g = tf.Graph()
-            # with g.as_default():
+
         self.graph = graph
         self.name = name
 
@@ -27,29 +26,3 @@ def __init__(self, name, graph):
     def is_training(self):
         is_training_op = self.graph.get_collection('is_training')
         return self.sess.run(is_training_op)[0]
-
-#     def __init__(self, name, model, trainer, sess):
-#         '''
-#         Constructor
-#         '''
-#         self.model = model
-#         self.trainer = trainer
-#         self.sess = sess
-#         self.train_step = trainer.train_step
-#         self.saver = tf.train.Saver(tf.global_variables(), scope=name, max_to_keep=None)
-# 
-#     def total_loss(self):
-#         return self.trainer.total_loss
-# 
-#     def forward(self, input_tensor):
-#         return self.model.forward(input_tensor)
-# 
-#     def save_model(self, tick):
-#         self.saver.save(self.sess, MODEL_SAVER_ID, global_step=tick)
-# 
-#     def restore_model(self, model_path, tick, verbose=False):
-#         ''' restore_model.
-# 
-#             Restore all the variables of the saved model.
-#         '''
-#         self.saver.restore(self.sess, osp.join(model_path, MODEL_SAVER_ID + '-' + str(int(tick))))