Adapt to python-protobuf >= 3.2.0

.gitignore

@@ -8,3 +8,8 @@
 # Python cache
 *.pyc
 
+# IDEA project files.
+.idea/
+
+# Generated files.
+*.npy

convert.py

@@ -29,10 +29,18 @@ def convert(def_path, caffemodel_path, data_output_path, code_output_path, phase
         if caffemodel_path is not None:
             data = transformer.transform_data()
             print_stderr('Saving data...')
+            # create directory if not existing
+            dirname = os.path.dirname(data_output_path)
+            if not os.path.exists(dirname):
+                os.makedirs(dirname)
             with open(data_output_path, 'wb') as data_out:
                 np.save(data_out, data)
         if code_output_path:
             print_stderr('Saving source...')
+            # create directory if not existing
+            dirname = os.path.dirname(code_output_path)
+            if not os.path.exists(dirname):
+                os.makedirs(dirname)
             with open(code_output_path, 'wb') as src_out:
                 src_out.write(transformer.transform_source())
         print_stderr('Done.')

examples/save_model/.gitignore

@@ -0,0 +1,11 @@
+checkpoint
+
+*.index
+*.meta
+*.pb
+*.pbtxt
+*.data-*-of-*
+
+*.py
+!save_model.py
+!__init__.py

examples/save_model/READMD.md

@@ -0,0 +1,17 @@
+# Example of Saving Model
+
+This is an example of how to save the GraphDef and variables of a converted model 
+in the Tensorflow official form. By doing this, the converted model can be 
+conveniently applied with Tensorflow APIs in other languages.
+
+For example, if a converted model is named "VGG", the generated code file should
+be named as "VGG.py", and the class name inside should remain "CaffeNet".
+
+The module "VGG" should be able to be directly imported. So put it inside the
+[save_graphdef](save_graphdef) folder, or add it to "sys.path".
+
+To save model variables, pass the path of the converted data file (e.g. VGG.npy)
+to the parameter "--data-input-path".
+
+A "VGG_frozen.pb' is also generated with all variables converted into constants
+in the saved graph.

examples/save_model/save_model.py

@@ -0,0 +1,51 @@
+#!/usr/bin/env python
+
+import argparse
+import os.path as osp
+import sys
+
+import tensorflow as tf
+from tensorflow.python.tools import freeze_graph
+from tensorflow.python.training import saver as saver_lib
+
+
+def save(name, data_input_path):
+    def getpardir(path): return osp.split(path)[0]
+    sys.path.append(getpardir(getpardir(getpardir(osp.realpath(__file__)))))
+    # Import the converted model's class
+    caffe_net_module = __import__(name)
+    with tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) as sess:
+        image_input = tf.placeholder(tf.float32, shape=[1, 227, 227, 3], name="data")
+        net = caffe_net_module.CaffeNet({'data': image_input})
+
+        # Save protocol buffer
+        pb_name = name + '.pb'
+        tf.train.write_graph(sess.graph_def, '.', pb_name + 'txt', True)
+        tf.train.write_graph(sess.graph_def, '.', pb_name, False)
+
+        if data_input_path is not None:
+            # Load the data
+            sess.run(tf.global_variables_initializer())
+            net.load(data_input_path, sess)
+            # Save the data
+            saver = saver_lib.Saver(tf.global_variables())
+            checkpoint_prefix = osp.join(osp.curdir, name + '.ckpt')
+            checkpoint_path = saver.save(sess, checkpoint_prefix)
+
+            # Freeze the graph
+            freeze_graph.freeze_graph(pb_name, "",
+                                      True, checkpoint_path, 'fc8/fc8',
+                                      'save/restore_all', 'save/Const:0',
+                                      name + '_frozen.pb', False, "")
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('name', help='Name of the converted model')
+    parser.add_argument('--data-input-path', help='Converted data input path')
+    args = parser.parse_args()
+    save(args.name, args.data_input_path)
+
+
+if __name__ == '__main__':
+    main()

kaffe/caffe/resolver.py

@@ -14,8 +14,8 @@ def import_caffe(self):
             self.caffe = caffe
         except ImportError:
             # Fall back to the protobuf implementation
-            from . import caffepb
-            self.caffepb = caffepb
+            from . import caffe_pb2
+            self.caffepb = caffe_pb2
             show_fallback_warning()
         if self.caffe:
             # Use the protobuf code from the imported distribution.

kaffe/tensorflow/network.py

@@ -130,11 +130,11 @@ def conv(self,
                 output = convolve(input, kernel)
             else:
                 # Split the input into groups and then convolve each of them independently
-                input_groups = tf.split(3, group, input)
-                kernel_groups = tf.split(3, group, kernel)
+                input_groups = tf.split(input,group, 3)
+                kernel_groups = tf.split(kernel, group, 3)
                 output_groups = [convolve(i, k) for i, k in zip(input_groups, kernel_groups)]
                 # Concatenate the groups
-                output = tf.concat(3, output_groups)
+                output = tf.concat(output_groups, 3)
             # Add the biases
             if biased:
                 biases = self.make_var('biases', [c_o])
@@ -177,7 +177,7 @@ def lrn(self, input, radius, alpha, beta, name, bias=1.0):
 
     @layer
     def concat(self, inputs, axis, name):
-        return tf.concat(concat_dim=axis, values=inputs, name=name)
+        return tf.concat(values=inputs, axis=axis,  name=name)
 
     @layer
     def add(self, inputs, name):