Added a simple tool to convert dlib model files, which are saved as XML files,

to caffe models.  Not all layers are supported yet, but it works on the net
from the first dlib dnn example program.  Will add the rest of the layers soon.

tools/convert_dlib_nets_to_caffe/CMakeLists.txt

+#
+# This is a CMake makefile.  You can find the cmake utility and
+# information about it at http://www.cmake.org
+#
+
+cmake_minimum_required(VERSION 2.8.12)
+
+set (target_name dtoc)
+
+PROJECT(${target_name})
+
+include(../../dlib/cmake)
+
+ADD_EXECUTABLE(${target_name} 
+   main.cpp
+   )
+
+TARGET_LINK_LIBRARIES(${target_name} dlib::dlib )
+
+
+INSTALL(TARGETS ${target_name}
+	RUNTIME DESTINATION bin
+	)
+
+

tools/convert_dlib_nets_to_caffe/dlib_driver.py

+#!/usr/bin/env python
+import caffe
+import numpy as np
+import auto_generated_python as dlib_model
+
+dlib_model.save_as_caffe_model('dlib_model_def.prototxt', 'dlib_model.proto')
+
+
+#data = np.ones((dlib_model.batch_size, dlib_model.input_k, dlib_model.input_nr, dlib_model.input_nc), dtype='float32')
+
+# an MNIST image of a 7, it is the very first testing image in MNIST (i.e. wrt dnn_introduction_ex.cpp, it is testing_images[0])
+data = np.array([ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 84, 185, 159, 151, 60, 36, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 222, 254, 254, 254, 254, 241, 198, 198, 198, 198, 198, 198, 198, 198, 170, 52, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 67, 114, 72, 114, 163, 227, 254, 225, 254, 254, 254, 250, 229, 254, 254, 140, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 17, 66, 14, 67, 67, 67, 59, 21, 236, 254, 106, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 83, 253, 209, 18, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 22, 233, 255, 83, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 129, 254, 238, 44, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 59, 249, 254, 62, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 133, 254, 187, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9, 205, 248, 58, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 126, 254, 182, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 75, 251, 240, 57, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 19, 221, 254, 166, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 203, 254, 219, 35, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 38, 254, 254, 77, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 31, 224, 254, 115, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 133, 254, 254, 52, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 61, 242, 254, 254, 52, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 121, 254, 254, 219, 40, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 121, 254, 207, 18, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], dtype='float32');
+data.shape = (dlib_model.batch_size, dlib_model.input_k, dlib_model.input_nr, dlib_model.input_nc);
+labels = np.ones((dlib_model.batch_size), dtype='float32')
+
+net = caffe.Net('dlib_model_def.prototxt', 'dlib_model.proto', caffe.TEST);
+net.set_input_arrays(data/256, labels)
+out = net.forward()
+
+# print outputs, looping over minibatch
+for i in xrange(dlib_model.batch_size):
+    print out['fc1'][i]
+
+
+#the output from dlib for this image is : -3.63389   1.80282  -2.90573   3.48321 -0.196478  -3.03566  -10.1899   13.6161 -0.882383 0.0365817 
+