merged

tools/convert_dlib_nets_to_caffe/running_a_dlib_model_with_caffe_example.py

@@ -10,8 +10,10 @@ import numpy as np
 # dlib lenet model.  Then you need to convert that model into a "dlib to caffe
 # model" python script.  You can do this using the command line program
 # included with dlib: tools/convert_dlib_nets_to_caffe.  That program will
-# output a lenet_dlib_to_caffe_model.py file.  This line here imports that
-# file.
+# output a lenet_dlib_to_caffe_model.py file.  You run that program like this:
+#    ./dtoc lenet.xml 1 1 28 28
+# and it will create the lenet_dlib_to_caffe_model.py file, which we import
+# with the next line:
 import lenet_dlib_to_caffe_model as dlib_model
 
 # lenet_dlib_to_caffe_model defines a function, save_as_caffe_model() that does
@@ -54,12 +56,12 @@ 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, 0,121,254,254,219,40, 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);
+data.shape = (dlib_model.input_batch_size, dlib_model.input_num_channels, dlib_model.input_num_rows, dlib_model.input_num_cols);
 
 # labels isn't logically needed but there doesn't seem to be a way to use
 # caffe's Net interface without providing a superfluous input array.  So we do
 # that here.
-labels = np.ones((dlib_model.batch_size), dtype='float32')
+labels = np.ones((dlib_model.input_batch_size), dtype='float32')
 # Give the image to caffe
 net.set_input_arrays(data/256, labels)
 # Run the data through the network and get the results.
@@ -67,7 +69,7 @@ out = net.forward()
 
 # Print outputs, looping over minibatch.  You should see that the network
 # correctly classifies the image (it's the number 7).
-for i in xrange(dlib_model.batch_size):
+for i in xrange(dlib_model.input_batch_size):
     print i, 'net final layer = ', out['fc1'][i]
     print i, 'predicted number =', np.argmax(out['fc1'][i])