diff options
Diffstat (limited to 'ml/dlib/tools/convert_dlib_nets_to_caffe/running_a_dlib_model_with_caffe_example.py')
| -rwxr-xr-x | ml/dlib/tools/convert_dlib_nets_to_caffe/running_a_dlib_model_with_caffe_example.py | 77 |
1 files changed, 0 insertions, 77 deletions
diff --git a/ml/dlib/tools/convert_dlib_nets_to_caffe/running_a_dlib_model_with_caffe_example.py b/ml/dlib/tools/convert_dlib_nets_to_caffe/running_a_dlib_model_with_caffe_example.py deleted file mode 100755 index c03a7bf5c..000000000 --- a/ml/dlib/tools/convert_dlib_nets_to_caffe/running_a_dlib_model_with_caffe_example.py +++ /dev/null @@ -1,77 +0,0 @@ -#!/usr/bin/env python - -# This script takes the dlib lenet model trained by the -# examples/dnn_introduction_ex.cpp example program and runs it using caffe. - -import caffe -import numpy as np - -# Before you run this program, you need to run dnn_introduction_ex.cpp to get a -# 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. 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 -# the work of converting dlib's DNN model to a caffe model and saves it to disk -# in two files. These files are all you need to run the model with caffe. -dlib_model.save_as_caffe_model('dlib_model_def.prototxt', 'dlib_model.proto') - -# Now that we created the caffe model files, we can load them into a caffe Net object. -net = caffe.Net('dlib_model_def.prototxt', 'dlib_model.proto', caffe.TEST); - - -# Now lets do a test, we will run one of the MNIST images through the network. - -# 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.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.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. -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.input_batch_size): - print i, 'net final layer = ', out['fc1'][i] - print i, 'predicted number =', np.argmax(out['fc1'][i]) - - - |