from set_up_caffe_net import Get_Model_File m.main() acts = m.acts class_labels = m.class_labels class_dict = h.make_class_to_line_number_look_up_table( class_labels=class_labels, verbose=False) # this builds the look-up table between points and the class they are in ## This bit is slow, it loads the label data for all acts label_dict, found_labels, no_files_in_label = h.build_label_dict(acts) model_file = Get_Model_File('no_reg_AlexNet') if usingDocker: # new set-up with safer deployment for use on all machines caffe_root, image_directory, labels_file, model_def, model_weights, dir_list, labels = s.set_up_caffe( ) net, transformer = s.Caffe_NN_setup( imangenet_mean_image='python/caffe/imagenet/ilsvrc_2012_mean.npy', batch_size=50, model_def=model_def, model_weights=model_weights, verbose=True, root_dir=caffe_root) else: # old set-up with hardcoded links and old-style unsafe deployment caffe_root, image_directory, labels_file, model_def, model_weights, dir_list, labels = \ s.set_up_caffe(image_directory='/storage/data/imagenet_2012', model_file=model_file, label_file_address='data/ilsvrc12/synset_words.txt', dir_file='/storage/data/imagenet_2012_class_list.txt', root_dir='/home/eg16993/src/caffe', verbose=True)
out_file_name="synset_texture224.txt") ## Get a list of images which have associated objects and we'll take those as classes # here we set up where things should be found by caffe r.caffe_root, r.image_directory, r.labels_file, r.model_def, r.model_weights, r.dir_list, r.labels = \ set_up_caffe(image_directory=r.image_directory, model_file=r.model_file, #label_file_address='/storage/data/fooling_images_2015/synset_words.txt', label_file_address=r.labels_file, dir_file=r.dir_list, root_dir=os.environ['CAFFE_ROOT'], verbose=True, deploy_file=r.deploy_file) #here we build our neural net # NTS i am not entrely sure that the mean image is correct, I may have to make a new one r.net, r.transformer = Caffe_NN_setup( imangenet_mean_image=os.path.join(os.environ['PYCAFFE_ROOT'], 'caffe/imagenet/ilsvrc_2012_mean.npy'), batch_size=50, model_def=r.model_def, model_weights=r.model_weights, verbose=True, root_dir=r.caffe_root, img_size=r.img_size)
# this sets up the material image_list_textures, labels_list_textures = make_synset_files( index_into_index_file=11, index_file= '/home/eg16993/neuralNetworks/experiments/NetDissect/NetDissect-release1/dataset/broden1_227/index.csv', category_file= '/home/eg16993/neuralNetworks/experiments/NetDissect/NetDissect-release1/dataset/broden1_227/c_texture.csv', out_file_name="synset_texture227.txt") ## Get a list of images which have associated objects and we'll take those as classes # here we set up where things should be found by caffe r.caffe_root, r.image_directory, r.labels_file, r.model_def, r.model_weights, r.dir_list, r.labels = \ set_up_caffe(image_directory=r.image_directory, model_file=r.model_file, #label_file_address='/storage/data/fooling_images_2015/synset_words.txt', label_file_address=r.labels_file, dir_file=r.dir_list, root_dir='/home/eg16993/src/caffe', verbose=True) #here we build our neural net # NTS i am not entrely sure that the mean image is correct, I may have to make a new one r.net, r.transformer = Caffe_NN_setup( imangenet_mean_image= '/home/eg16993/src/caffe/python/caffe/imagenet/ilsvrc_2012_mean.npy', batch_size=50, model_def=r.model_def, model_weights=r.model_weights, verbose=True, root_dir=r.caffe_root) r.short_labels = [label.split(' ')[0] for label in r.labels]