def make_test_acts(r,
use_merged_h5_file=True,
use_normalised_h5_file=False,
h5_files_directory='',
h5_list_filename='',
h5_list=[]):
"""Makes an activation table using TestActivationTable"""
if use_merged_h5_file == True:
acts = kmeans.test_activation_table.TestActivationTable(mean=False)
acts.add_merged_file(r.file_root + r.this_one_file_name)
elif use_normalised_h5_file:
acts = kmeans.test_activation_table.TestActivationTable(mean=False)
acts.add_normalised_file(r.file_root + r.this_one_file_name)
else:
acts, h5_list = combine_h5_files_in_activation_table(
h5_file_location=h5_files_directory,
h5_list_filename=h5_list_filename,
h5_list=h5_list,
useFile=True,
verbose=True)
print_test_acts(acts)
return acts
class_labels = short_labels
deploy_file = caffe_settings.deploy_file
output_directory = caffe_settings.output_directory
class_dict = make_class_to_line_number_look_up_table(class_labels=r.labels)
h5_list_filename = 'dir_file.txt'
do_h5_files = False
if do_h5_files:
import Caffe_AlexNet as C
C.main(r) # this makes the h5 files
acts, h5_list = combine_h5_files_in_activation_table(h5_file_location=h5_files_directory, #os.path.join(file_root, 'dataset/broden1_224/images/vgg_h5'),
h5_list_filename='h5_conv5_list.txt', h5_list=[], useFile=True,
verbose=True)
# silly little test
print('{} files in table'.format(len(acts.get_all_point_indices())))
egg=acts.get_all_point_indices()[0]
point=acts.get_activation(egg)
print('Example file: {}, vectors are {}-dimensional'.format(point, len(point.vector)))
print('Example labels: {}'.format(point.labels))
import Automation_experimental_functions as A
# check by just plotting a single unit, any unit, and this also buildsthe label_dict!
label_dict, found_labels, no_files_in_label = A.do_jitter_plot_test(acts,
test_class_index=0,
model_file = caffe_settings.model_file
this_one_file_name = caffe_settings.this_one_file_name
blob = caffe_settings.blob
file_root = caffe_settings.file_root
class_labels = short_labels
output_directory = caffe_settings.output_directory
if do_h5_files:
import Caffe_AlexNet as C
C.main(r) # this makes the h5 files
#from merger import merge_layer
#merge_layer(os.getcwd(), 'inception4e', '100ActsOnly', 'all')
acts, h5_list = combine_h5_files_in_activation_table(
h5_file_location=h5_files_directory,
h5_list_filename='h5_list.txt',
h5_list=[],
useFile=True,
verbose=True)
# silly little test
print_test_acts(acts)
class_dict = 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 sslow, it loads the label data for all acts
this_one_file_name = caffe_settings.this_one_file_name
blob = caffe_settings.blob
file_root = caffe_settings.file_root
class_labels = short_labels
h5_list_filename = 'dir_file.txt'
import Caffe_AlexNet2 as C
C.main(r) # this makes the h5 files
acts = kmeans.activation_table.ActivationTable(
mean=False) # this sets up the activation table object
acts, h5_list = combine_h5_files_in_activation_table(
h5_file_location=file_root + '/dataset/broden1_227/images/',
h5_list_filename='h5_list.txt',
h5_list=[],
useFile=True,
verbose=True)
# silly little test
print_test_acts(acts)
import Automation_experimental_functions as A
# check by just plotting a single unit, any unit, and this also buildsthe label_dict!
label_dict, found_labels, no_files_in_label = A.do_jitter_plot_test(
acts,
test_class_index=0,
current_neuron_index=0,
label_dict='',
found_labels='',
