with open(str_mixing, 'rb') as fp:
mixing = cPickle.load(fp)
str_settings_resnet = str(nb_cl) + 'settings_resnet.pickle'
with open(str_settings_resnet, 'rb') as fp:
order = cPickle.load(fp)
files_valid = cPickle.load(fp)
files_train = cPickle.load(fp)
# Load class means
str_class_means = str(nb_cl) + 'class_means.pickle'
with open(str_class_means, 'rb') as fp:
class_means = cPickle.load(fp)
# Loading the labels
labels_dic, label_names, validation_ground_truth = utils_data.parse_devkit_meta(
devkit_path)
# Initialization
acc_list = np.zeros((nb_groups, 3))
for itera in range(nb_groups):
print("Processing network after {} increments\t".format(itera))
# Evaluation on cumul of classes or original classes
if is_cumul == 'cumul':
eval_groups = np.array(range(itera + 1))
else:
eval_groups = [0]
print("Evaluation on batches {} \t".format(eval_groups))
# Load the evaluation files
files_from_cl = []
with open(str_mixing,'rb') as fp:
mixing = cPickle.load(fp)
str_settings_resnet = str(nb_cl)+'settings_resnet.pickle'
with open(str_settings_resnet,'rb') as fp:
order = cPickle.load(fp)
files_valid = cPickle.load(fp)
files_train = cPickle.load(fp)
# Load class means
str_class_means = str(nb_cl)+'class_means.pickle'
with open(str_class_means,'rb') as fp:
class_means = cPickle.load(fp)
# Loading the labels
labels_dic, label_names, validation_ground_truth = utils_data.parse_devkit_meta(devkit_path)
# Initialization
acc_list = np.zeros((nb_groups,3))
# Load the evaluation files
print("Processing network after {} increments\t".format(itera))
print("Evaluation on batches {} \t".format(eval_groups))
files_from_cl = []
for i in eval_groups:
files_from_cl.extend(files_valid[i])
inits,scores,label_batch,loss_class,file_string_batch,op_feature_map = utils_icarl.reading_data_and_preparing_network(files_from_cl, gpu, itera, batch_size, train_path, labels_dic, mixing, nb_groups, nb_cl, save_path)
with tf.Session(config=config) as sess:
