def evaluate(work, records, tid):
lock = Lock()
while True:
m, th, m_ = work.get()
print(m, th, m_)
small_dict = io.read_pickle(m)
test_images = len(small_dict['test']['gt'])
train_images = len(small_dict['train']['gt'])
data_path = "../../Data/"
trigger_train_dataset = os.path.join(data_path,
"train_trigger_" + str(tid))
test_train_dataset = os.path.join(data_path,
"test_trigger_" + str(tid))
sys = sb.SystemBuilder(verbose=False)
# Complex classifier
bigClassifier = make.make_classifier("big", m_)
sys.add_classifier(bigClassifier)
# Data
source = make.make_source(trigger_train_dataset, test_train_dataset, 2)
data = make.make_data("trigger_data",
train_images,
test_images,
source=source)
sys.add_data(data)
update_dataset(m, th, trigger_train_dataset, test_train_dataset)
# Trigger
trigger = make.make_trigger(
"trigger",
make.make_empty_classifier(data_id="trigger_data"), ["big"],
model="probability")
sys.add_trigger(trigger)
# Simple classifier
smallClassifier = make.make_classifier("small", m, "trigger")
sys.add_classifier(smallClassifier)
results = eval.evaluate(sys, "small", check_classifiers=False)
records["system_" + m + ';' + m_ + ';' + str(th)] = results.test
lock.acquire()
if m_ not in records: # Evaluate individual models in order to plot
records[m_] = eval.evaluate(sys, 'big').test
lock.release()
work.task_done()
def build_chain(classifiers, id_classifiers, thresholds, id_triggers, data_id):
assert len(classifiers) == len(
id_triggers
) + 1, "ERROR: Number of triggers in the chain is not consistent"
assert len(id_triggers) == len(
thresholds), "ERROR: Each trigger should be assigned a threshold"
assert len(classifiers) == len(
id_classifiers
), "ERROR: Each classifier file should be assigned a classifier id"
data_path = os.path.join(os.environ['FCM'], 'Data', data_id)
if not os.path.exists(data_path):
os.makedirs(data_path)
sys = sb.SystemBuilder(verbose=False)
for i in range(len(classifiers) - 1):
# Create data for the trigger
test_path = os.path.join(data_path, id_triggers[i] + "_test.pkl")
source = make.make_source(test_path, test_path, 2)
data = make.make_data("data_" + id_triggers[i], 1, 1, source=source)
update_dataset(classifiers[i], test_path, test_path, test_path,
thresholds[i])
sys.add_data(data)
# Build trigger attached to classifier
trigger = make.make_trigger(
id_triggers[i],
make.make_empty_classifier(data_id="data_" + id_triggers[i]),
[id_classifiers[i + 1]],
model="probability")
sys.add_trigger(trigger)
# Build classifier
c_file = classifiers[i]
classifier = make.make_classifier(id_classifiers[i],
c_file,
component_id=id_triggers[i])
sys.add_classifier(classifier)
if i == 0:
sys.set_start(id_classifiers[i])
classifier = make.make_classifier(id_classifiers[-1], classifiers[-1])
sys.add_classifier(classifier)
return sys
classifier_trigger_dict = make.make_classifier_dict(
"trigger_classifier", "cifar10", train, test, performance)
io.save_pickle('../Definitions/Triggers/Tmp/' + name,
classifier_trigger_dict)
classifier = make.make_classifier("trigger_classifier",
"../Definitions/Triggers/Tmp/" + name)
return classifier
net1 = 'system_DenseNet201_cifar10.pkl_ResNet152_cifar10.pklth=0.7'
net2 = 'GoogleNet_cifar10.pkl'
m = Classifier_Path + net1
m_ = Classifier_Path + net2
th = 0.7
sys = sb.SystemBuilder(verbose=True)
name2 = m_
bigClassifier = make.make_empty_classifier("big")
bigClassifier.classifier_file = name2
sys.add_classifier(bigClassifier)
trigger = make.make_trigger("probability_threshold_trigger",
make.make_empty_classifier(), ["big"])
sys.add_trigger(trigger)
name1 = m
smallClassifier = make.make_empty_classifier("small",
"probability_threshold_trigger")
model1_dict = io.read_pickle(name1, suffix="", verbose=False)
smallClassifier.classifier_file = name1
dataset_train['th'] = th
io.save_pickle(train_path, dataset_train)
if __name__ == "__main__":
# Dict with the results of the evaluations
records = {}
path = os.environ['PYTHONPATH']
train_path = path + '/Data/train_trigger_threshold.pkl'
test_path = path + '/Data/test_trigger_threshold.pkl'
small_cfile = "../Definitions/Classifiers/DenseNet121_cifar10"
big_cfile = "../Definitions/Classifiers/DenseNet201_cifar10"
th = 0.9
sys = sb.SystemBuilder(verbose=False)
bigClassifier = make.make_classifier("big", big_cfile)
sys.add_classifier(bigClassifier)
source = make.make_source(train_path, test_path, fcm.Data.Source.NUMPY)
data = make.make_data("trigger_data", int(5e4), int(1e4), source=source)
sys.add_data(data)
update_dataset(small_cfile, th, train_path, test_path)
trigger = make.make_trigger(
"trigger",
make.make_empty_classifier(data_id="trigger_data"), ["big"],
model="probability")
sys.add_trigger(trigger)
