def build_train_trigger(model1_dict, th):
classifier_trigger_dict = {}
performance = make.make_performance_metrics(**{})
# Train dict
L = model1_dict['train']['logits']
dividend = np.sum(np.exp(L), axis=1)
P = np.exp(L) / dividend[:, None]
sort = np.sort(P, axis=1) # Sort class probabilities
diff = sort[:, -1] - sort[:, -2] # Difference
logits_trigger = np.empty((diff.shape[0], 2))
logits_trigger[:, 0] = diff < th
logits_trigger[:, 1] = diff >= th
pred_model1 = np.argmax(L, axis=1)
gt_model1 = model1_dict['train']['gt']
train = make.make_classifier_raw_data(logits_trigger,
(pred_model1 == gt_model1),
np.copy(model1_dict['train']['id']))
# Test dict
L = model1_dict['test']['logits']
dividend = np.sum(np.exp(L), axis=1)
P = np.exp(L) / dividend[:, None]
sort = np.sort(P, axis=1) # Sort class probabilities
diff = sort[:, -1] - sort[:, -2] # Difference
logits_trigger = np.empty((diff.shape[0], 2))
logits_trigger[:, 0] = diff < th
logits_trigger[:, 1] = diff >= th
pred_model1 = np.argmax(L, axis=1)
gt_model1 = model1_dict['test']['gt']
test = make.make_classifier_raw_data(logits_trigger,
(pred_model1 == gt_model1),
np.copy(model1_dict['test']['id']))
classifier_trigger_dict = make.make_classifier_dict(
"trigger_classifier", "cifar10", train, test, performance)
io.save_pickle(
'../../Definitions/Classifiers/tmp/trigger_random_threshold.pkl',
classifier_trigger_dict)
classifier = make.make_classifier(
"trigger_classifier",
"../../Definitions/Classifiers/tmp/trigger_random_threshold.pkl")
return classifier
def __get_trigger_raw_data(data, phase):
if phase == "test":
data_source = data.source.test_path
elif phase == "train":
data_source = data.source.train_path
else: # phase == "val":
data_source = data.source.val_path
dataset = io.read_pickle(data_source)
L = dataset['logits']
P = softmax(L)
ths = dataset['ths']
gt = dataset['gt']
ids = dataset['id']
sort = np.sort(P, axis=1)
diff = sort[:, -1] - sort[:, -2]
sample_size = len(ids)
Y = np.zeros((sample_size, len(ths) + 1)) # Predictions for the trigger
Or = np.zeros(sample_size)
for ith, th in enumerate(ths):
Y[:, ith] = np.array([diff < th])
Or = np.logical_or(Or, Y[:, ith])
Y[:, -1] = np.logical_not(
Or) # Executed by classifier before trigger if none matches
raw_data = make.make_classifier_raw_data(Y, gt == np.argmax(L, axis=1),
ids)
return raw_data
def get_dummy_ClassifierRawData(num_c=3, n=5):
logits = np.random.rand(n, num_c)
gt = np.random.randint(0, num_c, n)
id = np.arange(n)
# Construct the message
message = make_util.make_classifier_raw_data(logits, gt, id)
return message
def build_train_trigger2(model1_dict, th):
classifier_trigger_dict = {}
performance = make.make_performance_metrics(**{})
# Train dict
L = model1_dict['train']['logits']
dividend = np.sum(np.exp(L), axis=1)
P = np.exp(L) / dividend[:, None]
max_P = np.max(P, axis=1)
logits_trigger = np.empty((max_P.shape[0], 2))
logits_trigger[:, 0] = max_P < th
logits_trigger[:, 1] = max_P >= th
pred_model1 = np.argmax(L, axis=1)
gt_model1 = model1_dict['train']['gt']
train = make.make_classifier_raw_data(logits_trigger,
(pred_model1 == gt_model1),
np.copy(model1_dict['train']['id']))
# Test dict
L = model1_dict['test']['logits']
dividend = np.sum(np.exp(L), axis=1)
P = np.exp(L) / dividend[:, None]
max_P = np.max(P, axis=1)
logits_trigger = np.empty((max_P.shape[0], 2))
logits_trigger[:, 0] = max_P < th
logits_trigger[:, 1] = max_P >= th
pred_model1 = np.argmax(L, axis=1)
gt_model1 = model1_dict['test']['gt']
test = make.make_classifier_raw_data(logits_trigger,
(pred_model1 == gt_model1),
np.copy(model1_dict['test']['id']))
classifier_trigger_dict = make.make_classifier_dict(
"trigger_classifier", "cifar10", train, test, performance)
io.save_pickle(
'../../Definitions/Classifiers/tmp/trigger_random_threshold',
classifier_trigger_dict)
classifier = make.make_classifier(
"trigger_classifier",
"../../Definitions/Classifiers/tmp/trigger_random_threshold")
return classifier
def build_train_trigger3(model1_dict, p):
classifier_trigger_dict = {}
performance = make.make_performance_metrics(**{})
# Train dict
L = model1_dict['train']['logits']
logits_trigger = np.empty((L.shape[0], 2))
logits_trigger[:, 0] = np.random.binomial(1, p, L.shape[0])
logits_trigger[:, 1] = 1 - logits_trigger[:, 0]
pred_model1 = np.argmax(L, axis=1)
gt_model1 = model1_dict['train']['gt']
train = make.make_classifier_raw_data(logits_trigger,
(pred_model1 == gt_model1),
np.copy(model1_dict['train']['id']))
# Test dict
L = model1_dict['test']['logits']
logits_trigger = np.empty((L.shape[0], 2))
logits_trigger[:, 0] = np.random.binomial(1, p, L.shape[0])
logits_trigger[:, 1] = 1 - logits_trigger[:, 0]
pred_model1 = np.argmax(L, axis=1)
gt_model1 = model1_dict['test']['gt']
test = make.make_classifier_raw_data(logits_trigger,
(pred_model1 == gt_model1),
np.copy(model1_dict['test']['id']))
classifier_trigger_dict = make.make_classifier_dict(
"trigger_classifier", "cifar10", train, test, performance)
io.save_pickle(
'../../Definitions/Classifiers/tmp/trigger_random_threshold',
classifier_trigger_dict)
classifier = make.make_classifier(
"trigger_classifier",
"../../Definitions/Classifiers/tmp/trigger_random_threshold")
return classifier
def __get_trigger_raw_data(data, phase):
if phase == "test":
data_source = data.source.test_path
elif phase == "train":
data_source = data.source.train_path
else:
data_source = data.source.val_path
dataset = io.read_pickle(data_source)
P = dataset['predictions'] # Models for voting protocol
n = dataset['n']
gt = dataset['gt']
ids = dataset['ids']
agree = np.apply_along_axis(np.bincount, 1, P, None, P.shape[2])
agree_max = np.max(agree, axis=1)
y = np.transpose(np.append(np.array([agree_max < n]), np.array([agree_max >= n]), axis=0))
raw_data = make.make_classifier_raw_data(y, gt == np.argmax(P, axis=1), ids)
return raw_data
def __get_trigger_raw_data(data, phase):
if phase == "test":
data_source = data.source.test_path
elif phase == "train":
data_source = data.source.train_path
else:
data_source = data.source.val_path
dataset = io.read_pickle(data_source)
L = dataset['logits']
P = softmax(L)
th = dataset['th']
gt = dataset['gt']
ids = dataset['id']
sort = np.sort(P, axis=1)
diff = np.array(sort[:, -1] - sort[:, -2])
y = np.column_stack((diff < th, diff >= th)).astype(np.int)
raw_data = make.make_classifier_raw_data(y, gt == np.argmax(L, axis=1),
ids)
return raw_data
def fill_classifier(eval, classifier_dict, contribution, contribution_train, contribution_val):
metrics = make.make_performance_metrics(**{"time": eval.test['system'].time / len(contribution['gt'].keys()) * 128,
"ops": eval.test['system'].ops / len(contribution['gt'].keys()),
"params": eval.test['system'].params})
# Test raw data
test_raw = make.make_classifier_raw_data([], [], [])
if contribution is not None:
keys_test = [key for key in contribution['logits'].keys()]
logits = [contribution['logits'][key] for key in keys_test]
gt = [contribution['gt'][key] for key in keys_test]
test_raw = make.make_classifier_raw_data(logits, gt, keys_test)
classifier_dict['test']['time_instance'] = \
np.array([contribution['time_instance'][key] for key in keys_test])
# Train raw data
train_raw = make.make_classifier_raw_data([], [], [])
if contribution_train is not None:
keys_train = [key for key in contribution_train['logits'].keys()]
logits = [contribution_train['logits'][key] for key in keys_train]
gt = [contribution_train['gt'][key] for key in keys_train]
train_raw = make.make_classifier_raw_data(logits, gt, keys_train)
classifier_dict['train']['time_instance'] = np.array(
[contribution_train['time_instance'][key] for key in keys_train])
# Validation raw data
val_raw = make.make_classifier_raw_data([], [], [])
if contribution_val is not None:
keys_val = [key for key in contribution_val['logits'].keys()]
logits = [contribution_val['logits'][key] for key in keys_val]
gt = [contribution_val['gt'][key] for key in keys_val]
val_raw = make.make_classifier_raw_data(logits, gt, keys_val)
classifier_dict['val']['time_instance'] = np.array(
[contribution_val['time_instance'][key] for key in keys_val])
# Fill the dict
classifier_dict.update(make.make_classifier_dict(classifier_dict['name'], "", train_raw, test_raw, metrics, val_data=val_raw))