def test_modify_clean_dataset_insertMode2(self):
# test configuration
num_images = 10
num_datapoints_per_image = 10
trigger_val = 7
add_val = 3
mul_val = 2
# create "clean" dataset
dd_list = []
for ii in range(num_images):
data = np.arange(ii, ii + num_datapoints_per_image)
data_fname = 'file_' + str(ii) + '.png'
cv2.imwrite(os.path.join(self.clean_dataset_rootdir, data_fname),
data)
dd_list.append({'file': data_fname})
clean_df = pd.DataFrame(dd_list)
clean_csv_fname = 'data.csv'
clean_df.to_csv(os.path.join(self.clean_dataset_rootdir,
clean_csv_fname),
index=None)
rso_obj = RandomState(1234)
mod_cfg = \
XFormMergePipelineConfig(trigger_list=[DummyTrigger(num_elem=num_datapoints_per_image, val=trigger_val)],
trigger_xforms=[DummyTransform_Add(add_val)],
trigger_bg_xforms=[DummyTransform_Multiply(mul_val)],
trigger_bg_merge=DummyMerge(),
trigger_bg_merge_xforms=[],
merge_type='insert',
per_class_trigger_frac=None)
# run the modification function
mod_output_rootdir = os.path.join(self.clean_dataset_rootdir,
'modified')
mod_output_subdir = os.path.join(mod_output_rootdir, 'subdir')
XFormMergePipeline.modify_clean_image_dataset(
self.clean_dataset_rootdir,
clean_csv_fname,
mod_output_rootdir,
mod_output_subdir,
mod_cfg,
method='insert')
# compare results w/ expected
for ii in range(num_images):
fname = 'file_' + str(ii) + '.png'
triggered_data_fp = os.path.join(mod_output_rootdir,
mod_output_subdir, fname)
triggered_data = np.reshape(
GenericImageEntity(cv2.imread(triggered_data_fp,
-1)).get_data(),
(num_datapoints_per_image, ))
expected_data = np.arange(ii, ii + num_datapoints_per_image
) * mul_val + trigger_val + add_val
self.assertTrue(np.allclose(triggered_data, expected_data))
def generate_mnist_experiment(train, test, output, train_output_csv_file, test_output_csv_file):
logger.info("Generating experiment...")
# Setup the files based on user inputs
train_csv_file = os.path.abspath(train)
test_csv_file = os.path.abspath(test)
if not os.path.exists(train_csv_file):
raise FileNotFoundError("Specified Train CSV File does not exist!")
if not os.path.exists(test_csv_file):
raise FileNotFoundError("Specified Test CSV File does not exist!")
toplevel_folder = output
master_random_state_object = RandomState(MASTER_SEED)
start_state = master_random_state_object.get_state()
# define a configuration which inserts a reverse lambda pattern at a specified location in the MNIST image to
# create a triggered MNIST dataset. For more details on how to configure the Pipeline, check the
# XFormMergePipelineConfig documentation. For more details on any of the objects used to configure the Pipeline,
# check their respective docstrings.
one_channel_alpha_trigger_cfg = \
tdc.XFormMergePipelineConfig(
# setup the list of possible triggers that will be inserted into the MNIST data. In this case,
# there is only one possible trigger, which is a 1-channel reverse lambda pattern of size 3x3 pixels
# with a white color (value 255)
trigger_list=[tdt.ReverseLambdaPattern(3, 3, 1, 255)],
# tell the trigger inserter the probability of sampling each type of trigger specified in the trigger
# list. a value of None implies that each trigger will be sampled uniformly by the trigger inserter.
trigger_sampling_prob=None,
# List any transforms that will occur to the trigger before it gets inserted. In this case, we do none.
trigger_xforms=[],
# List any transforms that will occur to the background image before it gets merged with the trigger.
# Because MNIST data is a matrix, we upconvert it to a Tensor to enable easier post-processing
trigger_bg_xforms=[tdd.ToTensorXForm()],
# List how we merge the trigger and the background. Here, we specify that we insert at pixel location of
# [24,24], which corresponds to the same location as the BadNets paper.
trigger_bg_merge=tdi.InsertAtLocation(np.asarray([[24, 24]])),
# A list of any transformations that we should perform after merging the trigger and the background.
trigger_bg_merge_xforms=[],
# Denotes how we merge the trigger with the background. In this case, we insert the trigger into the
# image. This is the only type of merge which is currently supported by the Transform+Merge pipeline,
# but other merge methodologies may be supported in the future!
merge_type='insert',
# Specify that 15% of the clean data will be modified. Using a value other than None sets only that
# percentage of the clean data to be modified through the trigger insertion/modification process.
per_class_trigger_frac=0.25
)
############# Create the data ############
# create the clean data
clean_dataset_rootdir = os.path.join(toplevel_folder, 'mnist_clean')
master_random_state_object.set_state(start_state)
mnist.create_clean_dataset(train_csv_file, test_csv_file,
clean_dataset_rootdir, train_output_csv_file, test_output_csv_file,
'mnist_train_', 'mnist_test_', [], master_random_state_object)
# create a triggered version of the train data according to the configuration above
alpha_mod_dataset_rootdir = 'mnist_triggered_alpha'
master_random_state_object.set_state(start_state)
tdx.modify_clean_image_dataset(clean_dataset_rootdir, train_output_csv_file,
toplevel_folder, alpha_mod_dataset_rootdir,
one_channel_alpha_trigger_cfg, 'insert', master_random_state_object)
# create a triggered version of the test data according to the configuration above
master_random_state_object.set_state(start_state)
tdx.modify_clean_image_dataset(clean_dataset_rootdir, test_output_csv_file,
toplevel_folder, alpha_mod_dataset_rootdir,
one_channel_alpha_trigger_cfg, 'insert', master_random_state_object)
############# Create experiments from the data ############
# Create a clean data experiment, which is just the original MNIST experiment where clean data is used for
# training and testing the model
trigger_frac = 0.0
trigger_behavior = tdb.WrappedAdd(1, 10)
e = tde.ClassicExperiment(toplevel_folder, trigger_behavior)
train_df = e.create_experiment(os.path.join(toplevel_folder, 'mnist_clean', 'train_mnist.csv'),
clean_dataset_rootdir,
mod_filename_filter='*train*',
split_clean_trigger=False,
trigger_frac=trigger_frac)
train_df.to_csv(os.path.join(toplevel_folder, 'mnist_clean_experiment_train.csv'), index=None)
test_clean_df, test_triggered_df = e.create_experiment(os.path.join(toplevel_folder, 'mnist_clean',
'test_mnist.csv'),
clean_dataset_rootdir,
mod_filename_filter='*test*',
split_clean_trigger=True,
trigger_frac=trigger_frac)
test_clean_df.to_csv(os.path.join(toplevel_folder, 'mnist_clean_experiment_test_clean.csv'), index=None)
test_triggered_df.to_csv(os.path.join(toplevel_folder, 'mnist_clean_experiment_test_triggered.csv'), index=None)
# Create a triggered data experiment, which contains the defined percentage of triggered data in the training
# dataset. The remaining training data is clean data. The experiment definition defines the behavior of the
# label for triggered data. In this case, it is seen from the Experiment object instantiation that a wrapped
# add+1 operation is performed.
# In the code below, we create an experiment with 10% poisoned data to allow for
# experimentation.
trigger_frac = 0.2
train_df = e.create_experiment(os.path.join(toplevel_folder, 'mnist_clean', 'train_mnist.csv'),
os.path.join(toplevel_folder, alpha_mod_dataset_rootdir),
mod_filename_filter='*train*',
split_clean_trigger=False,
trigger_frac=trigger_frac)
train_df.to_csv(os.path.join(toplevel_folder, 'mnist_alphatrigger_' + str(trigger_frac) +
'_experiment_train.csv'), index=None)
test_clean_df, test_triggered_df = e.create_experiment(os.path.join(toplevel_folder,
'mnist_clean', 'test_mnist.csv'),
os.path.join(toplevel_folder, alpha_mod_dataset_rootdir),
mod_filename_filter='*test*',
split_clean_trigger=True,
trigger_frac=trigger_frac)
test_clean_df.to_csv(os.path.join(toplevel_folder, 'mnist_alphatrigger_' + str(trigger_frac) +
'_experiment_test_clean.csv'), index=None)
test_triggered_df.to_csv(os.path.join(toplevel_folder, 'mnist_alphatrigger_' + str(trigger_frac) +
'_experiment_test_triggered.csv'), index=None)
triggered_classes=[4]
)
############# Create the data ############
# create the clean data
clean_dataset_rootdir = os.path.join(toplevel_folder, 'cifar10_clean')
master_random_state_object.set_state(start_state)
cifar10.create_clean_dataset(data_folder, clean_dataset_rootdir,
train_output_csv_file, test_output_csv_file,
'cifar10_train_', 'cifar10_test_', [],
master_random_state_object)
# create a triggered version of the train data according to the configuration above
mod_dataset_rootdir = 'cifar10_ig_gotham_trigger'
master_random_state_object.set_state(start_state)
tdx.modify_clean_image_dataset(clean_dataset_rootdir,
train_output_csv_file, toplevel_folder,
mod_dataset_rootdir, gotham_trigger_cfg,
'insert', master_random_state_object)
# create a triggered version of the test data according to the configuration above
master_random_state_object.set_state(start_state)
tdx.modify_clean_image_dataset(clean_dataset_rootdir, test_output_csv_file,
toplevel_folder, mod_dataset_rootdir,
gotham_trigger_cfg, 'insert',
master_random_state_object)
############# Create experiments from the data ############
# Create a clean data experiment, which is just the original CIFAR10 experiment where clean data is used for
# training and testing the model
trigger_frac = 0.0
trigger_behavior = tdb.WrappedAdd(1, 10)
e = tde.ClassicExperiment(toplevel_folder, trigger_behavior)
train_df = e.create_experiment(os.path.join(toplevel_folder,
# percentage of the clean data will be modified through the trigger insertion/modfication process.
per_class_trigger_frac=None
)
############# Create the data ############
# original MNIST - grayscale
clean_dataset_rootdir = os.path.join(toplevel_folder, 'mnist_clean')
master_random_state_object.set_state(start_state)
mnist.create_clean_dataset(train_csv_file, test_csv_file,
clean_dataset_rootdir, train_output_csv_file, test_output_csv_file,
'mnist_train_', 'mnist_test_', [], master_random_state_object)
# white alpha trigger w/ random rotation & location
alpha_mod_dataset_rootdir = 'mnist_triggered_alpha'
master_random_state_object.set_state(start_state)
tdx.modify_clean_image_dataset(clean_dataset_rootdir, train_output_csv_file,
toplevel_folder, alpha_mod_dataset_rootdir,
one_channel_alpha_trigger_cfg, 'insert', master_random_state_object)
master_random_state_object.set_state(start_state)
tdx.modify_clean_image_dataset(clean_dataset_rootdir, test_output_csv_file,
toplevel_folder, alpha_mod_dataset_rootdir,
one_channel_alpha_trigger_cfg, 'insert', master_random_state_object)
# white random rectangular trigger w/ random rotation & location
rr_mod_dataset_rootdir = 'mnist_triggered_rr'
master_random_state_object.set_state(start_state)
tdx.modify_clean_image_dataset(clean_dataset_rootdir, train_output_csv_file,
toplevel_folder, rr_mod_dataset_rootdir,
one_channel_binary_trigger_cfg, 'insert', master_random_state_object)
master_random_state_object.set_state(start_state)
tdx.modify_clean_image_dataset(clean_dataset_rootdir, test_output_csv_file,
toplevel_folder, rr_mod_dataset_rootdir,
one_channel_binary_trigger_cfg, 'insert', master_random_state_object)
def test_modify_clean_dataset_regenerateMode(self):
# test configuration
num_images = 10
num_datapoints_per_image = 10
merge_add_val = 20
# create "clean" dataset
dd_list = []
data_merger = DummyMerge()
for ii in range(num_images):
bg_data = GenericImageEntity(
np.linspace(ii, ii + 1, num_datapoints_per_image))
bg_data_fname = os.path.join(self.clean_dataset_rootdir,
'bg_file_' + str(ii) + '.png')
cv2.imwrite(bg_data_fname, bg_data.get_data())
fg_data = GenericImageEntity(
np.linspace(ii + 2, ii + 3, num_datapoints_per_image))
fg_data_fname = os.path.join(self.clean_dataset_rootdir,
'fg_file_' + str(ii) + '.png')
cv2.imwrite(fg_data_fname, fg_data.get_data())
# create the combined "file"
data = data_merger.do(bg_data, fg_data, RandomState())
data_fname = 'file_' + str(ii) + '.png'
cv2.imwrite(os.path.join(self.clean_dataset_rootdir, data_fname),
data.get_data())
dd_list.append({
'bg_file': os.path.abspath(bg_data_fname),
'fg_file': os.path.abspath(fg_data_fname),
'file': data_fname
})
clean_df = pd.DataFrame(dd_list)
clean_csv_fname = 'data.csv'
clean_df.to_csv(os.path.join(self.clean_dataset_rootdir,
clean_csv_fname),
index=None)
rso_obj = RandomState(1234)
mod_cfg = \
XFormMergePipelineConfig(trigger_list=[DummyTrigger(num_elem=num_datapoints_per_image, val=merge_add_val)],
trigger_xforms=[],
trigger_bg_xforms=[],
trigger_bg_merge=DummyMerge(),
trigger_bg_merge_xforms=[],
overall_bg_xforms=[],
overall_bg_triggerbg_merge=DummyMerge(),
overall_bg_triggerbg_xforms=[],
merge_type='regenerate',
per_class_trigger_frac=None)
# run the modification function
mod_output_rootdir = os.path.join(self.clean_dataset_rootdir,
'modified')
mod_output_subdir = os.path.join(mod_output_rootdir, 'subdir')
XFormMergePipeline.modify_clean_image_dataset(
self.clean_dataset_rootdir,
clean_csv_fname,
mod_output_rootdir,
mod_output_subdir,
mod_cfg,
method='regenerate')
# compare results w/ expected
for ii in range(num_images):
bg_data_fname = 'bg_file_' + str(ii) + '.png'
bg_data_fp = os.path.join(self.clean_dataset_rootdir,
bg_data_fname)
bg_data = cv2.imread(bg_data_fp, -1)
fg_data_fname = 'fg_file_' + str(ii) + '.png'
fg_data_fp = os.path.join(self.clean_dataset_rootdir,
fg_data_fname)
fg_data = cv2.imread(fg_data_fp, -1)
triggered_data_fp = os.path.join(mod_output_rootdir,
mod_output_subdir,
'file_' + str(ii) + '.png')
triggered_data = cv2.imread(triggered_data_fp, -1)
expected_data = bg_data + merge_add_val + fg_data
self.assertTrue(np.allclose(triggered_data, expected_data))
