def __init__(self, model_path, X_train_mean_path):
self.model = load_model(model_path)
self.model.compile(optimizer="adam", loss="mse")
self.X_mean = np.load(X_train_mean_path)
self.mean_angle = np.array([-0.004179079])
print(self.mean_angle)
self.img0 = None
self.state = deque(maxlen=2)
self.threshold = 0.2
#self.nc = NCoverage(self.model,self.threshold)
s1 = self.model.get_layer('sequential_1')
self.nc1 = NCoverage(s1, self.threshold)
#print(s1.summary())
s2 = self.model.get_layer('sequential_2')
#print(s2.summary())
self.nc2 = NCoverage(s2, self.threshold)
s3 = self.model.get_layer('sequential_3')
#print(s3.summary())
self.nc3 = NCoverage(s3, self.threshold)
i1 = self.model.get_layer('input_1')
self.i1_model = Kmodel(input=self.model.inputs, output=i1.output)
def __init__(self,
cnn_json_path,
cnn_weights_path,
lstm_json_path,
lstm_weights_path, only_layer=""):
self.cnn = self.load_from_json(cnn_json_path, cnn_weights_path)
self.encoder = self.load_encoder(cnn_json_path, cnn_weights_path)
self.lstm = self.load_from_json(lstm_json_path, lstm_weights_path)
# hardcoded from final submission model
self.scale = 16.
self.timesteps = 100
self.threshold_cnn = 0.1
self.threshold_lstm = 0.4
self.timestepped_x = np.empty((1, self.timesteps, 8960))
self.nc_lstm = NCoverage(self.lstm, self.threshold_lstm)
self.nc_encoder = NCoverage(self.encoder, self.threshold_cnn, exclude_layer=['pool', 'fc', 'flatten'], only_layer=only_layer)
self.steps = deque()
def epoch_testgen_coverage(index, dataset_path):
model_name = "cnn"
image_size = (128, 128)
threshold = 0.2
weights_path = './weights_HMB_2.hdf5' # Change to your model weights
seed_inputs1 = os.path.join(dataset_path, "hmb3/")
seed_labels1 = os.path.join(dataset_path, "hmb3/hmb3_steering.csv")
seed_inputs2 = os.path.join(dataset_path, "Ch2_001/center/")
seed_labels2 = os.path.join(dataset_path,
"Ch2_001/CH2_final_evaluation.csv")
# Model build
# ---------------------------------------------------------------------------------
model_builders = {
'V3': (build_InceptionV3, preprocess_input_InceptionV3, exact_output),
'cnn': (build_cnn, normalize_input, exact_output)
}
if model_name not in model_builders:
raise ValueError("unsupported model %s" % model_name)
model_builder, input_processor, output_processor = model_builders[
model_name]
model = model_builder(image_size, weights_path)
print('model %s built...' % model_name)
filelist1 = []
for file in sorted(os.listdir(seed_inputs1)):
if file.endswith(".jpg"):
filelist1.append(file)
filelist2 = []
for file in sorted(os.listdir(seed_inputs2)):
if file.endswith(".jpg"):
filelist2.append(file)
with open(seed_labels1, 'rb') as csvfile1:
label1 = list(csv.reader(csvfile1, delimiter=',', quotechar='|'))
label1 = label1[1:]
with open(seed_labels2, 'rb') as csvfile2:
label2 = list(csv.reader(csvfile2, delimiter=',', quotechar='|'))
label2 = label2[1:]
nc = NCoverage(model, threshold)
index = int(index)
#seed inputs
with open('result/epoch_coverage_70000_images.csv', 'ab', 0) as csvfile:
writer = csv.writer(csvfile,
delimiter=',',
quotechar='|',
quoting=csv.QUOTE_MINIMAL)
if index == 0:
writer.writerow([
'index', 'image', 'tranformation', 'param_name', 'param_value',
'threshold', 'covered_neurons', 'total_neurons',
'covered_detail', 'y_hat', 'label'
])
if index / 2 == 0:
if index % 2 == 0:
input_images = xrange(1, 501)
else:
input_images = xrange(501, 1001)
for i in input_images:
j = i * 5
csvrecord = []
seed_image = cv2.imread(
os.path.join(seed_inputs1, filelist1[j]))
seed_image = read_transformed_image(seed_image, image_size)
test_x = input_processor(seed_image.astype(np.float32))
#print('test data shape:', test_x.shape)
yhat = model.predict(test_x)
#print('steering angle: ', yhat)
ndict = nc.update_coverage(test_x)
new_covered1, new_total1, p = nc.curr_neuron_cov()
tempk = []
for k in ndict.keys():
if ndict[k]:
tempk.append(k)
tempk = sorted(tempk,
key=lambda element: (element[0], element[1]))
covered_detail = ';'.join(str(x)
for x in tempk).replace(',', ':')
nc.reset_cov_dict()
#print(covered_neurons)
#covered_neurons = nc.get_neuron_coverage(test_x)
#print('input covered {} neurons'.format(covered_neurons))
#print('total {} neurons'.format(total_neurons))
filename, ext = os.path.splitext(str(filelist1[j]))
if label1[j][0] != filename:
print(filename + " not found in the label file")
continue
if j < 2:
continue
csvrecord.append(j - 2)
csvrecord.append(str(filelist1[j]))
csvrecord.append('-')
csvrecord.append('-')
csvrecord.append('-')
csvrecord.append(threshold)
csvrecord.append(new_covered1)
csvrecord.append(new_total1)
csvrecord.append(covered_detail)
csvrecord.append(yhat[0][0])
csvrecord.append(label1[j][1])
print(csvrecord)
writer.writerow(csvrecord)
print("seed input done")
#Translation
if index / 2 >= 1 and index / 2 <= 10:
#for p in xrange(1,11):
p = index / 2
params = [p * 10, p * 10]
if index % 2 == 0:
input_images = xrange(1, 501)
else:
input_images = xrange(501, 1001)
for i in input_images:
j = i * 5
csvrecord = []
seed_image = cv2.imread(
os.path.join(seed_inputs1, filelist1[j]))
seed_image = image_translation(seed_image, params)
seed_image = read_transformed_image(seed_image, image_size)
#seed_image = read_image(os.path.join(seed_inputs1, filelist1[j]),image_size)
#new_covered1, new_total1, result = model.predict_fn(seed_image)
test_x = input_processor(seed_image.astype(np.float32))
#print('test data shape:', test_x.shape)
yhat = model.predict(test_x)
#print('steering angle: ', yhat)
ndict = nc.update_coverage(test_x)
new_covered1, new_total1, p = nc.curr_neuron_cov()
tempk = []
for k in ndict.keys():
if ndict[k] == True:
tempk.append(k)
tempk = sorted(tempk,
key=lambda element: (element[0], element[1]))
covered_detail = ';'.join(str(x)
for x in tempk).replace(',', ':')
nc.reset_cov_dict()
#print(covered_neurons)
#covered_neurons = nc.get_neuron_coverage(test_x)
#print('input covered {} neurons'.format(covered_neurons))
#print('total {} neurons'.format(total_neurons))
filename, ext = os.path.splitext(str(filelist1[j]))
if label1[j][0] != filename:
print(filename + " not found in the label file")
continue
if j < 2:
continue
csvrecord.append(j - 2)
csvrecord.append(str(filelist1[j]))
csvrecord.append('translation')
csvrecord.append('x:y')
csvrecord.append(':'.join(str(x) for x in params))
csvrecord.append(threshold)
csvrecord.append(new_covered1)
csvrecord.append(new_total1)
csvrecord.append(covered_detail)
csvrecord.append(yhat[0][0])
csvrecord.append(label1[j][1])
print(csvrecord)
writer.writerow(csvrecord)
print("translation done")
#Scale
if index / 2 >= 11 and index / 2 <= 20:
#for p in xrange(1,11):
p = index / 2 - 10
params = [p * 0.5 + 1, p * 0.5 + 1]
if index % 2 == 0:
input_images = xrange(1, 501)
else:
input_images = xrange(501, 1001)
for i in input_images:
j = i * 5
csvrecord = []
seed_image = cv2.imread(
os.path.join(seed_inputs1, filelist1[j]))
seed_image = image_scale(seed_image, params)
seed_image = read_transformed_image(seed_image, image_size)
#new_covered1, new_total1, result = model.predict_fn(seed_image)
test_x = input_processor(seed_image.astype(np.float32))
#print('test data shape:', test_x.shape)
yhat = model.predict(test_x)
#print('steering angle: ', yhat)
ndict = nc.update_coverage(test_x)
new_covered1, new_total1, p = nc.curr_neuron_cov()
tempk = []
for k in ndict.keys():
if ndict[k] == True:
tempk.append(k)
tempk = sorted(tempk,
key=lambda element: (element[0], element[1]))
covered_detail = ';'.join(str(x)
for x in tempk).replace(',', ':')
nc.reset_cov_dict()
#print(covered_neurons)
#covered_neurons = nc.get_neuron_coverage(test_x)
#print('input covered {} neurons'.format(covered_neurons))
#print('total {} neurons'.format(total_neurons))
filename, ext = os.path.splitext(str(filelist1[j]))
if label1[j][0] != filename:
print(filename + " not found in the label file")
continue
if j < 2:
continue
csvrecord.append(j - 2)
csvrecord.append(str(filelist1[j]))
csvrecord.append('scale')
csvrecord.append('x:y')
csvrecord.append(':'.join(str(x) for x in params))
csvrecord.append(threshold)
csvrecord.append(new_covered1)
csvrecord.append(new_total1)
csvrecord.append(covered_detail)
csvrecord.append(yhat[0][0])
csvrecord.append(label1[j][1])
print(csvrecord)
writer.writerow(csvrecord)
print("scale done")
#Shear
if index / 2 >= 21 and index / 2 <= 30:
#for p in xrange(1,11):
p = index / 2 - 20
#for p in xrange(1,11):
params = 0.1 * p
if index % 2 == 0:
input_images = xrange(1, 501)
else:
input_images = xrange(501, 1001)
for i in input_images:
j = i * 5
csvrecord = []
seed_image = cv2.imread(
os.path.join(seed_inputs1, filelist1[j]))
seed_image = image_shear(seed_image, params)
seed_image = read_transformed_image(seed_image, image_size)
#new_covered1, new_total1, result = model.predict_fn(seed_image)
test_x = input_processor(seed_image.astype(np.float32))
#print('test data shape:', test_x.shape)
yhat = model.predict(test_x)
#print('steering angle: ', yhat)
ndict = nc.update_coverage(test_x)
new_covered1, new_total1, p = nc.curr_neuron_cov()
tempk = []
for k in ndict.keys():
if ndict[k] == True:
tempk.append(k)
tempk = sorted(tempk,
key=lambda element: (element[0], element[1]))
covered_detail = ';'.join(str(x)
for x in tempk).replace(',', ':')
nc.reset_cov_dict()
#print(covered_neurons)
#covered_neurons = nc.get_neuron_coverage(test_x)
#print('input covered {} neurons'.format(covered_neurons))
#print('total {} neurons'.format(total_neurons))
filename, ext = os.path.splitext(str(filelist1[j]))
if label1[j][0] != filename:
print(filename + " not found in the label file")
continue
if j < 2:
continue
csvrecord.append(j - 2)
csvrecord.append(str(filelist1[j]))
csvrecord.append('shear')
csvrecord.append('factor')
csvrecord.append(params)
csvrecord.append(threshold)
csvrecord.append(new_covered1)
csvrecord.append(new_total1)
csvrecord.append(covered_detail)
csvrecord.append(yhat[0][0])
csvrecord.append(label1[j][1])
print(csvrecord)
writer.writerow(csvrecord)
print("sheer done")
#Rotation
if index / 2 >= 31 and index / 2 <= 40:
p = index / 2 - 30
params = p * 3
if index % 2 == 0:
input_images = xrange(1, 501)
else:
input_images = xrange(501, 1001)
for i in input_images:
j = i * 5
csvrecord = []
seed_image = cv2.imread(
os.path.join(seed_inputs1, filelist1[j]))
seed_image = image_rotation(seed_image, params)
seed_image = read_transformed_image(seed_image, image_size)
#new_covered1, new_total1, result = model.predict_fn(seed_image)
test_x = input_processor(seed_image.astype(np.float32))
#print('test data shape:', test_x.shape)
yhat = model.predict(test_x)
#print('steering angle: ', yhat)
ndict = nc.update_coverage(test_x)
new_covered1, new_total1, p = nc.curr_neuron_cov()
tempk = []
for k in ndict.keys():
if ndict[k] == True:
tempk.append(k)
tempk = sorted(tempk,
key=lambda element: (element[0], element[1]))
covered_detail = ';'.join(str(x)
for x in tempk).replace(',', ':')
nc.reset_cov_dict()
#print(covered_neurons)
#covered_neurons = nc.get_neuron_coverage(test_x)
#print('input covered {} neurons'.format(covered_neurons))
#print('total {} neurons'.format(total_neurons))
filename, ext = os.path.splitext(str(filelist1[j]))
if label1[j][0] != filename:
print(filename + " not found in the label file")
continue
if j < 2:
continue
csvrecord.append(j - 2)
csvrecord.append(str(filelist1[j]))
csvrecord.append('rotation')
csvrecord.append('angle')
csvrecord.append(params)
csvrecord.append(threshold)
csvrecord.append(new_covered1)
csvrecord.append(new_total1)
csvrecord.append(covered_detail)
csvrecord.append(yhat[0][0])
csvrecord.append(label1[j][1])
print(csvrecord)
writer.writerow(csvrecord)
print("rotation done")
#Contrast
if index / 2 >= 41 and index / 2 <= 50:
p = index / 2 - 40
params = 1 + p * 0.2
if index % 2 == 0:
input_images = xrange(1, 501)
else:
input_images = xrange(501, 1001)
for i in input_images:
j = i * 5
csvrecord = []
seed_image = cv2.imread(
os.path.join(seed_inputs1, filelist1[j]))
seed_image = image_contrast(seed_image, params)
seed_image = read_transformed_image(seed_image, image_size)
#new_covered1, new_total1, result = model.predict_fn(seed_image)
test_x = input_processor(seed_image.astype(np.float32))
#print('test data shape:', test_x.shape)
yhat = model.predict(test_x)
#print('steering angle: ', yhat)
ndict = nc.update_coverage(test_x)
new_covered1, new_total1, p = nc.curr_neuron_cov()
tempk = []
for k in ndict.keys():
if ndict[k] == True:
tempk.append(k)
tempk = sorted(tempk,
key=lambda element: (element[0], element[1]))
covered_detail = ';'.join(str(x)
for x in tempk).replace(',', ':')
nc.reset_cov_dict()
#print(covered_neurons)
#covered_neurons = nc.get_neuron_coverage(test_x)
#print('input covered {} neurons'.format(covered_neurons))
#print('total {} neurons'.format(total_neurons))
filename, ext = os.path.splitext(str(filelist1[j]))
if label1[j][0] != filename:
print(filename + " not found in the label file")
continue
if j < 2:
continue
csvrecord.append(j - 2)
csvrecord.append(str(filelist1[j]))
csvrecord.append('contrast')
csvrecord.append('gain')
csvrecord.append(params)
csvrecord.append(threshold)
csvrecord.append(new_covered1)
csvrecord.append(new_total1)
csvrecord.append(covered_detail)
csvrecord.append(yhat[0][0])
csvrecord.append(label1[j][1])
print(csvrecord)
writer.writerow(csvrecord)
print("contrast done")
#Brightness
if index / 2 >= 51 and index / 2 <= 60:
p = index / 2 - 50
params = p * 10
if index % 2 == 0:
input_images = xrange(1, 501)
else:
input_images = xrange(501, 1001)
for i in input_images:
j = i * 5
csvrecord = []
seed_image = cv2.imread(
os.path.join(seed_inputs1, filelist1[j]))
seed_image = image_brightness2(seed_image, params)
seed_image = read_transformed_image(seed_image, image_size)
#new_covered1, new_total1, result = model.predict_fn(seed_image)
test_x = input_processor(seed_image.astype(np.float32))
#print('test data shape:', test_x.shape)
yhat = model.predict(test_x)
#print('steering angle: ', yhat)
ndict = nc.update_coverage(test_x)
new_covered1, new_total1, p = nc.curr_neuron_cov()
tempk = []
for k in ndict.keys():
if ndict[k] == True:
tempk.append(k)
tempk = sorted(tempk,
key=lambda element: (element[0], element[1]))
covered_detail = ';'.join(str(x)
for x in tempk).replace(',', ':')
nc.reset_cov_dict()
#print(covered_neurons)
#covered_neurons = nc.get_neuron_coverage(test_x)
#print('input covered {} neurons'.format(covered_neurons))
#print('total {} neurons'.format(total_neurons))
filename, ext = os.path.splitext(str(filelist1[j]))
if label1[j][0] != filename:
print(filename + " not found in the label file")
continue
if j < 2:
continue
csvrecord.append(j - 2)
csvrecord.append(str(filelist1[j]))
csvrecord.append('brightness')
csvrecord.append('bias')
csvrecord.append(params)
csvrecord.append(threshold)
csvrecord.append(new_covered1)
csvrecord.append(new_total1)
csvrecord.append(covered_detail)
csvrecord.append(yhat[0][0])
csvrecord.append(label1[j][1])
print(csvrecord)
writer.writerow(csvrecord)
print("brightness done")
#blur
if index / 2 >= 61 and index / 2 <= 70:
p = index / 2 - 60
params = p
if index % 2 == 0:
input_images = xrange(1, 501)
else:
input_images = xrange(501, 1001)
for i in input_images:
j = i * 5
csvrecord = []
seed_image = cv2.imread(
os.path.join(seed_inputs1, filelist1[j]))
seed_image = image_blur(seed_image, params)
seed_image = read_transformed_image(seed_image, image_size)
#new_covered1, new_total1, result = model.predict_fn(seed_image)
test_x = input_processor(seed_image.astype(np.float32))
#print('test data shape:', test_x.shape)
yhat = model.predict(test_x)
#print('steering angle: ', yhat)
ndict = nc.update_coverage(test_x)
new_covered1, new_total1, p = nc.curr_neuron_cov()
tempk = []
for k in ndict.keys():
if ndict[k] == True:
tempk.append(k)
tempk = sorted(tempk,
key=lambda element: (element[0], element[1]))
covered_detail = ';'.join(str(x)
for x in tempk).replace(',', ':')
nc.reset_cov_dict()
#print(covered_neurons)
#covered_neurons = nc.get_neuron_coverage(test_x)
#print('input covered {} neurons'.format(covered_neurons))
#print('total {} neurons'.format(total_neurons))
filename, ext = os.path.splitext(str(filelist1[j]))
if label1[j][0] != filename:
print(filename + " not found in the label file")
continue
if j < 2:
continue
param_name = ""
if params == 1:
param_name = "averaging:3:3"
if params == 2:
param_name = "averaging:4:4"
if params == 3:
param_name = "averaging:5:5"
if params == 4:
param_name = "GaussianBlur:3:3"
if params == 5:
param_name = "GaussianBlur:5:5"
if params == 6:
param_name = "GaussianBlur:7:7"
if params == 7:
param_name = "medianBlur:3"
if params == 8:
param_name = "medianBlur:5"
if params == 9:
param_name = "averaging:6:6"
if params == 10:
param_name = "bilateralFilter:9:75:75"
csvrecord.append(j - 2)
csvrecord.append(str(filelist1[j]))
csvrecord.append('blur')
csvrecord.append(param_name)
csvrecord.append(param_name)
csvrecord.append(threshold)
csvrecord.append(new_covered1)
csvrecord.append(new_total1)
csvrecord.append(covered_detail)
csvrecord.append(yhat[0][0])
csvrecord.append(label1[j][1])
print(csvrecord)
writer.writerow(csvrecord)
print("all done")
def epoch_guided(dataset_path):
model_name = "cnn"
image_size = (128, 128)
threshold = 0.2
weights_path = './weights_HMB_2.hdf5' # Change to your model weights
seed_inputs1 = os.path.join(dataset_path, "hmb3/")
seed_labels1 = os.path.join(dataset_path, "hmb3/hmb3_steering.csv")
seed_inputs2 = os.path.join(dataset_path, "Ch2_001/center/")
seed_labels2 = os.path.join(dataset_path,
"Ch2_001/CH2_final_evaluation.csv")
new_inputs = "./new/"
# Model build
# ---------------------------------------------------------------------------------
model_builders = {
'V3': (build_InceptionV3, preprocess_input_InceptionV3, exact_output),
'cnn': (build_cnn, normalize_input, exact_output)
}
if model_name not in model_builders:
raise ValueError("unsupported model %s" % model_name)
model_builder, input_processor, output_processor = model_builders[
model_name]
model = model_builder(image_size, weights_path)
print('model %s built...' % model_name)
filelist1 = []
for file in sorted(os.listdir(seed_inputs1)):
if file.endswith(".jpg"):
filelist1.append(file)
truth = {}
with open(seed_labels1, 'rb') as csvfile1:
label1 = list(csv.reader(csvfile1, delimiter=',', quotechar='|'))
label1 = label1[1:]
for i in label1:
truth[i[0] + ".jpg"] = i[1]
newlist = []
for file in sorted(os.listdir(new_inputs)):
if file.endswith(".jpg"):
newlist.append(file)
flag = 0
#flag:0 start from beginning
#flag:1 initialize from pickle files
'''
Pickle files are used for continuing the search after rerunning the script.
Delete all pkl files and generated images for starting from the beginnning.
'''
if os.path.isfile("epoch_covdict2.pkl") and \
os.path.isfile("epoch_stack.pkl") and \
os.path.isfile("epoch_queue.pkl") and \
os.path.isfile("generated.pkl"):
with open('epoch_covdict2.pkl', 'rb') as input:
covdict = pickle.load(input)
with open('epoch_stack.pkl', 'rb') as input:
epoch_stack = pickle.load(input)
with open('epoch_queue.pkl', 'rb') as input:
epoch_queue = pickle.load(input)
with open('generated.pkl', 'rb') as input:
generated = pickle.load(input)
flag = 1
nc = NCoverage(model, threshold)
if flag == 0:
filewrite = "wb"
epoch_queue = deque()
epoch_stack = []
generated = 0
else:
nc.set_covdict(covdict)
filewrite = "ab"
print("initialize from files and continue from previous progress")
C = 0 # covered neurons
P = 0 # covered percentage
T = 0 # total neurons
transformations = [
image_translation, image_scale, image_shear, image_rotation,
image_contrast, image_brightness2, image_blur
]
params = []
params.append(list(xrange(-50, 50)))
params.append(list(map(lambda x: x * 0.1, list(xrange(5, 20)))))
params.append(list(map(lambda x: x * 0.1, list(xrange(-5, 5)))))
params.append(list(xrange(-30, 30)))
params.append(list(map(lambda x: x * 0.1, list(xrange(1, 20)))))
params.append(list(xrange(-21, 21)))
params.append(list(xrange(1, 11)))
maxtrynumber = 10
maximages = 200
cache = deque()
image_count = 0
#load nc, generation, population
with open('result/epoch_rq3_100_2.csv', filewrite, 0) as csvfile:
writer = csv.writer(csvfile,
delimiter=',',
quotechar='|',
quoting=csv.QUOTE_MINIMAL)
if flag == 0:
writer.writerow([
'id', 'seed image(root)', 'parent image',
'new generated image', 'number of generated images',
'total_covered', 'total_neurons', 'coverage_percentage',
'transformations', 'yhat', 'baseline', 'label'
])
#initialize population and coverage
print("compute coverage of original population")
input_images = xrange(1, 101)
for i in input_images:
j = i * 50
epoch_queue.append(os.path.join(seed_inputs1, filelist1[j]))
while len(epoch_queue) > 0:
current_seed_image = epoch_queue[0]
print(str(len(epoch_queue)) + " images are left.")
if len(epoch_stack) == 0:
epoch_stack.append(current_seed_image)
image = cv2.imread(current_seed_image)
test_x = read_transformed_image(image, image_size)
test_x = input_processor(test_x.astype(np.float32))
nc.update_coverage(test_x)
baseline_yhat = model.predict(test_x)
#image_count = 0
while len(epoch_stack) > 0:
try:
image_file = epoch_stack[-1]
print("current image in stack " + image_file)
image = cv2.imread(image_file)
new_generated = False
for i in xrange(maxtrynumber):
tid = random.sample([0, 1, 2, 3, 4, 5, 6], 2)
if len(cache) > 0:
tid[0] = cache.popleft()
transinfo = ""
new_image = image
for j in xrange(2):
transformation = transformations[tid[j]]
#random choose parameter
param = random.sample(params[tid[j]], 1)
param = param[0]
transinfo = transinfo + transformation.__name__ + ':' + str(
param) + ';'
print("transformation " + transformation.__name__ +
" parameter " + str(param))
new_image = transformation(new_image, param)
new_x = read_transformed_image(new_image, image_size)
test_x = input_processor(new_x.astype(np.float32))
if nc.is_testcase_increase_coverage(test_x):
print(
"Generated image increases coverage and will be added to population."
)
cache.append(tid[0])
cache.append(tid[1])
generated = generated + 1
#image_count = image_count + 1
name = os.path.basename(
current_seed_image) + '_' + str(
generated) + '.jpg'
name = os.path.join(new_inputs, name)
cv2.imwrite(name, new_image)
epoch_stack.append(name)
nc.update_coverage(test_x)
yhat = model.predict(test_x)
covered, total, p = nc.curr_neuron_cov()
C = covered
T = total
P = p
csvrecord = []
csvrecord.append(100 - len(epoch_queue))
csvrecord.append(
os.path.basename(current_seed_image))
if len(epoch_stack) >= 2:
parent = os.path.basename(epoch_stack[-2])
else:
parent = os.path.basename(current_seed_image)
child = os.path.basename(
current_seed_image) + '_' + str(
generated) + '.jpg'
csvrecord.append(parent)
csvrecord.append(child)
csvrecord.append(generated)
csvrecord.append(C)
csvrecord.append(T)
csvrecord.append(P)
csvrecord.append(transinfo)
csvrecord.append(yhat[0][0])
csvrecord.append(baseline_yhat[0][0])
csvrecord.append(
truth[os.path.basename(current_seed_image)])
print(csvrecord)
writer.writerow(csvrecord)
new_generated = True
break
else:
print(
"Generated image does not increase coverage.")
if not new_generated:
epoch_stack.pop()
save_object(epoch_stack, 'epoch_stack.pkl')
save_object(epoch_queue, 'epoch_queue.pkl')
save_object(nc.cov_dict, 'epoch_covdict2.pkl')
save_object(generated, 'generated.pkl')
except ValueError:
print("value error")
epoch_stack.pop()
save_object(epoch_stack, 'epoch_stack.pkl')
save_object(epoch_queue, 'epoch_queue.pkl')
epoch_queue.popleft()