def make_model(sess, model_to_run, model_path,
labels_path, randomize=False,):
"""Make an instance of a model.
Args:
sess: tf session instance.
model_to_run: a string that describes which model to make.
model_path: Path to models saved graph.
randomize: Start with random weights
labels_path: Path to models line separated class names text file.
Returns:
a model instance.
Raises:
ValueError: If model name is not valid.
"""
if model_to_run == 'InceptionV3':
mymodel = model.InceptionV3Wrapper_public(
sess, model_saved_path=model_path, labels_path=labels_path)
elif model_to_run == 'GoogleNet':
# common_typos_disable
mymodel = model.GoolgeNetWrapper_public(
sess, model_saved_path=model_path, labels_path=labels_path)
else:
raise ValueError('Invalid model name')
if randomize: # randomize the network!
sess.run(tf.global_variables_initializer())
return mymodel
def run(self):
self.sess = utils.create_session()
if self.model_to_run == 'InceptionV3':
self.mymodel = model.InceptionV3Wrapper_public(
self.sess, self.graph_path, self.labels_path)
if self.model_to_run == 'GoogleNet':
self.mymodel = model.GoolgeNetWrapper_public(
self.sess, self.graph_path, self.labels_path)
if self.model_to_run == 'XceptionHPV':
self.mymodel = model.XceptionHPVWrapper_public(
self.sess, self.graph_path, self.labels_path)
act_generator = act_gen.ImageActivationGenerator(self.mymodel,
self.source_dir,
self.activation_dir,
max_examples=100)
tf.logging.set_verbosity(0)
mytcav = tcav.TCAV(self.sess,
self.target,
self.concepts,
self.bottlenecks,
act_generator,
self.alphas,
cav_dir=self.cav_dir,
num_random_exp=10)
print('This may take a while... Go get coffee!')
results = mytcav.run(run_parallel=False)
print('done!')
# returns dictionary of plot data
plot_data = utils_plot.plot_results(
results,
os.path.join(self.project_dir, 'results/inceptionv3_tcav.png'),
num_random_exp=10)
def make_model(sess, model_to_run, model_path, labels_path):
if model_to_run == 'InceptionV3':
if '.h5' in model_path:
mymodel = load_model(model_path)
endpoints_v3 = dict(input=mymodel.inputs[0].name,
input_tensor=mymodel.inputs[0],
logit=mymodel.outputs[0].name,
prediction=mymodel.outputs[0].name,
prediction_tensor=mymodel.outputs[0])
mymodel = KerasModelWrapper(sess, labels_path, [299, 299, 3],
endpoints_v3, 'InceptionV3_public',
(-1, 1))
else:
mymodel = model.InceptionV3Wrapper_public(
sess, model_saved_path=model_path, labels_path=labels_path)
elif model_to_run == 'GoogleNet':
mymodel = model.GoogleNetWrapper_public(sess,
model_saved_path=model_path,
labels_path=labels_path)
else:
raise ValueError('Invalid model name')
return mymodel
def make_model(sess, model_to_run, model_path,
labels_path, randomize=False,):
"""Make an instance of a model.
Args:
sess: tf session instance.
model_to_run: a string that describes which model to make.
model_path: Path to models saved graph.
randomize: Start with random weights
labels_path: Path to models line separated class names text file.
Returns:
a model instance.
Raises:
ValueError: If model name is not valid.
"""
if model_to_run == 'InceptionV3':
if '.h5' in model_path:
mymodel = load_model(model_path)
endpoints = dict(
input=mymodel.inputs[0].name,
input_tensor=mymodel.inputs[0],
logit=mymodel.outputs[0].name,
prediction=mymodel.outputs[0].name,
prediction_tensor=mymodel.outputs[0])
mymodel = wrapper.KerasModelWrapper(sess,
labels_path, [299, 299, 3], endpoints,
'InceptionV3_public', (-1, 1))
else:
mymodel = model.InceptionV3Wrapper_public(
sess, model_saved_path=model_path, labels_path=labels_path)
elif model_to_run == 'GoogleNet':
# common_typos_disable
mymodel = model.GoolgeNetWrapper_public(
sess, model_saved_path=model_path, labels_path=labels_path)
else:
raise ValueError('Invalid model name')
if randomize: # randomize the network!
sess.run(tf.global_variables_initializer())
return mymodel