def load_model_by_name(self,
model_name,
logits=False,
input_range_type=1,
pre_filter=lambda x: x):
"""
:params logits: return logits(input of softmax layer) if True; return softmax output otherwise.
:params input_range_type: {1: [0,1], 2:[-0.5, 0.5], 3:[-1, 1]...}
"""
if model_name not in [
"cnn2", 'cnn2_adv_trained', 'cnn1', 'densenet', 'resnet20',
'resnet32', 'resnet44', 'resnet56', 'resnet110', 'lenet',
'distillation'
]:
raise NotImplementedError("Undefined model [%s] for %s." %
(model_name, self.dataset_name))
self.model_name = model_name
model_weights_fpath = "%s_%s.keras_weights.h5" % (self.dataset_name,
model_name)
model_weights_fpath = os.path.join('downloads/trained_models',
model_weights_fpath)
if model_name in ["cnn2", 'cnn2_adv_trained']:
model = cnn2_cifar10_model(logits=logits,
input_range_type=input_range_type,
pre_filter=pre_filter)
elif model_name == "cnn1":
model = cnn1_cifar10_model(logits=logits,
input_range_type=input_range_type,
pre_filter=pre_filter)
elif model_name == "densenet":
model = densenet_cifar10_model(logits=logits,
input_range_type=input_range_type,
pre_filter=pre_filter)
model_weights_fpath = get_densenet_weights_path(self.dataset_name)
elif model_name == "lenet":
model = lenet_cifar10_model(logits=logits,
input_range_type=input_range_type,
pre_filter=pre_filter)
elif model_name in ['resnet20']:
model = resnet20_cifar10_model()
elif model_name in ['resnet32']:
model = resnet32_cifar10_model()
elif model_name in ['resnet44']:
model = resnet44_cifar10_model()
elif model_name in ['resnet56']:
model = resnet56_cifar10_model()
elif model_name in ['resnet110']:
model = resnet110_cifar10_model()
elif model_name in ['distillation']:
model = distillation_cifar10_model()
print("\n===Defined TensorFlow model graph.")
#if model_name not in ['distillation']:
model.load_weights(model_weights_fpath)
print("---Loaded CIFAR-10-%s model.\n" % model_name)
return model
def load_model_by_name(self,
model_name,
logits=False,
input_range_type=1,
pre_filter=lambda x: x):
"""
:params logits: return logits(input of softmax layer) if True; return softmax output otherwise.
:params input_range_type: {1: [0,1], 2:[-0.5, 0.5], 3:[-1, 1]...}
"""
if model_name not in [
"cleverhans", 'cleverhans_adv_trained', 'carlini', 'densenet'
]:
raise NotImplementedError("Undefined model [%s] for %s." %
(model_name, self.dataset_name))
self.model_name = model_name
model_weights_fpath = "%s_%s.keras_weights.h5" % (self.dataset_name,
model_name)
model_weights_fpath = os.path.join(PARENT_DIR,
'downloads/trained_models',
model_weights_fpath)
if model_name in ["cleverhans", 'cleverhans_adv_trained']:
model = cleverhans_cifar10_model(logits=logits,
input_range_type=input_range_type,
pre_filter=pre_filter)
elif model_name == "carlini":
model = carlini_cifar10_model(logits=logits,
input_range_type=input_range_type,
pre_filter=pre_filter)
elif model_name == "densenet":
from models.densenet_models import densenet_cifar10_model
model = densenet_cifar10_model(logits=logits,
input_range_type=input_range_type,
pre_filter=pre_filter)
print("\n===Defined TensorFlow model graph.")
model.load_weights(model_weights_fpath)
print("---Loaded CIFAR-10-%s model.\n" % model_name)
return model