def create():
weights_path = zoo.fetch_weights(
'https://github.com/MadryLab/mnist_challenge_models/raw/master/secret.zip',
unzip=True
)
weights_path = os.path.join(weights_path, 'models/secret')
model = Model()
sess = tf.Session().__enter__()
saver = tf.train.Saver()
checkpoint = tf.train.latest_checkpoint(weights_path)
saver.restore(sess, checkpoint)
images = model.x_input
logits = model.pre_softmax
fmodel = foolbox.models.TensorFlowModel(images, logits, bounds=(0, 1))
return fmodel
def test_fetch_weights_unzipped() -> None:
weights_uri = "http://localhost:8080/weights.zip"
raw_body = _random_body(zipped=False)
# mock server
responses.add(responses.GET,
weights_uri,
body=raw_body,
status=200,
stream=True)
expected_path = _expected_path(weights_uri)
if os.path.exists(expected_path):
shutil.rmtree(expected_path) # make sure path does not exist already
file_path = fetch_weights(weights_uri)
exists_locally = os.path.exists(expected_path)
assert exists_locally
assert expected_path in file_path
def create():
# fetch weights
weights_path = zoo.fetch_weights(
'https://github.com/facebookresearch/ImageNet-Adversarial-Training/releases/download/v0.1/R152-Denoise.npz',
unzip=False)
# model constructer expects an ArgumentParser as argument
parser = argparse.ArgumentParser()
parser.add_argument('-d',
'--depth',
help='ResNet depth',
type=int,
default=152,
choices=[50, 101, 152])
parser.add_argument('--arch',
help='Name of architectures defined in nets.py',
default='ResNetDenoise')
args = parser.parse_args([])
model = getattr(nets, args.arch + 'Model')(args)
image = tf.placeholder(tf.float32, shape=(None, 3, 224, 224))
with TowerContext(tower_name='', is_training=False):
logits = model.get_logits(image)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
session = tf.Session(config=config)
with session.as_default():
model = get_model_loader(weights_path).init(session)
fmodel = TensorFlowModel(image,
logits,
channel_axis=1,
bounds=[0, 255.],
preprocessing=(127.5, 127.5))
return fmodel
def test_fetch_weights_zipped():
weights_uri = 'http://localhost:8080/weights.zip'
# mock server
raw_body = _random_body(zipped=True)
responses.add(responses.GET,
weights_uri,
body=raw_body,
status=200,
stream=True,
content_type='application/zip',
headers={'Accept-Encoding': 'gzip, deflate'})
expected_path = _expected_path(weights_uri)
if path_exists(expected_path):
shutil.rmtree(expected_path) # make sure path does not exist already
file_path = fetch_weights(weights_uri, unzip=True)
exists_locally = path_exists(expected_path)
assert exists_locally
assert expected_path in file_path
def create():
tf.enable_eager_execution()
# load pretrained weights
weights_path = zoo.fetch_weights(
'http://download.tensorflow.org/models/adversarial_logit_pairing/imagenet64_alp025_2018_06_26.ckpt.tar.gz',
unzip=True)
with tf.get_default_graph().as_default():
checkpoint = os.path.join(weights_path,
'imagenet64_alp025_2018_06_26.ckpt')
# load model
input_ = tf.keras.layers.Input(dtype=tf.float32, shape=(64, 64, 3))
model_fn_two_args = get_model('resnet_v2_50', 1001)
model_fn = lambda x: model_fn_two_args(x, is_training=False)
preprocessed = _normalize(input_)
logits = model_fn(preprocessed)[:, 1:]
# load pretrained weights into model
variables_to_restore = tf.contrib.framework.get_variables_to_restore()
saver = tf.train.Saver(variables_to_restore)
sess = tf.Session().__enter__()
saver.restore(sess, checkpoint)
def __call__(inputs):
sess = tf.get_default_session()
return sess.run(logits, feed_dict={input_: inputs})
# create foolbox model
preprocessing = None
bounds = (0, 255)
fmodel = foolbox.models.TensorFlowModel(__call__,
bounds=bounds,
preprocessing=preprocessing)
return fmodel
def test_no_uri_given():
assert fetch_weights(None) is None