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Cyprien de Masson d'Autume
59 lines
2.0 KiB
Python
59 lines
2.0 KiB
Python
# Copyright 2019 Deepmind Technologies Limited.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Submission to Unrestricted Adversarial Challenge."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import tensorflow as tf
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import tensorflow_hub as hub
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from unrestricted_advex import eval_kit
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def _preprocess_image(image):
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image = tf.image.central_crop(image, central_fraction=0.875)
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image = tf.image.resize_bilinear(image, [224, 224], align_corners=False)
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return image
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def test_preprocess(image):
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image = _preprocess_image(image)
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image = tf.subtract(image, 0.5)
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image = tf.multiply(image, 2.0)
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return image
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def main():
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g = tf.Graph()
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with g.as_default():
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input_tensor = tf.placeholder(tf.float32, (None, 224, 224, 3))
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x_np = test_preprocess(input_tensor)
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raw_module_1 = hub.Module(
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"https://tfhub.dev/deepmind/llr-pretrain-adv/latents/1")
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raw_module_2 = hub.Module(
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"https://tfhub.dev/deepmind/llr-pretrain-adv/linear/1")
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latents = raw_module_1(dict(inputs=x_np, decay_rate=0.1))
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logits = raw_module_2(dict(inputs=latents))
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logits = tf.squeeze(logits, axis=[1, 2])
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two_class_logits = tf.concat([tf.nn.relu(-logits[:, 1:]),
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tf.nn.relu(logits[:, 1:])], axis=1)
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sess = tf.train.SingularMonitoredSession()
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def model(x_np):
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return sess.run(two_class_logits, feed_dict={input_tensor: x_np})
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eval_kit.evaluate_bird_or_bicycle_model(model, model_name="llr_resnet")
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if __name__ == "__main__":
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main()
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