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Alvaro Sanchez-Gonzalez
55 lines
2.3 KiB
Python
55 lines
2.3 KiB
Python
# Lint as: python3
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# pylint: disable=g-bad-file-header
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# Copyright 2020 DeepMind Technologies Limited. All Rights Reserved.
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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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# ============================================================================
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"""Methods to calculate input noise."""
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import tensorflow.compat.v1 as tf
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from learning_to_simulate import learned_simulator
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def get_random_walk_noise_for_position_sequence(
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position_sequence, noise_std_last_step):
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"""Returns random-walk noise in the velocity applied to the position."""
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velocity_sequence = learned_simulator.time_diff(position_sequence)
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# We want the noise scale in the velocity at the last step to be fixed.
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# Because we are going to compose noise at each step using a random_walk:
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# std_last_step**2 = num_velocities * std_each_step**2
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# so to keep `std_last_step` fixed, we apply at each step:
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# std_each_step `std_last_step / np.sqrt(num_input_velocities)`
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# TODO(alvarosg): Make sure this is consistent with the value and
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# description provided in the paper.
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num_velocities = velocity_sequence.shape.as_list()[1]
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velocity_sequence_noise = tf.random.normal(
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tf.shape(velocity_sequence),
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stddev=noise_std_last_step / num_velocities ** 0.5,
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dtype=position_sequence.dtype)
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# Apply the random walk.
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velocity_sequence_noise = tf.cumsum(velocity_sequence_noise, axis=1)
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# Integrate the noise in the velocity to the positions, assuming
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# an Euler intergrator and a dt = 1, and adding no noise to the very first
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# position (since that will only be used to calculate the first position#
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# change.
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position_sequence_noise = tf.concat([
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tf.zeros_like(velocity_sequence_noise[:, 0:1]),
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tf.cumsum(velocity_sequence_noise, axis=1)], axis=1)
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return position_sequence_noise
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