cfd_eval.py

# pylint: disable=g-bad-file-header
# Copyright 2020 DeepMind Technologies Limited. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or  implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""Functions to build evaluation metrics for CFD data."""

import tensorflow.compat.v1 as tf

from common import NodeType


def _rollout(model, initial_state, num_steps):
    """Rolls out a model trajectory."""
    node_type = initial_state["node_type"][:, 0]
    mask = tf.logical_or(
        tf.equal(node_type, NodeType.NORMAL), tf.equal(node_type, NodeType.OUTFLOW)
    )

    def step_fn(step, velocity, trajectory):
        prediction = model({**initial_state, "velocity": velocity})
        # don't update boundary nodes
        next_velocity = tf.where(mask, prediction, velocity)
        trajectory = trajectory.write(step, velocity)
        return step + 1, next_velocity, trajectory

    _, _, output = tf.while_loop(
        cond=lambda step, cur, traj: tf.less(step, num_steps),
        body=step_fn,
        loop_vars=(0, initial_state["velocity"], tf.TensorArray(tf.float32, num_steps)),
        parallel_iterations=1,
    )
    return output.stack()


def evaluate(model, inputs):
    """Performs model rollouts and create stats."""
    initial_state = {k: v[0] for k, v in inputs.items()}
    num_steps = inputs["cells"].shape[0]
    prediction = _rollout(model, initial_state, num_steps)

    error = tf.reduce_mean((prediction - inputs["velocity"]) ** 2, axis=-1)
    scalars = {
        "mse_%d_steps" % horizon: tf.reduce_mean(error[1 : horizon + 1])
        for horizon in [1, 10, 20, 50, 100, 200]
    }
    traj_ops = {
        "faces": inputs["cells"],
        "mesh_pos": inputs["mesh_pos"],
        "gt_velocity": inputs["velocity"],
        "pred_velocity": prediction,
    }
    return scalars, traj_ops