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# Copyright 2023 The TensorFlow Authors. 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.
"""Abstraction of multi-task model."""
from typing import Text, Dict
import tensorflow as tf, tf_keras
class MultiTaskBaseModel(tf.Module):
"""Base class that holds multi-task model computation."""
def __init__(self, **kwargs):
super().__init__(**kwargs)
self._sub_tasks = self._instantiate_sub_tasks()
def _instantiate_sub_tasks(self) -> Dict[Text, tf_keras.Model]:
"""Abstract function that sets up the computation for each sub-task.
Returns:
A map from task name (as string) to a tf_keras.Model object that
represents the sub-task in the multi-task pool.
"""
raise NotImplementedError(
"_instantiate_sub_task_models() is not implemented.")
@property
def sub_tasks(self):
"""Fetch a map of task name (string) to task model (tf_keras.Model)."""
return self._sub_tasks
def initialize(self):
"""Optional function that loads a pre-train checkpoint."""
return
def build(self):
"""Builds the networks for tasks to make sure variables are created."""
# Try to build all sub tasks.
for task_model in self._sub_tasks.values():
# Assumes all the tf.Module models are built because we don't have any
# way to check them.
if isinstance(task_model, tf_keras.Model) and not task_model.built:
_ = task_model(task_model.inputs)
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