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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.
"""Base Model definition."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import abc
import re
import tensorflow as tf, tf_keras
from official.legacy.detection.modeling import checkpoint_utils
from official.legacy.detection.modeling import learning_rates
from official.legacy.detection.modeling import optimizers
def _make_filter_trainable_variables_fn(frozen_variable_prefix):
"""Creates a function for filtering trainable varialbes."""
def _filter_trainable_variables(variables):
"""Filters trainable varialbes.
Args:
variables: a list of tf.Variable to be filtered.
Returns:
filtered_variables: a list of tf.Variable filtered out the frozen ones.
"""
# frozen_variable_prefix: a regex string specifing the prefix pattern of
# the frozen variables' names.
filtered_variables = [
v for v in variables if not frozen_variable_prefix or
not re.match(frozen_variable_prefix, v.name)
]
return filtered_variables
return _filter_trainable_variables
class Model(object):
"""Base class for model function."""
__metaclass__ = abc.ABCMeta
def __init__(self, params):
self._use_bfloat16 = params.architecture.use_bfloat16
if params.architecture.use_bfloat16:
tf.compat.v2.keras.mixed_precision.set_global_policy('mixed_bfloat16')
# Optimization.
self._optimizer_fn = optimizers.OptimizerFactory(params.train.optimizer)
self._learning_rate = learning_rates.learning_rate_generator(
params.train.total_steps, params.train.learning_rate)
self._frozen_variable_prefix = params.train.frozen_variable_prefix
self._regularization_var_regex = params.train.regularization_variable_regex
self._l2_weight_decay = params.train.l2_weight_decay
# Checkpoint restoration.
self._checkpoint = params.train.checkpoint.as_dict()
# Summary.
self._enable_summary = params.enable_summary
self._model_dir = params.model_dir
@abc.abstractmethod
def build_outputs(self, inputs, mode):
"""Build the graph of the forward path."""
pass
@abc.abstractmethod
def build_model(self, params, mode):
"""Build the model object."""
pass
@abc.abstractmethod
def build_loss_fn(self):
"""Build the model object."""
pass
def post_processing(self, labels, outputs):
"""Post-processing function."""
return labels, outputs
def model_outputs(self, inputs, mode):
"""Build the model outputs."""
return self.build_outputs(inputs, mode)
def build_optimizer(self):
"""Returns train_op to optimize total loss."""
# Sets up the optimizer.
return self._optimizer_fn(self._learning_rate)
def make_filter_trainable_variables_fn(self):
"""Creates a function for filtering trainable varialbes."""
return _make_filter_trainable_variables_fn(self._frozen_variable_prefix)
def weight_decay_loss(self, trainable_variables):
reg_variables = [
v for v in trainable_variables
if self._regularization_var_regex is None or
re.match(self._regularization_var_regex, v.name)
]
return self._l2_weight_decay * tf.add_n(
[tf.nn.l2_loss(v) for v in reg_variables])
def make_restore_checkpoint_fn(self):
"""Returns scaffold function to restore parameters from v1 checkpoint."""
if 'skip_checkpoint_variables' in self._checkpoint:
skip_regex = self._checkpoint['skip_checkpoint_variables']
else:
skip_regex = None
return checkpoint_utils.make_restore_checkpoint_fn(
self._checkpoint['path'],
prefix=self._checkpoint['prefix'],
skip_regex=skip_regex)
def eval_metrics(self):
"""Returns tuple of metric function and its inputs for evaluation."""
raise NotImplementedError('Unimplemented eval_metrics')
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