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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.
"""Tests for official.core.train_utils."""
import json
import os
import pprint
import numpy as np
import tensorflow as tf, tf_keras
from official.core import exp_factory
from official.core import test_utils
from official.core import train_utils
from official.modeling import hyperparams
@exp_factory.register_config_factory('foo')
def foo():
"""Multitask experiment for test."""
experiment_config = hyperparams.Config(
default_params={
'runtime': {
'tpu': 'fake',
},
'task': {
'model': {
'model_id': 'bar',
},
},
'trainer': {
'train_steps': -1,
'validation_steps': -1,
},
})
return experiment_config
class TrainUtilsTest(tf.test.TestCase):
def test_get_leaf_nested_dict(self):
d = {'a': {'i': {'x': 5}}}
self.assertEqual(train_utils.get_leaf_nested_dict(d, ['a', 'i', 'x']), 5)
def test_get_leaf_nested_dict_not_leaf(self):
with self.assertRaisesRegex(KeyError, 'The value extracted with keys.*'):
d = {'a': {'i': {'x': 5}}}
train_utils.get_leaf_nested_dict(d, ['a', 'i'])
def test_get_leaf_nested_dict_path_not_exist_missing_key(self):
with self.assertRaisesRegex(KeyError, 'Path not exist while traversing .*'):
d = {'a': {'i': {'x': 5}}}
train_utils.get_leaf_nested_dict(d, ['a', 'i', 'y'])
def test_get_leaf_nested_dict_path_not_exist_out_of_range(self):
with self.assertRaisesRegex(KeyError, 'Path not exist while traversing .*'):
d = {'a': {'i': {'x': 5}}}
train_utils.get_leaf_nested_dict(d, ['a', 'i', 'z'])
def test_get_leaf_nested_dict_path_not_exist_meets_leaf(self):
with self.assertRaisesRegex(KeyError, 'Path not exist while traversing .*'):
d = {'a': {'i': 5}}
train_utils.get_leaf_nested_dict(d, ['a', 'i', 'z'])
def test_cast_leaf_nested_dict(self):
d = {'a': {'i': {'x': '123'}}, 'b': 456.5}
d = train_utils.cast_leaf_nested_dict(d, int)
self.assertEqual(d['a']['i']['x'], 123)
self.assertEqual(d['b'], 456)
def test_write_model_params_keras_model(self):
inputs = np.zeros([2, 3])
model = test_utils.FakeKerasModel()
model(inputs) # Must do forward pass to build the model.
filepath = os.path.join(self.create_tempdir(), 'model_params.txt')
train_utils.write_model_params(model, filepath)
actual = tf.io.gfile.GFile(filepath, 'r').read().splitlines()
expected = [
'fake_keras_model/dense/kernel:0 [3, 4]',
'fake_keras_model/dense/bias:0 [4]',
'fake_keras_model/dense_1/kernel:0 [4, 4]',
'fake_keras_model/dense_1/bias:0 [4]',
'',
'Total params: 36',
]
self.assertEqual(actual, expected)
def test_write_model_params_module(self):
inputs = np.zeros([2, 3], dtype=np.float32)
model = test_utils.FakeModule(3, name='fake_module')
model(inputs) # Must do forward pass to build the model.
filepath = os.path.join(self.create_tempdir(), 'model_params.txt')
train_utils.write_model_params(model, filepath)
actual = tf.io.gfile.GFile(filepath, 'r').read().splitlines()
expected = [
'fake_module/dense/b:0 [4]',
'fake_module/dense/w:0 [3, 4]',
'fake_module/dense_1/b:0 [4]',
'fake_module/dense_1/w:0 [4, 4]',
'',
'Total params: 36',
]
self.assertEqual(actual, expected)
def test_construct_experiment_from_flags(self):
options = train_utils.ParseConfigOptions(
experiment='foo',
config_file=[],
tpu='bar',
tf_data_service='',
params_override='task.model.model_id=new,'
'trainer.train_steps=10,'
'trainer.validation_steps=11')
builder = train_utils.ExperimentParser(options)
params_from_obj = builder.parse()
params_from_func = train_utils.parse_configuration(options)
pp = pprint.PrettyPrinter()
self.assertEqual(
pp.pformat(params_from_obj.as_dict()),
pp.pformat(params_from_func.as_dict()))
self.assertEqual(params_from_obj.runtime.tpu, 'bar')
self.assertEqual(params_from_obj.task.model.model_id, 'new')
self.assertEqual(params_from_obj.trainer.train_steps, 10)
self.assertEqual(params_from_obj.trainer.validation_steps, 11)
class BestCheckpointExporterTest(tf.test.TestCase):
def test_maybe_export(self):
model_dir = self.create_tempdir().full_path
best_ckpt_path = os.path.join(model_dir, 'best_ckpt-1')
metric_name = 'test_metric|metric_1'
exporter = train_utils.BestCheckpointExporter(
model_dir, metric_name, 'higher')
v = tf.Variable(1.0)
checkpoint = tf.train.Checkpoint(v=v)
ret = exporter.maybe_export_checkpoint(
checkpoint, {'test_metric': {'metric_1': 5.0}}, 100)
with self.subTest(name='Successful first save.'):
self.assertEqual(ret, True)
v_2 = tf.Variable(2.0)
checkpoint_2 = tf.train.Checkpoint(v=v_2)
checkpoint_2.restore(best_ckpt_path)
self.assertEqual(v_2.numpy(), 1.0)
v = tf.Variable(3.0)
checkpoint = tf.train.Checkpoint(v=v)
ret = exporter.maybe_export_checkpoint(
checkpoint, {'test_metric': {'metric_1': 6.0}}, 200)
with self.subTest(name='Successful better metic save.'):
self.assertEqual(ret, True)
v_2 = tf.Variable(2.0)
checkpoint_2 = tf.train.Checkpoint(v=v_2)
checkpoint_2.restore(best_ckpt_path)
self.assertEqual(v_2.numpy(), 3.0)
v = tf.Variable(5.0)
checkpoint = tf.train.Checkpoint(v=v)
ret = exporter.maybe_export_checkpoint(
checkpoint, {'test_metric': {'metric_1': 1.0}}, 300)
with self.subTest(name='Worse metic no save.'):
self.assertEqual(ret, False)
v_2 = tf.Variable(2.0)
checkpoint_2 = tf.train.Checkpoint(v=v_2)
checkpoint_2.restore(best_ckpt_path)
self.assertEqual(v_2.numpy(), 3.0)
def test_export_best_eval_metric(self):
model_dir = self.create_tempdir().full_path
metric_name = 'test_metric|metric_1'
exporter = train_utils.BestCheckpointExporter(model_dir, metric_name,
'higher')
exporter.export_best_eval_metric({'test_metric': {'metric_1': 5.0}}, 100)
with tf.io.gfile.GFile(os.path.join(model_dir, 'info.json'),
'rb') as reader:
metric = json.loads(reader.read())
self.assertAllEqual(
metric,
{'test_metric': {'metric_1': 5.0}, 'best_ckpt_global_step': 100.0})
def test_export_best_eval_metric_skips_non_scalar_values(self):
model_dir = self.create_tempdir().full_path
metric_name = 'test_metric|metric_1'
exporter = train_utils.BestCheckpointExporter(model_dir, metric_name,
'higher')
image = tf.zeros(shape=[16, 8, 1])
eval_logs = {'test_metric': {'metric_1': 5.0, 'image': image}}
exporter.export_best_eval_metric(eval_logs, 100)
with tf.io.gfile.GFile(os.path.join(model_dir, 'info.json'),
'rb') as reader:
metric = json.loads(reader.read())
self.assertAllEqual(
metric,
{'test_metric': {'metric_1': 5.0}, 'best_ckpt_global_step': 100.0})
if __name__ == '__main__':
tf.test.main()
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