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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.
"""Unit tests for the classifier trainer models."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import copy
import os
from absl.testing import parameterized
import tensorflow as tf, tf_keras
from official.legacy.image_classification import classifier_trainer
from official.legacy.image_classification import dataset_factory
from official.legacy.image_classification import test_utils
from official.legacy.image_classification.configs import base_configs
def get_trivial_model(num_classes: int) -> tf_keras.Model:
"""Creates and compiles trivial model for ImageNet dataset."""
model = test_utils.trivial_model(num_classes=num_classes)
lr = 0.01
optimizer = tf_keras.optimizers.SGD(learning_rate=lr)
loss_obj = tf_keras.losses.SparseCategoricalCrossentropy()
model.compile(optimizer=optimizer, loss=loss_obj, run_eagerly=True)
return model
def get_trivial_data() -> tf.data.Dataset:
"""Gets trivial data in the ImageNet size."""
def generate_data(_) -> tf.data.Dataset:
image = tf.zeros(shape=(224, 224, 3), dtype=tf.float32)
label = tf.zeros([1], dtype=tf.int32)
return image, label
dataset = tf.data.Dataset.range(1)
dataset = dataset.repeat()
dataset = dataset.map(
generate_data, num_parallel_calls=tf.data.experimental.AUTOTUNE)
dataset = dataset.prefetch(buffer_size=1).batch(1)
return dataset
class UtilTests(parameterized.TestCase, tf.test.TestCase):
"""Tests for individual utility functions within classifier_trainer.py."""
@parameterized.named_parameters(
('efficientnet-b0', 'efficientnet', 'efficientnet-b0', 224),
('efficientnet-b1', 'efficientnet', 'efficientnet-b1', 240),
('efficientnet-b2', 'efficientnet', 'efficientnet-b2', 260),
('efficientnet-b3', 'efficientnet', 'efficientnet-b3', 300),
('efficientnet-b4', 'efficientnet', 'efficientnet-b4', 380),
('efficientnet-b5', 'efficientnet', 'efficientnet-b5', 456),
('efficientnet-b6', 'efficientnet', 'efficientnet-b6', 528),
('efficientnet-b7', 'efficientnet', 'efficientnet-b7', 600),
('resnet', 'resnet', '', None),
)
def test_get_model_size(self, model, model_name, expected):
config = base_configs.ExperimentConfig(
model_name=model,
model=base_configs.ModelConfig(
model_params={
'model_name': model_name,
},))
size = classifier_trainer.get_image_size_from_model(config)
self.assertEqual(size, expected)
@parameterized.named_parameters(
('dynamic', 'dynamic', None, 'dynamic'),
('scalar', 128., None, 128.),
('float32', None, 'float32', 1),
('float16', None, 'float16', 128),
)
def test_get_loss_scale(self, loss_scale, dtype, expected):
config = base_configs.ExperimentConfig(
runtime=base_configs.RuntimeConfig(loss_scale=loss_scale),
train_dataset=dataset_factory.DatasetConfig(dtype=dtype))
ls = classifier_trainer.get_loss_scale(config, fp16_default=128)
self.assertEqual(ls, expected)
@parameterized.named_parameters(('float16', 'float16'),
('bfloat16', 'bfloat16'))
def test_initialize(self, dtype):
config = base_configs.ExperimentConfig(
runtime=base_configs.RuntimeConfig(
run_eagerly=False,
enable_xla=False,
per_gpu_thread_count=1,
gpu_thread_mode='gpu_private',
num_gpus=1,
dataset_num_private_threads=1,
),
train_dataset=dataset_factory.DatasetConfig(dtype=dtype),
model=base_configs.ModelConfig(),
)
class EmptyClass:
pass
fake_ds_builder = EmptyClass()
fake_ds_builder.dtype = dtype
fake_ds_builder.config = EmptyClass()
classifier_trainer.initialize(config, fake_ds_builder)
def test_resume_from_checkpoint(self):
"""Tests functionality for resuming from checkpoint."""
# Set the keras policy
tf_keras.mixed_precision.set_global_policy('mixed_bfloat16')
# Get the model, datasets, and compile it.
model = get_trivial_model(10)
# Create the checkpoint
model_dir = self.create_tempdir().full_path
train_epochs = 1
train_steps = 10
ds = get_trivial_data()
callbacks = [
tf_keras.callbacks.ModelCheckpoint(
os.path.join(model_dir, 'model.ckpt-{epoch:04d}'),
save_weights_only=True)
]
model.fit(
ds,
callbacks=callbacks,
epochs=train_epochs,
steps_per_epoch=train_steps)
# Test load from checkpoint
clean_model = get_trivial_model(10)
weights_before_load = copy.deepcopy(clean_model.get_weights())
initial_epoch = classifier_trainer.resume_from_checkpoint(
model=clean_model, model_dir=model_dir, train_steps=train_steps)
self.assertEqual(initial_epoch, 1)
self.assertNotAllClose(weights_before_load, clean_model.get_weights())
tf.io.gfile.rmtree(model_dir)
def test_serialize_config(self):
"""Tests functionality for serializing data."""
config = base_configs.ExperimentConfig()
model_dir = self.create_tempdir().full_path
classifier_trainer.serialize_config(params=config, model_dir=model_dir)
saved_params_path = os.path.join(model_dir, 'params.yaml')
self.assertTrue(os.path.exists(saved_params_path))
tf.io.gfile.rmtree(model_dir)
if __name__ == '__main__':
tf.test.main()
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