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default_input_generator_test.py
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# coding=utf-8
# Copyright 2024 The Tensor2Robot Authors.
#
# 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 estimator_models.input_generators.default_input_generator."""
import json
import os
import six
from tensor2robot.input_generators import default_input_generator
from tensor2robot.utils import tensorspec_utils
import tensorflow.compat.v1 as tf
from tensorflow.compat.v1 import estimator as tf_estimator
FLAGS = tf.app.flags.FLAGS
BATCH_SIZE = 2
NUM_BATCHES = 3
NUM_TASKS = 3
EXAMPLES_PER_TASK = 10
_int64_feature = (
lambda v: tf.train.Feature(int64_list=tf.train.Int64List(value=v)))
class DefaultInputGeneratorTest(tf.test.TestCase):
def _test_input_generator(self, input_generator, is_dataset=False):
feature_spec = tensorspec_utils.TensorSpecStruct()
feature_spec.state = tensorspec_utils.ExtendedTensorSpec(
shape=(64, 64, 3),
dtype=tf.uint8,
name='state/image',
data_format='jpeg')
feature_spec.action = tensorspec_utils.ExtendedTensorSpec(
shape=(2), dtype=tf.float32, name='pose')
label_spec = tensorspec_utils.TensorSpecStruct()
label_spec.reward = tensorspec_utils.ExtendedTensorSpec(
shape=(), dtype=tf.float32, name='reward')
with self.assertRaises(ValueError):
_ = input_generator.create_dataset_input_fn(
mode=tf_estimator.ModeKeys.TRAIN)
input_generator.set_feature_specifications(feature_spec, feature_spec)
input_generator.set_label_specifications(label_spec, label_spec)
np_features, np_labels = input_generator.create_dataset_input_fn(
mode=tf_estimator.ModeKeys.TRAIN)().make_one_shot_iterator().get_next()
np_features = tensorspec_utils.validate_and_pack(
feature_spec, np_features, ignore_batch=True)
np_labels = tensorspec_utils.validate_and_pack(
label_spec, np_labels, ignore_batch=True)
self.assertAllEqual([2, 64, 64, 3], np_features.state.shape)
self.assertAllEqual([2, 2], np_features.action.shape)
self.assertAllEqual((2,), np_labels.reward.shape)
def _test_multi_record_input_generator(
self, input_generator, is_dataset=False):
feature_spec = tensorspec_utils.TensorSpecStruct()
feature_spec.state = tensorspec_utils.ExtendedTensorSpec(
shape=(64, 64, 3),
dtype=tf.uint8,
name='state/image',
data_format='jpeg',
dataset_key='d1')
feature_spec.action = tensorspec_utils.ExtendedTensorSpec(
shape=(2), dtype=tf.float32, name='pose', dataset_key='d1')
label_spec = tensorspec_utils.TensorSpecStruct()
label_spec.reward = tensorspec_utils.ExtendedTensorSpec(
shape=(), dtype=tf.float32, name='reward', dataset_key='d1')
label_spec.reward_2 = tensorspec_utils.ExtendedTensorSpec(
shape=(), dtype=tf.float32, name='reward', dataset_key='d2')
input_generator.set_feature_specifications(feature_spec, feature_spec)
input_generator.set_label_specifications(label_spec, label_spec)
np_features, np_labels = input_generator.create_dataset_input_fn(
mode=tf_estimator.ModeKeys.TRAIN)().make_one_shot_iterator().get_next()
np_features = tensorspec_utils.validate_and_pack(
feature_spec, np_features, ignore_batch=True)
np_labels = tensorspec_utils.validate_and_pack(
label_spec, np_labels, ignore_batch=True)
self.assertAllEqual([2, 64, 64, 3], np_features.state.shape)
self.assertAllEqual([2, 2], np_features.action.shape)
self.assertAllEqual((2,), np_labels.reward.shape)
self.assertAllEqual((2,), np_labels.reward_2.shape)
def test_record_input_generator(self):
base_dir = 'tensor2robot'
file_pattern = os.path.join(
FLAGS.test_srcdir, base_dir, 'test_data/pose_env_test_data.tfrecord')
input_generator = default_input_generator.DefaultRecordInputGenerator(
file_patterns=file_pattern, batch_size=BATCH_SIZE)
self._test_input_generator(input_generator)
def test_multi_record_input_generator(self):
base_dir = 'tensor2robot'
file_pattern = os.path.join(
FLAGS.test_srcdir, base_dir, 'test_data/pose_env_test_data.tfrecord')
dataset_map = {'d1': file_pattern, 'd2': file_pattern}
input_generator = default_input_generator.DefaultRecordInputGenerator(
dataset_map=dataset_map, batch_size=BATCH_SIZE)
self._test_multi_record_input_generator(input_generator)
def test_multi_eval_record_input_generator(self):
base_dir = 'tensor2robot'
file_pattern = os.path.join(
FLAGS.test_srcdir, base_dir, 'test_data/pose_env_test_data.tfrecord')
eval_map = {'d1': file_pattern, 'd2': 'fubar'}
os.environ['TF_CONFIG'] = json.dumps({'multi_eval_name': 'd2'})
input_generator = default_input_generator.MultiEvalRecordInputGenerator(
eval_map=eval_map, batch_size=2)
self.assertEqual(input_generator._file_patterns, 'fubar')
self.assertEqual(default_input_generator.get_multi_eval_name(), 'd2')
def test_fractional_record_input_generator(self):
base_dir = 'tensor2robot'
file_pattern = os.path.join(
FLAGS.test_srcdir, base_dir, 'test_data/pose_env_test_data.tfrecord')
num_files = 10
fraction = 0.3
file_patterns = ','.join([file_pattern]*10)
input_generator = default_input_generator.FractionalRecordInputGenerator(
file_fraction=fraction, file_patterns=file_patterns,
batch_size=BATCH_SIZE)
self.assertEqual(
len(six.ensure_str(input_generator._file_patterns).split(',')),
int(fraction * num_files))
def test_weighted_record_input_generator(self):
base_dir = 'tensor2robot'
file_pattern = os.path.join(FLAGS.test_srcdir, base_dir,
'test_data/pose_env_test_data.tfrecord')
file_patterns = ','.join([file_pattern] * 10)
input_generator = default_input_generator.WeightedRecordInputGenerator(
file_patterns=file_patterns, batch_size=BATCH_SIZE)
self._test_input_generator(input_generator)
def test_random_dataset(self):
input_generator = default_input_generator.DefaultRandomInputGenerator(
batch_size=2)
self._test_input_generator(input_generator)
def test_constant_dataset(self):
input_generator = default_input_generator.DefaultConstantInputGenerator(
constant_value=1, batch_size=2)
self._test_input_generator(input_generator)
if __name__ == '__main__':
tf.test.main()