def test_profile():
config_file = "tests/fixtures/configs/for_profile.py"
expriment_id = "test_profile"
train_run(None, None, config_file, expriment_id, recreate=True)
setup_test_environment()
run(expriment_id, None, None, 2, [])
def test_export():
config_file = "tests/fixtures/configs/for_export.py"
expriment_id = "test_export"
train_run(None, None, config_file, expriment_id, recreate=True)
setup_test_environment()
run(expriment_id, None, (None, None), [], None)
def test_build_tfds_classification():
environment.setup_test_environment()
# Build TFDS Dataset
config_file = "tests/fixtures/configs/for_build_tfds_classification.py"
run(config_file, overwrite=True)
# Check if the builded dataset can be loaded with the same config file
expriment_id = "tfds_classification"
train_run(None, None, config_file, expriment_id, recreate=True)
# Check if the dataset was build correctly
train_data_num = 3
validation_data_num = 2
config = config_util.load(config_file)
train_dataset = setup_dataset(TFDSClassification,
subset="train",
batch_size=config.BATCH_SIZE,
pre_processor=config.PRE_PROCESSOR,
**config.DATASET.TFDS_KWARGS)
validation_dataset = setup_dataset(TFDSClassification,
subset="validation",
batch_size=config.BATCH_SIZE,
pre_processor=config.PRE_PROCESSOR,
**config.DATASET.TFDS_KWARGS)
assert train_dataset.num_per_epoch == train_data_num
assert validation_dataset.num_per_epoch == validation_data_num
for _ in range(train_data_num):
images, labels = train_dataset.feed()
assert isinstance(images, np.ndarray)
assert images.shape[0] == config.BATCH_SIZE
assert images.shape[1] == config.IMAGE_SIZE[0]
assert images.shape[2] == config.IMAGE_SIZE[1]
assert images.shape[3] == 3
assert isinstance(labels, np.ndarray)
assert labels.shape[0] == config.BATCH_SIZE
assert labels.shape[1] == train_dataset.num_classes
for _ in range(validation_data_num):
images, labels = validation_dataset.feed()
assert isinstance(images, np.ndarray)
assert images.shape[0] == config.BATCH_SIZE
assert images.shape[1] == config.IMAGE_SIZE[0]
assert images.shape[2] == config.IMAGE_SIZE[1]
assert images.shape[3] == 3
assert isinstance(labels, np.ndarray)
assert labels.shape[0] == config.BATCH_SIZE
assert labels.shape[1] == validation_dataset.num_classes
def test_predict_object_detection():
config_file = "tests/fixtures/configs/for_predict_object_detection.py"
expriment_id = "test_predict_object_detection"
train_run(None, None, config_file, expriment_id, recreate=True)
setup_test_environment()
run("tests/fixtures/sample_images",
"outputs",
expriment_id,
None,
None,
save_images=True)
def set_test_environment():
"""Set test environment"""
print("set test environment")
yield environment.setup_test_environment()
# By using a yield statement instead of return, all the code after the yield statement serves as the teardown code:
# See also: https://docs.pytest.org/en/latest/fixture.html#fixture-finalization-executing-teardown-code
environment.teardown_test_environment()
# 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.
# =============================================================================
import pytest
from executor.profile_model import run
from executor.train import run as train_run
from lmnet.environment import setup_test_environment
# Apply reset_default_graph() in conftest.py to all tests in this file.
# Set test environment
pytestmark = pytest.mark.usefixtures("reset_default_graph", "set_test_environment")
def test_profile():
config_file = "tests/fixtures/configs/for_profile.py"
expriment_id = "test_profile"
train_run(None, None, config_file, expriment_id, recreate=True)
setup_test_environment()
run(expriment_id, None, None, 2, [])
if __name__ == '__main__':
setup_test_environment()
test_profile()
for i in range(0, 10):
images, labels = next(dataset_iterator)
assert images.shape[0] == batch_size
assert labels.shape[0] == batch_size
def test_dataset_iterator_batch_order():
"""Assert that data given by iterator is same whether enabele_prefetch ture or false."""
batch_size = 8
dataset = Dummy(subset="train", batch_size=batch_size)
dataset_iterator = DatasetIterator(dataset, seed=10, enable_prefetch=False)
prefetch_dataset_iterator = DatasetIterator(dataset,
seed=10,
enable_prefetch=True)
for i in range(0, 30):
images, labels = next(dataset_iterator)
prefetch_images, prefetch_labels = next(prefetch_dataset_iterator)
assert np.all(images == prefetch_images)
assert np.all(labels == prefetch_labels)
if __name__ == '__main__':
from lmnet import environment
environment.setup_test_environment()
test_dataset_iterator_batch_size()
test_dataset_iterator_batch_order()
