def test_unused_subclass_model_hdf5_save_format(
self,
dummy_tf_subclassed_model,
dummy_x_train,
dummy_y_train,
dummy_x_test,
filepath,
):
"""Test TensorflowModelDataset cannot save subclassed user models in HDF5 format
Subclassed model
From TF docs
First of all, a subclassed model that has never been used cannot be saved.
That's because a subclassed model needs to be called on some data in order to
create its weights.
"""
hdf5_data_set = TensorFlowModelDataset(filepath=filepath,
save_args={"save_format": "h5"})
# demonstrating is a working model
dummy_tf_subclassed_model.fit(dummy_x_train,
dummy_y_train,
batch_size=64,
epochs=1)
dummy_tf_subclassed_model.predict(dummy_x_test)
pattern = (
r"Saving the model to HDF5 format requires the model to be a Functional model or a "
r"Sequential model. It does not work for subclassed models, because such models are "
r"defined via the body of a Python method, which isn\'t safely serializable. Consider "
r"saving to the Tensorflow SavedModel format \(by setting save_format=\"tf\"\) "
r"or using `save_weights`.")
with pytest.raises(DataSetError, match=pattern):
hdf5_data_set.save(dummy_tf_subclassed_model)
def test_catalog_release(self, mocker):
fs_mock = mocker.patch("fsspec.filesystem").return_value
filepath = "test.tf"
data_set = TensorFlowModelDataset(filepath=filepath)
assert data_set._version_cache.currsize == 0 # no cache if unversioned
data_set.release()
fs_mock.invalidate_cache.assert_called_once_with(filepath)
assert data_set._version_cache.currsize == 0
def test_hdf5_save_format(self, dummy_tf_base_model, dummy_x_test, filepath):
"""Test TensorflowModelDataset can save TF graph models in HDF5 format"""
hdf5_dataset = TensorFlowModelDataset(
filepath=filepath, save_args={"save_format": "h5"}
)
predictions = dummy_tf_base_model.predict(dummy_x_test)
hdf5_dataset.save(dummy_tf_base_model)
reloaded = hdf5_dataset.load()
new_predictions = reloaded.predict(dummy_x_test)
np.testing.assert_allclose(predictions, new_predictions, rtol=1e-6, atol=1e-6)
def test_fs_args(self, fs_args, mocker):
fs_mock = mocker.patch("fsspec.filesystem")
TensorFlowModelDataset("test.tf", fs_args=fs_args)
fs_mock.assert_called_once_with("file",
auto_mkdir=True,
storage_option="value")
def test_http_filesystem_no_versioning(self):
pattern = r"HTTP\(s\) DataSet doesn't support versioning\."
with pytest.raises(DataSetError, match=pattern):
TensorFlowModelDataset(
filepath="https://example.com/file.tf", version=Version(None, None)
)
def test_protocol_usage(self, filepath, instance_type):
"""Test that can be instantiated with mocked arbitrary file systems.
"""
data_set = TensorFlowModelDataset(filepath=filepath)
assert isinstance(data_set._fs, instance_type)
path = filepath.split(PROTOCOL_DELIMITER, 1)[-1]
assert str(data_set._filepath) == path
assert isinstance(data_set._filepath, PurePosixPath)
def test_protocol_usage(self, filepath, instance_type):
"""Test that can be instantiated with mocked arbitrary file systems.
"""
data_set = TensorFlowModelDataset(filepath=filepath)
assert isinstance(data_set._fs, instance_type)
# _strip_protocol() doesn't strip http(s) protocol
if data_set._protocol == "https":
path = filepath.split("://")[-1]
else:
path = data_set._fs._strip_protocol(filepath)
assert str(data_set._filepath) == path
assert isinstance(data_set._filepath, PurePosixPath)
def versioned_tf_model_dataset(filepath, load_version, save_version):
return TensorFlowModelDataset(filepath=filepath,
version=Version(load_version, save_version))
def tf_model_dataset(filepath, load_args, save_args, fs_args):
return TensorFlowModelDataset(filepath=filepath,
load_args=load_args,
save_args=save_args,
fs_args=fs_args)