def randomify_neuropod(output_path, input_spec, output_spec):
"""Uses neuropod input and output specs to automatically generate a neuropod package that complies to the spec and
produces random outputs.
This neuropod can be used as a stub, for testing purposes. A Tensorflow engine is used in the neuropod generated.
:param output_path: Output path where the neuropod will be written to.
:param input_spec: Neuropod input spec structure (a list of dictionaries)
:param output_spec: Neuropod output spec structure (a list of dictionaries)
:return:
"""
g = tf.Graph()
with g.as_default():
# Create a placeholder of a corresponding shape for each of the inputs in the input spec
node_name_mapping = _placeholdes_from_input_spec(input_spec)
# For each output, we create a random matrix generator of an appropriate type and size.
node_name_mapping.update(_random_from_output_spec(output_spec))
graph_def = g.as_graph_def()
create_tensorflow_neuropod(
neuropod_path=output_path,
model_name="random_model",
graph_def=graph_def,
node_name_mapping=node_name_mapping,
input_spec=input_spec,
output_spec=output_spec,
)
return output_path
def package_simple_addition_model(self, do_fail=False, **kwargs):
with TemporaryDirectory() as test_dir:
neuropod_path = os.path.join(test_dir, "test_neuropod")
# Get the input/output spec along with test data
kwargs.update(get_addition_model_spec(do_fail=do_fail))
# `create_tensorflow_neuropod` runs inference with the test data immediately
# after creating the neuropod. Raises a ValueError if the model output
# does not match the expected output.
create_tensorflow_neuropod(
neuropod_path=neuropod_path,
model_name="addition_model",
trackable_obj=create_tf_addition_model(),
**kwargs)
# Run some additional checks
check_addition_model(neuropod_path)
def package_simple_addition_model(self, do_fail=False):
with TemporaryDirectory() as test_dir:
neuropod_path = os.path.join(test_dir, "test_neuropod")
# `create_tensorflow_neuropod` runs inference with the test data immediately
# after creating the neuropod. Raises a ValueError if the model output
# does not match the expected output.
create_tensorflow_neuropod(
neuropod_path=neuropod_path,
model_name="addition_model",
graph_def=create_tf_addition_model(self.custom_op_path),
node_name_mapping={
"x": "some_namespace/in_x:0",
"y": "some_namespace/in_y:0",
"out": "some_namespace/out:0",
},
custom_ops=[self.custom_op_path, self.second_custom_op],
# Get the input/output spec along with test data
**get_addition_model_spec(do_fail=do_fail))
def package_accumulator_model(self, neuropod_path, init_op_name_as_list):
graph_def, init_op_name = create_tf_accumulator_model()
# `create_tensorflow_neuropod` runs inference with the test data immediately
# after creating the neuropod. Raises a ValueError if the model output
# does not match the expected output.
create_tensorflow_neuropod(
neuropod_path=neuropod_path,
model_name="accumulator_model",
graph_def=graph_def,
node_name_mapping={
"x": "some_namespace/in_x:0",
"out": "some_namespace/out:0",
},
input_spec=[
{
"name": "x",
"dtype": "float32",
"shape": ()
},
],
output_spec=[
{
"name": "out",
"dtype": "float32",
"shape": ()
},
],
init_op_names=[init_op_name]
if init_op_name_as_list else init_op_name,
test_input_data={
"x": np.float32(5.0),
},
test_expected_out={
"out": np.float32(5.0),
},
)
def package_simple_addition_model(self, do_fail=False, **kwargs):
with TemporaryDirectory() as test_dir:
neuropod_path = os.path.join(test_dir, "test_neuropod")
# Get the input/output spec along with test data
kwargs.update(get_addition_model_spec(do_fail=do_fail))
# `create_tensorflow_neuropod` runs inference with the test data immediately
# after creating the neuropod. Raises a ValueError if the model output
# does not match the expected output.
create_tensorflow_neuropod(
neuropod_path=neuropod_path,
model_name="addition_model",
graph_def=create_tf_addition_model(),
node_name_mapping={
"x": "some_namespace/in_x:0",
"y": "some_namespace/in_y:0",
# The `:0` is optional
"out": "some_namespace/out",
},
**kwargs)
# Run some additional checks
check_addition_model(neuropod_path)