def test_parameter_file_load_save():
module_creator = ModuleCreator(TSTNetNormal(), [(4, 3, 32, 32),
(4, 3, 32, 32)])
proto_variable_inputs = module_creator.get_proto_variable_inputs()
outputs = module_creator.module(*proto_variable_inputs)
g = nn.graph_def.get_default_graph_by_variable(outputs)
with create_temp_with_dir(nnp_file) as tmp_file:
g.save(tmp_file)
another = TSTNetNormal()
variable_inputs = module_creator.get_variable_inputs()
outputs = g(*variable_inputs)
ref_outputs = another(*variable_inputs)
# Should not equal
with pytest.raises(AssertionError) as excinfo:
forward_variable_and_check_equal(outputs, ref_outputs)
# load to local scope
with nn.parameter_scope('', another.parameter_scope):
nn.load_parameters(tmp_file)
another.update_parameter()
ref_outputs = another(*variable_inputs)
forward_variable_and_check_equal(outputs, ref_outputs)
def test_parameter_file_load_save_for_file_object(memory_buffer_format):
module_creator = ModuleCreator(TSTNetNormal(), [(4, 3, 32, 32),
(4, 3, 32, 32)])
variable_inputs = module_creator.get_variable_inputs()
a_module = module_creator.module
outputs = a_module(*variable_inputs)
another = TSTNetNormal()
ref_outputs = another(*variable_inputs)
extension = memory_buffer_format
# Should not equal
with pytest.raises(AssertionError) as excinfo:
forward_variable_and_check_equal(outputs, ref_outputs)
with io.BytesIO() as param_file:
nn.save_parameters(param_file,
a_module.get_parameters(),
extension=extension)
# load from file
with nn.parameter_scope('', another.parameter_scope):
nn.load_parameters(param_file, extension=extension)
another.update_parameter()
ref_outputs = another(*variable_inputs)
# should equal
forward_variable_and_check_equal(outputs, ref_outputs)
def test_module_load_save_parameter_file_io(extension, file_format):
module_creator = ModuleCreator(TSTNetNormal(), [(4, 3, 32, 32),
(4, 3, 32, 32)])
variable_inputs = module_creator.get_variable_inputs()
a_module = module_creator.module
outputs = a_module(*variable_inputs)
another = TSTNetNormal()
ref_outputs = another(*variable_inputs)
# Should not equal
with pytest.raises(AssertionError) as excinfo:
forward_variable_and_check_equal(outputs, ref_outputs)
if file_format == 'file_io':
with create_temp_with_dir("tmp{}".format(extension)) as param_file:
with open(param_file, "wb") as f:
a_module.save_parameters(f, extension=extension)
with open(param_file, "rb") as f:
another.load_parameters(f, extension=extension)
elif file_format == 'byte_io':
with io.BytesIO() as param_file:
a_module.save_parameters(param_file, extension=extension)
another.load_parameters(param_file, extension=extension)
elif file_format == 'str':
with create_temp_with_dir("tmp{}".format(extension)) as param_file:
a_module.save_parameters(param_file, extension=extension)
another.load_parameters(param_file, extension=extension)
ref_outputs = another(*variable_inputs)
# should equal
forward_variable_and_check_equal(outputs, ref_outputs)
def test_parameter_file_load_save_for_files(parameter_file):
module_creator = ModuleCreator(TSTNetNormal(), [(4, 3, 32, 32),
(4, 3, 32, 32)])
variable_inputs = module_creator.get_variable_inputs()
a_module = module_creator.module
outputs = a_module(*variable_inputs)
another = TSTNetNormal()
ref_outputs = another(*variable_inputs)
# Should not equal
with pytest.raises(AssertionError) as excinfo:
forward_variable_and_check_equal(outputs, ref_outputs)
with create_temp_with_dir(parameter_file) as tmp_file:
# save to file
nn.save_parameters(tmp_file, a_module.get_parameters())
# load from file
with nn.parameter_scope('', another.parameter_scope):
nn.load_parameters(tmp_file)
another.update_parameter()
ref_outputs = another(*variable_inputs)
# should equal
forward_variable_and_check_equal(outputs, ref_outputs)
def test_multiple_network():
"""This cases assume that user create network multiple times in a ProtoGraph.
Because no graph_name() is called to name each network, all computation graph
operators are collected into a network, it looks like multiple networks
are merged into a network. In testing time, we only need to feed concatenated
inputs to this network and check concatenate outputs.
"""
module_creators = [ModuleCreator(TSTNetNormal(), [(4, 3, 32, 32), (4, 3, 32, 32)]),
ModuleCreator(ResUnit(16), [(4, 3, 32, 32)]),
ModuleCreator(NestedTestNet(), [(4, 3, 32, 32), (4, 3, 32, 32)])]
# create graph_def by passing proto_variables as inputs
with nn.graph_def.graph() as g:
for module_creator in module_creators:
module = module_creator.module
# create proto variables as inputs
proto_variable_inputs = [nn.ProtoVariable(
shape) for shape in module_creator.input_shape]
# generate graph
outputs = module(*proto_variable_inputs)
for module_creator, network in zip(module_creators, g.networks.values()):
# create variable inputs and initialized by random value
variable_inputs = module_creator.get_variable_inputs()
# create network by module-like graph_def
outputs = network(*variable_inputs)
# create reference network by passing in variable inputs
ref_outputs = module_creator.module(*variable_inputs)
# check if outputs are equal
forward_variable_and_check_equal(outputs, ref_outputs)
def test_get_default_graph_def_by_name():
"""This case tests retrieving graph using nn.graph_def.get_default_graph() by
specifying the name of graph.
"""
module_creators = [ModuleCreator(TSTNetNormal(), [(4, 3, 32, 32), (4, 3, 32, 32)]),
ModuleCreator(ResUnit(16), [(4, 3, 32, 32)]),
ModuleCreator(NestedTestNet(), [(4, 3, 32, 32), (4, 3, 32, 32)])]
network_names = [
'network1',
'network2',
'network3'
]
for module_creator, network_name in zip(module_creators, network_names):
module = module_creator.module
# create proto variables as inputs
proto_variable_inputs = [nn.ProtoVariable(
shape) for shape in module_creator.input_shape]
with nn.graph_def.graph_name(network_name):
# generate graph
outputs = module(*proto_variable_inputs)
for module_creator, network_name in zip(module_creators, network_names):
# create variable inputs and initialized by random value
variable_inputs = module_creator.get_variable_inputs()
# get graph from default by name
g = nn.graph_def.get_default_graph(network_name)
# create network by module-like graph_def
outputs = g(*variable_inputs)
# create reference network by passing in variable inputs
ref_outputs = module_creator.module(*variable_inputs)
# check if outputs are equal
forward_variable_and_check_equal(outputs, ref_outputs)
def test_get_graph_def_by_name():
"""This cases assume that user creates multiple networks in a ProtoGraph.
User may specify the name of network(graph) they created by graph_name().
"""
module_creators = [
ModuleCreator(TSTNetNormal(), [(4, 3, 32, 32), (4, 3, 32, 32)]),
ModuleCreator(ResUnit(16), [(4, 3, 32, 32)]),
ModuleCreator(NestedTestNet(), [(4, 3, 32, 32), (4, 3, 32, 32)])
]
network_names = ['network1', 'network2', 'network3']
# create graph_def by passing proto_variables as inputs
with nn.graph_def.graph() as g:
for module_creator, network_name in zip(module_creators,
network_names):
module = module_creator.module
# create proto variables as inputs
proto_variable_inputs = [
nn.ProtoVariable(shape) for shape in module_creator.input_shape
]
with nn.graph_def.graph_name(network_name):
# generate graph
outputs = module(*proto_variable_inputs)
for module_creator, network_name in zip(module_creators, network_names):
# create variable inputs and initialized by random value
variable_inputs = module_creator.get_variable_inputs()
# create network by module-like graph_def
outputs = g[network_name](*variable_inputs)
# create reference network by passing in variable inputs
ref_outputs = module_creator.module(*variable_inputs)
# check if outputs are equal
forward_variable_and_check_equal(outputs, ref_outputs)
def test_parameter_file_load_save_using_global():
module_creator = ModuleCreator(TSTNetNormal(), [(4, 3, 32, 32),
(4, 3, 32, 32)])
proto_variable_inputs = module_creator.get_proto_variable_inputs()
outputs = module_creator.module(*proto_variable_inputs)
g = nn.graph_def.get_default_graph_by_variable(outputs)
g.save(nnp_file)
another = TSTNetNormal()
variable_inputs = module_creator.get_variable_inputs()
outputs = g(*variable_inputs)
ref_outputs = another(*variable_inputs)
# Should not equal
with pytest.raises(AssertionError) as excinfo:
forward_variable_and_check_equal(outputs, ref_outputs)
# load to global scope
nn.load_parameters(nnp_file)
params = nn.get_parameters()
another.set_parameters(params)
ref_outputs = another(*variable_inputs)
forward_variable_and_check_equal(outputs, ref_outputs)
# Create another graph with the same architecture
e2 = Example()
assert not e2.get_parameters(), "It doesn't have any parameters so far."
# Setting parameters from an existing model)
e2.set_parameters(e.get_parameters())
assert e.get_parameters() == e2.get_parameters(
), "They have the same parameters."
assert '@cb/conv/W' in e.get_parameters()
assert '@cb/conv/W' in e2.get_parameters()
@pytest.mark.parametrize(
"module_creator",
[ModuleCreator(TSTNetAbnormal(), [(4, 3, 32, 32), (4, 3, 32, 32)])])
def test_unsupported_module_definition(module_creator):
# Since we use global graphdef, we should reset it beforehand
# This is already done in test fixture.
# nn.graph_def.reset_default_graph()
# get module from test parameters
module = module_creator.module
# create variable inputs and initialized by random value
variable_inputs = module_creator.get_variable_inputs()
# create reference network by passing in variable inputs
ref_outputs = module(*variable_inputs)
# create proto variable inputs
e = Example()
h = e(x)
# Create another graph with the same architecture
e2 = Example()
assert not e2.get_parameters(), "It doesn't have any parameters so far."
# Setting parameters from an existing model)
e2.set_parameters(e.get_parameters())
assert e.get_parameters() == e2.get_parameters(), "They have the same parameters."
assert '@cb/conv/W' in e.get_parameters()
assert '@cb/conv/W' in e2.get_parameters()
@pytest.mark.parametrize("module_creator", [ModuleCreator(TSTNetAbnormal(), [(4, 3, 32, 32), (4, 3, 32, 32)])])
def test_unsupported_module_definition(module_creator):
# Since we use global graphdef, we should reset it beforehand
# This is already done in test fixture.
# nn.graph_def.reset_default_graph()
# get module from test parameters
module = module_creator.module
# create variable inputs and initialized by random value
variable_inputs = module_creator.get_variable_inputs()
# create reference network by passing in variable inputs
ref_outputs = module(*variable_inputs)
# create proto variable inputs
