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)
# create variable inputs and initialized by random value
variable_inputs = module_creator.get_variable_inputs()
# create network by module-like graph_def
outputs = g(*variable_inputs)
# create reference network by passing in variable inputs
ref_outputs = module(*variable_inputs)
# check if outputs are equal
forward_variable_and_check_equal(outputs, ref_outputs)
@pytest.mark.parametrize("module_creator", [
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)])
])
def test_simple_create_graph_def(module_creator):
# get module from test parameters
module = module_creator.module
# create proto variables as inputs
proto_variable_inputs = [
nn.ProtoVariable(shape) for shape in module_creator.input_shape
]
# create graph_def by passing proto_variables as inputs
outputs = module(*proto_variable_inputs)
# find out graph_def in global default graph
# create variable inputs and initialized by random value
variable_inputs = module_creator.get_variable_inputs()
# create network by module-like graph_def
outputs = g(*variable_inputs)
# create reference network by passing in variable inputs
ref_outputs = module(*variable_inputs)
# check if outputs are equal
forward_variable_and_check_equal(outputs, ref_outputs)
@pytest.mark.parametrize("module_creator", [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)])])
def test_simple_create_graph_def(module_creator):
# get module from test parameters
module = module_creator.module
# create proto variables as inputs
proto_variable_inputs = [nn.ProtoVariable(
shape) for shape in module_creator.input_shape]
# create graph_def by passing proto_variables as inputs
outputs = module(*proto_variable_inputs)
# find out graph_def in global default graph
g = nn.graph_def.get_default_graph()